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Unverified Commit 4ae91cb4 authored by Wei-JL's avatar Wei-JL Committed by GitHub
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Merge branch 'PaddlePaddle:dygraph' into dygraph

parents 5ce3af84 9b8f587e
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Showing with 49 additions and 27 deletions
tools/infer/utility.py
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...@@ -24,6 +24,7 @@ from paddle import inference ...@@ -24,6 +24,7 @@ from paddle import inference
import time import time
from ppocr.utils.logging import get_logger from ppocr.utils.logging import get_logger
def str2bool(v): def str2bool(v):
return v.lower() in ("true", "t", "1") return v.lower() in ("true", "t", "1")
...@@ -47,8 +48,8 @@ def init_args(): ...@@ -47,8 +48,8 @@ def init_args():
# DB parmas # DB parmas
parser.add_argument("--det_db_thresh", type=float, default=0.3) parser.add_argument("--det_db_thresh", type=float, default=0.3)
parser.add_argument("--det_db_box_thresh", type=float, default=0.5) parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6) parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
parser.add_argument("--max_batch_size", type=int, default=10) parser.add_argument("--max_batch_size", type=int, default=10)
parser.add_argument("--use_dilation", type=bool, default=False) parser.add_argument("--use_dilation", type=bool, default=False)
parser.add_argument("--det_db_score_mode", type=str, default="fast") parser.add_argument("--det_db_score_mode", type=str, default="fast")
...@@ -168,46 +169,67 @@ def create_predictor(args, mode, logger): ...@@ -168,46 +169,67 @@ def create_predictor(args, mode, logger):
if mode == "det": if mode == "det":
min_input_shape = { min_input_shape = {
"x": [1, 3, 50, 50], "x": [1, 3, 50, 50],
"conv2d_92.tmp_0": [1, 96, 20, 20], "conv2d_92.tmp_0": [1, 120, 20, 20],
"conv2d_91.tmp_0": [1, 96, 10, 10], "conv2d_91.tmp_0": [1, 24, 10, 10],
"conv2d_59.tmp_0": [1, 96, 20, 20], "conv2d_59.tmp_0": [1, 96, 20, 20],
"nearest_interp_v2_1.tmp_0": [1, 96, 10, 10], "nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20], "nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
"conv2d_124.tmp_0": [1, 96, 20, 20], "conv2d_124.tmp_0": [1, 256, 20, 20],
"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20], "nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_4.tmp_0": [1, 24, 20, 20], "nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
"nearest_interp_v2_5.tmp_0": [1, 24, 20, 20], "nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
"elementwise_add_7": [1, 56, 2, 2], "elementwise_add_7": [1, 56, 2, 2],
"nearest_interp_v2_0.tmp_0": [1, 96, 2, 2] "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
} }
max_input_shape = { max_input_shape = {
"x": [1, 3, 2000, 2000], "x": [1, 3, 2000, 2000],
"conv2d_92.tmp_0": [1, 96, 400, 400], "conv2d_92.tmp_0": [1, 120, 400, 400],
"conv2d_91.tmp_0": [1, 96, 200, 200], "conv2d_91.tmp_0": [1, 24, 200, 200],
"conv2d_59.tmp_0": [1, 96, 400, 400], "conv2d_59.tmp_0": [1, 96, 400, 400],
"nearest_interp_v2_1.tmp_0": [1, 96, 200, 200], "nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
"conv2d_124.tmp_0": [1, 256, 400, 400], "conv2d_124.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400], "nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400], "nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_4.tmp_0": [1, 24, 400, 400], "nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
"nearest_interp_v2_5.tmp_0": [1, 24, 400, 400], "nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
"elementwise_add_7": [1, 56, 400, 400], "elementwise_add_7": [1, 56, 400, 400],
"nearest_interp_v2_0.tmp_0": [1, 96, 400, 400] "nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
} }
opt_input_shape = { opt_input_shape = {
"x": [1, 3, 640, 640], "x": [1, 3, 640, 640],
"conv2d_92.tmp_0": [1, 96, 160, 160], "conv2d_92.tmp_0": [1, 120, 160, 160],
"conv2d_91.tmp_0": [1, 96, 80, 80], "conv2d_91.tmp_0": [1, 24, 80, 80],
"conv2d_59.tmp_0": [1, 96, 160, 160], "conv2d_59.tmp_0": [1, 96, 160, 160],
"nearest_interp_v2_1.tmp_0": [1, 96, 80, 80], "nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160], "nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
"conv2d_124.tmp_0": [1, 256, 160, 160], "conv2d_124.tmp_0": [1, 256, 160, 160],
"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160], "nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_4.tmp_0": [1, 24, 160, 160], "nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
"nearest_interp_v2_5.tmp_0": [1, 24, 160, 160], "nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
"elementwise_add_7": [1, 56, 40, 40], "elementwise_add_7": [1, 56, 40, 40],
"nearest_interp_v2_0.tmp_0": [1, 96, 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": elif mode == "rec":
min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]} min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]} max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
......
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