diff --git a/ppocr/data/rec/dataset_traversal.py b/ppocr/data/rec/dataset_traversal.py index ebee624ab74b2390323ab538627f459cb2353e8b..67cbf9b53ad7b877be8985d76627cdf97d49f423 100755 --- a/ppocr/data/rec/dataset_traversal.py +++ b/ppocr/data/rec/dataset_traversal.py @@ -214,6 +214,8 @@ class SimpleReader(object): self.mode = params['mode'] self.infer_img = params['infer_img'] self.use_tps = False + if "num_heads" in params: + self.num_heads = params['num_heads'] if "tps" in params: self.use_tps = True self.use_distort = False @@ -251,12 +253,19 @@ class SimpleReader(object): img = cv2.imread(single_img) if img.shape[-1] == 1 or len(list(img.shape)) == 2: img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - norm_img = process_image( - img=img, - image_shape=self.image_shape, - char_ops=self.char_ops, - tps=self.use_tps, - infer_mode=True) + if self.loss_type == 'srn': + norm_img = process_image_srn( + img=img, + image_shape=self.image_shape, + num_heads=self.num_heads, + max_text_length=self.max_text_length) + else: + norm_img = process_image( + img=img, + image_shape=self.image_shape, + char_ops=self.char_ops, + tps=self.use_tps, + infer_mode=True) yield norm_img else: with open(self.label_file_path, "rb") as fin: @@ -286,14 +295,25 @@ class SimpleReader(object): img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) label = substr[1] - outs = process_image( - img=img, - image_shape=self.image_shape, - label=label, - char_ops=self.char_ops, - loss_type=self.loss_type, - max_text_length=self.max_text_length, - distort=self.use_distort) + if self.loss_type == "srn": + outs = process_image_srn( + img=img, + image_shape=self.image_shape, + num_heads=self.num_heads, + max_text_length=self.max_text_length, + label=label, + char_ops=self.char_ops, + loss_type=self.loss_type) + + else: + outs = process_image( + img=img, + image_shape=self.image_shape, + label=label, + char_ops=self.char_ops, + loss_type=self.loss_type, + max_text_length=self.max_text_length, + distort=self.use_distort) if outs is None: continue yield outs diff --git a/ppocr/data/rec/img_tools.py b/ppocr/data/rec/img_tools.py index 527e0266ee33ac81e29b5610ed05f401860078a4..8b497e6b803ba0fffaefc3e12c366130504b9ce0 100755 --- a/ppocr/data/rec/img_tools.py +++ b/ppocr/data/rec/img_tools.py @@ -410,7 +410,8 @@ def resize_norm_img_srn(img, image_shape): def srn_other_inputs(image_shape, num_heads, - max_text_length): + max_text_length, + char_num): imgC, imgH, imgW = image_shape feature_dim = int((imgH / 8) * (imgW / 8)) @@ -418,7 +419,7 @@ def srn_other_inputs(image_shape, 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') - lbl_weight = np.array([37] * max_text_length).reshape((-1,1)).astype('int64') + lbl_weight = np.array([int(char_num-1)] * max_text_length).reshape((-1,1)).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]) @@ -441,17 +442,18 @@ def process_image_srn(img, loss_type=None): norm_img = resize_norm_img_srn(img, image_shape) norm_img = norm_img[np.newaxis, :] + char_num = char_ops.get_char_num() + [lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \ - srn_other_inputs(image_shape, num_heads, max_text_length) + srn_other_inputs(image_shape, num_heads, max_text_length,char_num) if label is not None: - char_num = char_ops.get_char_num() text = char_ops.encode(label) if len(text) == 0 or len(text) > max_text_length: return None else: if loss_type == "srn": - text_padded = [37] * max_text_length + text_padded = [int(char_num-1)] * max_text_length for i in range(len(text)): text_padded[i] = text[i] lbl_weight[i] = [1.0] diff --git a/ppocr/modeling/backbones/rec_resnet50_fpn.py b/ppocr/modeling/backbones/rec_resnet50_fpn.py index f6d72377fe4e2d3355a4510f070178ad48dd2a27..a0aef4878539879ac448706ba9f7a3e48d4f6605 100755 --- a/ppocr/modeling/backbones/rec_resnet50_fpn.py +++ b/ppocr/modeling/backbones/rec_resnet50_fpn.py @@ -81,6 +81,23 @@ class ResNet(): num_filters=num_filters[block], stride=stride_list[block] if i == 0 else 1, name=conv_name) F.append(conv) + else: + for block in range(len(depth)): + for i in range(depth[block]): + conv_name = "res" + str(block + 2) + chr(97 + i) + + if i == 0 and block != 0: + stride = (2, 1) + else: + stride = (1, 1) + + conv = self.basic_block( + input=conv, + num_filters=num_filters[block], + stride=stride, + if_first=block == i == 0, + name=conv_name) + F.append(conv) base = F[-1] for i in [-2, -3]: diff --git a/ppocr/utils/character.py b/ppocr/utils/character.py index c7c93fc557604a32d12343d929c119fd787ee126..b4b2021e02c9905623fd9fad5c9673543569c1c2 100755 --- a/ppocr/utils/character.py +++ b/ppocr/utils/character.py @@ -26,8 +26,6 @@ class CharacterOps(object): self.character_type = config['character_type'] self.loss_type = config['loss_type'] self.max_text_len = config['max_text_length'] - if self.loss_type == "srn" and self.character_type != "en": - raise Exception("SRN can only support in character_type == en") if self.character_type == "en": self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" dict_character = list(self.character_str) @@ -160,13 +158,15 @@ def cal_predicts_accuracy_srn(char_ops, acc_num = 0 img_num = 0 + char_num = char_ops.get_char_num() + total_len = preds.shape[0] img_num = int(total_len / max_text_len) for i in range(img_num): cur_label = [] cur_pred = [] for j in range(max_text_len): - if labels[j + i * max_text_len] != 37: #0 + if labels[j + i * max_text_len] != int(char_num-1): #0 cur_label.append(labels[j + i * max_text_len][0]) else: break @@ -178,7 +178,7 @@ def cal_predicts_accuracy_srn(char_ops, elif j == len(cur_label) and j == max_text_len: acc_num += 1 break - elif j == len(cur_label) and preds[j + i * max_text_len][0] == 37: + elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1): acc_num += 1 break acc = acc_num * 1.0 / img_num diff --git a/tools/infer_rec.py b/tools/infer_rec.py index 7a81b3d4cedc26616fa1194baa9e4431c2176150..fd70cd66dccc2cb755efbd10c4d16c9f7a97146d 100755 --- a/tools/infer_rec.py +++ b/tools/infer_rec.py @@ -140,12 +140,12 @@ def main(): preds = preds.reshape(-1) preds_text = char_ops.decode(preds) elif loss_type == "srn": - cur_pred = [] + char_num = char_ops.get_char_num() preds = np.array(predict[0]) preds = preds.reshape(-1) probs = np.array(predict[1]) ind = np.argmax(probs, axis=1) - valid_ind = np.where(preds != 37)[0] + valid_ind = np.where(preds != int(char_num-1))[0] if len(valid_ind) == 0: continue score = np.mean(probs[valid_ind, ind[valid_ind]])