Source code for pywick.models.classification.pyramid_resnet

"""
Implementation from paper: `Deep Pyramidal Residual Networks <https://arxiv.org/abs/1610.02915>`_.
Not pretrained.
"""

import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F

__all__ = ['PyResNet18', 'PyResNet34', 'PyResNet']

def make_conv_bn_relu(in_channels, out_channels, kernel_size=3, stride=1, padding=1, groups=1):
    return [
        nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, bias=False),
        nn.BatchNorm2d(out_channels),
        nn.ReLU(inplace=True),
    ]

def make_linear_bn_relu(in_channels, out_channels):
    return [
        nn.Linear(in_channels, out_channels, bias=False),
        nn.BatchNorm1d(out_channels),
        nn.ReLU(inplace=True),
    ]


def make_max_flat(out):
    flat = F.adaptive_max_pool2d(out,output_size=1)  ##nn.AdaptiveMaxPool2d(1)(out)
    flat = flat.view(flat.size(0), -1)
    return flat


def make_avg_flat(out):
    flat = F.adaptive_avg_pool2d(out,output_size=1)
    flat = flat.view(flat.size(0), -1)
    return flat


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1   = nn.BatchNorm2d(planes)
        self.relu  = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2   = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out





[docs]class PyResNet(nn.Module): def __init__(self, block, layers, in_shape=(3,256,256), num_classes=17): self.inplanes = 64 super(PyResNet, self).__init__() in_channels, height, width = in_shape # self.conv0 = nn.Sequential( # *make_conv_bn_relu(in_channels, 64, kernel_size=7, stride=2, padding=3, groups=1) # ) self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.layer1 = self.make_layer(block, 64, layers[0]) self.layer2 = self.make_layer(block, 128, layers[1], stride=2) self.layer3 = self.make_layer(block, 256, layers[2], stride=2) self.layer4 = self.make_layer(block, 512, layers[3], stride=2) self.fc2 = nn.Sequential( *make_linear_bn_relu(128 * block.expansion, 512), nn.Linear(512, num_classes), ) self.fc3 = nn.Sequential( *make_linear_bn_relu(256 * block.expansion, 512), nn.Linear(512, num_classes), ) self.fc4 = nn.Sequential( *make_linear_bn_relu(512 * block.expansion, 512), nn.Linear(512, num_classes), ) # self.fc = nn.Sequential( # *make_linear_bn_relu((128+256+512) * block.expansion, 1024), # nn.Linear(1024, num_classes) # ) # for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_()
[docs] def make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers)
def forward(self, x): #x = self.conv0(x) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) x = self.layer1(x) # 64, 64x64 x = self.layer2(x) #128, 32x32 flat2 = make_max_flat(x) ##make_avg_flat x = self.layer3(x) #256, 16x16 flat3 = make_max_flat(x) x = self.layer4(x) #512, 8x8 flat4 = make_max_flat(x) # x = torch.cat([flat2,flat3,flat4,],1) # x = self.fc(x) x = self.fc2(flat2) + self.fc3(flat3) + self.fc4(flat4) logit = x prob = torch.sigmoid(logit) return logit, prob
[docs]def PyResNet18(pretrained=None, **kwargs): """Not Pretrained""" if pretrained: raise NotImplementedError() model = PyResNet(BasicBlock, [2, 2, 2, 2], **kwargs) return model
[docs]def PyResNet34(pretrained=None, **kwargs): """Not Pretrained""" if pretrained: raise NotImplementedError() model = PyResNet(BasicBlock, [3, 4, 6, 3], **kwargs) return model
######################################################################################## if __name__ == '__main__': print( '%s: calling main function ... ' % os.path.basename(__file__)) # https://discuss.pytorch.org/t/print-autograd-graph/692/8 batch_size = 1 num_classes = 17 C,H,W = 3,256,256 inputs = torch.randn(batch_size,C,H,W) labels = torch.randn(batch_size,num_classes) in_shape = inputs.size()[1:] if 1: net = PyResNet34(in_shape=in_shape, num_classes=num_classes).cuda().train() x = inputs logits, probs = net.forward(x.cuda()) loss = nn.MultiLabelSoftMarginLoss()(logits, labels.cuda()) loss.backward() print(type(net)) print(net) print('probs') print(probs) #input('Press ENTER to continue.')