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- b6403f046c358030be646b54412c110bf9eea4e98ad2c933bae66b742dca3638 (11201d88326d8df379de18609eca1dc7c2c4038f)

.gitattributes

@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+---

config.json

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+{
+  "architectures": [
+    "SpiceCNNModelForImageClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_spice_cnn.SpiceCNNConfig",
+    "AutoModelForImageClassification": "modeling_spice_cnn.SpiceCNNModelForImageClassification"
+  },
+  "dropout_rate": 0.4,
+  "hidden_size": 128,
+  "in_channels": 1,
+  "kernel_size": 3,
+  "model_type": "spicecnn",
+  "num_classes": 10,
+  "num_filters": 16,
+  "padding": 1,
+  "pooling_size": 2,
+  "stride": 1,
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2"
+}

configuration_spice_cnn.py

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+from transformers import PretrainedConfig
+
+"""Spice CNN model configuration"""
+
+SPICE_CNN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "spicecloud/spice-cnn-base": "https://huggingface.co/spice-cnn-base/resolve/main/config.json"
+}
+
+
+# Define custom convnet configuration
+class SpiceCNNConfig(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`SpiceCNNModel`].
+    It is used to instantiate an SpiceCNN model according to the specified arguments,
+    defining the model architecture. Instantiating a configuration with the defaults
+    will yield a similar configuration to that of the SpiceCNN
+    [spicecloud/spice-cnn-base](https://huggingface.co/spicecloud/spice-cnn-base)
+    architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control
+    the model outputs. Read the documentation from [`PretrainedConfig`] for more
+    information.
+    """
+
+    model_type = "spicecnn"
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        num_classes: int = 10,
+        dropout_rate: float = 0.4,
+        hidden_size: int = 128,
+        num_filters: int = 16,
+        kernel_size: int = 3,
+        stride: int = 1,
+        padding: int = 1,
+        pooling_size: int = 2,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+        self.dropout_rate = dropout_rate
+        self.hidden_size = hidden_size
+        self.num_filters = num_filters
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.pooling_size = pooling_size

image_processing_spice_cnn.py

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+from typing import Dict, List, Optional, Union
+
+import numpy as np
+
+from transformers.image_processing_utils import (
+    BaseImageProcessor,
+    BatchFeature,
+    get_size_dict,
+)
+from transformers.image_transforms import (
+    normalize,
+    rescale,
+    resize,
+    to_channel_dimension_format,
+)
+from transformers.image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    make_list_of_images,
+    to_numpy_array,
+    valid_images,
+)
+from transformers.utils import TensorType
+
+
+class SpiceCNNImageProcessor(BaseImageProcessor):
+    """
+    Constructs a SpiceCNN image processor.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
+            size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
+        size (`dict`, *optional*, defaults to `{"height": 224, "width": 224}`):
+            Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
+            method.
+        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
+            `preprocess` method.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+            parameter in the `preprocess` method.
+        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
+            `preprocess` method.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+            method.
+        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+    """  # noqa
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: Optional[Dict[str, int]] = None,
+        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        size = size if size is not None else {"height": 224, "width": 224}
+        size = get_size_dict(size)
+        self.do_resize = do_resize
+        self.do_rescale = do_rescale
+        self.do_normalize = do_normalize
+        self.size = size
+        self.resample = resample
+        self.rescale_factor = rescale_factor
+        self.image_mean = (
+            image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+        )
+        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+
+    def resize(
+        self,
+        image: np.ndarray,
+        size: Dict[str, int],
+        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Resize an image to `(size["height"], size["width"])`.
+
+        Args:
+            image (`np.ndarray`):
+                Image to resize.
+            size (`Dict[str, int]`):
+                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
+            resample:
+                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
+            data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the output image. If unset, the channel dimension format of the input
+                image is used. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+
+        Returns:
+            `np.ndarray`: The resized image.
+        """  # noqa
+        size = get_size_dict(size)
+        if "height" not in size or "width" not in size:
+            raise ValueError(
+                f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"  # noqa
+            )
+        return resize(
+            image,
+            size=(size["height"], size["width"]),
+            resample=resample,
+            data_format=data_format,
+            **kwargs,
+        )
+
+    def rescale(
+        self,
+        image: np.ndarray,
+        scale: float,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Rescale an image by a scale factor. image = image * scale.
+
+        Args:
+            image (`np.ndarray`):
+                Image to rescale.
+            scale (`float`):
+                The scaling factor to rescale pixel values by.
+            data_format (`str` or `ChannelDimension`, *optional*):
+                The channel dimension format for the output image. If unset, the channel dimension format of the input
+                image is used. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+
+        Returns:
+            `np.ndarray`: The rescaled image.
+        """  # noqa
+        return rescale(image, scale=scale, data_format=data_format, **kwargs)
+
+    def normalize(
+        self,
+        image: np.ndarray,
+        mean: Union[float, List[float]],
+        std: Union[float, List[float]],
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Normalize an image. image = (image - image_mean) / image_std.
+
+        Args:
+            image (`np.ndarray`):
+                Image to normalize.
+            mean (`float` or `List[float]`):
+                Image mean to use for normalization.
+            std (`float` or `List[float]`):
+                Image standard deviation to use for normalization.
+            data_format (`str` or `ChannelDimension`, *optional*):
+                The channel dimension format for the output image. If unset, the channel dimension format of the input
+                image is used. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+
+        Returns:
+            `np.ndarray`: The normalized image.
+        """  # noqa
+        return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: Optional[bool] = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_rescale: Optional[bool] = None,
+        rescale_factor: Optional[float] = None,
+        do_normalize: Optional[bool] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
+        **kwargs,
+    ):
+        """
+        Preprocess an image or batch of images.
+
+        Args:
+            images (`ImageInput`):
+                Image to preprocess.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+                Dictionary in the format `{"height": h, "width": w}` specifying the size of the output image after
+                resizing.
+            resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
+                `PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
+                an effect if `do_resize` is set to `True`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image values between [0 - 1].
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use if `do_normalize` is set to `True`.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use if `do_normalize` is set to `True`.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+        """  # noqa
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        resample = resample if resample is not None else self.resample
+        rescale_factor = (
+            rescale_factor if rescale_factor is not None else self.rescale_factor
+        )
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+
+        size = size if size is not None else self.size
+        size_dict = get_size_dict(size)
+
+        images = make_list_of_images(images)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        if do_resize and size is None:
+            raise ValueError("Size must be specified if do_resize is True.")
+
+        if do_rescale and rescale_factor is None:
+            raise ValueError("Rescale factor must be specified if do_rescale is True.")
+
+        # All transformations expect numpy arrays.
+        images = [to_numpy_array(image) for image in images]
+
+        if do_resize:
+            images = [
+                self.resize(image=image, size=size_dict, resample=resample)
+                for image in images
+            ]
+
+        if do_rescale:
+            images = [
+                self.rescale(image=image, scale=rescale_factor) for image in images
+            ]
+
+        if do_normalize:
+            images = [
+                self.normalize(image=image, mean=image_mean, std=image_std)
+                for image in images
+            ]
+
+        images = [to_channel_dimension_format(image, data_format) for image in images]
+
+        data = {"pixel_values": images}
+        return BatchFeature(data=data, tensor_type=return_tensors)

modeling_spice_cnn.py

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+import torch.nn as nn
+
+# from torchsummary import summary
+
+from transformers import PreTrainedModel
+
+from .configuration_spice_cnn import SpiceCNNConfig
+
+
+class SpiceCNNModelForImageClassification(PreTrainedModel):
+    config_class = SpiceCNNConfig
+
+    def __init__(self, config: SpiceCNNConfig):
+        super().__init__(config)
+        layers = [
+            nn.Conv2d(
+                config.in_channels, 16, kernel_size=config.kernel_size, padding=1
+            ),
+            nn.BatchNorm2d(16),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=config.pooling_size),
+            nn.Conv2d(16, 32, kernel_size=config.kernel_size, padding=1),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=config.pooling_size),
+            nn.Conv2d(32, 64, kernel_size=config.kernel_size, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=config.pooling_size),
+            nn.Flatten(),
+            nn.Linear(64 * 3 * 3, 128),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(128, config.num_classes),
+        ]
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, tensor, labels=None):
+        logits = self.model(tensor)
+        if labels is not None:
+            loss_fnc = nn.CrossEntropyLoss()
+            loss = loss_fnc(logits, labels)
+            return {"loss": loss, "logits": logits}
+        return {"logits": logits}
+
+
+# config = SpiceCNNConfig(in_channels=1)
+# cnn = SpiceCNNModelForImageClassification(config)
+# summary(cnn, (1,28,28))

preprocessor_config.json

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+{
+  "auto_map": {
+    "AutoImageProcessor": "image_processing_spice_cnn.SpiceCNNImageProcessor"
+  },
+  "do_normalize": false,
+  "do_rescale": false,
+  "do_resize": true,
+  "image_mean": 0.5,
+  "image_processor_type": "SpiceCNNImageProcessor",
+  "image_std": 0.5,
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 28,
+    "width": 28
+  },
+  "trust_remote_code": true
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:252fb83eccdd41e7c54f6e114675db448b97f6d0cd3f203235fcdec0f2285a63
+size 402812