Comments (1)
Hi, it should be straightforward.
Change https://github.com/bryanyzhu/two-stream-pytorch/blob/master/models/flow_resnet.py#L146 this function. Right now, we only output x = self.fc_action(x)
, which is the prediction softmax scores. If you want the features, for example, from 4th convolution layer x = self.layer4(x)
. You can simply change x = self.layer4(x)
to feature = self.layer4(x)
, and output the feature like return feature
. So now your output is the feature.
You can directly use this script https://github.com/bryanyzhu/two-stream-pytorch/blob/master/scripts/eval_ucf101_pytorch/temporal_demo.py. The spatial_prediction = VideoTemporalPrediction(...)
is the feature you want.
from two-stream-pytorch.
Related Issues (20)
- About pre-trained Model HOT 2
- test video HOT 5
- Question about training the models together HOT 2
- Different running env? HOT 5
- Can you provide your results for loss and accuracy values of spatial and temporal training?
- The number of GPUs? HOT 1
- I use your restnet152 model parameters for testing, but in split_1 the accuracy is only 67.59%.
- Use the video input from the camera for action recognition HOT 7
- Problems about VideoSpatialPrediction.py HOT 2
- How is the two streams fused ? HOT 1
- What is the accuracy of UCF101?
- what's version of pytorch and cuda
- dense_flow 可不可以在windows安装 HOT 1
- 如果没有安装dense_flow,运行build_of.py文件,是不是不会运行出结果 HOT 1
- fusion two stream feature?
- a PROBLEM when using VGG as motion model
- 老师我想问下怎么late fusion呀 HOT 1
- 关于抽帧的图片存放路径 HOT 2
- video sampling rate in training two-stream network
- About parameter --new_length in training RGB videos
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from two-stream-pytorch.