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):
