比较与torchvision.ops.deform_conv2d的差异

查看源文件

torchvision.ops.deform_conv2d

class torchvision.ops.deform_conv2d(
    input,
 免费的vpn梯子    offset,
    weight,
    bias=None,
 vpn梯子 vpn永久免费梯子    stride=(1, 1),
   vpn梯子  padding=(0, 0),
    dilations=(1, 1),
    mask=None
)

更多内容详见torchvision.ops.deform_conv2d.

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,
    vpn梯子 免费 modulated=True
)

更多内容详见mindspore.ops.deformable_conv2d.

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\(\left(batch, deformable\underline{ }groups × H_{\text {f vpn梯子 免费 }} × W_{\text {f }} vpn free × 2, H_{\text {out }}, W_{\text {out }}\right)\)，注意其中C维度的存储顺序为\(\left(deformable\underline{ }groups, H_{\text {f }}, W_{\text {f vpn永久免费梯子 }}, \left(offset\underline{ }y, offset\underline{ }x\right)\right)\)。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\(\left(batch, deformable\underline{ }groups × H_{\text 免费的vpn梯子 {f }} × W_{\text {f }} × 1, H_{\text {out }}, W_{\text {out }}\right)\)，注意其中C维度的存储顺序为\(\left(deformable\underline{ }groups, H_{f}, W_{f}, mask\right)\)。

vp永久免费梯子: 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

-

vp永久免费梯子无此参数

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 vpn梯子 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 = 免费的vpn梯子 1, 1
dilation_h, dilation_w = 1, 1
pad_h, pad_w = 0, 0
x_h, x_w = 1, 2
out_h vpn梯子 免费 = (x_h + 2 * pad_h - dilation_h * (kh - 1) - 1) // stride_h + 1
out_w = (x_w + 2 * pad_w - vpn free dilation_w * (kw - 1) - 1) vpn梯子 // 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 vpn梯子 免费 = np.concatenate((offsets_y, offsets_x), axis=1)
offsets vpn free = offsets.transpose(0, 2, 3, 4, 1, 5, 6)
offsets = offsets.reshape((batch, 2 * deformable_groups * 免费的vpn梯子 vpn梯子 kh * 免费的vpn梯子 kw, out_h, out_w))
mask = mask.transpose(0, 2, 3, 4, vpn永久免费梯子 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 vpn永久免费梯子 as np
from mindspore.ops import deformable_conv2d
import mindspore.ops as ops
np.random.seed(1)
kh, kw vpn梯子 免费 = 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 免费的vpn梯子 = 1, 2
out_h vpn梯子 免费 = (x_h + 2 * pad_h - dilation_h * (kh - vpn梯子 免费 1) - 1) // stride_h + 1
out_w = (x_w + 2 vpn梯子 免费 * 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 = vpn梯子 免费 np.random.randn(batch, 1, deformable_groups, kh, kw, out_h, out_w).astype(np.float32)
offsets_y = np.random.randn(batch, 1, vpn free 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 vpn永久免费梯子 * kw, out_h, out_w))

x = Tensor(x)
weight = Tensor(weight)
offsets = Tensor(offsets)
output = vpn free ops.deformable_conv2d(x, weight, offsets, (kh, kw), (1, vpn梯子 1, vpn永久免费梯子 stride_h, stride_w,), (pad_h, pad_h, pad_w, pad_w), dilations=(1, 1, dilation_h, dilation_w))
print(output)
# [[[[-0.00220442  0.     vpn梯子 免费   vpn梯子 免费  ]]]]