224 lines
7.6 KiB
Python
224 lines
7.6 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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import torch
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from torch import nn
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from torch.nn import functional as F
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import torch.utils.model_zoo as model_zoo
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__all__ = ['mobilenetv2_1dot0', 'mobilenetv2_1dot4']
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model_urls = {
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# 1.0: top-1 71.3
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'mobilenetv2_1dot0': 'http://eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/mobilenetv2_1.0-0f5d2d8f.pth',
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# 1.4: top-1 73.9
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'mobilenetv2_1dot4': 'http://eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/mobilenetv2_1.4-4d0d3520.pth',
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}
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class ConvBlock(nn.Module):
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"""Basic convolutional block:
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convolution (bias discarded) + batch normalization + relu6.
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Args (following http://pytorch.org/docs/master/nn.html#torch.nn.Conv2d):
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in_c (int): number of input channels.
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out_c (int): number of output channels.
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k (int or tuple): kernel size.
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s (int or tuple): stride.
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p (int or tuple): padding.
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g (int): number of blocked connections from input channels
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to output channels (default: 1).
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"""
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def __init__(self, in_c, out_c, k, s=1, p=0, g=1):
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super(ConvBlock, self).__init__()
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self.conv = nn.Conv2d(in_c, out_c, k, stride=s, padding=p, bias=False, groups=g)
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self.bn = nn.BatchNorm2d(out_c)
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def forward(self, x):
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return F.relu6(self.bn(self.conv(x)))
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class Bottleneck(nn.Module):
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def __init__(self, in_channels, out_channels, expansion_factor, stride=1):
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super(Bottleneck, self).__init__()
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mid_channels = in_channels * expansion_factor
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self.use_residual = stride == 1 and in_channels == out_channels
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self.conv1 = ConvBlock(in_channels, mid_channels, 1)
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self.dwconv2 = ConvBlock(mid_channels, mid_channels, 3, stride, 1, g=mid_channels)
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self.conv3 = nn.Sequential(
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nn.Conv2d(mid_channels, out_channels, 1, bias=False),
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nn.BatchNorm2d(out_channels),
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)
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def forward(self, x):
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m = self.conv1(x)
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m = self.dwconv2(m)
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m = self.conv3(m)
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if self.use_residual:
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return x + m
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else:
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return m
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class MobileNetV2(nn.Module):
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"""
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MobileNetV2
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Reference:
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Sandler et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR 2018.
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"""
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def __init__(self, num_classes, width_mult=1, loss={'xent'}, fc_dims=None, dropout_p=None, **kwargs):
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super(MobileNetV2, self).__init__()
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self.loss = loss
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self.in_channels = int(32 * width_mult)
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self.feature_dim = int(1280 * width_mult) if width_mult > 1 else 1280
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# construct layers
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self.conv1 = ConvBlock(3, self.in_channels, 3, s=2, p=1)
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self.conv2 = self._make_layer(Bottleneck, 1, int(16 * width_mult), 1, 1)
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self.conv3 = self._make_layer(Bottleneck, 6, int(24 * width_mult), 2, 2)
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self.conv4 = self._make_layer(Bottleneck, 6, int(32 * width_mult), 3, 2)
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self.conv5 = self._make_layer(Bottleneck, 6, int(64 * width_mult), 4, 2)
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self.conv6 = self._make_layer(Bottleneck, 6, int(96 * width_mult), 3, 1)
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self.conv7 = self._make_layer(Bottleneck, 6, int(160 * width_mult), 3, 2)
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self.conv8 = self._make_layer(Bottleneck, 6, int(320 * width_mult), 1, 1)
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self.conv9 = ConvBlock(self.in_channels, self.feature_dim, 1)
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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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self.fc = self._construct_fc_layer(fc_dims, self.feature_dim, dropout_p)
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self.classifier = nn.Linear(self.feature_dim, num_classes)
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self._init_params()
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def _make_layer(self, block, t, c, n, s):
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# t: expansion factor
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# c: output channels
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# n: number of blocks
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# s: stride for first layer
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layers = []
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layers.append(block(self.in_channels, c, t, s))
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self.in_channels = c
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for i in range(1, n):
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layers.append(block(self.in_channels, c, t))
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return nn.Sequential(*layers)
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def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
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"""
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Construct fully connected layer
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- fc_dims (list or tuple): dimensions of fc layers, if None,
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no fc layers are constructed
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- input_dim (int): input dimension
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- dropout_p (float): dropout probability, if None, dropout is unused
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"""
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if fc_dims is None:
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self.feature_dim = input_dim
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return None
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assert isinstance(fc_dims, (list, tuple)), 'fc_dims must be either list or tuple, but got {}'.format(type(fc_dims))
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layers = []
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for dim in fc_dims:
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layers.append(nn.Linear(input_dim, dim))
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layers.append(nn.BatchNorm1d(dim))
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layers.append(nn.ReLU(inplace=True))
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if dropout_p is not None:
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layers.append(nn.Dropout(p=dropout_p))
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input_dim = dim
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self.feature_dim = fc_dims[-1]
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return nn.Sequential(*layers)
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def _init_params(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm1d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def featuremaps(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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x = self.conv3(x)
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x = self.conv4(x)
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x = self.conv5(x)
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x = self.conv6(x)
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x = self.conv7(x)
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x = self.conv8(x)
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x = self.conv9(x)
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return x
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def forward(self, x):
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f = self.featuremaps(x)
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v = self.global_avgpool(f)
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v = v.view(v.size(0), -1)
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if self.fc is not None:
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v = self.fc(v)
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if not self.training:
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return v
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y = self.classifier(v)
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if self.loss == {'xent'}:
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return y
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elif self.loss == {'xent', 'htri'}:
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return y, v
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else:
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raise KeyError("Unsupported loss: {}".format(self.loss))
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def init_pretrained_weights(model, model_url):
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"""
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Initialize model with pretrained weights.
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Layers that don't match with pretrained layers in name or size are kept unchanged.
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"""
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pretrain_dict = model_zoo.load_url(model_url)
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model_dict = model.state_dict()
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pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
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model_dict.update(pretrain_dict)
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model.load_state_dict(model_dict)
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print('Initialized model with pretrained weights from {}'.format(model_url))
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def mobilenetv2_1dot0(num_classes, loss, pretrained=True, **kwargs):
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model = MobileNetV2(
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num_classes,
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loss=loss,
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width_mult=1,
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fc_dims=None,
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['mobilenetv2_1dot0'])
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return model
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def mobilenetv2_1dot4(num_classes, loss, pretrained=True, **kwargs):
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model = MobileNetV2(
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num_classes,
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loss=loss,
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width_mult=1.4,
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fc_dims=None,
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['mobilenetv2_1dot4'])
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return model |