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ldm/thirdp/psp/helpers.py

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+# https://github.com/eladrich/pixel2style2pixel
+
+from collections import namedtuple
+import torch
+from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
+
+"""
+ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
+"""
+
+
+class Flatten(Module):
+	def forward(self, input):
+		return input.view(input.size(0), -1)
+
+
+def l2_norm(input, axis=1):
+	norm = torch.norm(input, 2, axis, True)
+	output = torch.div(input, norm)
+	return output
+
+
+class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
+	""" A named tuple describing a ResNet block. """
+
+
+def get_block(in_channel, depth, num_units, stride=2):
+	return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
+
+
+def get_blocks(num_layers):
+	if num_layers == 50:
+		blocks = [
+			get_block(in_channel=64, depth=64, num_units=3),
+			get_block(in_channel=64, depth=128, num_units=4),
+			get_block(in_channel=128, depth=256, num_units=14),
+			get_block(in_channel=256, depth=512, num_units=3)
+		]
+	elif num_layers == 100:
+		blocks = [
+			get_block(in_channel=64, depth=64, num_units=3),
+			get_block(in_channel=64, depth=128, num_units=13),
+			get_block(in_channel=128, depth=256, num_units=30),
+			get_block(in_channel=256, depth=512, num_units=3)
+		]
+	elif num_layers == 152:
+		blocks = [
+			get_block(in_channel=64, depth=64, num_units=3),
+			get_block(in_channel=64, depth=128, num_units=8),
+			get_block(in_channel=128, depth=256, num_units=36),
+			get_block(in_channel=256, depth=512, num_units=3)
+		]
+	else:
+		raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
+	return blocks
+
+
+class SEModule(Module):
+	def __init__(self, channels, reduction):
+		super(SEModule, self).__init__()
+		self.avg_pool = AdaptiveAvgPool2d(1)
+		self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
+		self.relu = ReLU(inplace=True)
+		self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
+		self.sigmoid = Sigmoid()
+
+	def forward(self, x):
+		module_input = x
+		x = self.avg_pool(x)
+		x = self.fc1(x)
+		x = self.relu(x)
+		x = self.fc2(x)
+		x = self.sigmoid(x)
+		return module_input * x
+
+
+class bottleneck_IR(Module):
+	def __init__(self, in_channel, depth, stride):
+		super(bottleneck_IR, self).__init__()
+		if in_channel == depth:
+			self.shortcut_layer = MaxPool2d(1, stride)
+		else:
+			self.shortcut_layer = Sequential(
+				Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+				BatchNorm2d(depth)
+			)
+		self.res_layer = Sequential(
+			BatchNorm2d(in_channel),
+			Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
+			Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)
+		)
+
+	def forward(self, x):
+		shortcut = self.shortcut_layer(x)
+		res = self.res_layer(x)
+		return res + shortcut
+
+
+class bottleneck_IR_SE(Module):
+	def __init__(self, in_channel, depth, stride):
+		super(bottleneck_IR_SE, self).__init__()
+		if in_channel == depth:
+			self.shortcut_layer = MaxPool2d(1, stride)
+		else:
+			self.shortcut_layer = Sequential(
+				Conv2d(in_channel, depth, (1, 1), stride, bias=False),
+				BatchNorm2d(depth)
+			)
+		self.res_layer = Sequential(
+			BatchNorm2d(in_channel),
+			Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
+			PReLU(depth),
+			Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
+			BatchNorm2d(depth),
+			SEModule(depth, 16)
+		)
+
+	def forward(self, x):
+		shortcut = self.shortcut_layer(x)
+		res = self.res_layer(x)
+		return res + shortcut

ldm/thirdp/psp/id_loss.py

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+# https://github.com/eladrich/pixel2style2pixel
+import torch
+from torch import nn
+from ldm.thirdp.psp.model_irse import Backbone
+
+
+class IDFeatures(nn.Module):
+    def __init__(self, model_path):
+        super(IDFeatures, self).__init__()
+        print('Loading ResNet ArcFace')
+        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
+        self.facenet.load_state_dict(torch.load(model_path, map_location="cpu"))
+        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
+        self.facenet.eval()
+
+    def forward(self, x, crop=False):
+        # Not sure of the image range here
+        if crop:
+            x = torch.nn.functional.interpolate(x, (256, 256), mode="area")
+            x = x[:, :, 35:223, 32:220]
+        x = self.face_pool(x)
+        x_feats = self.facenet(x)
+        return x_feats

ldm/thirdp/psp/model_irse.py

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+# https://github.com/eladrich/pixel2style2pixel
+
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
+from ldm.thirdp.psp.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
+
+"""
+Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
+"""
+
+
+class Backbone(Module):
+	def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
+		super(Backbone, self).__init__()
+		assert input_size in [112, 224], "input_size should be 112 or 224"
+		assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
+		assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
+		blocks = get_blocks(num_layers)
+		if mode == 'ir':
+			unit_module = bottleneck_IR
+		elif mode == 'ir_se':
+			unit_module = bottleneck_IR_SE
+		self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
+									  BatchNorm2d(64),
+									  PReLU(64))
+		if input_size == 112:
+			self.output_layer = Sequential(BatchNorm2d(512),
+			                               Dropout(drop_ratio),
+			                               Flatten(),
+			                               Linear(512 * 7 * 7, 512),
+			                               BatchNorm1d(512, affine=affine))
+		else:
+			self.output_layer = Sequential(BatchNorm2d(512),
+			                               Dropout(drop_ratio),
+			                               Flatten(),
+			                               Linear(512 * 14 * 14, 512),
+			                               BatchNorm1d(512, affine=affine))
+
+		modules = []
+		for block in blocks:
+			for bottleneck in block:
+				modules.append(unit_module(bottleneck.in_channel,
+										   bottleneck.depth,
+										   bottleneck.stride))
+		self.body = Sequential(*modules)
+
+	def forward(self, x):
+		x = self.input_layer(x)
+		x = self.body(x)
+		x = self.output_layer(x)
+		return l2_norm(x)
+
+
+def IR_50(input_size):
+	"""Constructs a ir-50 model."""
+	model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_101(input_size):
+	"""Constructs a ir-101 model."""
+	model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_152(input_size):
+	"""Constructs a ir-152 model."""
+	model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_SE_50(input_size):
+	"""Constructs a ir_se-50 model."""
+	model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_SE_101(input_size):
+	"""Constructs a ir_se-101 model."""
+	model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
+	return model
+
+
+def IR_SE_152(input_size):
+	"""Constructs a ir_se-152 model."""
+	model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
+	return model