Upload configuration_spice_cnn.py with huggingface_hub

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