47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
import math
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import numpy as np
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import torch
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from torch import nn
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class ImageNN(torch.nn.Module):
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def __init__(self, precision: np.float32 or np.float64, n_in_channels: int = 1, n_hidden_layers: int = 3,
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n_kernels: int = 32, kernel_size: int = 9):
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"""Simple CNN with `n_hidden_layers`, `n_kernels`, and `kernel_size` as hyperparameters"""
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super().__init__()
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ksize = kernel_size
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cnn = []
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for i in range(n_hidden_layers):
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cnn.append(torch.nn.Conv2d(
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in_channels=n_in_channels,
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out_channels=n_kernels,
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kernel_size=ksize,
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padding=int(ksize / 2),
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padding_mode="replicate"
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))
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kernel_size -= 2
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cnn.append(torch.nn.ReLU())
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n_in_channels = n_kernels
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self.hidden_layers = torch.nn.Sequential(*cnn)
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self.output_layer = torch.nn.Conv2d(
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in_channels=n_in_channels,
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out_channels=3,
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kernel_size=kernel_size,
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padding=int(kernel_size / 2)
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)
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if precision == np.float64:
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self.double()
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def forward(self, x):
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"""Apply CNN to input `x` of shape (N, n_channels, X, Y), where N=n_samples and X, Y are spatial dimensions"""
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cnn_out = self.hidden_layers(x) # apply hidden layers (N, n_in_channels, X, Y) -> (N, n_kernels, X, Y)
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pred = self.output_layer(cnn_out) # apply output layer (N, n_kernels, X, Y) -> (N, 1, X, Y)
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return pred
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