Function Differences with torch.nn.Conv2d
torch.nn.Conv2d
torch.nn.Conv2d(
in_channels=120,
out_channels=240,
kernel_size=4,
stride=1,
padding=0,
padding_mode='zeros',
dilation=1,
groups=1,
bias=True
)
For more information, see torch.nn.Conv2d.
mindspore.nn.Conv2d
class mindspore.nn.Conv2d(
in_channels=120,
out_channels=240,
kernel_size=4,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
groups=1,
has_bias=False,
weight_init='normal',
bias_init='zeros',
data_format='NCHW'
)(input_x)
For more information, see mindspore.nn.Conv2d.
Differences
PyTorch: No padding is applied to the input by default. bias is set to True by default.
MindSpore: Padding is applied to the input so the output’s dimensions match with input’s dimensions by default. If no padding is needed, set pad_mode to ‘valid’. has_bias is set to False by default.
Code Example
import mindspore
from mindspore import Tensor
import mindspore.nn as nn
import torch
import numpy as np
# In MindSpore
net = nn.Conv2d(120, 240, 4, stride=2, has_bias=True, weight_init='normal')
x = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
output = net(x).shape
print(output)
# Out:
# (1, 240, 512, 320)
# In MindSpore
net = nn.Conv2d(120, 240, 4, stride=2, pad_mode='valid', has_bias=True, weight_init='normal')
x = Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
output = net(x).shape
print(output)
# Out:
# (1, 240, 511, 319)
# In PyTorch
m = torch.nn.Conv2d(120, 240, 4, stride=2)
input = torch.rand(1, 120, 1024, 640)
output = m(input)
print(output.shape)
# Out:
# torch.Size([1, 240, 511, 319])