网站开发外包公司,快速开发工具网站,博物馆网站建设目的,网站如何做后台Pytorch文档
Pytorch 官方文档
https://pytorch.org/docs/stable/index.html
pytorch 里的一些基础tensor操作讲的不错
https://blog.csdn.net/abc13526222160/category_8614343.html
关于pytorch的Broadcast,合并与分割,数学运算,属性统计以及高阶操作
https://blog.csd…Pytorch文档
Pytorch 官方文档
https://pytorch.org/docs/stable/index.html
pytorch 里的一些基础tensor操作讲的不错
https://blog.csdn.net/abc13526222160/category_8614343.html
关于pytorch的Broadcast,合并与分割,数学运算,属性统计以及高阶操作
https://blog.csdn.net/abc13526222160/article/details/103520465
broadcast机制
对于涉及计算的两个tensor, 对于数量不匹配的dim, 可以进行自动重复拷贝使得dim进行批评
Broadcast它能维度扩展和expand一样它是自动扩展并且不需要拷贝数据能够节省内存。关键思想 [外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-WMOpueEB-1692628397278)(attachment:image.png)]
import sys, osimport torch, torchvision
import torch.nn as nn
import torch.nn.functional as Ffrom PIL import Image
import numpy as np
import pandas as pd
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt1. Pytorch索引与切片以及维度变换
TODO: https://zhangkaifang.blog.csdn.net/article/details/103517970
index, view, reshape, squeeze, unsqueeze, transpose/t, permute , expand/repeat transpose()函数表示矩阵的维度交换接受的参数为要交换的哪两个维度。
permute: transpose()函数一次只能两两交换。【b, c, h, w】 【b, w, h, c】比如原来一个人的图片交换过后图片可能不是人了我们还希望变成原来的样子可以看成多维度交换其中参数为新的维度顺序。同样的道理permute函数也会把内存的顺序给打乱因此要是涉及contious这个错误的时候需要额外添加.contiguous()函数来把内存的顺序变得连续。
2.1 合并与分割 cat, stack, split, chunk repeat
stack一般叠加在新的维度 concatenate在已有的dim上进行扩展, vstack, dstack, hstack分别是在dim [0, 1, 2]上进行叠加
a torch.rand([5, 5, 3])print(-*10 stack -*10)
t1 torch.stack([a, a], dim 0)
print(pre, a.shape, post,t1.shape)t1 torch.stack([a, a], dim 1)
print(pre, a.shape, post,t1.shape)t1 torch.stack([a, a], dim 2)
print(pre, a.shape, post,t1.shape)t1 torch.stack([a, a], dim 3)
print(pre, a.shape, post,t1.shape)print(-*10 concatenate -*10)
t1 torch.cat([a, a], dim 0)
print(pre, a.shape, post,t1.shape)t1 torch.cat([a, a], dim 1)
print(pre, a.shape, post,t1.shape)t1 torch.cat([a, a], dim 2)
print(pre, a.shape, post,t1.shape)print(-*10 v-h-d stack -*10)t1 torch.vstack([a, a])
print(pre, a.shape, post,t1.shape)t1 torch.hstack([a, a])
print(pre, a.shape, post,t1.shape)t1 torch.dstack([a, a])
print(pre, a.shape, post,t1.shape)
----------stack----------
pre torch.Size([5, 5, 3]) post torch.Size([2, 5, 5, 3])
pre torch.Size([5, 5, 3]) post torch.Size([5, 2, 5, 3])
pre torch.Size([5, 5, 3]) post torch.Size([5, 5, 2, 3])
pre torch.Size([5, 5, 3]) post torch.Size([5, 5, 3, 2])
----------concatenate----------
pre torch.Size([5, 5, 3]) post torch.Size([10, 5, 3])
pre torch.Size([5, 5, 3]) post torch.Size([5, 10, 3])
pre torch.Size([5, 5, 3]) post torch.Size([5, 5, 6])
----------v-h-d stack----------
pre torch.Size([5, 5, 3]) post torch.Size([10, 5, 3])
pre torch.Size([5, 5, 3]) post torch.Size([5, 10, 3])
pre torch.Size([5, 5, 3]) post torch.Size([5, 5, 6])torch.split函数 按照长度进行切分
torch.split(tensor, split_size_or_sections, dim0)
split_size_or_sections需要切分的大小(int or list ) dim切分维度
torch.chunk函数 按照数量进行切分
a torch.rand([6, 256, 10, 10 ])
print(a, a.shape)t1 torch.split(a, [3,3], dim 0)
print(dim %d%(0))
for ti in t1:print(ti.shape)print(dim %d%(1))
t1 torch.split(a, [128,128], dim 1)
for ti in t1:print(ti.shape)print(dim %d%(2))
t1 torch.split(a, [5,5], dim 2)
for ti in t1:print(ti.shape)a torch.Size([6, 256, 10, 10])
dim 0
torch.Size([3, 256, 10, 10])
torch.Size([3, 256, 10, 10])
dim 1
torch.Size([6, 128, 10, 10])
torch.Size([6, 128, 10, 10])
dim 2
torch.Size([6, 256, 5, 10])
torch.Size([6, 256, 5, 10])a torch.rand([6, 256, 10, 10 ])
print(a, a.shape)t1 torch.chunk(a, 2, dim 0)
print(dim %d%(0))
for ti in t1:print(ti.shape)print(dim %d%(1))
t1 torch.chunk(a, 2, dim 1)
for ti in t1:print(ti.shape)print(dim %d%(2))
t1 torch.chunk(a, 2, dim 2)
for ti in t1:print(ti.shape)a torch.Size([6, 256, 10, 10])
dim 0
torch.Size([3, 256, 10, 10])
torch.Size([3, 256, 10, 10])
dim 1
torch.Size([6, 128, 10, 10])
torch.Size([6, 128, 10, 10])
dim 2
torch.Size([6, 256, 5, 10])
torch.Size([6, 256, 5, 10])torch.repeat() np.tile()
参数为沿着不同维度的扩展, 对于一个给定的tensor 给出其dims以及repeat的次数类似于np.tile 由后往前进行匹配, 若超出当前的dims, 则新建一个dim, 进行repeat 当超出tensor 本身的 dims时第一个dim 新起一个dim, 即沿着batch方向进行扩展
参考资料见 https://pytorch.org/docs/stable/generated/torch.Tensor.repeat.html?highlightrepeat#torch.Tensor.repeat
a torch.tensor([1, 2, 3])
print(a.shape)
t1 a.repeat(5, 2)
print(-*10);print(t1.shape, a.shape); print(a,\n, t1)t1 a.repeat(2, 3, 2)
print(-*10);print(t1.shape, a.shape); print(a,\n, t1)a torch.tensor([[1, 2, 3], [4, 5, 6]])
print(a.shape)t1 a.repeat([1, 1])
print(-*10);print(t1.shape, a.shape); print(a,\n, t1)t1 a.repeat([2, 1]) # dim0 repeat 2
print(-*10);print(t1.shape, a.shape); print(a,\n, t1)t1 a.repeat([1, 2]) # dim1 repeat 2
print(-*10);print(t1.shape, a.shape); print(a,\n, t1)t1 a.repeat([2, 1, 1])
print(-*10);print(t1.shape, a.shape); print(a,\n, t1)
torch.Size([3])
----------
torch.Size([5, 6]) torch.Size([3])
tensor([1, 2, 3]) tensor([[1, 2, 3, 1, 2, 3],[1, 2, 3, 1, 2, 3],[1, 2, 3, 1, 2, 3],[1, 2, 3, 1, 2, 3],[1, 2, 3, 1, 2, 3]])
----------
torch.Size([2, 3, 6]) torch.Size([3])
tensor([1, 2, 3]) tensor([[[1, 2, 3, 1, 2, 3],[1, 2, 3, 1, 2, 3],[1, 2, 3, 1, 2, 3]],[[1, 2, 3, 1, 2, 3],[1, 2, 3, 1, 2, 3],[1, 2, 3, 1, 2, 3]]])
torch.Size([2, 3])
----------
torch.Size([2, 3]) torch.Size([2, 3])
tensor([[1, 2, 3],[4, 5, 6]]) tensor([[1, 2, 3],[4, 5, 6]])
----------
torch.Size([4, 3]) torch.Size([2, 3])
tensor([[1, 2, 3],[4, 5, 6]]) tensor([[1, 2, 3],[4, 5, 6],[1, 2, 3],[4, 5, 6]])
----------
torch.Size([2, 6]) torch.Size([2, 3])
tensor([[1, 2, 3],[4, 5, 6]]) tensor([[1, 2, 3, 1, 2, 3],[4, 5, 6, 4, 5, 6]])
----------
torch.Size([2, 2, 3]) torch.Size([2, 3])
tensor([[1, 2, 3],[4, 5, 6]]) tensor([[[1, 2, 3],[4, 5, 6]],[[1, 2, 3],[4, 5, 6]]])2.2 torch生成grid函数 torch.arange 类似于 np.arange [left, right), not include right. torch.linspace 包括 left, right, 且分割为若干个点 torch.meshgrid叠加后生成 x 和 y (opencv坐标系)下两个 h*w的tensor。为了组装为后期可以用的点所以需要先翻转后使用。
xs torch.linspace(-10, 10, 10)
ys torch.linspace(-5, 5, 10)x, y torch.meshgrid(xs, ys, indexingij)
z x yax plt.axes(projection 3d)
ax.plot_surface(x.numpy(), y.numpy(), z.numpy())
plt.show()[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-m1idlV1z-1692628397282)(output_15_0.png)]
def coords_grid(batch, ht, wd, device, ifReverse True):#ht,wd 55, 128coords torch.meshgrid(torch.arange(ht, devicedevice), torch.arange(wd, devicedevice))if ifReverse:#NOTE, coords 为 x, y opencv 坐标系下, y, x (numpy 坐标系)coords torch.stack(coords[::-1], dim0).float() # coords shape [ 2, ht, wd]else:#NOTE coords 为 x, y (numpy 坐标系下)coords torch.stack(coords, dim0).float()# 通过[::-1]转变为 y, x(numpy 坐标系)#coords torch.reshape(coords, [1, 2, ht, wd])coords coords.repeat(batch, 1, 1, 1)return coords
DEVICE cuda
grid_opencv coords_grid(2, 12, 5, DEVICE, ifReverseTrue)
grid_numpy coords_grid(2, 12, 5, DEVICE, ifReverse False)print(grid_numpy.shape, grid_opencv.shape)def visualize_grid(grid):# sample 1, x yplot_x grid[0, 0, :, :].reshape([-1]).cpu().numpy()plot_y grid[0, 1, :, :].reshape([-1]).cpu().numpy()return plot_x, plot_yplt.figure(figsize(8,6))
for pici, grid in enumerate([grid_opencv, grid_numpy]):plt.subplot(2, 1, pici 1)plt.title([opencv, numpy][pici])plot_x, plot_y visualize_grid(grid)for i in range(len(plot_x)):plt.scatter([plot_x[i]], [plot_y[i]], c grey, marker o)plt.annotate(xy (plot_x[i], plot_y[i]), s str(i))plt.show()
/home/chwei/.local/lib/python3.6/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]torch.Size([2, 2, 12, 5]) torch.Size([2, 2, 12, 5])[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-4XGp96GA-1692628397283)(output_17_2.png)]
3.1 数学运算
add/sub/mul/div加减乘除
a torch.rand(3, 4)
b torch.rand(4) # 这里基于pytorch 的broadcasting进行了dim 0的 broadcastc a bprint(a.shape, b.shape, c.shape)
#print(ab)
#print(torch.add(a, b))print(torch.all(torch.eq(a-b, torch.sub(a, b))))
print(torch.all(torch.eq(a*b, torch.mul(a, b))))
print(torch.all(torch.eq(a/b, torch.div(a, b))))
torch.Size([3, 4]) torch.Size([4]) torch.Size([3, 4])
tensor(True)
tensor(True)
tensor(True)3.2 torch.matmul函数 基础case 1: 两个tensor 都是1 dim If both tensors are 1-dimensional, the dot product (scalar) is returned. 基础case 2: 两个tensor 都是2 dims, If both arguments are 2-dimensional, the matrix-matrix product is returned. 基础case 3: 一个tensor 是1 dim, 另外一个 2dim. tensor with 1 dim进行扩展到2dim, 以适应 matrix-product If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed. If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned. 基础case 4: Batch Brocasted batch matrix-product 思路 把 first dim (batch dim) 排除 每个sample 进行 matrix-product or vector product 若 有一个tensor 其维度不匹配 沿着batch dim进行扩展broadcast之后计算
If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N 2), then a batched matrix multiply is returned.
If the first argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after.
If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable).
For example, if input is a ( j × 1 × n × n ) ( j × 1 × n × n ) (j \times 1 \times n \times n)(j×1×n×n) (j×1×n×n)(j×1×n×n) tensor and other is a ( k × n × n ) ( k × n × n ) (k \times n \times n)(k×n×n) (k×n×n)(k×n×n) tensor, out will be a ( j × k × n × n ) ( j × k × n × n ) (j \times k \times n \times n)(j×k×n×n) (j×k×n×n)(j×k×n×n) tensor.
这里 j j j 是 batch dim, 而 matrix product 执行的位置在于$ (1 \times n \times n) \times (k \times n \times n) $
Note that the broadcasting logic only looks at the batch dimensions when determining if the inputs are broadcastable, and not the matrix dimensions.
For example, if input is a ( j × 1 × n × m ) ( j × 1 × n × m ) (j \times 1 \times n \times m)(j×1×n×m) (j×1×n×m)(j×1×n×m) tensor and other is a ( k × m × p ) ( k × m × p ) (k \times m \times p)(k×m×p) (k×m×p)(k×m×p) tensor, these inputs are valid for broadcasting even though the final two dimensions (i.e. the matrix dimensions) are different. out will be a ( j × k × n × p ) ( j × k × n × p ) (j \times k \times n \times p)(j×k×n×p) (j×k×n×p)(j×k×n×p) tensor.
# vector x vector case 1
tensor1 torch.randn(3)
tensor2 torch.randn(3)
out torch.matmul(tensor1, tensor2)
print(input, tensor1.shape, tensor2.shape, output,out.shape)
# matrix x matrix case 2
tensor1 torch.randn(3, 1)
tensor2 torch.randn(1, 3)
out torch.matmul(tensor1, tensor2)
print(input, tensor1.shape, tensor2.shape, output,out.shape)# matrix x vector case 3
tensor1 torch.randn(3, 4)
tensor2 torch.randn(4)
out torch.matmul(tensor1, tensor2)
print(input, tensor1.shape, tensor2.shape, output,out.shape)# batched matrix x broadcasted vector
tensor1 torch.randn(10, 3, 4) # first dim is batch dim
tensor2 torch.randn(4)
out torch.matmul(tensor1, tensor2)
print(input, tensor1.shape, tensor2.shape, output,out.shape)# batched matrix x batched matrix
tensor1 torch.randn(10, 3, 4) # first dim is batch dim
tensor2 torch.randn(10, 4, 5) # first dim is batch dim
out torch.matmul(tensor1, tensor2)
# matrix product (3 * 4 ) * (4 * 5) - 3 * 5
print(input, tensor1.shape, tensor2.shape, output,out.shape) # batched matrix x broadcasted matrix
tensor1 torch.randn(10, 3, 4) # first dim is batch dim
tensor2 torch.randn(4, 5)
out torch.matmul(tensor1, tensor2)
# matrix product (3 * 4 ) * (4 * 5) - 3 * 5
print(input, tensor1.shape, tensor2.shape, output,out.shape) input torch.Size([3]) torch.Size([3]) output torch.Size([])
input torch.Size([3, 1]) torch.Size([1, 3]) output torch.Size([3, 3])
input torch.Size([3, 4]) torch.Size([4]) output torch.Size([3])
input torch.Size([10, 3, 4]) torch.Size([4]) output torch.Size([10, 3])
input torch.Size([10, 3, 4]) torch.Size([10, 4, 5]) output torch.Size([10, 3, 5])
input torch.Size([10, 3, 4]) torch.Size([4, 5]) output torch.Size([10, 3, 5])3.3 pow矩阵的次方以及sqrt/rsqrt/exp/log
a torch.full([2, 2], 3) # 使用torch.full函数创建一个shape[2, 2],元素全部为3的张量
print(a.pow(2))
print(torch.pow(a, 2))
print(a**2)b a**2
print(b.sqrt())
print(b.rsqrt()) # 平方根的导数print()
a torch.exp(torch.ones(2, 2))
print(a)
print(torch.log(a)) # 默认以e为底,使用2为底或者其他的自己设置.
tensor([[9, 9],[9, 9]])
tensor([[9, 9],[9, 9]])
tensor([[9, 9],[9, 9]])
tensor([[3., 3.],[3., 3.]])
tensor([[0.3333, 0.3333],[0.3333, 0.3333]])tensor([[2.7183, 2.7183],[2.7183, 2.7183]])
tensor([[1., 1.],[1., 1.]])3.4 round矩阵近似运算
.floor()向下取整.ceil()向上取整.trunc()截取整数.frac截取小数。
a torch.tensor(3.14)
# .floor()向下取整.ceil()向上取整.trunc()截取整数.frac截取小数。
print(a.floor(), a.ceil(), a.trunc(), a.frac())print(a.round())
b torch.tensor(3.5)
print(b.round())tensor(3.) tensor(4.) tensor(3.) tensor(0.1400)
tensor(3.)
tensor(4.)3.6. clamp(裁剪)用的多
主要用在梯度裁剪里面梯度离散(不需要从网络层面解决因为梯度非常小接近0)和梯度爆炸(梯度非常大,100已经算是大的了)。因此在网络训练不稳定的时候可以打印一下梯度的模看看w.grad.norm(2)表示梯度的二范数(一般100,1000已经算是大的了一般10以内算是合适的)。
a.clamp(min):表示tensor a中小于10的都赋值为10表示最小值为10grad torch.rand(2, 3)*15
print(grad)
print(grad.max(), grad.median(), grad.min())
print()
print(grad.clamp(10)) # 最小值限定为10小于10的都变为10print(grad.clamp(8, 15))
print(torch.clamp(grad, 8, 15))
tensor([[ 3.7078, 11.4988, 7.9875],[ 0.6747, 0.6269, 12.3761]])
tensor(12.3761) tensor(3.7078) tensor(0.6269)tensor([[10.0000, 11.4988, 10.0000],[10.0000, 10.0000, 12.3761]])
tensor([[ 8.0000, 11.4988, 8.0000],[ 8.0000, 8.0000, 12.3761]])
tensor([[ 8.0000, 11.4988, 8.0000],[ 8.0000, 8.0000, 12.3761]])4. 统计属性相关操作 TODO
4.1. norm范数,prod张量元素累乘(阶乘)
4.2. mean/sum/max/min/argmin/argmax
4.3. kthvalue()和topk()
这里 topk(3, dim1)(最大的3个)返回结果如下图所示如果把largest设置为False就是默认最小的几个。
这里 kthvalue(kdim1)表示第k小的(默认表示小的)。下面图中的一共10中可能第8小就是表示第3大。4.4. 比较运算符号,,,,!,
greater than表示大于等于。equal表示等于eq。5. batch检索
torch.where, gather, index_select, masked_select, nonzero函数
TODO: https://www.cnblogs.com/liangjianli/p/13754817.html#3-gather%E5%87%BD%E6%95%B0
5.1 torch.where
高阶操作where和gather
5.2 torch.gather
TODO 依旧不是很理解没有完全理解 gather 收集输入的特定维度指定位置的数值
注意torch.gather里的index tensor必须是longtensor类型
官方解释 https://pytorch.org/docs/stable/generated/torch.gather.html?highlightgather#torch.gather
out[i][j][k] input[index[i][j][k]][j][k] # if dim 0
out[i][j][k] input[i][index[i][j][k]][k] # if dim 1
out[i][j][k] input[i][j][index[i][j][k]] # if dim 2a torch.rand([4, 5])
indexes torch.LongTensor([[4, 2],[3, 1],[2, 4],[1,2]])# 这个在q-learning中的batch actions中常见注意一下
a_ torch.gather(a, dim 1, index indexes)print(a.shape, a_.shape, \n, a, \n, a_)indexes torch.LongTensor([[ 0, 2, 2], [1, 2, 3]])
a_ torch.gather(a, dim 0, index indexes)
print(a.shape, a_.shape, \n, a, \n, a_)torch.Size([4, 5]) torch.Size([4, 2]) tensor([[0.1330, 0.2177, 0.2177, 0.3287, 0.0809],[0.1803, 0.4438, 0.9827, 0.6353, 0.3548],[0.4980, 0.6792, 0.3885, 0.6338, 0.4985],[0.8367, 0.4682, 0.0805, 0.6161, 0.4861]]) tensor([[0.0809, 0.2177],[0.6353, 0.4438],[0.3885, 0.4985],[0.4682, 0.0805]])
torch.Size([4, 5]) torch.Size([2, 3]) tensor([[0.1330, 0.2177, 0.2177, 0.3287, 0.0809],[0.1803, 0.4438, 0.9827, 0.6353, 0.3548],[0.4980, 0.6792, 0.3885, 0.6338, 0.4985],[0.8367, 0.4682, 0.0805, 0.6161, 0.4861]]) tensor([[0.1330, 0.6792, 0.3885],[0.1803, 0.6792, 0.0805]])10. torch vision下的一些操作
10.1 F.grid_sample
根据位置 对 batch 里的 feature flow 进行采样可以理解为index, 但本函数允许index里为float类型采用bilinear插值实现 函数
torch.nn.functional.grid_sample(input, grid, modebilinear, padding_modezeros, align_cornersNone) iven an input and a flow-field grid, computes the output using input values and pixel locations from grid.
input: (N, C, Hin, Win) grid: (N, Hout, Wout, 2) , 其中最后一维为opencv 坐标系下的x y,
output: (N, C, Hout, Wout)
NOTE输入的grid,需根据 input 的Hin, Win进行归一化到 -1 1
grid specifies the sampling pixel locations normalized by the input spatial dimensions.
Therefore, it should have most values in the range of [-1, 1]. For example, values x -1, y -1 is the left-top pixel of input, and values x 1, y 1 is the right-bottom pixel of input.
参考资料https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html?highlightgrid_sample#torch.nn.functional.grid_sample 以下通过图片展示如何使用:
DEVICE cudadef load_image(imfile):img np.array(Image.open(imfile)).astype(np.uint8)img torch.from_numpy(img).permute(2, 0, 1).float() # h, w, dim - dim, h, wreturn img.to(DEVICE)def visualize(img):#input imgshape: 3, w, h# NOTE, dims convert, device convert, numpy convert, and dtype convertreturn img.permute(1, 2, 0).cpu().numpy().astype(np.uint8)def coords_grid(batch, ht, wd, device, ifReverse True):#ht,wd 55, 128coords torch.meshgrid(torch.arange(ht, devicedevice), torch.arange(wd, devicedevice))if ifReverse:#NOTE, coords 为 x, y opencv 坐标系下, y, x (numpy 坐标系)coords torch.stack(coords[::-1], dim0).float() # coords shape [ 2, ht, wd]else:#NOTE coords 为 x, y (numpy 坐标系下)coords torch.stack(coords, dim0).float()# 通过[::-1]转变为 y, x(numpy 坐标系)#coords torch.reshape(coords, [1, 2, ht, wd])coords coords.repeat(batch, 1, 1, 1)return coords
# 载入图片并显示
img1, img2 load_image(demo-frames/frame_0016.png), load_image(demo-frames/frame_0017.png)inputImg torch.cat([img1.unsqueeze(0), img2.unsqueeze(0)], 0)
print(img1.shape, img2.shape, inputImg.shape)#------ visualize -----------
img1_, img2_ visualize(img1), visualize(img2)plt.subplot(2, 1, 1)
plt.imshow(img1_)plt.subplot(2, 1, 2)
plt.imshow(img2_)
plt.show()torch.Size([3, 436, 1024]) torch.Size([3, 436, 1024]) torch.Size([2, 3, 436, 1024])[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-zA8xwrlK-1692628397284)(output_36_1.png)] # 生成grid
hImg, wImg img1.shape[-2:]grid coords_grid(batch 2, ht 100, wd 100, device cuda, ifReverse True)
print(grid.shape) # dim 1: y x(numpy 坐标系) x y (opencv坐标系)
torch.Size([2, 2, 100, 100])# grid归一化操作
grid grid.permute(0, 2, 3, 1)
ygrid, xgrid grid.split([1, 1], dim -1) # 这里的xgrid, ygrid指的是 numpy 坐标系
print(grid.shape, xgrid.shape, ygrid.shape)ygrid 2 * ygrid/(wImg - 1) -1
xgrid 2 * xgrid/(hImg - 1) -1grid torch.cat([ygrid, xgrid], dim-1)
grid grid.to(DEVICE)
# 输入的grid 最后一维为 opencv坐标系下的x yinputImg_ F.grid_sample(inputImg, grid, align_cornersTrue) # size 7040, 1, 9,9
torch.Size([2, 100, 100, 2]) torch.Size([2, 100, 100, 1]) torch.Size([2, 100, 100, 1])plt.subplot(1, 2, 1)
img_i visualize(inputImg[0, :, :, :])
plt.imshow(img_i)
plt.subplot(1, 2, 2)img_o visualize(inputImg_[0, :, :, :])
plt.imshow(img_o)
plt.show()print(inputImg.shape, img_i.shape, img_o.shape)[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-l9dvPqBJ-1692628397285)(output_39_0.png)]
torch.Size([2, 3, 436, 1024]) (436, 1024, 3) (100, 100, 3)上/下采样 方法1
F.interpolate
功能利用插值方法对输入的张量数组进行上\下采样操作换句话说就是科学合理地改变数组的尺寸大小尽量保持数据完整。
官方文档: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html?highlightf%20interpolate#torch.nn.functional.interpolate
img1, img2 load_image(demo-frames/frame_0016.png), load_image(demo-frames/frame_0017.png)
inputImg torch.cat([img1.unsqueeze(0), img2.unsqueeze(0)], 0)
print(img1.shape, img2.shape, inputImg.shape)
torch.Size([3, 436, 1024]) torch.Size([3, 436, 1024]) torch.Size([2, 3, 436, 1024])new_size (2* inputImg.shape[2], 2 * inputImg.shape[3])
mode bilinear
inputImg_ F.interpolate(inputImg, sizenew_size, modemode, align_cornersTrue)plt.subplot(2, 1, 1)
img_i visualize(inputImg[0, :, :, :])
plt.imshow(img_i)
plt.subplot(2, 1, 2)
img_o visualize(inputImg_[0, :, :, :])
plt.imshow(img_o)
plt.show()print(inputImg.shape, img_i.shape, img_o.shape)[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-CNtrY6FN-1692628397286)(output_43_0.png)] torch.Size([2, 3, 436, 1024]) (436, 1024, 3) (872, 2048, 3)
