# BSD 3-Clause License # Copyright (c) Soumith Chintala 2016, # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # Adaptation of the PyTorch torchvision MobileNetV2 without a classifier. # See source here: https://pytorch.org/vision/0.8/_modules/torchvision/models/mobilenet.html#mobilenet_v2 from torch import nn 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, embed_dim=1028, width_mult=1.0, inverted_residual_setting=None, round_nearest=8, block=None, norm_layer=None, ): """ MobileNet V2 main class Args: embed_dim (int): Number of channels in the final output. 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 = embed_dim / width_mult 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) # 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 return self.features(x) # return the features directly, no classifier or pooling def forward(self, x): return self._forward_impl(x)