CurricularFace

README.md

@@ -1,2 +1,49 @@
-# CurricularFace
-The code and pre-trained model are coming soon.
+## CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
+Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang
+
+This repository is the official PyTorch implementation of paper [CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition](). (The work has been accepted by [CVPR2020](http://cvpr2020.thecvf.com/))
+
+## Main requirements
+
+  * **torch == 1.1.0**
+  * **torchvision == 0.3.0**
+  * **tensorboardX == 1.7**
+  * **bcolz == 1.2.1**
+  * **Python 3**
+
+## Usage
+```bash
+# To train the model:
+sh train.sh
+# To evaluate the model:
+set the checkpoint dir in config.py
+sh evaluate.sh
+```
+You can change the experimental setting by simply modifying the parameter in the config.py
+
+## Model
+The IR101 pretrained model can be downloaded.
+IR101 Backbone: 
+[Baidu Cloud](link£ºhttps://pan.baidu.com/s/1bu-uocgSyFHf5pOPShhTyA 
+passwd£º5qa0), 
+[Google Drive](https://drive.google.com/open?id=1upOyrPzZ5OI3p6WkA5D5JFYCeiZuaPcp)
+
+## Citing this repository
+If you find this code useful in your research, please consider citing us:
+```
+@article{huang2020curricularface,
+	title={CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition},
+	author={Yuge Huang and Yuhan Wang and Ying Tai and  Xiaoming Liu and Pengcheng Shen and Shaoxin Li and Jilin Li, Feiyue Huang},
+	booktitle={CVPR},
+	pages={1--8},
+	year={2020}
+}
+```
+
+## Contacts
+If you have any questions about our work, please do not hesitate to contact us by emails.
+Yuge Huang: yugehuang@tencent.com
+Ying Tai: yingtai@tencent.com
+
+
+

backbone/__init__.py

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+

backbone/model_irse.py

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+import torch
+import torch.nn as nn
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout, MaxPool2d, \
+    AdaptiveAvgPool2d, Sequential, Module
+from collections import namedtuple
+
+
+# Support: ['IR_50', 'IR_101', 'IR_152', 'IR_SE_50', 'IR_SE_101', 'IR_SE_152']
+
+
+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 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)
+
+        nn.init.xavier_uniform_(self.fc1.weight.data)
+
+        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
+
+
+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)
+        ]
+
+    return blocks
+
+
+class Backbone(Module):
+    def __init__(self, input_size, num_layers, mode='ir'):
+        super(Backbone, self).__init__()
+        assert input_size[0] in [112, 224], "input_size should be [112, 112] or [224, 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[0] == 112:
+            self.output_layer = Sequential(BatchNorm2d(512),
+                                           Dropout(0.4),
+                                           Flatten(),
+                                           Linear(512 * 7 * 7, 512),
+                                           BatchNorm1d(512, affine=False))
+        else:
+            self.output_layer = Sequential(BatchNorm2d(512),
+                                           Dropout(0.4),
+                                           Flatten(),
+                                           Linear(512 * 14 * 14, 512),
+                                           BatchNorm1d(512, affine=False))
+
+        modules = []
+        for block in blocks:
+            for bottleneck in block:
+                modules.append(
+                    unit_module(bottleneck.in_channel,
+                                bottleneck.depth,
+                                bottleneck.stride))
+        self.body = Sequential(*modules)
+
+        self._initialize_weights()
+
+    def forward(self, x):
+        x = self.input_layer(x)
+        x = self.body(x)
+        conv_out = x.view(x.shape[0], -1)
+        x = self.output_layer(x)
+
+        return x, conv_out
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                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):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    m.bias.data.zero_()
+
+def IR_50(input_size):
+    """Constructs a ir-50 model.
+    """
+    model = Backbone(input_size, 50, 'ir')
+
+    return model
+
+
+def IR_101(input_size):
+    """Constructs a ir-101 model.
+    """
+    model = Backbone(input_size, 100, 'ir')
+
+    return model
+
+
+def IR_152(input_size):
+    """Constructs a ir-152 model.
+    """
+    model = Backbone(input_size, 152, 'ir')
+
+    return model
+
+
+def IR_SE_50(input_size):
+    """Constructs a ir_se-50 model.
+    """
+    model = Backbone(input_size, 50, 'ir_se')
+
+    return model
+
+
+def IR_SE_101(input_size):
+    """Constructs a ir_se-101 model.
+    """
+    model = Backbone(input_size, 100, 'ir_se')
+
+    return model
+
+
+def IR_SE_152(input_size):
+    """Constructs a ir_se-152 model.
+    """
+    model = Backbone(input_size, 152, 'ir_se')
+
+    return model