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221350e3a4
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132352 Approved by: https://github.com/ezyang ghstack dependencies: #132335, #132351
52 lines
1.2 KiB
Python
52 lines
1.2 KiB
Python
# Owner(s): ["module: onnx"]
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import torch
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import torch.nn as nn
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class DummyNet(nn.Module):
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def __init__(self, num_classes=1000):
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super().__init__()
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self.features = nn.Sequential(
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nn.LeakyReLU(0.02),
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nn.BatchNorm2d(3),
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nn.AvgPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False),
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)
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def forward(self, x):
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output = self.features(x)
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return output.view(-1, 1).squeeze(1)
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class ConcatNet(nn.Module):
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def forward(self, inputs):
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return torch.cat(inputs, 1)
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class PermuteNet(nn.Module):
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def forward(self, input):
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return input.permute(2, 3, 0, 1)
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class PReluNet(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.features = nn.Sequential(
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nn.PReLU(3),
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)
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def forward(self, x):
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output = self.features(x)
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return output
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class FakeQuantNet(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.fake_quant = torch.ao.quantization.FakeQuantize()
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self.fake_quant.disable_observer()
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def forward(self, x):
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output = self.fake_quant(x)
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return output
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