from torch import nn from .utils import load_state_dict_from_url from config import config as conf __all__ = ['MobileNetV2', 'mobilenet_v2'] model_urls = { 'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth', } 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 ConvBNReLU(nn.Sequential): def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1, norm_layer=None): padding = (kernel_size - 1) // 2 if norm_layer is None: norm_layer = nn.BatchNorm2d super(ConvBNReLU, self).__init__( nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False), norm_layer(out_planes), nn.ReLU6(inplace=True) ) class InvertedResidual(nn.Module): def __init__(self, inp, oup, stride, expand_ratio, norm_layer=None): super(InvertedResidual, self).__init__() self.stride = stride assert stride in [1, 2] if norm_layer is None: norm_layer = nn.BatchNorm2d hidden_dim = int(round(inp * expand_ratio)) self.use_res_connect = self.stride == 1 and inp == oup layers = [] if expand_ratio != 1: # pw layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer)) layers.extend([ # dw ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer), # pw-linear nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), norm_layer(oup), ]) self.conv = nn.Sequential(*layers) def forward(self, x): if self.use_res_connect: return x + self.conv(x) else: return self.conv(x) class MobileNetV2(nn.Module): def __init__(self, num_classes=conf.embedding_size, width_mult=1.0, inverted_residual_setting=None, round_nearest=8, block=None, norm_layer=None): """ MobileNet V2 main class Args: num_classes (int): Number of classes width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount inverted_residual_setting: Network structure round_nearest (int): Round the number of channels in each layer to be a multiple of this number Set to 1 to turn off rounding block: Module specifying inverted residual building block for mobilenet norm_layer: Module specifying the normalization layer to use """ super(MobileNetV2, self).__init__() if block is None: block = InvertedResidual if norm_layer is None: norm_layer = nn.BatchNorm2d input_channel = 32 last_channel = 1280 if inverted_residual_setting is None: inverted_residual_setting = [ # t, c, n, s [1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2], [6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2], [6, 320, 1, 1], ] # only check the first element, assuming user knows t,c,n,s are required if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4: raise ValueError("inverted_residual_setting should be non-empty " "or a 4-element list, got {}".format(inverted_residual_setting)) # building first layer input_channel = _make_divisible(input_channel * width_mult, round_nearest) self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest) features = [ConvBNReLU(3, input_channel, stride=2, norm_layer=norm_layer)] # building inverted residual blocks for t, c, n, s in inverted_residual_setting: output_channel = _make_divisible(c * width_mult, round_nearest) for i in range(n): stride = s if i == 0 else 1 features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer)) input_channel = output_channel # building last several layers features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer)) # make it nn.Sequential self.features = nn.Sequential(*features) # building classifier self.classifier = nn.Sequential( nn.Dropout(0.2), nn.Linear(self.last_channel, num_classes), ) # weight initialization for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.ones_(m.weight) nn.init.zeros_(m.bias) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.zeros_(m.bias) def _forward_impl(self, x): # This exists since TorchScript doesn't support inheritance, so the superclass method # (this one) needs to have a name other than `forward` that can be accessed in a subclass x = self.features(x) # Cannot use "squeeze" as batch-size can be 1 => must use reshape with x.shape[0] x = nn.functional.adaptive_avg_pool2d(x, 1).reshape(x.shape[0], -1) x = self.classifier(x) return x def forward(self, x): return self._forward_impl(x) def mobilenet_v2(pretrained=True, progress=True, **kwargs): """ Constructs a MobileNetV2 architecture from `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ model = MobileNetV2(**kwargs) if pretrained: state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'], progress=progress) src_state_dict = state_dict target_state_dict = model.state_dict() skip_keys = [] # skip mismatch size tensors in case of pretraining for k in src_state_dict.keys(): if k not in target_state_dict: continue if src_state_dict[k].size() != target_state_dict[k].size(): skip_keys.append(k) for k in skip_keys: del src_state_dict[k] missing_keys, unexpected_keys = model.load_state_dict(src_state_dict, strict=False) #.load_state_dict(state_dict) return model