Revert "Mmseg initial support (airctic#1079)" (airctic#1082)

This reverts commit 20c3ff5.

Author: FraPochetti

.github/workflows/build-pkg.yml

@@ -25,4 +25,4 @@ jobs:
         run: |
 #          pip install numpy
           pip install -e .
-          python -c "from icevision.all import *"
+          python -c "from icevision.all import *"

.github/workflows/ci-all-testing.yml

@@ -24,10 +24,10 @@ jobs:
 
     - name: Install package
       run: |
-          sh ./icevision_install.sh cpu
-          pip install -e ".[all,dev]"
-          pip install fiftyone
-          
+        sh ./icevision_install.sh cpu
+        pip install -e ".[all,dev]"
+        pip install fiftyone
+
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -45,4 +45,4 @@ jobs:
         file: ./coverage.xml
         flags: unittests
         name: codecov-umbrella
-        fail_ci_if_error: false
+        fail_ci_if_error: false

.gitignore

@@ -155,8 +155,3 @@ archives/
 notebooks/wandb/latest-run
 
 checkpoints
-
-wandb
-wandb/*
-
-*/artifacts/*

docker/Dockerfile

@@ -6,5 +6,4 @@ RUN pip install git+https://github.com/airctic/icedata.git -U
 RUN pip install yolov5-icevision -U 
 RUN pip install mmcv-full==1.3.7 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.0/index.html -U
 RUN pip install mmdet==2.13.0 -U
-RUN pip install mmsegmentation==0.17.0 -U
 RUN pip install ipywidgets

icevision/core/exceptions.py

@@ -1,10 +1,4 @@
-__all__ = [
-    "InvalidDataError",
-    "AutofixAbort",
-    "AbortParseRecord",
-    "InvalidMMSegModelType",
-    "PreTrainedVariantNotFound",
-]
+__all__ = ["InvalidDataError", "AutofixAbort", "AbortParseRecord"]
 
 
 class InvalidDataError(Exception):
@@ -17,11 +11,3 @@ class AutofixAbort(Exception):
 
 class AbortParseRecord(Exception):
     pass
-
-
-class InvalidMMSegModelType(Exception):
-    pass
-
-
-class PreTrainedVariantNotFound(Exception):
-    pass

icevision/core/mask.py

@@ -64,11 +64,10 @@ class MaskArray(Mask):
 
     # Arguments
         data: Mask array, with the dimensions: (num_instances, height, width)
-        pad_dim: bool
     """
 
-    def __init__(self, data: np.uint8, pad_dim: bool = True):
-        if pad_dim and (len(data.shape) == 2):
+    def __init__(self, data: np.uint8):
+        if len(data.shape) == 2:
             data = np.expand_dims(data, 0)
         self.data = data.astype(np.uint8)
 
@@ -138,21 +137,10 @@ class MaskFile(Mask):
     def __init__(self, filepath: Union[str, Path]):
         self.filepath = Path(filepath)
 
-    def to_mask(self, h=None, w=None):
-        mask_img = open_img(self.filepath, gray=True)
-
-        if (h is not None) and (w is not None):
-            # If the dimensions provided in h and w do not match the size of the mask, resize the mask accordingly
-            (w_org, h_org) = mask_img.size
-
-            # TODO: Check NEAREST is always the best option or only for binary?
-            if w_org != w or h_org != h:
-                mask_img = mask_img.resize((w, h), resample=PIL.Image.NEAREST)
-
-        mask = np.array(mask_img)
+    def to_mask(self, h, w):
+        mask = np.array(open_img(self.filepath, gray=True))
         obj_ids = np.unique(mask)[1:]
         masks = mask == obj_ids[:, None, None]
-
         return MaskArray(masks)
 
     def to_coco_rle(self, h, w) -> List[dict]:
@@ -287,26 +275,17 @@ def __init__(self, filepath: Union[str, Path], binary=False):
         self.filepath = Path(filepath)
         self.binary = binary
 
-    def to_mask(self, h, w, pad_dim=True):
+    def to_mask(self, h, w):
         # TODO: convert the 255 masks
         mask = open_img(self.filepath, gray=True)
-
-        # If the dimensions provided in h and w do not match the size of the mask, resize the mask accordingly
-        (w_org, h_org) = mask.size
-
-        # TODO: Check NEAREST is always the best option or only for binary?
-        if w_org != w or h_org != h:
-            mask = mask.resize((w, h), resample=PIL.Image.NEAREST)
-
         # HACK: because open_img now return PIL
         mask = np.array(mask)
 
         # convert 255 pixels to 1
         if self.binary:
             mask[mask == 255] = 1
 
-        # control array padding behaviour
-        return MaskArray(mask, pad_dim=pad_dim)
+        return MaskArray(mask[None])
 
     def to_coco_rle(self, h, w) -> List[dict]:
         raise NotImplementedError

icevision/metrics/__init__.py

@@ -1,6 +1,3 @@
 from icevision.metrics.metric import *
 from icevision.metrics.coco_metric import *
 from icevision.metrics.confusion_matrix import *
-from icevision.metrics.segmentation_accuracy import *
-from icevision.metrics.dice_coefficient import *
-from icevision.metrics.jaccard_index import *

icevision/metrics/dice_coefficient/__init__.py

@@ -30,5 +30,3 @@
 
 if SoftDependencies.sahi:
     from icevision.models import inference_sahi
-if SoftDependencies.mmseg:
-    from icevision.models import mmseg