add amp train

configs/det/det_mv3_db_amp.yml

+Global:
+  use_gpu: true
+  epoch_num: 1200
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/db_mv3/
+  save_epoch_step: 1200
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_db/predicts_db.txt
+
+AMP:
+  scale_loss: 1024.0
+  use_dynamic_loss_scaling: True
+
+Architecture:
+  model_type: det
+  algorithm: DB
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: DBFPN
+    out_channels: 256
+  Head:
+    name: DBHead
+    k: 50
+
+Loss:
+  name: DBLoss
+  balance_loss: true
+  main_loss_type: DiceLoss
+  alpha: 5
+  beta: 10
+  ohem_ratio: 3
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: DBPostProcess
+  thresh: 0.3
+  box_thresh: 0.6
+  max_candidates: 1000
+  unclip_ratio: 1.5
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [1.0]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
+            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
+      - EastRandomCropData:
+          size: [640, 640]
+          max_tries: 50
+          keep_ratio: true
+      - MakeBorderMap:
+          shrink_ratio: 0.4
+          thresh_min: 0.3
+          thresh_max: 0.7
+      - MakeShrinkMap:
+          shrink_ratio: 0.4
+          min_text_size: 8
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 16
+    num_workers: 8
+    use_shared_memory: False
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          image_shape: [736, 1280]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 8
+    use_shared_memory: False

tools/program.py

@@ -226,14 +226,29 @@ def train(config,
             images = batch[0]
             if use_srn:
                 model_average = True
-            if model_type == 'table' or extra_input:
-                preds = model(images, data=batch[1:])
+
+            # use amp
+            if scaler:
+                with paddle.amp.auto_cast():
+                    if model_type == 'table' or extra_input:
+                        preds = model(images, data=batch[1:])
+                    else:
+                        preds = model(images)
             else:
-                preds = model(images)
+                if model_type == 'table' or extra_input:
+                    preds = model(images, data=batch[1:])
+                else:
+                    preds = model(images)
             loss = loss_class(preds, batch)
             avg_loss = loss['loss']
-            avg_loss.backward()
-            optimizer.step()
+
+            if scaler:
+                scaled_avg_loss = scaler.scale(avg_loss)
+                scaled_avg_loss.backward()
+                scaler.minimize(optimizer, scaled_avg_loss)
+            else:
+                avg_loss.backward()
+                optimizer.step()
             optimizer.clear_grad()
 
             train_batch_cost += time.time() - batch_start

tools/train.py

@@ -102,6 +102,23 @@ def main(config, device, logger, vdl_writer):
     if valid_dataloader is not None:
         logger.info('valid dataloader has {} iters'.format(
             len(valid_dataloader)))
+
+    use_amp = True if "AMP" in config else False
+    if use_amp:
+        AMP_RELATED_FLAGS_SETTING = {
+            'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
+            'FLAGS_max_inplace_grad_add': 8,
+        }
+        paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
+        scale_loss = config["AMP"].get("scale_loss", 1.0)
+        use_dynamic_loss_scaling = config["AMP"].get("use_dynamic_loss_scaling",
+                                                     False)
+        scaler = paddle.amp.GradScaler(
+            init_loss_scaling=scale_loss,
+            use_dynamic_loss_scaling=use_dynamic_loss_scaling)
+    else:
+        scaler = None
+
     # start train
     program.train(config, train_dataloader, valid_dataloader, device, model,
                   loss_class, optimizer, lr_scheduler, post_process_class,