mirror of https://github.com/llvm/torch-mlir
54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
# -*- Python -*-
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# This file is licensed under a pytorch-style license
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# See frontends/pytorch/LICENSE for license information.
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from __future__ import print_function
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import argparse
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torchvision import datasets, transforms
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from torch.optim.lr_scheduler import StepLR
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import npcomp.frontends.pytorch as torch_mlir
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import npcomp.frontends.pytorch.test as test
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# RUN: python %s | FileCheck %s
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(1, 32, 3, 1)
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self.conv2 = nn.Conv2d(32, 64, 3, 1)
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self.maxpool2d = nn.MaxPool2d(2,2)
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#self.dropout1 = nn.Dropout2d(0.25)
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#self.dropout2 = nn.Dropout2d(0.5)
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self.fc1 = nn.Linear(9216, 128)
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self.fc2 = nn.Linear(128, 10)
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def forward(self, x):
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x = self.conv1(x)
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x = F.relu(x)
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x = self.conv2(x)
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x = self.maxpool2d(x)
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#x = self.dropout1(x)
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x = x.view((4,9216))
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x = self.fc1(x)
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x = F.relu(x)
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#x = self.dropout2(x)
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x = self.fc2(x)
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output = F.log_softmax(x, dim=1)
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return output
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def main():
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model = Net()
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tensor = torch.randn((4, 1, 28, 28))
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# CHECK: PASS! fwd check
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fwd_path = test.check_fwd(model, tensor)
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if __name__ == '__main__':
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main()
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