def predict(self, images):
"""
Runs forward pass of model.
Args:
images (BxTxHxWx3): Images to predict.
Returns:
dict.
"""
feed_dict = {
self.images_pl: images,
}
fetch_dict = self.make_fetch_dict(self.omegas_pred[0])
fetch_dict_deltas = {}
for delta_t, omega_delta in sorted(self.omegas_pred.items()):
if delta_t == 0:
continue
update_dict_entries(accumulator=fetch_dict_deltas,
appender=self.make_fetch_dict(omega_delta,
suffix='_delta'))
# DxBxTx... --> BxTxDx...
for k in fetch_dict_deltas:
fetch_dict_deltas[k] = tf.stack(fetch_dict_deltas[k], axis=2)
fetch_dict.update(fetch_dict_deltas)
results = self.sess.run(fetch_dict, feed_dict)
return results
def predict_all_images(self, all_images):
"""
Wrapper to predict entire sequence.
Because of edge padding, images at edges will have low quality
predictions since they don't have full field-of-view. Thus, we slide
a window of size T across the images and only keep the predictions
with full fov.
Args:
all_images (NxHxWx3): Images in sequence.
Returns:
dict
"""
B = self.batch_size
T = self.sequence_length
N = len(all_images)
H, W = self.img_size, self.img_size
# Need margin on both sides. Num good frames = T - 2 * margin.
margin = (self.fov - 1) // 2
g = self.sequence_length - 2 * margin
count = np.ceil(N / (g * B)).astype(int)
num_fill = count * B * g + T - N
images_padded = np.concatenate(
(
np.zeros((margin, H, W, 3)), # Front padding.
all_images,
np.zeros((num_fill, H, W, 3)), # Back padding.
),
axis=0)
images_batched = []
# [ m ][ g ][ m ] Slide over by g every time.
# [ m ][ g ][ m ]
for i in range(count * B):
images_batched.append(images_padded[i * g:i * g + T])
images_batched = np.reshape(images_batched, (count, B, T, H, W, 3))
results = {}
# results is the full frames results, pred is single frame result
for images in tqdm(images_batched):
pred = self.predict(images, )
update_dict_entries(results, pred)
# Results are now (CxBxTx...). Should be (Nx...).
new_results = {}
# k,v stands for key value
for k, v in results.items():
v = np.array(v)[:, :, margin:-margin]
old_shape = v.shape[3:]
new_v = v.reshape((-1, ) + old_shape)[:N]
new_results[k] = new_v
return new_results
def main(config):
t0 = time()
config = restore_config(config)
print('-' * 20)
print('Evaluating {}'.format(config.load_path))
json_path = get_result_path_name(
split=config.split,
load_path=config.load_path,
pred_mode=config.pred_mode,
datasets=config.test_datasets,
pred_dir=config.pred_dir,
)
if os.path.exists(json_path):
print(json_path, 'already exists!')
all_dataset_results = json.load(open(json_path, 'r'))
save_results(config, all_dataset_results)
print('Total time:', time() - t0)
print('-' * 20)
exit(0)
resnet_path = config.resnet_path if config.precomputed_phi else ''
model = Tester(
config,
pretrained_resnet_path=resnet_path,
sequence_length=config.T
)
all_dataset_results = {}
if config.pred_mode == 'const':
all_dataset_results.update({
'past': {},
'past_const': {},
'present': {},
'future': {},
'future_const': {},
})
for dataset in config.test_datasets:
print('Evaluating dataset:', dataset)
dataset_result = {}
if config.pred_mode == 'const':
dataset_result.update({
'past': {},
'past_const': {},
'present': {},
'future': {},
'future_const': {},
})
if dataset == 'h36m':
tf_paths = sorted(glob(os.path.join(
config.tf_dir,
dataset,
config.split,
'*cam03*.tfrecord',
)))
else:
tf_paths = sorted(glob(os.path.join(
config.tf_dir,
dataset,
config.split,
'*.tfrecord',
)))
if config.reverse:
tf_paths = tf_paths[::-1]
for i, fname in enumerate(tf_paths):
print('\n', '*' * 10)
print(dataset, '{}/{}'.format(i, len(tf_paths)))
print('Running on', os.path.basename(fname))
path_result = {}
if config.pred_mode == 'const':
path_result.update({
'past': {},
'past_const': {},
'present': {},
'future': {},
'future_const': {},
})
for p_id, s_ex in enumerate(tf.python_io.tf_record_iterator(fname)):
data = read_from_example(s_ex)
images = data['images']
if dataset == 'h36m':
images = images
data['poses'] = data['poses']
data['kps'] = data['kps']
data['gt3ds'] = data['gt3ds']
preds = get_predictions(
model=model,
images=images,
load_path=config.load_path,
tf_path=fname,
p_id=p_id,
pred_dir=config.pred_dir,
)
eval_path = get_eval_path_name(
load_path=config.load_path,
pred_mode=config.pred_mode,
tf_path=fname,
p_id=p_id,
pred_dir=config.pred_dir,
min_visible=config.min_visible,
)
if config.pred_mode == 'const':
errors_dict = test_sequence_const(
data=data,
preds=preds,
eval_path=eval_path,
has_3d=(dataset in DATASETS_3D),
min_visible=config.min_visible,
)
for k in errors_dict.keys():
extend_dict_entries(path_result[k], errors_dict[k])
else:
compute_mesh = config.split == 'test' and dataset == '3dpw'
errors = test_sequence(
data=data,
preds=preds,
eval_path=eval_path,
pred_mode=config.pred_mode,
has_3d=(dataset in DATASETS_3D),
min_visible=config.min_visible,
compute_mesh=compute_mesh,
)
extend_dict_entries(path_result, errors)
if config.pred_mode == 'const':
for k in path_result.keys():
update_dict_entries(dataset_result[k], path_result[k])
else:
update_dict_entries(dataset_result, path_result)
if config.pred_mode == 'const':
for pred_type, result in dataset_result.items():
mean_of_dict_values(result)
all_dataset_results[pred_type][dataset] = result
else:
mean_of_dict_values(dataset_result)
all_dataset_results[dataset] = dataset_result
save_results(config, all_dataset_results, json_path)
print('Total time:', time() - t0)
print('-' * 20)
