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mobilenet.py
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mobilenet.py
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"""MobileNet in PyTorch.
See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
for more details.
"""
from typing import Optional, Callable
import torch.nn as nn
import torch.nn.functional as F
from super_gradients.training.models import BaseClassifier
from super_gradients.module_interfaces import SupportsReplaceInputChannels
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)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
return out
class MobileNet(BaseClassifier, SupportsReplaceInputChannels):
# (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, in_channels: int = 3):
super(MobileNet, self).__init__()
self.backbone_mode = backbone_mode
self.conv1 = nn.Conv2d(in_channels, 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)
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
def replace_input_channels(self, in_channels: int, compute_new_weights_fn: Optional[Callable[[nn.Module, int], nn.Module]] = None):
from super_gradients.modules.weight_replacement_utils import replace_conv2d_input_channels
self.conv1 = replace_conv2d_input_channels(conv=self.conv1, in_channels=in_channels)
def get_input_channels(self) -> int:
return self.conv1.in_channels