Upload xception.py

xception.py

@@ -0,0 +1,460 @@
+"""
+Code from https://github.com/ondyari/FaceForensics
+Author: Andreas Rössler
+"""
+import os
+import argparse
+
+
+import torch
+# import pretrainedmodels
+import torch.nn as nn
+import torch.nn.functional as F
+# from lib.nets.xception import xception
+import math
+import torchvision
+
+# import math
+# import torch
+# import torch.nn as nn
+# import torch.nn.functional as F
+import torch.utils.model_zoo as model_zoo
+from torch.nn import init
+
+pretrained_settings = {
+    'xception': {
+        'imagenet': {
+            'url': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-b5690688.pth',
+            'input_space': 'RGB',
+            'input_size': [3, 299, 299],
+            'input_range': [0, 1],
+            'mean': [0.5, 0.5, 0.5],
+            'std': [0.5, 0.5, 0.5],
+            'num_classes': 1000,
+            'scale': 0.8975  # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
+        }
+    }
+}
+
+PRETAINED_WEIGHT_PATH = './pretrained_models/xception-b5690688.pth'
+
+class SeparableConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False):
+        super(SeparableConv2d, self).__init__()
+
+        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size,
+                               stride, padding, dilation, groups=in_channels, bias=bias)
+        self.pointwise = nn.Conv2d(
+            in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.pointwise(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(self, in_filters, out_filters, reps, strides=1, start_with_relu=True, grow_first=True):
+        super(Block, self).__init__()
+
+        if out_filters != in_filters or strides != 1:
+            self.skip = nn.Conv2d(in_filters, out_filters,
+                                  1, stride=strides, bias=False)
+            self.skipbn = nn.BatchNorm2d(out_filters)
+        else:
+            self.skip = None
+
+        self.relu = nn.ReLU(inplace=True)
+        rep = []
+
+        filters = in_filters
+        if grow_first:
+            rep.append(self.relu)
+            rep.append(SeparableConv2d(in_filters, out_filters,
+                                       3, stride=1, padding=1, bias=False))
+            rep.append(nn.BatchNorm2d(out_filters))
+            filters = out_filters
+
+        for i in range(reps-1):
+            rep.append(self.relu)
+            rep.append(SeparableConv2d(filters, filters,
+                                       3, stride=1, padding=1, bias=False))
+            rep.append(nn.BatchNorm2d(filters))
+
+        if not grow_first:
+            rep.append(self.relu)
+            rep.append(SeparableConv2d(in_filters, out_filters,
+                                       3, stride=1, padding=1, bias=False))
+            rep.append(nn.BatchNorm2d(out_filters))
+
+        if not start_with_relu:
+            rep = rep[1:]
+        else:
+            rep[0] = nn.ReLU(inplace=False)
+
+        if strides != 1:
+            rep.append(nn.MaxPool2d(3, strides, 1))
+        self.rep = nn.Sequential(*rep)
+
+    def forward(self, inp):
+        x = self.rep(inp)
+
+        if self.skip is not None:
+            skip = self.skip(inp)
+            skip = self.skipbn(skip)
+        else:
+            skip = inp
+
+        x += skip
+        return x
+
+
+def add_gaussian_noise(ins, mean=0, stddev=0.2):
+    noise = ins.data.new(ins.size()).normal_(mean, stddev)
+    return ins + noise
+
+
+class Xception(nn.Module):
+    """
+    Xception optimized for the ImageNet dataset, as specified in
+    https://arxiv.org/pdf/1610.02357.pdf
+    """
+
+    def __init__(self, num_classes=1000, inc=3):
+        """ Constructor
+        Args:
+            num_classes: number of classes
+        """
+        super(Xception, self).__init__()
+        self.num_classes = num_classes
+
+        # Entry flow
+        self.conv1 = nn.Conv2d(inc, 32, 3, 2, 0, bias=False)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.relu = nn.ReLU(inplace=True)
+
+        self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
+        self.bn2 = nn.BatchNorm2d(64)
+        # do relu here
+
+        self.block1 = Block(
+            64, 128, 2, 2, start_with_relu=False, grow_first=True)
+        self.block2 = Block(
+            128, 256, 2, 2, start_with_relu=True, grow_first=True)
+        self.block3 = Block(
+            256, 728, 2, 2, start_with_relu=True, grow_first=True)
+
+        # middle flow
+        self.block4 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True)
+        self.block5 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True)
+        self.block6 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True)
+        self.block7 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True)
+
+        self.block8 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True)
+        self.block9 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True)
+        self.block10 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True)
+        self.block11 = Block(
+            728, 728, 3, 1, start_with_relu=True, grow_first=True)
+
+        # Exit flow
+        self.block12 = Block(
+            728, 1024, 2, 2, start_with_relu=True, grow_first=False)
+
+        self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
+        self.bn3 = nn.BatchNorm2d(1536)
+
+        # do relu here
+        self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
+        self.bn4 = nn.BatchNorm2d(2048)
+
+        self.fc = nn.Linear(2048, num_classes)
+
+        # #------- init weights --------
+        # 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))
+        #     elif isinstance(m, nn.BatchNorm2d):
+        #         m.weight.data.fill_(1)
+        #         m.bias.data.zero_()
+        # #-----------------------------
+    def fea_part1_0(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        return x
+
+    def fea_part1_1(self, x):
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+
+        return x
+
+    def fea_part1(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+
+        return x
+
+    def fea_part2(self, x):
+        x = self.block1(x)
+        x = self.block2(x)
+        x = self.block3(x)
+
+        return x
+
+    def fea_part3(self, x):
+        x = self.block4(x)
+        x = self.block5(x)
+        x = self.block6(x)
+        x = self.block7(x)
+
+        return x
+
+    def fea_part4(self, x):
+        x = self.block8(x)
+        x = self.block9(x)
+        x = self.block10(x)
+        x = self.block11(x)
+
+        return x
+
+    def fea_part5(self, x):
+        x = self.block12(x)
+
+        x = self.conv3(x)
+        x = self.bn3(x)
+        x = self.relu(x)
+
+        x = self.conv4(x)
+        x = self.bn4(x)
+
+        return x
+
+    def features(self, input):
+        x = self.fea_part1(input)
+
+        x = self.fea_part2(x)
+        x = self.fea_part3(x)
+        x = self.fea_part4(x)
+
+        x = self.fea_part5(x)
+        return x
+
+    def classifier(self, features):
+        x = self.relu(features)
+
+        x = F.adaptive_avg_pool2d(x, (1, 1))
+        x = x.view(x.size(0), -1)
+        out = self.last_linear(x)
+        return out, x
+
+    def forward(self, input):
+        x = self.features(input)
+        out, x = self.classifier(x)
+        return out, x
+
+
+def xception(num_classes=1000, pretrained='imagenet', inc=3):
+    model = Xception(num_classes=num_classes, inc=inc)
+    if pretrained:
+        settings = pretrained_settings['xception'][pretrained]
+        assert num_classes == settings['num_classes'], \
+            "num_classes should be {}, but is {}".format(
+                settings['num_classes'], num_classes)
+
+        model = Xception(num_classes=num_classes)
+        model.load_state_dict(model_zoo.load_url(settings['url']))
+
+        model.input_space = settings['input_space']
+        model.input_size = settings['input_size']
+        model.input_range = settings['input_range']
+        model.mean = settings['mean']
+        model.std = settings['std']
+
+    # TODO: ugly
+    model.last_linear = model.fc
+    del model.fc
+    return model
+
+
+class TransferModel(nn.Module):
+    """
+    Simple transfer learning model that takes an imagenet pretrained model with
+    a fc layer as base model and retrains a new fc layer for num_out_classes
+    """
+
+    def __init__(self, modelchoice, num_out_classes=2, dropout=0.0,
+                 weight_norm=False, return_fea=False, inc=3):
+        super(TransferModel, self).__init__()
+        self.modelchoice = modelchoice
+        self.return_fea = return_fea
+
+        if modelchoice == 'xception':
+
+            def return_pytorch04_xception(pretrained=True):
+                # Raises warning "src not broadcastable to dst" but thats fine
+                model = xception(pretrained=False)
+                if pretrained:
+                    # Load model in torch 0.4+
+                    model.fc = model.last_linear
+                    del model.last_linear
+                    state_dict = torch.load(
+                        PRETAINED_WEIGHT_PATH)
+                    for name, weights in state_dict.items():
+                        if 'pointwise' in name:
+                            state_dict[name] = weights.unsqueeze(
+                                -1).unsqueeze(-1)
+                    model.load_state_dict(state_dict)
+                    model.last_linear = model.fc
+                    del model.fc
+                return model
+
+            self.model = return_pytorch04_xception()
+            # Replace fc
+            num_ftrs = self.model.last_linear.in_features
+            if not dropout:
+                if weight_norm:
+                    print('Using Weight_Norm')
+                    self.model.last_linear = nn.utils.weight_norm(
+                        nn.Linear(num_ftrs, num_out_classes), name='weight')
+                self.model.last_linear = nn.Linear(num_ftrs, num_out_classes)
+            else:
+                print('Using dropout', dropout)
+                if weight_norm:
+                    print('Using Weight_Norm')
+                    self.model.last_linear = nn.Sequential(
+                        nn.Dropout(p=dropout),
+                        nn.utils.weight_norm(
+                            nn.Linear(num_ftrs, num_out_classes), name='weight')
+                    )
+
+                self.model.last_linear = nn.Sequential(
+                    nn.Dropout(p=dropout),
+                    nn.Linear(num_ftrs, num_out_classes)
+                )
+
+            if inc != 3:
+                self.model.conv1 = nn.Conv2d(inc, 32, 3, 2, 0, bias=False)
+                nn.init.xavier_normal(self.model.conv1.weight.data, gain=0.02)
+
+        elif modelchoice == 'resnet50' or modelchoice == 'resnet18':
+            if modelchoice == 'resnet50':
+                self.model = torchvision.models.resnet50(pretrained=True)
+            if modelchoice == 'resnet18':
+                self.model = torchvision.models.resnet18(pretrained=True)
+            # Replace fc
+            num_ftrs = self.model.fc.in_features
+            if not dropout:
+                self.model.fc = nn.Linear(num_ftrs, num_out_classes)
+            else:
+                self.model.fc = nn.Sequential(
+                    nn.Dropout(p=dropout),
+                    nn.Linear(num_ftrs, num_out_classes)
+                )
+        else:
+            raise Exception('Choose valid model, e.g. resnet50')
+
+    def set_trainable_up_to(self, boolean=False, layername="Conv2d_4a_3x3"):
+        """
+        Freezes all layers below a specific layer and sets the following layers
+        to true if boolean else only the fully connected final layer
+        :param boolean:
+        :param layername: depends on lib, for inception e.g. Conv2d_4a_3x3
+        :return:
+        """
+        # Stage-1: freeze all the layers
+        if layername is None:
+            for i, param in self.model.named_parameters():
+                param.requires_grad = True
+                return
+        else:
+            for i, param in self.model.named_parameters():
+                param.requires_grad = False
+        if boolean:
+            # Make all layers following the layername layer trainable
+            ct = []
+            found = False
+            for name, child in self.model.named_children():
+                if layername in ct:
+                    found = True
+                    for params in child.parameters():
+                        params.requires_grad = True
+                ct.append(name)
+            if not found:
+                raise NotImplementedError('Layer not found, cant finetune!'.format(
+                    layername))
+        else:
+            if self.modelchoice == 'xception':
+                # Make fc trainable
+                for param in self.model.last_linear.parameters():
+                    param.requires_grad = True
+
+            else:
+                # Make fc trainable
+                for param in self.model.fc.parameters():
+                    param.requires_grad = True
+
+    def forward(self, x):
+        out, x = self.model(x)
+        if self.return_fea:
+            return out, x
+        else:
+            return out
+
+    def features(self, x):
+        x = self.model.features(x)
+        return x
+
+    def classifier(self, x):
+        out, x = self.model.classifier(x)
+        return out, x
+
+
+def model_selection(modelname, num_out_classes,
+                    dropout=None):
+    """
+    :param modelname:
+    :return: model, image size, pretraining<yes/no>, input_list
+    """
+    if modelname == 'xception':
+        return TransferModel(modelchoice='xception',
+                             num_out_classes=num_out_classes), 299, \
+            True, ['image'], None
+    elif modelname == 'resnet18':
+        return TransferModel(modelchoice='resnet18', dropout=dropout,
+                             num_out_classes=num_out_classes), \
+            224, True, ['image'], None
+    else:
+        raise NotImplementedError(modelname)
+
+
+if __name__ == '__main__':
+    model = TransferModel('xception', dropout=0.5)
+    print(model)
+    # model = model.cuda()
+    # from torchsummary import summary
+    # input_s = (3, image_size, image_size)
+    # print(summary(model, input_s))
+    dummy = torch.rand(10, 3, 256, 256)
+    out = model(dummy)
+    print(out.size())
+    x = model.features(dummy)
+    out, x = model.classifier(x)
+    print(out.size())
+    print(x.size())