Source code for super_gradients.training.models.mobilenet

'''MobileNet in PyTorch.

See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
for more details.
'''
import torch.nn as nn
import torch.nn.functional as F
from super_gradients.training.models.sg_module import SgModule


[docs]class Block(nn.Module): '''Depthwise conv + Pointwise conv''' def __init__(self, in_planes, out_planes, stride=1): super(Block, self).__init__() self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False) self.bn1 = nn.BatchNorm2d(in_planes) self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False) self.bn2 = nn.BatchNorm2d(out_planes)
[docs] def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = F.relu(self.bn2(self.conv2(out))) return out
[docs]class MobileNet(SgModule): # (128,2) means conv planes=128, conv stride=2, by default conv stride=1 cfg = [64, 128, (128, 2), 256, (256, 2), 512, 512, 512, 512, 512, (512, 2), 1024, (1024, 2)] def __init__(self, num_classes=10, backbone_mode=False, up_to_layer=None): super(MobileNet, self).__init__() self.backbone_mode = backbone_mode self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(32) self.layers = self._make_layers(in_planes=32, up_to_layer=up_to_layer if up_to_layer is not None else len(self.cfg)) if not self.backbone_mode: self.linear = nn.Linear(self.cfg[-1], num_classes) def _make_layers(self, in_planes, up_to_layer): layers = [] for x in self.cfg[:up_to_layer]: out_planes = x if isinstance(x, int) else x[0] stride = 1 if isinstance(x, int) else x[1] layers.append(Block(in_planes, out_planes, stride)) in_planes = out_planes return nn.Sequential(*layers)
[docs] def forward(self, x): """ :param up_to_layer: forward through the net layers up to a specific layer. if None, run all layers """ out = F.relu(self.bn1(self.conv1(x))) out = self.layers(out) if not self.backbone_mode: out = F.avg_pool2d(out, 2) out = out.view(out.size(0), -1) out = self.linear(out) return out