305 lines
12 KiB
Python
305 lines
12 KiB
Python
import torch.nn as nn
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from torch import set_grad_enabled, flatten, Tensor
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from torchvision.models import mobilenetv3
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from .nets_utils import EmbeddingRecorder
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import math
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'''MobileNetV3 in PyTorch.
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Paper： "Inverted Residuals and Linear Bottlenecks:Mobile Networks for Classification, Detection and Segmentation"
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Acknowlegement to:
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https://github.com/d-li14/mobilenetv3.pytorch/blob/master/mobilenetv3.py
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'''
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def _make_divisible(v, divisor, min_value=None):
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"""
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This function is taken from the original tf repo.
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It ensures that all layers have a channel number that is divisible by 8
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It can be seen here:
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https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
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"""
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class h_sigmoid(nn.Module):
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def __init__(self, inplace=True):
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super(h_sigmoid, self).__init__()
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self.relu = nn.ReLU6(inplace=inplace)
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def forward(self, x):
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return self.relu(x + 3) / 6
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class h_swish(nn.Module):
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def __init__(self, inplace=True):
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super(h_swish, self).__init__()
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self.sigmoid = h_sigmoid(inplace=inplace)
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def forward(self, x):
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return x * self.sigmoid(x)
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class SELayer(nn.Module):
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def __init__(self, channel, reduction=4):
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super(SELayer, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(channel, _make_divisible(channel // reduction, 8)),
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nn.ReLU(inplace=True),
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nn.Linear(_make_divisible(channel // reduction, 8), channel),
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h_sigmoid()
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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return x * y
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def conv_3x3_bn(inp, oup, stride, padding=1):
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return nn.Sequential(
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nn.Conv2d(inp, oup, 3, stride, padding, bias=False),
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nn.BatchNorm2d(oup),
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h_swish()
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)
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def conv_1x1_bn(inp, oup):
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return nn.Sequential(
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nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
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nn.BatchNorm2d(oup),
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h_swish()
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)
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class InvertedResidual(nn.Module):
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def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs):
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super(InvertedResidual, self).__init__()
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assert stride in [1, 2]
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self.identity = stride == 1 and inp == oup
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if inp == hidden_dim:
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self.conv = nn.Sequential(
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# dw
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nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
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bias=False),
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nn.BatchNorm2d(hidden_dim),
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h_swish() if use_hs else nn.ReLU(inplace=True),
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# Squeeze-and-Excite
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SELayer(hidden_dim) if use_se else nn.Identity(),
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# pw-linear
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nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
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nn.BatchNorm2d(oup),
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)
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else:
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self.conv = nn.Sequential(
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# pw
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nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
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nn.BatchNorm2d(hidden_dim),
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h_swish() if use_hs else nn.ReLU(inplace=True),
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# dw
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nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
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bias=False),
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nn.BatchNorm2d(hidden_dim),
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# Squeeze-and-Excite
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SELayer(hidden_dim) if use_se else nn.Identity(),
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h_swish() if use_hs else nn.ReLU(inplace=True),
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# pw-linear
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nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
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nn.BatchNorm2d(oup),
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)
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def forward(self, x):
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if self.identity:
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return x + self.conv(x)
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else:
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return self.conv(x)
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class MobileNetV3_32x32(nn.Module):
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def __init__(self, cfgs, mode, channel=3, num_classes=1000, record_embedding=False,
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no_grad=False, width_mult=1.):
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super(MobileNetV3_32x32, self).__init__()
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# setting of inverted residual blocks
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self.cfgs = cfgs
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assert mode in ['mobilenet_v3_large', 'mobilenet_v3_small']
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self.embedding_recorder = EmbeddingRecorder(record_embedding)
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self.no_grad = no_grad
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# building first layer
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input_channel = _make_divisible(16 * width_mult, 8)
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layers = [conv_3x3_bn(channel, input_channel, 2, padding=3 if channel == 1 else 1)]
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# building inverted residual blocks
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block = InvertedResidual
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for k, t, c, use_se, use_hs, s in self.cfgs:
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output_channel = _make_divisible(c * width_mult, 8)
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exp_size = _make_divisible(input_channel * t, 8)
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layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
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input_channel = output_channel
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self.features = nn.Sequential(*layers)
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# building last several layers
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self.conv = conv_1x1_bn(input_channel, exp_size)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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output_channel = {'mobilenet_v3_large': 1280, 'mobilenet_v3_small': 1024}
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output_channel = _make_divisible(output_channel[mode] * width_mult, 8) if width_mult > 1.0 else output_channel[
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mode]
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self.classifier = nn.Sequential(
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nn.Linear(exp_size, output_channel),
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h_swish(),
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nn.Dropout(0.2),
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self.embedding_recorder,
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nn.Linear(output_channel, num_classes),
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)
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self._initialize_weights()
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def forward(self, x):
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with set_grad_enabled(not self.no_grad):
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x = self.features(x)
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x = self.conv(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.classifier(x)
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return x
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def _initialize_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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m.weight.data.normal_(0, 0.01)
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m.bias.data.zero_()
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def get_last_layer(self):
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return self.classifier[-1]
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class MobileNetV3_224x224(mobilenetv3.MobileNetV3):
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def __init__(self, inverted_residual_setting, last_channel,
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channel=3, num_classes=1000, record_embedding=False, no_grad=False, **kwargs):
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super(MobileNetV3_224x224, self).__init__(inverted_residual_setting, last_channel,
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num_classes=num_classes, **kwargs)
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self.embedding_recorder = EmbeddingRecorder(record_embedding)
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self.fc = self.classifier[-1]
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self.classifier[-1] = self.embedding_recorder
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self.classifier.add_module("fc", self.fc)
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self.no_grad = no_grad
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def get_last_layer(self):
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return self.fc
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def _forward_impl(self, x: Tensor) -> Tensor:
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with set_grad_enabled(not self.no_grad):
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x = self.features(x)
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x = self.avgpool(x)
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x = flatten(x, 1)
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x = self.classifier(x)
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return x
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def MobileNetV3(arch: str, channel: int, num_classes: int, im_size, record_embedding: bool = False,
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no_grad: bool = False,
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pretrained: bool = False, **kwargs):
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arch = arch.lower()
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if pretrained:
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if channel != 3:
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raise NotImplementedError("Network Architecture for current dataset has not been implemented.")
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inverted_residual_setting, last_channel = mobilenetv3._mobilenet_v3_conf(arch)
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net = MobileNetV3_224x224(inverted_residual_setting=inverted_residual_setting, last_channel=last_channel,
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channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad,
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**kwargs)
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from torch.hub import load_state_dict_from_url
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state_dict = load_state_dict_from_url(mobilenetv3.model_urls[arch], progress=True)
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net.load_state_dict(state_dict)
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if num_classes != 1000:
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net.fc = nn.Linear(last_channel, num_classes)
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net.classifier[-1] = net.fc
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elif im_size[0] == 224 and im_size[1] == 224:
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if channel != 3:
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raise NotImplementedError("Network Architecture for current dataset has not been implemented.")
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inverted_residual_setting, last_channel = mobilenetv3._mobilenet_v3_conf(arch)
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net = MobileNetV3_224x224(inverted_residual_setting=inverted_residual_setting, last_channel=last_channel,
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channel=channel, num_classes=num_classes, record_embedding=record_embedding,
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no_grad=no_grad, **kwargs)
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elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or (
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channel == 3 and im_size[0] == 32 and im_size[1] == 32):
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if arch == "mobilenet_v3_large":
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cfgs = [
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# k, t, c, SE, HS, s
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[3, 1, 16, 0, 0, 1],
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[3, 4, 24, 0, 0, 2],
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[3, 3, 24, 0, 0, 1],
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[5, 3, 40, 1, 0, 2],
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[5, 3, 40, 1, 0, 1],
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[5, 3, 40, 1, 0, 1],
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[3, 6, 80, 0, 1, 2],
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[3, 2.5, 80, 0, 1, 1],
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[3, 2.3, 80, 0, 1, 1],
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[3, 2.3, 80, 0, 1, 1],
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[3, 6, 112, 1, 1, 1],
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[3, 6, 112, 1, 1, 1],
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[5, 6, 160, 1, 1, 2],
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[5, 6, 160, 1, 1, 1],
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[5, 6, 160, 1, 1, 1]
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]
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net = MobileNetV3_32x32(cfgs, arch, channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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elif arch == "mobilenet_v3_small":
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cfgs = [
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# k, t, c, SE, HS, s
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[3, 1, 16, 1, 0, 2],
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[3, 4.5, 24, 0, 0, 2],
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[3, 3.67, 24, 0, 0, 1],
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[5, 4, 40, 1, 1, 2],
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[5, 6, 40, 1, 1, 1],
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[5, 6, 40, 1, 1, 1],
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[5, 3, 48, 1, 1, 1],
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[5, 3, 48, 1, 1, 1],
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[5, 6, 96, 1, 1, 2],
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[5, 6, 96, 1, 1, 1],
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[5, 6, 96, 1, 1, 1],
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]
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net = MobileNetV3_32x32(cfgs, arch, channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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else:
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raise ValueError("Model architecture not found.")
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else:
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raise NotImplementedError("Network Architecture for current dataset has not been implemented.")
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return net
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def MobileNetV3Large(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False,
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pretrained: bool = False, **kwargs):
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return MobileNetV3("mobilenet_v3_large", channel, num_classes, im_size, record_embedding, no_grad,
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pretrained, **kwargs)
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def MobileNetV3Small(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False,
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pretrained: bool = False, **kwargs):
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return MobileNetV3("mobilenet_v3_small", channel, num_classes, im_size, record_embedding, no_grad,
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pretrained, **kwargs)
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