fix seed typo

configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_enhanced_ctc_loss.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 800
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_mobile_pp-OCRv2_enhanced_ctc_loss
+  save_epoch_step: 3
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  character_type: ch
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+  distributed: true
+  save_res_path: ./output/rec/predicts_mobile_pp-OCRv2_enhanced_ctc_loss.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Piecewise
+    decay_epochs : [700, 800]
+    values : [0.001, 0.0001]
+    warmup_epoch: 5
+  regularizer:
+    name: L2
+    factor: 2.0e-05
+
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV1Enhance
+    scale: 0.5
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 64
+  Head:
+    name: CTCHead
+    mid_channels: 96
+    fc_decay: 0.00002
+    return_feats: true
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - CTCLoss:
+      use_focal_loss: false
+      weight: 1.0
+  - CenterLoss:
+      weight: 0.05
+      num_classes: 6625
+      feat_dim: 96
+      init_center: false
+      center_file_path: "./train_center.pkl"
+  # you can also try to add ace loss on your own dataset
+  # - ACELoss:
+  #     weight: 0.1
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list:
+    - ./train_data/train_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug:
+    - CTCLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 32, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+        - label_ace
+  loader:
+    shuffle: true
+    batch_size_per_card: 128
+    drop_last: true
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data
+    label_file_list:
+    - ./train_data/val_list.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 32, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 128
+    num_workers: 8

ppocr/data/imaug/label_ops.py

@@ -215,6 +215,11 @@ class CTCLabelEncode(BaseRecLabelEncode):
         data['length'] = np.array(len(text))
         text = text + [0] * (self.max_text_len - len(text))
         data['label'] = np.array(text)
+
+        label = [0] * len(self.character)
+        for x in text:
+            label[x] += 1
+        data['label_ace'] = np.array(label)
         return data
 
     def add_special_char(self, dict_character):

ppocr/losses/__init__.py

@@ -52,7 +52,6 @@ def build_loss(config):
         'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss',
         'TableAttentionLoss', 'SARLoss', 'AsterLoss'
     ]
-
     config = copy.deepcopy(config)
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('loss only support {}'.format(

ppocr/losses/ace_loss.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+import paddle.nn as nn
+
+
+class ACELoss(nn.Layer):
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.loss_func = nn.CrossEntropyLoss(
+            weight=None,
+            ignore_index=0,
+            reduction='none',
+            soft_label=True,
+            axis=-1)
+
+    def __call__(self, predicts, batch):
+        if isinstance(predicts, (list, tuple)):
+            predicts = predicts[-1]
+        B, N = predicts.shape[:2]
+        div = paddle.to_tensor([N]).astype('float32')
+
+        predicts = nn.functional.softmax(predicts, axis=-1)
+        aggregation_preds = paddle.sum(predicts, axis=1)
+        aggregation_preds = paddle.divide(aggregation_preds, div)
+
+        length = batch[2].astype("float32")
+        batch = batch[3].astype("float32")
+        batch[:, 0] = paddle.subtract(div, length)
+
+        batch = paddle.divide(batch, div)
+
+        loss = self.loss_func(aggregation_preds, batch)
+
+        return {"loss_ace": loss}

ppocr/losses/center_loss.py

+#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os
+import pickle
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class CenterLoss(nn.Layer):
+    """
+    Reference: Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
+    """
+
+    def __init__(self,
+                 num_classes=6625,
+                 feat_dim=96,
+                 init_center=False,
+                 center_file_path=None):
+        super().__init__()
+        self.num_classes = num_classes
+        self.feat_dim = feat_dim
+        self.centers = paddle.randn(
+            shape=[self.num_classes, self.feat_dim]).astype(
+                "float64")  #random center
+
+        if init_center:
+            assert os.path.exists(
+                center_file_path
+            ), f"center path({center_file_path}) must exist when init_center is set as True."
+            with open(center_file_path, 'rb') as f:
+                char_dict = pickle.load(f)
+                for key in char_dict.keys():
+                    self.centers[key] = paddle.to_tensor(char_dict[key])
+
+    def __call__(self, predicts, batch):
+        assert isinstance(predicts, (list, tuple))
+        features, predicts = predicts
+
+        feats_reshape = paddle.reshape(
+            features, [-1, features.shape[-1]]).astype("float64")
+        label = paddle.argmax(predicts, axis=2)
+        label = paddle.reshape(label, [label.shape[0] * label.shape[1]])
+
+        batch_size = feats_reshape.shape[0]
+
+        #calc feat * feat   
+        dist1 = paddle.sum(paddle.square(feats_reshape), axis=1, keepdim=True)
+        dist1 = paddle.expand(dist1, [batch_size, self.num_classes])
+
+        #dist2 of centers
+        dist2 = paddle.sum(paddle.square(self.centers), axis=1,
+                           keepdim=True)  #num_classes
+        dist2 = paddle.expand(dist2,
+                              [self.num_classes, batch_size]).astype("float64")
+        dist2 = paddle.transpose(dist2, [1, 0])
+
+        #first x * x + y * y
+        distmat = paddle.add(dist1, dist2)
+        tmp = paddle.matmul(feats_reshape,
+                            paddle.transpose(self.centers, [1, 0]))
+        distmat = distmat - 2.0 * tmp
+
+        #generate the mask
+        classes = paddle.arange(self.num_classes).astype("int64")
+        label = paddle.expand(
+            paddle.unsqueeze(label, 1), (batch_size, self.num_classes))
+        mask = paddle.equal(
+            paddle.expand(classes, [batch_size, self.num_classes]),
+            label).astype("float64")  #get mask
+        dist = paddle.multiply(distmat, mask)
+        loss = paddle.sum(paddle.clip(dist, min=1e-12, max=1e+12)) / batch_size
+        return {'loss_center': loss}

ppocr/losses/combined_loss.py

@@ -15,6 +15,10 @@
 import paddle
 import paddle.nn as nn
 
+from .rec_ctc_loss import CTCLoss
+from .center_loss import CenterLoss
+from .ace_loss import ACELoss
+
 from .distillation_loss import DistillationCTCLoss
 from .distillation_loss import DistillationDMLLoss
 from .distillation_loss import DistillationDistanceLoss, DistillationDBLoss, DistillationDilaDBLoss

ppocr/losses/distillation_loss.py

@@ -112,7 +112,7 @@ class DistillationDMLLoss(DMLLoss):
                     if isinstance(loss, dict):
                         for key in loss:
                             loss_dict["{}_{}_{}_{}_{}".format(key, pair[
-                                0], pair[1], map_name, idx)] = loss[key]
+                                0], pair[1], self.maps_name, idx)] = loss[key]
                     else:
                         loss_dict["{}_{}_{}".format(self.name, self.maps_name[
                             _c], idx)] = loss

ppocr/losses/rec_ctc_loss.py

@@ -21,16 +21,24 @@ from paddle import nn
 
 
 class CTCLoss(nn.Layer):
-    def __init__(self, **kwargs):
+    def __init__(self, use_focal_loss=False, **kwargs):
         super(CTCLoss, self).__init__()
         self.loss_func = nn.CTCLoss(blank=0, reduction='none')
+        self.use_focal_loss = use_focal_loss
 
     def forward(self, predicts, batch):
+        if isinstance(predicts, (list, tuple)):
+            predicts = predicts[-1]
         predicts = predicts.transpose((1, 0, 2))
         N, B, _ = predicts.shape
         preds_lengths = paddle.to_tensor([N] * B, dtype='int64')
         labels = batch[1].astype("int32")
         label_lengths = batch[2].astype('int64')
         loss = self.loss_func(predicts, labels, preds_lengths, label_lengths)
+        if self.use_focal_loss:
+            weight = paddle.exp(-loss)
+            weight = paddle.subtract(paddle.to_tensor([1.0]), weight)
+            weight = paddle.square(weight) * self.focal_loss_alpha
+            loss = paddle.multiply(loss, weight)
         loss = loss.mean()  # sum
         return {'loss': loss}

ppocr/modeling/heads/rec_ctc_head.py

@@ -38,6 +38,7 @@ class CTCHead(nn.Layer):
                  out_channels,
                  fc_decay=0.0004,
                  mid_channels=None,
+                 return_feats=False,
                  **kwargs):
         super(CTCHead, self).__init__()
         if mid_channels is None:
@@ -66,14 +67,22 @@ class CTCHead(nn.Layer):
                 bias_attr=bias_attr2)
         self.out_channels = out_channels
         self.mid_channels = mid_channels
+        self.return_feats = return_feats
 
     def forward(self, x, targets=None):
         if self.mid_channels is None:
             predicts = self.fc(x)
         else:
-            predicts = self.fc1(x)
-            predicts = self.fc2(predicts)
-            
+            x = self.fc1(x)
+            predicts = self.fc2(x)
+
+        if self.return_feats:
+            result = (x, predicts)
+        else:
+            result = predicts
+
         if not self.training:
             predicts = F.softmax(predicts, axis=2)
-        return predicts
+            result = predicts
+
+        return result

ppocr/postprocess/__init__.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 import copy
+import platform
 
 __all__ = ['build_post_process']
 
@@ -28,7 +29,10 @@ from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, Di
     TableLabelDecode, NRTRLabelDecode, SARLabelDecode , SEEDLabelDecode
 from .cls_postprocess import ClsPostProcess
 from .pg_postprocess import PGPostProcess
-from .pse_postprocess import PSEPostProcess
+
+if platform.system() != "Windows":
+    # pse is not support in Windows
+    from .pse_postprocess import PSEPostProcess
 
 
 def build_post_process(config, global_config=None):

ppocr/postprocess/rec_postprocess.py

@@ -111,6 +111,8 @@ class CTCLabelDecode(BaseRecLabelDecode):
                                              character_type, use_space_char)
 
     def __call__(self, preds, label=None, *args, **kwargs):
+        if isinstance(preds, tuple):
+            preds = preds[-1]
         if isinstance(preds, paddle.Tensor):
             preds = preds.numpy()
         preds_idx = preds.argmax(axis=2)

tools/export_model.py

@@ -49,6 +49,12 @@ def export_single_model(model, arch_config, save_path, logger):
                 ]
         ]
         model = to_static(model, input_spec=other_shape)
+    elif arch_config["algorithm"] == "SAR":
+        other_shape = [
+            paddle.static.InputSpec(
+                shape=[None, 3, 48, 160], dtype="float32"),
+        ]
+        model = to_static(model, input_spec=other_shape)
     else:
         infer_shape = [3, -1, -1]
         if arch_config["model_type"] == "rec":

tools/infer/predict_rec.py

@@ -68,6 +68,13 @@ class TextRecognizer(object):
                 "character_dict_path": args.rec_char_dict_path,
                 "use_space_char": args.use_space_char
             }
+        elif self.rec_algorithm == "SAR":
+            postprocess_params = {
+                'name': 'SARLabelDecode',
+                "character_type": args.rec_char_type,
+                "character_dict_path": args.rec_char_dict_path,
+                "use_space_char": args.use_space_char
+            }
         self.postprocess_op = build_post_process(postprocess_params)
         self.predictor, self.input_tensor, self.output_tensors, self.config = \
             utility.create_predictor(args, 'rec', logger)
@@ -194,6 +201,41 @@ class TextRecognizer(object):
         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 __call__(self, img_list):
         img_num = len(img_list)
         # Calculate the aspect ratio of all text bars
@@ -216,11 +258,19 @@ 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 != "SRN":
+                if self.rec_algorithm != "SRN" and self.rec_algorithm != "SAR":
                     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)
+                elif 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)
                 else:
                     norm_img = self.process_image_srn(
                         img_list[indices[ino]], self.rec_image_shape, 8, 25)
@@ -266,6 +316,25 @@ class TextRecognizer(object):
                 if self.benchmark:
                     self.autolog.times.stamp()
                 preds = {"predict": outputs[2]}
+            elif self.rec_algorithm == "SAR":
+                valid_ratios = np.concatenate(valid_ratios)
+                inputs = [
+                    norm_img_batch,
+                    valid_ratios,
+                ]
+                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)
+                if self.benchmark:
+                    self.autolog.times.stamp()
+                preds = outputs[0]
             else:
                 self.input_tensor.copy_from_cpu(norm_img_batch)
                 self.predictor.run()

tools/program.py

@@ -394,6 +394,18 @@ def preprocess(is_train=False):
     config = load_config(FLAGS.config)
     merge_config(FLAGS.opt)
 
+    if is_train:
+        # save_config
+        save_model_dir = config['Global']['save_model_dir']
+        os.makedirs(save_model_dir, exist_ok=True)
+        with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
+            yaml.dump(
+                dict(config), f, default_flow_style=False, sort_keys=False)
+        log_file = '{}/train.log'.format(save_model_dir)
+    else:
+        log_file = None
+    logger = get_logger(name='root', log_file=log_file)
+
     # check if set use_gpu=True in paddlepaddle cpu version
     use_gpu = config['Global']['use_gpu']
     check_gpu(use_gpu)
@@ -403,22 +415,17 @@ def preprocess(is_train=False):
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
         'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
         'SEED']
+    windows_not_support_list = ['PSE']
+    if platform.system() == "Windows" and alg in windows_not_support_list:
+        logger.warning('{} is not support in Windows now'.format(
+            windows_not_support_list))
+        sys.exit()
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
     device = paddle.set_device(device)
 
     config['Global']['distributed'] = dist.get_world_size() != 1
-    if is_train:
-        # save_config
-        save_model_dir = config['Global']['save_model_dir']
-        os.makedirs(save_model_dir, exist_ok=True)
-        with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
-            yaml.dump(
-                dict(config), f, default_flow_style=False, sort_keys=False)
-        log_file = '{}/train.log'.format(save_model_dir)
-    else:
-        log_file = None
-    logger = get_logger(name='root', log_file=log_file)
+
     if config['Global']['use_visualdl']:
         from visualdl import LogWriter
         save_model_dir = config['Global']['save_model_dir']