syheliel opened a new issue, #17879:
URL: https://github.com/apache/tvm/issues/17879
### Environment
- torch 1.13.1
- apache-tvm 0.14.dev273
### Steps to reproduce
```
import torch
import tvm
from tvm import relay
import torch.nn as nn
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
# First convolutional layer
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32,
kernel_size=3, padding=1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
# Second convolutional layer
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64,
kernel_size=3, padding=1)
self.relu2 = nn.ReLU()
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
# Third convolutional layer
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128,
kernel_size=3, padding=1)
self.relu3 = nn.ReLU()
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
# Fully connected layers
self.fc1 = nn.Linear(128 * 4 * 4, 512)
self.relu4 = nn.ReLU()
self.dropout = nn.Dropout(0.5)
self.fc2 = nn.Linear(512, 10) # 10 classes for classification
def forward(self, x):
# First conv block
x = self.conv1(x)
x = self.relu1(x)
x = self.pool1(x)
# Second conv block
x = self.conv2(x)
x = self.relu2(x)
x = self.pool2(x)
# Third conv block
x = self.conv3(x)
x = self.relu3(x)
x = self.pool3(x)
# Flatten the output
x = x.view(x.size(0), -1)
# Fully connected layers
x = self.fc1(x)
x = self.relu4(x)
x = self.dropout(x)
x = self.fc2(x)
return x
# Example usage
if __name__ == "__main__":
# Create model instance
model = SimpleCNN()
# Create a sample input tensor (batch_size=1, channels=3, height=32,
width=32)
sample_input = torch.randn(1, 3, 32, 32)
scripted_model = torch.jit.script(model)
shape_list = ["sample_input", sample_input.shape]
mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
```
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