def inference(base_model_name, path_to_npz, data_format, input_files, plot):
model_func = get_base_model(base_model_name)
height, width = (368, 432)
e = measure(
lambda: TfPoseEstimator(path_to_npz,
model_func,
target_size=(width, height),
data_format=data_format),
'create TfPoseEstimator')
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_2d(base_model_name, base_npz_path, rgb_files, dep_files):
height, width, channel = (368, 432, 4)
base_model_func = get_base_model(base_model_name)
e_2d = measure(
lambda: TfPoseEstimator(base_npz_path, base_model_func,
(height, width, channel)),
'create TfPoseEstimator')
time0 = time.time()
for idx, (rgb_name, dep_name) in enumerate(zip(rgb_files, dep_files)):
input_2d, init_h, init_w = measure(
lambda: read_2dfiles(rgb_name, dep_name, height, width),
'read_2dfiles')
humans, heatMap, pafMap = measure(lambda: e_2d.inference(input_2d),
'e_2d.inference')
plot_humans(input_2d, heatMap, pafMap, humans, '%02d' % (idx + 1))
for h, pred_2d in enumerate(humans):
coords2d, _, coords2d_vis = tranform_keypoints2d(
pred_2d.body_parts, init_w, init_h, 0.1)
plot_human2d(rgb_name, dep_name, coords2d, h, coords2d_vis)
do_plot()
mean = (time.time() - time0) / len(rgb_files)
print('inference all took: %f, mean: %f, FPS: %f' %
(time.time() - time0, mean, 1.0 / mean))
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 inference(path_to_freezed_model, input_files):
h, w = 368, 432
e = measure(lambda: TfPoseestimatorLoader(path_to_freezed_model, target_size=(w, h)),
'create TfPoseestimatorLoader')
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))
human.score
}
result.append(item)
scores += item['score']
avg_score = scores / len(humans) if len(humans) > 0 else 0
logger.info('image: %s humans: %d anns: %d score: %f' %
(img_name, len(humans), len(anns), avg_score))
if config.EVAL.data_idx >= 0:
if humans:
for h in humans:
logger.info(h)
if config.EVAL.plot:
plot_humans(
image, heatMap, pafMap, humans, '%06d' % (img_idx + 1),
os.path.join(config.EVAL.eval_path, config.LOG.vis_path))
write_json = os.path.join(config.EVAL.eval_path, 'eval.json')
fp = open(write_json, 'w+')
json.dump(result, fp)
fp.close()
cocoDt = cocoGt.loadRes(write_json)
cocoEval = COCOeval(cocoGt, cocoDt, 'keypoints')
cocoEval.params.imgIds = keys
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
write_txt = os.path.join(config.EVAL.eval_path, 'eval.txt')
