Merge pull request #18 from FR-DC/0.0.5

0.0.5

.github/workflows/model.yml

@@ -0,0 +1,63 @@
+name: Model Training
+
+on:
+  pull_request:
+
+jobs:
+  build:
+
+    runs-on: self-hosted
+    container:
+      image: docker://ghcr.io/iterative/cml:0-dvc2-base1-gpu
+      volumes:
+        - /home/runner/work/frdc-ml/_github_home:/root
+      env:
+        AGENT_TOOLSDIRECTORY: "/root/venv"
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Force change owner
+        run: |
+          chown -R root: ~
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+
+      - name: Install via exported requirements.txt
+        run: |
+          python -m pip install --upgrade pip
+          python -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
+
+      - name: Set up gcloud
+        id: 'auth'
+        uses: 'google-github-actions/auth@v1'
+        with:
+          credentials_json: '${{ secrets.FRDC_DOWNLOAD_KEY }}'
+
+      - name: Set up Cloud SDK
+        uses: 'google-github-actions/setup-gcloud@v1'
+
+      - name: Set up WandB
+        run: |
+          echo "WANDB_API_KEY=${{ secrets.WANDB_API_KEY }}" >> $GITHUB_ENV
+
+      - name: Add src as PYTHONPATH
+        run: |
+          echo "PYTHONPATH=src" >> $GITHUB_ENV
+
+      - name: Run Model Training
+        run: |
+          python3 -m tests.model_tests.chestnut_dec_may.main
+
+      - name: Comment results via CML
+        run: |
+          cml comment update \
+          --target=pr \
+          --token ${{ secrets.GITHUB_TOKEN }} \
+          tests/model_tests/chestnut_dec_may/report.md

.github/workflows/python-package.yml

@@ -1,6 +1,3 @@
-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
-# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
-
 name: Python CI
 
 on:
@@ -34,7 +31,7 @@ jobs:
       - name: 'Set up Cloud SDK'
         uses: 'google-github-actions/setup-gcloud@v1'
 
-#     We don't necessarily need to install the CUDA version of torch, so we'll do with cpu
+      #     We don't necessarily need to install the CUDA version of torch, so we'll do with cpu
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
@@ -52,5 +49,3 @@ jobs:
       - name: Test with pytest
         run: |
           pytest
-
-

pyproject.toml

@@ -34,6 +34,7 @@ pytest = "^7.4.2"
 pre-commit = "^3.5.0"
 black = "^23.10.0"
 flake8 = "^6.1.0"
+wandb = "^0.16.0"
 
 
 [tool.poetry.group.glcm.dependencies]

pipeline/model_tests/chestnut_dec_may/evaluate.py → tests/model_tests/chestnut_dec_may/evaluate.py

@@ -7,8 +7,8 @@
 
 from frdc.train import FRDCDataModule
 from frdc.train import FRDCModule
-from pipeline.model_tests.chestnut_dec_may.preprocess import preprocess
-from pipeline.model_tests.utils import get_dataset
+from .preprocess import preprocess
+from tests.model_tests.utils import get_dataset
 
 # Get our Test
 # TODO: Ideally, we should have a separate dataset for testing.

pipeline/model_tests/chestnut_dec_may/main.py → tests/model_tests/chestnut_dec_may/main.py

@@ -3,6 +3,7 @@
 This test is done by training a model on the 20201218 dataset, then testing on
 the 20210510 dataset.
 """
+from pathlib import Path
 
 import lightning as pl
 import numpy as np
@@ -16,19 +17,30 @@
 
 from frdc.models import FaceNet
 from frdc.train import FRDCDataModule, FRDCModule
-from pipeline.model_tests.chestnut_dec_may.augmentation import augmentation
-from pipeline.model_tests.chestnut_dec_may.preprocess import preprocess
-from pipeline.model_tests.utils import get_dataset
+from tests.model_tests.chestnut_dec_may.augmentation import augmentation
+from tests.model_tests.chestnut_dec_may.preprocess import preprocess
+from tests.model_tests.utils import get_dataset
+from lightning.pytorch.loggers import WandbLogger
+import wandb
 
+assert wandb.run is None
 
-def train_val_test_split(x: TensorDataset) -> list[Dataset, Dataset, Dataset]:
+wandb.setup(wandb.Settings(program=__name__, program_relpath=__name__))
+run = wandb.init()
+logger = WandbLogger(name="chestnut_dec_may", project="frdc")
+
+
+def train_val_test_split(
+    x: TensorDataset,
+) -> list[Dataset, Dataset, Dataset]:
     # Defines how to split the dataset into train, val, test subsets.
     # TODO: Quite ugly as it uses the global variables segments_0 and
     #  segments_1. Will need to refactor this.
     return [
         Subset(x, list(range(len(segments_0)))),
         Subset(
-            x, list(range(len(segments_0), len(segments_0) + len(segments_1)))
+            x,
+            list(range(len(segments_0), len(segments_0) + len(segments_1))),
         ),
         [],
     ]
@@ -40,12 +52,13 @@ def train_val_test_split(x: TensorDataset) -> list[Dataset, Dataset, Dataset]:
     "chestnut_nature_park", "20210510", "90deg43m85pct255deg/map"
 )
 
+
 # Concatenate the datasets
 segments = [*segments_0, *segments_1]
 labels = [*labels_0, *labels_1]
 
 BATCH_SIZE = 5
-EPOCHS = 100
+EPOCHS = 50
 LR = 1e-3
 
 # Prepare the datamodule and trainer
@@ -66,9 +79,12 @@ def train_val_test_split(x: TensorDataset) -> list[Dataset, Dataset, Dataset]:
 
 trainer = pl.Trainer(
     max_epochs=EPOCHS,
+    # fast_dev_run=True,
     # Set the seed for reproducibility
     # TODO: Though this is set, the results are still not reproducible.
     deterministic=True,
+    # fast_dev_run=True,
+    accelerator="cpu",
     log_every_n_steps=4,
     callbacks=[
         # Stop training if the validation loss doesn't improve for 4 epochs
@@ -78,11 +94,13 @@ def train_val_test_split(x: TensorDataset) -> list[Dataset, Dataset, Dataset]:
         # Save the best model
         ModelCheckpoint(monitor="val_loss", mode="min", save_top_k=1),
     ],
+    logger=logger,
 )
 
 m = FRDCModule(
     # Our model is the "FaceNet" model
-    # TODO: It's not really the FaceNet model, but a modified version of it.
+    # TODO: It's not really the FaceNet model,
+    #  but a modified version of it.
     model_cls=FaceNet,
     model_kwargs=dict(n_out_classes=len(set(labels))),
     # We use the Adam optimizer
@@ -94,3 +112,15 @@ def train_val_test_split(x: TensorDataset) -> list[Dataset, Dataset, Dataset]:
 trainer.fit(m, datamodule=dm)
 # TODO: Quite hacky, but we need to save the label encoder for prediction.
 np.save("le.npy", dm.le.classes_)
+
+report = f"""
+# Chestnut Nature Park (Dec 2020 vs May 2021)
+[WandB Report]({run.get_url()})
+TODO: Authentication for researchers
+"""
+
+with open(Path(__file__).parent / "report.md", "w") as f:
+    f.write(report)
+
+
+wandb.finish()

pipeline/model_tests/chestnut_dec_may/preprocess.py → tests/model_tests/chestnut_dec_may/preprocess.py

@@ -1,10 +1,12 @@
 import numpy as np
 import torch
-from glcm_cupy import Features
+
+# from glcm_cupy import Features
 from torchvision.transforms.v2 import Resize
 
 from frdc.models import FaceNet
-from frdc.preprocess.glcm_padded import append_glcm_padded_cached
+
+# from frdc.preprocess.glcm_padded import append_glcm_padded_cached
 from frdc.preprocess.scale import scale_normal_per_band, scale_0_1_per_band
 
 
@@ -24,16 +26,17 @@ def segment_preprocess(ar: np.ndarray) -> torch.Tensor:
 
     # Add a small epsilon to avoid upper bound of 1.0
     ar = scale_0_1_per_band(ar, epsilon=0.001)
-    ar = append_glcm_padded_cached(
-        ar,
-        step_size=7,
-        bin_from=1,
-        bin_to=128,
-        radius=3,
-        features=(Features.MEAN,),
-    )
-    # We can then scale normal for better neural network convergence
+    # ar = append_glcm_padded_cached(
+    #     ar,
+    #     step_size=7,
+    #     bin_from=1,
+    #     bin_to=128,
+    #     radius=3,
+    #     features=(Features.MEAN,),
+    # )
+    # # We can then scale normal for better neural network convergence
     ar = scale_normal_per_band(ar)
+    ar = np.rollaxis(ar, axis=2)
 
     # TODO: Doesn't seem like we have any channel preprocessing here.
     # ar = np.stack([