if __name__ == "__main__": lprint("--------------------args------------------") for x in C.__dict__: lprint("%s : %s" % (x, repr(C.__dict__[x]))) lprint("------------------------------------------\n") if C.seed > 0: random.seed(C.seed) np.random.seed(C.seed) tc.manual_seed(C.seed) tc.cuda.set_device(C.gpus[0]) data = dataloader.run(name=C.name, force_reprocess=C.force_reprocess) lprint("got data.") lprint("size of train/valid/test = %d / %d / %d" % (len(data["train"]), len(data["valid"]), len(data["test"]))) sort_idx = data["sort_idx"].cuda(C.gpus[0]) net = GraphWriter( vocab=data["vocab"], entity_number=Con.max_entity_per_string, dropout=C.dropout, sort_idx=sort_idx, ) net = net.cuda(C.gpus[0]) if len(C.gpus) > 1: net = nn.DataParallel(net, C.gpus)
rgb_preds = 'record/spatial/spatial_video_preds.pickle' opf_preds = 'record/motion/motion_video_preds.pickle' with open(rgb_preds, 'rb') as f: rgb = pickle.load(f) f.close() with open(opf_preds, 'rb') as f: opf = pickle.load(f) f.close() dataloader = dataloader.spatial_dataloader(BATCH_SIZE=1, num_workers=1, path='/home/ubuntu/data/UCF101/spatial_no_sampled/', ucf_list='/home/ubuntu/cvlab/pytorch/ucf101_two_stream/github/UCF_list/', ucf_split='01') train_loader, val_loader, test_video = dataloader.run() video_level_preds = np.zeros((len(rgb.keys()), 101)) video_level_labels = np.zeros(len(rgb.keys())) correct = 0 ii = 0 for name in sorted(rgb.keys()): r = rgb[name] o = opf[name] label = int(test_video[name])-1 video_level_preds[ii, :] = (r+o) video_level_labels[ii] = label ii += 1 if np.argmax(r+o) == (label):
rgb_preds='record/spatial/spatial_video_preds.pickle' opf_preds = 'record/motion/motion_video_preds.pickle' with open(rgb_preds,'rb') as f: rgb =pickle.load(f) f.close() with open(opf_preds,'rb') as f: opf =pickle.load(f) f.close() dataloader = dataloader.spatial_dataloader(BATCH_SIZE=1, num_workers=1, path='/home/ubuntu/data/UCF101/spatial_no_sampled/', ucf_list='/home/ubuntu/cvlab/pytorch/ucf101_two_stream/github/UCF_list/', ucf_split='01') train_loader,val_loader,test_video = dataloader.run() video_level_preds = np.zeros((len(rgb.keys()),101)) video_level_labels = np.zeros(len(rgb.keys())) correct=0 ii=0 for name in sorted(rgb.keys()): r = rgb[name] o = opf[name] label = int(test_video[name])-1 video_level_preds[ii,:] = (r+o) video_level_labels[ii] = label ii+=1 if np.argmax(r+o) == (label):