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import torch import torch.nn as nn import torch.onnx # Define a deep convolutional neural network class DeepConvNet(nn.Module): def __init__(self, in_channels=3, num_classes=10): super(DeepConvNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1), # Layer 1 nn.ReLU(), nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1), # Layer 2 nn.ReLU(), nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1), # Layer 3 nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), # Layer 4 (Pooling) nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1), # Layer 5 nn.ReLU(), nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), # Layer 6 nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2) # Layer 7 (Pooling) ) self.classifier = nn.Sequential( nn.Flatten(), nn.Linear(512 * 16 * 16, 1024), # Fully connected layer nn.ReLU(), nn.Linear(1024, num_classes) # Output layer ) def forward(self, x): x = self.features(x) x = self.classifier(x) return x # Initialize the model model = DeepConvNet(in_channels=3, num_classes=10) model.eval() # Set to evaluation mode # Dummy input for ONNX export (batch size 1, 3 channels, 64x64 image) dummy_input = torch.randn(1, 3, 64, 64) # Path to save the ONNX model onnx_path = "deep_conv_net.onnx" # Export the model to ONNX torch.onnx.export( model, # Model to export dummy_input, # Example input onnx_path, # Path to save the ONNX file export_params=True, # Store trained parameter weights inside the model opset_version=11, # ONNX opset version input_names=["input"], # Input tensor name output_names=["output"] # Output tensor name ) print(f"Deep CNN model has been converted to ONNX and saved to {onnx_path}")
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