def test(model, dataset, test_data, use_gpu):
print("===> Test")
with torch.no_grad():
model.eval()
if config.SAVE_RESULTS:
h5_res = h5py.File(os.path.join(config.SAVE_DIR, 'result_test.h5'),
'w')
for key_idx, key in enumerate(test_data):
seq = dataset[key]['features'][...]
seq = torch.from_numpy(seq).unsqueeze(0)
if use_gpu: seq.cuda()
probs = model(seq)
probs = probs.data.cpu().squeeze().numpy()
cps = dataset[key]['change_points'][...]
num_frames = dataset[key]['n_frames'][...]
nfps = dataset[key]['n_frame_per_seg'][...].tolist()
positions = dataset[key]['picks'][...]
machine_summary = vsum_tool.generate_summary(
probs, cps, num_frames, nfps, positions)
if config.SAVE_RESULTS:
h5_res.create_dataset(key + '/score', data=probs)
h5_res.create_dataset(key + '/machine_summary',
data=machine_summary)
if config.SAVE_RESULTS:
h5_res.close()
def evaluate(model, dataset, test_keys, use_gpu):
print("===> Evaluation")
with torch.no_grad():
model.eval()
fms = []
eval_metric = 'avg' if config.METRIC == 'tvsum' else 'max'
if config.VERBOSE: table = [["No.", "Video", "F-Score"]]
if config.SAVE_RESULTS:
h5_res = h5py.File(os.path.join(config.SAVE_DIR, 'result.h5'), 'w')
for key_idx, key in enumerate(test_keys):
seq = dataset[key]['features'][...]
seq = torch.from_numpy(seq).unsqueeze(0)
if use_gpu: seq = seq.cuda()
probs = model(seq)
probs = probs.data.cpu().squeeze().numpy()
cps = dataset[key]['change_points'][...]
num_frames = dataset[key]['n_frames'][()]
nfps = dataset[key]['n_frame_per_seg'][...].tolist()
positions = dataset[key]['picks'][...]
# user_summary = dataset[key]['user_summary'][...]
machine_summary = vsum_tool.generate_summary(
probs, cps, num_frames, nfps, positions)
user_summary = machine_summary #dataset[key]['user_summary'][...] REMOVE IT
fm, _, _ = vsum_tool.evaluate_summary(machine_summary,
user_summary, eval_metric)
fms.append(fm)
if config.VERBOSE:
table.append([key_idx + 1, key, "{:.1%}".format(fm)])
if config.SAVE_RESULTS:
h5_res.create_dataset(key + '/score', data=probs)
h5_res.create_dataset(key + '/machine_summary',
data=machine_summary)
h5_res.create_dataset(key + '/gtscore',
data=dataset[key]['gtscore'][...])
h5_res.create_dataset(key + '/fm', data=fm)
if config.VERBOSE:
print(tabulate(table))
if config.SAVE_RESULTS: h5_res.close()
mean_fm = np.mean(fms)
print("Average F-Score {:.1%}".format(mean_fm))
return mean_fm
def evaluate(model, dataset, test_keys, use_gpu):
with torch.no_grad():
model.eval()
fms = []
table = [["No.", "Video", "F-score"]]
if not os.path.isdir(args.save_dir):
os.mkdir(args.save_dir)
h5_res = h5py.File(os.path.join(args.save_dir, 'result.h5'), 'w')
for key_idx, key in enumerate(test_keys):
seq = dataset[key]['features'][...]
seq = torch.from_numpy(seq).unsqueeze(0)
if use_gpu: seq = seq.cuda()
probs = model(seq)
probs = probs.data.cpu().squeeze().numpy()
cps = dataset[key]['change_points'][...]
num_frames = dataset[key]['n_frames'][()]
nfps = dataset[key]['n_frame_per_seg'][...].tolist()
positions = dataset[key]['picks'][...]
video_name = dataset[key]['video_name'][()]
fps = dataset[key]['fps'][()]
sum = 0
for i in range(len(nfps)):
sum += nfps[i]
machine_summary = vsum_tool.generate_summary(
probs, cps, num_frames, nfps, positions)
h5_res.create_dataset(key + '/score', data=probs)
h5_res.create_dataset(key + '/machine_summary',
data=machine_summary)
h5_res.create_dataset(key + '/video_name', data=video_name)
h5_res.create_dataset(key + '/fps', data=fps)
h5_res.close()