From f7ad610c3be0e94441204401ce86e04c1d1a3257 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 13:39:52 +0800
Subject: [PATCH 01/35] Move common functions to utils

---
 tests/model_tests/chestnut_dec_may/train.py | 157 ++------------------
 tests/model_tests/utils.py                  | 135 +++++++++++++++++
 2 files changed, 151 insertions(+), 141 deletions(-)

diff --git a/tests/model_tests/chestnut_dec_may/train.py b/tests/model_tests/chestnut_dec_may/train.py
index de390e6d..299eb708 100644
--- a/tests/model_tests/chestnut_dec_may/train.py
+++ b/tests/model_tests/chestnut_dec_may/train.py
@@ -9,7 +9,6 @@
 
 import lightning as pl
 import numpy as np
-import torch
 import wandb
 from lightning.pytorch.callbacks import (
     LearningRateMonitor,
@@ -17,150 +16,19 @@
     EarlyStopping,
 )
 from lightning.pytorch.loggers import WandbLogger
-from matplotlib import pyplot as plt
-from seaborn import heatmap
-from sklearn.metrics import confusion_matrix
 from sklearn.preprocessing import StandardScaler, OrdinalEncoder
-from torch.utils.data import DataLoader
-from torchvision.transforms import RandomVerticalFlip
-from torchvision.transforms.v2 import (
-    Compose,
-    ToImage,
-    ToDtype,
-    RandomVerticalFlip,
-    RandomCrop,
-    CenterCrop,
-)
-from torchvision.transforms.v2 import RandomHorizontalFlip
 
 from frdc.load import FRDCDataset
 from frdc.load.dataset import FRDCUnlabelledDataset
 from frdc.models.inceptionv3 import InceptionV3MixMatchModule
 from frdc.train.frdc_datamodule import FRDCDataModule
-
-THIS_DIR = Path(__file__).parent
-
-
-# TODO: Ideally, we should have a separate dataset for testing.
-
-
-# TODO: This is pretty hacky, I'm not sure if there's a better way to do this.
-#       Note that initializing datasets separately then concatenating them
-#       together is 4x slower than initializing a dataset then hacking into
-#       the __getitem__ method.
-class FRDCDatasetFlipped(FRDCDataset):
-    def __len__(self):
-        """Assume that the dataset is 4x larger than it actually is.
-
-        For example, for index 0, we return the original image. For index 1, we
-        return the horizontally flipped image and so on, until index 3.
-        Then, return the next image for index 4, and so on.
-        """
-        return super().__len__() * 4
-
-    def __getitem__(self, idx):
-        """Alter the getitem method to implement the logic above."""
-        x, y = super().__getitem__(int(idx // 4))
-        if idx % 4 == 0:
-            return x, y
-        elif idx % 4 == 1:
-            return RandomHorizontalFlip(p=1)(x), y
-        elif idx % 4 == 2:
-            return RandomVerticalFlip(p=1)(x), y
-        elif idx % 4 == 3:
-            return RandomHorizontalFlip(p=1)(RandomVerticalFlip(p=1)(x)), y
-
-
-def evaluate(ckpt_pth: Path | str | None = None) -> tuple[plt.Figure, float]:
-    ds = FRDCDatasetFlipped(
-        "chestnut_nature_park",
-        "20210510",
-        "90deg43m85pct255deg",
-        transform=preprocess,
-    )
-
-    if ckpt_pth is None:
-        # This fetches all possible checkpoints and gets the latest one
-        ckpt_pth = sorted(
-            THIS_DIR.glob("**/*.ckpt"), key=lambda x: x.stat().st_mtime_ns
-        )[-1]
-
-    m = InceptionV3MixMatchModule.load_from_checkpoint(ckpt_pth)
-    # Make predictions
-    trainer = pl.Trainer(logger=False)
-    pred = trainer.predict(m, dataloaders=DataLoader(ds, batch_size=32))
-
-    y_trues = []
-    y_preds = []
-    for y_true, y_pred in pred:
-        y_trues.append(y_true)
-        y_preds.append(y_pred)
-    y_trues = np.concatenate(y_trues)
-    y_preds = np.concatenate(y_preds)
-    acc = (y_trues == y_preds).mean()
-
-    # Plot the confusion matrix
-    cm = confusion_matrix(y_trues, y_preds)
-
-    plt.figure(figsize=(10, 10))
-
-    heatmap(
-        cm,
-        annot=True,
-        xticklabels=m.y_encoder.categories_[0],
-        yticklabels=m.y_encoder.categories_[0],
-        cbar=False,
-    )
-    plt.title(f"Accuracy: {acc:.2%}")
-    plt.tight_layout(pad=3)
-    plt.xlabel("Predicted Label")
-    plt.ylabel("True Label")
-    return plt.gcf(), acc
-
-
-def preprocess(x):
-    return Compose(
-        [
-            ToImage(),
-            ToDtype(torch.float32, scale=True),
-            CenterCrop(
-                [
-                    InceptionV3MixMatchModule.MIN_SIZE,
-                    InceptionV3MixMatchModule.MIN_SIZE,
-                ],
-            ),
-        ]
-    )(x)
-
-
-def train_preprocess(x):
-    return Compose(
-        [
-            ToImage(),
-            ToDtype(torch.float32, scale=True),
-            RandomCrop(
-                [
-                    InceptionV3MixMatchModule.MIN_SIZE,
-                    InceptionV3MixMatchModule.MIN_SIZE,
-                ],
-                pad_if_needed=True,
-                padding_mode="constant",
-                fill=0,
-            ),
-            RandomHorizontalFlip(),
-            RandomVerticalFlip(),
-        ]
-    )(x)
-
-
-def train_unl_preprocess(n_aug: int = 2):
-    def f(x):
-        # This simulates the n_aug of MixMatch
-        return (
-            [train_preprocess(x) for _ in range(n_aug)] if n_aug > 0 else None
-        )
-
-    return f
+from model_tests.utils import (
+    train_preprocess,
+    train_unl_preprocess,
+    preprocess,
+    evaluate,
+    FRDCDatasetFlipped,
+)
 
 
 def main(
@@ -250,7 +118,15 @@ def main(
             f"[WandB Report]({run.get_url()})"
         )
 
-    fig, acc = evaluate(Path(ckpt.best_model_path))
+    fig, acc = evaluate(
+        ds=FRDCDatasetFlipped(
+            "chestnut_nature_park",
+            "20210510",
+            "90deg43m85pct255deg",
+            transform=preprocess,
+        ),
+        ckpt_pth=Path(ckpt.best_model_path),
+    )
     wandb.log({"confusion_matrix": wandb.Image(fig)})
     wandb.log({"eval_accuracy": acc})
 
@@ -263,7 +139,6 @@ def main(
     TRAIN_ITERS = 25
     VAL_ITERS = 15
     LR = 1e-3
-    os.environ["GOOGLE_CLOUD_PROJECT"] = "frmodel"
 
     assert wandb.run is None
     wandb.setup(wandb.Settings(program=__name__, program_relpath=__name__))
diff --git a/tests/model_tests/utils.py b/tests/model_tests/utils.py
index 22640115..593bae11 100644
--- a/tests/model_tests/utils.py
+++ b/tests/model_tests/utils.py
@@ -1 +1,136 @@
+from pathlib import Path
+
+import lightning as pl
+import numpy as np
+import torch
+from matplotlib import pyplot as plt
+from seaborn import heatmap
+from sklearn.metrics import confusion_matrix
+from torch.utils.data import DataLoader
+from torchvision.transforms import RandomVerticalFlip
+from torchvision.transforms.v2 import (
+    Compose,
+    ToImage,
+    ToDtype,
+    RandomVerticalFlip,
+    RandomCrop,
+    CenterCrop,
+)
+from torchvision.transforms.v2 import RandomHorizontalFlip
+
+from frdc.load import FRDCDataset
+from frdc.models.inceptionv3 import InceptionV3MixMatchModule
+
+THIS_DIR = Path(__file__).parent
+
 BANDS = ["NB", "NG", "NR", "RE", "NIR"]
+
+
+class FRDCDatasetFlipped(FRDCDataset):
+    def __len__(self):
+        """Assume that the dataset is 4x larger than it actually is.
+
+        For example, for index 0, we return the original image. For index 1, we
+        return the horizontally flipped image and so on, until index 3.
+        Then, return the next image for index 4, and so on.
+        """
+        return super().__len__() * 4
+
+    def __getitem__(self, idx):
+        """Alter the getitem method to implement the logic above."""
+        x, y = super().__getitem__(int(idx // 4))
+        if idx % 4 == 0:
+            return x, y
+        elif idx % 4 == 1:
+            return RandomHorizontalFlip(p=1)(x), y
+        elif idx % 4 == 2:
+            return RandomVerticalFlip(p=1)(x), y
+        elif idx % 4 == 3:
+            return RandomHorizontalFlip(p=1)(RandomVerticalFlip(p=1)(x)), y
+
+
+def evaluate(
+    ds: FRDCDataset, ckpt_pth: Path | str | None = None
+) -> tuple[plt.Figure, float]:
+    if ckpt_pth is None:
+        # This fetches all possible checkpoints and gets the latest one
+        ckpt_pth = sorted(
+            THIS_DIR.glob("**/*.ckpt"), key=lambda x: x.stat().st_mtime_ns
+        )[-1]
+
+    m = InceptionV3MixMatchModule.load_from_checkpoint(ckpt_pth)
+    # Make predictions
+    trainer = pl.Trainer(logger=False)
+    pred = trainer.predict(m, dataloaders=DataLoader(ds, batch_size=32))
+
+    y_trues = []
+    y_preds = []
+    for y_true, y_pred in pred:
+        y_trues.append(y_true)
+        y_preds.append(y_pred)
+    y_trues = np.concatenate(y_trues)
+    y_preds = np.concatenate(y_preds)
+    acc = (y_trues == y_preds).mean()
+
+    # Plot the confusion matrix
+    cm = confusion_matrix(y_trues, y_preds)
+
+    plt.figure(figsize=(10, 10))
+
+    heatmap(
+        cm,
+        annot=True,
+        xticklabels=m.y_encoder.categories_[0],
+        yticklabels=m.y_encoder.categories_[0],
+        cbar=False,
+    )
+    plt.title(f"Accuracy: {acc:.2%}")
+    plt.tight_layout(pad=3)
+    plt.xlabel("Predicted Label")
+    plt.ylabel("True Label")
+    return plt.gcf(), acc
+
+
+def preprocess(x):
+    return Compose(
+        [
+            ToImage(),
+            ToDtype(torch.float32, scale=True),
+            CenterCrop(
+                [
+                    InceptionV3MixMatchModule.MIN_SIZE,
+                    InceptionV3MixMatchModule.MIN_SIZE,
+                ],
+            ),
+        ]
+    )(x)
+
+
+def train_preprocess(x):
+    return Compose(
+        [
+            ToImage(),
+            ToDtype(torch.float32, scale=True),
+            RandomCrop(
+                [
+                    InceptionV3MixMatchModule.MIN_SIZE,
+                    InceptionV3MixMatchModule.MIN_SIZE,
+                ],
+                pad_if_needed=True,
+                padding_mode="constant",
+                fill=0,
+            ),
+            RandomHorizontalFlip(),
+            RandomVerticalFlip(),
+        ]
+    )(x)
+
+
+def train_unl_preprocess(n_aug: int = 2):
+    def f(x):
+        # This simulates the n_aug of MixMatch
+        return (
+            [train_preprocess(x) for _ in range(n_aug)] if n_aug > 0 else None
+        )
+
+    return f

From 01b9849098838cc3e824543370316272a446e38d Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 13:40:00 +0800
Subject: [PATCH 02/35] Suppress gcs environ warning

---
 src/frdc/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/frdc/conf.py b/src/frdc/conf.py
index bb3716c1..c94806fe 100644
--- a/src/frdc/conf.py
+++ b/src/frdc/conf.py
@@ -9,6 +9,7 @@
 
 ROOT_DIR = Path(__file__).parents[2]
 LOCAL_DATASET_ROOT_DIR = ROOT_DIR / "rsc"
+os.environ["GOOGLE_CLOUD_PROJECT"] = "frmodel"
 GCS_PROJECT_ID = "frmodel"
 GCS_BUCKET_NAME = "frdc-ds"
 GCS_CREDENTIALS = None

From 643a98d88417a75037f65cb5de8b2955dabef677 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 14:02:25 +0800
Subject: [PATCH 03/35] Fix missing label-studio-sdk

---
 poetry.lock    | 647 +++++++++++++++++++++++++++++++++++--------------
 pyproject.toml |   1 +
 2 files changed, 471 insertions(+), 177 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 39ed7d6a..5775a26d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,16 @@
 # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
+[[package]]
+name = "annotated-types"
+version = "0.6.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
+    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+]
+
 [[package]]
 name = "appdirs"
 version = "1.4.4"
@@ -13,29 +24,33 @@ files = [
 
 [[package]]
 name = "black"
-version = "23.11.0"
+version = "23.12.0"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "black-23.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:412f56bab20ac85927f3a959230331de5614aecda1ede14b373083f62ec24e6f"},
-    {file = "black-23.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d136ef5b418c81660ad847efe0e55c58c8208b77a57a28a503a5f345ccf01394"},
-    {file = "black-23.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:6c1cac07e64433f646a9a838cdc00c9768b3c362805afc3fce341af0e6a9ae9f"},
-    {file = "black-23.11.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:cf57719e581cfd48c4efe28543fea3d139c6b6f1238b3f0102a9c73992cbb479"},
-    {file = "black-23.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:698c1e0d5c43354ec5d6f4d914d0d553a9ada56c85415700b81dc90125aac244"},
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-    {file = "black-23.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:58e5f4d08a205b11800332920e285bd25e1a75c54953e05502052738fe16b3b5"},
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-    {file = "black-23.11.0-cp38-cp38-win_amd64.whl", hash = "sha256:fc7f6a44d52747e65a02558e1d807c82df1d66ffa80a601862040a43ec2e3142"},
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 ]
 
 [package.dependencies]
@@ -49,7 +64,7 @@ typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)"]
+d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
@@ -275,31 +290,34 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 fastrlock = ">=0.5"
-numpy = ">=1.20,<1.27"
+numpy = ">=1.20,<1.29"
 
 [package.extras]
-all = ["Cython (>=0.29.22,<3)", "optuna (>=2.0)", "scipy (>=1.6,<1.13)"]
+all = ["Cython (>=0.29.22,<3)", "optuna (>=2.0)", "scipy (>=1.6,<1.14)"]
 stylecheck = ["autopep8 (==1.5.5)", "flake8 (==3.8.4)", "mypy (==1.4.1)", "pbr (==5.5.1)", "pycodestyle (==2.6.0)", "types-setuptools (==57.4.14)"]
 test = ["hypothesis (>=6.37.2,<6.55.0)", "pytest (>=7.2)"]
 
@@ -320,13 +338,13 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "distlib"
-version = "0.3.7"
+version = "0.3.8"
 description = "Distribution utilities"
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 python-versions = "*"
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 ]
 
 [[package]]
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 ]
 
 [package.extras]
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+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"]
 graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["munkres", "scipy"]
+interpolatable = ["munkres", "pycairo", "scipy"]
 lxml = ["lxml (>=4.0,<5)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
@@ -586,13 +604,13 @@ tqdm = ">=4.66.1,<5.0.0"
 
 [[package]]
 name = "google-api-core"
-version = "2.14.0"
+version = "2.15.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -608,13 +626,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.24.0"
+version = "2.25.2"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-auth-2.24.0.tar.gz", hash = "sha256:2ec7b2a506989d7dbfdbe81cb8d0ead8876caaed14f86d29d34483cbe99c57af"},
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+    {file = "google-auth-2.25.2.tar.gz", hash = "sha256:42f707937feb4f5e5a39e6c4f343a17300a459aaf03141457ba505812841cc40"},
+    {file = "google_auth-2.25.2-py2.py3-none-any.whl", hash = "sha256:473a8dfd0135f75bb79d878436e568f2695dce456764bf3a02b6f8c540b1d256"},
 ]
 
 [package.dependencies]
@@ -631,13 +649,13 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-cloud-core"
-version = "2.3.3"
+version = "2.4.1"
 description = "Google Cloud API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-core-2.3.3.tar.gz", hash = "sha256:37b80273c8d7eee1ae816b3a20ae43585ea50506cb0e60f3cf5be5f87f1373cb"},
-    {file = "google_cloud_core-2.3.3-py2.py3-none-any.whl", hash = "sha256:fbd11cad3e98a7e5b0343dc07cb1039a5ffd7a5bb96e1f1e27cee4bda4a90863"},
+    {file = "google-cloud-core-2.4.1.tar.gz", hash = "sha256:9b7749272a812bde58fff28868d0c5e2f585b82f37e09a1f6ed2d4d10f134073"},
+    {file = "google_cloud_core-2.4.1-py2.py3-none-any.whl", hash = "sha256:a9e6a4422b9ac5c29f79a0ede9485473338e2ce78d91f2370c01e730eab22e61"},
 ]
 
 [package.dependencies]
@@ -645,17 +663,17 @@ google-api-core = ">=1.31.6,<2.0.dev0 || >2.3.0,<3.0.0dev"
 google-auth = ">=1.25.0,<3.0dev"
 
 [package.extras]
-grpc = ["grpcio (>=1.38.0,<2.0dev)"]
+grpc = ["grpcio (>=1.38.0,<2.0dev)", "grpcio-status (>=1.38.0,<2.0.dev0)"]
 
 [[package]]
 name = "google-cloud-storage"
-version = "2.13.0"
+version = "2.14.0"
 description = "Google Cloud Storage API client library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-cloud-storage-2.13.0.tar.gz", hash = "sha256:f62dc4c7b6cd4360d072e3deb28035fbdad491ac3d9b0b1815a12daea10f37c7"},
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+    {file = "google-cloud-storage-2.14.0.tar.gz", hash = "sha256:2d23fcf59b55e7b45336729c148bb1c464468c69d5efbaee30f7201dd90eb97e"},
+    {file = "google_cloud_storage-2.14.0-py2.py3-none-any.whl", hash = "sha256:8641243bbf2a2042c16a6399551fbb13f062cbc9a2de38d6c0bb5426962e9dbd"},
 ]
 
 [package.dependencies]
@@ -751,13 +769,13 @@ testing = ["pytest"]
 
 [[package]]
 name = "google-resumable-media"
-version = "2.6.0"
+version = "2.7.0"
 description = "Utilities for Google Media Downloads and Resumable Uploads"
 optional = false
 python-versions = ">= 3.7"
 files = [
-    {file = "google-resumable-media-2.6.0.tar.gz", hash = "sha256:972852f6c65f933e15a4a210c2b96930763b47197cdf4aa5f5bea435efb626e7"},
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+    {file = "google-resumable-media-2.7.0.tar.gz", hash = "sha256:5f18f5fa9836f4b083162064a1c2c98c17239bfda9ca50ad970ccf905f3e625b"},
+    {file = "google_resumable_media-2.7.0-py2.py3-none-any.whl", hash = "sha256:79543cfe433b63fd81c0844b7803aba1bb8950b47bedf7d980c38fa123937e08"},
 ]
 
 [package.dependencies]
@@ -769,13 +787,13 @@ requests = ["requests (>=2.18.0,<3.0.0dev)"]
 
 [[package]]
 name = "googleapis-common-protos"
-version = "1.61.0"
+version = "1.62.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "googleapis-common-protos-1.61.0.tar.gz", hash = "sha256:8a64866a97f6304a7179873a465d6eee97b7a24ec6cfd78e0f575e96b821240b"},
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+    {file = "googleapis-common-protos-1.62.0.tar.gz", hash = "sha256:83f0ece9f94e5672cced82f592d2a5edf527a96ed1794f0bab36d5735c996277"},
+    {file = "googleapis_common_protos-1.62.0-py2.py3-none-any.whl", hash = "sha256:4750113612205514f9f6aa4cb00d523a94f3e8c06c5ad2fee466387dc4875f07"},
 ]
 
 [package.dependencies]
@@ -786,13 +804,13 @@ grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
 [[package]]
 name = "identify"
-version = "2.5.32"
+version = "2.5.33"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "identify-2.5.32-py2.py3-none-any.whl", hash = "sha256:0b7656ef6cba81664b783352c73f8c24b39cf82f926f78f4550eda928e5e0545"},
-    {file = "identify-2.5.32.tar.gz", hash = "sha256:5d9979348ec1a21c768ae07e0a652924538e8bce67313a73cb0f681cf08ba407"},
+    {file = "identify-2.5.33-py2.py3-none-any.whl", hash = "sha256:d40ce5fcd762817627670da8a7d8d8e65f24342d14539c59488dc603bf662e34"},
+    {file = "identify-2.5.33.tar.gz", hash = "sha256:161558f9fe4559e1557e1bff323e8631f6a0e4837f7497767c1782832f16b62d"},
 ]
 
 [package.extras]
@@ -811,13 +829,13 @@ files = [
 
 [[package]]
 name = "imageio"
-version = "2.33.0"
+version = "2.33.1"
 description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "imageio-2.33.0-py3-none-any.whl", hash = "sha256:d580d6576d0ae39c459a444a23f6f61fe72123a3df2264f5fce8c87784a4be2e"},
-    {file = "imageio-2.33.0.tar.gz", hash = "sha256:39999d05eb500089e60be467dd7d618f56e142229b44c3961c2b420eeb538d7e"},
+    {file = "imageio-2.33.1-py3-none-any.whl", hash = "sha256:c5094c48ccf6b2e6da8b4061cd95e1209380afafcbeae4a4e280938cce227e1d"},
+    {file = "imageio-2.33.1.tar.gz", hash = "sha256:78722d40b137bd98f5ec7312119f8aea9ad2049f76f434748eb306b6937cc1ce"},
 ]
 
 [package.dependencies]
@@ -994,6 +1012,38 @@ files = [
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 ]
 
+[[package]]
+name = "label-studio-sdk"
+version = "0.0.32"
+description = "Label Studio annotation tool"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.dependencies]
+label-studio-tools = ">=0.0.1"
+lxml = ">=4.2.5"
+pydantic = ">1.7,<3"
+requests = ">=2.22.0,<3"
+
+[[package]]
+name = "label-studio-tools"
+version = "0.0.3"
+description = "Label studio common tools"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
+[package.dependencies]
+appdirs = ">=1.4.3"
+lxml = ">=4.2.5"
+
 [[package]]
 name = "lazy-loader"
 version = "0.3"
@@ -1009,6 +1059,113 @@ files = [
 lint = ["pre-commit (>=3.3)"]
 test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
 
+[[package]]
+name = "lxml"
+version = "4.9.4"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
+files = [
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 [[package]]
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+
+[package.extras]
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+
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+    {file = "pydantic_core-2.14.5-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c97bee68898f3f4344eb02fec316db93d9700fb1e6a5b760ffa20d71d9a46ce3"},
+    {file = "pydantic_core-2.14.5-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b9b759b77f5337b4ea024f03abc6464c9f35d9718de01cfe6bae9f2e139c397e"},
+    {file = "pydantic_core-2.14.5-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:439c9afe34638ace43a49bf72d201e0ffc1a800295bed8420c2a9ca8d5e3dbb3"},
+    {file = "pydantic_core-2.14.5-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:ba39688799094c75ea8a16a6b544eb57b5b0f3328697084f3f2790892510d144"},
+    {file = "pydantic_core-2.14.5-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ccd4d5702bb90b84df13bd491be8d900b92016c5a455b7e14630ad7449eb03f8"},
+    {file = "pydantic_core-2.14.5-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:81982d78a45d1e5396819bbb4ece1fadfe5f079335dd28c4ab3427cd95389944"},
+    {file = "pydantic_core-2.14.5-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:7f8210297b04e53bc3da35db08b7302a6a1f4889c79173af69b72ec9754796b8"},
+    {file = "pydantic_core-2.14.5-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:8c8a8812fe6f43a3a5b054af6ac2d7b8605c7bcab2804a8a7d68b53f3cd86e00"},
+    {file = "pydantic_core-2.14.5-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:206ed23aecd67c71daf5c02c3cd19c0501b01ef3cbf7782db9e4e051426b3d0d"},
+    {file = "pydantic_core-2.14.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c2027d05c8aebe61d898d4cffd774840a9cb82ed356ba47a90d99ad768f39789"},
+    {file = "pydantic_core-2.14.5-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:40180930807ce806aa71eda5a5a5447abb6b6a3c0b4b3b1b1962651906484d68"},
+    {file = "pydantic_core-2.14.5-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:615a0a4bff11c45eb3c1996ceed5bdaa2f7b432425253a7c2eed33bb86d80abc"},
+    {file = "pydantic_core-2.14.5-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f5e412d717366e0677ef767eac93566582518fe8be923361a5c204c1a62eaafe"},
+    {file = "pydantic_core-2.14.5-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:513b07e99c0a267b1d954243845d8a833758a6726a3b5d8948306e3fe14675e3"},
+    {file = "pydantic_core-2.14.5.tar.gz", hash = "sha256:6d30226dfc816dd0fdf120cae611dd2215117e4f9b124af8c60ab9093b6e8e71"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
+
 [[package]]
 name = "pyflakes"
 version = "3.1.0"
@@ -1771,13 +2064,13 @@ stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
 
 [[package]]
 name = "sentry-sdk"
-version = "1.38.0"
+version = "1.39.1"
 description = "Python client for Sentry (https://sentry.io)"
 optional = false
 python-versions = "*"
 files = [
-    {file = "sentry-sdk-1.38.0.tar.gz", hash = "sha256:8feab81de6bbf64f53279b085bd3820e3e737403b0a0d9317f73a2c3374ae359"},
-    {file = "sentry_sdk-1.38.0-py2.py3-none-any.whl", hash = "sha256:0017fa73b8ae2d4e57fd2522ee3df30453715b29d2692142793ec5d5f90b94a6"},
+    {file = "sentry-sdk-1.39.1.tar.gz", hash = "sha256:320a55cdf9da9097a0bead239c35b7e61f53660ef9878861824fd6d9b2eaf3b5"},
+    {file = "sentry_sdk-1.39.1-py2.py3-none-any.whl", hash = "sha256:81b5b9ffdd1a374e9eb0c053b5d2012155db9cbe76393a8585677b753bd5fdc1"},
 ]
 
 [package.dependencies]
@@ -1965,13 +2258,13 @@ files = [
 
 [[package]]
 name = "tifffile"
-version = "2023.9.26"
+version = "2023.12.9"
 description = "Read and write TIFF files"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "tifffile-2023.9.26-py3-none-any.whl", hash = "sha256:1de47fa945fddaade256e25ad4f375ae65547f3c1354063aded881c32a64cf89"},
-    {file = "tifffile-2023.9.26.tar.gz", hash = "sha256:67e355e4595aab397f8405d04afe1b4ae7c6f62a44e22d933fee1a571a48c7ae"},
+    {file = "tifffile-2023.12.9-py3-none-any.whl", hash = "sha256:9b066e4b1a900891ea42ffd33dab8ba34c537935618b9893ddef42d7d422692f"},
+    {file = "tifffile-2023.12.9.tar.gz", hash = "sha256:9dd1da91180a6453018a241ff219e1905f169384355cd89c9ef4034c1b46cdb8"},
 ]
 
 [package.dependencies]
@@ -2013,13 +2306,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.8.0"
+version = "4.9.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
-    {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
+    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
+    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
 ]
 
 [[package]]
@@ -2071,13 +2364,13 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [[package]]
 name = "wandb"
-version = "0.16.0"
+version = "0.16.1"
 description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "wandb-0.16.0-py3-none-any.whl", hash = "sha256:e103142a5ecdb158d29441c2bf9f935ae149ed562377f7cebffd2a6f7c9de949"},
-    {file = "wandb-0.16.0.tar.gz", hash = "sha256:8d9875f1d8d75fee32dc51f6727bc277ce4f3869d7319ccf5f36ce596597402a"},
+    {file = "wandb-0.16.1-py3-none-any.whl", hash = "sha256:1d7423f92520984585bae9693bb637ae08d3e0c1d75ad4b34215bc44431f114c"},
+    {file = "wandb-0.16.1.tar.gz", hash = "sha256:ffe6e8dd8cc8fcd72010c1246fb3d6d226b37c4f111f3f94308a1c0ae28a2fec"},
 ]
 
 [package.dependencies]
@@ -2101,12 +2394,12 @@ typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
 async = ["httpx (>=0.23.0)"]
 aws = ["boto3"]
 azure = ["azure-identity", "azure-storage-blob"]
+core = ["wandb-core (>=0.17.0b2)"]
 gcp = ["google-cloud-storage"]
 kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
 launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "typing-extensions"]
 media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"]
 models = ["cloudpickle"]
-nexus = ["wandb-core (>=0.17.0b1)"]
 perf = ["orjson"]
 sweeps = ["sweeps (>=0.2.0)"]
 
@@ -2245,4 +2538,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "d0a398b3738e8aa3771631995f2eb161ac863807b20f7161852e323da11a9463"
+content-hash = "c6a5c39be3bd546e1a659a64d0e358026b0e7936823f3aee9cdc728448f2833b"
diff --git a/pyproject.toml b/pyproject.toml
index 524fa9cb..b453d666 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,6 +17,7 @@ matplotlib = "^3.7.3"
 google-cloud-storage = "^2.10.0"
 tqdm = "^4.66.1"
 xxhash = "^3.4.1"
+label-studio-sdk = "^0.0.32"
 
 #torch = {version="^2.1.0", source="pytorch"}
 #torchvision = {version="^0.16.0", source="pytorch"}

From dd8a18d5a7cc4664ce0c2692fcac25aa8fd706cc Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 14:07:15 +0800
Subject: [PATCH 04/35] Set default LABEL_STUDIO_API_KEY

---
 src/frdc/conf.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/frdc/conf.py b/src/frdc/conf.py
index c94806fe..99fd2f87 100644
--- a/src/frdc/conf.py
+++ b/src/frdc/conf.py
@@ -7,6 +7,8 @@
 import requests
 from google.cloud import storage as gcs
 
+logger = logging.getLogger(__name__)
+
 ROOT_DIR = Path(__file__).parents[2]
 LOCAL_DATASET_ROOT_DIR = ROOT_DIR / "rsc"
 os.environ["GOOGLE_CLOUD_PROJECT"] = "frmodel"
@@ -14,9 +16,9 @@
 GCS_BUCKET_NAME = "frdc-ds"
 GCS_CREDENTIALS = None
 LABEL_STUDIO_URL = "http://localhost:8080"
-LABEL_STUDIO_API_KEY = os.environ["LABEL_STUDIO_API_KEY"]
 
-logger = logging.getLogger(__name__)
+if not (LABEL_STUDIO_API_KEY := os.environ.get("LABEL_STUDIO_API_KEY", None)):
+    logger.warning("LABEL_STUDIO_API_KEY not set")
 
 BAND_CONFIG = OrderedDict(
     {

From e16480b1104e62c0fa3cde5ba0c6ae6710acaa40 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 14:17:28 +0800
Subject: [PATCH 05/35] Fix bad typing version

---
 src/frdc/load/label_studio.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/frdc/load/label_studio.py b/src/frdc/load/label_studio.py
index 54a0fce1..b8287ff3 100644
--- a/src/frdc/load/label_studio.py
+++ b/src/frdc/load/label_studio.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import logging
 from pathlib import Path
 from warnings import warn

From 6a85fa7d38028eef25f273bc7280270dfe461080 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 14:58:25 +0800
Subject: [PATCH 06/35] Unignore shell files, add cml entrypoint

---
 .gitignore | 1 -
 cml.sh     | 9 +++++++++
 2 files changed, 9 insertions(+), 1 deletion(-)
 create mode 100644 cml.sh

diff --git a/.gitignore b/.gitignore
index 4fcff576..e522579c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -166,7 +166,6 @@ rsc/**/*.tif
 
 **/*/lightning_logs
 *.zip
-*.sh
 *.ckpt
 /rsc
 **/wandb/
diff --git a/cml.sh b/cml.sh
new file mode 100644
index 00000000..900fa3d1
--- /dev/null
+++ b/cml.sh
@@ -0,0 +1,9 @@
+set -a
+source .env
+set +a
+
+cml runner launch \
+  --token=${GH_CML_TOKEN} \
+  --labels="cml-gpu" \
+  --idle-timeout="1h" --driver=github
+

From 8e71ad1753d7d634617ab402cb399913f6fc6a72 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 15:10:53 +0800
Subject: [PATCH 07/35] Update model-tests.yml

---
 .github/workflows/model-tests.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index c61342aa..36ff2052 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -63,6 +63,7 @@ jobs:
       # Do not do cd as it'll break PYTHONPATH.
       - name: Run Model Training
         run: |
+          sleep 1000
           python3 -m tests.model_tests.chestnut_dec_may.train
 
       - name: Comment results via CML

From 68effdba7192b505d830628dd80a552b95a3afd1 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 15:28:44 +0800
Subject: [PATCH 08/35] Update model-tests.yml

---
 .github/workflows/model-tests.yml | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index 36ff2052..b4463e63 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -4,6 +4,12 @@ on:
   pull_request:
     branches: ['main']
   workflow_dispatch:
+    inputs:
+      debug_enabled:
+        type: boolean
+        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
+        required: false
+        default: false
 
 
 jobs:
@@ -33,8 +39,8 @@ jobs:
 
       - name: Install via exported requirements.txt
         run: |
-          python -m pip install --upgrade pip
-          python -m pip install flake8 pytest poetry
+          python3 -m pip install --upgrade pip
+          python3 -m pip install flake8 pytest poetry
           poetry export --with dev --without-hashes -o requirements.txt
           pip3 install -r requirements.txt
           pip3 install torch torchvision torchaudio
@@ -60,6 +66,11 @@ jobs:
         run: |
           echo "PYTHONPATH=src" >> $GITHUB_ENV
 
+      # Enable tmate debugging of manually-triggered workflows if the input option was provided
+      - name: Setup tmate session
+        uses: mxschmitt/action-tmate@v3
+        if: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_enabled }}
+
       # Do not do cd as it'll break PYTHONPATH.
       - name: Run Model Training
         run: |

From 046e14023cbafec82ec9acbc2fec692eceb6579e Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 15:53:04 +0800
Subject: [PATCH 09/35] Attempt to fix tests not found

---
 .github/workflows/model-tests.yml | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index b4463e63..ce864b3e 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -61,21 +61,20 @@ jobs:
         run: |
           echo "WANDB_API_KEY=${{ secrets.WANDB_API_KEY }}" >> $GITHUB_ENV
 
-      # Our project has src as a source path, explicitly add that in.
-      - name: Add src as PYTHONPATH
-        run: |
-          echo "PYTHONPATH=src" >> $GITHUB_ENV
+      - name: Add directories to PYTHONPATH
+        run: >
+          echo "PYTHONPATH=${{ github.workspace }}/src:\
+          ${{ github.workspace }}/tests" >> $GITHUB_ENV
 
       # Enable tmate debugging of manually-triggered workflows if the input option was provided
       - name: Setup tmate session
         uses: mxschmitt/action-tmate@v3
         if: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_enabled }}
 
-      # Do not do cd as it'll break PYTHONPATH.
       - name: Run Model Training
+        working-directory: ${{ github.workspace }}/tests
         run: |
-          sleep 1000
-          python3 -m tests.model_tests.chestnut_dec_may.train
+          python3 -m model_tests.chestnut_dec_may.train
 
       - name: Comment results via CML
         run: |

From 79c3a99118b625b22a7ab39fd4e54ef946cb3f5e Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 19:21:25 +0800
Subject: [PATCH 10/35] Attempt to connect to label studio in cml

---
 .github/workflows/model-tests.yml | 7 +++++--
 src/frdc/conf.py                  | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index ce864b3e..fb35d8f1 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -14,17 +14,19 @@ on:
 
 jobs:
   build:
-
     runs-on: self-hosted
     container:
       image: docker://ghcr.io/iterative/cml:0-dvc2-base1-gpu
       volumes:
+        # This mounts and persists the venv between runs
         - /home/runner/work/frdc-ml/_github_home:/root
+        # This mounts the resources folder
+        - /home/runner/work/frdc-ml/_github_home/rsc:/__w/FRDC-ML/FRDC-ML/rsc
       env:
         # This is where setup-python will install and cache the venv
         AGENT_TOOLSDIRECTORY: "/root/venv"
-      options: --gpus all
 
+      options: --gpus all --network label-studio
     steps:
       - uses: actions/checkout@v3
 
@@ -74,6 +76,7 @@ jobs:
       - name: Run Model Training
         working-directory: ${{ github.workspace }}/tests
         run: |
+          git config --global --add safe.directory /__w/FRDC-ML/FRDC-ML'
           python3 -m model_tests.chestnut_dec_may.train
 
       - name: Comment results via CML
diff --git a/src/frdc/conf.py b/src/frdc/conf.py
index 99fd2f87..2e3c5127 100644
--- a/src/frdc/conf.py
+++ b/src/frdc/conf.py
@@ -15,7 +15,7 @@
 GCS_PROJECT_ID = "frmodel"
 GCS_BUCKET_NAME = "frdc-ds"
 GCS_CREDENTIALS = None
-LABEL_STUDIO_URL = "http://localhost:8080"
+LABEL_STUDIO_URL = "http://label-studio:8080"
 
 if not (LABEL_STUDIO_API_KEY := os.environ.get("LABEL_STUDIO_API_KEY", None)):
     logger.warning("LABEL_STUDIO_API_KEY not set")

From 4bb29e389bdb5b5286bdead068aec0936b52e072 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 20:14:06 +0800
Subject: [PATCH 11/35] Remove network line

---
 .github/workflows/model-tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index fb35d8f1..628e2f5a 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -26,7 +26,7 @@ jobs:
         # This is where setup-python will install and cache the venv
         AGENT_TOOLSDIRECTORY: "/root/venv"
 
-      options: --gpus all --network label-studio
+      options: --gpus all
     steps:
       - uses: actions/checkout@v3
 

From 6084fbbdce074d2c094c732315d20c781196cb19 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 20:29:23 +0800
Subject: [PATCH 12/35] Update docker-compose.yml

---
 src/label-studio/docker-compose.yml | 143 +++++++++++++++-------------
 1 file changed, 77 insertions(+), 66 deletions(-)

diff --git a/src/label-studio/docker-compose.yml b/src/label-studio/docker-compose.yml
index 2efb1d1d..4a6d909f 100644
--- a/src/label-studio/docker-compose.yml
+++ b/src/label-studio/docker-compose.yml
@@ -1,66 +1,77 @@
-version: "3.9"
-services:
-  nginx:
-    build: .
-    image: heartexlabs/label-studio:latest
-    restart: unless-stopped
-    ports:
-      - "8080:8085"
-      - "8081:8086"
-    depends_on:
-      - app
-    environment:
-      - LABEL_STUDIO_HOST=${LABEL_STUDIO_HOST:-}
-    #   Optional: Specify SSL termination certificate & key
-    #   Just drop your cert.pem and cert.key into folder 'deploy/nginx/certs'
-    #      - NGINX_SSL_CERT=/certs/cert.pem
-    #      - NGINX_SSL_CERT_KEY=/certs/cert.key
-    volumes:
-      - ./mydata:/label-studio/data:rw
-      - ./deploy/nginx/certs:/certs:ro
-    #   Optional: Override nginx default conf
-    #      - ./deploy/my.conf:/etc/nginx/nginx.conf
-    command: nginx
-
-  app:
-    stdin_open: true
-    tty: true
-    build: .
-    image: heartexlabs/label-studio:latest
-    restart: unless-stopped
-    expose:
-      - "8000"
-    depends_on:
-      - db
-    environment:
-      - DJANGO_DB=default
-      - POSTGRE_NAME=postgres
-      - POSTGRE_USER=postgres
-      - POSTGRE_PASSWORD=
-      - POSTGRE_PORT=5432
-      - POSTGRE_HOST=db
-      - LABEL_STUDIO_HOST=${LABEL_STUDIO_HOST:-}
-      - JSON_LOG=1
-    #      - LOG_LEVEL=DEBUG
-    volumes:
-      - ./mydata:/label-studio/data:rw
-    command: label-studio-uwsgi
-
-  db:
-    image: postgres:11.5
-    hostname: db
-    restart: unless-stopped
-    # Optional: Enable TLS on PostgreSQL
-    # Just drop your server.crt and server.key into folder 'deploy/pgsql/certs'
-    # NOTE: Both files must have permissions u=rw (0600) or less
-    #    command: >
-    #      -c ssl=on
-    #      -c ssl_cert_file=/var/lib/postgresql/certs/server.crt
-    #      -c ssl_key_file=/var/lib/postgresql/certs/server.key
-    ports:
-      - "5432:5432"
-    environment:
-      - POSTGRES_HOST_AUTH_METHOD=trust
-    volumes:
-      - ${POSTGRES_DATA_DIR:-./postgres-data}:/var/lib/postgresql/data
-      - ./deploy/pgsql/certs:/var/lib/postgresql/certs:ro
+version: "3.9"
+services:
+  nginx:
+    build: .
+    image: heartexlabs/label-studio:latest
+    restart: unless-stopped
+    ports:
+      - "8080:8085"
+      - "8081:8086"
+    depends_on:
+      - app
+    environment:
+      - LABEL_STUDIO_HOST=${LABEL_STUDIO_HOST:-}
+    #   Optional: Specify SSL termination certificate & key
+    #   Just drop your cert.pem and cert.key into folder 'deploy/nginx/certs'
+    #      - NGINX_SSL_CERT=/certs/cert.pem
+    #      - NGINX_SSL_CERT_KEY=/certs/cert.key
+    volumes:
+      - ./mydata:/label-studio/data:rw
+      - ./deploy/nginx/certs:/certs:ro
+    #   Optional: Override nginx default conf
+    #      - ./deploy/my.conf:/etc/nginx/nginx.conf
+    command: nginx
+    networks:
+      - label-studio
+
+  app:
+    stdin_open: true
+    tty: true
+    build: .
+    image: heartexlabs/label-studio:latest
+    restart: unless-stopped
+    expose:
+      - "8000"
+    depends_on:
+      - db
+    environment:
+      - DJANGO_DB=default
+      - POSTGRE_NAME=postgres
+      - POSTGRE_USER=postgres
+      - POSTGRE_PASSWORD=
+      - POSTGRE_PORT=5432
+      - POSTGRE_HOST=db
+      - LABEL_STUDIO_HOST=${LABEL_STUDIO_HOST:-}
+      - JSON_LOG=1
+    #      - LOG_LEVEL=DEBUG
+    volumes:
+      - ./mydata:/label-studio/data:rw
+    command: label-studio-uwsgi
+    networks:
+      - label-studio
+
+  db:
+    image: postgres:11.5
+    hostname: db
+    restart: unless-stopped
+    # Optional: Enable TLS on PostgreSQL
+    # Just drop your server.crt and server.key into folder 'deploy/pgsql/certs'
+    # NOTE: Both files must have permissions u=rw (0600) or less
+    #    command: >
+    #      -c ssl=on
+    #      -c ssl_cert_file=/var/lib/postgresql/certs/server.crt
+    #      -c ssl_key_file=/var/lib/postgresql/certs/server.key
+    ports:
+      - "5432:5432"
+    environment:
+      - POSTGRES_HOST_AUTH_METHOD=trust
+    volumes:
+      - ${POSTGRES_DATA_DIR:-./postgres-data}:/var/lib/postgresql/data
+      - ./deploy/pgsql/certs:/var/lib/postgresql/certs:ro
+    networks:
+      - label-studio
+
+networks:
+  label-studio:
+    name: label-studio
+#    driver: bridge

From 9a7f4080af3f27053a9c72d787730daba06764e0 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 20:39:48 +0800
Subject: [PATCH 13/35] Force owner

---
 .github/workflows/model-tests.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index 628e2f5a..84c656c7 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -28,11 +28,10 @@ jobs:
 
       options: --gpus all
     steps:
-      - uses: actions/checkout@v3
-
       - name: Force change owner
         run: |
           chown -R root: ~
+      - uses: actions/checkout@v3
 
       - name: Set up Python 3.11
         uses: actions/setup-python@v4

From 2ebc298ee70257c3d5d5de3a884bdec0fe644c1b Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 20:41:20 +0800
Subject: [PATCH 14/35] don't mount rsc

---
 .github/workflows/model-tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index 84c656c7..033bf341 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -21,7 +21,7 @@ jobs:
         # This mounts and persists the venv between runs
         - /home/runner/work/frdc-ml/_github_home:/root
         # This mounts the resources folder
-        - /home/runner/work/frdc-ml/_github_home/rsc:/__w/FRDC-ML/FRDC-ML/rsc
+#        - /home/runner/work/frdc-ml/_github_home/rsc:/__w/FRDC-ML/FRDC-ML/rsc
       env:
         # This is where setup-python will install and cache the venv
         AGENT_TOOLSDIRECTORY: "/root/venv"

From 4b13ce7d69681d054642fc699c987f6f3c59a2b3 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 21:00:04 +0800
Subject: [PATCH 15/35] Attempt to get label-studio via docker host

---
 .github/workflows/model-tests.yml |   6 +-
 src/frdc/conf.py                  | 151 +++++++++++++++---------------
 2 files changed, 81 insertions(+), 76 deletions(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index 033bf341..06bb253a 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -21,11 +21,15 @@ jobs:
         # This mounts and persists the venv between runs
         - /home/runner/work/frdc-ml/_github_home:/root
         # This mounts the resources folder
-#        - /home/runner/work/frdc-ml/_github_home/rsc:/__w/FRDC-ML/FRDC-ML/rsc
+        # - /home/runner/work/frdc-ml/_github_home/rsc:/__w/FRDC-ML/FRDC-ML/rsc
       env:
         # This is where setup-python will install and cache the venv
         AGENT_TOOLSDIRECTORY: "/root/venv"
 
+        # This uses the host's exposed services
+        LABEL_STUDIO_HOST: "host.docker.internal"
+        LABEL_STUDIO_API_KEY: "${{ secrets.LABEL_STUDIO_API_KEY }}"
+
       options: --gpus all
     steps:
       - name: Force change owner
diff --git a/src/frdc/conf.py b/src/frdc/conf.py
index 2e3c5127..89986988 100644
--- a/src/frdc/conf.py
+++ b/src/frdc/conf.py
@@ -1,75 +1,76 @@
-import logging
-import os
-from collections import OrderedDict
-from pathlib import Path
-
-import label_studio_sdk as label_studio
-import requests
-from google.cloud import storage as gcs
-
-logger = logging.getLogger(__name__)
-
-ROOT_DIR = Path(__file__).parents[2]
-LOCAL_DATASET_ROOT_DIR = ROOT_DIR / "rsc"
-os.environ["GOOGLE_CLOUD_PROJECT"] = "frmodel"
-GCS_PROJECT_ID = "frmodel"
-GCS_BUCKET_NAME = "frdc-ds"
-GCS_CREDENTIALS = None
-LABEL_STUDIO_URL = "http://label-studio:8080"
-
-if not (LABEL_STUDIO_API_KEY := os.environ.get("LABEL_STUDIO_API_KEY", None)):
-    logger.warning("LABEL_STUDIO_API_KEY not set")
-
-BAND_CONFIG = OrderedDict(
-    {
-        "WB": ("*result.tif", lambda x: x[..., 2:3]),
-        "WG": ("*result.tif", lambda x: x[..., 1:2]),
-        "WR": ("*result.tif", lambda x: x[..., 0:1]),
-        "NB": ("result_Blue.tif", lambda x: x),
-        "NG": ("result_Green.tif", lambda x: x),
-        "NR": ("result_Red.tif", lambda x: x),
-        "RE": ("result_RedEdge.tif", lambda x: x),
-        "NIR": ("result_NIR.tif", lambda x: x),
-    }
-)
-
-BAND_MAX_CONFIG: dict[str, tuple[int, int]] = {
-    "WR": (0, 2**8),
-    "WG": (0, 2**8),
-    "WB": (0, 2**8),
-    "NR": (0, 2**14),
-    "NG": (0, 2**14),
-    "NB": (0, 2**14),
-    "RE": (0, 2**14),
-    "NIR": (0, 2**14),
-}
-
-try:
-    logger.info("Connecting to GCS...")
-    GCS_CLIENT = gcs.Client(
-        project=GCS_PROJECT_ID,
-        credentials=GCS_CREDENTIALS,
-    )
-    GCS_BUCKET = GCS_CLIENT.bucket(GCS_BUCKET_NAME)
-    logger.info("Connected to GCS.")
-except Exception as e:
-    logger.warning(
-        "Could not connect to GCS. Will not be able to download files. "
-        "GCS_CLIENT will be None."
-    )
-    GCS_CLIENT = None
-
-try:
-    logger.info("Connecting to Label Studio...")
-    requests.get(LABEL_STUDIO_URL)
-    LABEL_STUDIO_CLIENT = label_studio.Client(
-        url=LABEL_STUDIO_URL,
-        api_key=LABEL_STUDIO_API_KEY,
-    )
-    logger.info("Connected to Label Studio.")
-except requests.exceptions.ConnectionError:
-    logger.warning(
-        f"Could not connect to Label Studio at {LABEL_STUDIO_URL}. "
-        f"LABEL_STUDIO_CLIENT will be None."
-    )
-    LABEL_STUDIO_CLIENT = None
+import logging
+import os
+from collections import OrderedDict
+from pathlib import Path
+
+import label_studio_sdk as label_studio
+import requests
+from google.cloud import storage as gcs
+
+logger = logging.getLogger(__name__)
+
+ROOT_DIR = Path(__file__).parents[2]
+LOCAL_DATASET_ROOT_DIR = ROOT_DIR / "rsc"
+os.environ["GOOGLE_CLOUD_PROJECT"] = "frmodel"
+GCS_PROJECT_ID = "frmodel"
+GCS_BUCKET_NAME = "frdc-ds"
+GCS_CREDENTIALS = None
+LABEL_STUDIO_HOST = os.environ.get("LABEL_STUDIO_HOST", "localhost")
+LABEL_STUDIO_URL = f"http://{LABEL_STUDIO_HOST}:8080"
+
+if not (LABEL_STUDIO_API_KEY := os.environ.get("LABEL_STUDIO_API_KEY", None)):
+    logger.warning("LABEL_STUDIO_API_KEY not set")
+
+BAND_CONFIG = OrderedDict(
+    {
+        "WB": ("*result.tif", lambda x: x[..., 2:3]),
+        "WG": ("*result.tif", lambda x: x[..., 1:2]),
+        "WR": ("*result.tif", lambda x: x[..., 0:1]),
+        "NB": ("result_Blue.tif", lambda x: x),
+        "NG": ("result_Green.tif", lambda x: x),
+        "NR": ("result_Red.tif", lambda x: x),
+        "RE": ("result_RedEdge.tif", lambda x: x),
+        "NIR": ("result_NIR.tif", lambda x: x),
+    }
+)
+
+BAND_MAX_CONFIG: dict[str, tuple[int, int]] = {
+    "WR": (0, 2**8),
+    "WG": (0, 2**8),
+    "WB": (0, 2**8),
+    "NR": (0, 2**14),
+    "NG": (0, 2**14),
+    "NB": (0, 2**14),
+    "RE": (0, 2**14),
+    "NIR": (0, 2**14),
+}
+
+try:
+    logger.info("Connecting to GCS...")
+    GCS_CLIENT = gcs.Client(
+        project=GCS_PROJECT_ID,
+        credentials=GCS_CREDENTIALS,
+    )
+    GCS_BUCKET = GCS_CLIENT.bucket(GCS_BUCKET_NAME)
+    logger.info("Connected to GCS.")
+except Exception as e:
+    logger.warning(
+        "Could not connect to GCS. Will not be able to download files. "
+        "GCS_CLIENT will be None."
+    )
+    GCS_CLIENT = None
+
+try:
+    logger.info("Connecting to Label Studio...")
+    requests.get(LABEL_STUDIO_URL)
+    LABEL_STUDIO_CLIENT = label_studio.Client(
+        url=LABEL_STUDIO_URL,
+        api_key=LABEL_STUDIO_API_KEY,
+    )
+    logger.info("Connected to Label Studio.")
+except requests.exceptions.ConnectionError:
+    logger.warning(
+        f"Could not connect to Label Studio at {LABEL_STUDIO_URL}. "
+        f"LABEL_STUDIO_CLIENT will be None."
+    )
+    LABEL_STUDIO_CLIENT = None

From ede2a5c9cd4c65fb1b8843dc5e6998f882394bf4 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 21:03:26 +0800
Subject: [PATCH 16/35] Update conf and compose

---
 .dvcignore | 3 ---
 1 file changed, 3 deletions(-)
 delete mode 100644 .dvcignore

diff --git a/.dvcignore b/.dvcignore
deleted file mode 100644
index 51973055..00000000
--- a/.dvcignore
+++ /dev/null
@@ -1,3 +0,0 @@
-# Add patterns of files dvc should ignore, which could improve
-# the performance. Learn more at
-# https://dvc.org/doc/user-guide/dvcignore

From 2d1efdd206c9523846b9e83243297ce6adca1909 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 21:05:10 +0800
Subject: [PATCH 17/35] Fix incorrect git config

---
 .github/workflows/model-tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index 06bb253a..93ee9e14 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -79,7 +79,7 @@ jobs:
       - name: Run Model Training
         working-directory: ${{ github.workspace }}/tests
         run: |
-          git config --global --add safe.directory /__w/FRDC-ML/FRDC-ML'
+          git config --global --add safe.directory /__w/FRDC-ML/FRDC-ML
           python3 -m model_tests.chestnut_dec_may.train
 
       - name: Comment results via CML

From b55afdd096ba315d5de2b0f37cf183bfc669e1a0 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 21:15:15 +0800
Subject: [PATCH 18/35] Add a check for Label Studio server up

---
 .github/workflows/model-tests.yml | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index 93ee9e14..feb1ffba 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -2,7 +2,7 @@ name: Model Training
 
 on:
   pull_request:
-    branches: ['main']
+    branches: [ 'main' ]
   workflow_dispatch:
     inputs:
       debug_enabled:
@@ -32,10 +32,15 @@ jobs:
 
       options: --gpus all
     steps:
+      - uses: actions/checkout@v3
+
       - name: Force change owner
         run: |
           chown -R root: ~
-      - uses: actions/checkout@v3
+
+      - name: Check if Label Studio Server is up
+        run: |
+          curl --fail --silent --head http://${{ env.LABEL_STUDIO_HOST }} || exit 1
 
       - name: Set up Python 3.11
         uses: actions/setup-python@v4

From 2de41eb63c59f0ef07e41ccb60aec92204c28f64 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 21:21:19 +0800
Subject: [PATCH 19/35] Improve formatting for report

---
 tests/model_tests/chestnut_dec_may/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/model_tests/chestnut_dec_may/train.py b/tests/model_tests/chestnut_dec_may/train.py
index 299eb708..9c2b3c96 100644
--- a/tests/model_tests/chestnut_dec_may/train.py
+++ b/tests/model_tests/chestnut_dec_may/train.py
@@ -114,8 +114,8 @@ def main(
 
     with open(Path(__file__).parent / "report.md", "w") as f:
         f.write(
-            f"# Chestnut Nature Park (Dec 2020 vs May 2021)"
-            f"[WandB Report]({run.get_url()})"
+            f"# Chestnut Nature Park (Dec 2020 vs May 2021)\n"
+            f"- Results: [WandB Report]({run.get_url()})"
         )
 
     fig, acc = evaluate(

From a4f00c2a9d9a859c82d43f50c9d56c5b4575723a Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 20 Dec 2023 21:21:28 +0800
Subject: [PATCH 20/35] Fix issue with env substitution

---
 .github/workflows/model-tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/model-tests.yml b/.github/workflows/model-tests.yml
index feb1ffba..ca734adb 100644
--- a/.github/workflows/model-tests.yml
+++ b/.github/workflows/model-tests.yml
@@ -40,7 +40,7 @@ jobs:
 
       - name: Check if Label Studio Server is up
         run: |
-          curl --fail --silent --head http://${{ env.LABEL_STUDIO_HOST }} || exit 1
+          curl --fail --silent --head http://host.docker.internal:8080 || exit 1
 
       - name: Set up Python 3.11
         uses: actions/setup-python@v4

From bf92c2c1ccfc8b072ab6d943149c6f9b20a24a45 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 14:19:48 +0800
Subject: [PATCH 21/35] Remove unused evaluate script

---
 src/frdc/evaluate/__init__.py |  2 --
 src/frdc/evaluate/evaluate.py | 31 -------------------------------
 2 files changed, 33 deletions(-)
 delete mode 100644 src/frdc/evaluate/evaluate.py

diff --git a/src/frdc/evaluate/__init__.py b/src/frdc/evaluate/__init__.py
index 48354a4b..8b137891 100644
--- a/src/frdc/evaluate/__init__.py
+++ b/src/frdc/evaluate/__init__.py
@@ -1,3 +1 @@
-from .evaluate import dummy_evaluate
 
-__all__ = ["dummy_evaluate"]
diff --git a/src/frdc/evaluate/evaluate.py b/src/frdc/evaluate/evaluate.py
deleted file mode 100644
index a0ccfbed..00000000
--- a/src/frdc/evaluate/evaluate.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from typing import Callable
-
-import numpy as np
-from sklearn.base import ClassifierMixin
-
-
-def dummy_evaluate(
-    *,
-    feature_extraction: Callable[[np.ndarray], np.ndarray],
-    classifier: ClassifierMixin,
-    X_test: np.ndarray,
-    y_test: np.ndarray,
-) -> float:
-    """Dummy Evaluation function.
-
-    Notes:
-        This is obviously not final. This is just a placeholder to get the
-         pipeline working.
-
-    Args:
-        feature_extraction: The feature extraction function.
-        classifier: The classifier.
-        X_test: X_test is the test image numpy array of shape (N, H, W, C).
-        y_test: y_test is the test class label a numpy array of shape (N,).
-
-    Returns:
-        The score of the model.
-    """
-    # TODO: Replace this with how the model scores
-
-    return classifier.score(feature_extraction(X_test), y_test)

From 77ba78a33bed09b7bf4051e889795634311cda20 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 14:20:44 +0800
Subject: [PATCH 22/35] Make GCS error clearer

---
 src/frdc/conf.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/frdc/conf.py b/src/frdc/conf.py
index 89986988..5098025d 100644
--- a/src/frdc/conf.py
+++ b/src/frdc/conf.py
@@ -56,6 +56,8 @@
 except Exception as e:
     logger.warning(
         "Could not connect to GCS. Will not be able to download files. "
+        "Check that you've (1) Installed the GCS CLI and (2) Set up the"
+        "ADC with `gcloud auth application-default login`. "
         "GCS_CLIENT will be None."
     )
     GCS_CLIENT = None

From 5c4a36cfbd672593293dc07ebe1fd30c9f762f63 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 14:20:51 +0800
Subject: [PATCH 23/35] Fix missing default on exception

---
 src/frdc/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/frdc/conf.py b/src/frdc/conf.py
index 5098025d..e2f5958a 100644
--- a/src/frdc/conf.py
+++ b/src/frdc/conf.py
@@ -61,6 +61,7 @@
         "GCS_CLIENT will be None."
     )
     GCS_CLIENT = None
+    GCS_BUCKET = None
 
 try:
     logger.info("Connecting to Label Studio...")

From d46f4e3fb56ae6b5becd2ba81389fd9dd3119e2b Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 14:21:03 +0800
Subject: [PATCH 24/35] Add dev container spec

---
 .devcontainer/devcontainer.json | 16 ++++++++++++++++
 Dockerfile                      | 19 +++++++++++++++++++
 2 files changed, 35 insertions(+)
 create mode 100644 .devcontainer/devcontainer.json
 create mode 100644 Dockerfile

diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 00000000..9b0313b8
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,16 @@
+{
+  "name": "frdc",
+  "build": {
+    "context": "../",
+    "dockerfile": "Dockerfile",
+  },
+  "containerEnv": {
+    "LABEL_STUDIO_HOST": "host.docker.internal",
+  },
+  "runArgs": [
+    "--gpus=all",
+  ],
+  "hostRequirements": {
+    "gpu": true,
+  }
+}
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 00000000..e1378f66
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,19 @@
+FROM pytorch/pytorch:2.1.2-cuda12.1-cudnn8-runtime as torch
+WORKDIR /devcontainer
+
+COPY ./pyproject.toml /devcontainer/pyproject.toml
+
+RUN apt-get update
+RUN apt-get install git -y
+
+RUN pip3 install --upgrade pip && \
+    pip3 install poetry
+
+RUN conda init bash \
+    && . ~/.bashrc \
+    && conda activate base \
+    && poetry config virtualenvs.create false \
+    && poetry install --with dev --no-interaction --no-ansi
+
+RUN apt-get install curl -y && curl -sSL https://sdk.cloud.google.com | bash
+ENV PATH $PATH:/root/google-cloud-sdk/bin
\ No newline at end of file

From c2ba141e24d02c47d88691ca199087c63a553c4b Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 14:22:54 +0800
Subject: [PATCH 25/35] Delete rsc.dvc

---
 rsc.dvc | 424 --------------------------------------------------------
 1 file changed, 424 deletions(-)
 delete mode 100644 rsc.dvc

diff --git a/rsc.dvc b/rsc.dvc
deleted file mode 100644
index f9ae0e31..00000000
--- a/rsc.dvc
+++ /dev/null
@@ -1,424 +0,0 @@
-outs:
-- hash: md5
-  path: rsc
-  files:
-  - relpath: DEBUG/0/bounds.csv
-    md5: ad253d8dfe0cbbc8107647b11d9a2ce2
-    size: 325
-    cloud:
-      frdc-ds:
-        etag: 08b6ab869c8de682031001
-        version_id: '1701154195084726'
-  - relpath: DEBUG/0/result.jpg
-    md5: 36e390821a45f70fc442bbd5d881f495
-    size: 7361
-    cloud:
-      frdc-ds:
-        etag: 08ccc6cc8191e682031001
-        version_id: '1701155213353804'
-  - relpath: DEBUG/0/result.tif
-    md5: c700d23b1332113e74051d0b0d187d6e
-    size: 60154
-    cloud:
-      frdc-ds:
-        etag: 0893da859c8de682031001
-        version_id: '1701154195074323'
-  - relpath: DEBUG/0/result_Blue.tif
-    md5: 663cb540356deb8811d0b779e4435bf5
-    size: 60134
-    cloud:
-      frdc-ds:
-        etag: 08fada859c8de682031001
-        version_id: '1701154195074426'
-  - relpath: DEBUG/0/result_Green.tif
-    md5: 2643f64a2e775d4ee95583e108ab5e9c
-    size: 60134
-    cloud:
-      frdc-ds:
-        etag: 08bc99869c8de682031001
-        version_id: '1701154195082428'
-  - relpath: DEBUG/0/result_NIR.tif
-    md5: 946c2eedf7e658fd9924b0a99b3282fa
-    size: 60134
-    cloud:
-      frdc-ds:
-        etag: 08879c869c8de682031001
-        version_id: '1701154195082759'
-  - relpath: DEBUG/0/result_Red.tif
-    md5: f566accc812ae46a0147b0078d0f4411
-    size: 60134
-    cloud:
-      frdc-ds:
-        etag: 0884fa869c8de682031001
-        version_id: '1701154195094788'
-  - relpath: DEBUG/0/result_RedEdge.tif
-    md5: ab998dba488a0fd7c362fab04c1b4034
-    size: 60134
-    cloud:
-      frdc-ds:
-        etag: 08a98c869c8de682031001
-        version_id: '1701154195080745'
-  - relpath: casuarina/20220418/183deg/dsm.tif
-    md5: 43260454e85e6f5795a5c17213de3334
-    size: 36290602
-    cloud:
-      frdc-ds:
-        etag: 08fd818aa08de682031001
-        version_id: '1701154203533565'
-  - relpath: casuarina/20220418/183deg/gsddsm.tif
-    md5: f67e3c112989145af0cc0b5a0478fa22
-    size: 98938
-    cloud:
-      frdc-ds:
-        etag: 08ceb6879c8de682031001
-        version_id: '1701154195102542'
-  - relpath: casuarina/20220418/183deg/result.jpg
-    md5: ceae7eab3f8a39485e3b4f5c5b7d47b3
-    size: 13379694
-    cloud:
-      frdc-ds:
-        etag: 08be83e98191e682031001
-        version_id: '1701155213820350'
-  - relpath: casuarina/20220418/183deg/result.tif
-    md5: e355405f060d55a6b7374b3542fdc3c1
-    size: 197880775
-    cloud:
-      frdc-ds:
-        etag: 08f2c3ebaf8de682031001
-        version_id: '1701154236588530'
-  - relpath: casuarina/20220418/183deg/result_Blue.tif
-    md5: 03bf7028f6527db900b95d7621463cce
-    size: 223640924
-    cloud:
-      frdc-ds:
-        etag: 08b4bf9eac8de682031001
-        version_id: '1701154229034932'
-  - relpath: casuarina/20220418/183deg/result_Green.tif
-    md5: d475aef93075d7375f38f73895d5fdb8
-    size: 226577241
-    cloud:
-      frdc-ds:
-        etag: 088b9682ac8de682031001
-        version_id: '1701154228570891'
-  - relpath: casuarina/20220418/183deg/result_NIR.tif
-    md5: 2c9cc4d48cf9316dea3a6f536fe5a3ac
-    size: 222016430
-    cloud:
-      frdc-ds:
-        etag: 089be4c1ab8de682031001
-        version_id: '1701154227515931'
-  - relpath: casuarina/20220418/183deg/result_Red.tif
-    md5: 73977a9d21760bbbcc8b42f79bcb4692
-    size: 226754875
-    cloud:
-      frdc-ds:
-        etag: 08e99287ac8de682031001
-        version_id: '1701154228652393'
-  - relpath: casuarina/20220418/183deg/result_RedEdge.tif
-    md5: 3f04518f4d8a35e2efdc3c3d6187860f
-    size: 223730363
-    cloud:
-      frdc-ds:
-        etag: 08ffbea1ac8de682031001
-        version_id: '1701154229084031'
-  - relpath: casuarina/20220418/183deg/segment.tif
-    md5: 830f7ca57c34ac3a0eb6a852cb272373
-    size: 26708
-    cloud:
-      frdc-ds:
-        etag: 08ccdd859c8de682031001
-        version_id: '1701154195074764'
-  - relpath: casuarina/20220418/93deg/dsm.tif
-    md5: 5fbd6841a0b7426bd01d4a3b68d06a9c
-    size: 36780878
-    cloud:
-      frdc-ds:
-        etag: 08f8f3c9a08de682031001
-        version_id: '1701154204580344'
-  - relpath: casuarina/20220418/93deg/gsddsm.tif
-    md5: ac659a3f17b2e21dc852a04af246906b
-    size: 91346
-    cloud:
-      frdc-ds:
-        etag: 08c798869c8de682031001
-        version_id: '1701154195082311'
-  - relpath: casuarina/20220418/93deg/result.jpg
-    md5: e0f493e7704cf377b4f41132313950d1
-    size: 13131131
-    cloud:
-      frdc-ds:
-        etag: 08bffae48191e682031001
-        version_id: '1701155213753663'
-  - relpath: casuarina/20220418/93deg/result.tif
-    md5: 83feb031787b457f5017db9acdebf625
-    size: 195394032
-    cloud:
-      frdc-ds:
-        etag: 088df3e1aa8de682031001
-        version_id: '1701154225944973'
-  - relpath: casuarina/20220418/93deg/result_Blue.tif
-    md5: d97ce2b5a44e896810e6401b4f5ab506
-    size: 220670499
-    cloud:
-      frdc-ds:
-        etag: 08d5d5b0b28de682031001
-        version_id: '1701154241915605'
-  - relpath: casuarina/20220418/93deg/result_Green.tif
-    md5: 40ef3a8c81ba28366ba47a1fa4989702
-    size: 223797748
-    cloud:
-      frdc-ds:
-        etag: 08d3ec8cac8de682031001
-        version_id: '1701154228745811'
-  - relpath: casuarina/20220418/93deg/result_NIR.tif
-    md5: a2ce084c7ba1c5f5a6b0b4a1dc93a98c
-    size: 219316999
-    cloud:
-      frdc-ds:
-        etag: 08f4b0fdab8de682031001
-        version_id: '1701154228492404'
-  - relpath: casuarina/20220418/93deg/result_Red.tif
-    md5: 65144fb2a827f6dd1723538afd375902
-    size: 224060322
-    cloud:
-      frdc-ds:
-        etag: 0885ce9fac8de682031001
-        version_id: '1701154229053189'
-  - relpath: casuarina/20220418/93deg/result_RedEdge.tif
-    md5: ceda32971f11a11af00a4f1838475e16
-    size: 221188044
-    cloud:
-      frdc-ds:
-        etag: 08f8dceaab8de682031001
-        version_id: '1701154228186744'
-  - relpath: casuarina/20220418/93deg/segment.tif
-    md5: ea46f1c799137aa0fd0256b20c5a66d6
-    size: 25383
-    cloud:
-      frdc-ds:
-        etag: 0881da859c8de682031001
-        version_id: '1701154195074305'
-  - relpath: chestnut_nature_park/20201218/bounds.csv
-    md5: d7043890368db04693865c2451c35a1f
-    size: 1408
-    cloud:
-      frdc-ds:
-        etag: 08e98f869c8de682031001
-        version_id: '1701154195081193'
-  - relpath: chestnut_nature_park/20201218/dsm.tif
-    md5: fbf608de495c51dace8bda86cadb4b65
-    size: 14482537
-    cloud:
-      frdc-ds:
-        etag: 08b8a1c79c8de682031001
-        version_id: '1701154196148408'
-  - relpath: chestnut_nature_park/20201218/result.jpg
-    md5: 91c1cff8671c332b20e3ca128431d6d7
-    size: 4780586
-    cloud:
-      frdc-ds:
-        etag: 08ddf0c49c8de682031001
-        version_id: '1701154196109405'
-  - relpath: chestnut_nature_park/20201218/result.tif
-    md5: d08c5b89a54b24ae85dd5d15e2a462fb
-    size: 80415617
-    cloud:
-      frdc-ds:
-        etag: 08fafec5a58de682031001
-        version_id: '1701154215001978'
-  - relpath: chestnut_nature_park/20201218/result_Blue.tif
-    md5: ad5eacd90476ad6a56111009f958a30b
-    size: 95205235
-    cloud:
-      frdc-ds:
-        etag: 08cebef79b8de682031001
-        version_id: '1701154194841422'
-  - relpath: chestnut_nature_park/20201218/result_Green.tif
-    md5: 5c85ec286f81edecaa5f2978086a6f79
-    size: 96849701
-    cloud:
-      frdc-ds:
-        etag: 08d2eff6a08de682031001
-        version_id: '1701154205317074'
-  - relpath: chestnut_nature_park/20201218/result_NIR.tif
-    md5: fa7df3dece555c289aea03c2c996195d
-    size: 94504684
-    cloud:
-      frdc-ds:
-        etag: 08b180c2a68de682031001
-        version_id: '1701154217033777'
-  - relpath: chestnut_nature_park/20201218/result_Red.tif
-    md5: 09b07ef8678e8f4ce058fd091760eab6
-    size: 96915726
-    cloud:
-      frdc-ds:
-        etag: 088cd0d0a18de682031001
-        version_id: '1701154206787596'
-  - relpath: chestnut_nature_park/20201218/result_RedEdge.tif
-    md5: 097c3c39629534dcdcb8f0b645ad9736
-    size: 95540119
-    cloud:
-      frdc-ds:
-        etag: 08ffa289a78de682031001
-        version_id: '1701154218201471'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/bounds.csv
-    md5: 58f03982df33a8b52c61e51daf6b450b
-    size: 1452
-    cloud:
-      frdc-ds:
-        etag: 08b2b8859c8de682031001
-        version_id: '1701154195070002'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/dsm.tif
-    md5: 041257e74edb3d9719debc3d9fe7d4dd
-    size: 20773786
-    cloud:
-      frdc-ds:
-        etag: 08f7b2809f8de682031001
-        version_id: '1701154201278839'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/gsddsm.tif
-    md5: 45b28f2f3735de83d65aea19e2697676
-    size: 45878
-    cloud:
-      frdc-ds:
-        etag: 08f691869c8de682031001
-        version_id: '1701154195081462'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/result.jpg
-    md5: d012b232cc1faed2871ebf5150d20cc1
-    size: 6941116
-    cloud:
-      frdc-ds:
-        etag: 08cfbae38191e682031001
-        version_id: '1701155213729103'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/result.tif
-    md5: 43541fa1f15171ba8548fb9b6ae77cfe
-    size: 103859274
-    cloud:
-      frdc-ds:
-        etag: 089b99c3a78de682031001
-        version_id: '1701154219150491'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/result_Blue.tif
-    md5: 87bef8f7bff639951ffa452f6ef9bc3f
-    size: 117714429
-    cloud:
-      frdc-ds:
-        etag: 08d8b18ca88de682031001
-        version_id: '1701154220349656'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/result_Green.tif
-    md5: c01de4a50b5caa01ace693e5191b3870
-    size: 119925274
-    cloud:
-      frdc-ds:
-        etag: 08bdfcbaa78de682031001
-        version_id: '1701154219015741'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/result_NIR.tif
-    md5: 2d842ef0d2f43e5269c1fc456aeb746d
-    size: 117211078
-    cloud:
-      frdc-ds:
-        etag: 08fb9adaa78de682031001
-        version_id: '1701154219527547'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/result_Red.tif
-    md5: 214d97f9eacf93ce29c6898858b7f146
-    size: 119913392
-    cloud:
-      frdc-ds:
-        etag: 08e5ecdda78de682031001
-        version_id: '1701154219587173'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/result_RedEdge.tif
-    md5: b45f71ffe2c14280100e22c23ba871ab
-    size: 118263239
-    cloud:
-      frdc-ds:
-        etag: 089798e6a88de682031001
-        version_id: '1701154221820951'
-  - relpath: chestnut_nature_park/20210510/90deg43m85pct255deg/segment.tif
-    md5: 86191f2ab6e7374ce98c83491ce43527
-    size: 10021
-    cloud:
-      frdc-ds:
-        etag: 08b5e5859c8de682031001
-        version_id: '1701154195075765'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/dsm.tif
-    md5: 99fa34f9ca8dc66d95b9b2fb144b334f
-    size: 10857218
-    cloud:
-      frdc-ds:
-        etag: 08dc84e09d8de682031001
-        version_id: '1701154198651484'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/gsddsm.tif
-    md5: 09f4143d1083c743f6a2bbed2dba5d81
-    size: 55570
-    cloud:
-      frdc-ds:
-        etag: 089e95869c8de682031001
-        version_id: '1701154195081886'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result.jpg
-    md5: cfc21b5bf466b57e888733f37d922017
-    size: 3243560
-    cloud:
-      frdc-ds:
-        etag: 08edc4db8191e682031001
-        version_id: '1701155213599341'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result.tif
-    md5: 6b99351bd16f05fafc2f96e2945ec094
-    size: 51407149
-    cloud:
-      frdc-ds:
-        etag: 08fca18da48de682031001
-        version_id: '1701154211975420'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result_Blue.tif
-    md5: c240b4e607af2cdadc3d4fc96181e005
-    size: 59362235
-    cloud:
-      frdc-ds:
-        etag: 08b3d484a58de682031001
-        version_id: '1701154213931571'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result_Green.tif
-    md5: 08d7b7e3e4823b0cf9c53692d6b55f5b
-    size: 60380606
-    cloud:
-      frdc-ds:
-        etag: 08ebe3a2a58de682031001
-        version_id: '1701154214425067'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result_NIR.tif
-    md5: ab3509fac188fe452d01957237181f31
-    size: 59034799
-    cloud:
-      frdc-ds:
-        etag: 0893bcfca48de682031001
-        version_id: '1701154213797395'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result_Red.tif
-    md5: 6b0531431f0d898aacda6334c10fca8a
-    size: 60369184
-    cloud:
-      frdc-ds:
-        etag: 08e8a58ea58de682031001
-        version_id: '1701154214089448'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result_RedEdge.tif
-    md5: 75e67bc7c1b01e060c68062354192b8e
-    size: 59567305
-    cloud:
-      frdc-ds:
-        etag: 08f0dc91ad8de682031001
-        version_id: '1701154230922864'
-  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/segment.tif
-    md5: 2b5856c859b44e54517b7891fd53bd60
-    size: 12784
-    cloud:
-      frdc-ds:
-        etag: 08838e869c8de682031001
-        version_id: '1701154195080963'
-  - relpath: chestnut_nature_park/20210510/Join/adding -90deg 60m data/cloud.las
-    md5: a4755ad17e0c0f2686a1fb9cacc712b7
-    size: 74248398
-    cloud:
-      frdc-ds:
-        etag: 0892daa1a48de682031001
-        version_id: '1701154212310290'
-  - relpath: chestnut_nature_park/20210510/Join/cloud.las
-    md5: 8ce9bccccab90a8a5c17ad5b3a982f0d
-    size: 61187376
-    cloud:
-      frdc-ds:
-        etag: 08fda5a8a58de682031001
-        version_id: '1701154214515453'

From 276fa174e0dcac7def581b0e715c97dcc875f760 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 14:41:50 +0800
Subject: [PATCH 26/35] Get api key from host

---
 .devcontainer/devcontainer.json | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index 9b0313b8..ca281b81 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -6,6 +6,7 @@
   },
   "containerEnv": {
     "LABEL_STUDIO_HOST": "host.docker.internal",
+    "LABEL_STUDIO_API_KEY": "${localEnv:LABEL_STUDIO_API_KEY}",
   },
   "runArgs": [
     "--gpus=all",

From 70b275ebb3a9b9bed4559a851e437d306c0226f6 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 15:10:40 +0800
Subject: [PATCH 27/35] Add missing lightning dep

---
 Dockerfile | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index e1378f66..9951dc95 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -7,7 +7,8 @@ RUN apt-get update
 RUN apt-get install git -y
 
 RUN pip3 install --upgrade pip && \
-    pip3 install poetry
+    pip3 install poetry && \
+    pip3 install lightning
 
 RUN conda init bash \
     && . ~/.bashrc \
@@ -16,4 +17,4 @@ RUN conda init bash \
     && poetry install --with dev --no-interaction --no-ansi
 
 RUN apt-get install curl -y && curl -sSL https://sdk.cloud.google.com | bash
-ENV PATH $PATH:/root/google-cloud-sdk/bin
\ No newline at end of file
+ENV PATH $PATH:/root/google-cloud-sdk/bin

From a1d79c158cfc927cca4a7319e54609e50e64de84 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 15:10:53 +0800
Subject: [PATCH 28/35] Add uncommentable local W&B setup

---
 tests/model_tests/chestnut_dec_may/train.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tests/model_tests/chestnut_dec_may/train.py b/tests/model_tests/chestnut_dec_may/train.py
index 9c2b3c96..f31ee825 100644
--- a/tests/model_tests/chestnut_dec_may/train.py
+++ b/tests/model_tests/chestnut_dec_may/train.py
@@ -4,7 +4,10 @@
 the 20210510 dataset.
 """
 
-import os
+# Uncomment this to run the W&B monitoring locally
+# import os
+# os.environ["WANDB_MODE"] = "offline"
+
 from pathlib import Path
 
 import lightning as pl

From 5d457abd3c17cf7d1884b3d87bd7b891741b7f5c Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 15:46:13 +0800
Subject: [PATCH 29/35] Update getting started docs for dev container

---
 Writerside/topics/Getting-Started.md     | 125 ++++++++++++++++++-----
 Writerside/writerside.cfg                |   2 +-
 docs/HelpTOC.json                        |   2 +-
 docs/custom-k-aug-dataloaders.html       |   6 +-
 docs/getting-started.html                |  24 +++--
 docs/icon-192.png                        | Bin 0 -> 337 bytes
 docs/icon-512.png                        | Bin 0 -> 1103 bytes
 docs/load-dataset.html                   |   8 +-
 docs/load-gcs.html                       |   6 +-
 docs/mix-match-module.html               |  12 +--
 docs/mix-match.html                      |   2 +-
 docs/model-test-chestnut-may-dec.html    |   2 +-
 docs/overview.html                       |   2 +-
 docs/preprocessing-extract-segments.html |  16 +--
 docs/preprocessing-glcm-padded.html      |   4 +-
 docs/preprocessing-morphology.html       |   6 +-
 docs/preprocessing-scale.html            |   4 +-
 docs/retrieve-our-datasets.html          |  10 +-
 docs/site.webmanifest                    |  11 ++
 docs/train-frdc-lightning.html           |   4 +-
 20 files changed, 167 insertions(+), 79 deletions(-)
 create mode 100644 docs/icon-192.png
 create mode 100644 docs/icon-512.png
 create mode 100644 docs/site.webmanifest

diff --git a/Writerside/topics/Getting-Started.md b/Writerside/topics/Getting-Started.md
index 7615174d..10fe4398 100644
--- a/Writerside/topics/Getting-Started.md
+++ b/Writerside/topics/Getting-Started.md
@@ -10,7 +10,7 @@
     </step>
     <step>Start by cloning our repository.
         <code-block lang="shell">
-          git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git
+          git clone https://github.com/FR-DC/FRDC-ML.git
         </code-block>
     </step>
     <step>Then, create a Python Virtual Env <code>pyvenv</code>
@@ -60,6 +60,26 @@
     </step>
 </procedure>
 
+<procedure title="Use a Dev. Container" id="install-dev-con">
+    <tip>
+        Only use Dev. Containers if you're familiar with your IDEs, it's highly
+        dependent on clicking around the IDE.
+    </tip>
+    <warning>Do not set up a new environment, it'll be included in the environment.</warning>
+    <step>
+        Ensure that you have installed pre-requisites for respective IDEs. 
+        <a href="https://code.visualstudio.com/docs/remote/containers#_system-requirements"> VSCode </a>
+        <a href="https://www.jetbrains.com/help/idea/prerequisites-for-dev-containers.html"> IntelliJ </a>
+    </step>
+    <step>Start by cloning our repository.
+        <code-block lang="shell">
+          git clone https://github.com/FR-DC/FRDC-ML.git
+        </code-block>
+    </step>
+    <step>Follow steps for respective IDEs to set up the Dev. Container.</step>
+    <step>Activate the virtual environment. The venv is located in <code>/opt/venv</code></step>
+</procedure>
+
 <procedure title="Setting Up Google Cloud" id="gcloud">
     <step>
         We use Google Cloud to store our datasets. To set up Google Cloud,
@@ -86,6 +106,49 @@
     </step>
 </procedure>
 
+<procedure title="Setting Up Label Studio" id="ls">
+    <tip>This is only necessary if any task requires Label Studio annotations</tip>
+    <step>
+        We use Label Studio to annotate our datasets.
+        We won't go through how to install Label Studio, for contributors, it
+        should be up on <code>localhost:8080</code>.
+    </step>
+    <step>
+        Then, retrieve your own API key from Label Studio.
+        <a href="http://localhost:8080/user/account"> Go to your account page </a>
+        and copy the API key. <br/></step>
+    <step> Set your API key as an environment variable.
+        <tabs>
+<tab title="Windows">
+        In Windows, go to "Edit environment variables for
+        your account" and add this as a new environment variable with name
+        <code>LABEL_STUDIO_API_KEY</code>.
+</tab>
+<tab title="Linux">
+        Export it as an environment variable.
+        <code-block lang="shell">export LABEL_STUDIO_API_KEY=...</code-block>
+</tab>
+</tabs>
+    </step>
+</procedure>
+
+
+<procedure title="Setting Up Weight and Biases" id="wandb">
+    <step>
+        We use W&B to track our experiments. To set up W&B,
+        <a href="https://docs.wandb.ai/quickstart">
+          install the W&B CLI
+        </a>
+    </step>
+    <step>
+        Then, 
+        <a href="https://docs.wandb.ai/quickstart">
+          authenticate your account
+        </a>.
+        <code-block lang="shell">wandb login</code-block>
+    </step>
+</procedure>
+
 <procedure title="Pre-commit Hooks" collapsible="true">
     <note>This is optional but recommended.
     Pre-commit hooks are a way to ensure that your code is formatted correctly.
@@ -98,30 +161,45 @@
     </step>
 </procedure>
 
-<procedure title="Running the Tests" collapsible="true" id="tests">
+<procedure title="Running the Tests" id="tests">
     <step>
         Run the tests to make sure everything is working
         <code-block lang="shell">
             pytest
         </code-block>
     </step>
-<step>
-    In case of errors:
-    <deflist>
-        <def title="google.auth.exceptions.DefaultCredentialsError">
-            If you get this error, it means that you haven't authenticated your
-            Google Cloud account.
-            See <a anchor="gcloud">Setting Up Google Cloud</a>
-        </def>
-        <def title="ModuleNotFoundError" collapsible="true">
-            If you get this error, it means that you haven't installed the
-            dependencies.
-            See <a anchor="install">Installing the Dev. Environment</a>
-        </def>
-    </deflist>
-</step>
 </procedure>
 
+## Troubleshooting
+
+### ModuleNotFoundError
+
+It's likely that your `src` and `tests` directories are not in `PYTHONPATH`.
+To fix this, run the following command:
+
+```shell
+export PYTHONPATH=$PYTHONPATH:./src:./tests
+```
+
+Or, set it in your IDE, for example, IntelliJ allows setting directories as
+**Source Roots**.
+
+### google.auth.exceptions.DefaultCredentialsError
+
+It's likely that you haven't authenticated your Google Cloud account.
+See [Setting Up Google Cloud](#gcloud)
+
+### Couldn't connect to Label Studio
+
+Label Studio must be running locally, exposed on `localhost:8080`. Furthermore,
+you need to specify the `LABEL_STUDIO_API_KEY` environment variable. See 
+[Setting Up Label Studio](#ls)
+
+### Cannot login to W&B
+
+You need to authenticate your W&B account. See [Setting Up Weight and Biases](#wandb)
+If you're facing difficulties, set the `WANDB_MODE` environment variable to `offline`
+to disable W&B.
 
 ## Our Repository Structure
 
@@ -132,7 +210,6 @@ help you understand where to put your code.
 graph LR
     FRDC -- " Core Dependencies " --> src/frdc/
     FRDC -- " Resources " --> rsc/
-    FRDC -- " Pipeline " --> pipeline/
     FRDC -- " Tests " --> tests/
     FRDC -- " Repo Dependencies " --> pyproject.toml,poetry.lock
     src/frdc/ -- " Dataset Loaders " --> ./load/
@@ -140,7 +217,6 @@ graph LR
     src/frdc/ -- " Train Deps " --> ./train/
     src/frdc/ -- " Model Architectures " --> ./models/
     rsc/ -- " Datasets ... " --> ./dataset_name/
-    pipeline/ -- " Model Training Pipeline " --> ./model_tests/
 ```
 
 src/frdc/
@@ -149,11 +225,8 @@ src/frdc/
 rsc/
 : Resources. These are usually cached datasets
 
-pipeline/
-: Pipeline code. These are the full ML tests of our pipeline.
-
 tests/
-: PyTest tests. These are unit tests & integration tests.
+: PyTest tests. These are unit, integration, and model tests.
 
 ### Unit, Integration, and Pipeline Tests
 
@@ -161,7 +234,7 @@ We have 3 types of tests:
 
 - Unit Tests are usually small, single function tests.
 - Integration Tests are larger tests that tests a mock pipeline.
-- Pipeline Tests are the true production pipeline tests that will generate a
+- Model Tests are the true production pipeline tests that will generate a
   model.
 
 ### Where Should I contribute?
@@ -176,9 +249,9 @@ at the <code>src/frdc/</code> directory.
 By adding a new component, you'll need to add a new test. Take a look at the
 <code>tests/</code> directory.
 </def>
-<def title="Changing the pipeline">
+<def title="Changing the model pipeline">
 If you're a ML Researcher, you'll probably be changing the pipeline. Take a
-look at the <code>pipeline/</code> directory.
+look at the <code>tests/model_tests/</code> directory.
 </def>
 <def title="Adding a dependency">
 If you're adding a new dependency, use <code>poetry add PACKAGE</code> and
diff --git a/Writerside/writerside.cfg b/Writerside/writerside.cfg
index 39e81f21..9e1b0444 100644
--- a/Writerside/writerside.cfg
+++ b/Writerside/writerside.cfg
@@ -4,5 +4,5 @@
 <ihp version="2.0">
     <topics dir="topics" web-path="topics"/>
     <images dir="images" web-path="images"/>
-    <instance src="d.tree" web-path="/d/" version="0.0.7"/>
+    <instance src="d.tree" web-path="/d/" version="0.0.8"/>
 </ihp>
\ No newline at end of file
diff --git a/docs/HelpTOC.json b/docs/HelpTOC.json
index 54d3f877..107c112d 100644
--- a/docs/HelpTOC.json
+++ b/docs/HelpTOC.json
@@ -1 +1 @@
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\ No newline at end of file
+{"entities":{"pages":{"Overview":{"id":"Overview","title":"Overview","url":"overview.html","level":0,"tabIndex":0},"Getting-Started":{"id":"Getting-Started","title":"Getting Started","url":"getting-started.html","level":0,"tabIndex":1},"e8e19623_38829":{"id":"e8e19623_38829","title":"Tutorials","level":0,"pages":["Retrieve-our-Datasets"],"tabIndex":2},"Retrieve-our-Datasets":{"id":"Retrieve-our-Datasets","title":"Retrieve our Datasets","url":"retrieve-our-datasets.html","level":1,"parentId":"e8e19623_38829","tabIndex":0},"mix-match":{"id":"mix-match","title":"MixMatch","url":"mix-match.html","level":0,"pages":["mix-match-module","custom-k-aug-dataloaders"],"tabIndex":3},"mix-match-module":{"id":"mix-match-module","title":"MixMatch Module","url":"mix-match-module.html","level":1,"parentId":"mix-match","tabIndex":0},"custom-k-aug-dataloaders":{"id":"custom-k-aug-dataloaders","title":"Custom K-Aug Dataloaders","url":"custom-k-aug-dataloaders.html","level":1,"parentId":"mix-match","tabIndex":1},"e8e19623_38834":{"id":"e8e19623_38834","title":"Model Tests","level":0,"pages":["Model-Test-Chestnut-May-Dec"],"tabIndex":4},"Model-Test-Chestnut-May-Dec":{"id":"Model-Test-Chestnut-May-Dec","title":"Model Test Chestnut May-Dec","url":"model-test-chestnut-may-dec.html","level":1,"parentId":"e8e19623_38834","tabIndex":0},"e8e19623_38836":{"id":"e8e19623_38836","title":"API","level":0,"pages":["load.dataset","load.gcs","preprocessing.scale","preprocessing.extract_segments","preprocessing.morphology","preprocessing.glcm_padded","train.frdc_lightning"],"tabIndex":5},"load.dataset":{"id":"load.dataset","title":"load.dataset","url":"load-dataset.html","level":1,"parentId":"e8e19623_38836","tabIndex":0},"load.gcs":{"id":"load.gcs","title":"load.gcs","url":"load-gcs.html","level":1,"parentId":"e8e19623_38836","tabIndex":1},"preprocessing.scale":{"id":"preprocessing.scale","title":"preprocessing.scale","url":"preprocessing-scale.html","level":1,"parentId":"e8e19623_38836","tabIndex":2},"preprocessing.extract_segments":{"id":"preprocessing.extract_segments","title":"preprocessing.extract_segments","url":"preprocessing-extract-segments.html","level":1,"parentId":"e8e19623_38836","tabIndex":3},"preprocessing.morphology":{"id":"preprocessing.morphology","title":"preprocessing.morphology","url":"preprocessing-morphology.html","level":1,"parentId":"e8e19623_38836","tabIndex":4},"preprocessing.glcm_padded":{"id":"preprocessing.glcm_padded","title":"preprocessing.glcm_padded","url":"preprocessing-glcm-padded.html","level":1,"parentId":"e8e19623_38836","tabIndex":5},"train.frdc_lightning":{"id":"train.frdc_lightning","title":"train.frdc_datamodule \u0026 frdc_module","url":"train-frdc-lightning.html","level":1,"parentId":"e8e19623_38836","tabIndex":6}}},"topLevelIds":["Overview","Getting-Started","e8e19623_38829","mix-match","e8e19623_38834","e8e19623_38836"]}
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diff --git a/docs/custom-k-aug-dataloaders.html b/docs/custom-k-aug-dataloaders.html
index f3bc78b7..487648dc 100644
--- a/docs/custom-k-aug-dataloaders.html
+++ b/docs/custom-k-aug-dataloaders.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.0968833"><meta name="build-number" content="${buildNumber}">       <title>Custom K-Aug Dataloaders | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"loading-multiple-augmented-versions-of-the-same-image","level":0,"title":"Loading Multiple Augmented Versions of the Same Image","anchor":"#loading-multiple-augmented-versions-of-the-same-image"},{"id":"premature-end-of-epoch-due-to-small-labelled-set","level":0,"title":"Premature End of Epoch due to Small Labelled Set","anchor":"#premature-end-of-epoch-due-to-small-labelled-set"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Custom K-Aug Dataloaders | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/custom-k-aug-dataloaders.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Custom K-Aug Dataloaders | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/custom-k-aug-dataloaders.html#webpage", "url": "/d/0.0.7/custom-k-aug-dataloaders.html", "name": "Custom K-Aug Dataloaders | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="custom-k-aug-dataloaders" data-main-title="Custom K-Aug Dataloaders" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="mix-match.md|MixMatch"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="custom-k-aug-dataloaders"   id="custom-k-aug-dataloaders.md">Custom K-Aug Dataloaders</h1>  <p id="943bd38_133">In MixMatch, implementing the data loading methods is quite unconventional.</p><ol class="list _decimal" id="943bd38_134" type="1"><li class="list__item" id="943bd38_135"><p>We need to load multiple augmented versions of the same image into the same batch.</p></li><li class="list__item" id="943bd38_136"><p>The labelled set is usually too small, causing a premature end to the epoch as it runs out of samples to draw from faster than the unlabelled set.</p></li></ol><p id="943bd38_137">This can be rather tricky to implement in PyTorch. This tutorial will illustrate how we did it.</p><section class="chapter"><h2 id="loading-multiple-augmented-versions-of-the-same-image" data-toc="loading-multiple-augmented-versions-of-the-same-image"   >Loading Multiple Augmented Versions of the Same Image</h2><p id="943bd38_138">See: <code class="code" id="943bd38_139">frdc/load/dataset.py</code> <code class="code" id="943bd38_140">FRDCDataset.__getitem__</code></p><p id="943bd38_141">In MixMatch, a single train batch must consist of:</p><ol class="list _decimal" id="943bd38_142" type="1"><li class="list__item" id="943bd38_143"><p>A batch of labeled images</p></li><li class="list__item" id="943bd38_144"><p>K batches of unlabeled images</p></li></ol><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 518.203125 302"  xmlns="http://www.w3.org/2000/svg" width="518.203125" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; 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white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Unl. Batch 1</span></div></foreignObject></g></g><g transform="translate(425.9296875, 185)" id="flowchart-E-93" class="node default default flowchart-label"><rect height="34" width="130.421875" y="-17" x="-65.2109375" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-57.7109375, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="115.421875"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Unl. Batch i</span></div></foreignObject></g></g><g transform="translate(425.9296875, 269)" id="flowchart-F-95" class="node default default flowchart-label"><rect height="34" width="135.0625" y="-17" x="-67.53125" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-60.03125, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="120.0625"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Unl. Batch K</span></div></foreignObject></g></g></g></g></g></svg><p id="943bd38_146">Keep in mind that the unlabelled batch, is a single batch of images, not separate draws of batches. It is then &quot;duplicated&quot; K times, and each copy is augmented differently.</p><section class="chapter"><h3 id="solution-1-custom-dataset" data-toc="solution-1-custom-dataset"   >Solution 1: Custom Dataset</h3><p id="943bd38_147">To solve this, we need to understand the role of both a <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" id="943bd38_148"   data-external="true" rel="noopener noreferrer" >Dataset</a> and a <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" id="943bd38_149"   data-external="true" rel="noopener noreferrer" >DataLoader</a>.</p><ul class="list _ul" id="943bd38_150"><li class="list__item" id="943bd38_151"><p>A <code class="code" id="943bd38_152">Dataset</code> represents a collection of data, responsible for loading and returning something.</p></li><li class="list__item" id="943bd38_153"><p>A <code class="code" id="943bd38_154">DataLoader</code> draws samples from a Dataset and returns batched samples.</p></li></ul><p id="943bd38_155">The key here is that a <code class="code" id="943bd38_156">Dataset</code> is not limited to returning 1 sample at a time, we can make it return the K augmented versions of the same image.</p><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 270.78125 218"  xmlns="http://www.w3.org/2000/svg" width="270.78125" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; 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white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Sample i</span></div></foreignObject></g></g><g transform="translate(198.875, 185)" id="flowchart-D-149" class="node default default flowchart-label"><rect height="34" width="111.8125" y="-17" x="-55.90625" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-48.40625, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="96.8125"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Sample K</span></div></foreignObject></g></g></g></g></g></svg><p id="943bd38_158">In code, this is done by subclassing the <code class="code" id="943bd38_159">Dataset</code> class and overriding the <code class="code" id="943bd38_160">__getitem__</code> method.</p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1419417">        <title>Custom K-Aug Dataloaders | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"loading-multiple-augmented-versions-of-the-same-image","level":0,"title":"Loading Multiple Augmented Versions of the Same Image","anchor":"#loading-multiple-augmented-versions-of-the-same-image"},{"id":"premature-end-of-epoch-due-to-small-labelled-set","level":0,"title":"Premature End of Epoch due to Small Labelled Set","anchor":"#premature-end-of-epoch-due-to-small-labelled-set"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Custom K-Aug Dataloaders | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/custom-k-aug-dataloaders.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Custom K-Aug Dataloaders | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/custom-k-aug-dataloaders.html#webpage", "url": "/d/0.0.7/custom-k-aug-dataloaders.html", "name": "Custom K-Aug Dataloaders | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="custom-k-aug-dataloaders" data-main-title="Custom K-Aug Dataloaders" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="mix-match.md|MixMatch"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="custom-k-aug-dataloaders"   id="custom-k-aug-dataloaders.md">Custom K-Aug Dataloaders</h1>  <p id="a70305a_1767">In MixMatch, implementing the data loading methods is quite unconventional.</p><ol class="list _decimal" id="a70305a_1768" type="1"><li class="list__item" id="a70305a_1769"><p>We need to load multiple augmented versions of the same image into the same batch.</p></li><li class="list__item" id="a70305a_1770"><p>The labelled set is usually too small, causing a premature end to the epoch as it runs out of samples to draw from faster than the unlabelled set.</p></li></ol><p id="a70305a_1771">This can be rather tricky to implement in PyTorch. This tutorial will illustrate how we did it.</p><section class="chapter"><h2 id="loading-multiple-augmented-versions-of-the-same-image" data-toc="loading-multiple-augmented-versions-of-the-same-image">Loading Multiple Augmented Versions of the Same Image</h2><p id="a70305a_1772">See: <code class="code" id="a70305a_1773">frdc/load/dataset.py</code> <code class="code" id="a70305a_1774">FRDCDataset.__getitem__</code></p><p id="a70305a_1775">In MixMatch, a single train batch must consist of:</p><ol class="list _decimal" id="a70305a_1776" type="1"><li class="list__item" id="a70305a_1777"><p>A batch of labeled images</p></li><li class="list__item" id="a70305a_1778"><p>K batches of unlabeled images</p></li></ol><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 518.203125 302"  xmlns="http://www.w3.org/2000/svg" width="518.203125" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; 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white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Unl. Batch 1</span></div></foreignObject></g></g><g transform="translate(425.9296875, 185)" id="flowchart-E-7617" class="node default default flowchart-label"><rect height="34" width="130.421875" y="-17" x="-65.2109375" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-57.7109375, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="115.421875"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Unl. Batch i</span></div></foreignObject></g></g><g transform="translate(425.9296875, 269)" id="flowchart-F-7619" class="node default default flowchart-label"><rect height="34" width="135.0625" y="-17" x="-67.53125" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-60.03125, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="120.0625"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Unl. Batch K</span></div></foreignObject></g></g></g></g></g></svg><p id="a70305a_1780">Keep in mind that the unlabelled batch, is a single batch of images, not separate draws of batches. It is then &quot;duplicated&quot; K times, and each copy is augmented differently.</p><section class="chapter"><h3 id="solution-1-custom-dataset" data-toc="solution-1-custom-dataset">Solution 1: Custom Dataset</h3><p id="a70305a_1781">To solve this, we need to understand the role of both a <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" id="a70305a_1782" data-external="true" rel="noopener noreferrer">Dataset</a> and a <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" id="a70305a_1783" data-external="true" rel="noopener noreferrer">DataLoader</a>.</p><ul class="list _ul" id="a70305a_1784"><li class="list__item" id="a70305a_1785"><p>A <code class="code" id="a70305a_1786">Dataset</code> represents a collection of data, responsible for loading and returning something.</p></li><li class="list__item" id="a70305a_1787"><p>A <code class="code" id="a70305a_1788">DataLoader</code> draws samples from a Dataset and returns batched samples.</p></li></ul><p id="a70305a_1789">The key here is that a <code class="code" id="a70305a_1790">Dataset</code> is not limited to returning 1 sample at a time, we can make it return the K augmented versions of the same image.</p><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 270.78125 218"  xmlns="http://www.w3.org/2000/svg" width="270.78125" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; 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white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Sample</span></div></foreignObject></g></g><g transform="translate(198.875, 17)" id="flowchart-B-7723" class="node default default flowchart-label"><rect height="34" width="110.984375" y="-17" x="-55.4921875" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-47.9921875, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="95.984375"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Sample 1</span></div></foreignObject></g></g><g transform="translate(198.875, 101)" id="flowchart-C-7725" class="node default default flowchart-label"><rect height="34" width="107.15625" y="-17" x="-53.578125" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-46.078125, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="92.15625"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Sample i</span></div></foreignObject></g></g><g transform="translate(198.875, 185)" id="flowchart-D-7727" class="node default default flowchart-label"><rect height="34" width="111.8125" y="-17" x="-55.90625" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-48.40625, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="96.8125"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Aug Sample K</span></div></foreignObject></g></g></g></g></g></svg><p id="a70305a_1792">In code, this is done by subclassing the <code class="code" id="a70305a_1793">Dataset</code> class and overriding the <code class="code" id="a70305a_1794">__getitem__</code> method.</p><div class="code-block" data-lang="python">
 def duplicate(x):
     return x, deepcopy(x), deepcopy(x)
 
@@ -10,7 +10,7 @@
     def __getitem__(self, index):
         x, y = self.dataset[index]
         return self.aug(x), y
-</div><p id="943bd38_162">In the above example, we have a <code class="code" id="943bd38_163">Dataset</code> that returns 3 duplicate versions of the same image. By leveraging this technique, we can create a <code class="code" id="943bd38_164">Dataset</code> that returns K augmented versions of the same image as a tuple</p></section></section><section class="chapter"><h2 id="premature-end-of-epoch-due-to-small-labelled-set" data-toc="premature-end-of-epoch-due-to-small-labelled-set"   >Premature End of Epoch due to Small Labelled Set</h2><p id="943bd38_165">See: <code class="code" id="943bd38_166">frdc/train/frdc_datamodule.py</code></p><p id="943bd38_167">In MixMatch, the definition of an &quot;epoch&quot; is a bit different. Instead of implying that we have seen all the data once, it implies that we've drawn <code class="code" id="943bd38_168">N</code> batches. The <code class="code" id="943bd38_169">N</code> is referred to as the number of iterations per epoch.</p><p id="943bd38_170">Take for example, a labelled set of numbers [1, 2, 3] and an unlabelled set [4, 5, 6, 7, 8, 9, 10]. With batch size of 2, we'll run out of labelled samples after 2 iterations, but we'll still have 3 more iterations for the unlabelled set.</p><ul class="list _ul" id="943bd38_171"><li class="list__item" id="943bd38_172"><p>Draw 1: [1, 2], [4, 5]</p></li><li class="list__item" id="943bd38_173"><p>Draw 2: [3], [6, 7].</p></li><li class="list__item" id="943bd38_174"><p>Epoch ends.</p></li></ul><section class="chapter"><h3 id="solution-2-random-sampling" data-toc="solution-2-random-sampling"   >Solution 2: Random Sampling</h3><p id="943bd38_175">To fix this, instead of sequentially sampling the labelled set (and the unlabelled set), we can sample them randomly. This way, we can ensure that it never runs out.</p><ul class="list _ul" id="943bd38_176"><li class="list__item" id="943bd38_177"><p>Draw 1: [1, 3], [7, 5]</p></li><li class="list__item" id="943bd38_178"><p>Draw 2: [2, 1], [4, 9]</p></li><li class="list__item" id="943bd38_179"><p>Draw 3: [3, 2], [8, 6]</p></li><li class="list__item" id="943bd38_180"><p>... and so on.</p></li></ul><p id="943bd38_181">Luckily, PyTorch's <code class="code" id="943bd38_182">DataLoader</code> supports random sampling. We just need to use <code class="code" id="943bd38_183">RandomSampler</code> instead of <code class="code" id="943bd38_184">SequentialSampler</code> (which is the default).</p><div class="code-block" data-lang="python"         >
+</div><p id="a70305a_1796">In the above example, we have a <code class="code" id="a70305a_1797">Dataset</code> that returns 3 duplicate versions of the same image. By leveraging this technique, we can create a <code class="code" id="a70305a_1798">Dataset</code> that returns K augmented versions of the same image as a tuple</p></section></section><section class="chapter"><h2 id="premature-end-of-epoch-due-to-small-labelled-set" data-toc="premature-end-of-epoch-due-to-small-labelled-set">Premature End of Epoch due to Small Labelled Set</h2><p id="a70305a_1799">See: <code class="code" id="a70305a_1800">frdc/train/frdc_datamodule.py</code></p><p id="a70305a_1801">In MixMatch, the definition of an &quot;epoch&quot; is a bit different. Instead of implying that we have seen all the data once, it implies that we've drawn <code class="code" id="a70305a_1802">N</code> batches. The <code class="code" id="a70305a_1803">N</code> is referred to as the number of iterations per epoch.</p><p id="a70305a_1804">Take for example, a labelled set of numbers [1, 2, 3] and an unlabelled set [4, 5, 6, 7, 8, 9, 10]. With batch size of 2, we'll run out of labelled samples after 2 iterations, but we'll still have 3 more iterations for the unlabelled set.</p><ul class="list _ul" id="a70305a_1805"><li class="list__item" id="a70305a_1806"><p>Draw 1: [1, 2], [4, 5]</p></li><li class="list__item" id="a70305a_1807"><p>Draw 2: [3], [6, 7].</p></li><li class="list__item" id="a70305a_1808"><p>Epoch ends.</p></li></ul><section class="chapter"><h3 id="solution-2-random-sampling" data-toc="solution-2-random-sampling">Solution 2: Random Sampling</h3><p id="a70305a_1809">To fix this, instead of sequentially sampling the labelled set (and the unlabelled set), we can sample them randomly. This way, we can ensure that it never runs out.</p><ul class="list _ul" id="a70305a_1810"><li class="list__item" id="a70305a_1811"><p>Draw 1: [1, 3], [7, 5]</p></li><li class="list__item" id="a70305a_1812"><p>Draw 2: [2, 1], [4, 9]</p></li><li class="list__item" id="a70305a_1813"><p>Draw 3: [3, 2], [8, 6]</p></li><li class="list__item" id="a70305a_1814"><p>... and so on.</p></li></ul><p id="a70305a_1815">Luckily, PyTorch's <code class="code" id="a70305a_1816">DataLoader</code> supports random sampling. We just need to use <code class="code" id="a70305a_1817">RandomSampler</code> instead of <code class="code" id="a70305a_1818">SequentialSampler</code> (which is the default).</p><div class="code-block" data-lang="python">
 from torch.utils.data import DataLoader, RandomSampler
 
 dl = DataLoader(
@@ -21,4 +21,4 @@
         replacement=False,
     )
 )
-</div><p id="943bd38_186">This will ensure that the &quot;epoch&quot; ends when we've drawn <code class="code" id="943bd38_187">train_iters</code> batches</p></section></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="mix-match-module.html">MixMatch Module</a>   <a class="navigation-links__next" href="model-test-chestnut-may-dec.html">Model Test Chestnut May-Dec</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+</div><p id="a70305a_1820">This will ensure that the &quot;epoch&quot; ends when we've drawn <code class="code" id="a70305a_1821">train_iters</code> batches</p></section></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="mix-match-module.html" class="navigation-links__prev">MixMatch Module</a><a href="model-test-chestnut-may-dec.html" class="navigation-links__next">Model Test Chestnut May-Dec</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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diff --git a/docs/getting-started.html b/docs/getting-started.html
index 172d307d..7cd27c91 100644
--- a/docs/getting-started.html
+++ b/docs/getting-started.html
@@ -1,20 +1,24 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.0998842"><meta name="build-number" content="${buildNumber}">       <title>Getting Started | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/getting-started.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/getting-started.html#webpage", "url": "/d/0.0.7/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"   id="Getting-Started.md">Getting Started</h1>  <section class="procedure-steps"   ><h3 id="install" data-toc="install">Installing the Dev. Environment</h3 ><ol class="list _decimal"><li class="list__item" id="ee0d787c_13755"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="ee0d787c_13756">pyproject.toml</code></p><div class="code-block" data-lang="ini"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1510751">        <title>Getting Started | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"troubleshooting","level":0,"title":"Troubleshooting","anchor":"#troubleshooting"},{"id":"our-repository-structure","level":0,"title":"Our Repository Structure","anchor":"#our-repository-structure"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Getting Started | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/getting-started.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Getting Started | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/getting-started.html#webpage", "url": "/d/0.0.7/getting-started.html", "name": "Getting Started | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="Getting-Started" data-main-title="Getting Started" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Getting-Started"   id="Getting-Started.md">Getting Started</h1>  <section class="procedure-steps"><h3 id="install" data-toc="install">Installing the Dev. Environment</h3><ol class="list _decimal"><li class="list__item" id="4fa05b84_225839"><p>Ensure that you have the right version of Python. The required Python version can be seen in <code class="code" id="4fa05b84_225840">pyproject.toml</code></p><div class="code-block" data-lang="ini">
             [tool.poetry.dependencies]
             python = &quot;...&quot;
-        </div></li><li class="list__item" id="ee0d787c_13758"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash"         >
-          git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git
-        </div></li><li class="list__item" id="ee0d787c_13760"><p>Then, create a Python Virtual Env <code class="code" id="ee0d787c_13761">pyvenv</code></p><div class="tabs" id="ee0d787c_13762"><div class="tabs__content" data-gtm="tab" id="ee0d787c_13763" data-title="Windows"><div class="code-block" data-lang="bash"    data-title="Shell"     >python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="ee0d787c_13765" data-title="Linux"><div class="code-block" data-lang="bash"    data-title="Shell"     >python3 -m venv venv/</div></div></div></li><li class="list__item" id="ee0d787c_13767"><p><a href="https://python-poetry.org/docs/" id="ee0d787c_13768"   data-external="true" rel="noopener noreferrer" >Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash"         >poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="ee0d787c_13770"><p>If <code class="code" id="ee0d787c_13771">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="ee0d787c_13772"><p>Activate the virtual environment </p><div class="tabs" id="ee0d787c_13773"><div class="tabs__content" data-gtm="tab" id="ee0d787c_13774" data-title="Windows"><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></li><li class="list__item" id="4fa05b84_225842"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash">
+          git clone https://github.com/FR-DC/FRDC-ML.git
+        </div></li><li class="list__item" id="4fa05b84_225844"><p>Then, create a Python Virtual Env <code class="code" id="4fa05b84_225845">pyvenv</code></p><div class="tabs" id="4fa05b84_225846"><div class="tabs__content" data-gtm="tab" id="4fa05b84_225847" data-title="Windows"><div class="code-block" data-lang="bash" data-title="Shell">python -m venv venv/</div></div><div class="tabs__content" data-gtm="tab" id="4fa05b84_225849" data-title="Linux"><div class="code-block" data-lang="bash" data-title="Shell">python3 -m venv venv/</div></div></div></li><li class="list__item" id="4fa05b84_225851"><p><a href="https://python-poetry.org/docs/" id="4fa05b84_225852" data-external="true" rel="noopener noreferrer">Install Poetry</a> Then check if it's installed with </p><div class="code-block" data-lang="bash">poetry --version</div><aside class="prompt" data-type="warning" data-title="" id="4fa05b84_225854"><p>If <code class="code" id="4fa05b84_225855">poetry</code> is not found, it's likely not in the user PATH.</p></aside></li><li class="list__item" id="4fa05b84_225856"><p>Activate the virtual environment </p><div class="tabs" id="4fa05b84_225857"><div class="tabs__content" data-gtm="tab" id="4fa05b84_225858" data-title="Windows"><div class="code-block" data-lang="bash" data-title="Shell">
             cd venv/Scripts
             activate
             cd ../..
-        </div></div><div class="tabs__content" data-gtm="tab" id="ee0d787c_13776" data-title="Linux"><div class="code-block" data-lang="bash"    data-title="Shell"     >
+        </div></div><div class="tabs__content" data-gtm="tab" id="4fa05b84_225860" data-title="Linux"><div class="code-block" data-lang="bash" data-title="Shell">
             source venv/bin/activate
-        </div></div></div></li><li class="list__item" id="ee0d787c_13778"><p>Install the dependencies. You should be in the same directory as <code class="code" id="ee0d787c_13779">pyproject.toml</code></p><div class="code-block" data-lang="bash"         >
+        </div></div></div></li><li class="list__item" id="4fa05b84_225862"><p>Install the dependencies. You should be in the same directory as <code class="code" id="4fa05b84_225863">pyproject.toml</code></p><div class="code-block" data-lang="bash">
             poetry install --with dev
-        </div></li><li class="list__item" id="ee0d787c_13781"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash"         >
+        </div></li><li class="list__item" id="4fa05b84_225865"><p>Install Pre-Commit Hooks </p><div class="code-block" data-lang="bash">
             pre-commit install
-        </div></li></ol></section><section class="procedure-steps"   ><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3 ><ol class="list _decimal"><li class="list__item" id="ee0d787c_13783"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="ee0d787c_13784"   data-external="true" rel="noopener noreferrer" >install the Google Cloud CLI</a></p></li><li class="list__item" id="ee0d787c_13785"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="ee0d787c_13786"   data-external="true" rel="noopener noreferrer" >authenticate your account</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth login</div></li><li class="list__item" id="ee0d787c_13788"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="ee0d787c_13789"   data-external="true" rel="noopener noreferrer" >set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash"         >gcloud auth application-default login</div></li><li class="list__item" id="ee0d787c_13791"><p>To make sure everything is working, <a href="#tests" id="ee0d787c_13792" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >run the tests</a>.</p></li></ol></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="ee0d787c_13793" data-toc="ee0d787c_13793">Pre-commit Hooks</h3 ></div><div class="collapse__content" ><aside class="prompt" data-type="note" data-title="" id="ee0d787c_13794"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list" ><li class="list__item" id="ee0d787c_13795"><div class="code-block" data-lang="bash"         >
+        </div></li></ol></section><section class="procedure-steps"><h3 id="install-dev-con" data-toc="install-dev-con">Use a Dev. Container</h3><aside class="prompt" data-type="tip" data-title="" id="4fa05b84_225867"><p>Only use Dev. Containers if you're familiar with your IDEs, it's highly dependent on clicking around the IDE.</p></aside><aside class="prompt" data-type="warning" data-title="" id="4fa05b84_225868"><p>Do not set up a new environment, it'll be included in the environment.</p></aside><ol class="list _decimal"><li class="list__item" id="4fa05b84_225869"><p>Ensure that you have installed pre-requisites for respective IDEs. <a href="https://code.visualstudio.com/docs/remote/containers#_system-requirements" id="4fa05b84_225870" data-external="true" rel="noopener noreferrer">VSCode</a> <a href="https://www.jetbrains.com/help/idea/prerequisites-for-dev-containers.html" id="4fa05b84_225871" data-external="true" rel="noopener noreferrer">IntelliJ</a></p></li><li class="list__item" id="4fa05b84_225872"><p>Start by cloning our repository. </p><div class="code-block" data-lang="bash">
+          git clone https://github.com/FR-DC/FRDC-ML.git
+        </div></li><li class="list__item" id="4fa05b84_225874"><p>Follow steps for respective IDEs to set up the Dev. Container.</p></li><li class="list__item" id="4fa05b84_225875"><p>Activate the virtual environment. The venv is located in <code class="code" id="4fa05b84_225876">/opt/venv</code></p></li></ol></section><section class="procedure-steps"><h3 id="gcloud" data-toc="gcloud">Setting Up Google Cloud</h3><ol class="list _decimal"><li class="list__item" id="4fa05b84_225877"><p>We use Google Cloud to store our datasets. To set up Google Cloud, <a href="https://cloud.google.com/sdk/docs/install" id="4fa05b84_225878" data-external="true" rel="noopener noreferrer">install the Google Cloud CLI</a></p></li><li class="list__item" id="4fa05b84_225879"><p>Then, <a href="https://cloud.google.com/sdk/docs/initializing" id="4fa05b84_225880" data-external="true" rel="noopener noreferrer">authenticate your account</a>. </p><div class="code-block" data-lang="bash">gcloud auth login</div></li><li class="list__item" id="4fa05b84_225882"><p>Finally, <a href="https://cloud.google.com/docs/authentication/provide-credentials-adc" id="4fa05b84_225883" data-external="true" rel="noopener noreferrer">set up Application Default Credentials (ADC)</a>. </p><div class="code-block" data-lang="bash">gcloud auth application-default login</div></li><li class="list__item" id="4fa05b84_225885"><p>To make sure everything is working, <a href="#tests" id="4fa05b84_225886" data-tooltip="It's likely that your src and tests directories are not in PYTHONPATH. To fix this, run the following command:">run the tests</a>.</p></li></ol></section><section class="procedure-steps"><h3 id="ls" data-toc="ls">Setting Up Label Studio</h3><aside class="prompt" data-type="tip" data-title="" id="4fa05b84_225887"><p>This is only necessary if any task requires Label Studio annotations</p></aside><ol class="list _decimal"><li class="list__item" id="4fa05b84_225888"><p>We use Label Studio to annotate our datasets. We won't go through how to install Label Studio, for contributors, it should be up on <code class="code" id="4fa05b84_225889">localhost:8080</code>.</p></li><li class="list__item" id="4fa05b84_225890"><p>Then, retrieve your own API key from Label Studio. <a href="http://localhost:8080/user/account" id="4fa05b84_225891" data-external="true" rel="noopener noreferrer">Go to your account page</a> and copy the API key. </p><br></li><li class="list__item" id="4fa05b84_225893"><p>Set your API key as an environment variable. </p><div class="tabs" id="4fa05b84_225894"><div class="tabs__content" data-gtm="tab" id="4fa05b84_225895" data-title="Windows"><p>In Windows, go to &quot;Edit environment variables for your account&quot; and add this as a new environment variable with name <code class="code" id="4fa05b84_225896">LABEL_STUDIO_API_KEY</code>.</p></div><div class="tabs__content" data-gtm="tab" id="4fa05b84_225897" data-title="Linux"><p>Export it as an environment variable. </p><div class="code-block" data-lang="bash" data-title="Shell">export LABEL_STUDIO_API_KEY=...</div></div></div></li></ol></section><section class="procedure-steps"><h3 id="wandb" data-toc="wandb">Setting Up Weight and Biases</h3><ol class="list _decimal"><li class="list__item" id="4fa05b84_225899"><p>We use W&amp;B to track our experiments. To set up W&amp;B, <a href="https://docs.wandb.ai/quickstart" id="4fa05b84_225900" data-external="true" rel="noopener noreferrer">install the W&amp;B CLI</a></p></li><li class="list__item" id="4fa05b84_225901"><p>Then, <a href="https://docs.wandb.ai/quickstart" id="4fa05b84_225902" data-external="true" rel="noopener noreferrer">authenticate your account</a>. </p><div class="code-block" data-lang="bash">wandb login</div></li></ol></section><section class="procedure-steps"><div class="collapse"><div class="collapse__title"><h3 id="4fa05b84_225904" data-toc="4fa05b84_225904">Pre-commit Hooks</h3></div><div class="collapse__content"><aside class="prompt" data-type="note" data-title="" id="4fa05b84_225905"><p>This is optional but recommended. Pre-commit hooks are a way to ensure that your code is formatted correctly. This is done by running a series of checks before you commit your code.</p></aside><ul class="list"><li class="list__item" id="4fa05b84_225906"><div class="code-block" data-lang="bash">
             pre-commit install
-        </div></li></ul></div ></div></section><section class="procedure-steps"   > <div class="collapse" ><div class="collapse__title"  ><h3 id="tests" data-toc="tests">Running the Tests</h3 ></div><div class="collapse__content" ><ol class="list _decimal"><li class="list__item" id="ee0d787c_13797"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash"         >
+        </div></li></ul></div></div></section><section class="procedure-steps"><h3 id="tests" data-toc="tests">Running the Tests</h3><ul class="list"><li class="list__item" id="4fa05b84_225908"><p>Run the tests to make sure everything is working </p><div class="code-block" data-lang="bash">
             pytest
-        </div></li><li class="list__item" id="ee0d787c_13799"><p>In case of errors: </p><dl id="ee0d787c_13800" data-style="title-top"><dt id="ee0d787c_13801" data-expandable="false">google.auth.exceptions.DefaultCredentialsError</dt><dd><p>If you get this error, it means that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="ee0d787c_13802" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Setting Up Google Cloud</a></p></dd><dt id="ee0d787c_13803" data-expandable="false">ModuleNotFoundError</dt><dd><p>If you get this error, it means that you haven't installed the dependencies. See <a href="#install" id="ee0d787c_13804" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Installing the Dev. Environment</a></p></dd></dl></li></ol></div ></div></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure"   >Our Repository Structure</h2><p id="ee0d787c_13805">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="ee0d787c_13811" data-expandable="false">rsc/</dt><dd><p id="ee0d787c_13813">Resources. These are usually cached datasets</p></dd><dt id="ee0d787c_13814" data-expandable="false">pipeline/</dt><dd><p id="ee0d787c_13816">Pipeline code. These are the full ML tests of our pipeline.</p></dd><dt id="ee0d787c_13817" data-expandable="false">tests/</dt><dd><p id="ee0d787c_13819">PyTest tests. These are unit tests &amp; integration tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests"   >Unit, Integration, and Pipeline Tests</h3><p id="ee0d787c_13820">We have 3 types of tests:</p><ul class="list _ul" id="ee0d787c_13821"><li class="list__item" id="ee0d787c_13822"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="ee0d787c_13823"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="ee0d787c_13824"><p>Pipeline Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute"   >Where Should I contribute?</h3><dl id="ee0d787c_13825" data-style="title-top"><dt id="ee0d787c_13826" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="ee0d787c_13827">src/frdc/</code> directory.</p></dd><dt id="ee0d787c_13828" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="ee0d787c_13829">tests/</code> directory.</p></dd><dt id="ee0d787c_13830" data-expandable="false">Changing the pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="ee0d787c_13831">pipeline/</code> directory.</p></dd><dt id="ee0d787c_13832" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="ee0d787c_13833">poetry add PACKAGE</code> and commit the changes to <code class="code" id="ee0d787c_13834">pyproject.toml</code> and <code class="code" id="ee0d787c_13835">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="ee0d787c_13836"><p>E.g. Adding <code class="code" id="ee0d787c_13837">numpy</code> is the same as <code class="code" id="ee0d787c_13838">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="overview.html">Overview</a>   <a class="navigation-links__next" href="retrieve-our-datasets.html">Retrieve our Datasets</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+        </div></li></ul></section><section class="chapter"><h2 id="troubleshooting" data-toc="troubleshooting">Troubleshooting</h2><section class="chapter"><h3 id="modulenotfounderror" data-toc="modulenotfounderror">ModuleNotFoundError</h3><p id="4fa05b84_225910">It's likely that your <code class="code" id="4fa05b84_225911">src</code> and <code class="code" id="4fa05b84_225912">tests</code> directories are not in <code class="code" id="4fa05b84_225913">PYTHONPATH</code>. To fix this, run the following command:</p><div class="code-block" data-lang="bash">
+export PYTHONPATH=$PYTHONPATH:./src:./tests
+</div><p id="4fa05b84_225915">Or, set it in your IDE, for example, IntelliJ allows setting directories as <span class="control" id="4fa05b84_225916">Source Roots</span>.</p></section><section class="chapter"><h3 id="google-auth-exceptions-defaultcredentialserror" data-toc="google-auth-exceptions-defaultcredentialserror">google.auth.exceptions.DefaultCredentialsError</h3><p id="4fa05b84_225917">It's likely that you haven't authenticated your Google Cloud account. See <a href="#gcloud" id="4fa05b84_225918" data-tooltip="It's likely that your src and tests directories are not in PYTHONPATH. To fix this, run the following command:">Setting Up Google Cloud</a></p></section><section class="chapter"><h3 id="couldn-t-connect-to-label-studio" data-toc="couldn-t-connect-to-label-studio">Couldn't connect to Label Studio</h3><p id="4fa05b84_225919">Label Studio must be running locally, exposed on <code class="code" id="4fa05b84_225920">localhost:8080</code>. Furthermore, you need to specify the <code class="code" id="4fa05b84_225921">LABEL_STUDIO_API_KEY</code> environment variable. See <a href="#ls" id="4fa05b84_225922" data-tooltip="It's likely that your src and tests directories are not in PYTHONPATH. To fix this, run the following command:">Setting Up Label Studio</a></p></section><section class="chapter"><h3 id="cannot-login-to-w-b" data-toc="cannot-login-to-w-b">Cannot login to W&amp;B</h3><p id="4fa05b84_225923">You need to authenticate your W&amp;B account. See <a href="#wandb" id="4fa05b84_225924" data-tooltip="It's likely that your src and tests directories are not in PYTHONPATH. To fix this, run the following command:">Setting Up Weight and Biases</a> If you're facing difficulties, set the <code class="code" id="4fa05b84_225925">WANDB_MODE</code> environment variable to <code class="code" id="4fa05b84_225926">offline</code> to disable W&amp;B.</p></section></section><section class="chapter"><h2 id="our-repository-structure" data-toc="our-repository-structure">Our Repository Structure</h2><p id="4fa05b84_225927">Before starting development, take a look at our repository structure. 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These are the unit components of our pipeline.</p></dd><dt id="4fa05b84_225933" data-expandable="false">rsc/</dt><dd><p id="4fa05b84_225935">Resources. These are usually cached datasets</p></dd><dt id="4fa05b84_225936" data-expandable="false">tests/</dt><dd><p id="4fa05b84_225938">PyTest tests. These are unit, integration, and model tests.</p></dd></dl><section class="chapter"><h3 id="unit-integration-and-pipeline-tests" data-toc="unit-integration-and-pipeline-tests">Unit, Integration, and Pipeline Tests</h3><p id="4fa05b84_225939">We have 3 types of tests:</p><ul class="list _ul" id="4fa05b84_225940"><li class="list__item" id="4fa05b84_225941"><p>Unit Tests are usually small, single function tests.</p></li><li class="list__item" id="4fa05b84_225942"><p>Integration Tests are larger tests that tests a mock pipeline.</p></li><li class="list__item" id="4fa05b84_225943"><p>Model Tests are the true production pipeline tests that will generate a model.</p></li></ul></section><section class="chapter"><h3 id="where-should-i-contribute" data-toc="where-should-i-contribute">Where Should I contribute?</h3><dl id="4fa05b84_225944" data-style="title-top"><dt id="4fa05b84_225945" data-expandable="false">Changing a small component</dt><dd><p>If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the <code class="code" id="4fa05b84_225946">src/frdc/</code> directory.</p></dd><dt id="4fa05b84_225947" data-expandable="false">Adding a test</dt><dd><p>By adding a new component, you'll need to add a new test. Take a look at the <code class="code" id="4fa05b84_225948">tests/</code> directory.</p></dd><dt id="4fa05b84_225949" data-expandable="false">Changing the model pipeline</dt><dd><p>If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the <code class="code" id="4fa05b84_225950">tests/model_tests/</code> directory.</p></dd><dt id="4fa05b84_225951" data-expandable="false">Adding a dependency</dt><dd><p>If you're adding a new dependency, use <code class="code" id="4fa05b84_225952">poetry add PACKAGE</code> and commit the changes to <code class="code" id="4fa05b84_225953">pyproject.toml</code> and <code class="code" id="4fa05b84_225954">poetry.lock</code>. </p><aside class="prompt" data-type="note" data-title="" id="4fa05b84_225955"><p>E.g. Adding <code class="code" id="4fa05b84_225956">numpy</code> is the same as <code class="code" id="4fa05b84_225957">poetry add numpy</code></p></aside></dd></dl></section></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="overview.html" class="navigation-links__prev">Overview</a><a href="retrieve-our-datasets.html" class="navigation-links__next">Retrieve our Datasets</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.095883"><meta name="build-number" content="${buildNumber}">       <title>load.dataset | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"filters","level":0,"title":"Filters","anchor":"#filters"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.dataset | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/load-dataset.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.dataset | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/load-dataset.html#webpage", "url": "/d/0.0.7/load-dataset.html", "name": "load.dataset | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="load.dataset" data-main-title="load.dataset" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.dataset"   id="load.dataset.md">load.dataset</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;92a10ff7_376\&quot;\u003eLoad dataset objects from our GCS bucket.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="92a10ff7_336"><p id="92a10ff7_337">You need to Set-Up <a href="getting-started.html#gcloud" id="92a10ff7_338" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Google Cloud</a> with the appropriate permissions to use this library.</p></aside><section class="chapter"><h2 id="usage" data-toc="usage"   >Usage</h2><p id="92a10ff7_340">Firstly, to load a dataset instance, you need to initiliaze a <code class="code" id="92a10ff7_341">FRDCDataset</code> object, providing the site, date, and version.</p><p id="92a10ff7_342">For example, to load our Chestnut Nature Park dataset.</p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1419417">        <title>load.dataset | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"filters","level":0,"title":"Filters","anchor":"#filters"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.dataset | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/load-dataset.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.dataset | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/load-dataset.html#webpage", "url": "/d/0.0.7/load-dataset.html", "name": "load.dataset | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="load.dataset" data-main-title="load.dataset" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.dataset"   id="load.dataset.md">load.dataset</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;f15e534c_1779\&quot;\u003eLoad dataset objects from our GCS bucket.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="f15e534c_1739"><p id="f15e534c_1740">You need to Set-Up <a href="getting-started.html#gcloud" id="f15e534c_1741" data-tooltip="It's likely that your src and tests directories are not in PYTHONPATH. To fix this, run the following command:">Google Cloud</a> with the appropriate permissions to use this library.</p></aside><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="f15e534c_1743">Firstly, to load a dataset instance, you need to initiliaze a <code class="code" id="f15e534c_1744">FRDCDataset</code> object, providing the site, date, and version.</p><p id="f15e534c_1745">For example, to load our Chestnut Nature Park dataset.</p><div class="code-block" data-lang="python">
 from frdc.load import FRDCDataset
 
 ds = FRDCDataset(site='chestnut_nature_park',
                  date='20201218',
                  version=None)
-</div><p id="92a10ff7_344">Then, we can use the <code class="code" id="92a10ff7_345">ds</code> object to load objects of the dataset:</p><div class="code-block" data-lang="python"         >
+</div><p id="f15e534c_1747">Then, we can use the <code class="code" id="f15e534c_1748">ds</code> object to load objects of the dataset:</p><div class="code-block" data-lang="python">
 ar, order = ds.get_ar_bands()
 d = ds.get_ar_bands_as_dict()
 bounds, labels = ds.get_bounds_and_labels()
-</div><ul class="list _ul" id="92a10ff7_347"><li class="list__item" id="92a10ff7_348"><p><code class="code" id="92a10ff7_349">ar</code> is a stacked NDArray of the hyperspectral bands of shape (H x W x C)</p></li><li class="list__item" id="92a10ff7_350"><p><code class="code" id="92a10ff7_351">order</code> is a list of strings, containing the names of the bands, ordered according to the channels of <code class="code" id="92a10ff7_352">ar</code></p></li><li class="list__item" id="92a10ff7_353"><p><code class="code" id="92a10ff7_354">d</code> is a dictionary of the hyperspectral bands of shape (H x W), keyed by the band names</p></li><li class="list__item" id="92a10ff7_355"><p><code class="code" id="92a10ff7_356">bounds</code> is a list of bounding boxes, in the format of <code class="code" id="92a10ff7_357">Rect</code>, a <code class="code" id="92a10ff7_358">namedtuple</code> of x0, y0, x1, y1</p></li><li class="list__item" id="92a10ff7_359"><p><code class="code" id="92a10ff7_360">labels</code> is a list of strings, containing the labels of the bounding boxes, ordered according to <code class="code" id="92a10ff7_361">bounds</code></p></li></ul><aside class="prompt" data-type="note" data-title="" id="92a10ff7_362"><p id="92a10ff7_363"><code class="code" id="92a10ff7_364">get_ar_bands()</code> and <code class="code" id="92a10ff7_365">get_ar_bands_as_dict()</code> retrieves the same data, but <code class="code" id="92a10ff7_366">get_ar_bands()</code> is a convenience function that stacks the bands into a single NDArray, and returns the channel order as well.</p></aside></section><section class="chapter"><h2 id="filters" data-toc="filters"   >Filters</h2><p id="92a10ff7_367">You can also selectively get the channels for both <code class="code" id="92a10ff7_368">get_ar_bands()</code> and <code class="code" id="92a10ff7_369">get_ar_bands_as_dict()</code> by providing a list of strings to the <code class="code" id="92a10ff7_370">bands</code> argument.</p><p id="92a10ff7_371">For example, to get the Wideband RGB bands, you can do:</p><div class="code-block" data-lang="python"         >
+</div><ul class="list _ul" id="f15e534c_1750"><li class="list__item" id="f15e534c_1751"><p><code class="code" id="f15e534c_1752">ar</code> is a stacked NDArray of the hyperspectral bands of shape (H x W x C)</p></li><li class="list__item" id="f15e534c_1753"><p><code class="code" id="f15e534c_1754">order</code> is a list of strings, containing the names of the bands, ordered according to the channels of <code class="code" id="f15e534c_1755">ar</code></p></li><li class="list__item" id="f15e534c_1756"><p><code class="code" id="f15e534c_1757">d</code> is a dictionary of the hyperspectral bands of shape (H x W), keyed by the band names</p></li><li class="list__item" id="f15e534c_1758"><p><code class="code" id="f15e534c_1759">bounds</code> is a list of bounding boxes, in the format of <code class="code" id="f15e534c_1760">Rect</code>, a <code class="code" id="f15e534c_1761">namedtuple</code> of x0, y0, x1, y1</p></li><li class="list__item" id="f15e534c_1762"><p><code class="code" id="f15e534c_1763">labels</code> is a list of strings, containing the labels of the bounding boxes, ordered according to <code class="code" id="f15e534c_1764">bounds</code></p></li></ul><aside class="prompt" data-type="note" data-title="" id="f15e534c_1765"><p id="f15e534c_1766"><code class="code" id="f15e534c_1767">get_ar_bands()</code> and <code class="code" id="f15e534c_1768">get_ar_bands_as_dict()</code> retrieves the same data, but <code class="code" id="f15e534c_1769">get_ar_bands()</code> is a convenience function that stacks the bands into a single NDArray, and returns the channel order as well.</p></aside></section><section class="chapter"><h2 id="filters" data-toc="filters">Filters</h2><p id="f15e534c_1770">You can also selectively get the channels for both <code class="code" id="f15e534c_1771">get_ar_bands()</code> and <code class="code" id="f15e534c_1772">get_ar_bands_as_dict()</code> by providing a list of strings to the <code class="code" id="f15e534c_1773">bands</code> argument.</p><p id="f15e534c_1774">For example, to get the Wideband RGB bands, you can do:</p><div class="code-block" data-lang="python">
 ar, order = ds.get_ar_bands(bands=['WR', 'WG', 'WB'])
 d = ds.get_ar_bands_as_dict(bands=['WR', 'WG', 'WB'])
-</div><p id="92a10ff7_373">This will also alter the channel order to the order of the bands provided.</p><p id="92a10ff7_374">See <a href="load-gcs.html#configuration" id="92a10ff7_375" data-tooltip="If you need granular control over"  >load.gcs</a> for configuration options.</p></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="model-test-chestnut-may-dec.html">Model Test Chestnut May-Dec</a>   <a class="navigation-links__next" href="load-gcs.html">load.gcs</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
\ No newline at end of file
+</div><p id="f15e534c_1776">This will also alter the channel order to the order of the bands provided.</p><p id="f15e534c_1777">See <a href="load-gcs.html#configuration" id="f15e534c_1778" data-tooltip="If you need granular control over">load.gcs</a> for configuration options.</p></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="model-test-chestnut-may-dec.html" class="navigation-links__prev">Model Test Chestnut May-Dec</a><a href="load-gcs.html" class="navigation-links__next">load.gcs</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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diff --git a/docs/load-gcs.html b/docs/load-gcs.html
index e135d6e1..f02e3959 100644
--- a/docs/load-gcs.html
+++ b/docs/load-gcs.html
@@ -1,11 +1,11 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.095883"><meta name="build-number" content="${buildNumber}">       <title>load.gcs | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"usage","level":0,"title":"Usage","anchor":"#usage"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.gcs | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/load-gcs.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.gcs | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/load-gcs.html#webpage", "url": "/d/0.0.7/load-gcs.html", "name": "load.gcs | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="load.gcs" data-main-title="load.gcs" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.gcs"   id="load.gcs.md">load.gcs</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;573e5df3_2997\&quot;\u003eLow-level GCS utilities to automatically download and load objects from GCS.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="573e5df3_2951"><p id="573e5df3_2952"><a href="getting-started.html#gcloud" id="573e5df3_2953" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Set-Up Google Cloud</a> to use this library.</p></aside><section class="chapter"><h2 id="usage" data-toc="usage"   >Usage</h2><p id="573e5df3_2955">These are defined in the top-level load.gcs module.</p><dl id="573e5df3_2956" data-style="title-top"><dt id="573e5df3_2957" data-expandable="false">list_gcs_datasets</dt><dd><p>Lists all datasets in the bucket as a DataFrame. This works by checking which folders have a specific file, which we call the <code class="code" id="573e5df3_2958">anchor</code>.</p></dd></dl><aside class="prompt" data-type="tip" data-title="" id="573e5df3_2959"><p id="573e5df3_2960">All functions below will download the file if it doesn't exist locally. It also checks the hash of the file, thus will not redundantly download</p></aside><dl id="573e5df3_2961" data-style="title-top"><dt id="573e5df3_2962" data-expandable="false">download</dt><dd><p>Downloads a file from Google Cloud Storage and returns the local file path.</p></dd><dt id="573e5df3_2963" data-expandable="false">open_file</dt><dd><p>Downloads and opens a file from Google Cloud Storage. Returns a file handle.</p></dd><dt id="573e5df3_2964" data-expandable="false">open_image</dt><dd><p>Downloads and returns the PIL image from Google Cloud Storage.</p></dd></dl><section class="chapter"><h3 id="pathing" data-toc="pathing"   >Pathing</h3><p id="573e5df3_2965">The path to specify is relative to the bucket, which is <code class="code" id="573e5df3_2966">frdc-ds</code> by default.</p><p id="573e5df3_2967">For example this filesystem on GCS:</p><div class="code-block" data-lang="none"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1419417">        <title>load.gcs | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"usage","level":0,"title":"Usage","anchor":"#usage"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="load.gcs | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/load-gcs.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="load.gcs | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/load-gcs.html#webpage", "url": "/d/0.0.7/load-gcs.html", "name": "load.gcs | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="load.gcs" data-main-title="load.gcs" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="load.gcs"   id="load.gcs.md">load.gcs</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;b7a1143d_1778\&quot;\u003eLow-level GCS utilities to automatically download and load objects from GCS.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="warning" data-title="" id="b7a1143d_1732"><p id="b7a1143d_1733"><a href="getting-started.html#gcloud" id="b7a1143d_1734" data-tooltip="It's likely that your src and tests directories are not in PYTHONPATH. To fix this, run the following command:">Set-Up Google Cloud</a> to use this library.</p></aside><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="b7a1143d_1736">These are defined in the top-level load.gcs module.</p><dl id="b7a1143d_1737" data-style="title-top"><dt id="b7a1143d_1738" data-expandable="false">list_gcs_datasets</dt><dd><p>Lists all datasets in the bucket as a DataFrame. This works by checking which folders have a specific file, which we call the <code class="code" id="b7a1143d_1739">anchor</code>.</p></dd></dl><aside class="prompt" data-type="tip" data-title="" id="b7a1143d_1740"><p id="b7a1143d_1741">All functions below will download the file if it doesn't exist locally. It also checks the hash of the file, thus will not redundantly download</p></aside><dl id="b7a1143d_1742" data-style="title-top"><dt id="b7a1143d_1743" data-expandable="false">download</dt><dd><p>Downloads a file from Google Cloud Storage and returns the local file path.</p></dd><dt id="b7a1143d_1744" data-expandable="false">open_file</dt><dd><p>Downloads and opens a file from Google Cloud Storage. Returns a file handle.</p></dd><dt id="b7a1143d_1745" data-expandable="false">open_image</dt><dd><p>Downloads and returns the PIL image from Google Cloud Storage.</p></dd></dl><section class="chapter"><h3 id="pathing" data-toc="pathing">Pathing</h3><p id="b7a1143d_1746">The path to specify is relative to the bucket, which is <code class="code" id="b7a1143d_1747">frdc-ds</code> by default.</p><p id="b7a1143d_1748">For example this filesystem on GCS:</p><div class="code-block" data-lang="none">
 # On Google Cloud Storage
 frdc-ds
 ├── chestnut_nature_park
 │   └── 20201218
 │       └── 90deg
 │           └── bounds.json
-</div><p id="573e5df3_2969">To download <code class="code" id="573e5df3_2970">bounds.json</code>, use <code class="code" id="573e5df3_2971">download(r&quot;chestnut_nature_park/20201218/90deg/bounds.json&quot;)</code>. By default, all files will be downloaded to <code class="code" id="573e5df3_2972">PROJ_DIR/rsc/...</code>.</p><div class="code-block" data-lang="none"         >
+</div><p id="b7a1143d_1750">To download <code class="code" id="b7a1143d_1751">bounds.json</code>, use <code class="code" id="b7a1143d_1752">download(r&quot;chestnut_nature_park/20201218/90deg/bounds.json&quot;)</code>. By default, all files will be downloaded to <code class="code" id="b7a1143d_1753">PROJ_DIR/rsc/...</code>.</p><div class="code-block" data-lang="none">
 # On local filesystem
 PROJ_DIR
 ├── rsc
@@ -13,4 +13,4 @@
 │       └── 20201218
 │           └── 90deg
 │               └── bounds.json
-</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="configuration" data-toc="configuration"   >Configuration</h3></div><div class="collapse__content"><p id="573e5df3_2974">If you need granular control over</p><ul class="list _ul" id="573e5df3_2975"><li class="list__item" id="573e5df3_2976"><p>where the files are downloaded</p></li><li class="list__item" id="573e5df3_2977"><p>the credentials used</p></li><li class="list__item" id="573e5df3_2978"><p>the project used</p></li><li class="list__item" id="573e5df3_2979"><p>the bucket used</p></li></ul><p id="573e5df3_2980">Then edit <code class="code" id="573e5df3_2981">conf.py</code>.</p><dl id="573e5df3_2982" data-style="title-top"><dt id="573e5df3_2983" data-expandable="false">GCS_CREDENTIALS</dt><dd><p><b id="573e5df3_2984">Google Cloud credentials.</b></p><br><p> A <code class="code" id="573e5df3_2986">google.oauth2.service_account.Credentials</code> object. See the object documentation for more information.</p></dd><dt id="573e5df3_2987" data-expandable="false">LOCAL_DATASET_ROOT_DIR</dt><dd><p><b id="573e5df3_2988">Local directory to download files to.</b></p><br><p> Path to a directory, or a <code class="code" id="573e5df3_2990">Path</code> object.</p></dd><dt id="573e5df3_2991" data-expandable="false">GCS_PROJECT_ID</dt><dd><p><b id="573e5df3_2992">Google Cloud project ID.</b></p><br></dd><dt id="573e5df3_2994" data-expandable="false">GCS_BUCKET_NAME</dt><dd><p><b id="573e5df3_2995">Google Cloud Storage bucket name.</b></p><br></dd></dl></div></div></section></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="load-dataset.html">load.dataset</a>   <a class="navigation-links__next" href="preprocessing-scale.html">preprocessing.scale</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="configuration" data-toc="configuration">Configuration</h3></div><div class="collapse__content"><p id="b7a1143d_1755">If you need granular control over</p><ul class="list _ul" id="b7a1143d_1756"><li class="list__item" id="b7a1143d_1757"><p>where the files are downloaded</p></li><li class="list__item" id="b7a1143d_1758"><p>the credentials used</p></li><li class="list__item" id="b7a1143d_1759"><p>the project used</p></li><li class="list__item" id="b7a1143d_1760"><p>the bucket used</p></li></ul><p id="b7a1143d_1761">Then edit <code class="code" id="b7a1143d_1762">conf.py</code>.</p><dl id="b7a1143d_1763" data-style="title-top"><dt id="b7a1143d_1764" data-expandable="false">GCS_CREDENTIALS</dt><dd><p><b id="b7a1143d_1765">Google Cloud credentials.</b></p><br><p> A <code class="code" id="b7a1143d_1767">google.oauth2.service_account.Credentials</code> object. See the object documentation for more information.</p></dd><dt id="b7a1143d_1768" data-expandable="false">LOCAL_DATASET_ROOT_DIR</dt><dd><p><b id="b7a1143d_1769">Local directory to download files to.</b></p><br><p> Path to a directory, or a <code class="code" id="b7a1143d_1771">Path</code> object.</p></dd><dt id="b7a1143d_1772" data-expandable="false">GCS_PROJECT_ID</dt><dd><p><b id="b7a1143d_1773">Google Cloud project ID.</b></p><br></dd><dt id="b7a1143d_1775" data-expandable="false">GCS_BUCKET_NAME</dt><dd><p><b id="b7a1143d_1776">Google Cloud Storage bucket name.</b></p><br></dd></dl></div></div></section></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="load-dataset.html" class="navigation-links__prev">load.dataset</a><a href="preprocessing-scale.html" class="navigation-links__next">preprocessing.scale</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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diff --git a/docs/mix-match-module.html b/docs/mix-match-module.html
index f622c5c5..e8d5df2e 100644
--- a/docs/mix-match-module.html
+++ b/docs/mix-match-module.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.095883"><meta name="build-number" content="${buildNumber}">       <title>MixMatch Module | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"quick-recap","level":0,"title":"Quick Recap","anchor":"#quick-recap"},{"id":"mixmatch","level":0,"title":"MixMatch","anchor":"#mixmatch"},{"id":"design-choices","level":0,"title":"Design Choices","anchor":"#design-choices"},{"id":"references","level":0,"title":"References","anchor":"#references"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="MixMatch Module | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/mix-match-module.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="MixMatch Module | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/mix-match-module.html#webpage", "url": "/d/0.0.7/mix-match-module.html", "name": "MixMatch Module | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="mix-match-module" data-main-title="MixMatch Module" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="mix-match.md|MixMatch"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="mix-match-module"   id="mix-match-module.md">MixMatch Module</h1>  <p id="85c3129d_134">See <code class="code" id="85c3129d_135">frdc/train/mixmatch_module.py</code>.</p><section class="chapter"><h2 id="quick-recap" data-toc="quick-recap"   >Quick Recap</h2><p id="85c3129d_136">We will go over the essential parts of the code here. Before that, we revise some of the concepts that are used in the code.</p><section class="chapter"><h3 id="abstract-methods" data-toc="abstract-methods"   >Abstract Methods</h3><p id="85c3129d_137">In Python, we can define abstract methods using the <code class="code" id="85c3129d_138">abc</code> module. Just like other OOP languages, abstract methods are methods that must be implemented by the child class.</p><p id="85c3129d_139">For example:</p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.142942">        <title>MixMatch Module | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"quick-recap","level":0,"title":"Quick Recap","anchor":"#quick-recap"},{"id":"mixmatch","level":0,"title":"MixMatch","anchor":"#mixmatch"},{"id":"design-choices","level":0,"title":"Design Choices","anchor":"#design-choices"},{"id":"references","level":0,"title":"References","anchor":"#references"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="MixMatch Module | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/mix-match-module.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="MixMatch Module | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/mix-match-module.html#webpage", "url": "/d/0.0.7/mix-match-module.html", "name": "MixMatch Module | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="mix-match-module" data-main-title="MixMatch Module" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="mix-match.md|MixMatch"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="mix-match-module"   id="mix-match-module.md">MixMatch Module</h1>  <p id="27233123_1779">See <code class="code" id="27233123_1780">frdc/train/mixmatch_module.py</code>.</p><section class="chapter"><h2 id="quick-recap" data-toc="quick-recap">Quick Recap</h2><p id="27233123_1781">We will go over the essential parts of the code here. Before that, we revise some of the concepts that are used in the code.</p><section class="chapter"><h3 id="abstract-methods" data-toc="abstract-methods">Abstract Methods</h3><p id="27233123_1782">In Python, we can define abstract methods using the <code class="code" id="27233123_1783">abc</code> module. Just like other OOP languages, abstract methods are methods that must be implemented by the child class.</p><p id="27233123_1784">For example:</p><div class="code-block" data-lang="python">
 from abc import ABC, abstractmethod
 
 
@@ -11,7 +11,7 @@
 class MyChildClass(MyAbstractClass):
     def my_abstract_method(self):
         print(&quot;Hello World!&quot;)
-</div></section><section class="chapter"><h3 id="nn-module-lightningmodule" data-toc="nn-module-lightningmodule"   >nn.Module &amp; LightningModule</h3><p id="85c3129d_141">If you're unfamiliar with PyTorch, you should read the <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html" id="85c3129d_142"   data-external="true" rel="noopener noreferrer" >nn.Module Documentation</a>.</p><p id="85c3129d_143"><code class="code" id="85c3129d_144">nn.Module</code> is the base class for all neural network modules in PyTorch. While <code class="code" id="85c3129d_145">LightningModule</code> is a PyTorch Lightning class that extends <code class="code" id="85c3129d_146">nn.Module</code>, providing it with additional functionality that reduces boilerplate code.</p><p id="85c3129d_147">By implementing it as a <code class="code" id="85c3129d_148">LightningModule</code>, we also enter the PyTorch Lightning ecosystem, which provides us with a lot of useful features such as logging, early stopping, and more.</p><section class="chapter"><h4 id="what-do-we-implement-in-a-module" data-toc="what-do-we-implement-in-a-module"   >What do we implement in a Module?</h4><p id="85c3129d_149">One key component that <code class="code" id="85c3129d_150">nn.Module</code> requires, is the model. So for example:</p><div class="code-block" data-lang="python"         >
+</div></section><section class="chapter"><h3 id="nn-module-lightningmodule" data-toc="nn-module-lightningmodule">nn.Module &amp; LightningModule</h3><p id="27233123_1786">If you're unfamiliar with PyTorch, you should read the <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html" id="27233123_1787" data-external="true" rel="noopener noreferrer">nn.Module Documentation</a>.</p><p id="27233123_1788"><code class="code" id="27233123_1789">nn.Module</code> is the base class for all neural network modules in PyTorch. While <code class="code" id="27233123_1790">LightningModule</code> is a PyTorch Lightning class that extends <code class="code" id="27233123_1791">nn.Module</code>, providing it with additional functionality that reduces boilerplate code.</p><p id="27233123_1792">By implementing it as a <code class="code" id="27233123_1793">LightningModule</code>, we also enter the PyTorch Lightning ecosystem, which provides us with a lot of useful features such as logging, early stopping, and more.</p><section class="chapter"><h4 id="what-do-we-implement-in-a-module" data-toc="what-do-we-implement-in-a-module">What do we implement in a Module?</h4><p id="27233123_1794">One key component that <code class="code" id="27233123_1795">nn.Module</code> requires, is the model. So for example:</p><div class="code-block" data-lang="python">
 class MyModule(nn.Module):
     def __init__(self):
         super().__init__()
@@ -23,7 +23,7 @@
 
     def forward(self, x):
         return self.model(x)
-</div><p id="85c3129d_152">PyTorch Lightning builds on top of it, requiring <code class="code" id="85c3129d_153">training_step</code> and <code class="code" id="85c3129d_154">validation_step</code>. Each &quot;step&quot; is a batch of data, and the model is trained on it. So for example:</p><div class="code-block" data-lang="python"         >
+</div><p id="27233123_1797">PyTorch Lightning builds on top of it, requiring <code class="code" id="27233123_1798">training_step</code> and <code class="code" id="27233123_1799">validation_step</code>. Each &quot;step&quot; is a batch of data, and the model is trained on it. So for example:</p><div class="code-block" data-lang="python">
 class MyModule(LightningModule):
     def __init__(self): ...
 
@@ -40,7 +40,7 @@
         y_hat = self(x)
         loss = F.cross_entropy(y_hat, y)
         return loss
-</div><p id="85c3129d_156">Usually, the training and validation steps are the same, but in some cases, such as MixMatch, they are different. In MixMatch, we not only use a different loss function for train, we also handle a batch differently. The PyTorch Lightning framework allows us to separate the two, and implement them separately.</p></section></section><section class="chapter"><h3 id="model-embedded-preprocessing-on-before-batch-transfer" data-toc="model-embedded-preprocessing-on-before-batch-transfer"   >Model Embedded Preprocessing on_before_batch_transfer</h3><p id="85c3129d_157">In PyTorch Lightning, we can also inject a step before the batch is passed to the model. 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This hook is called after the backward pass, and allows us to do custom operations. 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It implements a real-world example of MixMatch.</p><p id="85c3129d_169">As a summary:</p><ol class="list _decimal" id="85c3129d_170" type="1"><li class="list__item" id="85c3129d_171"><p>We learned what is an abstract method, and how to implement it</p></li><li class="list__item" id="85c3129d_172"><p>We implement the model in <code class="code" id="85c3129d_173">LightningModule</code> much like we would in <code class="code" id="85c3129d_174">nn.Module</code></p></li><li class="list__item" id="85c3129d_175"><p>We implement <code class="code" id="85c3129d_176">on_before_batch_transfer</code> to preprocess the batch</p></li><li class="list__item" id="85c3129d_177"><p>Finally, we implement <code class="code" id="85c3129d_178">on_after_backward</code> to update the EMA model</p></li></ol><p id="85c3129d_179">With the above in mind, let's look at the MixMatch implementation.</p><dl id="85c3129d_180" data-style="title-top"><dt id="85c3129d_181" data-expandable="false">forward (abstract)</dt><dd><p id="85c3129d_182">Forward pass of the model</p></dd><dt id="85c3129d_183" data-expandable="false">ema_model (abstract)</dt><dd><p id="85c3129d_184">The model that is used for EMA. We expect this property to be implemented by the child class.</p></dd><dt id="85c3129d_185" data-expandable="false">update_ema (abstract)</dt><dd><p id="85c3129d_186">The method to update the EMA model. We expect this method to be implemented by the child class.</p></dd><dt id="85c3129d_187" data-expandable="false">loss_unl_scaler (static)</dt><dd><p id="85c3129d_188">Takes in the current progress of the training, 0.0 to 1.0, where 0.0 is the start of the training, and 1.0 is the end. Then, returns the multiplier for the unlabeled loss.</p></dd><dt id="85c3129d_189" data-expandable="false">loss_lbl (static)</dt><dd><p id="85c3129d_190">Implements the loss for labeled data. Takes in the predicted labels and the ground truth labels, and returns the loss. This is cross entropy for MixMatch.</p></dd><dt id="85c3129d_191" data-expandable="false">loss_unl (static)</dt><dd><p id="85c3129d_192">Implements the loss for unlabeled data. Takes in the predicted labels and the ground truth labels, and returns the loss. This is MSE for MixMatch.</p></dd><dt id="85c3129d_193" data-expandable="false">mixup</dt><dd><p id="85c3129d_194">Takes in the data and the labels, the beta distribution parameter, and returns the mixed data and labels.</p></dd><dt id="85c3129d_195" data-expandable="false">sharpen</dt><dd><p id="85c3129d_196">Takes in the labels and temperature, and returns the sharpened labels.</p></dd><dt id="85c3129d_197" data-expandable="false">guess_labels</dt><dd><p id="85c3129d_198">Takes in the unlabeled data, and returns the guessed labels.</p></dd><dt id="85c3129d_199" data-expandable="false">progress</dt><dd><p id="85c3129d_200">The current progress of the training, 0.0 to 1.0, where 0.0 is the start of the training, and 1.0 is the end.</p></dd><dt id="85c3129d_201" data-expandable="false">training_step</dt><dd><p id="85c3129d_202">The training step runs through 1 batch of data, and returns the loss. Note that this is significantly different from validation step, as we handle the K-Augmented data differently.</p></dd><dt id="85c3129d_203" data-expandable="false">test / validation_step</dt><dd><p id="85c3129d_204">The test / validation step runs through 1 batch of data, and returns the loss.</p></dd><dt id="85c3129d_205" data-expandable="false">predict_step</dt><dd><p id="85c3129d_206">The predict step runs through 1 batch of data, and returns the actual decoded labels.</p></dd><dt id="85c3129d_207" data-expandable="false">on_after_backward</dt><dd><p id="85c3129d_208">The on_after_backward hook is called after the backward pass, and allows us to do custom operations. In our case, we use it to update the EMA model.</p></dd><dt id="85c3129d_209" data-expandable="false">on_before_batch_transfer</dt><dd><p id="85c3129d_210">The on_before_batch_transfer hook is called before the batch is transferred to the GPU. 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+</div><p id="27233123_1801">Usually, the training and validation steps are the same, but in some cases, such as MixMatch, they are different. In MixMatch, we not only use a different loss function for train, we also handle a batch differently. The PyTorch Lightning framework allows us to separate the two, and implement them separately.</p></section></section><section class="chapter"><h3 id="model-embedded-preprocessing-on-before-batch-transfer" data-toc="model-embedded-preprocessing-on-before-batch-transfer">Model Embedded Preprocessing on_before_batch_transfer</h3><p id="27233123_1802">In PyTorch Lightning, we can also inject a step before the batch is passed to the model. 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It implements a real-world example of MixMatch.</p><p id="27233123_1814">As a summary:</p><ol class="list _decimal" id="27233123_1815" type="1"><li class="list__item" id="27233123_1816"><p>We learned what is an abstract method, and how to implement it</p></li><li class="list__item" id="27233123_1817"><p>We implement the model in <code class="code" id="27233123_1818">LightningModule</code> much like we would in <code class="code" id="27233123_1819">nn.Module</code></p></li><li class="list__item" id="27233123_1820"><p>We implement <code class="code" id="27233123_1821">on_before_batch_transfer</code> to preprocess the batch</p></li><li class="list__item" id="27233123_1822"><p>Finally, we implement <code class="code" id="27233123_1823">on_after_backward</code> to update the EMA model</p></li></ol><p id="27233123_1824">With the above in mind, let's look at the MixMatch implementation.</p><dl id="27233123_1825" data-style="title-top"><dt id="27233123_1826" data-expandable="false">forward (abstract)</dt><dd><p id="27233123_1827">Forward pass of the model</p></dd><dt id="27233123_1828" data-expandable="false">ema_model (abstract)</dt><dd><p id="27233123_1829">The model that is used for EMA. We expect this property to be implemented by the child class.</p></dd><dt id="27233123_1830" data-expandable="false">update_ema (abstract)</dt><dd><p id="27233123_1831">The method to update the EMA model. We expect this method to be implemented by the child class.</p></dd><dt id="27233123_1832" data-expandable="false">loss_unl_scaler (static)</dt><dd><p id="27233123_1833">Takes in the current progress of the training, 0.0 to 1.0, where 0.0 is the start of the training, and 1.0 is the end. Then, returns the multiplier for the unlabeled loss.</p></dd><dt id="27233123_1834" data-expandable="false">loss_lbl (static)</dt><dd><p id="27233123_1835">Implements the loss for labeled data. Takes in the predicted labels and the ground truth labels, and returns the loss. This is cross entropy for MixMatch.</p></dd><dt id="27233123_1836" data-expandable="false">loss_unl (static)</dt><dd><p id="27233123_1837">Implements the loss for unlabeled data. Takes in the predicted labels and the ground truth labels, and returns the loss. This is MSE for MixMatch.</p></dd><dt id="27233123_1838" data-expandable="false">mixup</dt><dd><p id="27233123_1839">Takes in the data and the labels, the beta distribution parameter, and returns the mixed data and labels.</p></dd><dt id="27233123_1840" data-expandable="false">sharpen</dt><dd><p id="27233123_1841">Takes in the labels and temperature, and returns the sharpened labels.</p></dd><dt id="27233123_1842" data-expandable="false">guess_labels</dt><dd><p id="27233123_1843">Takes in the unlabeled data, and returns the guessed labels.</p></dd><dt id="27233123_1844" data-expandable="false">progress</dt><dd><p id="27233123_1845">The current progress of the training, 0.0 to 1.0, where 0.0 is the start of the training, and 1.0 is the end.</p></dd><dt id="27233123_1846" data-expandable="false">training_step</dt><dd><p id="27233123_1847">The training step runs through 1 batch of data, and returns the loss. Note that this is significantly different from validation step, as we handle the K-Augmented data differently.</p></dd><dt id="27233123_1848" data-expandable="false">test / validation_step</dt><dd><p id="27233123_1849">The test / validation step runs through 1 batch of data, and returns the loss.</p></dd><dt id="27233123_1850" data-expandable="false">predict_step</dt><dd><p id="27233123_1851">The predict step runs through 1 batch of data, and returns the actual decoded labels.</p></dd><dt id="27233123_1852" data-expandable="false">on_after_backward</dt><dd><p id="27233123_1853">The on_after_backward hook is called after the backward pass, and allows us to do custom operations. In our case, we use it to update the EMA model.</p></dd><dt id="27233123_1854" data-expandable="false">on_before_batch_transfer</dt><dd><p id="27233123_1855">The on_before_batch_transfer hook is called before the batch is transferred to the GPU. 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flowchart-label"><rect height="34" width="103.859375" y="-17" x="-51.9296875" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-44.4296875, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="88.859375"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">update_ema</span></div></foreignObject></g></g><g transform="translate(301.2421875, 185)" id="flowchart-VC-7861" class="node default default flowchart-label"><rect height="34" width="124.9375" y="-17" x="-62.46875" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-54.96875, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="109.9375"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">validation_step</span></div></foreignObject></g></g><g transform="translate(301.2421875, 269)" id="flowchart-VL-7862" class="node default default flowchart-label"><rect height="34" width="41.265625" y="-17" x="-20.6328125" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-13.1328125, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="26.265625"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">loss</span></div></foreignObject></g></g></g></g></g></svg><p id="27233123_1858">Finally, we show an example of how to use the MixMatch module:</p><div class="code-block" data-lang="python">
 from sklearn.preprocessing import StandardScaler, OrdinalEncoder
 
 from frdc.train.mixmatch_module import MixMatchModule
@@ -60,7 +60,7 @@
    sharpen_temp=0.5,
    mix_beta_alpha=0.75,
 )
-</div><p id="85c3129d_215">In particular, we need to supply some transformations for the preprocessing step. In this case, we use <code class="code" id="85c3129d_216">StandardScaler</code> to scale the data, and <code class="code" id="85c3129d_217">OrdinalEncoder</code> to encode the labels.</p><ol class="list _decimal" id="85c3129d_218" type="1"><li class="list__item" id="85c3129d_219"><p>It's best if standardization is done only on the training data, and not the validation data to better fit real-world scenarios.</p></li><li class="list__item" id="85c3129d_220"><p>We use <code class="code" id="85c3129d_221">OrdinalEncoder</code> as it handles unseen labels. So if a class doesn't show up in the training data, it will be encoded as <code class="code" id="85c3129d_222">np.nan</code>, and will not participate in the loss calculation.</p></li></ol><aside class="prompt" data-type="warning" data-title="" id="85c3129d_223"><p id="85c3129d_224">Due to the nature of how we handle unseen labels, the performance can be an overestimate. This is because the model will not be penalized for misclassifying unseen labels.</p></aside></section><section class="chapter"><h2 id="design-choices" data-toc="design-choices"   >Design Choices</h2><section class="chapter"><h3 id="static-method-overriding" data-toc="static-method-overriding"   >Static Method Overriding</h3><p id="85c3129d_225">We implement many functions as static, as we believe that a functional style reduces dependencies, thus making the code easier to test and debug.</p><p id="85c3129d_226">Furthermore, it allows the subclasses to easily override the functions, to customize the behavior of the MixMatch module.</p><p id="85c3129d_227">For example, the <code class="code" id="85c3129d_228">loss_unl_scaler</code> function is static, thus, we can implement our own scaling function, and pass it to the MixMatch module.</p><div class="code-block" data-lang="python"         >
+</div><p id="27233123_1860">In particular, we need to supply some transformations for the preprocessing step. In this case, we use <code class="code" id="27233123_1861">StandardScaler</code> to scale the data, and <code class="code" id="27233123_1862">OrdinalEncoder</code> to encode the labels.</p><ol class="list _decimal" id="27233123_1863" type="1"><li class="list__item" id="27233123_1864"><p>It's best if standardization is done only on the training data, and not the validation data to better fit real-world scenarios.</p></li><li class="list__item" id="27233123_1865"><p>We use <code class="code" id="27233123_1866">OrdinalEncoder</code> as it handles unseen labels. So if a class doesn't show up in the training data, it will be encoded as <code class="code" id="27233123_1867">np.nan</code>, and will not participate in the loss calculation.</p></li></ol><aside class="prompt" data-type="warning" data-title="" id="27233123_1868"><p id="27233123_1869">Due to the nature of how we handle unseen labels, the performance can be an overestimate. This is because the model will not be penalized for misclassifying unseen labels.</p></aside></section><section class="chapter"><h2 id="design-choices" data-toc="design-choices">Design Choices</h2><section class="chapter"><h3 id="static-method-overriding" data-toc="static-method-overriding">Static Method Overriding</h3><p id="27233123_1870">We implement many functions as static, as we believe that a functional style reduces dependencies, thus making the code easier to test and debug.</p><p id="27233123_1871">Furthermore, it allows the subclasses to easily override the functions, to customize the behavior of the MixMatch module.</p><p id="27233123_1872">For example, the <code class="code" id="27233123_1873">loss_unl_scaler</code> function is static, thus, we can implement our own scaling function, and pass it to the MixMatch module.</p><div class="code-block" data-lang="python">
 def my_loss_unl_scaler(progress: float) -&gt; float:
     return progress ** 2
     
@@ -68,4 +68,4 @@
     @staticmethod
     def loss_unl_scaler(progress: float) -&gt; float:
         return my_loss_unl_scaler(progress)
-</div><p id="85c3129d_230">If we had used a method instead, we would have to consider instance state, which would make it harder to override.</p></section><section class="chapter"><h3 id="why-not-use-dataclasses" data-toc="why-not-use-dataclasses"   >Why not use Dataclasses?</h3><p id="85c3129d_231">One of the biggest caveats of <code class="code" id="85c3129d_232">nn.Module</code> is that it requires <code class="code" id="85c3129d_233">super().__init__()</code> to be called before anything is assigned. While <code class="code" id="85c3129d_234">dataclass</code> can leverage <code class="code" id="85c3129d_235">__post_init__</code> to do the same, we felt that this was too much of a hassle to save a few keystrokes. Thus, we opted to use <code class="code" id="85c3129d_236">__init__</code> instead, while more verbose, it is more explicit.</p></section><section class="chapter"><h3 id="why-use-pytorch-lightning" data-toc="why-use-pytorch-lightning"   >Why use PyTorch Lightning?</h3><p id="85c3129d_237">While we did hit some road blocks implementing SSL, due to its complex and unconventional nature, we felt that the benefits of using PyTorch Lightning outweighed the cons.</p><p id="85c3129d_238"><code class="code" id="85c3129d_239">on_before_batch_transfer</code> and <code class="code" id="85c3129d_240">on_after_backward</code> are unconventional hooks, and we had to do some digging to find them. It can be argued that by just writing explicit code, we can avoid the need for these hooks, but the PyTorch ecosystem fixes many other issues, so we closed an eye on this.</p></section></section><section class="chapter"><h2 id="references" data-toc="references"   >References</h2><ul class="list _ul" id="85c3129d_241"><li class="list__item" id="85c3129d_242"><p><a href="https://github.com/YU1ut/MixMatch-pytorch/tree/master" id="85c3129d_243"   data-external="true" rel="noopener noreferrer" >YU1ut's PyTorch Implementation</a></p></li><li class="list__item" id="85c3129d_244"><p><a href="https://github.com/google-research/mixmatch" id="85c3129d_245"   data-external="true" rel="noopener noreferrer" >Google Research's TensorFlow Implementation</a></p></li></ul></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="mix-match.html">MixMatch</a>   <a class="navigation-links__next" href="custom-k-aug-dataloaders.html">Custom K-Aug Dataloaders</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+</div><p id="27233123_1875">If we had used a method instead, we would have to consider instance state, which would make it harder to override.</p></section><section class="chapter"><h3 id="why-not-use-dataclasses" data-toc="why-not-use-dataclasses">Why not use Dataclasses?</h3><p id="27233123_1876">One of the biggest caveats of <code class="code" id="27233123_1877">nn.Module</code> is that it requires <code class="code" id="27233123_1878">super().__init__()</code> to be called before anything is assigned. While <code class="code" id="27233123_1879">dataclass</code> can leverage <code class="code" id="27233123_1880">__post_init__</code> to do the same, we felt that this was too much of a hassle to save a few keystrokes. Thus, we opted to use <code class="code" id="27233123_1881">__init__</code> instead, while more verbose, it is more explicit.</p></section><section class="chapter"><h3 id="why-use-pytorch-lightning" data-toc="why-use-pytorch-lightning">Why use PyTorch Lightning?</h3><p id="27233123_1882">While we did hit some road blocks implementing SSL, due to its complex and unconventional nature, we felt that the benefits of using PyTorch Lightning outweighed the cons.</p><p id="27233123_1883"><code class="code" id="27233123_1884">on_before_batch_transfer</code> and <code class="code" id="27233123_1885">on_after_backward</code> are unconventional hooks, and we had to do some digging to find them. It can be argued that by just writing explicit code, we can avoid the need for these hooks, but the PyTorch ecosystem fixes many other issues, so we closed an eye on this.</p></section></section><section class="chapter"><h2 id="references" data-toc="references">References</h2><ul class="list _ul" id="27233123_1886"><li class="list__item" id="27233123_1887"><p><a href="https://github.com/YU1ut/MixMatch-pytorch/tree/master" id="27233123_1888" data-external="true" rel="noopener noreferrer">YU1ut's PyTorch Implementation</a></p></li><li class="list__item" id="27233123_1889"><p><a href="https://github.com/google-research/mixmatch" id="27233123_1890" data-external="true" rel="noopener noreferrer">Google Research's TensorFlow Implementation</a></p></li></ul></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="mix-match.html" class="navigation-links__prev">MixMatch</a><a href="custom-k-aug-dataloaders.html" class="navigation-links__next">Custom K-Aug Dataloaders</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.0968833"><meta name="build-number" content="${buildNumber}">       <title>MixMatch | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"implementation-details","level":0,"title":"Implementation Details","anchor":"#implementation-details"},{"id":"references","level":0,"title":"References","anchor":"#references"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="MixMatch | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/mix-match.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="MixMatch | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/mix-match.html#webpage", "url": "/d/0.0.7/mix-match.html", "name": "MixMatch | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="mix-match" data-main-title="MixMatch" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="mix-match"   id="mix-match.md">MixMatch</h1>  <p id="4d1f0585_137">In FRDC-ML, we leverage semi-supervised learning to improve the model's performance through better augmentation consistency and using even unlabelled data.</p><p id="4d1f0585_138">The algorithm we use is <a href="https://arxiv.org/abs/1905.02249" id="4d1f0585_139"   data-external="true" rel="noopener noreferrer" >MixMatch</a>. A state-of-the-art semi-supervised learning algorithm. It is based on the idea of consistency regularization, which encourages models to predict the same class even after augmentations that occur naturally in the real world.</p><aside class="prompt" data-type="tip" data-title="" id="4d1f0585_140"><p id="4d1f0585_141">In other words, a picture of a dog should be classified as a dog even if it is horizontally flipped, offset, or is of a different size. Consistency regularization encourages the model to predict the same class consistently.</p></aside><p id="4d1f0585_142">Our implementation of MixMatch is a refactored version of <a href="https://github.com/YU1ut/MixMatch-pytorch/tree/master" id="4d1f0585_143"   data-external="true" rel="noopener noreferrer" >YU1ut/MixMatch-pytorch</a> We've refactored the code to follow more modern PyTorch practices, allowing us to utilize it with modern PyTorch frameworks such as PyTorch Lightning.</p><p id="4d1f0585_144">We won't go through the details of MixMatch here, see <a href="https://fr-dc.github.io/MixMatch-PyTorch-CIFAR10/pipeline.html" id="4d1f0585_145"   data-external="true" rel="noopener noreferrer" >Our Documentation</a> in our <a href="https://github.com/FR-DC/MixMatch-PyTorch-CIFAR10" id="4d1f0585_146"   data-external="true" rel="noopener noreferrer" >MixMatch-PyTorch-CIFAR10</a> repository for more details.</p><section class="chapter"><h2 id="implementation-details" data-toc="implementation-details"   >Implementation Details</h2><ol class="list _decimal" id="4d1f0585_147" type="1"><li class="list__item" id="4d1f0585_148"><p id="4d1f0585_149">How we implemented the MixMatch logic <a href="mix-match-module.html" id="4d1f0585_150" data-tooltip="See frdc/train/mixmatch_module.py."  >MixMatchModule</a></p></li><li class="list__item" id="4d1f0585_151"><p id="4d1f0585_152">How we implemented the unique MixMatch data loading logic <a href="custom-k-aug-dataloaders.html" id="4d1f0585_153" data-tooltip="In MixMatch, implementing the data loading methods is quite unconventional."  >Custom MixMatch Data Loading</a></p></li></ol></section><section class="chapter"><h2 id="references" data-toc="references"   >References</h2><ul class="list _ul" id="4d1f0585_154"><li class="list__item" id="4d1f0585_155"><p><a href="https://github.com/YU1ut/MixMatch-pytorch/tree/master" id="4d1f0585_156"   data-external="true" rel="noopener noreferrer" >YU1ut's PyTorch Implementation</a></p></li><li class="list__item" id="4d1f0585_157"><p><a href="https://github.com/google-research/mixmatch" id="4d1f0585_158"   data-external="true" rel="noopener noreferrer" >Google Research's TensorFlow Implementation</a></p></li></ul></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="retrieve-our-datasets.html">Retrieve our Datasets</a>   <a class="navigation-links__next" href="mix-match-module.html">MixMatch Module</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1419417">        <title>MixMatch | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"implementation-details","level":0,"title":"Implementation Details","anchor":"#implementation-details"},{"id":"references","level":0,"title":"References","anchor":"#references"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="MixMatch | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/mix-match.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="MixMatch | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/mix-match.html#webpage", "url": "/d/0.0.7/mix-match.html", "name": "MixMatch | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="mix-match" data-main-title="MixMatch" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="mix-match"   id="mix-match.md">MixMatch</h1>  <p id="8cc90250_1820">In FRDC-ML, we leverage semi-supervised learning to improve the model's performance through better augmentation consistency and using even unlabelled data.</p><p id="8cc90250_1821">The algorithm we use is <a href="https://arxiv.org/abs/1905.02249" id="8cc90250_1822" data-external="true" rel="noopener noreferrer">MixMatch</a>. A state-of-the-art semi-supervised learning algorithm. It is based on the idea of consistency regularization, which encourages models to predict the same class even after augmentations that occur naturally in the real world.</p><aside class="prompt" data-type="tip" data-title="" id="8cc90250_1823"><p id="8cc90250_1824">In other words, a picture of a dog should be classified as a dog even if it is horizontally flipped, offset, or is of a different size. Consistency regularization encourages the model to predict the same class consistently.</p></aside><p id="8cc90250_1825">Our implementation of MixMatch is a refactored version of <a href="https://github.com/YU1ut/MixMatch-pytorch/tree/master" id="8cc90250_1826" data-external="true" rel="noopener noreferrer">YU1ut/MixMatch-pytorch</a> We've refactored the code to follow more modern PyTorch practices, allowing us to utilize it with modern PyTorch frameworks such as PyTorch Lightning.</p><p id="8cc90250_1827">We won't go through the details of MixMatch here, see <a href="https://fr-dc.github.io/MixMatch-PyTorch-CIFAR10/pipeline.html" id="8cc90250_1828" data-external="true" rel="noopener noreferrer">Our Documentation</a> in our <a href="https://github.com/FR-DC/MixMatch-PyTorch-CIFAR10" id="8cc90250_1829" data-external="true" rel="noopener noreferrer">MixMatch-PyTorch-CIFAR10</a> repository for more details.</p><section class="chapter"><h2 id="implementation-details" data-toc="implementation-details">Implementation Details</h2><ol class="list _decimal" id="8cc90250_1830" type="1"><li class="list__item" id="8cc90250_1831"><p id="8cc90250_1832">How we implemented the MixMatch logic <a href="mix-match-module.html" id="8cc90250_1833" data-tooltip="See frdc/train/mixmatch_module.py.">MixMatchModule</a></p></li><li class="list__item" id="8cc90250_1834"><p id="8cc90250_1835">How we implemented the unique MixMatch data loading logic <a href="custom-k-aug-dataloaders.html" id="8cc90250_1836" data-tooltip="In MixMatch, implementing the data loading methods is quite unconventional.">Custom MixMatch Data Loading</a></p></li></ol></section><section class="chapter"><h2 id="references" data-toc="references">References</h2><ul class="list _ul" id="8cc90250_1837"><li class="list__item" id="8cc90250_1838"><p><a href="https://github.com/YU1ut/MixMatch-pytorch/tree/master" id="8cc90250_1839" data-external="true" rel="noopener noreferrer">YU1ut's PyTorch Implementation</a></p></li><li class="list__item" id="8cc90250_1840"><p><a href="https://github.com/google-research/mixmatch" id="8cc90250_1841" data-external="true" rel="noopener noreferrer">Google Research's TensorFlow Implementation</a></p></li></ul></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="retrieve-our-datasets.html" class="navigation-links__prev">Retrieve our Datasets</a><a href="mix-match-module.html" class="navigation-links__next">MixMatch Module</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.0988847"><meta name="build-number" content="${buildNumber}">       <title>Model Test Chestnut May-Dec | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"motivation","level":0,"title":"Motivation","anchor":"#motivation"},{"id":"methodology","level":0,"title":"Methodology","anchor":"#methodology"},{"id":"model","level":0,"title":"Model","anchor":"#model"},{"id":"preprocessing","level":0,"title":"Preprocessing","anchor":"#preprocessing"},{"id":"hyperparameters","level":0,"title":"Hyperparameters","anchor":"#hyperparameters"},{"id":"results","level":0,"title":"Results","anchor":"#results"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Model Test Chestnut May-Dec | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/model-test-chestnut-may-dec.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Model Test Chestnut May-Dec | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/model-test-chestnut-may-dec.html#webpage", "url": "/d/0.0.7/model-test-chestnut-may-dec.html", "name": "Model Test Chestnut May-Dec | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="Model-Test-Chestnut-May-Dec" data-main-title="Model Test Chestnut May-Dec" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="Model Tests"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Model-Test-Chestnut-May-Dec"   id="Model-Test-Chestnut-May-Dec.md">Model Test Chestnut May-Dec</h1>  <p id="43cd37dc_133">This test is used to evaluate the model performance on the Chestnut Nature Park May &amp; December dataset.</p><p id="43cd37dc_134">See this script in <code class="code" id="43cd37dc_135">model_tests/chestnut_dec_may/train.py</code>.</p><section class="chapter"><h2 id="motivation" data-toc="motivation"   >Motivation</h2><p id="43cd37dc_136">The usage of this model will be to classify trees in unseen datasets under different conditions. In this test, we'll evaluate it under a different season.</p><p id="43cd37dc_137">A caveat is that it'll be evaluated on the same set of trees, so it's not a representative of a field-test. However, given difficulties of yielding datasets, this still gives us a good preliminary idea of how the model will perform in different conditions.</p></section><section class="chapter"><h2 id="methodology" data-toc="methodology"   >Methodology</h2><p id="43cd37dc_138">We train on the December dataset, and test on the May dataset.</p><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 512.25 74"  xmlns="http://www.w3.org/2000/svg" width="512.25" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; stroke-dasharray: 1, 0;" class="arrowMarkerPath" d="M 0 0 L 10 5 L 0 10 z"></path></marker><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="0" viewBox="0 0 10 10" class="marker flowchart" id="flowchart-pointStart"><path style="stroke-width: 1; 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white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">MayDataset</span></div></foreignObject></g></g></g></g></g></svg><p id="43cd37dc_140">Despite not having any true unlabelled data, we use <a href="mix-match.html" id="43cd37dc_141" data-tooltip="In FRDC-ML, we leverage semi-supervised learning to improve the model's performance through better augmentation consistency and using even unlabelled data."  >MixMatch</a> by treating the labelled data of the December dataset as unlabelled data.</p><aside class="prompt" data-type="warning" data-title="" id="43cd37dc_142"><p id="43cd37dc_143">Ideally, we should have a Validation set to tune the hyperparameters, but given the limitations of the dataset, we'll skip this step.</p></aside></section><section class="chapter"><h2 id="model" data-toc="model"   >Model</h2><p id="43cd37dc_144">The current Model used is a simple InceptionV3 Transfer Learning model, with the last layer replaced with a fully connected layer(s).</p><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 654.5625 104"  xmlns="http://www.w3.org/2000/svg" width="654.5625" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; 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This indicates that the model has saturated and is not able to learn anymore from the training set. There's no indication of overfitting as the validation loss just plateaus.</p><p id="43cd37dc_165"><a href="https://wandb.ai/frdc/FRDC-ML-tests_model_tests_chestnut_dec_may" id="43cd37dc_166"   data-external="true" rel="noopener noreferrer" >W&amp;B Dashboard</a></p><section class="chapter"><h3 id="caveats" data-toc="caveats"   >Caveats</h3><ul class="list _ul" id="43cd37dc_167"><li class="list__item" id="43cd37dc_168"><p>The test set is very small, so the results are not very representative.</p></li><li class="list__item" id="43cd37dc_169"><p>The test set is the same set of trees, so it's not a true test of the model performance in different conditions.</p></li><li class="list__item" id="43cd37dc_170"><p>There are many classes with 1 sample, so the model may not be able to learn the features of these classes well.</p></li></ul></section></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="custom-k-aug-dataloaders.html">Custom K-Aug Dataloaders</a>   <a class="navigation-links__next" href="load-dataset.html">load.dataset</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1500755">        <title>Model Test Chestnut May-Dec | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"motivation","level":0,"title":"Motivation","anchor":"#motivation"},{"id":"methodology","level":0,"title":"Methodology","anchor":"#methodology"},{"id":"model","level":0,"title":"Model","anchor":"#model"},{"id":"preprocessing","level":0,"title":"Preprocessing","anchor":"#preprocessing"},{"id":"hyperparameters","level":0,"title":"Hyperparameters","anchor":"#hyperparameters"},{"id":"results","level":0,"title":"Results","anchor":"#results"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Model Test Chestnut May-Dec | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/model-test-chestnut-may-dec.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Model Test Chestnut May-Dec | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/model-test-chestnut-may-dec.html#webpage", "url": "/d/0.0.7/model-test-chestnut-may-dec.html", "name": "Model Test Chestnut May-Dec | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="Model-Test-Chestnut-May-Dec" data-main-title="Model Test Chestnut May-Dec" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="Model Tests"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Model-Test-Chestnut-May-Dec"   id="Model-Test-Chestnut-May-Dec.md">Model Test Chestnut May-Dec</h1>  <p id="677787d8_1749">This test is used to evaluate the model performance on the Chestnut Nature Park May &amp; December dataset.</p><p id="677787d8_1750">See this script in <code class="code" id="677787d8_1751">model_tests/chestnut_dec_may/train.py</code>.</p><section class="chapter"><h2 id="motivation" data-toc="motivation">Motivation</h2><p id="677787d8_1752">The usage of this model will be to classify trees in unseen datasets under different conditions. In this test, we'll evaluate it under a different season.</p><p id="677787d8_1753">A caveat is that it'll be evaluated on the same set of trees, so it's not a representative of a field-test. However, given difficulties of yielding datasets, this still gives us a good preliminary idea of how the model will perform in different conditions.</p></section><section class="chapter"><h2 id="methodology" data-toc="methodology">Methodology</h2><p id="677787d8_1754">We train on the December dataset, and test on the May dataset.</p><svg aria-roledescription="flowchart-v2" role="graphics-document document" viewBox="-8 -8 512.25 74"  xmlns="http://www.w3.org/2000/svg" width="512.25" id="mermaid"><g><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="10" viewBox="0 0 12 20" class="marker flowchart" id="flowchart-pointEnd"><path style="stroke-width: 1; stroke-dasharray: 1, 0;" class="arrowMarkerPath" d="M 0 0 L 10 5 L 0 10 z"></path></marker><marker orient="auto" markerHeight="12" markerWidth="12" markerUnits="userSpaceOnUse" refY="5" refX="0" viewBox="0 0 10 10" class="marker flowchart" id="flowchart-pointStart"><path style="stroke-width: 1; 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white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Vertical Flip</span></div></foreignObject></g></g><g transform="translate(707.921875, 269)" id="flowchart-G-7910" class="node default default flowchart-label"><rect height="34" width="196.84375" y="-17" x="-98.421875" ry="0" rx="0" style="" class="basic label-container"></rect><g transform="translate(-90.921875, -9.5)" style="" class="label"><rect></rect><foreignObject height="19" width="181.84375"><div style="display: inline-block; white-space: nowrap;" xmlns="http://www.w3.org/1999/xhtml"><span class="nodeLabel">Horizontal &amp; Vertical Flip</span></div></foreignObject></g></g></g></g></g></svg><p id="677787d8_1770">For evaluation, we evaluate that the model should be invariant to horizontal and vertical flips, as well as the original image.</p></section><section class="chapter"><h2 id="hyperparameters" data-toc="hyperparameters">Hyperparameters</h2><p id="677787d8_1771">The following hyperparameters are used:</p><ul class="list _ul" id="677787d8_1772"><li class="list__item" id="677787d8_1773"><p>Optimizer: Adam</p></li><li class="list__item" id="677787d8_1774"><p>Learning Rate: 1e-3</p></li><li class="list__item" id="677787d8_1775"><p>Batch Size: 32</p></li><li class="list__item" id="677787d8_1776"><p>Epochs: 10</p></li><li class="list__item" id="677787d8_1777"><p>Train Iterations: 25~100</p></li><li class="list__item" id="677787d8_1778"><p>Validation Iterations: 10~25</p></li><li class="list__item" id="677787d8_1779"><p>Early Stopping: 4</p></li></ul></section><section class="chapter"><h2 id="results" data-toc="results">Results</h2><p id="677787d8_1780">We evaluate around 40% accuracy on the test set, compared to 100% for the training set. This indicates that the model has saturated and is not able to learn anymore from the training set. There's no indication of overfitting as the validation loss just plateaus.</p><p id="677787d8_1781"><a href="https://wandb.ai/frdc/FRDC-ML-tests_model_tests_chestnut_dec_may" id="677787d8_1782" data-external="true" rel="noopener noreferrer">W&amp;B Dashboard</a></p><section class="chapter"><h3 id="caveats" data-toc="caveats">Caveats</h3><ul class="list _ul" id="677787d8_1783"><li class="list__item" id="677787d8_1784"><p>The test set is very small, so the results are not very representative.</p></li><li class="list__item" id="677787d8_1785"><p>The test set is the same set of trees, so it's not a true test of the model performance in different conditions.</p></li><li class="list__item" id="677787d8_1786"><p>There are many classes with 1 sample, so the model may not be able to learn the features of these classes well.</p></li></ul></section></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="custom-k-aug-dataloaders.html" class="navigation-links__prev">Custom K-Aug Dataloaders</a><a href="load-dataset.html" class="navigation-links__next">load.dataset</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.0988847"><meta name="build-number" content="${buildNumber}">       <title>Overview | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/overview.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/overview.html#webpage", "url": "/d/0.0.7/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"   id="Overview.md">Overview</h1>  <p id="1da610ba_140">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="1da610ba_141">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="1da610ba_142"><a href="getting-started.html" id="1da610ba_143" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  ><span class="control" id="1da610ba_144">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects"   >Other Projects</h2><dl id="1da610ba_145" data-style="title-top"><dt id="1da610ba_146" data-expandable="false">FRDC-UI</dt><dd><p id="1da610ba_148"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="1da610ba_149"   data-external="true" rel="noopener noreferrer" >The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">   <a class="navigation-links__next" href="getting-started.html">Getting Started</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.142942">        <title>Overview | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"other-projects","level":0,"title":"Other Projects","anchor":"#other-projects"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Overview | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/overview.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Overview | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/overview.html#webpage", "url": "/d/0.0.7/overview.html", "name": "Overview | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="Overview" data-main-title="Overview" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs=""  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Overview"   id="Overview.md">Overview</h1>  <p id="33838681_1865">Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).</p><p id="33838681_1866">This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.</p><p id="33838681_1867"><a href="getting-started.html" id="33838681_1868" data-tooltip="It's likely that your src and tests directories are not in PYTHONPATH. To fix this, run the following command:"><span class="control" id="33838681_1869">Get started here</span></a></p><section class="chapter"><h2 id="other-projects" data-toc="other-projects">Other Projects</h2><dl id="33838681_1870" data-style="title-top"><dt id="33838681_1871" data-expandable="false">FRDC-UI</dt><dd><p id="33838681_1873"><a href="https://github.com/Forest-Recovery-Digital-Companion/FRDC-UI/" id="33838681_1874" data-external="true" rel="noopener noreferrer">The User Interface Repository</a> for FRDC, a WebApp GUI for ecologists to adjust annotations.</p></dd></dl></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="getting-started.html" class="navigation-links__next">Getting Started</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.095883"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.extract_segments | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.extract_segments | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/preprocessing-extract-segments.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.extract_segments | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/preprocessing-extract-segments.html#webpage", "url": "/d/0.0.7/preprocessing-extract-segments.html", "name": "preprocessing.extract_segments | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="preprocessing.extract_segments" data-main-title="preprocessing.extract_segments" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.extract_segments"   id="preprocessing.extract_segments.md">preprocessing.extract_segments</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;cfc7b30f_191\&quot;\u003eExtracts segments from a label classification or bounds and labels.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions"   >Functions</h2><dl id="cfc7b30f_132" data-style="title-left" data-title-width="medium"><dt id="cfc7b30f_133" data-expandable="false">extract_segments_from_labels</dt><dd><p><b id="cfc7b30f_134">Extracts segments from a label classification.</b></p></dd><dt id="cfc7b30f_135" data-expandable="false">extract_segments_from_bounds</dt><dd><p><b id="cfc7b30f_136">Extracts segments from <code class="code" id="cfc7b30f_137">Rect</code> bounds.</b></p></dd><dt id="cfc7b30f_138" data-expandable="false">remove_small_segments_from_labels</dt><dd><p><b id="cfc7b30f_139">Removes small segments from a label classification.</b></p></dd></dl><section class="chapter"><h3 id="extract-with-boundaries" data-toc="extract-with-boundaries"   >Extract with Boundaries</h3><p id="cfc7b30f_140">A boundary is a <code class="code" id="cfc7b30f_141">Rect</code> object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.</p><p id="cfc7b30f_142">It simply slices the original image to the bounding box. The origin is the top left corner of the image.</p><div class="tabs" id="cfc7b30f_143"><div class="tabs__content" data-gtm="tab" id="cfc7b30f_144" data-title="Cropped = True"><div class="code-block" data-lang="none"    data-title="None"     >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1500755">        <title>preprocessing.extract_segments | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link 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property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/preprocessing-extract-segments.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.extract_segments | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/preprocessing-extract-segments.html#webpage", "url": "/d/0.0.7/preprocessing-extract-segments.html", "name": "preprocessing.extract_segments | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="preprocessing.extract_segments" data-main-title="preprocessing.extract_segments" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.extract_segments"   id="preprocessing.extract_segments.md">preprocessing.extract_segments</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;7ac56e62_1777\&quot;\u003eExtracts segments from a label classification or bounds and labels.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><dl id="7ac56e62_1718" data-style="title-left" data-title-width="medium"><dt id="7ac56e62_1719" data-expandable="false">extract_segments_from_labels</dt><dd><p><b id="7ac56e62_1720">Extracts segments from a label classification.</b></p></dd><dt id="7ac56e62_1721" data-expandable="false">extract_segments_from_bounds</dt><dd><p><b id="7ac56e62_1722">Extracts segments from <code class="code" id="7ac56e62_1723">Rect</code> bounds.</b></p></dd><dt id="7ac56e62_1724" data-expandable="false">remove_small_segments_from_labels</dt><dd><p><b id="7ac56e62_1725">Removes small segments from a label classification.</b></p></dd></dl><section class="chapter"><h3 id="extract-with-boundaries" data-toc="extract-with-boundaries">Extract with Boundaries</h3><p id="7ac56e62_1726">A boundary is a <code class="code" id="7ac56e62_1727">Rect</code> object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.</p><p id="7ac56e62_1728">It simply slices the original image to the bounding box. The origin is the top left corner of the image.</p><div class="tabs" id="7ac56e62_1729"><div class="tabs__content" data-gtm="tab" id="7ac56e62_1730" data-title="Cropped = True"><div class="code-block" data-lang="none" data-title="None">
 +-----------------+                +-----------+      
 | Original        |                | Segmented |      
 | Image           |                | Image     |      
@@ -9,7 +9,7 @@
 +-----+-----+-----+   1, 2, 0, 2   +-----+-----+
 |  7  |  8  |  9  |   x0 y0 x1 y1  |  8  |  9  |
 +-----+-----+-----+                +-----+-----+
-</div></div><div class="tabs__content" data-gtm="tab" id="cfc7b30f_146" data-title="Cropped = False"><div class="code-block" data-lang="none"    data-title="None"     >
+</div></div><div class="tabs__content" data-gtm="tab" id="7ac56e62_1732" data-title="Cropped = False"><div class="code-block" data-lang="none" data-title="None">
 +-----------------+                +-----------------+      
 | Original        |                | Segmented       |      
 | Image           |                | Image           |      
@@ -20,7 +20,7 @@
 +-----+-----+-----+   1, 2, 0, 2   +-----+-----+-----+      
 |  7  |  8  |  9  |   x0 y0 x1 y1  |  0  |  8  |  9  |      
 +-----+-----+-----+                +-----+-----+-----+      
-</div></div></div><aside class="prompt" data-type="warning" data-title="" id="cfc7b30f_148"><p>The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing with the bounds, make sure that you're slicing the correct axis. </p><p id="cfc7b30f_149">The correct syntax should be <code class="code" id="cfc7b30f_150">ar[y0:y1,x0:x1]</code>.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-with-labels" data-toc="extract-with-labels"   >Extract with Labels</h3></div><div class="collapse__content"><p id="cfc7b30f_151">A label classification is a <code class="code" id="cfc7b30f_152">np.ndarray</code> where each pixel is mapped to a segment. The segments are mapped to a unique integer. In our project, the 0th label is the background.</p><p id="cfc7b30f_153">For example, a label classification of 3 segments will look like this:</p><div class="code-block" data-lang="none"         >
+</div></div></div><aside class="prompt" data-type="warning" data-title="" id="7ac56e62_1734"><p>The shape of an NDArray is usually H x W x C. Thus, if you're manually slicing with the bounds, make sure that you're slicing the correct axis. </p><p id="7ac56e62_1735">The correct syntax should be <code class="code" id="7ac56e62_1736">ar[y0:y1,x0:x1]</code>.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-with-labels" data-toc="extract-with-labels">Extract with Labels</h3></div><div class="collapse__content"><p id="7ac56e62_1737">A label classification is a <code class="code" id="7ac56e62_1738">np.ndarray</code> where each pixel is mapped to a segment. The segments are mapped to a unique integer. In our project, the 0th label is the background.</p><p id="7ac56e62_1739">For example, a label classification of 3 segments will look like this:</p><div class="code-block" data-lang="none">
 +-----------------+  +-----------------+
 | Label           |  | Original        |
 | Classification  |  | Image           |
@@ -31,7 +31,7 @@
 +-----+-----+-----+  +-----+-----+-----+
 |  1  |  1  |  0  |  |  7  |  8  |  9  |
 +-----+-----+-----+  +-----+-----+-----+
-</div><p id="cfc7b30f_155">The extraction will take the <span class="control" id="cfc7b30f_156">minimum bounding box</span> of each segment and return a list of segments.</p><p id="cfc7b30f_157">For example, the label 1 and 2 extracted images will be</p><div class="tabs" id="cfc7b30f_158"><div class="tabs__content" data-gtm="tab" id="cfc7b30f_159" data-title="Cropped = True"><div class="code-block" data-lang="none"    data-title="None"     >
+</div><p id="7ac56e62_1741">The extraction will take the <span class="control" id="7ac56e62_1742">minimum bounding box</span> of each segment and return a list of segments.</p><p id="7ac56e62_1743">For example, the label 1 and 2 extracted images will be</p><div class="tabs" id="7ac56e62_1744"><div class="tabs__content" data-gtm="tab" id="7ac56e62_1745" data-title="Cropped = True"><div class="code-block" data-lang="none" data-title="None">
 +-----------+  +-----------+
 | Extracted |  | Extracted |
 | Segment 1 |  | Segment 2 |
@@ -42,7 +42,7 @@
 +-----+-----+  +-----+-----+
 |  7  |  8  |  
 +-----+-----+  
-</div></div><div class="tabs__content" data-gtm="tab" id="cfc7b30f_161" data-title="Cropped = False"><div class="code-block" data-lang="none"    data-title="None"     >
+</div></div><div class="tabs__content" data-gtm="tab" id="7ac56e62_1747" data-title="Cropped = False"><div class="code-block" data-lang="none" data-title="None">
 +-----------------+  +-----------------+
 | Extracted       |  | Extracted       |
 | Segment 1       |  | Segment 2       |
@@ -53,7 +53,7 @@
 +-----+-----+-----+  +-----+-----+-----+
 |  7  |  8  |  0  |  |  0  |  0  |  0  |
 +-----+-----+-----+  +-----+-----+-----+
-</div></div></div><ul class="list _ul" id="cfc7b30f_163"><li class="list__item" id="cfc7b30f_164"><p>If <span class="control" id="cfc7b30f_165">cropped is False</span>, the segments are padded with 0s to the original image size. While this can ensure shape consistency, it can consume more memory for large images.</p></li><li class="list__item" id="cfc7b30f_166"><p>If <span class="control" id="cfc7b30f_167">cropped is True</span>, the segments are cropped to the minimum bounding box. This can save memory, but the shape of the segments will be inconsistent.</p></li></ul></div></div></section></section><section class="chapter"><h2 id="usage" data-toc="usage"   >Usage</h2><section class="chapter"><h3 id="extract-from-bounds-and-labels" data-toc="extract-from-bounds-and-labels"   >Extract from Bounds and Labels</h3><p id="cfc7b30f_168">Extract segments from bounds and labels.</p><div class="code-block" data-lang="python"         >
+</div></div></div><ul class="list _ul" id="7ac56e62_1749"><li class="list__item" id="7ac56e62_1750"><p>If <span class="control" id="7ac56e62_1751">cropped is False</span>, the segments are padded with 0s to the original image size. While this can ensure shape consistency, it can consume more memory for large images.</p></li><li class="list__item" id="7ac56e62_1752"><p>If <span class="control" id="7ac56e62_1753">cropped is True</span>, the segments are cropped to the minimum bounding box. This can save memory, but the shape of the segments will be inconsistent.</p></li></ul></div></div></section></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><section class="chapter"><h3 id="extract-from-bounds-and-labels" data-toc="extract-from-bounds-and-labels">Extract from Bounds and Labels</h3><p id="7ac56e62_1754">Extract segments from bounds and labels.</p><div class="code-block" data-lang="python">
 import numpy as np
 from frdc.load import FRDCDataset
 from frdc.preprocess.extract_segments import extract_segments_from_bounds
@@ -65,7 +65,7 @@
 bounds, labels = ds.get_bounds_and_labels()
 
 segments: list[np.ndarray] = extract_segments_from_bounds(ar, bounds)
-</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-from-auto-segmentation" data-toc="extract-from-auto-segmentation"   >Extract from Auto-Segmentation</h3></div><div class="collapse__content"><p id="cfc7b30f_170">Extract segments from a label classification.</p><div class="code-block" data-lang="python"         >
+</div></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="extract-from-auto-segmentation" data-toc="extract-from-auto-segmentation">Extract from Auto-Segmentation</h3></div><div class="collapse__content"><p id="7ac56e62_1756">Extract segments from a label classification.</p><div class="code-block" data-lang="python">
 from skimage.morphology import remove_small_objects, remove_small_holes
 import numpy as np
 
@@ -91,4 +91,4 @@
                                               min_height=10, min_width=10)
 
 segments: list[np.ndarray] = extract_segments_from_labels(ar, ar_labels)
-</div><aside class="prompt" data-type="note" data-title="" id="cfc7b30f_172"><p id="cfc7b30f_173">The <code class="code" id="cfc7b30f_174">remove_small_objects</code> and <code class="code" id="cfc7b30f_175">remove_small_holes</code> are used to remove small noise from the binary mask. This is recommended and used in the original paper.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api"   >API</h2><dl id="cfc7b30f_176" data-style="title-top"><dt id="cfc7b30f_177" data-expandable="false">extract_segments_from_labels(ar, ar_labels, cropped)</dt><dd><p><b id="cfc7b30f_178">Extracts segments from a label classification.</b></p><br><p> <code class="code" id="cfc7b30f_180">ar_labels</code> is a label classification as a <code class="code" id="cfc7b30f_181">np.ndarray</code></p></dd><dt id="cfc7b30f_182" data-expandable="false">extract_segments_from_bounds(ar, bounds, cropped)</dt><dd><p><b id="cfc7b30f_183">Extracts segments from <code class="code" id="cfc7b30f_184">Rect</code> bounds.</b></p><br><p> <code class="code" id="cfc7b30f_186">bounds</code> is a list of <code class="code" id="cfc7b30f_187">Rect</code> bounds.</p></dd><dt id="cfc7b30f_188" data-expandable="false">remove_small_segments_from_labels(ar_labels, min_height, min_width)</dt><dd><p><b id="cfc7b30f_189">Removes small segments from a label classification.</b></p><br></dd></dl></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-scale.html">preprocessing.scale</a>   <a class="navigation-links__next" href="preprocessing-morphology.html">preprocessing.morphology</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+</div><aside class="prompt" data-type="note" data-title="" id="7ac56e62_1758"><p id="7ac56e62_1759">The <code class="code" id="7ac56e62_1760">remove_small_objects</code> and <code class="code" id="7ac56e62_1761">remove_small_holes</code> are used to remove small noise from the binary mask. This is recommended and used in the original paper.</p></aside></div></div></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="7ac56e62_1762" data-style="title-top"><dt id="7ac56e62_1763" data-expandable="false">extract_segments_from_labels(ar, ar_labels, cropped)</dt><dd><p><b id="7ac56e62_1764">Extracts segments from a label classification.</b></p><br><p> <code class="code" id="7ac56e62_1766">ar_labels</code> is a label classification as a <code class="code" id="7ac56e62_1767">np.ndarray</code></p></dd><dt id="7ac56e62_1768" data-expandable="false">extract_segments_from_bounds(ar, bounds, cropped)</dt><dd><p><b id="7ac56e62_1769">Extracts segments from <code class="code" id="7ac56e62_1770">Rect</code> bounds.</b></p><br><p> <code class="code" id="7ac56e62_1772">bounds</code> is a list of <code class="code" id="7ac56e62_1773">Rect</code> bounds.</p></dd><dt id="7ac56e62_1774" data-expandable="false">remove_small_segments_from_labels(ar_labels, min_height, min_width)</dt><dd><p><b id="7ac56e62_1775">Removes small segments from a label classification.</b></p><br></dd></dl></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="preprocessing-scale.html" class="navigation-links__prev">preprocessing.scale</a><a href="preprocessing-morphology.html" class="navigation-links__next">preprocessing.morphology</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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diff --git a/docs/preprocessing-glcm-padded.html b/docs/preprocessing-glcm-padded.html
index ff0639fd..ed865bac 100644
--- a/docs/preprocessing-glcm-padded.html
+++ b/docs/preprocessing-glcm-padded.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.095883"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.glcm_padded | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.glcm_padded | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/preprocessing-glcm-padded.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.glcm_padded | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/preprocessing-glcm-padded.html#webpage", "url": "/d/0.0.7/preprocessing-glcm-padded.html", "name": "preprocessing.glcm_padded | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="preprocessing.glcm_padded" data-main-title="preprocessing.glcm_padded" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.glcm_padded"   id="preprocessing.glcm_padded.md">preprocessing.glcm_padded</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;19dba91f_193\&quot;\u003eComputes the GLCM of the NDArray bands with padding.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="19dba91f_129"><p id="19dba91f_130">This is largely a handy wrapper around my <a href="https://github.com/Eve-ning/glcm-cupy" id="19dba91f_131"   data-external="true" rel="noopener noreferrer" >glcm-cupy</a> package. This auto-computes the necessary padding so that the GLCM is the same size as the original image.</p></aside><aside class="prompt" data-type="warning" data-title="" id="19dba91f_132"><p id="19dba91f_133">The GLCM computation is rather slow, so it is recommended to use it only if necessary.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions"   >Functions</h2><aside class="prompt" data-type="warning" data-title="" id="19dba91f_134"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="19dba91f_135" data-style="title-left" data-title-width="medium"><dt id="19dba91f_136" data-expandable="false">glcm_padded</dt><dd><p><b id="19dba91f_137">Computes the GLCM of the NDArray bands with padding.</b></p></dd><dt id="19dba91f_138" data-expandable="false">glcm_padded_cached</dt><dd><p><b id="19dba91f_139">Computes the GLCM of the NDArray bands with padding, and caches it.</b></p></dd><dt id="19dba91f_140" data-expandable="false">append_glcm_padded_cached</dt><dd><p><b id="19dba91f_141">Computes the GLCM of the NDArray bands with padding, and caches it and also appends it onto the original array.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage"   >Usage</h2><p id="19dba91f_142">We show a few examples of how to use the GLCM functions.</p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1419417">        <title>preprocessing.glcm_padded | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.glcm_padded | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/preprocessing-glcm-padded.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.glcm_padded | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/preprocessing-glcm-padded.html#webpage", "url": "/d/0.0.7/preprocessing-glcm-padded.html", "name": "preprocessing.glcm_padded | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="preprocessing.glcm_padded" data-main-title="preprocessing.glcm_padded" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.glcm_padded"   id="preprocessing.glcm_padded.md">preprocessing.glcm_padded</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;954a0762_1758\&quot;\u003eComputes the GLCM of the NDArray bands with padding.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="954a0762_1694"><p id="954a0762_1695">This is largely a handy wrapper around my <a href="https://github.com/Eve-ning/glcm-cupy" id="954a0762_1696" data-external="true" rel="noopener noreferrer">glcm-cupy</a> package. This auto-computes the necessary padding so that the GLCM is the same size as the original image.</p></aside><aside class="prompt" data-type="warning" data-title="" id="954a0762_1697"><p id="954a0762_1698">The GLCM computation is rather slow, so it is recommended to use it only if necessary.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="954a0762_1699"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="954a0762_1700" data-style="title-left" data-title-width="medium"><dt id="954a0762_1701" data-expandable="false">glcm_padded</dt><dd><p><b id="954a0762_1702">Computes the GLCM of the NDArray bands with padding.</b></p></dd><dt id="954a0762_1703" data-expandable="false">glcm_padded_cached</dt><dd><p><b id="954a0762_1704">Computes the GLCM of the NDArray bands with padding, and caches it.</b></p></dd><dt id="954a0762_1705" data-expandable="false">append_glcm_padded_cached</dt><dd><p><b id="954a0762_1706">Computes the GLCM of the NDArray bands with padding, and caches it and also appends it onto the original array.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="954a0762_1707">We show a few examples of how to use the GLCM functions.</p><div class="code-block" data-lang="python">
 import numpy as np
 from glcm_cupy import Features
 
@@ -23,4 +23,4 @@
 ar_glcm_cached_appended = append_glcm_padded_cached(ar, bin_from=1, bin_to=4,
                                                     radius=3)
 
-</div><ul class="list _ul" id="19dba91f_144"><li class="list__item" id="19dba91f_145"><p><code class="code" id="19dba91f_146">ar_glcm</code> is the GLCM of the original array, with the last dimension being the GLCM features. The number of features is determined by the <code class="code" id="19dba91f_147">features</code> parameter, which defaults to all features.</p></li><li class="list__item" id="19dba91f_148"><p><code class="code" id="19dba91f_149">ar_glcm_2_features</code> selects only 2 features, with the last dimension being the 2 GLCM features specified.</p></li><li class="list__item" id="19dba91f_150"><p><code class="code" id="19dba91f_151">ar_glcm_cached</code> caches the GLCM so that if you call it again, it will return the cached version. It stores its data at the project root dir, under <code class="code" id="19dba91f_152">.cache/</code>.</p></li><li class="list__item" id="19dba91f_153"><p><code class="code" id="19dba91f_154">ar_glcm_cached_appended</code> is a wrapper around <code class="code" id="19dba91f_155">ar_glcm_cached</code>, it appends the GLCM features onto the original array. It's equivalent to calling <code class="code" id="19dba91f_156">ar_glcm_cached</code> and then <code class="code" id="19dba91f_157">np.concatenate</code> on the final axes.</p></li></ul><section class="chapter"><h3 id="caching" data-toc="caching"   >Caching</h3><p id="19dba91f_158">GLCM is an expensive operation, thus we recommend to cache it if the input parameters will be the same. This is especially useful if you're experimenting with the same dataset with constant parameters.</p><aside class="prompt" data-type="warning" data-title="" id="19dba91f_159"><p id="19dba91f_160">This cache is automatically invalidated if the parameters change. Thus, if you perform augmentation, the cache will not be used and will be recomputed. This can be wasteful, so it is recommended to perform augmentation after the GLCM computation if possible.</p></aside><aside class="prompt" data-type="note" data-title="" id="19dba91f_161"><p id="19dba91f_162">The cache is stored at the project root dir, under <code class="code" id="19dba91f_163">.cache/</code>. It is safe to delete this folder if you want to clear the cache.</p></aside></section></section><section class="chapter"><h2 id="api" data-toc="api"   >API</h2><dl id="19dba91f_164" data-style="title-top"><dt id="19dba91f_165" data-expandable="false">glcm_padded(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="19dba91f_166">Computes the GLCM of the NDArray bands with padding.</b></p><br><ul class="list _ul" id="19dba91f_168"><li class="list__item" id="19dba91f_169"><p><code class="code" id="19dba91f_170">ar</code> is the input array</p></li><li class="list__item" id="19dba91f_171"><p><code class="code" id="19dba91f_172">bin_from</code> is the upper bound of the input</p></li><li class="list__item" id="19dba91f_173"><p><code class="code" id="19dba91f_174">bin_to</code> is the upper bound of the GLCM input, i.e. the resolution that GLCM operates on</p></li><li class="list__item" id="19dba91f_175"><p><code class="code" id="19dba91f_176">radius</code> is the radius of the GLCM</p></li><li class="list__item" id="19dba91f_177"><p><code class="code" id="19dba91f_178">step_size</code> is the step size of the GLCM</p></li><li class="list__item" id="19dba91f_179"><p><code class="code" id="19dba91f_180">features</code> is the list of GLCM features to compute</p></li></ul><p> The return shape is </p><svg xmlns="http://www.w3.org/2000/svg" width="30.394ex" height="1.645ex" role="img" focusable="false" viewBox="0 -705 13434.3 727" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.05ex;"><defs><path id="MJX-2-TEX-I-1D43B" d="M228 637Q194 637 192 641Q191 643 191 649Q191 673 202 682Q204 683 219 683Q260 681 355 681Q389 681 418 681T463 682T483 682Q499 682 499 672Q499 670 497 658Q492 641 487 638H485Q483 638 480 638T473 638T464 637T455 637Q416 636 405 634T387 623Q384 619 355 500Q348 474 340 442T328 395L324 380Q324 378 469 378H614L615 381Q615 384 646 504Q674 619 674 627T617 637Q594 637 587 639T580 648Q580 650 582 660Q586 677 588 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xlink:href="#MJX-2-TEX-N-4C" transform="translate(785,0)"></use><use data-c="43" xlink:href="#MJX-2-TEX-N-43" transform="translate(1410,0)"></use><use data-c="4D" xlink:href="#MJX-2-TEX-N-4D" transform="translate(2132,0)"></use><use data-c="20" xlink:href="#MJX-2-TEX-N-20" transform="translate(3049,0)"></use><use data-c="46" xlink:href="#MJX-2-TEX-N-46" transform="translate(3299,0)"></use><use data-c="65" xlink:href="#MJX-2-TEX-N-65" transform="translate(3952,0)"></use><use data-c="61" xlink:href="#MJX-2-TEX-N-61" transform="translate(4396,0)"></use><use data-c="74" xlink:href="#MJX-2-TEX-N-74" transform="translate(4896,0)"></use><use data-c="75" xlink:href="#MJX-2-TEX-N-75" transform="translate(5285,0)"></use><use data-c="72" xlink:href="#MJX-2-TEX-N-72" transform="translate(5841,0)"></use><use data-c="65" xlink:href="#MJX-2-TEX-N-65" transform="translate(6233,0)"></use><use data-c="73" xlink:href="#MJX-2-TEX-N-73" transform="translate(6677,0)"></use></g></g></g></svg><p> See <code 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data-c="74" xlink:href="#MJX-4-TEX-N-74" transform="translate(4896,0)"></use><use data-c="75" xlink:href="#MJX-4-TEX-N-75" transform="translate(5285,0)"></use><use data-c="72" xlink:href="#MJX-4-TEX-N-72" transform="translate(5841,0)"></use><use data-c="65" xlink:href="#MJX-4-TEX-N-65" transform="translate(6233,0)"></use><use data-c="73" xlink:href="#MJX-4-TEX-N-73" transform="translate(6677,0)"></use></g><g data-mml-node="mo" transform="translate(14045.6,0)"><use data-c="2B" xlink:href="#MJX-4-TEX-N-2B"></use></g><g data-mml-node="mi" transform="translate(15045.8,0)"><use data-c="1D436" xlink:href="#MJX-4-TEX-I-1D436"></use></g><g data-mml-node="mo" transform="translate(15805.8,0)"><use data-c="29" xlink:href="#MJX-4-TEX-N-29"></use></g></g></g></svg><p> The function automatically flattens the last 2 dimensions of the GLCM features, and appends it onto the original array.</p></dd></dl></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-morphology.html">preprocessing.morphology</a>   <a class="navigation-links__next" href="train-frdc-lightning.html">train.frdc_datamodule &amp; frdc_module</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+</div><ul class="list _ul" id="954a0762_1709"><li class="list__item" id="954a0762_1710"><p><code class="code" id="954a0762_1711">ar_glcm</code> is the GLCM of the original array, with the last dimension being the GLCM features. The number of features is determined by the <code class="code" id="954a0762_1712">features</code> parameter, which defaults to all features.</p></li><li class="list__item" id="954a0762_1713"><p><code class="code" id="954a0762_1714">ar_glcm_2_features</code> selects only 2 features, with the last dimension being the 2 GLCM features specified.</p></li><li class="list__item" id="954a0762_1715"><p><code class="code" id="954a0762_1716">ar_glcm_cached</code> caches the GLCM so that if you call it again, it will return the cached version. It stores its data at the project root dir, under <code class="code" id="954a0762_1717">.cache/</code>.</p></li><li class="list__item" id="954a0762_1718"><p><code class="code" id="954a0762_1719">ar_glcm_cached_appended</code> is a wrapper around <code class="code" id="954a0762_1720">ar_glcm_cached</code>, it appends the GLCM features onto the original array. It's equivalent to calling <code class="code" id="954a0762_1721">ar_glcm_cached</code> and then <code class="code" id="954a0762_1722">np.concatenate</code> on the final axes.</p></li></ul><section class="chapter"><h3 id="caching" data-toc="caching">Caching</h3><p id="954a0762_1723">GLCM is an expensive operation, thus we recommend to cache it if the input parameters will be the same. This is especially useful if you're experimenting with the same dataset with constant parameters.</p><aside class="prompt" data-type="warning" data-title="" id="954a0762_1724"><p id="954a0762_1725">This cache is automatically invalidated if the parameters change. Thus, if you perform augmentation, the cache will not be used and will be recomputed. This can be wasteful, so it is recommended to perform augmentation after the GLCM computation if possible.</p></aside><aside class="prompt" data-type="note" data-title="" id="954a0762_1726"><p id="954a0762_1727">The cache is stored at the project root dir, under <code class="code" id="954a0762_1728">.cache/</code>. It is safe to delete this folder if you want to clear the cache.</p></aside></section></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="954a0762_1729" data-style="title-top"><dt id="954a0762_1730" data-expandable="false">glcm_padded(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="954a0762_1731">Computes the GLCM of the NDArray bands with padding.</b></p><br><ul class="list _ul" id="954a0762_1733"><li class="list__item" id="954a0762_1734"><p><code class="code" id="954a0762_1735">ar</code> is the input array</p></li><li class="list__item" id="954a0762_1736"><p><code class="code" id="954a0762_1737">bin_from</code> is the upper bound of the input</p></li><li class="list__item" id="954a0762_1738"><p><code class="code" id="954a0762_1739">bin_to</code> is the upper bound of the GLCM input, i.e. the resolution that GLCM operates on</p></li><li class="list__item" id="954a0762_1740"><p><code class="code" id="954a0762_1741">radius</code> is the radius of the GLCM</p></li><li class="list__item" id="954a0762_1742"><p><code class="code" id="954a0762_1743">step_size</code> is the step size of the GLCM</p></li><li class="list__item" id="954a0762_1744"><p><code class="code" id="954a0762_1745">features</code> is the list of GLCM features to compute</p></li></ul><p> The return shape is </p><svg xmlns="http://www.w3.org/2000/svg" width="30.394ex" height="1.645ex" role="img" focusable="false" viewBox="0 -705 13434.3 727" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.05ex;"><defs><path id="MJX-1-TEX-I-1D43B" d="M228 637Q194 637 192 641Q191 643 191 649Q191 673 202 682Q204 683 219 683Q260 681 355 681Q389 681 418 681T463 682T483 682Q499 682 499 672Q499 670 497 658Q492 641 487 638H485Q483 638 480 638T473 638T464 637T455 637Q416 636 405 634T387 623Q384 619 355 500Q348 474 340 442T328 395L324 380Q324 378 469 378H614L615 381Q615 384 646 504Q674 619 674 627T617 637Q594 637 587 639T580 648Q580 650 582 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384T93 424T143 442T187 447H198Q238 447 268 432L283 424L292 431Q302 440 314 448H322H326Q329 448 335 442V310L329 304H301Q295 310 295 316Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><use data-c="1D43B" xlink:href="#MJX-1-TEX-I-1D43B"></use></g><g data-mml-node="mo" transform="translate(1110.2,0)"><use data-c="D7" xlink:href="#MJX-1-TEX-N-D7"></use></g><g data-mml-node="mi" transform="translate(2110.4,0)"><use data-c="1D44A" xlink:href="#MJX-1-TEX-I-1D44A"></use></g><g data-mml-node="mo" transform="translate(3380.7,0)"><use data-c="D7" xlink:href="#MJX-1-TEX-N-D7"></use></g><g data-mml-node="mi" transform="translate(4380.9,0)"><use data-c="1D436" xlink:href="#MJX-1-TEX-I-1D436"></use></g><g data-mml-node="mo" transform="translate(5363.1,0)"><use data-c="D7" xlink:href="#MJX-1-TEX-N-D7"></use></g><g data-mml-node="mtext" transform="translate(6363.3,0)"><use data-c="47" xlink:href="#MJX-1-TEX-N-47"></use><use data-c="4C" xlink:href="#MJX-1-TEX-N-4C" transform="translate(785,0)"></use><use data-c="43" xlink:href="#MJX-1-TEX-N-43" transform="translate(1410,0)"></use><use data-c="4D" xlink:href="#MJX-1-TEX-N-4D" transform="translate(2132,0)"></use><use data-c="20" xlink:href="#MJX-1-TEX-N-20" transform="translate(3049,0)"></use><use data-c="46" xlink:href="#MJX-1-TEX-N-46" transform="translate(3299,0)"></use><use data-c="65" xlink:href="#MJX-1-TEX-N-65" transform="translate(3952,0)"></use><use data-c="61" xlink:href="#MJX-1-TEX-N-61" transform="translate(4396,0)"></use><use data-c="74" xlink:href="#MJX-1-TEX-N-74" transform="translate(4896,0)"></use><use data-c="75" xlink:href="#MJX-1-TEX-N-75" transform="translate(5285,0)"></use><use data-c="72" xlink:href="#MJX-1-TEX-N-72" transform="translate(5841,0)"></use><use data-c="65" xlink:href="#MJX-1-TEX-N-65" transform="translate(6233,0)"></use><use data-c="73" xlink:href="#MJX-1-TEX-N-73" transform="translate(6677,0)"></use></g></g></g></svg><p> See <code class="code" id="954a0762_1747">glcm_cupy</code> for the GLCM Features.</p></dd><dt id="954a0762_1748" data-expandable="false">glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="954a0762_1749">Computes the GLCM of the NDArray bands with padding, and caches it.</b></p><br><p> See <code class="code" id="954a0762_1751">glcm_padded</code> for the parameters and output shape</p></dd><dt id="954a0762_1752" data-expandable="false">append_glcm_padded_cached(ar, bin_from, bin_to, radius, step_size, features)</dt><dd><p><b id="954a0762_1753">Computes the GLCM of the NDArray bands with padding, and caches it and also appends it onto the original array.</b></p><br><p> See <code class="code" id="954a0762_1755">glcm_padded</code> for the parameters</p><br><p> The return shape is: </p><svg xmlns="http://www.w3.org/2000/svg" width="36.64ex" height="2.262ex" role="img" focusable="false" viewBox="0 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transform="translate(4396,0)"></use><use data-c="74" xlink:href="#MJX-3-TEX-N-74" transform="translate(4896,0)"></use><use data-c="75" xlink:href="#MJX-3-TEX-N-75" transform="translate(5285,0)"></use><use data-c="72" xlink:href="#MJX-3-TEX-N-72" transform="translate(5841,0)"></use><use data-c="65" xlink:href="#MJX-3-TEX-N-65" transform="translate(6233,0)"></use><use data-c="73" xlink:href="#MJX-3-TEX-N-73" transform="translate(6677,0)"></use></g><g data-mml-node="mo" transform="translate(14045.6,0)"><use data-c="2B" xlink:href="#MJX-3-TEX-N-2B"></use></g><g data-mml-node="mi" transform="translate(15045.8,0)"><use data-c="1D436" xlink:href="#MJX-3-TEX-I-1D436"></use></g><g data-mml-node="mo" transform="translate(15805.8,0)"><use data-c="29" xlink:href="#MJX-3-TEX-N-29"></use></g></g></g></svg><p> The function automatically flattens the last 2 dimensions of the GLCM features, and appends it onto the original array.</p></dd></dl></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="preprocessing-morphology.html" class="navigation-links__prev">preprocessing.morphology</a><a href="train-frdc-lightning.html" class="navigation-links__next">train.frdc_datamodule &amp; frdc_module</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.0968833"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.morphology | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.morphology | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/preprocessing-morphology.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.morphology | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/preprocessing-morphology.html#webpage", "url": "/d/0.0.7/preprocessing-morphology.html", "name": "preprocessing.morphology | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="preprocessing.morphology" data-main-title="preprocessing.morphology" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.morphology"   id="preprocessing.morphology.md">preprocessing.morphology</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;19e6981_159\&quot;\u003ePerforms morphological operations on the NDArray bands.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="19e6981_131"><p id="19e6981_132">This is currently only used for auto-segmentation. If you want to use predefined segmentation see <a href="preprocessing-extract-segments.html" id="19e6981_133" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >preprocessing.extract_segments</a>.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions"   >Functions</h2><aside class="prompt" data-type="warning" data-title="" id="19e6981_134"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="19e6981_135" data-style="title-left" data-title-width="medium"><dt id="19e6981_136" data-expandable="false">threshold_binary_mask</dt><dd><p><b id="19e6981_137">Thresholds a selected NDArray bands to yield a binary mask.</b></p></dd><dt id="19e6981_138" data-expandable="false">binary_watershed</dt><dd><p><b id="19e6981_139">Performs watershed on a binary mask to yield a mapped label classification</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage"   >Usage</h2><p id="19e6981_140">Perform auto-segmentation on a dataset to yield a label classification.</p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1419417">        <title>preprocessing.morphology | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="preprocessing.morphology | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/preprocessing-morphology.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="preprocessing.morphology | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/preprocessing-morphology.html#webpage", "url": "/d/0.0.7/preprocessing-morphology.html", "name": "preprocessing.morphology | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="preprocessing.morphology" data-main-title="preprocessing.morphology" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.morphology"   id="preprocessing.morphology.md">preprocessing.morphology</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;d002349b_1738\&quot;\u003ePerforms morphological operations on the NDArray bands.\u003c/p\u003e&quot;]}"></div> <aside class="prompt" data-type="tip" data-title="" id="d002349b_1710"><p id="d002349b_1711">This is currently only used for auto-segmentation. If you want to use predefined segmentation see <a href="preprocessing-extract-segments.html" id="d002349b_1712" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.">preprocessing.extract_segments</a>.</p></aside><section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="d002349b_1713"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="d002349b_1714" data-style="title-left" data-title-width="medium"><dt id="d002349b_1715" data-expandable="false">threshold_binary_mask</dt><dd><p><b id="d002349b_1716">Thresholds a selected NDArray bands to yield a binary mask.</b></p></dd><dt id="d002349b_1717" data-expandable="false">binary_watershed</dt><dd><p><b id="d002349b_1718">Performs watershed on a binary mask to yield a mapped label classification</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><p id="d002349b_1719">Perform auto-segmentation on a dataset to yield a label classification.</p><div class="code-block" data-lang="python">
 from frdc.load import FRDCDataset
 from frdc.preprocess.morphology import (
     threshold_binary_mask, binary_watershed
@@ -10,6 +10,6 @@
 ar, order = ds.get_ar_bands()
 mask = threshold_binary_mask(ar, order.index('NIR'), 90 / 256)
 ar_label = binary_watershed(mask)
-</div></section><section class="chapter"><h2 id="api" data-toc="api"   >API</h2><dl id="19e6981_142" data-style="title-top"><dt id="19e6981_143" data-expandable="false">threshold_binary_mask(ar, band_idx, threshold_value)</dt><dd><p><b id="19e6981_144">Thresholds a selected NDArray bands to yield a binary mask as <code class="code" id="19e6981_145">np.ndarray</code></b></p><br><p> This is equivalent to </p><div class="code-block" data-lang="python"         >
+</div></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="d002349b_1721" data-style="title-top"><dt id="d002349b_1722" data-expandable="false">threshold_binary_mask(ar, band_idx, threshold_value)</dt><dd><p><b id="d002349b_1723">Thresholds a selected NDArray bands to yield a binary mask as <code class="code" id="d002349b_1724">np.ndarray</code></b></p><br><p> This is equivalent to </p><div class="code-block" data-lang="python">
 ar[..., band_idx] &gt; threshold_value
-</div></dd><dt id="19e6981_148" data-expandable="false">binary_watershed(ar_mask, peaks_footprint, watershed_compactness)</dt><dd><p><b id="19e6981_149">Performs watershed on a binary mask to yield a mapped label classification as a <code class="code" id="19e6981_150">np.ndarray</code></b></p><br><ul class="list _ul" id="19e6981_152"><li class="list__item" id="19e6981_153"><p><code class="code" id="19e6981_154">peaks_footprint</code> is the footprint of <code class="code" id="19e6981_155">skimage.feature.peak_local_max</code></p></li><li class="list__item" id="19e6981_156"><p><code class="code" id="19e6981_157">watershed_compactness</code> is the compactness of <code class="code" id="19e6981_158">skimage.morphology.watershed</code></p></li></ul></dd></dl></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>   <a class="navigation-links__next" href="preprocessing-glcm-padded.html">preprocessing.glcm_padded</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
\ No newline at end of file
+</div></dd><dt id="d002349b_1727" data-expandable="false">binary_watershed(ar_mask, peaks_footprint, watershed_compactness)</dt><dd><p><b id="d002349b_1728">Performs watershed on a binary mask to yield a mapped label classification as a <code class="code" id="d002349b_1729">np.ndarray</code></b></p><br><ul class="list _ul" id="d002349b_1731"><li class="list__item" id="d002349b_1732"><p><code class="code" id="d002349b_1733">peaks_footprint</code> is the footprint of <code class="code" id="d002349b_1734">skimage.feature.peak_local_max</code></p></li><li class="list__item" id="d002349b_1735"><p><code class="code" id="d002349b_1736">watershed_compactness</code> is the compactness of <code class="code" id="d002349b_1737">skimage.morphology.watershed</code></p></li></ul></dd></dl></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="preprocessing-extract-segments.html" class="navigation-links__prev">preprocessing.extract_segments</a><a href="preprocessing-glcm-padded.html" class="navigation-links__next">preprocessing.glcm_padded</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
\ No newline at end of file
diff --git a/docs/preprocessing-scale.html b/docs/preprocessing-scale.html
index c8213f8e..b846445e 100644
--- a/docs/preprocessing-scale.html
+++ b/docs/preprocessing-scale.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.095883"><meta name="build-number" content="${buildNumber}">       <title>preprocessing.scale | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" 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"inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="preprocessing.scale" data-main-title="preprocessing.scale" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.scale"   id="preprocessing.scale.md">preprocessing.scale</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;d5a9e2d8_148\&quot;\u003eScales the NDArray bands.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions"   >Functions</h2><aside class="prompt" data-type="warning" data-title="" id="d5a9e2d8_133"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="d5a9e2d8_134" data-style="title-left" data-title-width="medium"><dt id="d5a9e2d8_135" data-expandable="false">scale_0_1_per_band</dt><dd><p><b id="d5a9e2d8_136">Scales the NDArray bands to [0, 1] per band.</b></p><svg xmlns="http://www.w3.org/2000/svg" width="32.393ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 14317.9 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-1-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 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transform="translate(10911.9,0)"><use data-c="6D" xlink:href="#MJX-1-TEX-N-6D"></use><use data-c="69" xlink:href="#MJX-1-TEX-N-69" transform="translate(833,0)"></use><use data-c="6E" xlink:href="#MJX-1-TEX-N-6E" transform="translate(1111,0)"></use></g><g data-mml-node="mo" transform="translate(12578.9,0)"><use data-c="28" xlink:href="#MJX-1-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(12967.9,0)"><use data-c="1D465" xlink:href="#MJX-1-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(13539.9,0)"><use data-c="29" xlink:href="#MJX-1-TEX-N-29"></use></g><g data-mml-node="mo" transform="translate(13928.9,0)"><use data-c="29" xlink:href="#MJX-1-TEX-N-29"></use></g></g></g></svg></dd><dt id="d5a9e2d8_138" data-expandable="false">scale_normal_per_band</dt><dd><p><b id="d5a9e2d8_139">Scales the NDArray bands to zero mean unit variance per band.</b></p><svg xmlns="http://www.w3.org/2000/svg" width="9.607ex" height="2.262ex" role="img" focusable="false" 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data-mml-node="mi" transform="translate(389,0)"><use data-c="1D465" xlink:href="#MJX-3-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(1183.2,0)"><use data-c="2212" xlink:href="#MJX-3-TEX-N-2212"></use></g><g data-mml-node="mi" transform="translate(2183.4,0)"><use data-c="1D707" xlink:href="#MJX-3-TEX-I-1D707"></use></g><g data-mml-node="mo" transform="translate(2786.4,0)"><use data-c="29" xlink:href="#MJX-3-TEX-N-29"></use></g><g data-mml-node="TeXAtom" data-mjx-texclass="ORD" transform="translate(3175.4,0)"><g data-mml-node="mo"><use data-c="2F" xlink:href="#MJX-3-TEX-N-2F"></use></g></g><g data-mml-node="mi" transform="translate(3675.4,0)"><use data-c="1D70E" xlink:href="#MJX-3-TEX-I-1D70E"></use></g></g></g></svg></dd><dt id="d5a9e2d8_141" data-expandable="false">scale_static_per_band</dt><dd><p><b id="d5a9e2d8_142">Scales the NDArray bands by a predefined configuration.</b> Take a look at <code class="code" id="d5a9e2d8_143">frdc.conf.BAND_MAX_CONFIG</code> for an example. </p><svg xmlns="http://www.w3.org/2000/svg" width="17.379ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 7681.5 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-5-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path id="MJX-5-TEX-I-1D465" d="M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 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data-mml-node="mi"><use data-c="1D450" xlink:href="#MJX-5-TEX-I-1D450"></use></g><g data-mml-node="mn" transform="translate(466,-150) scale(0.707)"><use data-c="31" xlink:href="#MJX-5-TEX-N-31"></use></g></g><g data-mml-node="mo" transform="translate(5422.8,0)"><use data-c="2212" xlink:href="#MJX-5-TEX-N-2212"></use></g><g data-mml-node="msub" transform="translate(6423,0)"><g data-mml-node="mi"><use data-c="1D450" xlink:href="#MJX-5-TEX-I-1D450"></use></g><g data-mml-node="mn" transform="translate(466,-150) scale(0.707)"><use data-c="30" xlink:href="#MJX-5-TEX-N-30"></use></g></g><g data-mml-node="mo" transform="translate(7292.5,0)"><use data-c="29" xlink:href="#MJX-5-TEX-N-29"></use></g></g></g></svg></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage"   >Usage</h2><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1419417">        <title>preprocessing.scale | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"functions","level":0,"title":"Functions","anchor":"#functions"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" 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"url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="preprocessing.scale" data-main-title="preprocessing.scale" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="preprocessing.scale"   id="preprocessing.scale.md">preprocessing.scale</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;b4caadb1_1744\&quot;\u003eScales the NDArray bands.\u003c/p\u003e&quot;]}"></div> <section class="chapter"><h2 id="functions" data-toc="functions">Functions</h2><aside class="prompt" data-type="warning" data-title="" id="b4caadb1_1729"><p>Assumes shape H x W x C, where C is the number of bands.</p></aside><dl id="b4caadb1_1730" data-style="title-left" data-title-width="medium"><dt id="b4caadb1_1731" data-expandable="false">scale_0_1_per_band</dt><dd><p><b id="b4caadb1_1732">Scales the NDArray bands to [0, 1] per band.</b></p><svg xmlns="http://www.w3.org/2000/svg" width="32.393ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 14317.9 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-2-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path id="MJX-2-TEX-I-1D465" d="M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 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xlink:href="#MJX-2-TEX-N-28"></use></g><g data-mml-node="mo" transform="translate(6478.4,0)"><use data-c="6D" xlink:href="#MJX-2-TEX-N-6D"></use><use data-c="61" xlink:href="#MJX-2-TEX-N-61" transform="translate(833,0)"></use><use data-c="78" xlink:href="#MJX-2-TEX-N-78" transform="translate(1333,0)"></use></g><g data-mml-node="mo" transform="translate(8339.4,0)"><use data-c="28" xlink:href="#MJX-2-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(8728.4,0)"><use data-c="1D465" xlink:href="#MJX-2-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(9300.4,0)"><use data-c="29" xlink:href="#MJX-2-TEX-N-29"></use></g><g data-mml-node="mo" transform="translate(9911.7,0)"><use data-c="2212" xlink:href="#MJX-2-TEX-N-2212"></use></g><g data-mml-node="mo" transform="translate(10911.9,0)"><use data-c="6D" xlink:href="#MJX-2-TEX-N-6D"></use><use data-c="69" xlink:href="#MJX-2-TEX-N-69" transform="translate(833,0)"></use><use data-c="6E" xlink:href="#MJX-2-TEX-N-6E" transform="translate(1111,0)"></use></g><g data-mml-node="mo" transform="translate(12578.9,0)"><use data-c="28" xlink:href="#MJX-2-TEX-N-28"></use></g><g data-mml-node="mi" transform="translate(12967.9,0)"><use data-c="1D465" xlink:href="#MJX-2-TEX-I-1D465"></use></g><g data-mml-node="mo" transform="translate(13539.9,0)"><use data-c="29" xlink:href="#MJX-2-TEX-N-29"></use></g><g data-mml-node="mo" transform="translate(13928.9,0)"><use data-c="29" xlink:href="#MJX-2-TEX-N-29"></use></g></g></g></svg></dd><dt id="b4caadb1_1734" data-expandable="false">scale_normal_per_band</dt><dd><p><b id="b4caadb1_1735">Scales the NDArray bands to zero mean unit variance per band.</b></p><svg xmlns="http://www.w3.org/2000/svg" width="9.607ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 4246.4 1000" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" style="vertical-align: -0.566ex;"><defs><path id="MJX-4-TEX-N-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 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transform="translate(1183.2,0)"><use data-c="2212" xlink:href="#MJX-5-TEX-N-2212"></use></g><g data-mml-node="msub" transform="translate(2183.4,0)"><g data-mml-node="mi"><use data-c="1D450" xlink:href="#MJX-5-TEX-I-1D450"></use></g><g data-mml-node="mn" transform="translate(466,-150) scale(0.707)"><use data-c="30" xlink:href="#MJX-5-TEX-N-30"></use></g></g><g data-mml-node="mo" transform="translate(3053,0)"><use data-c="29" xlink:href="#MJX-5-TEX-N-29"></use></g><g data-mml-node="TeXAtom" data-mjx-texclass="ORD" transform="translate(3442,0)"><g data-mml-node="mo"><use data-c="2F" xlink:href="#MJX-5-TEX-N-2F"></use></g></g><g data-mml-node="mo" transform="translate(3942,0)"><use data-c="28" xlink:href="#MJX-5-TEX-N-28"></use></g><g data-mml-node="msub" transform="translate(4331,0)"><g data-mml-node="mi"><use data-c="1D450" xlink:href="#MJX-5-TEX-I-1D450"></use></g><g data-mml-node="mn" transform="translate(466,-150) scale(0.707)"><use data-c="31" xlink:href="#MJX-5-TEX-N-31"></use></g></g><g data-mml-node="mo" transform="translate(5422.8,0)"><use data-c="2212" xlink:href="#MJX-5-TEX-N-2212"></use></g><g data-mml-node="msub" transform="translate(6423,0)"><g data-mml-node="mi"><use data-c="1D450" xlink:href="#MJX-5-TEX-I-1D450"></use></g><g data-mml-node="mn" transform="translate(466,-150) scale(0.707)"><use data-c="30" xlink:href="#MJX-5-TEX-N-30"></use></g></g><g data-mml-node="mo" transform="translate(7292.5,0)"><use data-c="29" xlink:href="#MJX-5-TEX-N-29"></use></g></g></g></svg></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><div class="code-block" data-lang="python">
 from frdc.load import FRDCDataset
 from frdc.preprocess.scale import (
     scale_0_1_per_band, scale_normal_per_band, scale_static_per_band
@@ -12,4 +12,4 @@
 ar_01 = scale_0_1_per_band(ar)
 ar_norm = scale_normal_per_band(ar)
 ar_static = scale_static_per_band(ar, order, BAND_MAX_CONFIG)
-</div><aside class="prompt" data-type="tip" data-title="" id="d5a9e2d8_146"><p id="d5a9e2d8_147">The static scaling has a default config, which was inferred by our capturing device.</p></aside></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="load-gcs.html">load.gcs</a>   <a class="navigation-links__next" href="preprocessing-extract-segments.html">preprocessing.extract_segments</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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+</div><aside class="prompt" data-type="tip" data-title="" id="b4caadb1_1742"><p id="b4caadb1_1743">The static scaling has a default config, which was inferred by our capturing device.</p></aside></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="load-gcs.html" class="navigation-links__prev">load.gcs</a><a href="preprocessing-extract-segments.html" class="navigation-links__next">preprocessing.extract_segments</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
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diff --git a/docs/retrieve-our-datasets.html b/docs/retrieve-our-datasets.html
index 644e51f5..bbeecb47 100644
--- a/docs/retrieve-our-datasets.html
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@@ -1,10 +1,10 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.0968833"><meta name="build-number" content="${buildNumber}">       <title>Retrieve our Datasets | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"prerequisites","level":0,"title":"Prerequisites","anchor":"#prerequisites"},{"id":"retrieve-the-data","level":0,"title":"Retrieve the Data","anchor":"#retrieve-the-data"},{"id":"segment-the-data","level":0,"title":"Segment the Data","anchor":"#segment-the-data"},{"id":"plot-the-data-optional","level":0,"title":"Plot the Data (Optional)","anchor":"#plot-the-data-optional"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Retrieve our Datasets | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/retrieve-our-datasets.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Retrieve our Datasets | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/retrieve-our-datasets.html#webpage", "url": "/d/0.0.7/retrieve-our-datasets.html", "name": "Retrieve our Datasets | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="Retrieve-our-Datasets" data-main-title="Retrieve our Datasets" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="Tutorials"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Retrieve-our-Datasets"   id="Retrieve-our-Datasets.md">Retrieve our Datasets</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;2119d5ce_228\&quot;\u003eA tutorial to retrieve our datasets\u003c/p\u003e&quot;]}"></div> <p id="2119d5ce_139">In this tutorial, we'll learn how to :</p><ul class="list _ul" id="2119d5ce_140"><li class="list__item" id="2119d5ce_141"><p>Retrieve FRDC's Hyperspectral Image Data as <code class="code" id="2119d5ce_142">np.ndarray</code></p></li><li class="list__item" id="2119d5ce_143"><p>Retrieve FRDC's Ground Truth bounds and labels</p></li><li class="list__item" id="2119d5ce_144"><p>Slice/segment the image data by the bounds</p></li></ul><section class="chapter"><h2 id="prerequisites" data-toc="prerequisites"   >Prerequisites</h2><ul class="list _ul" id="2119d5ce_145"><li class="list__item" id="2119d5ce_146"><p>New here? <a href="getting-started.html" id="2119d5ce_147" data-tooltip="Before starting development, take a look at our repository structure. This will help you understand where to put your code."  >Get Started</a>.</p></li><li class="list__item" id="2119d5ce_148"><p>Setup the Google Cloud Authorization to download the data.</p></li></ul></section><section class="chapter"><h2 id="retrieve-the-data" data-toc="retrieve-the-data"   >Retrieve the Data</h2><p id="2119d5ce_149">To retrieve the data, use <a href="load-dataset.html" id="2119d5ce_150" data-tooltip="You need to Set-Up Google Cloud with the appropriate permissions to use this library."  >FRDCDataset</a></p><p id="2119d5ce_151">Here, we'll download and load our</p><ul class="list _ul" id="2119d5ce_152"><li class="list__item" id="2119d5ce_153"><p><code class="code" id="2119d5ce_154">ar</code>: Hyperspectral Image Data</p></li><li class="list__item" id="2119d5ce_155"><p><code class="code" id="2119d5ce_156">order</code>: The order of the bands</p></li><li class="list__item" id="2119d5ce_157"><p><code class="code" id="2119d5ce_158">bounds</code>: The bounds of the trees (segments)</p></li><li class="list__item" id="2119d5ce_159"><p><code class="code" id="2119d5ce_160">labels</code>: The labels of the trees (segments)</p></li></ul><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1500755">        <title>Retrieve our Datasets | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"prerequisites","level":0,"title":"Prerequisites","anchor":"#prerequisites"},{"id":"retrieve-the-data","level":0,"title":"Retrieve the Data","anchor":"#retrieve-the-data"},{"id":"segment-the-data","level":0,"title":"Segment the Data","anchor":"#segment-the-data"},{"id":"plot-the-data-optional","level":0,"title":"Plot the Data (Optional)","anchor":"#plot-the-data-optional"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="Retrieve our Datasets | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/retrieve-our-datasets.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="Retrieve our Datasets | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/retrieve-our-datasets.html#webpage", "url": "/d/0.0.7/retrieve-our-datasets.html", "name": "Retrieve our Datasets | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="Retrieve-our-Datasets" data-main-title="Retrieve our Datasets" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="Tutorials"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="Retrieve-our-Datasets"   id="Retrieve-our-Datasets.md">Retrieve our Datasets</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;e05cca52_1921\&quot;\u003eA tutorial to retrieve our datasets\u003c/p\u003e&quot;]}"></div> <p id="e05cca52_1832">In this tutorial, we'll learn how to :</p><ul class="list _ul" id="e05cca52_1833"><li class="list__item" id="e05cca52_1834"><p>Retrieve FRDC's Hyperspectral Image Data as <code class="code" id="e05cca52_1835">np.ndarray</code></p></li><li class="list__item" id="e05cca52_1836"><p>Retrieve FRDC's Ground Truth bounds and labels</p></li><li class="list__item" id="e05cca52_1837"><p>Slice/segment the image data by the bounds</p></li></ul><section class="chapter"><h2 id="prerequisites" data-toc="prerequisites">Prerequisites</h2><ul class="list _ul" id="e05cca52_1838"><li class="list__item" id="e05cca52_1839"><p>New here? <a href="getting-started.html" id="e05cca52_1840" data-tooltip="It's likely that your src and tests directories are not in PYTHONPATH. To fix this, run the following command:">Get Started</a>.</p></li><li class="list__item" id="e05cca52_1841"><p>Setup the Google Cloud Authorization to download the data.</p></li></ul></section><section class="chapter"><h2 id="retrieve-the-data" data-toc="retrieve-the-data">Retrieve the Data</h2><p id="e05cca52_1842">To retrieve the data, use <a href="load-dataset.html" id="e05cca52_1843" data-tooltip="You need to Set-Up Google Cloud with the appropriate permissions to use this library.">FRDCDataset</a></p><p id="e05cca52_1844">Here, we'll download and load our</p><ul class="list _ul" id="e05cca52_1845"><li class="list__item" id="e05cca52_1846"><p><code class="code" id="e05cca52_1847">ar</code>: Hyperspectral Image Data</p></li><li class="list__item" id="e05cca52_1848"><p><code class="code" id="e05cca52_1849">order</code>: The order of the bands</p></li><li class="list__item" id="e05cca52_1850"><p><code class="code" id="e05cca52_1851">bounds</code>: The bounds of the trees (segments)</p></li><li class="list__item" id="e05cca52_1852"><p><code class="code" id="e05cca52_1853">labels</code>: The labels of the trees (segments)</p></li></ul><div class="code-block" data-lang="python">
 from frdc.load.dataset import FRDCDataset
 
 ds = FRDCDataset(site=&quot;chestnut_nature_park&quot;, date=&quot;20201218&quot;, version=None)
 ar, order = ds.get_ar_bands()
 bounds, labels = ds.get_bounds_and_labels()
-</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="what-datasets-are-there" data-toc="what-datasets-are-there"   >What Datasets are there?</h3></div><div class="collapse__content"><aside class="prompt" data-type="tip" data-title="" id="2119d5ce_162"><p id="2119d5ce_163">To know what datasets are available, you can run <a href="load-gcs.html" id="2119d5ce_164" data-tooltip="Set-Up Google Cloud to use this library."  >load.gcs</a>'s <code class="code" id="2119d5ce_165">list_gcs_datasets()</code> method</p></aside><aside class="prompt" data-type="warning" data-title="" id="2119d5ce_166"><p id="2119d5ce_167">Note that some datasets do not have <code class="code" id="2119d5ce_168">bounds</code> and <code class="code" id="2119d5ce_169">labels</code> available as they have not been annotated yet.</p></aside><div class="code-block" data-lang="python"         >
+</div><section class="chapter"><div class="collapse"><div class="collapse__title"><h3 id="what-datasets-are-there" data-toc="what-datasets-are-there">What Datasets are there?</h3></div><div class="collapse__content"><aside class="prompt" data-type="tip" data-title="" id="e05cca52_1855"><p id="e05cca52_1856">To know what datasets are available, you can run <a href="load-gcs.html" id="e05cca52_1857" data-tooltip="Set-Up Google Cloud to use this library.">load.gcs</a>'s <code class="code" id="e05cca52_1858">list_gcs_datasets()</code> method</p></aside><aside class="prompt" data-type="warning" data-title="" id="e05cca52_1859"><p id="e05cca52_1860">Note that some datasets do not have <code class="code" id="e05cca52_1861">bounds</code> and <code class="code" id="e05cca52_1862">labels</code> available as they have not been annotated yet.</p></aside><div class="code-block" data-lang="python">
 from frdc.load.gcs import list_gcs_datasets 
 print(list_gcs_datasets())
 # 0  DEBUG/0
@@ -12,7 +12,7 @@
 # 2  casuarina/20220418/93deg
 # 3  chestnut_nature_park/20201218
 # ...
-</div><ul class="list _ul" id="2119d5ce_171"><li class="list__item" id="2119d5ce_172"><p>The first part of the path is the <code class="code" id="2119d5ce_173">site</code>, and the second part is the <code class="code" id="2119d5ce_174">date</code>.</p></li><li class="list__item" id="2119d5ce_175"><p>The <code class="code" id="2119d5ce_176">version</code> is the rest of the path, if there isn't any, use <code class="code" id="2119d5ce_177">None</code>.</p></li></ul><div class="tabs" id="2119d5ce_178"><div class="tabs__content" data-gtm="tab" id="2119d5ce_179" data-title="ds/date/ver/"><ul class="list _ul" id="2119d5ce_180"><li class="list__item" id="2119d5ce_181"><p><code class="code" id="2119d5ce_182">site=&quot;ds&quot;</code></p></li><li class="list__item" id="2119d5ce_183"><p><code class="code" id="2119d5ce_184">date=&quot;date&quot;</code></p></li><li class="list__item" id="2119d5ce_185"><p><code class="code" id="2119d5ce_186">version=&quot;ver&quot;</code></p></li></ul></div><div class="tabs__content" data-gtm="tab" id="2119d5ce_187" data-title="ds/date/ver/01/data/"><ul class="list _ul" id="2119d5ce_188"><li class="list__item" id="2119d5ce_189"><p><code class="code" id="2119d5ce_190">site=&quot;ds&quot;</code></p></li><li class="list__item" id="2119d5ce_191"><p><code class="code" id="2119d5ce_192">date=&quot;date&quot;</code></p></li><li class="list__item" id="2119d5ce_193"><p><code class="code" id="2119d5ce_194">version=&quot;ver/01/data&quot;</code></p></li></ul></div><div class="tabs__content" data-gtm="tab" id="2119d5ce_195" data-title="ds/date/"><ul class="list _ul" id="2119d5ce_196"><li class="list__item" id="2119d5ce_197"><p><code class="code" id="2119d5ce_198">site=&quot;ds&quot;</code></p></li><li class="list__item" id="2119d5ce_199"><p><code class="code" id="2119d5ce_200">date=&quot;date&quot;</code></p></li><li class="list__item" id="2119d5ce_201"><p><code class="code" id="2119d5ce_202">version=None</code></p></li></ul></div></div></div></div></section></section><section class="chapter"><h2 id="segment-the-data" data-toc="segment-the-data"   >Segment the Data</h2><p id="2119d5ce_203">To segment the data, use <a href="preprocessing-extract-segments.html" id="2119d5ce_204" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >Extract Segments</a>.</p><p id="2119d5ce_205">Here, we'll segment the data by the bounds.</p><div class="code-block" data-lang="python"         >
+</div><ul class="list _ul" id="e05cca52_1864"><li class="list__item" id="e05cca52_1865"><p>The first part of the path is the <code class="code" id="e05cca52_1866">site</code>, and the second part is the <code class="code" id="e05cca52_1867">date</code>.</p></li><li class="list__item" id="e05cca52_1868"><p>The <code class="code" id="e05cca52_1869">version</code> is the rest of the path, if there isn't any, use <code class="code" id="e05cca52_1870">None</code>.</p></li></ul><div class="tabs" id="e05cca52_1871"><div class="tabs__content" data-gtm="tab" id="e05cca52_1872" data-title="ds/date/ver/"><ul class="list _ul" id="e05cca52_1873"><li class="list__item" id="e05cca52_1874"><p><code class="code" id="e05cca52_1875">site=&quot;ds&quot;</code></p></li><li class="list__item" id="e05cca52_1876"><p><code class="code" id="e05cca52_1877">date=&quot;date&quot;</code></p></li><li class="list__item" id="e05cca52_1878"><p><code class="code" id="e05cca52_1879">version=&quot;ver&quot;</code></p></li></ul></div><div class="tabs__content" data-gtm="tab" id="e05cca52_1880" data-title="ds/date/ver/01/data/"><ul class="list _ul" id="e05cca52_1881"><li class="list__item" id="e05cca52_1882"><p><code class="code" id="e05cca52_1883">site=&quot;ds&quot;</code></p></li><li class="list__item" id="e05cca52_1884"><p><code class="code" id="e05cca52_1885">date=&quot;date&quot;</code></p></li><li class="list__item" id="e05cca52_1886"><p><code class="code" id="e05cca52_1887">version=&quot;ver/01/data&quot;</code></p></li></ul></div><div class="tabs__content" data-gtm="tab" id="e05cca52_1888" data-title="ds/date/"><ul class="list _ul" id="e05cca52_1889"><li class="list__item" id="e05cca52_1890"><p><code class="code" id="e05cca52_1891">site=&quot;ds&quot;</code></p></li><li class="list__item" id="e05cca52_1892"><p><code class="code" id="e05cca52_1893">date=&quot;date&quot;</code></p></li><li class="list__item" id="e05cca52_1894"><p><code class="code" id="e05cca52_1895">version=None</code></p></li></ul></div></div></div></div></section></section><section class="chapter"><h2 id="segment-the-data" data-toc="segment-the-data">Segment the Data</h2><p id="e05cca52_1896">To segment the data, use <a href="preprocessing-extract-segments.html" id="e05cca52_1897" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.">Extract Segments</a>.</p><p id="e05cca52_1898">Here, we'll segment the data by the bounds.</p><div class="code-block" data-lang="python">
 from frdc.load.dataset import FRDCDataset
 from frdc.preprocess.extract_segments import extract_segments_from_bounds
 
@@ -20,7 +20,7 @@
 ar, order = ds.get_ar_bands()
 bounds, labels = ds.get_bounds_and_labels()
 segments = extract_segments_from_bounds(ar, bounds)
-</div><p id="2119d5ce_207"><code class="code" id="2119d5ce_208">segments</code> is a list of <code class="code" id="2119d5ce_209">np.ndarray</code> of shape H, W, C, representing a tree. The order of <code class="code" id="2119d5ce_210">segments</code> is the same as <code class="code" id="2119d5ce_211">labels</code>, so you can use <code class="code" id="2119d5ce_212">labels</code> to identify the tree.</p><aside class="prompt" data-type="tip" data-title="" id="2119d5ce_213"><p id="2119d5ce_214">While we have not used <code class="code" id="2119d5ce_215">order</code> in our example, it's useful to determine the order of the bands in <code class="code" id="2119d5ce_216">ar</code> in other applications.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h2 id="plot-the-data-optional" data-toc="plot-the-data-optional"   >Plot the Data (Optional)</h2></div><div class="collapse__content"><p id="2119d5ce_217">We can then use these data to plot out the first tree segment.</p><div class="code-block" data-lang="python"         >
+</div><p id="e05cca52_1900"><code class="code" id="e05cca52_1901">segments</code> is a list of <code class="code" id="e05cca52_1902">np.ndarray</code> of shape H, W, C, representing a tree. The order of <code class="code" id="e05cca52_1903">segments</code> is the same as <code class="code" id="e05cca52_1904">labels</code>, so you can use <code class="code" id="e05cca52_1905">labels</code> to identify the tree.</p><aside class="prompt" data-type="tip" data-title="" id="e05cca52_1906"><p id="e05cca52_1907">While we have not used <code class="code" id="e05cca52_1908">order</code> in our example, it's useful to determine the order of the bands in <code class="code" id="e05cca52_1909">ar</code> in other applications.</p></aside></section><section class="chapter"><div class="collapse"><div class="collapse__title"><h2 id="plot-the-data-optional" data-toc="plot-the-data-optional">Plot the Data (Optional)</h2></div><div class="collapse__content"><p id="e05cca52_1910">We can then use these data to plot out the first tree segment.</p><div class="code-block" data-lang="python">
 import matplotlib.pyplot as plt
 
 from frdc.load.dataset import FRDCDataset
@@ -38,4 +38,4 @@
 plt.imshow(segment_0_rgb_scaled)
 plt.title(f&quot;Tree {labels[0]}&quot;)
 plt.show()
-</div><p id="2119d5ce_219">See also: <a href="preprocessing-scale.html" id="2119d5ce_220" data-tooltip="The static scaling has a default config, which was inferred by our capturing device."  >preprocessing.scale.scale_0_1_per_band</a></p><p id="2119d5ce_221">MatPlotLib cannot show the data correctly as-is, so we need to</p><ul class="list _ul" id="2119d5ce_222"><li class="list__item" id="2119d5ce_223"><p>Convert the data from BGR to RGB</p></li><li class="list__item" id="2119d5ce_224"><p>Scale the data to 0-1 per band</p></li></ul><aside class="prompt" data-type="note" data-title="" id="2119d5ce_225"><p id="2119d5ce_226">Remember that the library returns the band <code class="code" id="2119d5ce_227">order</code>? This is useful in debugging the data. If we had shown it in BGR, it'll look off!</p></aside></div></div></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="getting-started.html">Getting Started</a>   <a class="navigation-links__next" href="mix-match.html">MixMatch</a>  </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
\ No newline at end of file
+</div><p id="e05cca52_1912">See also: <a href="preprocessing-scale.html" id="e05cca52_1913" data-tooltip="The static scaling has a default config, which was inferred by our capturing device.">preprocessing.scale.scale_0_1_per_band</a></p><p id="e05cca52_1914">MatPlotLib cannot show the data correctly as-is, so we need to</p><ul class="list _ul" id="e05cca52_1915"><li class="list__item" id="e05cca52_1916"><p>Convert the data from BGR to RGB</p></li><li class="list__item" id="e05cca52_1917"><p>Scale the data to 0-1 per band</p></li></ul><aside class="prompt" data-type="note" data-title="" id="e05cca52_1918"><p id="e05cca52_1919">Remember that the library returns the band <code class="code" id="e05cca52_1920">order</code>? This is useful in debugging the data. If we had shown it in BGR, it'll look off!</p></aside></div></div></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="getting-started.html" class="navigation-links__prev">Getting Started</a><a href="mix-match.html" class="navigation-links__next">MixMatch</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
\ No newline at end of file
diff --git a/docs/site.webmanifest b/docs/site.webmanifest
new file mode 100644
index 00000000..fe6a9303
--- /dev/null
+++ b/docs/site.webmanifest
@@ -0,0 +1,11 @@
+{
+    "name": "JetBrains",
+    "short_name": "JetBrains",
+    "icons": [
+      { "src": "icon-192.png", "type": "image/png", "sizes": "192x192" },
+      { "src": "icon-512.png", "type": "image/png", "sizes": "512x512" }
+    ],
+    "theme_color": "#000000",
+    "background_color": "#000000",
+    "display": "standalone"
+}
\ No newline at end of file
diff --git a/docs/train-frdc-lightning.html b/docs/train-frdc-lightning.html
index ebcd6054..1886de79 100644
--- a/docs/train-frdc-lightning.html
+++ b/docs/train-frdc-lightning.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-20T12:31:22.095883"><meta name="build-number" content="${buildNumber}">       <title>train.frdc_datamodule &amp; frdc_module | Documentation</title><script id="virtual-toc-data" type="application/json">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script id="topic-shortcuts" type="application/json"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png"><link rel="manifest" href="https://jetbrains.com/site.webmanifest"><link rel="mask-icon" href="https://jetbrains.com/safari-pinned-tab.svg" color="#000000"><meta name="msapplication-TileColor" content="#000000"/><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"/><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"/><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"/><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"/><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png"/>  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"/><meta property="og:description" content=""/><meta property="og:image" content=""/><meta property="og:site_name" content="Documentation Help"/><meta property="og:type" content="website"/><meta property="og:locale" content="en_US"/><meta property="og:url" content="/d/0.0.7/train-frdc-lightning.html"/><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json"> { "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/train-frdc-lightning.html#webpage", "url": "/d/0.0.7/train-frdc-lightning.html", "name": "train.frdc_datamodule &amp; frdc_module | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json"> { "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="train.frdc_lightning" data-main-title="train.frdc_datamodule &amp; frdc_module" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="train.frdc_lightning"   id="train.frdc_lightning.md">train.frdc_datamodule &amp; frdc_module</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;aab859be_176\&quot;\u003eThe FRDC PyTorch LightningDataModule and LightningModule.\u003c/p\u003e&quot;]}"></div> <p id="aab859be_129">These are FRDC specific LightningDataModule and LightningModule, a core component in the PyTorch Lightning ecosystem to provide a simple interface to train and evaluate models.</p><section class="chapter"><h2 id="classes" data-toc="classes"   >Classes</h2><aside class="prompt" data-type="note" data-title="" id="aab859be_130"><p id="aab859be_131">It's optional to use these classes, you can use your own training loop if you want. We'll use these for our training pipeline.</p></aside><dl id="aab859be_132" data-style="title-left" data-title-width="medium"><dt id="aab859be_133" data-expandable="false">FRDCDataModule</dt><dd><p><b id="aab859be_134">The FRDC PyTorch Lightning DataModule.</b></p></dd><dt id="aab859be_135" data-expandable="false">FRDCModule</dt><dd><p><b id="aab859be_136">The FRDC PyTorch Lightning Module.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage"   >Usage</h2><aside class="prompt" data-type="tip" data-title="" id="aab859be_137"><p id="aab859be_138">See our training pipeline for a full example</p></aside></section><section class="chapter"><h2 id="api" data-toc="api"   >API</h2><dl id="aab859be_139" data-style="title-top"><dt id="aab859be_140" data-expandable="false">FRDCDataModule(segments, labels, preprocess, augmentation, train_val_test_split, batch_size)</dt><dd><p><b id="aab859be_141">Initializes the FRDC PyTorch Lightning DataModule.</b></p><br><ul class="list _ul" id="aab859be_143"><li class="list__item" id="aab859be_144"><p><code class="code" id="aab859be_145">segments</code>, <code class="code" id="aab859be_146">labels</code> are retrieved from </p><ul class="list _ul" id="aab859be_147"><li class="list__item" id="aab859be_148"><p><a href="load-dataset.html" id="aab859be_149" data-tooltip="You need to Set-Up Google Cloud with the appropriate permissions to use this library."  >FRDCDataset</a></p></li><li class="list__item" id="aab859be_150"><p><a href="preprocessing-extract-segments.html" id="aab859be_151" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates."  >Segmentation</a></p></li></ul></li><li class="list__item" id="aab859be_152"><p><code class="code" id="aab859be_153">preprocess</code> is a function that takes in a segment and returns a preprocessed segment. In particular, it should accept a list of NumPy NDArrays and return a single stacked PyToch Tensor.</p></li><li class="list__item" id="aab859be_154"><p><code class="code" id="aab859be_155">augmentation</code> is a function that takes in a segment and returns an augmented segment. In particular, it takes in a PyTorch Tensor and returns another.</p></li><li class="list__item" id="aab859be_156"><p><code class="code" id="aab859be_157">train_val_test_split</code> is a function that takes a TensorDataset and returns a list of 3 TensorDatasets, for train, val and test respectively.</p></li><li class="list__item" id="aab859be_158"><p><code class="code" id="aab859be_159">batch_size</code> is the batch size.</p></li></ul><aside class="prompt" data-type="note" data-title="" id="aab859be_160"><p>For now, the <code class="code" id="aab859be_161">augmentation</code> is only applied to training data</p></aside></dd><dt id="aab859be_162" data-expandable="false">FRDCModule(model_cls, model_kwargs, optim_cls, optim_kwargs)</dt><dd><p><b id="aab859be_163">Initializes the FRDC PyTorch Lightning Module.</b></p><br><ul class="list _ul" id="aab859be_165"><li class="list__item" id="aab859be_166"><p><code class="code" id="aab859be_167">model_cls</code> is the Class of the model.</p></li><li class="list__item" id="aab859be_168"><p><code class="code" id="aab859be_169">model_kwargs</code> is the kwargs to pass to the model.</p></li><li class="list__item" id="aab859be_170"><p><code class="code" id="aab859be_171">optim_cls</code> is the Class of the optimizer.</p></li><li class="list__item" id="aab859be_172"><p><code class="code" id="aab859be_173">optim_kwargs</code> is the kwargs to pass to the optimizer.</p></li></ul><p> Internally, the module will initialize the model and optimizer as follows: </p><div class="code-block" data-lang="python"         >
+<!DOCTYPE html SYSTEM "about:legacy-compat"><html lang="en-US" data-colors-preset="contrast" data-primary-color="#307FFF"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="UTF-8"><meta name="robots" content="noindex">  <meta name="built-on" content="2023-12-26T15:45:01.1419417">        <title>train.frdc_datamodule &amp; frdc_module | Documentation</title><script type="application/json" id="virtual-toc-data">[{"id":"classes","level":0,"title":"Classes","anchor":"#classes"},{"id":"usage","level":0,"title":"Usage","anchor":"#usage"},{"id":"api","level":0,"title":"API","anchor":"#api"}]</script><script type="application/json" id="topic-shortcuts"></script><link href="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.css" rel="stylesheet">   <link rel="apple-touch-icon" sizes="180x180" href="https://jetbrains.com/apple-touch-icon.png"><link rel="icon" type="image/png" sizes="32x32" href="https://jetbrains.com/favicon-32x32.png"><link rel="icon" type="image/png" sizes="16x16" href="https://jetbrains.com/favicon-16x16.png">  <link rel="manifest" href="site.webmanifest">  <meta name="msapplication-TileColor" content="#000000"><meta name="msapplication-TileImage" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-144x144.png"><meta name="msapplication-square70x70logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-70x70.png"><meta name="msapplication-square150x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-150x150.png"><meta name="msapplication-wide310x150logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x150.png"><meta name="msapplication-square310x310logo" content="https://resources.jetbrains.com/storage/ui/favicons/mstile-310x310.png">  <meta name="image" content=""><!-- Open Graph --><meta property="og:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"><meta property="og:description" content=""><meta property="og:image" content=""><meta property="og:site_name" content="Documentation Help"><meta property="og:type" content="website"><meta property="og:locale" content="en_US"><meta property="og:url" content="/d/0.0.7/train-frdc-lightning.html"><!-- End Open Graph --><!-- Twitter Card --><meta name="twitter:card" content="summary_large_image"><meta name="twitter:site" content=""><meta name="twitter:title" content="train.frdc_datamodule &amp; frdc_module | Documentation"><meta name="twitter:description" content=""><meta name="twitter:creator" content=""><meta name="twitter:image:src" content=""><!-- End Twitter Card --><!-- Schema.org WebPage --><script type="application/ld+json">{ "@context": "http://schema.org", "@type": "WebPage", "@id": "/d/0.0.7/train-frdc-lightning.html#webpage", "url": "/d/0.0.7/train-frdc-lightning.html", "name": "train.frdc_datamodule &amp; frdc_module | Documentation", "description": "", "image": "", "inLanguage":"en-US" }</script><!-- End Schema.org --><!-- Schema.org WebSite --><script type="application/ld+json">{ "@type": "WebSite", "@id": "/d/#website", "url": "/d/", "name": "Documentation Help" }</script><!-- End Schema.org --></head>      <body data-id="train.frdc_lightning" data-main-title="train.frdc_datamodule &amp; frdc_module" data-article-props="{&quot;seeAlsoStyle&quot;:&quot;links&quot;}"  data-template="article"  data-breadcrumbs="API"  >   <div class="wrapper"><main class="panel _main"><header class="panel__header"><div class="container"><h3>Documentation 0.0.7 Help</h3><div class="panel-trigger"></div></div></header><section class="panel__content"><div class="container"><article class="article" data-shortcut-switcher="inactive"><h1 data-toc="train.frdc_lightning"   id="train.frdc_lightning.md">train.frdc_datamodule &amp; frdc_module</h1> <div class="micro-format" data-content="{&quot;microFormat&quot;:[&quot;\u003cp id\u003d\&quot;2fcccc5f_1733\&quot;\u003eThe FRDC PyTorch LightningDataModule and LightningModule.\u003c/p\u003e&quot;]}"></div> <p id="2fcccc5f_1686">These are FRDC specific LightningDataModule and LightningModule, a core component in the PyTorch Lightning ecosystem to provide a simple interface to train and evaluate models.</p><section class="chapter"><h2 id="classes" data-toc="classes">Classes</h2><aside class="prompt" data-type="note" data-title="" id="2fcccc5f_1687"><p id="2fcccc5f_1688">It's optional to use these classes, you can use your own training loop if you want. We'll use these for our training pipeline.</p></aside><dl id="2fcccc5f_1689" data-style="title-left" data-title-width="medium"><dt id="2fcccc5f_1690" data-expandable="false">FRDCDataModule</dt><dd><p><b id="2fcccc5f_1691">The FRDC PyTorch Lightning DataModule.</b></p></dd><dt id="2fcccc5f_1692" data-expandable="false">FRDCModule</dt><dd><p><b id="2fcccc5f_1693">The FRDC PyTorch Lightning Module.</b></p></dd></dl></section><section class="chapter"><h2 id="usage" data-toc="usage">Usage</h2><aside class="prompt" data-type="tip" data-title="" id="2fcccc5f_1694"><p id="2fcccc5f_1695">See our training pipeline for a full example</p></aside></section><section class="chapter"><h2 id="api" data-toc="api">API</h2><dl id="2fcccc5f_1696" data-style="title-top"><dt id="2fcccc5f_1697" data-expandable="false">FRDCDataModule(segments, labels, preprocess, augmentation, train_val_test_split, batch_size)</dt><dd><p><b id="2fcccc5f_1698">Initializes the FRDC PyTorch Lightning DataModule.</b></p><br><ul class="list _ul" id="2fcccc5f_1700"><li class="list__item" id="2fcccc5f_1701"><p><code class="code" id="2fcccc5f_1702">segments</code>, <code class="code" id="2fcccc5f_1703">labels</code> are retrieved from </p><ul class="list _ul" id="2fcccc5f_1704"><li class="list__item" id="2fcccc5f_1705"><p><a href="load-dataset.html" id="2fcccc5f_1706" data-tooltip="You need to Set-Up Google Cloud with the appropriate permissions to use this library.">FRDCDataset</a></p></li><li class="list__item" id="2fcccc5f_1707"><p><a href="preprocessing-extract-segments.html" id="2fcccc5f_1708" data-tooltip="A boundary is a Rect object that represents the minimum bounding box of a segment, with x0, y0, x1, y1 coordinates.">Segmentation</a></p></li></ul></li><li class="list__item" id="2fcccc5f_1709"><p><code class="code" id="2fcccc5f_1710">preprocess</code> is a function that takes in a segment and returns a preprocessed segment. In particular, it should accept a list of NumPy NDArrays and return a single stacked PyToch Tensor.</p></li><li class="list__item" id="2fcccc5f_1711"><p><code class="code" id="2fcccc5f_1712">augmentation</code> is a function that takes in a segment and returns an augmented segment. In particular, it takes in a PyTorch Tensor and returns another.</p></li><li class="list__item" id="2fcccc5f_1713"><p><code class="code" id="2fcccc5f_1714">train_val_test_split</code> is a function that takes a TensorDataset and returns a list of 3 TensorDatasets, for train, val and test respectively.</p></li><li class="list__item" id="2fcccc5f_1715"><p><code class="code" id="2fcccc5f_1716">batch_size</code> is the batch size.</p></li></ul><aside class="prompt" data-type="note" data-title="" id="2fcccc5f_1717"><p>For now, the <code class="code" id="2fcccc5f_1718">augmentation</code> is only applied to training data</p></aside></dd><dt id="2fcccc5f_1719" data-expandable="false">FRDCModule(model_cls, model_kwargs, optim_cls, optim_kwargs)</dt><dd><p><b id="2fcccc5f_1720">Initializes the FRDC PyTorch Lightning Module.</b></p><br><ul class="list _ul" id="2fcccc5f_1722"><li class="list__item" id="2fcccc5f_1723"><p><code class="code" id="2fcccc5f_1724">model_cls</code> is the Class of the model.</p></li><li class="list__item" id="2fcccc5f_1725"><p><code class="code" id="2fcccc5f_1726">model_kwargs</code> is the kwargs to pass to the model.</p></li><li class="list__item" id="2fcccc5f_1727"><p><code class="code" id="2fcccc5f_1728">optim_cls</code> is the Class of the optimizer.</p></li><li class="list__item" id="2fcccc5f_1729"><p><code class="code" id="2fcccc5f_1730">optim_kwargs</code> is the kwargs to pass to the optimizer.</p></li></ul><p> Internally, the module will initialize the model and optimizer as follows: </p><div class="code-block" data-lang="python">
 model = model_cls(**model_kwargs)
 optim = optim_cls(model.parameters(), **optim_kwargs)
-</div><aside class="prompt" data-type="note" data-title="" id="aab859be_175"><p>We do not accept the instances of the Model and Optimizer so that we can pickle them.</p></aside></dd></dl></section><div class="last-modified"> Last modified: 20 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom">  <a class="navigation-links__prev" href="preprocessing-glcm-padded.html">preprocessing.glcm_padded</a>   </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
\ No newline at end of file
+</div><aside class="prompt" data-type="note" data-title="" id="2fcccc5f_1732"><p>We do not accept the instances of the Model and Optimizer so that we can pickle them.</p></aside></dd></dl></section><div class="last-modified">Last modified: 26 December 2023</div><div data-feedback-placeholder="true"></div><div class="navigation-links _bottom"> <a href="preprocessing-glcm-padded.html" class="navigation-links__prev">preprocessing.glcm_padded</a> </div></article><div id="disqus_thread"></div></div></section></main></div>  <script src="https://resources.jetbrains.com/writerside/apidoc/6.6.6-b205/app.js"></script></body></html>
\ No newline at end of file

From 3ad231b037edec6effe1208ede4bc258e6a26dbd Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 15:47:27 +0800
Subject: [PATCH 30/35] Update README.md

---
 README.md | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 9ddd6c92..d497d13a 100644
--- a/README.md
+++ b/README.md
@@ -54,14 +54,6 @@ To illustrate this, take a look at how
 `tests/model_tests/chestnut_dec_may/train.py` is written. It pulls in relevant
 modules from each stage and constructs a pipeline.
 
-
-> Initially, we evaluated a few ML E2E solutions, despite them offering great
-> functionality, their flexibility was
-> limited. From a dev perspective, **Active Learning** was a gray area, and we
-> foresee heavy shoehorning.
-> Ultimately, we decided that the risk was too great, thus we resort to
-> creating our own solution.
-
 ## Contributing
 
 ### Pre-commit Hooks
@@ -80,3 +72,5 @@ If you're using `pip` instead of `poetry`, run the following commands:
 pip install pre-commit
 pre-commit install
 ```
+
+Alternatively, you can use Black configured with your own IDE.

From 3eb0b40a792286b235486c6baeee3ac0f14e82f9 Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 15:53:17 +0800
Subject: [PATCH 31/35] Update devcontainer.json

---
 .devcontainer/devcontainer.json | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index ca281b81..5ca24e93 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -1,8 +1,7 @@
 {
   "name": "frdc",
   "build": {
-    "context": "../",
-    "dockerfile": "Dockerfile",
+    "dockerfile": "../Dockerfile",
   },
   "containerEnv": {
     "LABEL_STUDIO_HOST": "host.docker.internal",

From d021af7cbb825c88fcb8885bd118f6f51cfca35c Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 16:29:08 +0800
Subject: [PATCH 32/35] Attempt to fix codespace problem

---
 Dockerfile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 9951dc95..6b535c26 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -3,8 +3,8 @@ WORKDIR /devcontainer
 
 COPY ./pyproject.toml /devcontainer/pyproject.toml
 
-RUN apt-get update
-RUN apt-get install git -y
+RUN apt update && apt upgrade
+RUN apt install git -y
 
 RUN pip3 install --upgrade pip && \
     pip3 install poetry && \
@@ -16,5 +16,5 @@ RUN conda init bash \
     && poetry config virtualenvs.create false \
     && poetry install --with dev --no-interaction --no-ansi
 
-RUN apt-get install curl -y && curl -sSL https://sdk.cloud.google.com | bash
+RUN apt install curl -y && curl -sSL https://sdk.cloud.google.com | bash
 ENV PATH $PATH:/root/google-cloud-sdk/bin

From 2636cf1454a0d2a34b770829fa55f558d242e4ef Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Tue, 26 Dec 2023 16:38:02 +0800
Subject: [PATCH 33/35] Update Dockerfile

---
 Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Dockerfile b/Dockerfile
index 6b535c26..526daecb 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -3,7 +3,7 @@ WORKDIR /devcontainer
 
 COPY ./pyproject.toml /devcontainer/pyproject.toml
 
-RUN apt update && apt upgrade
+RUN apt update -y && apt upgrade -y
 RUN apt install git -y
 
 RUN pip3 install --upgrade pip && \

From bac614adb1e19d71d512e4d52824038146755b7f Mon Sep 17 00:00:00 2001
From: Eve-ning <johnchangqi@hotmail.com>
Date: Wed, 27 Dec 2023 16:24:11 +0800
Subject: [PATCH 34/35] Force Dockerfile to LF

---
 .gitattributes | 0
 Dockerfile     | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 .gitattributes
 mode change 100644 => 100755 Dockerfile

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 00000000..e69de29b
diff --git a/Dockerfile b/Dockerfile
old mode 100644
new mode 100755

From 399fc54ea766b9c1dcb9699cac337a5908ce895e Mon Sep 17 00:00:00 2001
From: Evening <johnchangqi@hotmail.com>
Date: Wed, 27 Dec 2023 16:24:51 +0800
Subject: [PATCH 35/35] Force Dockerfile to LF

---
 .gitattributes | 1 +
 Dockerfile     | 0
 2 files changed, 1 insertion(+)
 mode change 100755 => 100644 Dockerfile

diff --git a/.gitattributes b/.gitattributes
index e69de29b..d28cb2fc 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -0,0 +1 @@
+Dockerfile text=auto eol=lf
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
old mode 100755
new mode 100644