update slim models and benchmark (#3077)

* update slim models and benchmark

configs/slim/prune/ppyolo_mbv3_large_prune_fpgm.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams
+slim: Pruner
+
+Pruner:
+  criterion: fpgm
+  pruned_params: ['conv2d_62.w_0', 'conv2d_63.w_0', 'conv2d_64.w_0',
+                  'conv2d_65.w_0', 'conv2d_66.w_0', 'conv2d_67.w_0']
+  pruned_ratios: [0.75, 0.75, 0.75, 0.75, 0.75, 0.75]
+  print_params: True

configs/slim/prune/ppyolo_r50vd_prune_fpgm.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams
+slim: Pruner
+
+Pruner:
+  criterion: fpgm
+  pruned_params: ['conv2d_56.w_0', 'conv2d_57.w_0', 'conv2d_58.w_0',
+                  'conv2d_59.w_0', 'conv2d_60.w_0', 'conv2d_61.w_0',
+                  'conv2d_63.w_0', 'conv2d_64.w_0', 'conv2d_65.w_0',
+                  'conv2d_66.w_0', 'conv2d_67.w_0', 'conv2d_68.w_0',
+                  'conv2d_70.w_0', 'conv2d_71.w_0', 'conv2d_72.w_0',
+                  'conv2d_73.w_0', 'conv2d_74.w_0', 'conv2d_75.w_0']
+  pruned_ratios: [0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.875,0.875,0.875,0.875,0.875,0.875]
+  print_params: False

configs/slim/prune/yolov3_darknet_prune_fpgm.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams
+slim: Pruner
+
+Pruner:
+  criterion: fpgm
+  pruned_params: ['conv2d_52.w_0', 'conv2d_53.w_0', 'conv2d_54.w_0',
+                  'conv2d_55.w_0', 'conv2d_56.w_0', 'conv2d_57.w_0',
+                  'conv2d_59.w_0', 'conv2d_60.w_0', 'conv2d_61.w_0',
+                  'conv2d_62.w_0', 'conv2d_63.w_0', 'conv2d_64.w_0',
+                  'conv2d_66.w_0', 'conv2d_67.w_0', 'conv2d_68.w_0',
+                  'conv2d_69.w_0', 'conv2d_70.w_0', 'conv2d_71.w_0']
+  pruned_ratios: [0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.875,0.875,0.875,0.875,0.875,0.875]
+  print_params: True

configs/slim/quant/ppyolo_mbv3_large_qat.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams
+slim: QAT
+
+QAT:
+  quant_config: {
+    'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max',
+    'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.99,
+    'quantizable_layer_type': ['Conv2D', 'Linear']}
+  print_model: True
+
+PPYOLOFPN:
+  in_channels: [160, 368]
+  coord_conv: true
+  conv_block_num: 0
+  spp: true
+  drop_block: false

configs/slim/quant/ppyolo_r50vd_qat_pact.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolo_r50vd_dcn_1x_coco.pdparams
+slim: QAT
+
+QAT:
+  quant_config: {
+    'activation_preprocess_type': 'PACT',
+    'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max',
+    'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.9,
+    'quantizable_layer_type': ['Conv2D', 'Linear']}
+  print_model: True
+
+epoch: 50
+
+LearningRate:
+  base_lr: 0.0005
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones:
+    - 30
+    - 45
+  - !LinearWarmup
+    start_factor: 0.
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+PPYOLOFPN:
+  coord_conv: true
+  block_size: 3
+  keep_prob: 0.9
+  spp: true
+  drop_block: false

configs/slim/quant/ppyolov2_r50vd_dcn_qat.yml

+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyolov2_r50vd_dcn_365e_coco.pdparams
+slim: QAT
+
+QAT:
+  quant_config: {
+    'activation_preprocess_type': 'PACT',
+    'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max',
+    'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.9,
+    'quantizable_layer_type': ['Conv2D', 'Linear']}
+  print_model: True
+
+epoch: 50
+snapshot_epoch: 8
+LearningRate:
+  base_lr: 0.0005
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones:
+    - 30
+    - 45
+  - !LinearWarmup
+    start_factor: 0.
+    steps: 2000
+
+TrainReader:
+   batch_size: 8
+
+PPYOLOPAN:
+  drop_block: false
+  block_size: 3
+  keep_prob: 0.9
+  spp: true

configs/slim/quant/yolov3_mobilenet_v3_qat.yml

@@ -4,21 +4,21 @@ slim: QAT
 
 QAT:
   quant_config: {
-    'weight_preprocess_type': 'PACT',
+    'activation_preprocess_type': 'PACT',
     'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max',
     'weight_bits': 8, 'activation_bits': 8, 'dtype': 'int8', 'window_size': 10000, 'moving_rate': 0.9,
     'quantizable_layer_type': ['Conv2D', 'Linear']}
   print_model: True
 
-epoch: 30
+epoch: 50
 LearningRate:
   base_lr: 0.0001
   schedulers:
   - !PiecewiseDecay
     gamma: 0.1
     milestones:
-    - 25
-    - 28
+    - 35
+    - 45
   - !LinearWarmup
     start_factor: 0.
-    steps: 2000
+    steps: 1000

deploy/README.md

@@ -6,7 +6,7 @@
   - `C++`语言部署 ，支持`CPU`、`GPU`和`XPU`环境，支持在`Linux`、`Windows`系统下部署，支持`NV Jetson`嵌入式设备上部署。请参考文档[C++部署](cpp/README.md)。
   - `TensorRT`加速：请参考文档[TensorRT预测部署教程](TENSOR_RT.md)
 - 服务器端部署：使用[PaddleServing](./serving/README.md)部署。
-- 手机移动端部署：使用[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在手机移动端部署。
+- 手机移动端部署：使用[Paddle-Lite](./lite/README.md) 在手机移动端部署。
 
 
 ## 1.模型导出

ppdet/engine/trainer.py

@@ -498,7 +498,7 @@ class Trainer(object):
         }]
 
         # dy2st and save model
-        if 'slim' not in self.cfg or self.cfg['slim'] != 'QAT':
+        if 'slim' not in self.cfg or self.cfg['slim_type'] != 'QAT':
             static_model = paddle.jit.to_static(
                 self.model, input_spec=input_spec)
             # NOTE: dy2st do not pruned program, but jit.save will prune program

ppdet/slim/__init__.py

@@ -53,6 +53,7 @@ def build_slim_model(cfg, slim_cfg, mode='train'):
         if mode == 'train':
             load_pretrain_weight(model, cfg.pretrain_weights)
         slim = create(cfg.slim)
+        cfg['slim_type'] = cfg.slim
         cfg['model'] = slim(model)
         cfg['slim'] = slim
         if mode != 'train':