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import torch |
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from jaxtyping import Float |
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from torch import Tensor, nn |
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def to_binary_mask(x: Float[Tensor, "batch 1 nx ny"]) -> Float[Tensor, "batch 1 nx ny"]: |
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""" |
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converts x into a binary mask using torch exp. |
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""" |
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return nn.functional.sigmoid(x) |
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class ConvHead(nn.Module): |
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""" |
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Simple convolution head which uses pointwise convolutions to generate a segmentation map. |
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The segmentation map is binary. All convolutions are done pointwise (kernel size = 1) |
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""" |
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def __init__( |
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self, |
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hidden_dim: int, |
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in_dim: int, |
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n_layers: int, |
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dropout: float, |
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activation: str, |
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out_dim: int = 1, |
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) -> None: |
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super(ConvHead, self).__init__() |
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activation_fn = None |
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if activation == "relu": |
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activation_fn = nn.ReLU() |
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elif activation == "gelu": |
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activation_fn = nn.GELU() |
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else: |
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raise NotImplementedError( |
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f"Activation function not implemented {activation_fn}" |
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) |
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layers = [] |
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layers.append(nn.Conv2d(in_dim, hidden_dim, 1)) |
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layers.append(activation_fn) |
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if dropout != 0: |
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layers.append(nn.Dropout(p=dropout)) |
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for _ in range(n_layers): |
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layers.append(nn.Conv2d(hidden_dim, hidden_dim, 1)) |
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layers.append(activation_fn) |
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if dropout != 0: |
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layers.append(nn.Dropout(p=dropout)) |
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layers.append(nn.Conv2d(hidden_dim, 1, 1)) |
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self.layers = nn.Sequential(*layers) |
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def forward(self, x: Float[Tensor, "batch channel nx ny"]): |
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return to_binary_mask(self.layers(x)) |
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