fix tps and fix trt
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796898e0f8
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616ad6a179
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@ -230,15 +230,10 @@ class GridGenerator(nn.Layer):
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def build_inv_delta_C_paddle(self, C):
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""" Return inv_delta_C which is needed to calculate T """
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F = self.F
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hat_C = paddle.zeros((F, F), dtype='float64') # F x F
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for i in range(0, F):
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for j in range(i, F):
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if i == j:
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hat_C[i, j] = 1
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else:
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r = paddle.norm(C[i] - C[j])
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hat_C[i, j] = r
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hat_C[j, i] = r
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hat_eye = paddle.eye(F, dtype='float64') # F x F
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tmp1 = C.reshape([1, F, 2])
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tmp2 = C.reshape([F, 1, 2])
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hat_C = paddle.norm(tmp1 - tmp2, axis=2) + hat_eye
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hat_C = (hat_C**2) * paddle.log(hat_C)
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delta_C = paddle.concat( # F+3 x F+3
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[
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@ -235,12 +235,13 @@ def create_predictor(args, mode, logger):
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config.enable_tensorrt_engine(
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precision_mode=inference.PrecisionType.Float32,
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max_batch_size=args.max_batch_size,
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min_subgraph_size=10) # skip the minmum trt subgraph
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if mode == "det" and "mobile" in model_file_path:
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min_subgraph_size=3) # skip the minmum trt subgraph
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if mode == "det":
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min_input_shape = {
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"x": [1, 3, 50, 50],
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"conv2d_92.tmp_0": [1, 96, 20, 20],
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"conv2d_91.tmp_0": [1, 96, 10, 10],
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"conv2d_59.tmp_0": [1, 96, 20, 20],
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"nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
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"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
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"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
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@ -253,6 +254,7 @@ def create_predictor(args, mode, logger):
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"x": [1, 3, 2000, 2000],
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"conv2d_92.tmp_0": [1, 96, 400, 400],
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"conv2d_91.tmp_0": [1, 96, 200, 200],
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"conv2d_59.tmp_0": [1, 96, 400, 400],
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"nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
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"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
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"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
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@ -265,6 +267,7 @@ def create_predictor(args, mode, logger):
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"x": [1, 3, 640, 640],
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"conv2d_92.tmp_0": [1, 96, 160, 160],
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"conv2d_91.tmp_0": [1, 96, 80, 80],
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"conv2d_59.tmp_0": [1, 96, 160, 160],
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"nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
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"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
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"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
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@ -273,31 +276,6 @@ def create_predictor(args, mode, logger):
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"elementwise_add_7": [1, 56, 40, 40],
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"nearest_interp_v2_0.tmp_0": [1, 96, 40, 40]
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}
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if mode == "det" and "server" in model_file_path:
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min_input_shape = {
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"x": [1, 3, 50, 50],
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"conv2d_59.tmp_0": [1, 96, 20, 20],
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"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
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"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
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"nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
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"nearest_interp_v2_5.tmp_0": [1, 24, 20, 20]
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}
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max_input_shape = {
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"x": [1, 3, 2000, 2000],
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"conv2d_59.tmp_0": [1, 96, 400, 400],
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"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
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"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
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"nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
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"nearest_interp_v2_5.tmp_0": [1, 24, 400, 400]
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}
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opt_input_shape = {
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"x": [1, 3, 640, 640],
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"conv2d_59.tmp_0": [1, 96, 160, 160],
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"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
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"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
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"nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
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"nearest_interp_v2_5.tmp_0": [1, 24, 160, 160]
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}
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elif mode == "rec":
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min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
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max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
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