Hi!
I am following this tutorial about MaskRCNN:
Training Mask R-CNN Models with PyTorch – Christian Mills
I executed all blocks from the beginning and followed the instructions step by step.
However, if I reach the block entitled " Test the transforms" which reads
# Extract the labels for the sample
labels = [shape['label'] for shape in annotation_df.loc[file_id]['shapes']]
# Extract the polygon points for segmentation mask
shape_points = [shape['points'] for shape in annotation_df.loc[file_id]['shapes']]
# Format polygon points for PIL
xy_coords = [[tuple(p) for p in points] for points in shape_points]
# Generate mask images from polygons
mask_imgs = [create_polygon_mask(sample_img.size, xy) for xy in xy_coords]
# Convert mask images to tensors
masks = torch.concat([Mask(transforms.PILToTensor()(mask_img), dtype=torch.bool) for mask_img in mask_imgs])
# Generate bounding box annotations from segmentation masks
bboxes = BoundingBoxes(data=torchvision.ops.masks_to_boxes(masks), format='xyxy', canvas_size=sample_img.size[::-1])
# Get colors for dataset sample
sample_colors = [int_colors[i] for i in [class_names.index(label) for label in labels]]
# Prepare mask and bounding box targets
targets = {
'masks': Mask(masks),
'boxes': bboxes,
'labels': torch.Tensor([class_names.index(label) for label in labels])
}
# Crop the image
cropped_img, targets = iou_crop(sample_img, targets)
# Resize the image
resized_img, targets = resize_max(cropped_img, targets)
# Pad the image
padded_img, targets = pad_square(resized_img, targets)
# Ensure the padded image is the target size
resize = transforms.Resize([train_sz] * 2, antialias=True)
resized_padded_img, targets = resize(padded_img, targets)
sanitized_img, targets = transforms.SanitizeBoundingBoxes()(resized_padded_img, targets)
# Annotate the sample image with segmentation masks
annotated_tensor = draw_segmentation_masks(
image=transforms.PILToTensor()(sanitized_img),
masks=targets['masks'],
alpha=0.3,
colors=sample_colors
)
# Annotate the sample image with labels and bounding boxes
annotated_tensor = draw_bboxes(
image=annotated_tensor,
boxes=targets['boxes'],
labels=[class_names[int(label.item())] for label in targets['labels']],
colors=sample_colors
)
# # Display the annotated image
display(tensor_to_pil(annotated_tensor))
pd.Series({
"Source Image:": sample_img.size,
"Cropped Image:": cropped_img.size,
"Resized Image:": resized_img.size,
"Padded Image:": padded_img.size,
"Resized Padded Image:": resized_padded_img.size,
}).to_frame().style.hide(axis='columns')
I am running into the following error:
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call
last)
Cell In[33], line 28
25 cropped_img, targets = iou_crop(sample_img, targets)
27 # Resize the image
---> 28 resized_img, targets = resize_max(cropped_img, targets)
30 # Pad the image
31 padded_img, targets = pad_square(resized_img, targets)
File
~\anaconda3\envs\test-env\lib\site-packages\torch\nn\modules\module.py:1739,
in Module._wrapped_call_impl(self, *args, **kwargs)
1737 return self._compiled_call_impl(*args, **kwargs) # type:
ignore[misc]
1738 else:
-> 1739 return self._call_impl(*args, **kwargs)
File
~\anaconda3\envs\test-env\lib\site-packages\torch\nn\modules\module.py:1750,
in Module._call_impl(self, *args, **kwargs)
1745 # If we don't have any hooks, we want to skip the rest of the logic in
1746 # this function, and just call forward.
1747 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1748 or _global_backward_pre_hooks or _global_backward_hooks
1749 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1750 return forward_call(*args, **kwargs)
1752 result = None
1753 called_always_called_hooks = set()
File
~\anaconda3\envs\test-env\lib\site-packages\torchvision\transforms\v2\_transform.py:68,
in Transform.forward(self, *inputs)
63 needs_transform_list = self._needs_transform_list(flat_inputs)
64 params = self.make_params(
65 [inpt for (inpt, needs_transform) in zip(flat_inputs,
needs_transform_list) if needs_transform]
66 )
---> 68 flat_outputs = [
69 self.transform(inpt, params) if needs_transform else inpt
70 for (inpt, needs_transform) in zip(flat_inputs,
needs_transform_list)
71 ]
73 return tree_unflatten(flat_outputs, spec)
File
~\anaconda3\envs\test-env\lib\site-packages\torchvision\transforms\v2\_transform.py:69,
in <listcomp>(.0)
63 needs_transform_list = self._needs_transform_list(flat_inputs)
64 params = self.make_params(
65 [inpt for (inpt, needs_transform) in zip(flat_inputs,
needs_transform_list) if needs_transform]
66 )
68 flat_outputs = [
---> 69 self.transform(inpt, params) if needs_transform else inpt
70 for (inpt, needs_transform) in zip(flat_inputs,
needs_transform_list)
71 ]
73 return tree_unflatten(flat_outputs, spec)
File
~\anaconda3\envs\test-env\lib\site-packages\torchvision\transforms\v2\_transform.py:55,
in Transform.transform(self, inpt, params)
51 def transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
52 """Method to override for custom transforms.
53
54 See
:ref:`sphx_glr_auto_examples_transforms_plot_custom_transforms.py`"""
---> 55 raise NotImplementedError
NotImplementedError:
I cannot manage to solve this problem which seems to be related to torchvision.transforms.v2._transform which contains the following:
def transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
"""Method to override for custom transforms.
See :ref:`sphx_glr_auto_examples_transforms_plot_custom_transforms.py`"""
raise NotImplementedError
Do you have an idea?
Any help is appreciated!