def _default_config(args):
config = flow.function_config()
config.default_logical_view(flow.scope.consistent_view())
config.default_data_type(flow.float)
if args.use_fp16:
config.enable_auto_mixed_precision(True)
return config
def _default_config(args):
config = flow.function_config()
config.default_logical_view(flow.scope.consistent_view())
config.default_data_type(flow.float)
if args.use_fp16:
config.enable_auto_mixed_precision(True)
if args.use_xla:
config.use_xla_jit(True)
config.enable_fuse_add_to_output(True)
if args.use_tensorrt:
config.use_tensorrt(True)
if args.use_int8_online or args.use_int8_offline:
config.tensorrt.use_int8()
elif args.use_int8_online or args.use_int8_offline:
raise Exception(
"You can set use_int8_online or use_int8_offline only after use_tensorrt is True!"
)
if args.use_int8_offline:
int8_calibration_path = "./int8_calibration"
config.tensorrt.int8_calibration(int8_calibration_path)
if args.use_int8_offline and args.use_int8_online:
raise ValueError(
"You cannot use use_int8_offline or use_int8_online at the same time!"
)
return config
def GetFunctionConfig(args):
config = flow.function_config()
config.enable_auto_mixed_precision(args.use_fp16)
if args.use_xla:
config.use_xla_jit(True)
config.enable_fuse_add_to_output(True)
config.enable_fuse_model_update_ops(True)
return config
def make_func_config(args):
config = flow.function_config()
if args.use_fp16:
config.enable_auto_mixed_precision(True)
config.prune_parallel_cast_ops(True)
config.enable_fuse_add_to_output(True)
config.enable_fuse_model_update_ops(True)
config.enable_fuse_cast_scale(True)
# turn on the flag of none-distributed-optimizer
config.enable_non_distributed_optimizer(False)
return config
def get_train_config():
config = flow.function_config()
config.default_data_type(flow.float)
return config
if name is None:
name = "Mini_Reader_uniqueID"
return (flow.user_op_builder(name).Op("MiniReader").Output("out").Attr(
"data_dir", minidata_dir).Attr("data_part_num", data_part_num).Attr(
"batch_size",
batch_size).Attr("part_name_prefix", part_name_prefix).Attr(
"random_shuffle", random_shuffle).Attr(
"shuffle_after_epoch", shuffle_after_epoch).Attr(
"part_name_suffix_length",
part_name_suffix_length).Attr("shuffle_buffer_size",
shuffle_buffer_size).
Build().InferAndTryRun().RemoteBlobList()[0])
config = flow.function_config()
config.default_data_type(flow.double)
@flow.global_function("train", config)
def test_job() -> tp.Numpy:
batch_size = 10
with flow.scope.placement("cpu", "0:0"):
miniRecord = MiniReader(
"./",
batch_size=batch_size,
data_part_num=2,
part_name_suffix_length=3,
random_shuffle=True,
shuffle_after_epoch=True,
)
def get_val_config():
config = flow.function_config()
config.default_logical_view(flow.scope.consistent_view())
config.default_data_type(flow.float)
return config
def get_train_config():
config = flow.function_config()
config.default_data_type(flow.float)
config.default_logical_view(flow.scope.mirrored_view())
return config
from Scnet.utils.clsidx_to_labels import clsidx_2_labels
def load_image(image_path='data/img_red.png'):
print(image_path)
im = Image.open(image_path)
im = im.resize((224, 224))
im = im.convert('RGB') # 有的图像是单通道的，不加转换会报错
im = np.array(im).astype('float32')
im = (im - args.rgb_mean) / args.rgb_std
im = np.transpose(im, (2, 0, 1))
im = np.expand_dims(im, axis=0)
return np.ascontiguousarray(im, 'float32')
@flow.global_function("predict", flow.function_config())
def InferenceNet(images: tp.Numpy.Placeholder((1, 3, 224, 224),
dtype=flow.float)) -> tp.Numpy:
body, logits = resnet50(images, args, training=False)
predictions = flow.nn.softmax(logits)
return predictions
def main():
flow.env.log_dir(args.log_dir)
assert os.path.isdir(args.model_load_dir)
check_point = flow.train.CheckPoint()
check_point.load(args.model_load_dir)
image = load_image(args.image_path)
def GetFunctionConfig(args):
config = flow.function_config()
config.enable_auto_mixed_precision(args.use_fp16)
return config
w = img_width
h = img_height
origin_h = img.shape[1] # orignal height
origin_w = img.shape[2] # orignal width
resize_img = resize_image(img, origin_h, origin_w, h, w) # resize img
# normalize
resize_img[0] = (resize_img[0] - norm_mean[0]) / norm_std[0]
resize_img[1] = (resize_img[1] - norm_mean[1]) / norm_std[1]
resize_img[2] = (resize_img[2] - norm_mean[2]) / norm_std[2]
result_list.append(resize_img) # image list
results = np.asarray(result_list).astype(np.float32)
return results
@flow.global_function(flow.function_config())
def faceseg_job(image=flow.FixedTensorDef((1,3,256,256), dtype=flow.float)):
feature = LinkNet34(image,trainable=False,batch_size=1) # use linknet34 model to segment face
return feature
def faceSeg(img_path,model_para_path):
# input image preprocess
query_images = batch_image_preprocess(img_path,256,256)
feature = faceseg_job(query_images).get()
return feature
