Merge pull request #4798 from WenmuZhou/tipc

add rec fpgm to tipc

deploy/slim/prune/export_prune_model.py

@@ -52,12 +52,17 @@ def main(config, device, logger, vdl_writer):
         config['Architecture']["Head"]['out_channels'] = char_num
     model = build_model(config['Architecture'])
 
-    flops = paddle.flops(model, [1, 3, 640, 640])
+    if config['Architecture']['model_type'] == 'det':
-    logger.info(f"FLOPs before pruning: {flops}")
+        input_shape = [1, 3, 640, 640]
+    elif config['Architecture']['model_type'] == 'rec':
+        input_shape = [1, 3, 32, 320]
+
+    flops = paddle.flops(model, input_shape)
+    logger.info("FLOPs before pruning: {}".format(flops))
 
     from paddleslim.dygraph import FPGMFilterPruner
     model.train()
-    pruner = FPGMFilterPruner(model, [1, 3, 640, 640])
+    pruner = FPGMFilterPruner(model, input_shape)
 
     # build metric
     eval_class = build_metric(config['Metric'])
@@ -65,8 +70,13 @@ def main(config, device, logger, vdl_writer):
     def eval_fn():
         metric = program.eval(model, valid_dataloader, post_process_class,
                               eval_class)
-        logger.info(f"metric['hmean']: {metric['hmean']}")
+        if config['Architecture']['model_type'] == 'det':
-        return metric['hmean']
+            main_indicator = 'hmean'
+        else:
+            main_indicator = 'acc'
+        logger.info("metric[{}]: {}".format(main_indicator, metric[
+            main_indicator]))
+        return metric[main_indicator]
 
     params_sensitive = pruner.sensitive(
         eval_func=eval_fn,
@@ -81,18 +91,22 @@ def main(config, device, logger, vdl_writer):
     # calculate pruned params's ratio
     params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02)
     for key in params_sensitive.keys():
-        logger.info(f"{key}, {params_sensitive[key]}")
+        logger.info("{}, {}".format(key, params_sensitive[key]))
 
     plan = pruner.prune_vars(params_sensitive, [0])
 
-    flops = paddle.flops(model, [1, 3, 640, 640])
+    flops = paddle.flops(model, input_shape)
-    logger.info(f"FLOPs after pruning: {flops}")
+    logger.info("FLOPs after pruning: {}".format(flops))
 
     # load pretrain model
     load_model(config, model)
     metric = program.eval(model, valid_dataloader, post_process_class,
                           eval_class)
-    logger.info(f"metric['hmean']: {metric['hmean']}")
+    if config['Architecture']['model_type'] == 'det':
+        main_indicator = 'hmean'
+    else:
+        main_indicator = 'acc'
+    logger.info("metric['']: {}".format(main_indicator, metric[main_indicator]))
 
     # start export model
     from paddle.jit import to_static

deploy/slim/prune/sensitivity_anal.py

@@ -73,13 +73,18 @@ def main(config, device, logger, vdl_writer):
         char_num = len(getattr(post_process_class, 'character'))
         config['Architecture']["Head"]['out_channels'] = char_num
     model = build_model(config['Architecture'])
+    if config['Architecture']['model_type'] == 'det':
+        input_shape = [1, 3, 640, 640]
+    elif config['Architecture']['model_type'] == 'rec':
+        input_shape = [1, 3, 32, 320]
+    flops = paddle.flops(model, input_shape)
 
-    flops = paddle.flops(model, [1, 3, 640, 640])
     logger.info("FLOPs before pruning: {}".format(flops))
 
     from paddleslim.dygraph import FPGMFilterPruner
     model.train()
-    pruner = FPGMFilterPruner(model, [1, 3, 640, 640])
+
+    pruner = FPGMFilterPruner(model, input_shape)
 
     # build loss
     loss_class = build_loss(config['Loss'])
@@ -107,8 +112,14 @@ def main(config, device, logger, vdl_writer):
     def eval_fn():
         metric = program.eval(model, valid_dataloader, post_process_class,
                               eval_class, False)
-        logger.info("metric['hmean']: {}".format(metric['hmean']))
+        if config['Architecture']['model_type'] == 'det':
-        return metric['hmean']
+            main_indicator = 'hmean'
+        else:
+            main_indicator = 'acc'
+
+        logger.info("metric[{}]: {}".format(main_indicator, metric[
+            main_indicator]))
+        return metric[main_indicator]
 
     run_sensitive_analysis = False
     """
@@ -149,7 +160,7 @@ def main(config, device, logger, vdl_writer):
 
     plan = pruner.prune_vars(params_sensitive, [0])
 
-    flops = paddle.flops(model, [1, 3, 640, 640])
+    flops = paddle.flops(model, input_shape)
     logger.info("FLOPs after pruning: {}".format(flops))
 
     # start train

ppocr/modeling/backbones/rec_mobilenet_v3.py

@@ -26,8 +26,10 @@ class MobileNetV3(nn.Layer):
                  scale=0.5,
                  large_stride=None,
                  small_stride=None,
+                 disable_se=False,
                  **kwargs):
         super(MobileNetV3, self).__init__()
+        self.disable_se = disable_se
         if small_stride is None:
             small_stride = [2, 2, 2, 2]
         if large_stride is None:
@@ -101,6 +103,7 @@ class MobileNetV3(nn.Layer):
         block_list = []
         inplanes = make_divisible(inplanes * scale)
         for (k, exp, c, se, nl, s) in cfg:
+            se = se and not self.disable_se
             block_list.append(
                 ResidualUnit(
                     in_channels=inplanes,

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml

@@ -0,0 +1,102 @@
+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_chinese_lite_v2.0
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  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
+  # for data or label process
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+    disable_se: True
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/ic15_data
+    label_file_list: ["train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: train_data/ic15_data
+    label_file_list: ["train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/train_infer_python.txt

@@ -0,0 +1,51 @@
+===========================train_params===========================
+model_name:ch_ppocr_mobile_v2.0_rec_FPGM
+python:python3.7
+gpu_list:0
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/ic15_data/test/word_1.png
+null:null
+##
+trainer:fpgm_train
+norm_train:null
+pact_train:null
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./pretrain_models/ch_ppocr_mobile_v2.0_rec_train/best_accuracy
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:null
+quant_export:null
+fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml -o 
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:null
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+null:null
+--benchmark:True
+null:null

test_tipc/prepare.sh

@@ -61,6 +61,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
         wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar  --no-check-certificate
         cd ./inference/ && tar xf det_r50_vd_db_v2.0_train.tar && cd ../
     fi
+    if [ ${model_name} == "ch_ppocr_mobile_v2.0_rec_FPGM" ]; then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf ch_ppocr_mobile_v2.0_rec_train.tar && cd ../
+    fi
 
 elif [ ${MODE} = "whole_train_whole_infer" ];then
     wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate