Skip to content

GitLab

Commit 721c76b4 authored by LDOUBLEV's avatar LDOUBLEV
Browse files

fix conflict

parents 98162be4 b77f9ec0
Hide whitespace changes
Inline Side-by-side
Showing with 313 additions and 196 deletions
tools/infer/predict_system.py
View file @ 721c76b4
......@@ -49,11 +49,19 @@ class TextSystem(object):
if self.use_angle_cls:
self.text_classifier = predict_cls.TextClassifier(args)
def print_draw_crop_rec_res(self, img_crop_list, rec_res):
self.args = args
self.crop_image_res_index = 0
def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res):
os.makedirs(output_dir, exist_ok=True)
bbox_num = len(img_crop_list)
for bno in range(bbox_num):
cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
logger.info(bno, rec_res[bno])
cv2.imwrite(
os.path.join(output_dir,
f"mg_crop_{bno+self.crop_image_res_index}.jpg"),
img_crop_list[bno])
logger.debug(f"{bno}, {rec_res[bno]}")
self.crop_image_res_index += bbox_num
def __call__(self, img, cls=True):
ori_im = img.copy()
......@@ -80,7 +88,9 @@ class TextSystem(object):
rec_res, elapse = self.text_recognizer(img_crop_list)
logger.debug("rec_res num : {}, elapse : {}".format(
len(rec_res), elapse))
# self.print_draw_crop_rec_res(img_crop_list, rec_res)
if self.args.save_crop_res:
self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list,
rec_res)
filter_boxes, filter_rec_res = [], []
for box, rec_reuslt in zip(dt_boxes, rec_res):
text, score = rec_reuslt
......@@ -135,17 +145,17 @@ def main(args):
if not flag:
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
logger.debug("error in loading image:{}".format(image_file))
continue
starttime = time.time()
dt_boxes, rec_res = text_sys(img)
elapse = time.time() - starttime
total_time += elapse
logger.info(
logger.debug(
str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse))
for text, score in rec_res:
logger.info("{}, {:.3f}".format(text, score))
logger.debug("{}, {:.3f}".format(text, score))
if is_visualize:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
......@@ -160,19 +170,17 @@ def main(args):
scores,
drop_score=drop_score,
font_path=font_path)
draw_img_save = "./inference_results/"
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
draw_img_save_dir = args.draw_img_save_dir
os.makedirs(draw_img_save_dir, exist_ok=True)
if flag:
image_file = image_file[:-3] + "png"
cv2.imwrite(
os.path.join(draw_img_save, os.path.basename(image_file)),
os.path.join(draw_img_save_dir, os.path.basename(image_file)),
draw_img[:, :, ::-1])
logger.info("The visualized image saved in {}".format(
os.path.join(draw_img_save, os.path.basename(image_file))))
logger.debug("The visualized image saved in {}".format(
os.path.join(draw_img_save_dir, os.path.basename(image_file))))
logger.info("The predict total time is {}".format(time.time() - _st))
logger.info("\nThe predict total time is {}".format(total_time))
if args.benchmark:
text_sys.text_detector.autolog.report()
text_sys.text_recognizer.autolog.report()
......
tools/infer/utility.py 100755 → 100644
View file @ 721c76b4
......@@ -17,7 +17,7 @@ import os
import sys
import cv2
import numpy as np
import json
import paddle
from PIL import Image, ImageDraw, ImageFont
import math
from paddle import inference
......@@ -74,7 +74,6 @@ def init_args():
parser.add_argument("--rec_algorithm", type=str, default='CRNN')
parser.add_argument("--rec_model_dir", type=str)
parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
parser.add_argument("--rec_char_type", type=str, default='ch')
parser.add_argument("--rec_batch_num", type=int, default=6)
parser.add_argument("--max_text_length", type=int, default=25)
parser.add_argument(
......@@ -97,7 +96,6 @@ def init_args():
parser.add_argument(
"--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
parser.add_argument("--e2e_pgnet_polygon", type=str2bool, default=True)
parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
# params for text classifier
......@@ -111,7 +109,13 @@ def init_args():
parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
parser.add_argument("--cpu_threads", type=int, default=10)
parser.add_argument("--use_pdserving", type=str2bool, default=False)
parser.add_argument("--warmup", type=str2bool, default=True)
parser.add_argument("--warmup", type=str2bool, default=False)
#
parser.add_argument(
"--draw_img_save_dir", type=str, default="./inference_results")
parser.add_argument("--save_crop_res", type=str2bool, default=False)
parser.add_argument("--crop_res_save_dir", type=str, default="./output")
# multi-process
parser.add_argument("--use_mp", type=str2bool, default=False)
......@@ -122,6 +126,7 @@ def init_args():
parser.add_argument("--save_log_path", type=str, default="./log_output/")
parser.add_argument("--show_log", type=str2bool, default=True)
parser.add_argument("--use_onnx", type=str2bool, default=False)
return parser
......@@ -145,158 +150,167 @@ def create_predictor(args, mode, logger):
if model_dir is None:
logger.info("not find {} model file path {}".format(mode, model_dir))
sys.exit(0)
model_file_path = model_dir + "/inference.pdmodel"
params_file_path = model_dir + "/inference.pdiparams"
if not os.path.exists(model_file_path):
raise ValueError("not find model file path {}".format(model_file_path))
if not os.path.exists(params_file_path):
raise ValueError("not find params file path {}".format(
params_file_path))
config = inference.Config(model_file_path, params_file_path)
if hasattr(args, 'precision'):
if args.precision == "fp16" and args.use_tensorrt:
precision = inference.PrecisionType.Half
elif args.precision == "int8":
precision = inference.PrecisionType.Int8
else:
precision = inference.PrecisionType.Float32
if args.use_onnx:
import onnxruntime as ort
model_file_path = model_dir
if not os.path.exists(model_file_path):
raise ValueError("not find model file path {}".format(
model_file_path))
sess = ort.InferenceSession(model_file_path)
return sess, sess.get_inputs()[0], None, None
else:
precision = inference.PrecisionType.Float32
if args.use_gpu:
gpu_id = get_infer_gpuid()
if gpu_id is None:
raise ValueError(
"Not found GPU in current device. Please check your device or set args.use_gpu as False"
)
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
precision_mode=precision,
max_batch_size=args.max_batch_size,
min_subgraph_size=args.min_subgraph_size)
# skip the minmum trt subgraph
if mode == "det":
min_input_shape = {
"x": [1, 3, 50, 50],
"conv2d_92.tmp_0": [1, 120, 20, 20],
"conv2d_91.tmp_0": [1, 24, 10, 10],
"conv2d_59.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
"nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
"conv2d_124.tmp_0": [1, 256, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
"elementwise_add_7": [1, 56, 2, 2],
"nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
}
max_input_shape = {
"x": [1, 3, 2000, 2000],
"conv2d_92.tmp_0": [1, 120, 400, 400],
"conv2d_91.tmp_0": [1, 24, 200, 200],
"conv2d_59.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
"conv2d_124.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
"elementwise_add_7": [1, 56, 400, 400],
"nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
}
opt_input_shape = {
"x": [1, 3, 640, 640],
"conv2d_92.tmp_0": [1, 120, 160, 160],
"conv2d_91.tmp_0": [1, 24, 80, 80],
"conv2d_59.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
"nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
"conv2d_124.tmp_0": [1, 256, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
"elementwise_add_7": [1, 56, 40, 40],
"nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
}
min_pact_shape = {
"nearest_interp_v2_26.tmp_0": [1, 256, 20, 20],
"nearest_interp_v2_27.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_28.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_29.tmp_0": [1, 64, 20, 20]
}
max_pact_shape = {
"nearest_interp_v2_26.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_27.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_28.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_29.tmp_0": [1, 64, 400, 400]
}
opt_pact_shape = {
"nearest_interp_v2_26.tmp_0": [1, 256, 160, 160],
"nearest_interp_v2_27.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_28.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_29.tmp_0": [1, 64, 160, 160]
}
min_input_shape.update(min_pact_shape)
max_input_shape.update(max_pact_shape)
opt_input_shape.update(opt_pact_shape)
elif mode == "rec":
min_input_shape = {"x": [1, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls":
min_input_shape = {"x": [1, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
model_file_path = model_dir + "/inference.pdmodel"
params_file_path = model_dir + "/inference.pdiparams"
if not os.path.exists(model_file_path):
raise ValueError("not find model file path {}".format(
model_file_path))
if not os.path.exists(params_file_path):
raise ValueError("not find params file path {}".format(
params_file_path))
config = inference.Config(model_file_path, params_file_path)
if hasattr(args, 'precision'):
if args.precision == "fp16" and args.use_tensorrt:
precision = inference.PrecisionType.Half
elif args.precision == "int8":
precision = inference.PrecisionType.Int8
else:
precision = inference.PrecisionType.Float32
else:
min_input_shape = {"x": [1, 3, 10, 10]}
max_input_shape = {"x": [1, 3, 1000, 1000]}
opt_input_shape = {"x": [1, 3, 500, 500]}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)
precision = inference.PrecisionType.Float32
if args.use_gpu:
gpu_id = get_infer_gpuid()
if gpu_id is None:
logger.warning(
"GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jeston."
)
config.enable_use_gpu(args.gpu_mem, 0)
if args.use_tensorrt:
config.enable_tensorrt_engine(
workspace_size=1 << 30,
precision_mode=precision,
max_batch_size=args.max_batch_size,
min_subgraph_size=args.min_subgraph_size)
# skip the minmum trt subgraph
if mode == "det":
min_input_shape = {
"x": [1, 3, 50, 50],
"conv2d_92.tmp_0": [1, 120, 20, 20],
"conv2d_91.tmp_0": [1, 24, 10, 10],
"conv2d_59.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
"nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
"conv2d_124.tmp_0": [1, 256, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
"elementwise_add_7": [1, 56, 2, 2],
"nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
}
max_input_shape = {
"x": [1, 3, 1280, 1280],
"conv2d_92.tmp_0": [1, 120, 400, 400],
"conv2d_91.tmp_0": [1, 24, 200, 200],
"conv2d_59.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
"conv2d_124.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
"elementwise_add_7": [1, 56, 400, 400],
"nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
}
opt_input_shape = {
"x": [1, 3, 640, 640],
"conv2d_92.tmp_0": [1, 120, 160, 160],
"conv2d_91.tmp_0": [1, 24, 80, 80],
"conv2d_59.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
"nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
"conv2d_124.tmp_0": [1, 256, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
"elementwise_add_7": [1, 56, 40, 40],
"nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
}
min_pact_shape = {
"nearest_interp_v2_26.tmp_0": [1, 256, 20, 20],
"nearest_interp_v2_27.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_28.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_29.tmp_0": [1, 64, 20, 20]
}
max_pact_shape = {
"nearest_interp_v2_26.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_27.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_28.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_29.tmp_0": [1, 64, 400, 400]
}
opt_pact_shape = {
"nearest_interp_v2_26.tmp_0": [1, 256, 160, 160],
"nearest_interp_v2_27.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_28.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_29.tmp_0": [1, 64, 160, 160]
}
min_input_shape.update(min_pact_shape)
max_input_shape.update(max_pact_shape)
opt_input_shape.update(opt_pact_shape)
elif mode == "rec":
min_input_shape = {"x": [1, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1024]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls":
min_input_shape = {"x": [1, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
else:
min_input_shape = {"x": [1, 3, 10, 10]}
max_input_shape = {"x": [1, 3, 512, 512]}
opt_input_shape = {"x": [1, 3, 256, 256]}
config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
opt_input_shape)
else:
config.disable_gpu()
if hasattr(args, "cpu_threads"):
config.set_cpu_math_library_num_threads(args.cpu_threads)
else:
# default cpu threads as 10
config.set_cpu_math_library_num_threads(10)
if args.enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
# enable memory optim
config.enable_memory_optim()
#config.disable_glog_info()
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
if mode == 'table':
config.delete_pass("fc_fuse_pass") # not supported for table
config.switch_use_feed_fetch_ops(False)
config.switch_ir_optim(True)
# create predictor
predictor = inference.create_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
input_tensor = predictor.get_input_handle(name)
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_handle(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors, config
config.disable_gpu()
if hasattr(args, "cpu_threads"):
config.set_cpu_math_library_num_threads(args.cpu_threads)
else:
# default cpu threads as 10
config.set_cpu_math_library_num_threads(10)
if args.enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
if args.precision == "fp16":
config.enable_mkldnn_bfloat16()
# enable memory optim
config.enable_memory_optim()
config.disable_glog_info()
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
if mode == 'table':
config.delete_pass("fc_fuse_pass") # not supported for table
config.switch_use_feed_fetch_ops(False)
config.switch_ir_optim(True)
# create predictor
predictor = inference.create_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
input_tensor = predictor.get_input_handle(name)
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_handle(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors, config
def get_infer_gpuid():
cmd = "nvidia-smi"
res = os.popen(cmd).readlines()
if len(res) == 0:
return None
cmd = "env | grep CUDA_VISIBLE_DEVICES"
env_cuda = os.popen(cmd).readlines()
if len(env_cuda) == 0:
......@@ -589,5 +603,12 @@ def get_rotate_crop_image(img, points):
return dst_img
def check_gpu(use_gpu):
if use_gpu and not paddle.is_compiled_with_cuda():
use_gpu = False
return use_gpu
if __name__ == '__main__':
pass
tools/infer_cls.py
View file @ 721c76b4
......@@ -32,7 +32,7 @@ import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list
import tools.program as program
......@@ -47,7 +47,7 @@ def main():
# build model
model = build_model(config['Architecture'])
init_model(config, model)
load_model(config, model)
# create data ops
transforms = []
......
tools/infer_det.py
View file @ 721c76b4
......@@ -34,7 +34,7 @@ import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model, load_dygraph_params
from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list
import tools.program as program
......@@ -59,7 +59,7 @@ def main():
# build model
model = build_model(config['Architecture'])
_ = load_dygraph_params(config, model, logger, None)
load_model(config, model)
# build post process
post_process_class = build_post_process(config['PostProcess'])
......
tools/infer_e2e.py
View file @ 721c76b4
......@@ -34,7 +34,7 @@ import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list
import tools.program as program
......@@ -68,7 +68,7 @@ def main():
# build model
model = build_model(config['Architecture'])
init_model(config, model)
load_model(config, model)
# build post process
post_process_class = build_post_process(config['PostProcess'],
......
tools/infer_rec.py
View file @ 721c76b4
......@@ -33,7 +33,7 @@ import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list
import tools.program as program
......@@ -58,7 +58,7 @@ def main():
model = build_model(config['Architecture'])
init_model(config, model)
load_model(config, model)
# create data ops
transforms = []
......@@ -75,9 +75,7 @@ def main():
'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
]
elif config['Architecture']['algorithm'] == "SAR":
op[op_name]['keep_keys'] = [
'image', 'valid_ratio'
]
op[op_name]['keep_keys'] = ['image', 'valid_ratio']
else:
op[op_name]['keep_keys'] = ['image']
transforms.append(op)
......
tools/infer_table.py
View file @ 721c76b4
......@@ -34,11 +34,12 @@ from paddle.jit import to_static
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.save_load import load_model
from ppocr.utils.utility import get_image_file_list
import tools.program as program
import cv2
def main(config, device, logger, vdl_writer):
global_config = config['Global']
......@@ -53,7 +54,7 @@ def main(config, device, logger, vdl_writer):
model = build_model(config['Architecture'])
init_model(config, model, logger)
load_model(config, model)
# create data ops
transforms = []
......@@ -104,4 +105,3 @@ def main(config, device, logger, vdl_writer):
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
main(config, device, logger, vdl_writer)
tools/program.py
View file @ 721c76b4
......@@ -159,7 +159,8 @@ def train(config,
eval_class,
pre_best_model_dict,
logger,
vdl_writer=None):
vdl_writer=None,
scaler=None):
cal_metric_during_train = config['Global'].get('cal_metric_during_train',
False)
log_smooth_window = config['Global']['log_smooth_window']
......@@ -211,15 +212,15 @@ def train(config,
for epoch in range(start_epoch, epoch_num + 1):
train_dataloader = build_dataloader(
config, 'Train', device, logger, seed=epoch)
train_batch_cost = 0.0
train_reader_cost = 0.0
batch_sum = 0
batch_start = time.time()
train_run_cost = 0.0
total_samples = 0
reader_start = time.time()
max_iter = len(train_dataloader) - 1 if platform.system(
) == "Windows" else len(train_dataloader)
for idx, batch in enumerate(train_dataloader):
profiler.add_profiler_step(profiler_options)
train_reader_cost += time.time() - batch_start
train_reader_cost += time.time() - reader_start
if idx >= max_iter:
break
lr = optimizer.get_lr()
......@@ -230,16 +231,34 @@ def train(config,
preds = model(images, data=batch[1:])
if model_type == "kie":
preds = model(batch)
train_start = time.time()
# use amp
if scaler:
with paddle.amp.auto_cast():
if model_type == 'table' or extra_input:
preds = model(images, data=batch[1:])
else:
preds = model(images)
else:
preds = model(images)
if model_type == 'table' or extra_input:
preds = model(images, data=batch[1:])
else:
preds = model(images)
loss = loss_class(preds, batch)
avg_loss = loss['loss']
avg_loss.backward()
optimizer.step()
if scaler:
scaled_avg_loss = scaler.scale(avg_loss)
scaled_avg_loss.backward()
scaler.minimize(optimizer, scaled_avg_loss)
else:
avg_loss.backward()
optimizer.step()
optimizer.clear_grad()
train_batch_cost += time.time() - batch_start
batch_sum += len(images)
train_run_cost += time.time() - train_start
total_samples += len(images)
if not isinstance(lr_scheduler, float):
lr_scheduler.step()
......@@ -270,12 +289,13 @@ def train(config,
logs = train_stats.log()
strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f} s, batch_cost: {:.5f} s, samples: {}, ips: {:.5f}'.format(
epoch, epoch_num, global_step, logs, train_reader_cost /
print_batch_step, train_batch_cost / print_batch_step,
batch_sum, batch_sum / train_batch_cost)
print_batch_step, (train_reader_cost + train_run_cost) /
print_batch_step, total_samples,
total_samples / (train_reader_cost + train_run_cost))
logger.info(strs)
train_batch_cost = 0.0
train_reader_cost = 0.0
batch_sum = 0
train_run_cost = 0.0
total_samples = 0
# eval
if global_step > start_eval_step and \
(global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
......@@ -328,7 +348,7 @@ def train(config,
global_step)
global_step += 1
optimizer.clear_grad()
batch_start = time.time()
reader_start = time.time()
if dist.get_rank() == 0:
save_model(
model,
......@@ -369,7 +389,11 @@ def eval(model,
with paddle.no_grad():
total_frame = 0.0
total_time = 0.0
pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
pbar = tqdm(
total=len(valid_dataloader),
desc='eval model:',
position=0,
leave=True)
max_iter = len(valid_dataloader) - 1 if platform.system(
) == "Windows" else len(valid_dataloader)
for idx, batch in enumerate(valid_dataloader):
......@@ -404,6 +428,55 @@ def eval(model,
return metric
def update_center(char_center, post_result, preds):
result, label = post_result
feats, logits = preds
logits = paddle.argmax(logits, axis=-1)
feats = feats.numpy()
logits = logits.numpy()
for idx_sample in range(len(label)):
if result[idx_sample][0] == label[idx_sample][0]:
feat = feats[idx_sample]
logit = logits[idx_sample]
for idx_time in range(len(logit)):
index = logit[idx_time]
if index in char_center.keys():
char_center[index][0] = (
char_center[index][0] * char_center[index][1] +
feat[idx_time]) / (char_center[index][1] + 1)
char_center[index][1] += 1
else:
char_center[index] = [feat[idx_time], 1]
return char_center
def get_center(model, eval_dataloader, post_process_class):
pbar = tqdm(total=len(eval_dataloader), desc='get center:')
max_iter = len(eval_dataloader) - 1 if platform.system(
) == "Windows" else len(eval_dataloader)
char_center = dict()
for idx, batch in enumerate(eval_dataloader):
if idx >= max_iter:
break
images = batch[0]
start = time.time()
preds = model(images)
batch = [item.numpy() for item in batch]
# Obtain usable results from post-processing methods
post_result = post_process_class(preds, batch[1])
#update char_center
char_center = update_center(char_center, post_result, preds)
pbar.update(1)
pbar.close()
for key in char_center.keys():
char_center[key] = char_center[key][0]
return char_center
def preprocess(is_train=False):
FLAGS = ArgsParser().parse_args()
profiler_options = FLAGS.profiler_options
......
tools/train.py
View file @ 721c76b4
......@@ -35,7 +35,7 @@ from ppocr.losses import build_loss
from ppocr.optimizer import build_optimizer
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model, load_dygraph_params
from ppocr.utils.save_load import load_model
import tools.program as program
dist.get_world_size()
......@@ -97,15 +97,32 @@ def main(config, device, logger, vdl_writer):
# build metric
eval_class = build_metric(config['Metric'])
# load pretrain model
pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer)
pre_best_model_dict = load_model(config, model, optimizer)
logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
if valid_dataloader is not None:
logger.info('valid dataloader has {} iters'.format(
len(valid_dataloader)))
use_amp = config["Global"].get("use_amp", False)
if use_amp:
AMP_RELATED_FLAGS_SETTING = {
'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
'FLAGS_max_inplace_grad_add': 8,
}
paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
scale_loss = config["Global"].get("scale_loss", 1.0)
use_dynamic_loss_scaling = config["Global"].get(
"use_dynamic_loss_scaling", False)
scaler = paddle.amp.GradScaler(
init_loss_scaling=scale_loss,
use_dynamic_loss_scaling=use_dynamic_loss_scaling)
else:
scaler = None
# start train
program.train(config, train_dataloader, valid_dataloader, device, model,
loss_class, optimizer, lr_scheduler, post_process_class,
eval_class, pre_best_model_dict, logger, vdl_writer)
eval_class, pre_best_model_dict, logger, vdl_writer, scaler)
def test_reader(config, device, logger):
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment