Mmdet scnet support

docs/autogen.py

@@ -10,10 +10,7 @@
 from loguru import logger
 
 PAGES = {
-    "parser.md": [
-        "icevision.parsers.Parser",
-        "icevision.parsers.Parser.parse",
-    ],
+    "parser.md": ["icevision.parsers.Parser", "icevision.parsers.Parser.parse",],
     "dataset.md": [
         "icevision.data.dataset.Dataset",
         "icevision.data.dataset.Dataset.from_images",
@@ -130,9 +127,7 @@ def copy_examples(examples_dir, destination_dir):
 
         from_to = f"{file} -> {destination_file}"
         logger.opt(colors=True).log(
-            "INFO",
-            "️<green><bold>Copying Examples: {}</></>",
-            from_to,
+            "INFO", "️<green><bold>Copying Examples: {}</></>", from_to,
         )
 
 
@@ -197,9 +192,7 @@ def py_to_nb_md(dest_dir):
 def nb_to_md(src_dir, nb_folder, dest_dir):
     notebooks_dir = src_dir / nb_folder
     logger.opt(colors=True).log(
-        "INFO",
-        "️<green><bold>Notebooks folder: {}</></>",
-        notebooks_dir,
+        "INFO", "️<green><bold>Notebooks folder: {}</></>", notebooks_dir,
     )
 
     for file_path in os.listdir(notebooks_dir):
@@ -221,18 +214,14 @@ def nb_to_md(src_dir, nb_folder, dest_dir):
         tutobooks.nb_to_md(nb_path, md_path, images_path)
         from_to = f"{file_name} -> {file_name_md}"
         logger.opt(colors=True).log(
-            "INFO",
-            "️<green><bold>Converting to Notebook: {}</></>",
-            from_to,
+            "INFO", "️<green><bold>Converting to Notebook: {}</></>", from_to,
         )
 
 
 def examples_to_md(dest_dir):
     examples_dir = icevision_dir / "examples"
     logger.opt(colors=True).log(
-        "INFO",
-        "️<green><bold>Examples folder: {}</></>",
-        examples_dir,
+        "INFO", "️<green><bold>Examples folder: {}</></>", examples_dir,
     )
 
     for file_path in os.listdir(examples_dir):
@@ -266,9 +255,7 @@ def generate(dest_dir: Path):
     if os.path.exists(dest_dir):
         print("Removing sources folder:", dest_dir)
         logger.opt(colors=True).log(
-            "INFO",
-            "️<magenta><bold>\nRemoving sources folder: {}</></>",
-            dest_dir,
+            "INFO", "️<magenta><bold>\nRemoving sources folder: {}</></>", dest_dir,
         )
         shutil.rmtree(dest_dir)
     os.makedirs(dest_dir)
@@ -277,9 +264,7 @@ def generate(dest_dir: Path):
     copy_tree(str(icevision_dir / "images"), str(template_images_dir))
     from_to = f"root/images -> docs/images"
     logger.opt(colors=True).log(
-        "INFO",
-        "️<green><bold>\nCopying images folder: {}</></>",
-        from_to,
+        "INFO", "️<green><bold>\nCopying images folder: {}</></>", from_to,
     )
 
     # Generate APIs Documentation
@@ -298,9 +283,7 @@ def generate(dest_dir: Path):
     shutil.copyfile("manifest.webmanifest", dest_dir / "manifest.webmanifest")
     from_to = f"root/manifest.webmanifest -> docs/manifest.webmanifest"
     logger.opt(colors=True).log(
-        "INFO",
-        "️<green><bold>\nCopying webmanifest file: {}</></>",
-        from_to,
+        "INFO", "️<green><bold>\nCopying webmanifest file: {}</></>", from_to,
     )
 
     # Auto generate the index.md file using the README.md file and the index.md file in templates folder
@@ -321,9 +304,7 @@ def generate(dest_dir: Path):
         filename for filename in os.listdir(dir_to_search) if filename.endswith(".md")
     ]
     logger.opt(colors=True).log(
-        "INFO",
-        "️<green><bold>\nCopying .md files root folder: {}</></>",
-        fnamelist,
+        "INFO", "️<green><bold>\nCopying .md files root folder: {}</></>", fnamelist,
     )
 
     for fname in fnamelist:
@@ -332,9 +313,7 @@ def generate(dest_dir: Path):
         shutil.copyfile(fname_src, fname_dst)
         from_to = f"{fname} -> {fname.lower()}"
         logger.opt(colors=True).log(
-            "INFO",
-            "️<light-blue><bold>file: {}</></>",
-            from_to,
+            "INFO", "️<light-blue><bold>file: {}</></>", from_to,
         )
 
     # Copy static .md files from the docs folder
@@ -353,9 +332,7 @@ def generate(dest_dir: Path):
         shutil.copyfile(fname_src, fname_dst)
         from_to = f"{fname} -> {fname.lower()}"
         logger.opt(colors=True).log(
-            "INFO",
-            "️<light-blue><bold>Copying files: {}</></>",
-            from_to,
+            "INFO", "️<light-blue><bold>Copying files: {}</></>", from_to,
         )
 
     # Copy images folder from the template folder to the destination folder
@@ -366,9 +343,7 @@ def generate(dest_dir: Path):
     copy_tree(str(template_images_dir), str(dest_images_dir))
     from_to = f"{template_images_dir} -> {dest_images_dir}"
     logger.opt(colors=True).log(
-        "INFO",
-        "️<green><bold>Copying Images: {}</></>",
-        from_to,
+        "INFO", "️<green><bold>Copying Images: {}</></>", from_to,
     )
 
     # Copy css folder
@@ -377,9 +352,7 @@ def generate(dest_dir: Path):
     copy_tree(css_dir_src, css_dir_dest)
     from_to = f"{css_dir_src} -> {css_dir_dest}"
     logger.opt(colors=True).log(
-        "INFO",
-        "️<green><bold>Copying CSS files: {}</></>",
-        from_to,
+        "INFO", "️<green><bold>Copying CSS files: {}</></>", from_to,
     )
 
     # Copy js folder
@@ -389,9 +362,7 @@ def generate(dest_dir: Path):
     copy_tree(js_dir_src, js_dir_dest)
     from_to = f"{js_dir_src} -> {js_dir_dest}"
     logger.opt(colors=True).log(
-        "INFO",
-        "️<green><bold>Copying JS files: {}</></>",
-        from_to,
+        "INFO", "️<green><bold>Copying JS files: {}</></>", from_to,
     )
 
     # Generate .md files form Jupyter Notebooks located in the /notebooks folder
@@ -402,26 +373,22 @@ def generate(dest_dir: Path):
 
     # albumentations
     shutil.copyfile(
-        icevision_dir / "icevision/tfms/README.md",
-        dest_dir / "albumentations.md",
+        icevision_dir / "icevision/tfms/README.md", dest_dir / "albumentations.md",
     )
 
     # Models
     shutil.copyfile(
-        icevision_dir / "icevision/models/README.md",
-        dest_dir / "models.md",
+        icevision_dir / "icevision/models/README.md", dest_dir / "models.md",
     )
 
     # Backbones
     shutil.copyfile(
-        icevision_dir / "icevision/backbones/README.md",
-        dest_dir / "backbones.md",
+        icevision_dir / "icevision/backbones/README.md", dest_dir / "backbones.md",
     )
 
     # README DOCS
     shutil.copyfile(
-        icevision_dir / "docs/README.md",
-        dest_dir / "readme_mkdocs.md",
+        icevision_dir / "docs/README.md", dest_dir / "readme_mkdocs.md",
     )
 
 

icevision/core/components/composite.py

@@ -73,10 +73,7 @@ def set_task_components(self, components: Sequence[TaskComponent]):
 
     # TODO: rename reduce_on_all_tasks_components
     def reduce_on_components(
-        self,
-        fn_name: str,
-        reduction: Optional[str] = None,
-        **fn_kwargs,
+        self, fn_name: str, reduction: Optional[str] = None, **fn_kwargs,
     ) -> Dict[str, Any]:
         results = {}
         for task, composite in self.task_composites.items():

icevision/data/dataset.py

@@ -19,9 +19,7 @@ class Dataset:
     """
 
     def __init__(
-        self,
-        records: List[dict],
-        tfm: Optional[Transform] = None,
+        self, records: List[dict], tfm: Optional[Transform] = None,
     ):
         self.records = records
         self.tfm = tfm

icevision/metrics/confusion_matrix/confusion_matrix.py

@@ -75,9 +75,7 @@ def finalize(self):
         assert len(self.target_labels) == len(self.predicted_labels)
         label_ids = list(self.class_map._class2id.values())
         self.confusion_matrix = sklearn.metrics.confusion_matrix(
-            y_true=self.target_labels,
-            y_pred=self.predicted_labels,
-            labels=label_ids,
+            y_true=self.target_labels, y_pred=self.predicted_labels, labels=label_ids,
         )
         if self.print_summary:
             print(self.confusion_matrix)

icevision/models/base_show_results.py

@@ -21,8 +21,5 @@ def base_show_results(
     preds = predict_fn(model, records, **predict_kwargs)
 
     show_preds(
-        preds,
-        denormalize_fn=denormalize_fn,
-        ncols=ncols,
-        show=show,
+        preds, denormalize_fn=denormalize_fn, ncols=ncols, show=show,
     )

icevision/models/checkpoint.py

@@ -158,9 +158,7 @@ def model_from_checkpoint(
     img_size=None,
     map_location=None,
     strict=False,
-    revise_keys=[
-        (r"^module\.", ""),
-    ],
+    revise_keys=[(r"^module\.", ""),],
     eval_mode=True,
     logger=None,
 ):

icevision/models/fastai/unet/fastai/learner.py

@@ -16,11 +16,7 @@ def learner(
     cbs = L(UnetCallback()) + L(cbs)
 
     learn = adapted_fastai_learner(
-        dls=dls,
-        model=model,
-        cbs=cbs,
-        loss_func=loss_func,
-        **kwargs,
+        dls=dls, model=model, cbs=cbs, loss_func=loss_func, **kwargs,
     )
 
     return learn

icevision/models/fastai/unet/lightning/model_adapter.py

@@ -47,9 +47,7 @@ def validation_step(self, batch, batch_idx):
             loss = self.loss_func(preds, yb)
 
             preds = unet.convert_raw_predictions(
-                batch=xb,
-                raw_preds=preds,
-                records=records,
+                batch=xb, raw_preds=preds, records=records,
             )
 
         self.accumulate_metrics(preds)

icevision/models/fastai/unet/prediction.py

@@ -23,10 +23,7 @@ def _predict_batch(
 
     raw_preds = model(images)
     preds = convert_raw_predictions(
-        batch=batch,
-        raw_preds=raw_preds,
-        records=records,
-        keep_images=keep_images,
+        batch=batch, raw_preds=raw_preds, records=records, keep_images=keep_images,
     )
 
     return preds

icevision/models/inference.py

@@ -106,9 +106,7 @@ def _end2end_detect(
 
 
 def process_bbox_predictions(
-    pred: Prediction,
-    img: PIL.Image.Image,
-    transforms: List[Any],
+    pred: Prediction, img: PIL.Image.Image, transforms: List[Any],
 ) -> List[Dict[str, Any]]:
     """
     Postprocess prediction.

icevision/models/interpretation.py

@@ -154,9 +154,7 @@ def _loop(self, dl, model, losses_stats, device):
         return samples_plus_losses, losses_stats
 
     def get_losses(
-        self,
-        model: nn.Module,
-        dataset: Dataset,
+        self, model: nn.Module, dataset: Dataset,
     ) -> Tuple[List[dict], dict]:
         """
         Gets a dataset and a model as input and returns losses calculated per image + losses stats.
@@ -244,8 +242,7 @@ def plot_top_losses(
         ]
 
         show_preds(
-            preds=sorted_preds[:n_samples],
-            annotations=anns[:n_samples],
+            preds=sorted_preds[:n_samples], annotations=anns[:n_samples],
         )
         model.train()
         return sorted_samples, sorted_preds, losses_stats

icevision/models/mmdet/common/bbox/two_stage/model.py

@@ -13,13 +13,15 @@ def model(
     num_classes: int,
     checkpoints_path: Optional[Union[str, Path]] = "checkpoints",
     force_download=False,
+    cfg_options=None,
 ) -> nn.Module:
 
     cfg, weights_path = create_model_config(
         backbone=backbone,
         pretrained=backbone.pretrained,
         checkpoints_path=checkpoints_path,
         force_download=force_download,
+        cfg_options=None,
     )
 
     return build_model(
@@ -29,4 +31,5 @@ def model(
         pretrained=backbone.pretrained,
         checkpoints_path=checkpoints_path,
         force_download=force_download,
+        cfg_options=cfg_options,
     )

icevision/models/mmdet/fastai/learner.py

@@ -26,11 +26,7 @@ def mmdetection_learner(
     """
 
     learn = adapted_fastai_learner(
-        dls=dls,
-        model=model,
-        cbs=cbs,
-        loss_func=loss_fn,
-        **learner_kwargs,
+        dls=dls, model=model, cbs=cbs, loss_func=loss_fn, **learner_kwargs,
     )
 
     return learn

icevision/models/mmdet/models/__init__.py

@@ -3,6 +3,7 @@
 from icevision.models.mmdet.models import yolox
 from icevision.models.mmdet.models import retinanet
 from icevision.models.mmdet.models import fcos
+from icevision.models.mmdet.models import scnet
 from icevision.models.mmdet.models import vfnet
 from icevision.models.mmdet.models import cornernet
 from icevision.models.mmdet.models import centripetalnet

icevision/models/mmdet/models/scnet/__init__.py

@@ -0,0 +1,24 @@
+from icevision.models.mmdet.models.scnet import backbones
+from icevision.models.mmdet.common.bbox.two_stage import *
+from icevision.models.interpretation import Interpretation, _move_to_device
+from icevision.models.mmdet.common.interpretation_utils import (
+    sum_losses_mmdet,
+    loop_mmdet,
+)
+
+_LOSSES_DICT = {
+    "loss_rpn_cls": [],
+    "loss_rpn_bbox": [],
+    "loss_cls": [],
+    "loss_bbox": [],
+    "loss_mask": [],
+    "loss_total": [],
+}
+interp = Interpretation(
+    losses_dict=_LOSSES_DICT,
+    valid_dl=valid_dl,
+    infer_dl=infer_dl,
+    predict_from_dl=predict_from_dl,
+)
+
+interp._loop = loop_mmdet

icevision/models/mmdet/models/scnet/backbones/__init__.py

@@ -0,0 +1 @@
+from icevision.models.mmdet.models.scnet.backbones.resnet_fpn import *