delete other rec algorithm

deploy/pphuman/config/infer_cfg_ppvehicle.yml

@@ -19,7 +19,6 @@ VEHICLE_PLATE:
   det_model_dir: output_inference/ch_PP-OCRv3_det_infer/
   det_limit_side_len: 480
   det_limit_type: "max"
-  rec_algorithm: "SVTR_LCNet"
   rec_model_dir: output_inference/ch_PP-OCRv3_rec_infer/
   rec_image_shape: [3, 48, 320]
   rec_batch_num: 6

deploy/pphuman/ppvehicle/vehicle_plate.py

@@ -151,7 +151,6 @@ class TextRecognizer(object):
     def __init__(self, args, cfg, use_gpu=True):
         self.rec_image_shape = cfg['rec_image_shape']
         self.rec_batch_num = cfg['rec_batch_num']
-        self.rec_algorithm = cfg['rec_algorithm']
         word_dict_path = cfg['word_dict_path']
         use_space_char = True
 
@@ -160,30 +159,6 @@ class TextRecognizer(object):
             "character_dict_path": word_dict_path,
             "use_space_char": use_space_char
         }
-        if self.rec_algorithm == "SRN":
-            postprocess_params = {
-                'name': 'SRNLabelDecode',
-                "character_dict_path": word_dict_path,
-                "use_space_char": use_space_char
-            }
-        elif self.rec_algorithm == "RARE":
-            postprocess_params = {
-                'name': 'AttnLabelDecode',
-                "character_dict_path": word_dict_path,
-                "use_space_char": use_space_char
-            }
-        elif self.rec_algorithm == 'NRTR':
-            postprocess_params = {
-                'name': 'NRTRLabelDecode',
-                "character_dict_path": word_dict_path,
-                "use_space_char": use_space_char
-            }
-        elif self.rec_algorithm == "SAR":
-            postprocess_params = {
-                'name': 'SARLabelDecode',
-                "character_dict_path": word_dict_path,
-                "use_space_char": use_space_char
-            }
         self.postprocess_op = build_post_process(postprocess_params)
         self.predictor, self.input_tensor, self.output_tensors, self.config = \
             create_predictor(args, cfg, 'rec')
@@ -191,15 +166,6 @@ class TextRecognizer(object):
 
     def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
-        if self.rec_algorithm == 'NRTR':
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-            # return padding_im
-            image_pil = Image.fromarray(np.uint8(img))
-            img = image_pil.resize([100, 32], Image.ANTIALIAS)
-            img = np.array(img)
-            norm_img = np.expand_dims(img, -1)
-            norm_img = norm_img.transpose((2, 0, 1))
-            return norm_img.astype(np.float32) / 128. - 1.
 
         assert imgC == img.shape[2]
         imgW = int((imgH * max_wh_ratio))
@@ -214,10 +180,6 @@ class TextRecognizer(object):
             resized_w = imgW
         else:
             resized_w = int(math.ceil(imgH * ratio))
-        if self.rec_algorithm == 'RARE':
-            if resized_w > self.rec_image_shape[2]:
-                resized_w = self.rec_image_shape[2]
-            imgW = self.rec_image_shape[2]
         resized_image = cv2.resize(img, (resized_w, imgH))
         resized_image = resized_image.astype('float32')
         resized_image = resized_image.transpose((2, 0, 1)) / 255
@@ -227,124 +189,6 @@ class TextRecognizer(object):
         padding_im[:, :, 0:resized_w] = resized_image
         return padding_im
 
-    def resize_norm_img_svtr(self, img, image_shape):
-
-        imgC, imgH, imgW = image_shape
-        resized_image = cv2.resize(
-            img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
-        resized_image = resized_image.astype('float32')
-        resized_image = resized_image.transpose((2, 0, 1)) / 255
-        resized_image -= 0.5
-        resized_image /= 0.5
-        return resized_image
-
-    def resize_norm_img_srn(self, img, image_shape):
-        imgC, imgH, imgW = image_shape
-
-        img_black = np.zeros((imgH, imgW))
-        im_hei = img.shape[0]
-        im_wid = img.shape[1]
-
-        if im_wid <= im_hei * 1:
-            img_new = cv2.resize(img, (imgH * 1, imgH))
-        elif im_wid <= im_hei * 2:
-            img_new = cv2.resize(img, (imgH * 2, imgH))
-        elif im_wid <= im_hei * 3:
-            img_new = cv2.resize(img, (imgH * 3, imgH))
-        else:
-            img_new = cv2.resize(img, (imgW, imgH))
-
-        img_np = np.asarray(img_new)
-        img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
-        img_black[:, 0:img_np.shape[1]] = img_np
-        img_black = img_black[:, :, np.newaxis]
-
-        row, col, c = img_black.shape
-        c = 1
-
-        return np.reshape(img_black, (c, row, col)).astype(np.float32)
-
-    def srn_other_inputs(self, image_shape, num_heads, max_text_length):
-
-        imgC, imgH, imgW = image_shape
-        feature_dim = int((imgH / 8) * (imgW / 8))
-
-        encoder_word_pos = np.array(range(0, feature_dim)).reshape(
-            (feature_dim, 1)).astype('int64')
-        gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
-            (max_text_length, 1)).astype('int64')
-
-        gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
-        gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
-            [-1, 1, max_text_length, max_text_length])
-        gsrm_slf_attn_bias1 = np.tile(
-            gsrm_slf_attn_bias1,
-            [1, num_heads, 1, 1]).astype('float32') * [-1e9]
-
-        gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
-            [-1, 1, max_text_length, max_text_length])
-        gsrm_slf_attn_bias2 = np.tile(
-            gsrm_slf_attn_bias2,
-            [1, num_heads, 1, 1]).astype('float32') * [-1e9]
-
-        encoder_word_pos = encoder_word_pos[np.newaxis, :]
-        gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
-
-        return [
-            encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
-            gsrm_slf_attn_bias2
-        ]
-
-    def process_image_srn(self, img, image_shape, num_heads, max_text_length):
-        norm_img = self.resize_norm_img_srn(img, image_shape)
-        norm_img = norm_img[np.newaxis, :]
-
-        [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
-            self.srn_other_inputs(image_shape, num_heads, max_text_length)
-
-        gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
-        gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
-        encoder_word_pos = encoder_word_pos.astype(np.int64)
-        gsrm_word_pos = gsrm_word_pos.astype(np.int64)
-
-        return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
-                gsrm_slf_attn_bias2)
-
-    def resize_norm_img_sar(self, img, image_shape,
-                            width_downsample_ratio=0.25):
-        imgC, imgH, imgW_min, imgW_max = image_shape
-        h = img.shape[0]
-        w = img.shape[1]
-        valid_ratio = 1.0
-        # make sure new_width is an integral multiple of width_divisor.
-        width_divisor = int(1 / width_downsample_ratio)
-        # resize
-        ratio = w / float(h)
-        resize_w = math.ceil(imgH * ratio)
-        if resize_w % width_divisor != 0:
-            resize_w = round(resize_w / width_divisor) * width_divisor
-        if imgW_min is not None:
-            resize_w = max(imgW_min, resize_w)
-        if imgW_max is not None:
-            valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
-            resize_w = min(imgW_max, resize_w)
-        resized_image = cv2.resize(img, (resize_w, imgH))
-        resized_image = resized_image.astype('float32')
-        # norm 
-        if image_shape[0] == 1:
-            resized_image = resized_image / 255
-            resized_image = resized_image[np.newaxis, :]
-        else:
-            resized_image = resized_image.transpose((2, 0, 1)) / 255
-        resized_image -= 0.5
-        resized_image /= 0.5
-        resize_shape = resized_image.shape
-        padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
-        padding_im[:, :, 0:resize_w] = resized_image
-        pad_shape = padding_im.shape
-
-        return padding_im, resize_shape, pad_shape, valid_ratio
-
     def predict_text(self, img_list):
         img_num = len(img_list)
         # Calculate the aspect ratio of all text bars
@@ -367,103 +211,16 @@ class TextRecognizer(object):
                 wh_ratio = w * 1.0 / h
                 max_wh_ratio = max(max_wh_ratio, wh_ratio)
             for ino in range(beg_img_no, end_img_no):
-
-                if self.rec_algorithm == "SAR":
-                    norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
-                        img_list[indices[ino]], self.rec_image_shape)
-                    norm_img = norm_img[np.newaxis, :]
-                    valid_ratio = np.expand_dims(valid_ratio, axis=0)
-                    valid_ratios = []
-                    valid_ratios.append(valid_ratio)
-                    norm_img_batch.append(norm_img)
-                elif self.rec_algorithm == "SRN":
-                    norm_img = self.process_image_srn(
-                        img_list[indices[ino]], self.rec_image_shape, 8, 25)
-                    encoder_word_pos_list = []
-                    gsrm_word_pos_list = []
-                    gsrm_slf_attn_bias1_list = []
-                    gsrm_slf_attn_bias2_list = []
-                    encoder_word_pos_list.append(norm_img[1])
-                    gsrm_word_pos_list.append(norm_img[2])
-                    gsrm_slf_attn_bias1_list.append(norm_img[3])
-                    gsrm_slf_attn_bias2_list.append(norm_img[4])
-                    norm_img_batch.append(norm_img[0])
-                elif self.rec_algorithm == "SVTR":
-                    norm_img = self.resize_norm_img_svtr(img_list[indices[ino]],
-                                                         self.rec_image_shape)
-                    norm_img = norm_img[np.newaxis, :]
-                    norm_img_batch.append(norm_img)
-                else:
                 norm_img = self.resize_norm_img(img_list[indices[ino]],
                                                 max_wh_ratio)
                 norm_img = norm_img[np.newaxis, :]
                 norm_img_batch.append(norm_img)
             norm_img_batch = np.concatenate(norm_img_batch)
             norm_img_batch = norm_img_batch.copy()
-
-            if self.rec_algorithm == "SRN":
-                encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
-                gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
-                gsrm_slf_attn_bias1_list = np.concatenate(
-                    gsrm_slf_attn_bias1_list)
-                gsrm_slf_attn_bias2_list = np.concatenate(
-                    gsrm_slf_attn_bias2_list)
-
-                inputs = [
-                    norm_img_batch,
-                    encoder_word_pos_list,
-                    gsrm_word_pos_list,
-                    gsrm_slf_attn_bias1_list,
-                    gsrm_slf_attn_bias2_list,
-                ]
-                if self.use_onnx:
-                    input_dict = {}
-                    input_dict[self.input_tensor.name] = norm_img_batch
-                    outputs = self.predictor.run(self.output_tensors,
-                                                 input_dict)
-                    preds = {"predict": outputs[2]}
-                else:
-                    input_names = self.predictor.get_input_names()
-                    for i in range(len(input_names)):
-                        input_tensor = self.predictor.get_input_handle(
-                            input_names[i])
-                        input_tensor.copy_from_cpu(inputs[i])
-                    self.predictor.run()
-                    outputs = []
-                    for output_tensor in self.output_tensors:
-                        output = output_tensor.copy_to_cpu()
-                        outputs.append(output)
-                    preds = {"predict": outputs[2]}
-            elif self.rec_algorithm == "SAR":
-                valid_ratios = np.concatenate(valid_ratios)
-                inputs = [
-                    norm_img_batch,
-                    valid_ratios,
-                ]
-                if self.use_onnx:
-                    input_dict = {}
-                    input_dict[self.input_tensor.name] = norm_img_batch
-                    outputs = self.predictor.run(self.output_tensors,
-                                                 input_dict)
-                    preds = outputs[0]
-                else:
-                    input_names = self.predictor.get_input_names()
-                    for i in range(len(input_names)):
-                        input_tensor = self.predictor.get_input_handle(
-                            input_names[i])
-                        input_tensor.copy_from_cpu(inputs[i])
-                    self.predictor.run()
-                    outputs = []
-                    for output_tensor in self.output_tensors:
-                        output = output_tensor.copy_to_cpu()
-                        outputs.append(output)
-                    preds = outputs[0]
-            else:
             if self.use_onnx:
                 input_dict = {}
                 input_dict[self.input_tensor.name] = norm_img_batch
-                    outputs = self.predictor.run(self.output_tensors,
-                                                 input_dict)
+                outputs = self.predictor.run(self.output_tensors, input_dict)
                 preds = outputs[0]
             else:
                 self.input_tensor.copy_from_cpu(norm_img_batch)

deploy/pphuman/ppvehicle/vehicle_plateutils.py

@@ -185,7 +185,6 @@ def create_predictor(args, cfg, mode):
 def get_output_tensors(cfg, mode, predictor):
     output_names = predictor.get_output_names()
     output_tensors = []
-    if mode == "rec" and cfg['rec_algorithm'] in ["CRNN", "SVTR_LCNet"]:
     output_name = 'softmax_0.tmp_0'
     if output_name in output_names:
         return [predictor.get_output_handle(output_name)]
@@ -193,10 +192,6 @@ def get_output_tensors(cfg, mode, predictor):
         for output_name in output_names:
             output_tensor = predictor.get_output_handle(output_name)
             output_tensors.append(output_tensor)
-    else:
-        for output_name in output_names:
-            output_tensor = predictor.get_output_handle(output_name)
-            output_tensors.append(output_tensor)
     return output_tensors