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""" |
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This model is built based on |
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https://github.com/ricvolpi/generalize-unseen-domains/blob/master/model.py |
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""" |
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import torch.nn as nn |
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from torch.nn import functional as F |
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from dassl.utils import init_network_weights |
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from .build import BACKBONE_REGISTRY |
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from .backbone import Backbone |
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class CNN(Backbone): |
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def __init__(self): |
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super().__init__() |
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self.conv1 = nn.Conv2d(3, 64, 5) |
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self.conv2 = nn.Conv2d(64, 128, 5) |
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self.fc3 = nn.Linear(5 * 5 * 128, 1024) |
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self.fc4 = nn.Linear(1024, 1024) |
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self._out_features = 1024 |
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def _check_input(self, x): |
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H, W = x.shape[2:] |
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assert ( |
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H == 32 and W == 32 |
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), "Input to network must be 32x32, " "but got {}x{}".format(H, W) |
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def forward(self, x): |
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self._check_input(x) |
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x = self.conv1(x) |
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x = F.relu(x) |
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x = F.max_pool2d(x, 2) |
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x = self.conv2(x) |
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x = F.relu(x) |
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x = F.max_pool2d(x, 2) |
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x = x.view(x.size(0), -1) |
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x = self.fc3(x) |
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x = F.relu(x) |
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x = self.fc4(x) |
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x = F.relu(x) |
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return x |
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@BACKBONE_REGISTRY.register() |
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def cnn_digitsingle(**kwargs): |
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model = CNN() |
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init_network_weights(model, init_type="kaiming") |
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return model |
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