可变形卷积pytorch版本解读

1. 前言

1：import argparse 2：parser = argparse.ArgumentParser() 3：parser.add_argument() 4：parser.parse_args() 

torch.utils.data.DataLoader：PyTorch中数据读取的一个重要接口是torch.utils.data.DataLoader，该接口定义在dataloader.py脚本中，只要是用PyTorch来训练模型基本都会用到该接口，

注意：

该接口主要用来将自定义的数据读取接口的输出或者PyTorch已有的数据读取接口的输入按照batch size封装成Tensor，后续只需要再包装成Variable即可作为模型的输入，

因此该接口有点承上启下的作用，比较重要。

2. test.py

from __future__ import print_function import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.autograd import Variable from deform_conv import DeformConv2D from time import time # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=32, metavar='N', help='input batch size for training (default: 32)') parser.add_argument('--test-batch-size', type=int, default=32, metavar='N', help='input batch size for testing (default: 32)') parser.add_argument('--epochs', type=int, default=10, metavar='N', help='number of epochs to train (default: 10)') parser.add_argument('--lr', type=float, default=0.01, metavar='LR', help='learning rate (default: 0.01)') parser.add_argument('--momentum', type=float, default=0.5, metavar='M', help='SGD momentum (default: 0.5)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() ''' 编写与设备无关的代码 （可用时受益于 GPU 加速，不可用时会倒退回 CPU）时，选择并保存适当的 torch.device, 不失为一种好方法，它可用于确定存储张量的位置。 device = torch.device('cuda' if args.cuda else 'cpu') ''' torch.manual_seed(args.seed) #为CPU设置种子用于生成随机数，以使得结果是确定的 if args.cuda: '''为当前GPU设置随机种子； 如果使用多个GPU，应该使用torch.cuda.manual_seed_all()为所有的GPU设置种子。 ''' torch.cuda.manual_seed(args.seed) kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} """ 加载数据。组合数据集和采样器，提供数据上的单或多进程迭代器 参数： dataset：Dataset类型，从其中加载数据 batch_size：int，可选。每个batch加载多少样本 shuffle：bool，可选。为True时表示每个epoch都对数据进行洗牌 sampler：Sampler，可选。从数据集中采样样本的方法。 num_workers：int，可选。加载数据时使用多少子进程。默认值为0，表示在主进程中加载数据。 collate_fn：callable，可选。 pin_memory：bool，可选 drop_last：bool，可选。True表示如果最后剩下不完全的batch,丢弃。False表示不丢弃。 """ train_loader = torch.utils.data.DataLoader( datasets.MNIST('./MNIST', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.batch_size, shuffle=True, kwargs) test_loader = torch.utils.data.DataLoader( datasets.MNIST('./MNIST', train=False, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.test_batch_size, shuffle=True, kwargs) class DeformNet(nn.Module): def __init__(self): super(DeformNet, self).__init__() self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1) self.bn1 = nn.BatchNorm2d(32) self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) self.bn2 = nn.BatchNorm2d(64) self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1) self.bn3 = nn.BatchNorm2d(128) self.offsets = nn.Conv2d(128, 18, kernel_size=3, padding=1) self.conv4 = DeformConv2D(128, 128, kernel_size=3, padding=1) self.bn4 = nn.BatchNorm2d(128) self.classifier = nn.Linear(128, 10) def forward(self, x): # convs x = F.relu(self.conv1(x)) x = self.bn1(x) x = F.relu(self.conv2(x)) x = self.bn2(x) x = F.relu(self.conv3(x)) x = self.bn3(x) # deformable convolution offsets = self.offsets(x) x = F.relu(self.conv4(x, offsets)) x = self.bn4(x) x = F.avg_pool2d(x, kernel_size=28, stride=1).view(x.size(0), -1) x = self.classifier(x) return F.log_softmax(x, dim=1) class PlainNet(nn.Module): def __init__(self): super(PlainNet, self).__init__() self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1) self.bn1 = nn.BatchNorm2d(32) self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) self.bn2 = nn.BatchNorm2d(64) self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1) self.bn3 = nn.BatchNorm2d(128) self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1) self.bn4 = nn.BatchNorm2d(128) self.classifier = nn.Linear(128, 10) def forward(self, x): # convs x = F.relu(self.conv1(x)) x = self.bn1(x) x = F.relu(self.conv2(x)) x = self.bn2(x) x = F.relu(self.conv3(x)) x = self.bn3(x) x = F.relu(self.conv4(x)) x = self.bn4(x) x = F.avg_pool2d(x, kernel_size=28, stride=1).view(x.size(0), -1) x = self.classifier(x) return F.log_softmax(x, dim=1) model = DeformNet() ''' 将所有的模型参数移动到GPU上 if args.cuda: model.cuda()将所有的模型参数移动到GPU上 ''' def init_weights(m): if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): nn.init.xavier_uniform(m.weight, gain=nn.init.calculate_gain('relu')) if m.bias is not None: m.bias.data = torch.FloatTensor(m.bias.shape[0]).zero_() def init_conv_offset(m): m.weight.data = torch.zeros_like(m.weight.data) if m.bias is not None: m.bias.data = torch.FloatTensor(m.bias.shape[0]).zero_() model.apply(init_weights) model.offsets.apply(init_conv_offset) if args.cuda: model.cuda() optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) def train(epoch): model.train() #把module设成training模式，对Dropout和BatchNorm有影响 for batch_idx, (data, target) in enumerate(train_loader): ''' Variable类对Tensor对象进行封装，会保存该张量对应的梯度，以及对生成该张量的函数grad_fn的一个引用。 如果该张量是用户创建的，grad_fn是None，称这样的Variable为叶子Variable。 ''' data, target = Variable(data), Variable(target) if args.cuda: data, target = data.cuda(), target.cuda() optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) #负log似然损失 loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.data[0])) def test(): model.eval() #把module设置为评估模式，只对Dropout和BatchNorm模块有影响 test_loss = 0 correct = 0 for data, target in test_loader: if args.cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data, volatile=True), Variable(target) output = model(data) test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability correct += pred.eq(target.data.view_as(pred)).cpu().sum() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) for epoch in range(1, args.epochs + 1): since = time() train(epoch) iter = time() - since print("Spends {}s for each training epoch".format(iter/args.epochs)) test() 

3. deform_conv.py

from torch.autograd import Variable, Function import torch from torch import nn import numpy as np class DeformConv2D(nn.Module): def __init__(self, inc, outc, kernel_size=3, padding=1, bias=None): super(DeformConv2D, self).__init__() self.kernel_size = kernel_size self.padding = padding self.zero_padding = nn.ZeroPad2d(padding) self.conv_kernel = nn.Conv2d(inc, outc, kernel_size=kernel_size, stride=kernel_size, bias=bias) def forward(self, x, offset): dtype = offset.data.type() ks = self.kernel_size N = offset.size(1) // 2 # Change offset's order from [x1, x2, ..., y1, y2, ...] to [x1, y1, x2, y2, ...] # Codes below are written to make sure same results of MXNet implementation. # You can remove them, and it won't influence the module's performance. offsets_index = Variable(torch.cat([torch.arange(0, 2*N, 2), torch.arange(1, 2*N+1, 2)]), requires_grad=False).type_as(x).long() offsets_index = offsets_index.unsqueeze(dim=0).unsqueeze(dim=-1).unsqueeze(dim=-1).expand(*offset.size()) offset = torch.gather(offset, dim=1, index=offsets_index) # ------------------------------------------------------------------------ if self.padding: x = self.zero_padding(x) # (b, 2N, h, w) p = self._get_p(offset, dtype) # (b, h, w, 2N) p = p.contiguous().permute(0, 2, 3, 1) q_lt = Variable(p.data, requires_grad=False).floor() q_rb = q_lt + 1 q_lt = torch.cat([torch.clamp(q_lt[..., :N], 0, x.size(2)-1), torch.clamp(q_lt[..., N:], 0, x.size(3)-1)], dim=-1).long() q_rb = torch.cat([torch.clamp(q_rb[..., :N], 0, x.size(2)-1), torch.clamp(q_rb[..., N:], 0, x.size(3)-1)], dim=-1).long() q_lb = torch.cat([q_lt[..., :N], q_rb[..., N:]], -1) q_rt = torch.cat([q_rb[..., :N], q_lt[..., N:]], -1) # (b, h, w, N) mask = torch.cat([p[..., :N].lt(self.padding)+p[..., :N].gt(x.size(2)-1-self.padding), p[..., N:].lt(self.padding)+p[..., N:].gt(x.size(3)-1-self.padding)], dim=-1).type_as(p) mask = mask.detach() floor_p = p - (p - torch.floor(p)) p = p*(1-mask) + floor_p*mask p = torch.cat([torch.clamp(p[..., :N], 0, x.size(2)-1), torch.clamp(p[..., N:], 0, x.size(3)-1)], dim=-1) # bilinear kernel (b, h, w, N) g_lt = (1 + (q_lt[..., :N].type_as(p) - p[..., :N])) * (1 + (q_lt[..., N:].type_as(p) - p[..., N:])) g_rb = (1 - (q_rb[..., :N].type_as(p) - p[..., :N])) * (1 - (q_rb[..., N:].type_as(p) - p[..., N:])) g_lb = (1 + (q_lb[..., :N].type_as(p) - p[..., :N])) * (1 - (q_lb[..., N:].type_as(p) - p[..., N:])) g_rt = (1 - (q_rt[..., :N].type_as(p) - p[..., :N])) * (1 + (q_rt[..., N:].type_as(p) - p[..., N:])) # (b, c, h, w, N) x_q_lt = self._get_x_q(x, q_lt, N) x_q_rb = self._get_x_q(x, q_rb, N) x_q_lb = self._get_x_q(x, q_lb, N) x_q_rt = self._get_x_q(x, q_rt, N) # (b, c, h, w, N) x_offset = g_lt.unsqueeze(dim=1) * x_q_lt + \ g_rb.unsqueeze(dim=1) * x_q_rb + \ g_lb.unsqueeze(dim=1) * x_q_lb + \ g_rt.unsqueeze(dim=1) * x_q_rt x_offset = self._reshape_x_offset(x_offset, ks) out = self.conv_kernel(x_offset) return out def _get_p_n(self, N, dtype): p_n_x, p_n_y = np.meshgrid(range(-(self.kernel_size-1)//2, (self.kernel_size-1)//2+1), range(-(self.kernel_size-1)//2, (self.kernel_size-1)//2+1), indexing='ij') # (2N, 1) p_n = np.concatenate((p_n_x.flatten(), p_n_y.flatten())) p_n = np.reshape(p_n, (1, 2*N, 1, 1)) p_n = Variable(torch.from_numpy(p_n).type(dtype), requires_grad=False) return p_n @staticmethod def _get_p_0(h, w, N, dtype): p_0_x, p_0_y = np.meshgrid(range(1, h+1), range(1, w+1), indexing='ij') p_0_x = p_0_x.flatten().reshape(1, 1, h, w).repeat(N, axis=1) p_0_y = p_0_y.flatten().reshape(1, 1, h, w).repeat(N, axis=1) p_0 = np.concatenate((p_0_x, p_0_y), axis=1) p_0 = Variable(torch.from_numpy(p_0).type(dtype), requires_grad=False) return p_0 def _get_p(self, offset, dtype): N, h, w = offset.size(1)//2, offset.size(2), offset.size(3) # (1, 2N, 1, 1) p_n = self._get_p_n(N, dtype) # (1, 2N, h, w) p_0 = self._get_p_0(h, w, N, dtype) p = p_0 + p_n + offset return p def _get_x_q(self, x, q, N): b, h, w, _ = q.size() padded_w = x.size(3) c = x.size(1) # (b, c, h*w) x = x.contiguous().view(b, c, -1) # (b, h, w, N) index = q[..., :N]*padded_w + q[..., N:] # offset_x*w + offset_y # (b, c, h*w*N) index = index.contiguous().unsqueeze(dim=1).expand(-1, c, -1, -1, -1).contiguous().view(b, c, -1) x_offset = x.gather(dim=-1, index=index).contiguous().view(b, c, h, w, N) return x_offset @staticmethod def _reshape_x_offset(x_offset, ks): b, c, h, w, N = x_offset.size() x_offset = torch.cat([x_offset[..., s:s+ks].contiguous().view(b, c, h, w*ks) for s in range(0, N, ks)], dim=-1) x_offset = x_offset.contiguous().view(b, c, h*ks, w*ks) return x_offset 

4. 最后说明

可变形卷积在网络结构书写时，需要经过以下两步

self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1) self.bn3 = nn.BatchNorm2d(128) self.offsets = nn.Conv2d(128, 18, kernel_size=3, padding=1) self.conv4 = DeformConv2D(128, 128, kernel_size=3, padding=1)