add BiSeNetV2. (#1470)

example/auto_compression/detection/configs/picodet_s_qat_dis.yaml

 Global:
   reader_config: ./configs/picodet_reader.yml
-  input_list: ['image', 'scale_factor']
   Evaluation: True
   model_dir: ./picodet_s_416_coco_lcnet/
   model_filename: model.pdmodel

example/auto_compression/detection/configs/ppyoloe_l_qat_dis.yaml

 
 Global:
   reader_config: configs/yolo_reader.yml
-  input_list: ['image', 'scale_factor']
   arch: YOLO
   Evaluation: True
   model_dir: ./ppyoloe_crn_l_300e_coco

example/auto_compression/detection/configs/ppyoloe_s_qat_dis.yaml

 
 Global:
   reader_config: configs/yolo_reader.yml
-  input_list: ['image']
   arch: PPYOLOE    # When export exclude_nms=True, need set arch: PPYOLOE
   Evaluation: True
   model_dir: ./ppyoloe_crn_s_300e_coco

example/auto_compression/detection/configs/ssd_mbv1_voc_qat_dis.yaml

 Global:
   reader_config: configs/ssd_reader.yml
-  input_list: ['image', 'scale_factor', 'im_shape']
   Evaluation: True
   model_dir: ./ssd_mobilenet_v1_300_120e_voc # Model Link: https://bj.bcebos.com/v1/paddle-slim-models/act/ssd_mobilenet_v1_300_120e_voc.tar
   model_filename: model.pdmodel

example/auto_compression/detection/configs/tinypose_qat_dis.yaml

 Global:
   arch: 'keypoint'
   reader_config: configs/tinypose_reader.yml
-  input_list: ['image']
   Evaluation: True
   model_dir: ./tinypose_128x96
   model_filename: model.pdmodel

example/auto_compression/detection/configs/yolov3_mbv1_qat_dis.yaml

 Global:
   reader_config: configs/yolo_reader.yml
-  input_list: ['image', 'im_shape', 'scale_factor']
   Evaluation: True
   model_dir: ./yolov3_mobilenet_v1_270e_coco  # Model Link: https://paddledet.bj.bcebos.com/models/slim/yolov3_mobilenet_v1_270e_coco.tar
   model_filename: model.pdmodel

example/auto_compression/detection/run.py

@@ -24,6 +24,7 @@ from paddleslim.common import load_config as load_slim_config
 from paddleslim.auto_compression import AutoCompression
 from keypoint_utils import keypoint_post_process
 from post_process import PPYOLOEPostProcess
+from paddleslim.common.dataloader import get_feed_vars
 
 
 def argsparser():
@@ -134,6 +135,9 @@ def main():
     train_loader = create('EvalReader')(reader_cfg['TrainDataset'],
                                         reader_cfg['worker_num'],
                                         return_list=True)
+    global_config['input_list'] = get_feed_vars(
+        global_config['model_dir'], global_config['model_filename'],
+        global_config['params_filename'])
     train_loader = reader_wrapper(train_loader, global_config['input_list'])
 
     if 'Evaluation' in global_config.keys() and global_config[

example/auto_compression/image_classification/configs/EfficientNetB0/prune_dis.yaml

 Global:
-  input_name: x
   model_dir: EfficientNetB0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/EfficientNetB0/qat_dis.yaml

 Global:
-  input_name: x
   model_dir: EfficientNetB0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/GhostNet_x1_0/prune_dis.yaml

 Global:
-  input_name: inputs
   model_dir: GhostNet_x1_0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/GhostNet_x1_0/qat_dis.yaml

 Global:
-  input_name: inputs
   model_dir: GhostNet_x1_0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/InceptionV3/prune_dis.yaml

 Global:
-  input_name: x
   model_dir: InceptionV3_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/InceptionV3/qat_dis.yaml

 Global:
-  input_name: x
   model_dir: save_quant_inception
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/MobileNetV1/prune_dis.yaml

 Global:
-  input_name: inputs
   model_dir: MobileNetV1_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/MobileNetV1/qat_dis.yaml

 Global:
-  input_name: inputs
   model_dir: MobileNetV1_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/MobileNetV3_large_x1_0/prune_dis.yaml

 Global:
-  input_name: inputs
   model_dir: MobileNetV3_large_x1_0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/MobileNetV3_large_x1_0/qat_dis.yaml

 Global:
-  input_name: inputs
   model_dir: MobileNetV3_large_x1_0_ssld_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/PPHGNet_tiny/prune_dis.yaml

 Global:
-  input_name: x
   model_dir: PPHGNet_tiny_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/PPHGNet_tiny/qat_dis.yaml

 Global:
-  input_name: x
   model_dir: PPHGNet_tiny_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/PPLCNetV2_base/prune_dis.yaml

 Global:
-  input_name: x
   model_dir: PPLCNetV2_base_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/PPLCNetV2_base/qat_dis.yaml

 Global:
-  input_name: x
   model_dir: PPLCNetV2_base_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/PPLCNet_x1_0/prune_dis.yaml

 Global:
-  input_name: x
   model_dir: PPLCNet_x1_0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/PPLCNet_x1_0/qat_dis.yaml

 Global:
-  input_name: x
   model_dir: PPLCNet_x1_0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/ResNet50_vd/prune_dis.yaml

 Global:
-  input_name: inputs
   model_dir: ResNet50_vd_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/ResNet50_vd/qat_dis.yaml

 Global:
-  input_name: inputs
   model_dir: ResNet50_vd_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/ShuffleNetV2_x1_0/prune_dis.yaml

 Global:
-  input_name: inputs
   model_dir: ShuffleNetV2_x1_0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/ShuffleNetV2_x1_0/qat_dis.yaml

 Global:
-  input_name: inputs
   model_dir: ShuffleNetV2_x1_0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/SqueezeNet1_0/prune_dis.yaml

 Global:
-  input_name: inputs
   model_dir: SqueezeNet1_0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/SqueezeNet1_0/qat_dis.yaml

 Global:
-  input_name: inputs
   model_dir: SqueezeNet1_0_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/configs/SwinTransformer_base_patch4_window7_224/qat_dis.yaml

 Global:
-  input_name: inputs
   model_dir: SwinTransformer_base_patch4_window7_224_infer
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams

example/auto_compression/image_classification/run.py

@@ -27,6 +27,7 @@ from paddle.io import DataLoader
 from imagenet_reader import ImageNetDataset
 from paddleslim.common import load_config as load_slim_config
 from paddleslim.auto_compression import AutoCompression
+from paddleslim.common.dataloader import get_feed_vars
 
 
 def argsparser():
@@ -57,6 +58,9 @@ def argsparser():
 
 # yapf: enable
 def reader_wrapper(reader, input_name):
+    if isinstance(input_name, list) and len(input_name) == 1:
+        input_name = input_name[0]
+
     def gen():
         for i, (imgs, label) in enumerate(reader()):
             yield {input_name: imgs}
@@ -175,6 +179,9 @@ def main():
         shuffle=True,
         drop_last=True,
         num_workers=0)
+    global_config['input_name'] = get_feed_vars(
+        global_config['model_dir'], global_config['model_filename'],
+        global_config['params_filename'])
     train_dataloader = reader_wrapper(train_loader, global_config['input_name'])
 
     ac = AutoCompression(

example/auto_compression/ocr/configs/ppocrv3_det_qat_dist.yaml

@@ -4,7 +4,6 @@ Global:
   model_filename: inference.pdmodel
   params_filename: inference.pdiparams
   algorithm: DB
-  input_name: 'x'
   
 Distillation:
   alpha: 1.0

example/auto_compression/ocr/run.py

@@ -28,7 +28,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-
+from paddleslim.common.dataloader import get_feed_vars
 logger = get_logger(__name__, level=logging.INFO)
 
 
@@ -54,6 +54,9 @@ def argsparser():
 
 
 def reader_wrapper(reader, input_name):
+    if isinstance(input_name, list) and len(input_name) == 1:
+        input_name = input_name[0]
+
     def gen():
         for i, batch in enumerate(reader()):
             yield {input_name: batch[0]}
@@ -131,6 +134,9 @@ def main():
         all_config['TrainConfig']['learning_rate']['T_max'] = steps
         print('total training steps:', steps)
 
+    global_config['input_name'] = get_feed_vars(
+        global_config['model_dir'], global_config['model_filename'],
+        global_config['params_filename'])
     ac = AutoCompression(
         model_dir=global_config['model_dir'],
         model_filename=global_config['model_filename'],

example/auto_compression/semantic_segmentation/README.md

@@ -30,6 +30,8 @@
 | UNet | 量化训练 |  64.93 | - |10.228| [config](./configs/unet/unet_qat.yaml) | [model](https://bj.bcebos.com/v1/paddle-slim-models/act/PaddleSeg/qat/unet_qat.tar) |
 | Deeplabv3-ResNet50 | Baseline |  79.90 | -|12.766| -| [model](https://paddleseg.bj.bcebos.com/tipc/easyedge/RES-paddle2-Deeplabv3-ResNet50.zip)|
 | Deeplabv3-ResNet50 | 量化训练 |  79.26 | - |8.839|[config](./configs/deeplabv3/deeplabv3_qat.yaml) | [model](https://bj.bcebos.com/v1/paddle-slim-models/act/PaddleSeg/qat/deeplabv3_qat.tar) |
+| BiSeNetV2 | Baseline |  73.17 | -|35.61| -| [model](https://bj.bcebos.com/v1/paddle-slim-models/act/PaddleSeg/BiSeNetV2.tar)|
+| BiSeNetV2 | 量化训练 |  73.20 | - |15.94|[config](./configs/BiSeNetV2/BiSeNetV2_qat.yaml) | [model](https://bj.bcebos.com/v1/paddle-slim-models/act/PaddleSeg/qat/BiSeNetV2_qat.tar) |
 
 - ARM CPU测试环境：`高通骁龙710处理器(SDM710 2*A75(2.2GHz) 6*A55(1.7GHz))`；
 
@@ -47,7 +49,7 @@
 
 - PaddlePaddle >= 2.3 （可从[Paddle官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)下载安装）
 - PaddleSlim >= 2.3
-- PaddleSeg >= 2.5
+- PaddleSeg == 2.5.0
 
 安装paddlepaddle：
 ```shell
@@ -67,13 +69,13 @@ pip install paddleslim
 git clone https://github.com/PaddlePaddle/PaddleSlim.git
 ```
 
-安装paddleseg
+安装paddleseg 2.5.0
 
 ```shell
 pip install paddleseg==2.5.0
 ```
 
-注：安装[PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg)的目的只是为了直接使用PaddleSeg中的Dataloader组件，不涉及模型组网等。推荐安装PaddleSeg 2.5.0, 不同版本的PaddleSeg的Dataloader返回数据的格式略有不同.
+注：安装[PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg)的目的只是为了直接使用PaddleSeg中的Dataloader组件，不涉及模型组网等。本示例需安装**PaddleSeg 2.5.0**, 不同版本的PaddleSeg的Dataloader返回数据的格式略有不同.
 
 #### 3.2 准备数据集
 

example/auto_compression/semantic_segmentation/configs/BiSeNetV2/BiSeNetV2_qat.yaml

+Global:
+  reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
+  model_dir: ./BiSeNetV2
+  model_filename: model.pdmodel
+  params_filename: model.pdiparams
+  batch_size: 4
+
+Distillation:
+  alpha: 1.0
+  loss: l2
+  node:
+  - conv2d_103.tmp_1
+
+Quantization:
+  onnx_format: True
+  quantize_op_types:
+  - conv2d
+  - depthwise_conv2d
+
+TrainConfig:
+  epochs: 5
+  eval_iter: 360
+  logging_iter: 100
+  learning_rate: 
+    type: PiecewiseDecay
+    boundaries: [2]
+    values: [0.00001, 0.000001]
+  optimizer_builder:
+    optimizer: 
+      type: SGD
+    weight_decay: 0.0005
\ No newline at end of file

example/auto_compression/semantic_segmentation/configs/deeplabv3/deeplabv3_qat.yaml

 Global:
-  reader_config: configs/deeplabv3/deeplabv3_reader.yml
+  reader_config: configs/dataset/cityscapes_1024x512_scale1.0.yml
   model_dir: ./RES-paddle2-Deeplabv3-ResNet50
   model_filename: model
   params_filename: params
+  batch_size: 4
 
 Distillation:
   alpha: 1.0

example/auto_compression/semantic_segmentation/configs/deeplabv3/deeplabv3_reader.yml

-batch_size: 4
-train_dataset:
-  type: Cityscapes
-  dataset_root: data/cityscapes
-  transforms:
-    - type: ResizeStepScaling
-      min_scale_factor: 0.5
-      max_scale_factor: 2.0
-      scale_step_size: 0.25
-    - type: RandomPaddingCrop
-      crop_size: [1024, 512]
-    - type: RandomHorizontalFlip
-    - type: RandomDistort
-      brightness_range: 0.5
-      contrast_range: 0.5
-      saturation_range: 0.5
-    - type: Normalize
-  mode: train
-val_dataset:
-  type: Cityscapes
-  dataset_root: data/cityscapes
-  transforms:
-    - type: Normalize
-  mode: val

example/auto_compression/semantic_segmentation/configs/unet/unet_qat.yaml

@@ -19,6 +19,7 @@ TrainConfig:
   epochs: 20
   eval_iter: 360
   learning_rate: 0.0001
-  optimizer: SGD
-  optim_args:
-    weight_decay: 4.0e-05
+  optimizer_builder:
+    optimizer: 
+      type: SGD
+    weight_decay: 4.0e-05  

example/auto_compression/semantic_segmentation/run.py

@@ -18,12 +18,12 @@ import random
 import paddle
 import numpy as np
 from paddleseg.cvlibs import Config as PaddleSegDataConfig
-from paddleseg.utils import worker_init_fn
+from paddleseg.utils import worker_init_fn, metrics
+from paddleseg.core.infer import reverse_transform
 
 from paddleslim.auto_compression import AutoCompression
 from paddleslim.common import load_config as load_slim_config
-from paddleseg.core.infer import reverse_transform
-from paddleseg.utils import metrics
+from paddleslim.common.dataloader import get_feed_vars
 
 
 def argsparser():
@@ -63,7 +63,7 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
     print("Start evaluating (total_samples: {}, total_iters: {})...".format(
         len(eval_dataset), total_iters))
 
-    for iter, (image, label) in enumerate(loader):
+    for iters, (image, label) in enumerate(loader):
         paddle.enable_static()
 
         label = np.array(label).astype('int64')
@@ -97,6 +97,8 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
         intersect_area_all = intersect_area_all + intersect_area
         pred_area_all = pred_area_all + pred_area
         label_area_all = label_area_all + label_area
+        if iters % 100 == 0:
+            print("Eval iter:", iters)
 
     class_iou, miou = metrics.mean_iou(intersect_area_all, pred_area_all,
                                        label_area_all)
@@ -113,11 +115,14 @@ def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
     return miou
 
 
-def reader_wrapper(reader):
+def reader_wrapper(reader, input_name):
+    if isinstance(input_name, list) and len(input_name) == 1:
+        input_name = input_name[0]
+
     def gen():
         for i, data in enumerate(reader()):
             imgs = np.array(data[0])
-            yield {"x": imgs}
+            yield {input_name: imgs}
 
     return gen
 
@@ -139,9 +144,10 @@ def main(args):
     train_dataset = data_cfg.train_dataset
     global eval_dataset
     eval_dataset = data_cfg.val_dataset
+    batch_size = config.get('batch_size')
     batch_sampler = paddle.io.DistributedBatchSampler(
         train_dataset,
-        batch_size=data_cfg.batch_size,
+        batch_size=batch_size if batch_size else data_cfg.batch_size,
         shuffle=True,
         drop_last=True)
     train_loader = paddle.io.DataLoader(
@@ -151,7 +157,10 @@ def main(args):
         num_workers=0,
         return_list=True,
         worker_init_fn=worker_init_fn)
-    train_dataloader = reader_wrapper(train_loader)
+
+    input_name = get_feed_vars(config['model_dir'], config['model_filename'],
+                               config['params_filename'])
+    train_dataloader = reader_wrapper(train_loader, input_name)
 
     nranks = paddle.distributed.get_world_size()
     rank_id = paddle.distributed.get_rank()

paddleslim/auto_compression/utils/predict.py

@@ -5,6 +5,7 @@ from ...analysis import TableLatencyPredictor
 from .prune_model import get_sparse_model, get_prune_model
 from .fake_ptq import post_quant_fake
 from ...common.load_model import load_inference_model
+import platform
 
 
 def with_variable_shape(model_dir, model_filename=None, params_filename=None):