Upload modeling_spice_cnn.py with huggingface_hub

modeling_spice_cnn.py

@@ -0,0 +1,49 @@
+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))