| import unittest | |
| import numpy as np | |
| import os | |
| from glob import glob | |
| class TestFeatureAlign(unittest.TestCase): | |
| def test_caffe_pytorch_feat_align(self): | |
| caffe_feat_path = "/export/home/lxy/cvpalgo-fast-reid/tools/deploy/caffe_R50_output" | |
| pytorch_feat_path = "/export/home/lxy/cvpalgo-fast-reid/demo/logs/R50_256x128_pytorch_feat_output" | |
| feat_filenames = os.listdir(caffe_feat_path) | |
| for feat_name in feat_filenames: | |
| caffe_feat = np.load(os.path.join(caffe_feat_path, feat_name)) | |
| pytorch_feat = np.load(os.path.join(pytorch_feat_path, feat_name)) | |
| sim = np.dot(caffe_feat, pytorch_feat.transpose())[0][0] | |
| assert sim > 0.97, f"Got similarity {sim} and feature of {feat_name} is not aligned" | |
| def test_model_performance(self): | |
| caffe_feat_path = "/export/home/lxy/cvpalgo-fast-reid/tools/deploy/caffe_R50_output" | |
| feat_filenames = os.listdir(caffe_feat_path) | |
| feats = [] | |
| for feat_name in feat_filenames: | |
| caffe_feat = np.load(os.path.join(caffe_feat_path, feat_name)) | |
| feats.append(caffe_feat) | |
| from ipdb import set_trace; set_trace() | |
| if __name__ == '__main__': | |
| unittest.main() | |