102 lines
4.7 KiB
Python
102 lines
4.7 KiB
Python
import torch
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import torch.nn
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class MyCNN(torch.nn.Module):
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def __init__(self,
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input_channels: int,
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input_size: tuple[int, int]):
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super().__init__()
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# input_layer = torch.nn.Conv2d(in_channels=input_channels,
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# out_channels=hidden_channels[0],
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# kernel_size=kernel_size[0],
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# padding='same' if (stride[0] == 1 or stride[0] == 0) else 'valid',
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# stride=stride[0],
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# bias=not use_batchnorm)
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# hidden_layers = [torch.nn.Conv2d(hidden_channels[i - 1],
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# hidden_channels[i],
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# kernel_size[i],
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# padding='same' if (stride[i] == 1 or stride[i] == 0) else 'valid',
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# stride=stride[i],
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# bias=not use_batchnorm)
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# for i in range(1, len(hidden_channels))]
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# self.output_layer = torch.nn.Linear(hidden_channels[-1] * input_size[0] * input_size[1], output_channels)
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self.layers = torch.nn.Sequential(
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torch.nn.Conv2d(in_channels=input_channels, out_channels=64, kernel_size=3, padding='same', bias=False),
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torch.nn.BatchNorm2d(64),
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torch.nn.MaxPool2d(kernel_size=3, stride=2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding='same', bias=False),
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torch.nn.BatchNorm2d(128),
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torch.nn.MaxPool2d(kernel_size=3, stride=2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding='same', bias=False),
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torch.nn.BatchNorm2d(256),
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torch.nn.MaxPool2d(kernel_size=3, stride=2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding='same', bias=False),
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torch.nn.BatchNorm2d(512),
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torch.nn.MaxPool2d(kernel_size=3, stride=2),
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torch.nn.ReLU(),
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torch.nn.Flatten(),
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torch.nn.Linear(in_features=12800, out_features=4096, bias=False),
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torch.nn.ReLU(),
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torch.nn.Linear(in_features=4096, out_features=4096, bias=False),
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torch.nn.ReLU(),
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torch.nn.Linear(in_features=4096, out_features=20, bias=False),
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torch.nn.Softmax(dim=1)
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)
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# self.layers = torch.nn.Sequential(
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# torch.nn.Conv2d(in_channels=input_channels, out_channels=64, kernel_size=1, padding='same', bias=False),
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# torch.nn.BatchNorm2d(64),
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# torch.nn.ReLU(),
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# torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=7, padding='same', bias=False),
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# torch.nn.BatchNorm2d(64),
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# torch.nn.ReLU(),
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# torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding='same', bias=False),
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# torch.nn.BatchNorm2d(64),
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# torch.nn.ReLU(),
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# torch.nn.MaxPool2d(kernel_size=3, padding=1),
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# torch.nn.Dropout2d(0.1),
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# torch.nn.Conv2d(in_channels=64, out_channels=32, kernel_size=5, padding='same', bias=False),
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# torch.nn.BatchNorm2d(32),
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# torch.nn.ReLU(),
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# torch.nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding='same', bias=False),
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# torch.nn.BatchNorm2d(32),
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# torch.nn.ReLU(),
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# torch.nn.Conv2d(in_channels=32, out_channels=32, kernel_size=1, padding='same', bias=False),
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# torch.nn.BatchNorm2d(32),
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# torch.nn.ReLU(),
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# torch.nn.MaxPool2d(kernel_size=3, padding=1),
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# torch.nn.Dropout2d(0.1),
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# torch.nn.Conv2d(in_channels=32, out_channels=16, kernel_size=3, padding='same', bias=False),
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# torch.nn.BatchNorm2d(16),
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# torch.nn.ReLU(),
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# torch.nn.Conv2d(in_channels=16, out_channels=16, kernel_size=1, padding='same', bias=False),
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# torch.nn.BatchNorm2d(16),
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# torch.nn.ReLU(),
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# torch.nn.MaxPool2d(kernel_size=3, padding=1),
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# torch.nn.Flatten(),
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# torch.nn.Dropout(0.25),
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# torch.nn.Linear(in_features=256, out_features=512),
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# torch.nn.ReLU(),
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# torch.nn.Linear(in_features=512, out_features=20, bias=False),
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# # torch.nn.Softmax(dim=1),
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# )
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def forward(self, input_images: torch.Tensor) -> torch.Tensor:
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return self.layers(input_images)
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def __repr__(self):
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return str(self.layers)
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# model = MyCNN()
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