Create mobilenetv2.py

src/models/backbones/mobilenetv2.py

@@ -0,0 +1,199 @@
+""" This file is adapted from https://github.com/thuyngch/Human-Segmentation-PyTorch"""
+
+import math
+import json
+from functools import reduce
+
+import torch
+from torch import nn
+
+
+#------------------------------------------------------------------------------
+#  Useful functions
+#------------------------------------------------------------------------------
+
+def _make_divisible(v, divisor, min_value=None):
+	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
+
+
+def conv_bn(inp, oup, stride):
+	return nn.Sequential(
+		nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+		nn.BatchNorm2d(oup),
+		nn.ReLU6(inplace=True)
+	)
+
+
+def conv_1x1_bn(inp, oup):
+	return nn.Sequential(
+		nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+		nn.BatchNorm2d(oup),
+		nn.ReLU6(inplace=True)
+	)
+
+
+#------------------------------------------------------------------------------
+#  Class of Inverted Residual block
+#------------------------------------------------------------------------------
+
+class InvertedResidual(nn.Module):
+	def __init__(self, inp, oup, stride, expansion, dilation=1):
+		super(InvertedResidual, self).__init__()
+		self.stride = stride
+		assert stride in [1, 2]
+
+		hidden_dim = round(inp * expansion)
+		self.use_res_connect = self.stride == 1 and inp == oup
+
+		if expansion == 1:
+			self.conv = nn.Sequential(
+				# dw
+				nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
+				nn.BatchNorm2d(hidden_dim),
+				nn.ReLU6(inplace=True),
+				# pw-linear
+				nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+				nn.BatchNorm2d(oup),
+			)
+		else:
+			self.conv = nn.Sequential(
+				# pw
+				nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
+				nn.BatchNorm2d(hidden_dim),
+				nn.ReLU6(inplace=True),
+				# dw
+				nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
+				nn.BatchNorm2d(hidden_dim),
+				nn.ReLU6(inplace=True),
+				# pw-linear
+				nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+				nn.BatchNorm2d(oup),
+			)
+
+	def forward(self, x):
+		if self.use_res_connect:
+			return x + self.conv(x)
+		else:
+			return self.conv(x)
+
+
+#------------------------------------------------------------------------------
+#  Class of MobileNetV2
+#------------------------------------------------------------------------------
+
+class MobileNetV2(nn.Module):
+	def __init__(self, in_channels, alpha=1.0, expansion=6, num_classes=1000):
+		super(MobileNetV2, self).__init__()
+		self.in_channels = in_channels
+		self.num_classes = num_classes
+		input_channel = 32
+		last_channel = 1280
+		interverted_residual_setting = [
+			# t, c, n, s
+			[1        , 16, 1, 1],
+			[expansion, 24, 2, 2],
+			[expansion, 32, 3, 2],
+			[expansion, 64, 4, 2],
+			[expansion, 96, 3, 1],
+			[expansion, 160, 3, 2],
+			[expansion, 320, 1, 1],
+		]
+
+		# building first layer
+		input_channel = _make_divisible(input_channel*alpha, 8)
+		self.last_channel = _make_divisible(last_channel*alpha, 8) if alpha > 1.0 else last_channel
+		self.features = [conv_bn(self.in_channels, input_channel, 2)]
+
+		# building inverted residual blocks
+		for t, c, n, s in interverted_residual_setting:
+			output_channel = _make_divisible(int(c*alpha), 8)
+			for i in range(n):
+				if i == 0:
+					self.features.append(InvertedResidual(input_channel, output_channel, s, expansion=t))
+				else:
+					self.features.append(InvertedResidual(input_channel, output_channel, 1, expansion=t))
+				input_channel = output_channel
+
+		# building last several layers
+		self.features.append(conv_1x1_bn(input_channel, self.last_channel))
+
+		# make it nn.Sequential
+		self.features = nn.Sequential(*self.features)
+
+		# building classifier
+		if self.num_classes is not None:
+			self.classifier = nn.Sequential(
+				nn.Dropout(0.2),
+				nn.Linear(self.last_channel, num_classes),
+			)
+
+		# Initialize weights
+		self._init_weights()
+
+	def forward(self, x):
+		# Stage1
+		x = self.features[0](x)
+		x = self.features[1](x)
+		# Stage2
+		x = self.features[2](x)
+		x = self.features[3](x)
+		# Stage3
+		x = self.features[4](x)
+		x = self.features[5](x)
+		x = self.features[6](x)
+		# Stage4
+		x = self.features[7](x)
+		x = self.features[8](x)
+		x = self.features[9](x)
+		x = self.features[10](x)
+		x = self.features[11](x)
+		x = self.features[12](x)
+		x = self.features[13](x)
+		# Stage5
+		x = self.features[14](x)
+		x = self.features[15](x)
+		x = self.features[16](x)
+		x = self.features[17](x)
+		x = self.features[18](x)
+
+		# Classification
+		if self.num_classes is not None:
+			x = x.mean(dim=(2,3))
+			x = self.classifier(x)
+			
+		# Output
+		return x
+
+	def _load_pretrained_model(self, pretrained_file):
+		pretrain_dict = torch.load(pretrained_file, map_location='cpu')
+		model_dict = {}
+		state_dict = self.state_dict()
+		print("[MobileNetV2] Loading pretrained model...")
+		for k, v in pretrain_dict.items():
+			if k in state_dict:
+				model_dict[k] = v
+			else:
+				print(k, "is ignored")
+		state_dict.update(model_dict)
+		self.load_state_dict(state_dict)
+
+	def _init_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_()