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Merge pull request #3069 from LDOUBLEV/bm_dyg 

fix save_log_path as null
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Double_V 2021-06-28 20:33:53 +08:00 committed by GitHub
parent bc99998693 465ef3bffd
commit 6f64faeab4
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 12 additions and 37 deletions
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11
ppocr/modeling/transforms/tps.py
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@ -230,15 +230,8 @@ class GridGenerator(nn.Layer):
def build_inv_delta_C_paddle(self, C):
""" Return inv_delta_C which is needed to calculate T """
F = self.F
hat_C = paddle.zeros((F, F), dtype='float64') # F x F
for i in range(0, F):
for j in range(i, F):
if i == j:
hat_C[i, j] = 1
else:
r = paddle.norm(C[i] - C[j])
hat_C[i, j] = r
hat_C[j, i] = r
hat_eye = paddle.eye(F, dtype='float64') # F x F
hat_C = paddle.norm(C.reshape([1, F, 2]) - C.reshape([F, 1, 2]), axis=2) + hat_eye
hat_C = (hat_C**2) * paddle.log(hat_C)
delta_C = paddle.concat( # F+3 x F+3
[

1
tools/infer/predict_det.py
View File

@ -237,3 +237,4 @@ if __name__ == "__main__":
"det_res_{}".format(img_name_pure))
cv2.imwrite(img_path, src_im)
logger.info("The visualized image saved in {}".format(img_path))

3
tools/infer/predict_rec.py
View File

@ -322,7 +322,8 @@ def main(args):
'total_time_s': rec_time_dict['total_time']
}
benchmark_log = benchmark_utils.PaddleInferBenchmark(
text_recognizer.config, model_info, data_info, perf_info, mems)
text_recognizer.config, model_info, data_info, perf_info, mems,
args.save_log_path)
benchmark_log("Rec")

34
tools/infer/utility.py
View File

@ -37,6 +37,7 @@ def init_args():
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--ir_optim", type=str2bool, default=True)
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
parser.add_argument("--min_subgraph_size", type=int, default=3)
parser.add_argument("--precision", type=str, default="fp32")
parser.add_argument("--gpu_mem", type=int, default=500)
@ -236,12 +237,14 @@ def create_predictor(args, mode, logger):
config.enable_tensorrt_engine(
precision_mode=inference.PrecisionType.Float32,
max_batch_size=args.max_batch_size,
min_subgraph_size=3) # skip the minmum trt subgraph
if mode == "det" and "mobile" in model_file_path:
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, 96, 20, 20],
"conv2d_91.tmp_0": [1, 96, 10, 10],
"conv2d_59.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
@ -254,6 +257,7 @@ def create_predictor(args, mode, logger):
"x": [1, 3, 2000, 2000],
"conv2d_92.tmp_0": [1, 96, 400, 400],
"conv2d_91.tmp_0": [1, 96, 200, 200],
"conv2d_59.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
@ -266,6 +270,7 @@ def create_predictor(args, mode, logger):
"x": [1, 3, 640, 640],
"conv2d_92.tmp_0": [1, 96, 160, 160],
"conv2d_91.tmp_0": [1, 96, 80, 80],
"conv2d_59.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
@ -274,31 +279,6 @@ def create_predictor(args, mode, logger):
"elementwise_add_7": [1, 56, 40, 40],
"nearest_interp_v2_0.tmp_0": [1, 96, 40, 40]
}
if mode == "det" and "server" in model_file_path:
min_input_shape = {
"x": [1, 3, 50, 50],
"conv2d_59.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 24, 20, 20]
}
max_input_shape = {
"x": [1, 3, 2000, 2000],
"conv2d_59.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
"nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
"nearest_interp_v2_5.tmp_0": [1, 24, 400, 400]
}
opt_input_shape = {
"x": [1, 3, 640, 640],
"conv2d_59.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
"nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
"nearest_interp_v2_5.tmp_0": [1, 24, 160, 160]
}
elif mode == "rec":
min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}