The code below was taken from here:
\n enum\n inspect\nfrom typing Iterable, List, Optional, , \n\ndef verify_out_features_out_indices(\n out_features: Optional[Iterable[str]], out_indices: Optional[Iterable[int]], stage_names: Optional[Iterable[str]]\n):\n \n stage_names is None:\n raise ValueError()\n\n out_features is not None:\n not isinstance(out_features, (list,)):\n raise ValueError()\n any(feat not stage_names feat out_features):\n raise ValueError()\n\n out_indices is not None:\n not isinstance(out_indices, (list, tuple)):\n raise ValueError()\n any(idx >= len(stage_names) idx out_indices):\n raise ValueError()\n\n out_features is not None and out_indices is not None:\n len(out_features) != len(out_indices):\n raise ValueError()\n out_features != [stage_names[idx] idx out_indices]:\n raise ValueError()\n\ndef _align_output_features_output_indices(\n out_features: Optional[List[str]],\n out_indices: Optional[[List[int], [int]]],\n stage_names: List[str],\n):\n \n out_indices is None and out_features is None:\n out_indices = [len(stage_names) - ]\n out_features = [stage_names[-]]\n elif out_indices is None and out_features is not None:\n out_indices = [stage_names.index(layer) layer out_features]\n elif out_features is None and out_indices is not None:\n out_features = [stage_names[idx] idx out_indices]\n out_features, out_indices\n\ndef get_aligned_output_features_output_indices(\n out_features: Optional[List[str]],\n out_indices: Optional[[List[int], [int]]],\n stage_names: List[str],\n) -> [List[str], List[int]]:\n \n \n verify_out_features_out_indices(out_features=out_features, out_indices=out_indices, stage_names=stage_names)\n output_features, output_indices = _align_output_features_output_indices(\n out_features=out_features, out_indices=out_indices, stage_names=stage_names\n )\n \n verify_out_features_out_indices(out_features=output_features, out_indices=output_indices, stage_names=stage_names)\n output_features, output_indices\n\nclass BackboneConfigMixin:\n \n\n \n def out_features(self):\n self._out_features\n\n .setter\n def out_features(self, out_features: List[str]):\n \n self._out_features, self._out_indices = get_aligned_output_features_output_indices(\n out_features=out_features, out_indices=None, stage_names=self.stage_names\n )\n\n \n def out_indices(self):\n self._out_indices\n\n .setter\n def out_indices(self, out_indices: [[int], List[int]]):\n \n self._out_features, self._out_indices = get_aligned_output_features_output_indices(\n out_features=None, out_indices=out_indices, stage_names=self.stage_names\n )\n\n def to_dict(self):\n \n output = super().to_dict()\n output[] = output.pop()\n output[] = output.pop()\n output\nUsing this, I wanted to transformers.ConvNextV2Model, I wanted to try reasoning with it. (Sorry. Convnextv1-small was used in the last competition). The code above is used for the Config of ConvNextV2Model(for some reason, from transformers import ConvNeXTV2Config was not possible.). Continued below:
from transformers import AutoImageProcessor, ConvNextV2Model\nimport torch\nfrom PIL import Image\n\nimage = ('competition data/converted_train_images///png') # Please change accordingly.\nprint('image.mode is', image.mode)\n\nimage_processor = from\nconvnextv2config = \nmodel = .from\n\ninputs = image\n\n torch.no:\n outputs = model(**inputs)\nimage.mode is L\n---------------------------------------------------------------------------\nValueError Traceback (most recent call last)\nCell In, line \n image_processor = from\n model = .from\n---> inputs = image\n torch.no:\n outputs = model(**inputs)\n\nFile c:\\Users\\x\\anaconda3\\lib\\site-packages\\transformers\\image_processing_utils.py:, self, images, **kwargs)\n def -> BatchFeature:\n \n--> return self.preprocess(images, **kwargs)\n\nFile c:\\Users\\x\\anaconda3\\lib\\site-packages\\transformers\\models\\convnext\\image_processing_convnext.py:, preprocess(self, images, do_resize, size, crop_pct, resample, do_rescale, rescale_factor, do_normalize, image_mean, image_std, return_tensors, data_format, input_data_format, **kwargs)\n images = \n input_data_format is None:\n # We assume that all images have the same channel dimension format.\n--> input_data_format = infer\n do_resize:\n images = num_channels:\n return ChannelDimension.FIRST\n\nValueError: Unsupported number image dimensions: \nWhy 2? What should I do?
", "messages": [ { "id": 2413761, "postDate": "2023-08-29T06:28:04.727Z", "content": "\n\nimage.mode is L
\n
You are reading the image with a single-channel i.e. greyscale. Read the image as RGB.
\nImage.().()\nCurrent image shape is (h, w), required is (h, w, 3) or (3, h, w) (depending on the model).
", "rawMarkdown": "> image.mode is L\n\nYou are reading the image with a single-channel i.e. greyscale. Read the image as RGB.\n```\nImage.open('path.png').convert('RGB')\n```\nCurrent image shape is (h, w), required is (h, w, 3) or (3, h, w) (depending on the model).", "votes": 3, "replies": [ { "id": 2414480, "postDate": "2023-08-29T17:04:51.423Z", "content": "Thanks, I should have converted the image. Now it works.
", "rawMarkdown": "Thanks, I should have converted the image. Now it works." } ] }, { "id": 2413573, "postDate": "2023-08-29T02:48:10.850Z", "content": "The code below was taken from here:
\n enum\n inspect\nfrom typing Iterable, List, Optional, , \n\ndef verify_out_features_out_indices(\n out_features: Optional[Iterable[str]], out_indices: Optional[Iterable[int]], stage_names: Optional[Iterable[str]]\n):\n \n stage_names is None:\n raise ValueError()\n\n out_features is not None:\n not isinstance(out_features, (list,)):\n raise ValueError()\n any(feat not stage_names feat out_features):\n raise ValueError()\n\n out_indices is not None:\n not isinstance(out_indices, (list, tuple)):\n raise ValueError()\n any(idx >= len(stage_names) idx out_indices):\n raise ValueError()\n\n out_features is not None and out_indices is not None:\n len(out_features) != len(out_indices):\n raise ValueError()\n out_features != [stage_names[idx] idx out_indices]:\n raise ValueError()\n\ndef _align_output_features_output_indices(\n out_features: Optional[List[str]],\n out_indices: Optional[[List[int], [int]]],\n stage_names: List[str],\n):\n \n out_indices is None and out_features is None:\n out_indices = [len(stage_names) - ]\n out_features = [stage_names[-]]\n elif out_indices is None and out_features is not None:\n out_indices = [stage_names.index(layer) layer out_features]\n elif out_features is None and out_indices is not None:\n out_features = [stage_names[idx] idx out_indices]\n out_features, out_indices\n\ndef get_aligned_output_features_output_indices(\n out_features: Optional[List[str]],\n out_indices: Optional[[List[int], [int]]],\n stage_names: List[str],\n) -> [List[str], List[int]]:\n \n \n verify_out_features_out_indices(out_features=out_features, out_indices=out_indices, stage_names=stage_names)\n output_features, output_indices = _align_output_features_output_indices(\n out_features=out_features, out_indices=out_indices, stage_names=stage_names\n )\n \n verify_out_features_out_indices(out_features=output_features, out_indices=output_indices, stage_names=stage_names)\n output_features, output_indices\n\nclass BackboneConfigMixin:\n \n\n \n def out_features(self):\n self._out_features\n\n .setter\n def out_features(self, out_features: List[str]):\n \n self._out_features, self._out_indices = get_aligned_output_features_output_indices(\n out_features=out_features, out_indices=None, stage_names=self.stage_names\n )\n\n \n def out_indices(self):\n self._out_indices\n\n .setter\n def out_indices(self, out_indices: [[int], List[int]]):\n \n self._out_features, self._out_indices = get_aligned_output_features_output_indices(\n out_features=None, out_indices=out_indices, stage_names=self.stage_names\n )\n\n def to_dict(self):\n \n output = super().to_dict()\n output[] = output.pop()\n output[] = output.pop()\n output\nUsing this, I wanted to transformers.ConvNextV2Model, I wanted to try reasoning with it. (Sorry. Convnextv1-small was used in the last competition). The code above is used for the Config of ConvNextV2Model(for some reason, from transformers import ConvNeXTV2Config was not possible.). Continued below:
from transformers import AutoImageProcessor, ConvNextV2Model\nimport torch\nfrom PIL import Image\n\nimage = ('competition data/converted_train_images///png') # Please change accordingly.\nprint('image.mode is', image.mode)\n\nimage_processor = from\nconvnextv2config = \nmodel = .from\n\ninputs = image\n\n torch.no:\n outputs = model(**inputs)\nimage.mode is L\n---------------------------------------------------------------------------\nValueError Traceback (most recent call last)\nCell In, line \n image_processor = from\n model = .from\n---> inputs = image\n torch.no:\n outputs = model(**inputs)\n\nFile c:\\Users\\x\\anaconda3\\lib\\site-packages\\transformers\\image_processing_utils.py:, self, images, **kwargs)\n def -> BatchFeature:\n \n--> return self.preprocess(images, **kwargs)\n\nFile c:\\Users\\x\\anaconda3\\lib\\site-packages\\transformers\\models\\convnext\\image_processing_convnext.py:, preprocess(self, images, do_resize, size, crop_pct, resample, do_rescale, rescale_factor, do_normalize, image_mean, image_std, return_tensors, data_format, input_data_format, **kwargs)\n images = \n input_data_format is None:\n # We assume that all images have the same channel dimension format.\n--> input_data_format = infer\n do_resize:\n images = num_channels:\n return ChannelDimension.FIRST\n\nValueError: Unsupported number image dimensions: \nWhy 2? What should I do?
", "rawMarkdown": "# Source Code\n____\nThe code below was taken from [here](https://github.com/huggingface/transformers/blob/main/src/transformers/utils/backbone_utils.py#L18):\n```\nimport enum\nimport inspect\nfrom typing import Iterable, List, Optional, Tuple, Union\n\ndef verify_out_features_out_indices(\n out_features: Optional[Iterable[str]], out_indices: Optional[Iterable[int]], stage_names: Optional[Iterable[str]]\n):\n \"\"\"\n Verify that out_indices and out_features are valid for the given stage_names.\n \"\"\"\n if stage_names is None:\n raise ValueError(\"Stage_names must be set for transformers backbones\")\n\n if out_features is not None:\n if not isinstance(out_features, (list,)):\n raise ValueError(f\"out_features must be a list {type(out_features)}\")\n if any(feat not in stage_names for feat in out_features):\n raise ValueError(f\"out_features must be a subset of stage_names: {stage_names} got {out_features}\")\n\n if out_indices is not None:\n if not isinstance(out_indices, (list, tuple)):\n raise ValueError(f\"out_indices must be a list or tuple, got {type(out_indices)}\")\n if any(idx >= len(stage_names) for idx in out_indices):\n raise ValueError(\"out_indices must be valid indices for stage_names {stage_names}, got {out_indices}\")\n\n if out_features is not None and out_indices is not None:\n if len(out_features) != len(out_indices):\n raise ValueError(\"out_features and out_indices should have the same length if both are set\")\n if out_features != [stage_names[idx] for idx in out_indices]:\n raise ValueError(\"out_features and out_indices should correspond to the same stages if both are set\")\n\ndef _align_output_features_output_indices(\n out_features: Optional[List[str]],\n out_indices: Optional[Union[List[int], Tuple[int]]],\n stage_names: List[str],\n):\n \"\"\"\n Finds the corresponding `out_features` and `out_indices` for the given `stage_names`.\n\n The logic is as follows:\n - `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the\n `out_indices`.\n - `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the\n `out_features`.\n - `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.\n - `out_indices` and `out_features` set: input `out_indices` and `out_features` are returned.\n\n Args:\n out_features (`List[str]`): The names of the features for the backbone to output.\n out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.\n stage_names (`List[str]`): The names of the stages of the backbone.\n \"\"\"\n if out_indices is None and out_features is None:\n out_indices = [len(stage_names) - 1]\n out_features = [stage_names[-1]]\n elif out_indices is None and out_features is not None:\n out_indices = [stage_names.index(layer) for layer in out_features]\n elif out_features is None and out_indices is not None:\n out_features = [stage_names[idx] for idx in out_indices]\n return out_features, out_indices\n\ndef get_aligned_output_features_output_indices(\n out_features: Optional[List[str]],\n out_indices: Optional[Union[List[int], Tuple[int]]],\n stage_names: List[str],\n) -> Tuple[List[str], List[int]]:\n \"\"\"\n Get the `out_features` and `out_indices` so that they are aligned.\n\n The logic is as follows:\n - `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the\n `out_indices`.\n - `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the\n `out_features`.\n - `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.\n - `out_indices` and `out_features` set: they are verified to be aligned.\n\n Args:\n out_features (`List[str]`): The names of the features for the backbone to output.\n out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.\n stage_names (`List[str]`): The names of the stages of the backbone.\n \"\"\"\n # First verify that the out_features and out_indices are valid\n verify_out_features_out_indices(out_features=out_features, out_indices=out_indices, stage_names=stage_names)\n output_features, output_indices = _align_output_features_output_indices(\n out_features=out_features, out_indices=out_indices, stage_names=stage_names\n )\n # Verify that the aligned out_features and out_indices are valid\n verify_out_features_out_indices(out_features=output_features, out_indices=output_indices, stage_names=stage_names)\n return output_features, output_indices\n\nclass BackboneConfigMixin:\n \"\"\"\n A Mixin to support handling the `out_features` and `out_indices` attributes for the backbone configurations.\n \"\"\"\n\n @property\n def out_features(self):\n return self._out_features\n\n @out_features.setter\n def out_features(self, out_features: List[str]):\n \"\"\"\n Set the out_features attribute. This will also update the out_indices attribute to match the new out_features.\n \"\"\"\n self._out_features, self._out_indices = get_aligned_output_features_output_indices(\n out_features=out_features, out_indices=None, stage_names=self.stage_names\n )\n\n @property\n def out_indices(self):\n return self._out_indices\n\n @out_indices.setter\n def out_indices(self, out_indices: Union[Tuple[int], List[int]]):\n \"\"\"\n Set the out_indices attribute. This will also update the out_features attribute to match the new out_indices.\n \"\"\"\n self._out_features, self._out_indices = get_aligned_output_features_output_indices(\n out_features=None, out_indices=out_indices, stage_names=self.stage_names\n )\n\n def to_dict(self):\n \"\"\"\n Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig` to\n include the `out_features` and `out_indices` attributes.\n \"\"\"\n output = super().to_dict()\n output[\"out_features\"] = output.pop(\"_out_features\")\n output[\"out_indices\"] = output.pop(\"_out_indices\")\n return output\n```\n\nUsing this, I wanted to [transformers.ConvNextV2Model](https://huggingface.co/docs/transformers/model_doc/convnextv2#transformers.ConvNextV2Model ), I wanted to try reasoning with it. ~~This model was used in the 1st solution of the last RSNA competition~~(Sorry. Convnextv1-small was used in the last competition). The code above is used for the Config of ConvNextV2Model(for some reason, `from transformers import ConvNeXTV2Config` was not possible.). Continued below:\n\n```\nfrom transformers import AutoImageProcessor, ConvNextV2Model\nimport torch\nfrom PIL import Image\n\nimage = Image.open('competition data/converted_train_images/19/14374/0000.png') # Please change accordingly.\nprint('image.mode is', image.mode)\n\nimage_processor = AutoImageProcessor.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\nconvnextv2config = ConvNextV2Config(num_channels=1)\nmodel = ConvNextV2Model(convnextv2config).from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n\ninputs = image_processor(image, return_tensors=\"pt\")\n\nwith torch.no_grad():\n outputs = model(**inputs)\n```\n\n# Output\n___\n```\nimage.mode is L\n---------------------------------------------------------------------------\nValueError Traceback (most recent call last)\nCell In[7], line 14\n 11 image_processor = AutoImageProcessor.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n 12 model = ConvNextV2Model(convnextv2config).from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n---> 14 inputs = image_processor(image, return_tensors=\"pt\")\n 16 with torch.no_grad():\n 17 outputs = model(**inputs)\n\nFile c:\\Users\\x\\anaconda3\\lib\\site-packages\\transformers\\image_processing_utils.py:546, in BaseImageProcessor.__call__(self, images, **kwargs)\n 544 def __call__(self, images, **kwargs) -> BatchFeature:\n 545 \"\"\"Preprocess an image or a batch of images.\"\"\"\n--> 546 return self.preprocess(images, **kwargs)\n\nFile c:\\Users\\x\\anaconda3\\lib\\site-packages\\transformers\\models\\convnext\\image_processing_convnext.py:285, in ConvNextImageProcessor.preprocess(self, images, do_resize, size, crop_pct, resample, do_rescale, rescale_factor, do_normalize, image_mean, image_std, return_tensors, data_format, input_data_format, **kwargs)\n 281 images = [to_numpy_array(image) for image in images]\n 283 if input_data_format is None:\n 284 # We assume that all images have the same channel dimension format.\n--> 285 input_data_format = infer_channel_dimension_format(images[0])\n 287 if do_resize:\n 288 images = [\n 289 self.resize(\n 290 image=image, size=size, crop_pct=crop_pct, resample=resample, input_data_format=input_data_format\n 291 )\n 292 for image in images\n...\n--> 170 raise ValueError(f\"Unsupported number of image dimensions: {image.ndim}\")\n 172 if image.shape[first_dim] in num_channels:\n 173 return ChannelDimension.FIRST\n\nValueError: Unsupported number of image dimensions: 2\n```\n\nWhy 2? What should I do?" } ], "comments": [ { "id": 2413761, "author_name": "Priya Nagda", "author_url": "", "post_date": "2023-08-29T06:28:04.727000", "content": "\n\nimage.mode is L
\n
You are reading the image with a single-channel i.e. greyscale. Read the image as RGB.
\nImage.().()\nCurrent image shape is (h, w), required is (h, w, 3) or (3, h, w) (depending on the model).
", "votes": 3, "replies": [ { "id": 2414480, "author_name": "Ataracsia", "author_url": "", "post_date": "2023-08-29T17:04:51.423000", "content": "Thanks, I should have converted the image. Now it works.
", "votes": 0, "replies": [] } ] } ], "raw_markdown_by_id": { "2413761": "> image.mode is L\n\nYou are reading the image with a single-channel i.e. greyscale. Read the image as RGB.\n```\nImage.open('path.png').convert('RGB')\n```\nCurrent image shape is (h, w), required is (h, w, 3) or (3, h, w) (depending on the model).", "2413573": "# Source Code\n____\nThe code below was taken from [here](https://github.com/huggingface/transformers/blob/main/src/transformers/utils/backbone_utils.py#L18):\n```\nimport enum\nimport inspect\nfrom typing import Iterable, List, Optional, Tuple, Union\n\ndef verify_out_features_out_indices(\n out_features: Optional[Iterable[str]], out_indices: Optional[Iterable[int]], stage_names: Optional[Iterable[str]]\n):\n \"\"\"\n Verify that out_indices and out_features are valid for the given stage_names.\n \"\"\"\n if stage_names is None:\n raise ValueError(\"Stage_names must be set for transformers backbones\")\n\n if out_features is not None:\n if not isinstance(out_features, (list,)):\n raise ValueError(f\"out_features must be a list {type(out_features)}\")\n if any(feat not in stage_names for feat in out_features):\n raise ValueError(f\"out_features must be a subset of stage_names: {stage_names} got {out_features}\")\n\n if out_indices is not None:\n if not isinstance(out_indices, (list, tuple)):\n raise ValueError(f\"out_indices must be a list or tuple, got {type(out_indices)}\")\n if any(idx >= len(stage_names) for idx in out_indices):\n raise ValueError(\"out_indices must be valid indices for stage_names {stage_names}, got {out_indices}\")\n\n if out_features is not None and out_indices is not None:\n if len(out_features) != len(out_indices):\n raise ValueError(\"out_features and out_indices should have the same length if both are set\")\n if out_features != [stage_names[idx] for idx in out_indices]:\n raise ValueError(\"out_features and out_indices should correspond to the same stages if both are set\")\n\ndef _align_output_features_output_indices(\n out_features: Optional[List[str]],\n out_indices: Optional[Union[List[int], Tuple[int]]],\n stage_names: List[str],\n):\n \"\"\"\n Finds the corresponding `out_features` and `out_indices` for the given `stage_names`.\n\n The logic is as follows:\n - `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the\n `out_indices`.\n - `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the\n `out_features`.\n - `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.\n - `out_indices` and `out_features` set: input `out_indices` and `out_features` are returned.\n\n Args:\n out_features (`List[str]`): The names of the features for the backbone to output.\n out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.\n stage_names (`List[str]`): The names of the stages of the backbone.\n \"\"\"\n if out_indices is None and out_features is None:\n out_indices = [len(stage_names) - 1]\n out_features = [stage_names[-1]]\n elif out_indices is None and out_features is not None:\n out_indices = [stage_names.index(layer) for layer in out_features]\n elif out_features is None and out_indices is not None:\n out_features = [stage_names[idx] for idx in out_indices]\n return out_features, out_indices\n\ndef get_aligned_output_features_output_indices(\n out_features: Optional[List[str]],\n out_indices: Optional[Union[List[int], Tuple[int]]],\n stage_names: List[str],\n) -> Tuple[List[str], List[int]]:\n \"\"\"\n Get the `out_features` and `out_indices` so that they are aligned.\n\n The logic is as follows:\n - `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the\n `out_indices`.\n - `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the\n `out_features`.\n - `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.\n - `out_indices` and `out_features` set: they are verified to be aligned.\n\n Args:\n out_features (`List[str]`): The names of the features for the backbone to output.\n out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.\n stage_names (`List[str]`): The names of the stages of the backbone.\n \"\"\"\n # First verify that the out_features and out_indices are valid\n verify_out_features_out_indices(out_features=out_features, out_indices=out_indices, stage_names=stage_names)\n output_features, output_indices = _align_output_features_output_indices(\n out_features=out_features, out_indices=out_indices, stage_names=stage_names\n )\n # Verify that the aligned out_features and out_indices are valid\n verify_out_features_out_indices(out_features=output_features, out_indices=output_indices, stage_names=stage_names)\n return output_features, output_indices\n\nclass BackboneConfigMixin:\n \"\"\"\n A Mixin to support handling the `out_features` and `out_indices` attributes for the backbone configurations.\n \"\"\"\n\n @property\n def out_features(self):\n return self._out_features\n\n @out_features.setter\n def out_features(self, out_features: List[str]):\n \"\"\"\n Set the out_features attribute. This will also update the out_indices attribute to match the new out_features.\n \"\"\"\n self._out_features, self._out_indices = get_aligned_output_features_output_indices(\n out_features=out_features, out_indices=None, stage_names=self.stage_names\n )\n\n @property\n def out_indices(self):\n return self._out_indices\n\n @out_indices.setter\n def out_indices(self, out_indices: Union[Tuple[int], List[int]]):\n \"\"\"\n Set the out_indices attribute. This will also update the out_features attribute to match the new out_indices.\n \"\"\"\n self._out_features, self._out_indices = get_aligned_output_features_output_indices(\n out_features=None, out_indices=out_indices, stage_names=self.stage_names\n )\n\n def to_dict(self):\n \"\"\"\n Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig` to\n include the `out_features` and `out_indices` attributes.\n \"\"\"\n output = super().to_dict()\n output[\"out_features\"] = output.pop(\"_out_features\")\n output[\"out_indices\"] = output.pop(\"_out_indices\")\n return output\n```\n\nUsing this, I wanted to [transformers.ConvNextV2Model](https://huggingface.co/docs/transformers/model_doc/convnextv2#transformers.ConvNextV2Model ), I wanted to try reasoning with it. ~~This model was used in the 1st solution of the last RSNA competition~~(Sorry. Convnextv1-small was used in the last competition). The code above is used for the Config of ConvNextV2Model(for some reason, `from transformers import ConvNeXTV2Config` was not possible.). Continued below:\n\n```\nfrom transformers import AutoImageProcessor, ConvNextV2Model\nimport torch\nfrom PIL import Image\n\nimage = Image.open('competition data/converted_train_images/19/14374/0000.png') # Please change accordingly.\nprint('image.mode is', image.mode)\n\nimage_processor = AutoImageProcessor.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\nconvnextv2config = ConvNextV2Config(num_channels=1)\nmodel = ConvNextV2Model(convnextv2config).from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n\ninputs = image_processor(image, return_tensors=\"pt\")\n\nwith torch.no_grad():\n outputs = model(**inputs)\n```\n\n# Output\n___\n```\nimage.mode is L\n---------------------------------------------------------------------------\nValueError Traceback (most recent call last)\nCell In[7], line 14\n 11 image_processor = AutoImageProcessor.from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n 12 model = ConvNextV2Model(convnextv2config).from_pretrained(\"facebook/convnextv2-tiny-1k-224\")\n---> 14 inputs = image_processor(image, return_tensors=\"pt\")\n 16 with torch.no_grad():\n 17 outputs = model(**inputs)\n\nFile c:\\Users\\x\\anaconda3\\lib\\site-packages\\transformers\\image_processing_utils.py:546, in BaseImageProcessor.__call__(self, images, **kwargs)\n 544 def __call__(self, images, **kwargs) -> BatchFeature:\n 545 \"\"\"Preprocess an image or a batch of images.\"\"\"\n--> 546 return self.preprocess(images, **kwargs)\n\nFile c:\\Users\\x\\anaconda3\\lib\\site-packages\\transformers\\models\\convnext\\image_processing_convnext.py:285, in ConvNextImageProcessor.preprocess(self, images, do_resize, size, crop_pct, resample, do_rescale, rescale_factor, do_normalize, image_mean, image_std, return_tensors, data_format, input_data_format, **kwargs)\n 281 images = [to_numpy_array(image) for image in images]\n 283 if input_data_format is None:\n 284 # We assume that all images have the same channel dimension format.\n--> 285 input_data_format = infer_channel_dimension_format(images[0])\n 287 if do_resize:\n 288 images = [\n 289 self.resize(\n 290 image=image, size=size, crop_pct=crop_pct, resample=resample, input_data_format=input_data_format\n 291 )\n 292 for image in images\n...\n--> 170 raise ValueError(f\"Unsupported number of image dimensions: {image.ndim}\")\n 172 if image.shape[first_dim] in num_channels:\n 173 return ChannelDimension.FIRST\n\nValueError: Unsupported number of image dimensions: 2\n```\n\nWhy 2? What should I do?" } }