def main():
args = parse_args()
if args.amp:
import os
os.environ['AMP'] = "1"
cfg = get_config(args.config, overrides=args.override)
_, world_size = get_dist_info()
parallel = world_size != 1
if parallel:
paddle.distributed.init_parallel_env()
if args.test:
test_model(cfg, weights=args.weights, parallel=parallel)
elif args.train_dali:
train_dali(cfg, weights=args.weights, parallel=parallel)
elif args.multigrid:
train_model_multigrid(cfg, world_size, validate=args.validate)
else:
train_model(cfg,
weights=args.weights,
parallel=parallel,
validate=args.validate,
fleet=args.fleet,
amp=args.amp)
def main():
args = parse_args()
cfg, model_name = _trim(get_config(args.config, show=False), args)
print(f"Building model({model_name})...")
model = build_model(cfg)
assert osp.isfile(
args.pretrained_params
), f"pretrained params ({args.pretrained_params} is not a file path.)"
if not os.path.isdir(args.output_path):
os.makedirs(args.output_path)
print(f"Loading params from ({args.pretrained_params})...")
params = paddle.load(args.pretrained_params)
model.set_dict(params)
model.eval()
model = to_static(model,
input_spec=[
paddle.static.InputSpec(shape=[
None, args.num_seg, 3, args.img_size,
args.img_size
],
dtype='float32'),
])
paddle.jit.save(model, osp.join(args.output_path, model_name))
print(
f"model ({model_name}) has been already saved in ({args.output_path}).")
def main():
args = parse_args()
cfg, model_name = _trim(get_config(args.config, show=False))
print(f"Building model({model_name})...")
model = build_model(cfg)
params_info = paddle.summary(model, (1, 8, 3, 224, 224))
print(params_info)
def main():
args = parse_args()
cfg = get_config(args.config, overrides=args.override)
dataset = build_dataset((cfg.DATASET.test, cfg.PIPELINE.test))
_, world_size = get_dist_info()
parallel = world_size != 1
if parallel:
paddle.distributed.init_parallel_env()
model = build_model(cfg.MODEL)
test_model(model, dataset, cfg, args.weights, world_size)
def main():
args = parse_args()
cfg, model_name = _trim(get_config(args.config, show=False), args)
print(f"Building model({model_name})...")
model = build_model(cfg)
img_size = args.img_size
num_seg = args.num_seg
#NOTE: only support tsm now, will refine soon
params_info = paddle.summary(model, (1, num_seg, 3, img_size, img_size))
print(params_info)
if args.FLOPs:
flops_info = paddle.flops(model, [1, num_seg, 3, img_size, img_size],
print_detail=True)
print(flops_info)
def main():
args = parse_args()
cfg = get_config(args.config, overrides=args.override)
_, world_size = get_dist_info()
parallel = world_size != 1
if parallel:
paddle.distributed.init_parallel_env()
if args.test:
test_model(cfg, weights=args.weights, parallel=parallel)
else:
train_model(cfg,
weights=args.weights,
parallel=parallel,
validate=args.validate)
def main():
args = parse_args()
cfg, model_name = trim_config(get_config(args.config, show=False))
print(f"Building model({model_name})...")
model = build_model(cfg.MODEL)
assert osp.isfile(
args.pretrained_params
), f"pretrained params ({args.pretrained_params} is not a file path.)"
if not os.path.isdir(args.output_path):
os.makedirs(args.output_path)
print(f"Loading params from ({args.pretrained_params})...")
params = paddle.load(args.pretrained_params)
model.set_dict(params)
model.eval()
input_spec = get_input_spec(cfg.INFERENCE, model_name)
model = to_static(model, input_spec=input_spec)
paddle.jit.save(model, osp.join(args.output_path, model_name))
print(
f"model ({model_name}) has been already saved in ({args.output_path}).")
def main():
args = parse_args()
cfg = get_config(args.config, show=False)
model_name = cfg.model_name
print(f"Inference model({model_name})...")
InferenceHelper = build_inference_helper(cfg.INFERENCE)
if args.enable_benchmark:
assert args.use_gpu is True
# HALF precission predict only work when using tensorrt
if args.use_fp16 is True:
assert args.use_tensorrt is True
predictor = create_paddle_predictor(args)
# get input_tensor and output_tensor
input_names = predictor.get_input_names()
output_names = predictor.get_output_names()
input_tensor_list = []
output_tensor_list = []
for item in input_names:
input_tensor_list.append(predictor.get_input_handle(item))
for item in output_names:
output_tensor_list.append(predictor.get_output_handle(item))
test_num = 500
test_time = 0.0
if not args.enable_benchmark:
# Prepare input
inputs = InferenceHelper.preprocess(args.input_file)
# Run inference
for i in range(len(input_tensor_list)):
input_tensor_list[i].copy_from_cpu(inputs[i])
predictor.run()
output = []
for j in range(len(output_tensor_list)):
output.append(output_tensor_list[j].copy_to_cpu())
# Post process output
InferenceHelper.postprocess(output)
else: # benchmark only for ppTSM
for i in range(0, test_num + 10):
inputs = []
inputs.append(
np.random.rand(args.batch_size, 8, 3, 224,
224).astype(np.float32))
start_time = time.time()
for j in range(len(input_tensor_list)):
input_tensor_list[j].copy_from_cpu(inputs[j])
predictor.run()
output = []
for j in range(len(output_tensor_list)):
output.append(output_tensor_list[j].copy_to_cpu())
if i >= 10:
test_time += time.time() - start_time
#time.sleep(0.01) # sleep for T4 GPU
fp_message = "FP16" if args.use_fp16 else "FP32"
trt_msg = "using tensorrt" if args.use_tensorrt else "not using tensorrt"
print("{0}\t{1}\t{2}\tbatch size: {3}\ttime(ms): {4}".format(
model_name, trt_msg, fp_message, args.batch_size,
1000 * test_time / test_num))
