def inference(base_model_name, path_to_npz, data_format, input_files, plot):
model_func = get_model(base_model_name)
height, width = (368, 432)
e = measure(
lambda: TfPoseEstimator2(path_to_npz,
model_func,
target_size=(width, height),
data_format=data_format),
'create TfPoseEstimator2')
t0 = time.time()
for idx, img_name in enumerate(input_files):
image = measure(
lambda: read_imgfile(
img_name, width, height, data_format=data_format),
'read_imgfile')
humans, heatMap, pafMap = measure(lambda: e.inference(image),
'e.inference')
tl.logging.info('got %d humans from %s' % (len(humans), img_name))
if humans:
for h in humans:
tl.logging.debug(h)
if plot:
if data_format == 'channels_first':
image = image.transpose([1, 2, 0])
plot_humans(image, heatMap, pafMap, humans, '%02d' % (idx + 1))
tot = time.time() - t0
mean = tot / len(input_files)
tl.logging.info('inference all took: %f, mean: %f, FPS: %f' %
(tot, mean, 1.0 / mean))
def inference(base_model_name, path_to_npz, data_format, input_files, plot):
def model_func(n_pos, target_size):
full_model = get_full_model_func(base_model_name)
return full_model(n_pos, target_size, data_format=data_format)
height, width = (368, 432)
e = measure(
lambda: TfPoseEstimator2(
path_to_npz, model_func, target_size=(width, height)),
'create TfPoseEstimator2')
t0 = time.time()
for idx, img_name in enumerate(input_files):
image = measure(
lambda: read_imgfile(
img_name, width, height, data_format=data_format),
'read_imgfile')
humans = measure(lambda: e.inference(image, resize_out_ratio=8.0),
'e.inference')
tl.logging.info('got %d humans from %s' % (len(humans), img_name))
if humans:
for h in humans:
tl.logging.debug(h)
if plot:
plot_humans(e, image, humans, '%02d' % (idx + 1))
tot = time.time() - t0
mean = tot / len(input_files)
tl.logging.info('inference all took: %f, mean: %f, FPS: %f' %
(tot, mean, 1.0 / mean))
def inference(path_to_freezed_model, input_files):
h, w = 368, 432
e = measure(
lambda: TfPoseEstimator2Loader(path_to_freezed_model,
target_size=(w, h)),
'create TfPoseEstimator2Loader')
for idx, img_name in enumerate(input_files):
image = read_imgfile(img_name, w, h)
humans, heatMap, pafMap = measure(lambda: e.inference(image),
'inference')
print('got %d humans from %s' % (len(humans), img_name))
if humans:
for h in humans:
print(h)
plot_humans(image, heatMap, pafMap, humans, '%02d' % (idx + 1))
def inference(path_to_freezed_model, input_files):
e = measure(
lambda: TfPoseEstimator2Loader(path_to_freezed_model,
target_size=(432, 368)),
'create TfPoseEstimator2Loader')
for idx, img_name in enumerate(input_files):
image = read_imgfile(img_name, None, None)
humans = measure(lambda: e.inference(image, resize_out_ratio=8.0),
'inference')
print('got %d humans from %s' % (len(humans), img_name))
if humans:
for h in humans:
print(h)
plot_humans(e, image, humans, '%02d' % (idx + 1))
def main():
args = parse_args()
height, width, channel = 368, 432, 3
images = []
for name in args.images.split(','):
x = read_imgfile(
name, width, height,
'channels_first') # channels_first is required for tensorRT
images.append(x)
model_func = _get_model_func(args.base_model)
model_inputs, model_outputs = model_func()
input_names = [p.name[:-2] for p in model_inputs]
output_names = [p.name[:-2] for p in model_outputs]
print('input names: %s' % ','.join(input_names))
print('output names: %s' %
','.join(output_names)) # outputs/conf,outputs/paf
# with tf.Session() as sess:
sess = tf.InteractiveSession()
measure(lambda: tl.files.load_and_assign_npz_dict(args.path_to_npz, sess),
'load npz')
frozen_graph = tf.graph_util.convert_variables_to_constants(
sess, sess.graph_def, output_names)
tf_model = tf.graph_util.remove_training_nodes(frozen_graph)
uff_model = measure(lambda: uff.from_tensorflow(tf_model, output_names),
'uff.from_tensorflow')
print('uff model created')
parser = uffparser.create_uff_parser()
inputOrder = 0 # NCHW, https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/c_api/_nv_uff_parser_8h_source.html
parser.register_input(input_names[0], (channel, height, width), inputOrder)
for name in output_names:
parser.register_output(name)
G_LOGGER = trt.infer.ConsoleLogger(trt.infer.LogSeverity.INFO)
max_batch_size = 1
max_workspace_size = 1 << 30
engine = measure(
lambda: trt.utils.uff_to_trt_engine(
G_LOGGER, uff_model, parser, max_batch_size, max_workspace_size),
'trt.utils.uff_to_trt_engine')
print('engine created')
f_height, f_width = (height / 8, width / 8
) # TODO: derive from model_outputs
post_process = PostProcessor((height, width), (f_height, f_width),
'channels_first')
for idx, x in enumerate(images):
conf, paf = measure(lambda: infer(engine, x, 1), 'infer')
humans, heat_up, paf_up = measure(lambda: post_process(conf, paf),
'post_process')
print('got %d humans' % (len(humans)))
plot_humans(x.transpose([1, 2, 0]), heat_up, paf_up, humans,
'%02d' % (idx + 1))
def export_model(model_func, checkpoint_dir, path_to_npz, graph_filename):
mkdir_p(checkpoint_dir)
model_parameters = model_func()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
measure(lambda: tl.files.load_and_assign_npz_dict(path_to_npz, sess),
'load npz')
measure(lambda: save_graph(sess, checkpoint_dir, graph_filename),
'save_graph')
measure(lambda: save_model(sess, checkpoint_dir), 'save_model')
print('model_parameters:')
for p in model_parameters:
print('%s :: %s' % (p.name, p.shape))
parser.add_argument('--data-format',
type=str,
default='channels_last',
help='channels_last | channels_first.')
parser.add_argument('--plot',
type=bool,
default=False,
help='draw the results')
parser.add_argument('--repeat',
type=int,
default=1,
help='repeat the images for n times for profiling.')
parser.add_argument('--limit',
type=int,
default=100,
help='max number of images.')
return parser.parse_args()
def main():
args = parse_args()
image_files = ([f for f in args.images.split(',') if f] *
args.repeat)[:args.limit]
inference(args.base_model, args.path_to_npz, args.data_format, image_files,
args.plot)
if __name__ == '__main__':
measure(main)
