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[Official] Exploring Temporal Coherence for More General Video Face Forgery Detection(ICCV 2021)

Python 100.00%

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ftcn's Issues

Bug in test_on_raw_video

            l_post = len(post_module)
            post_module = post_module * (pad_length // l_post + 1)
            post_module = post_module[:pad_length]
            assert len(post_module) == pad_length

            pre_module = inner_index + inner_index[1:-1][::-1]
            l_pre = len(post_module)
            pre_module = pre_module * (pad_length // l_pre + 1)
            pre_module = pre_module[-pad_length:]
            assert len(pre_module) == pad_length

the code

 l_pre = len(post_module)

should be replaced by

 l_pre = len(pre_module)

is it right?

关于模型结构的问题

按文章中的结构，每个ResBlock中a、b、c三个kernel的size分别应为[1,1,1]，[3,1,1]与[1,1,1]。
但代码所输出结构与文中结构不符（如下），或许是理解错误，烦请解惑：
res2:

  (s2): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=[1, 1, 1], bias=False)
      (branch1_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (branch2): BottleneckTransform(
        (a): Conv3d(64, 64, kernel_size=[3, 1, 1], stride=[1, 1, 1], padding=[1, 0, 0], bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=[3, 1, 1], stride=[1, 1, 1], padding=[1, 0, 0], bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 64, kernel_size=[3, 1, 1], stride=[1, 1, 1], padding=[1, 0, 0], bias=False)
        (a_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(64, 64, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(64, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )

res3:

(s3): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(256, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
      (branch1_bn): Sequential(
        (0): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (1): MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=0, dilation=1, ceil_mode=False)
      )
      (branch2): BottleneckTransform(
        (a): Conv3d(256, 128, kernel_size=[3, 1, 1], stride=[1, 1, 1], padding=[1, 0, 0], bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): Sequential(
          (0): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=0, dilation=1, ceil_mode=False)
        )
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 128, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 128, kernel_size=[3, 1, 1], stride=[1, 1, 1], padding=[1, 0, 0], bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 128, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (a_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(128, 128, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(128, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )

res4:

(s4): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(512, 1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
      (branch1_bn): Sequential(
        (0): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (1): MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=0, dilation=1, ceil_mode=False)
      )
      (branch2): BottleneckTransform(
        (a): Conv3d(512, 256, kernel_size=[3, 1, 1], stride=[1, 1, 1], padding=[1, 0, 0], bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): Sequential(
          (0): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=0, dilation=1, ceil_mode=False)
        )
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=[3, 1, 1], stride=[1, 1, 1], padding=[1, 0, 0], bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res3): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res4): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=[3, 1, 1], stride=[1, 1, 1], padding=[1, 0, 0], bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res5): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (a_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(256, 256, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(256, 1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )

res5:

(s5): ResStage(
    (pathway0_res0): ResBlock(
      (branch1): Conv3d(1024, 2048, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
      (branch1_bn): Sequential(
        (0): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (1): MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=0, dilation=1, ceil_mode=False)
      )
      (branch2): BottleneckTransform(
        (a): Conv3d(1024, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (a_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(512, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): Sequential(
          (0): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (1): MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=0, dilation=1, ceil_mode=False)
        )
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(512, 2048, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res1): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(2048, 512, kernel_size=[3, 1, 1], stride=[1, 1, 1], padding=[1, 0, 0], bias=False)
        (a_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(512, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(512, 2048, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
    (pathway0_res2): ResBlock(
      (branch2): BottleneckTransform(
        (a): Conv3d(2048, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (a_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (a_relu): ReLU(inplace=True)
        (b): Conv3d(512, 512, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], dilation=[1, 1, 1], bias=False)
        (b_bn): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (b_relu): ReLU(inplace=True)
        (c): Conv3d(512, 2048, kernel_size=[1, 1, 1], stride=[1, 1, 1], padding=[0, 0, 0], bias=False)
        (c_bn): BatchNorm3d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (relu): ReLU(inplace=True)
    )
  )

inference on video which consists of more than one faces

I've run the code with video which consists of more than one face. The video looks like this:

However, I found out that the output is restricted to only one face. Has anyone here tried to tweak the code so it can predict on video which consists of more than one face?

训练中视频帧采样间隔

您好，请问训练中一个视频中各帧的采样间隔是多少？非常感谢

请问训练过程中有进行数据增强操作吗？

AttributeError: 'Conv3d' object has no attribute 'device'

I used my own device to run and google colab but getting the same error

test_on_raw_video 用上面自带的video测试全程都是fake

测试过程中有一个WARN 请问是什么意思如何解决

训练代码

请问您可以开源一下训练的代码嘛

训练代码开源

您好，请问您什么时候能开放数据预处理以及训练代码呢？

Error LeakyReLU.cu:29

I ran

python test_on_raw_video.py examples/shining.mp4 output

It returned me this error. Anyone has idea why this happened?
Thanks!

THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/THCUNN/generic/LeakyReLU.cu line=29 error=209 : no kernel image is available for execution on the device
Traceback (most recent call last):
  File "test_on_raw_video.py", line 50, in <module>
    input_file, return_frames=True, max_size=max_frame
  File "/data/test_tools/common.py", line 85, in detect_all
    [detector.detect(item) for item in partition(frames, 50)]
  File "/data/test_tools/common.py", line 85, in <listcomp>
    [detector.detect(item) for item in partition(frames, 50)]
  File "/data/test_tools/ct/detection/detector.py", line 36, in detect
    return batch_detect(self.model, np.array(images), self.device)
  File "/data/test_tools/ct/detection/alignment.py", line 567, in batch_detect
    loc, conf, landms = net(img)  # forward pass
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/data/test_tools/ct/detection/alignment.py", line 253, in forward
    out = self.body(inputs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torchvision/models/_utils.py", line 63, in forward
    x = module(x)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py", line 100, in forward
    input = module(input)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py", line 100, in forward
    input = module(input)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 532, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/activation.py", line 559, in forward
    return F.leaky_relu(input, self.negative_slope, self.inplace)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py", line 1061, in leaky_relu
    result = torch._C._nn.leaky_relu_(input, negative_slope)
RuntimeError: cuda runtime error (209) : no kernel image is available for execution on the device at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/THCUNN/generic/LeakyReLU.cu:29

Any plans on releasing trainer code?

我用正常的视频测试，也显示fake。。。

May I ask when the training code will be released？

Question about the structure of ResNet3D

您好，代码中conv1的kernel size为[5,7,7]，stride为[1,2,2]。而论文中kernel size为[5,1,1]，stride为[1,1,1]。
请问，是否可以给出论文中实际使用的，完整的模型结构呢？

temp_kernel[0][0] = [5]
self.s1 = stem_helper.VideoModelStem(
    dim_in=cfg.DATA.INPUT_CHANNEL_NUM,
    dim_out=[width_per_group],
    kernel=[temp_kernel[0][0] + [7, 7]],
    stride=[[1, 2, 2]],
    padding=[[temp_kernel[0][0][0] // 2, 3, 3]],
    norm_module=self.norm_module)

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