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Commit 616ad6a1 authored by LDOUBLEV's avatar LDOUBLEV
Browse files

fix tps and fix trt

parent 796898e0
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Showing with 9 additions and 36 deletions
ppocr/modeling/transforms/tps.py
View file @ 616ad6a1
...@@ -230,15 +230,10 @@ class GridGenerator(nn.Layer): ...@@ -230,15 +230,10 @@ class GridGenerator(nn.Layer):
def build_inv_delta_C_paddle(self, C): def build_inv_delta_C_paddle(self, C):
""" Return inv_delta_C which is needed to calculate T """ """ Return inv_delta_C which is needed to calculate T """
F = self.F F = self.F
hat_C = paddle.zeros((F, F), dtype='float64') # F x F hat_eye = paddle.eye(F, dtype='float64') # F x F
for i in range(0, F): tmp1 = C.reshape([1, F, 2])
for j in range(i, F): tmp2 = C.reshape([F, 1, 2])
if i == j: hat_C = paddle.norm(tmp1 - tmp2, axis=2) + hat_eye
hat_C[i, j] = 1
else:
r = paddle.norm(C[i] - C[j])
hat_C[i, j] = r
hat_C[j, i] = r
hat_C = (hat_C**2) * paddle.log(hat_C) hat_C = (hat_C**2) * paddle.log(hat_C)
delta_C = paddle.concat( # F+3 x F+3 delta_C = paddle.concat( # F+3 x F+3
[ [
......
tools/infer/utility.py
View file @ 616ad6a1
...@@ -235,12 +235,13 @@ def create_predictor(args, mode, logger): ...@@ -235,12 +235,13 @@ def create_predictor(args, mode, logger):
config.enable_tensorrt_engine( config.enable_tensorrt_engine(
precision_mode=inference.PrecisionType.Float32, precision_mode=inference.PrecisionType.Float32,
max_batch_size=args.max_batch_size, max_batch_size=args.max_batch_size,
min_subgraph_size=10) # skip the minmum trt subgraph min_subgraph_size=3) # skip the minmum trt subgraph
if mode == "det" and "mobile" in model_file_path: 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, 96, 20, 20],
"conv2d_91.tmp_0": [1, 96, 10, 10], "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_1.tmp_0": [1, 96, 10, 10],
"nearest_interp_v2_2.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_3.tmp_0": [1, 24, 20, 20],
...@@ -253,6 +254,7 @@ def create_predictor(args, mode, logger): ...@@ -253,6 +254,7 @@ def create_predictor(args, mode, logger):
"x": [1, 3, 2000, 2000], "x": [1, 3, 2000, 2000],
"conv2d_92.tmp_0": [1, 96, 400, 400], "conv2d_92.tmp_0": [1, 96, 400, 400],
"conv2d_91.tmp_0": [1, 96, 200, 200], "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_1.tmp_0": [1, 96, 200, 200],
"nearest_interp_v2_2.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_3.tmp_0": [1, 24, 400, 400],
...@@ -265,6 +267,7 @@ def create_predictor(args, mode, logger): ...@@ -265,6 +267,7 @@ def create_predictor(args, mode, logger):
"x": [1, 3, 640, 640], "x": [1, 3, 640, 640],
"conv2d_92.tmp_0": [1, 96, 160, 160], "conv2d_92.tmp_0": [1, 96, 160, 160],
"conv2d_91.tmp_0": [1, 96, 80, 80], "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_1.tmp_0": [1, 96, 80, 80],
"nearest_interp_v2_2.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_3.tmp_0": [1, 24, 160, 160],
...@@ -273,31 +276,6 @@ def create_predictor(args, mode, logger): ...@@ -273,31 +276,6 @@ def create_predictor(args, mode, logger):
"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, 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": 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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