""" Creates a MobileNetV3 Model as defined in: Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. (2019). Searching for MobileNetV3 arXiv preprint arXiv:1905.02244. """ import torch.nn as nn import math from utils.learning import freeze_params def _make_divisible(v, divisor, min_value=None): """ This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py :param v: :param divisor: :param min_value: :return: """ if min_value is None: min_value = divisor new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_v < 0.9 * v: new_v += divisor return new_v class h_sigmoid(nn.Module): def __init__(self, inplace=True): super(h_sigmoid, self).__init__() self.relu = nn.ReLU6(inplace=inplace) def forward(self, x): return self.relu(x + 3) / 6 class h_swish(nn.Module): def __init__(self, inplace=True): super(h_swish, self).__init__() self.sigmoid = h_sigmoid(inplace=inplace) def forward(self, x): return x * self.sigmoid(x) class SELayer(nn.Module): def __init__(self, channel, reduction=4): super(SELayer, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(channel, _make_divisible(channel // reduction, 8)), nn.ReLU(inplace=True), nn.Linear(_make_divisible(channel // reduction, 8), channel), h_sigmoid()) def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) return x * y def conv_3x3_bn(inp, oup, stride, norm_layer=nn.BatchNorm2d): return nn.Sequential(nn.Conv2d(inp, oup, 3, stride, 1, bias=False), norm_layer(oup), h_swish()) def conv_1x1_bn(inp, oup, norm_layer=nn.BatchNorm2d): return nn.Sequential(nn.Conv2d(inp, oup, 1, 1, 0, bias=False), norm_layer(oup), h_swish()) class InvertedResidual(nn.Module): def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs, dilation=1, norm_layer=nn.BatchNorm2d): super(InvertedResidual, self).__init__() assert stride in [1, 2] self.identity = stride == 1 and inp == oup if inp == hidden_dim: self.conv = nn.Sequential( # dw nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2 * dilation, dilation=dilation, groups=hidden_dim, bias=False), norm_layer(hidden_dim), h_swish() if use_hs else nn.ReLU(inplace=True), # Squeeze-and-Excite SELayer(hidden_dim) if use_se else nn.Identity(), # pw-linear nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), norm_layer(oup), ) else: self.conv = nn.Sequential( # pw nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), norm_layer(hidden_dim), h_swish() if use_hs else nn.ReLU(inplace=True), # dw nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2 * dilation, dilation=dilation, groups=hidden_dim, bias=False), norm_layer(hidden_dim), # Squeeze-and-Excite SELayer(hidden_dim) if use_se else nn.Identity(), h_swish() if use_hs else nn.ReLU(inplace=True), # pw-linear nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), norm_layer(oup), ) def forward(self, x): if self.identity: return x + self.conv(x) else: return self.conv(x) class MobileNetV3Large(nn.Module): def __init__(self, output_stride=16, norm_layer=nn.BatchNorm2d, width_mult=1., freeze_at=0): super(MobileNetV3Large, self).__init__() """ Constructs a MobileNetV3-Large model """ cfgs = [ # k, t, c, SE, HS, s [3, 1, 16, 0, 0, 1], [3, 4, 24, 0, 0, 2], [3, 3, 24, 0, 0, 1], [5, 3, 40, 1, 0, 2], [5, 3, 40, 1, 0, 1], [5, 3, 40, 1, 0, 1], [3, 6, 80, 0, 1, 2], [3, 2.5, 80, 0, 1, 1], [3, 2.3, 80, 0, 1, 1], [3, 2.3, 80, 0, 1, 1], [3, 6, 112, 1, 1, 1], [3, 6, 112, 1, 1, 1], [5, 6, 160, 1, 1, 2], [5, 6, 160, 1, 1, 1], [5, 6, 160, 1, 1, 1] ] self.cfgs = cfgs # building first layer input_channel = _make_divisible(16 * width_mult, 8) layers = [conv_3x3_bn(3, input_channel, 2, norm_layer)] # building inverted residual blocks block = InvertedResidual now_stride = 2 rate = 1 for k, t, c, use_se, use_hs, s in self.cfgs: if now_stride == output_stride: dilation = rate rate *= s s = 1 else: dilation = 1 now_stride *= s output_channel = _make_divisible(c * width_mult, 8) exp_size = _make_divisible(input_channel * t, 8) layers.append( block(input_channel, exp_size, output_channel, k, s, use_se, use_hs, dilation, norm_layer)) input_channel = output_channel self.features = nn.Sequential(*layers) self.conv = conv_1x1_bn(input_channel, exp_size, norm_layer) # building last several layers self._initialize_weights() feature_4x = self.features[0:4] feautre_8x = self.features[4:7] feature_16x = self.features[7:13] feature_32x = self.features[13:] self.stages = [feature_4x, feautre_8x, feature_16x, feature_32x] self.freeze(freeze_at) def forward(self, x): xs = [] for stage in self.stages: x = stage(x) xs.append(x) xs[-1] = self.conv(xs[-1]) return xs def _initialize_weights(self): 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)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): n = m.weight.size(1) m.weight.data.normal_(0, 0.01) m.bias.data.zero_() def freeze(self, freeze_at): if freeze_at >= 1: for m in self.stages[0][0]: freeze_params(m) for idx, stage in enumerate(self.stages, start=2): if freeze_at >= idx: freeze_params(stage)