My code is like below.
import numpy as np
import torch
import torch.nn as nn
import cupy as cp
from torchviz import make_dot
from torchinfo import summary
from torchsummary import summary as summary_
def get_filter_torch(*args, **kwargs):
class TraversabilityFilter(nn.Module):
def __init__(self, w1, w2, w3, w_out, device="cuda", use_bias=False):
super(TraversabilityFilter, self).__init__()
self.conv1 = nn.Conv2d(1, 4, 3, dilation=1, padding=0, bias=use_bias)
self.conv2 = nn.Conv2d(1, 4, 3, dilation=2, padding=0, bias=use_bias)
self.conv3 = nn.Conv2d(1, 4, 3, dilation=3, padding=0, bias=use_bias)
self.conv_out = nn.Conv2d(12, 1, 1, bias=use_bias)
# Set weights.
self.conv1.weight = nn.Parameter(torch.from_numpy(w1).float())
self.conv2.weight = nn.Parameter(torch.from_numpy(w2).float())
self.conv3.weight = nn.Parameter(torch.from_numpy(w3).float())
self.conv_out.weight = nn.Parameter(torch.from_numpy(w_out).float())
def __call__(self, elevation_cupy):
# Convert cupy tensor to pytorch.
elevation_cupy = elevation_cupy.astype(cp.float32)
elevation = torch.as_tensor(elevation_cupy, device=self.conv1.weight.device)
print("input: ",elevation.shape)
with torch.no_grad():
out1 = self.conv1(elevation.view(-1, 1, elevation.shape[0], elevation.shape[1]))
out2 = self.conv2(elevation.view(-1, 1, elevation.shape[0], elevation.shape[1]))
out3 = self.conv3(elevation.view(-1, 1, elevation.shape[0], elevation.shape[1]))
out1 = out1[:, :, 2:-2, 2:-2]
out2 = out2[:, :, 1:-1, 1:-1]
out = torch.cat((out1, out2, out3), dim=1)
out = self.conv_out(out.abs())
out = torch.exp(-out)
print("output: ",out.shape)
return out
traversability_filter = TraversabilityFilter(*args, **kwargs).cuda().eval()
return traversability_filter
# Define the weight values (you need to provide actual weight values)
w1 = np.random.randn(4, 1, 3, 3) # Shape: (out_channels, in_channels, kernel_height, kernel_width)
w2 = np.random.randn(4, 1, 3, 3) # Shape: (out_channels, in_channels, kernel_height, kernel_width)
w3 = np.random.randn(4, 1, 3, 3) # Shape: (out_channels, in_channels, kernel_height, kernel_width)
w_out = np.random.randn(1, 12, 1, 1) # Shape: (out_channels, in_channels, kernel_height, kernel_width)
model = get_filter_torch(w1, w2, w3, w_out)
cell_n = 200
x = cp.random.randn(cell_n, cell_n, dtype=cp.float32)
output = model(x)
print(model)
input_size=(200,200)
summary(model)
summary_(model, input_size)
When I run the code, the result is like below.
input: torch.Size([200, 200])
output: torch.Size([1, 1, 194, 194])
TraversabilityFilter(
(conv1): Conv2d(1, 4, kernel_size=(3, 3), stride=(1, 1), bias=False)
(conv2): Conv2d(1, 4, kernel_size=(3, 3), stride=(1, 1), dilation=(2, 2), bias=False)
(conv3): Conv2d(1, 4, kernel_size=(3, 3), stride=(1, 1), dilation=(3, 3), bias=False)
(conv_out): Conv2d(12, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)
)
=================================================================
Layer (type:depth-idx) Param #
=================================================================
TraversabilityFilter --
├─Conv2d: 1-1 36
├─Conv2d: 1-2 36
├─Conv2d: 1-3 36
├─Conv2d: 1-4 12
=================================================================
Total params: 120
Trainable params: 120
Non-trainable params: 0
=================================================================
input: torch.Size([2, 200, 200])
Traceback (most recent call last):
File "/home/Documents/relay/temp/visual.py", line 72, in <module>
summary_(model, input_size)
File "/home/Documents/2levation_ws/venv/myenv/lib/python3.8/site-packages/torchsummary/torchsummary.py", line 72, in summary
model(*x)
File "/home/Documents/relay/temp/visual.py", line 34, in __call__
out1 = self.conv1(elevation.view(-1, 1, elevation.shape[0], elevation.shape[1]))
File "/home/Documents/2levation_ws/venv/myenv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1212, in _call_impl
result = forward_call(*input, **kwargs)
File "/home/Documents/2levation_ws/venv/myenv/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 463, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/home/Documents/2levation_ws/venv/myenv/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 459, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Calculated padded input size per channel: (2 x 200). Kernel size: (3 x 3). Kernel size can't be greater than actual input size
The error occurs because the input size specified in the summary function doesn't match the actual input size used when applying the model. Even though the input size during usage is (200, 200), specifying input_size=(1, 200, 200) still results in the same error. How can this be resolved? My pytorch version is like below.
torch==1.13.1+cu116
torchaudio==0.13.1+cu116
torchinfo==1.8.0
torchsummary==1.5.1
torchvision==0.14.1+cu116
torchviz==0.0.2
And I use python 3.8.10.
Torchsummary works by running your model on a sample input and observing the shapes of intermediate results. Torchsummary does this by creating a random tensor of (2, your_shape). That is, it takes the shape you suggested and prepends a dimension of size 2, so an input shape of (200, 200) becomes (2, 200, 200).
Also, note that while your model expects a cupy.ndarray as an input, Torchsummary will pass a torch.Tensor. This is perfectly fine, because by convention classes that inherit from nn.Module should accept torch.Tensors rather than other types.
Finally, elevation.view(-1, 1, elevation.shape[0], elevation.shape[1]) assumes that elevation.shape[0] and elevation.shape[1] will be your input_size, so (200, 200). This is a very strong assumption and it is better to assume that the last two dimensions will be (200, 200).
Here's a working version of your code but modified to take into account what I just described.
import numpy as np
import torch
import torch.nn as nn
import cupy as cp
from torchviz import make_dot
from torchinfo import summary
from torchsummary import summary as summary_
def get_filter_torch(*args, **kwargs):
class TraversabilityFilter(nn.Module):
def __init__(self, w1, w2, w3, w_out, device="cuda", use_bias=False):
super(TraversabilityFilter, self).__init__()
self.conv1 = nn.Conv2d(1, 4, 3, dilation=1, padding=0, bias=use_bias)
self.conv2 = nn.Conv2d(1, 4, 3, dilation=2, padding=0, bias=use_bias)
self.conv3 = nn.Conv2d(1, 4, 3, dilation=3, padding=0, bias=use_bias)
self.conv_out = nn.Conv2d(12, 1, 1, bias=use_bias)
# Set weights.
self.conv1.weight = nn.Parameter(torch.from_numpy(w1).float())
self.conv2.weight = nn.Parameter(torch.from_numpy(w2).float())
self.conv3.weight = nn.Parameter(torch.from_numpy(w3).float())
self.conv_out.weight = nn.Parameter(torch.from_numpy(w_out).float())
def __call__(self, elevation_cupy):
# Convert cupy tensor to pytorch IF NEEDED
if isinstance(elevation_cupy, cp.ndarray):
elevation_cupy = elevation_cupy.astype(cp.float32)
elevation = torch.as_tensor(elevation_cupy, device=self.conv1.weight.device)
elif isinstance(elevation_cupy, torch.Tensor):
elevation = elevation_cupy
else:
raise TypeError()
print("input: ", elevation.shape)
# use last two axes
with torch.no_grad():
out1 = self.conv1(elevation.view(-1, 1, elevation.shape[-2], elevation.shape[-1]))
out2 = self.conv2(elevation.view(-1, 1, elevation.shape[-2], elevation.shape[-1]))
out3 = self.conv3(elevation.view(-1, 1, elevation.shape[-2], elevation.shape[-1]))
out1 = out1[:, :, 2:-2, 2:-2]
out2 = out2[:, :, 1:-1, 1:-1]
out = torch.cat((out1, out2, out3), dim=1)
out = self.conv_out(out.abs())
out = torch.exp(-out)
print("output: ", out.shape)
return out
traversability_filter = TraversabilityFilter(*args, **kwargs).cuda().eval()
return traversability_filter
# Define the weight values (you need to provide actual weight values)
w1 = np.random.randn(4, 1, 3, 3) # Shape: (out_channels, in_channels, kernel_height, kernel_width)
w2 = np.random.randn(4, 1, 3, 3) # Shape: (out_channels, in_channels, kernel_height, kernel_width)
w3 = np.random.randn(4, 1, 3, 3) # Shape: (out_channels, in_channels, kernel_height, kernel_width)
w_out = np.random.randn(1, 12, 1, 1) # Shape: (out_channels, in_channels, kernel_height, kernel_width)
model = get_filter_torch(w1, w2, w3, w_out)
cell_n = 200
x = cp.random.randn(cell_n, cell_n, dtype=cp.float32)
output = model(x)
print(model)
input_size = (200, 200)
summary(model)
summary_(model, input_size)