Hey , I compared the code with your other repository "m2tr", I found that your deleted the def extract_textures in efficientnet.py, and this caused the code to report an error —— 'EfficientNet' object has no attribute 'extract_textures'

We tried to load the corresponding weights for the model but found them to be inconsistent, including c23 and c40. We don't understand why, it looks like the keys are wrong.

RuntimeError: Error(s) in loading state_dict for M2TR: Missing key(s) in state_dict: "layers.2.0.t.attention.query_embedding.weight", "layers.2.0.t.attention.query_embedding.bias", "layers.2.0.t.attention.value_embedding.weight", "layers.2.0.t.attention.value_embedding.bias", "layers.2.0.t.attention.key_embedding.weight", "layers.2.0.t.attention.key_embedding.bias", "layers.2.0.t.attention.output_linear.0.weight", "layers.2.0.t.attention.output_linear.0.bias", "layers.2.0.t.attention.output_linear.1.weight", "layers.2.0.t.attention.output_linear.1.bias", "layers.2.0.t.attention.output_linear.1.running_mean", "layers.2.0.t.attention.output_linear.1.running_var", "layers.2.0.t.feed_forward.conv.0.weight", "layers.2.0.t.feed_forward.conv.0.bias", "layers.2.0.t.feed_forward.conv.1.weight", "layers.2.0.t.feed_forward.conv.1.bias", "layers.2.0.t.feed_forward.conv.1.running_mean", "layers.2.0.t.feed_forward.conv.1.running_var", "layers.2.0.t.feed_forward.conv.3.weight", "layers.2.0.t.feed_forward.conv.3.bias", "layers.2.0.t.feed_forward.conv.4.weight", "layers.2.0.t.feed_forward.conv.4.bias", "layers.2.0.t.feed_forward.conv.4.running_mean", "layers.2.0.t.feed_forward.conv.4.running_var", "layers.2.1.filter.complex_weight", "layers.2.1.feed_forward.conv.0.weight", "layers.2.1.feed_forward.conv.0.bias", "layers.2.1.feed_forward.conv.1.weight", "layers.2.1.feed_forward.conv.1.bias", "layers.2.1.feed_forward.conv.1.running_mean", "layers.2.1.feed_forward.conv.1.running_var", "layers.2.1.feed_forward.conv.3.weight", "layers.2.1.feed_forward.conv.3.bias", "layers.2.1.feed_forward.conv.4.weight", "layers.2.1.feed_forward.conv.4.bias", "layers.2.1.feed_forward.conv.4.running_mean", "layers.2.1.feed_forward.conv.4.running_var", "layers.2.2.conv1.weight", "layers.2.2.conv1.bias", "layers.2.2.conv2.weight", "layers.2.2.conv2.bias", "layers.2.2.conv3.weight", "layers.2.2.conv3.bias", "layers.2.2.conv4.0.weight", "layers.2.2.conv4.0.bias", "layers.2.2.conv4.1.weight", "layers.2.2.conv4.1.bias", "layers.2.2.conv4.1.running_mean", "layers.2.2.conv4.1.running_var", "layers.3.0.t.attention.query_embedding.weight", "layers.3.0.t.attention.query_embedding.bias", "layers.3.0.t.attention.value_embedding.weight", "layers.3.0.t.attention.value_embedding.bias", "layers.3.0.t.attention.key_embedding.weight", "layers.3.0.t.attention.key_embedding.bias", "layers.3.0.t.attention.output_linear.0.weight", "layers.3.0.t.attention.output_linear.0.bias", "layers.3.0.t.attention.output_linear.1.weight", "layers.3.0.t.attention.output_linear.1.bias", "layers.3.0.t.attention.output_linear.1.running_mean", "layers.3.0.t.attention.output_linear.1.running_var", "layers.3.0.t.feed_forward.conv.0.weight", "layers.3.0.t.feed_forward.conv.0.bias", "layers.3.0.t.feed_forward.conv.1.weight", "layers.3.0.t.feed_forward.conv.1.bias", "layers.3.0.t.feed_forward.conv.1.running_mean", "layers.3.0.t.feed_forward.conv.1.running_var", "layers.3.0.t.feed_forward.conv.3.weight", "layers.3.0.t.feed_forward.conv.3.bias", "layers.3.0.t.feed_forward.conv.4.weight", "layers.3.0.t.feed_forward.conv.4.bias", "layers.3.0.t.feed_forward.conv.4.running_mean", "layers.3.0.t.feed_forward.conv.4.running_var", "layers.3.1.filter.complex_weight", "layers.3.1.feed_forward.conv.0.weight", "layers.3.1.feed_forward.conv.0.bias", "layers.3.1.feed_forward.conv.1.weight", "layers.3.1.feed_forward.conv.1.bias", "layers.3.1.feed_forward.conv.1.running_mean", "layers.3.1.feed_forward.conv.1.running_var", "layers.3.1.feed_forward.conv.3.weight", "layers.3.1.feed_forward.conv.3.bias", "layers.3.1.feed_forward.conv.4.weight", "layers.3.1.feed_forward.conv.4.bias", "layers.3.1.feed_forward.conv.4.running_mean", "layers.3.1.feed_forward.conv.4.running_var", "layers.3.2.conv1.weight", "layers.3.2.conv1.bias", "layers.3.2.conv2.weight", "layers.3.2.conv2.bias", "layers.3.2.conv3.weight", "layers.3.2.conv3.bias", "layers.3.2.conv4.0.weight", "layers.3.2.conv4.0.bias", "layers.3.2.conv4.1.weight", "layers.3.2.conv4.1.bias", "layers.3.2.conv4.1.running_mean", "layers.3.2.conv4.1.running_var", "classifier.projection.weight", "classifier.projection.bias". Unexpected key(s) in state_dict: "classifier.weight", "classifier.bias". size mismatch for model._fc.weight: copying a param with shape torch.Size([1, 1792]) from checkpoint, the shape in current model is torch.Size([2, 1792]). size mismatch for model._fc.bias: copying a param with shape torch.Size([1]) from checkpoint, the shape in current model is torch.Size([2]).

Hope this can be fixed soon.

RuntimeError: CUDA error: invalid device ordinal CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Excuse me, how to solve this error.

when I training MAT, The training is stuck, and it's not over. I checked a lot, but I didn't find the relevant information.

CPU shows running, Do you know what the problem is?

Hi,

Thanks for your excellent summary. I have reproduced the related results on the dataset "raw" of FF-DF. But I got the results as shown: The overall values are on the high side, where meso4, MesoInception4 is not the correct one.

PS: My dataset where the original is real, manipulated is fake, where fake and real each have 1000 videos * 128 frames. I follow your default setup of the code for extracting frames from the video. Is it normal? Or is there something I ignore?

thanks~