fix tps and fix trt

ppocr/modeling/transforms/tps.py

@@ -230,15 +230,10 @@ 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
+        tmp1 = C.reshape([1, F, 2])
+        tmp2 = C.reshape([F, 1, 2])
+        hat_C = paddle.norm(tmp1 - tmp2, axis=2) + hat_eye
         hat_C = (hat_C**2) * paddle.log(hat_C)
         delta_C = paddle.concat(  # F+3 x F+3
             [

tools/infer/utility.py

@@ -235,12 +235,13 @@ 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=10)  # skip the minmum trt subgraph
-        if mode == "det" and "mobile" in model_file_path:
+                min_subgraph_size=3)  # 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],
@@ -253,6 +254,7 @@ 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],
@@ -265,6 +267,7 @@ 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],
@@ -273,31 +276,6 @@ 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]}