利用pytorch复现spatial pyramid pooling层
Posted 琴影
tags:
篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了利用pytorch复现spatial pyramid pooling层相关的知识,希望对你有一定的参考价值。
sppnet不讲了,懒得写。。。直接上代码
1 from math import floor, ceil 2 import torch 3 import torch.nn as nn 4 import torch.nn.functional as F 5 6 class SpatialPyramidPooling2d(nn.Module): 7 r"""apply spatial pyramid pooling over a 4d input(a mini-batch of 2d inputs 8 with additional channel dimension) as described in the paper 9 ‘Spatial Pyramid Pooling in deep convolutional Networks for visual recognition‘ 10 Args: 11 num_level: 12 pool_type: max_pool, avg_pool, Default:max_pool 13 By the way, the target output size is num_grid: 14 num_grid = 0 15 for i in range num_level: 16 num_grid += (i + 1) * (i + 1) 17 num_grid = num_grid * channels # channels is the channel dimension of input data 18 examples: 19 >>> input = torch.randn((1,3,32,32), dtype=torch.float32) 20 >>> net = torch.nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size=3,stride=1),21 nn.ReLU(),22 SpatialPyramidPooling2d(num_level=2,pool_type=‘avg_pool‘),23 nn.Linear(32 * (1*1 + 2*2), 10)) 24 >>> output = net(input) 25 """ 26 27 def __init__(self, num_level, pool_type=‘max_pool‘): 28 super(SpatialPyramidPooling2d, self).__init__() 29 self.num_level = num_level 30 self.pool_type = pool_type 31 32 def forward(self, x): 33 N, C, H, W = x.size() 34 for i in range(self.num_level): 35 level = i + 1 36 kernel_size = (ceil(H / level), ceil(W / level)) 37 stride = (ceil(H / level), ceil(W / level)) 38 padding = (floor((kernel_size[0] * level - H + 1) / 2), floor((kernel_size[1] * level - W + 1) / 2)) 39 40 if self.pool_type == ‘max_pool‘: 41 tensor = (F.max_pool2d(x, kernel_size=kernel_size, stride=stride, padding=padding)).view(N, -1) 42 else: 43 tensor = (F.avg_pool2d(x, kernel_size=kernel_size, stride=stride, padding=padding)).view(N, -1) 44 45 if i == 0: 46 res = tensor 47 else: 48 res = torch.cat((res, tensor), 1) 49 return res 50 def __repr__(self): 51 return self.__class__.__name__ + ‘(‘ 52 + ‘num_level = ‘ + str(self.num_level) 53 + ‘, pool_type = ‘ + str(self.pool_type) + ‘)‘ 54 55 56 class SPPNet(nn.Module): 57 def __init__(self, num_level=3, pool_type=‘max_pool‘): 58 super(SPPNet,self).__init__() 59 self.num_level = num_level 60 self.pool_type = pool_type 61 self.feature = nn.Sequential(nn.Conv2d(3,64,3),62 nn.ReLU(),63 nn.MaxPool2d(2),64 nn.Conv2d(64,64,3),65 nn.ReLU()) 66 self.num_grid = self._cal_num_grids(num_level) 67 self.spp_layer = SpatialPyramidPooling2d(num_level) 68 self.linear = nn.Sequential(nn.Linear(self.num_grid * 64, 512),69 nn.Linear(512, 10)) 70 def _cal_num_grids(self, level): 71 count = 0 72 for i in range(level): 73 count += (i + 1) * (i + 1) 74 return count 75 76 def forward(self, x): 77 x = self.feature(x) 78 x = self.spp_layer(x) 79 print(x.size()) 80 x = self.linear(x) 81 return x 82 83 if __name__ == ‘__main__‘: 84 a = torch.rand((1,3,64,64)) 85 net = SPPNet() 86 output = net(a) 87 print(output)
以上是关于利用pytorch复现spatial pyramid pooling层的主要内容,如果未能解决你的问题,请参考以下文章
facenet-retinaface快速复现 实现 facenet-retinaface-pytorch 人脸识别 windows上 使用cpu实现
facenet-retinaface快速复现 实现 facenet-retinaface-pytorch 人脸识别 windows上 使用cpu实现
每天讲解一点PyTorch 17Spatial Affinity代码实现分析
每天讲解一点PyTorch 17Spatial Affinity代码实现分析