比较与torchvision.ops.deform_conv2d的差异
torchvision.ops.deform_conv2d
class torchvision.ops.deform_conv2d(
input,
offset,
weight,
bias=None,
stride=(1, 1),
padding=(0, 0),
dilations=(1, 1),
mask=None
)
mindspore.ops.deformable_conv2d
class mindspore.ops.deformable_conv2d(
x,
weight,
offsets,
kernel_size,
strides,
padding,
bias=None,
dilations=(1, 1, 1, 1),
groups=1,
deformable_groups=1,
modulated=True
)
差异对比
PyTorch: 参数offsets是一个四维Tensor,存储x和y坐标的偏移。数据格式为“NCHW”,shape为\(\left(batch, deformable\underline{ }groups × H_{\text {f }} × W_{\text {f }} × 2, H_{\text {out }}, W_{\text {out }}\right)\),注意其中C维度的存储顺序为\(\left(deformable\underline{ }groups, H_{\text {f }}, W_{\text {f }}, \left(offset\underline{ }y, offset\underline{ }x\right)\right)\)。参数mask是一个四维Tensor,存储可变形卷积的输入掩码mask。数据格式为“NCHW”,shape为\(\left(batch, deformable\underline{ }groups × H_{\text {f }} × W_{\text {f }} × 1, H_{\text {out }}, W_{\text {out }}\right)\),注意其中C维度的存储顺序为\(\left(deformable\underline{ }groups, H_{f}, W_{f}, mask\right)\)。
MindSpore: 一个四维Tensor,存储x和y坐标的偏移,以及可变形卷积的输入掩码mask。数据格式为“NCHW”,shape为\(\left(batch, 3 × deformable\underline{ }groups × H_{\text {f }} × W_{\text {f }}, H_{\text {out }}, W_{\text {out }}\right)\),注意其中C维度的存储顺序为\(\left(\left(offset\underline{ }x, offset\underline{ }y, mask\right), deformable\underline{ }groups, H_{f}, W_{f}\right)\)。
分类 |
子类 |
PyTorch |
MindSpore |
差异 |
---|---|---|---|---|
参数 |
参数1 |
input |
x |
功能一致,参数名不同 |
参数2 |
offset |
offsets |
MindSpore的offsets参数包含PyTorch的offset和mask两个参数 |
|
参数3 |
weight |
weight |
- |
|
参数4 |
- |
kernel_size |
Pytorch无此参数 |
|
参数5 |
mask |
- |
MindSpore无此参数 |
|
参数6 |
bias |
bias |
- |
|
参数7 |
stride |
strides |
功能一致,参数名不同 |
|
参数8 |
padding |
padding |
- |
|
参数9 |
dilations |
dilations |
- |
|
参数10 |
- |
groups |
Pytorch无此参数 |
|
参数11 |
- |
deformable_groups |
Pytorch无此参数 |
|
参数12 |
- |
modulated |
Pytorch无此参数 |
代码示例
# PyTorch
import torch
from torch import tensor
import numpy as np
from torchvision.ops import deform_conv2d
np.random.seed(1)
kh, kw = 1, 1
batch = 1
deformable_groups = 1
stride_h, stride_w = 1, 1
dilation_h, dilation_w = 1, 1
pad_h, pad_w = 0, 0
x_h, x_w = 1, 2
out_h = (x_h + 2 * pad_h - dilation_h * (kh - 1) - 1) // stride_h + 1
out_w = (x_w + 2 * pad_w - dilation_w * (kw - 1) - 1) // stride_w + 1
x = np.random.randn(batch, 64, x_h, x_w).astype(np.float32)
weight = np.random.randn(batch, 64, kh, kw).astype(np.float32)
offsets_x = np.random.randn(batch, 1, deformable_groups, kh, kw, out_h, out_w).astype(np.float32)
offsets_y = np.random.randn(batch, 1, deformable_groups, kh, kw, out_h, out_w).astype(np.float32)
mask = np.random.randn(batch, 1, deformable_groups, kh, kw, out_h, out_w).astype(np.float32)
offsets = np.concatenate((offsets_y, offsets_x), axis=1)
offsets = offsets.transpose(0, 2, 3, 4, 1, 5, 6)
offsets = offsets.reshape((batch, 2 * deformable_groups * kh * kw, out_h, out_w))
mask = mask.transpose(0, 2, 3, 4, 1, 5, 6)
mask = mask.reshape((batch, 1 * deformable_groups * kh * kw, out_h, out_w))
x = torch.from_numpy(x.copy().astype(np.float32))
weight = torch.from_numpy(weight.copy().astype(np.float32))
offsets = torch.from_numpy(offsets.copy().astype(np.float32))
mask = torch.from_numpy(mask.copy().astype(np.float32))
output = deform_conv2d(x, offsets, weight, stride=(stride_h, stride_w), padding=(pad_h, pad_w), dilation=(dilation_h, dilation_w), mask=mask)
print(output)
# tensor([[[[-0.0022, 0.0000]]]])
# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.nn as nn
import numpy as np
from mindspore.ops import deformable_conv2d
import mindspore.ops as ops
np.random.seed(1)
kh, kw = 1, 1
batch = 1
deformable_groups = 1
stride_h, stride_w = 1, 1
dilation_h, dilation_w = 1, 1
pad_h, pad_w = 0, 0
x_h, x_w = 1, 2
out_h = (x_h + 2 * pad_h - dilation_h * (kh - 1) - 1) // stride_h + 1
out_w = (x_w + 2 * pad_w - dilation_w * (kw - 1) - 1) // stride_w + 1
x = np.random.randn(batch, 64, x_h, x_w).astype(np.float32)
weight = np.random.randn(batch, 64, kh, kw).astype(np.float32)
offsets_x = np.random.randn(batch, 1, deformable_groups, kh, kw, out_h, out_w).astype(np.float32)
offsets_y = np.random.randn(batch, 1, deformable_groups, kh, kw, out_h, out_w).astype(np.float32)
mask = np.random.randn(batch, 1, deformable_groups, kh, kw, out_h, out_w).astype(np.float32)
offsets = np.concatenate((offsets_x, offsets_y, mask), axis=1)
offsets = offsets.reshape((batch, 3 * deformable_groups * kh * kw, out_h, out_w))
x = Tensor(x)
weight = Tensor(weight)
offsets = Tensor(offsets)
output = ops.deformable_conv2d(x, weight, offsets, (kh, kw), (1, 1, stride_h, stride_w,), (pad_h, pad_h, pad_w, pad_w), dilations=(1, 1, dilation_h, dilation_w))
print(output)
# [[[[-0.00220442 0. ]]]]