add demo for auto-compress (#1078)

demo/auto_compression/pp-humanseg/README.md

+# 使用预测模型进行自动压缩示例
+
+本示例将介绍如何使用PaddleSeg中预测模型进行自动压缩训练。
+
+以[PP-HumanSeg-Lite](https://github.com/PaddlePaddle/PaddleSeg/tree/develop/contrib/PP-HumanSeg#portrait-segmentation)模型为例，使用自动压缩接口分别进行了蒸馏稀疏训练和蒸馏量化训练实验，并在SD710上使用单线程测试加速效果，其压缩结果和测速结果如下所示：
+| 压缩方式  | Total IoU | 耗时(ms)<br>thread=1 | 加速比 |
+|:-----:|:----------:|:---------:| :------:|
+| Baseline |  0.9287 | 56.363 | - |
+| 非结构化稀疏 |  0.9235 | 37.712 | 49.456% |
+| 量化 |  0.9284 | 49.656 | 13.506% |
+
+## 自动压缩训练流程
+
+### 1. 准备数据集
+
+参考[PaddleSeg数据准备文档](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.5/docs/data/marker/marker_cn.md)
+
+### 2. 准备待压缩模型
+
+PaddleSeg 是基于飞桨 PaddlePaddle 开发的端到端图像分割开发套件，涵盖了高精度和轻量级等不同方向的大量高质量分割模型。
+安装 PaddleSeg 指令如下：
+```
+pip install paddleseg
+```
+PaddleSeg 环境依赖详见[安装文档](https://github.com/PaddlePaddle/PaddleSeg/blob/develop/docs/install_cn.md)。
+
+#### 2.1 下载代码
+```
+git clone https://github.com/PaddlePaddle/PaddleSeg.git
+```
+#### 2.2 准备预训练模型
+
+在 PaddleSeg 目录下执行如下指令，下载预训练模型。
+``` shell
+wget https://paddleseg.bj.bcebos.com/dygraph/ppseg/ppseg_lite_portrait_398x224.tar.gz
+tar -xzf ppseg_lite_portrait_398x224.tar.gz
+```
+#### 2.3 导出预测模型
+
+在 PaddleSeg 目录下执行如下命令，则预测模型会保存在 inference_model 文件夹。
+```shell
+# 设置1张可用的卡
+export CUDA_VISIBLE_DEVICES=0
+# windows下请执行以下命令
+# set CUDA_VISIBLE_DEVICES=0
+python export.py \
+       --config configs/pp_humanseg_lite/pp_humanseg_lite_export_398x224.yml \
+       --model_path ppseg_lite_portrait_398x224/model.pdparams \
+       --save_dir inference_model
+       --with_softmax
+```
+或直接下载 PP-HumanSeg-Lite 的预测模型：
+```shell
+wegt https://paddleseg.bj.bcebos.com/dygraph/ppseg/ppseg_lite_portrait_398x224_with_softmax.tar.gz
+tar -xzf ppseg_lite_portrait_398x224_with_softmax.tar.gz
+```
+
+### 3. 多策略融合压缩
+
+每一个小章节代表一种多策略融合压缩方式。
+
+### 3.1 进行蒸馏稀疏压缩
+自动压缩训练需要准备 config 文件、数据集 dataloader 以及测试函数（``eval_function``）。
+#### 3.1.1 配置config
+
+使用自动压缩进行蒸馏和非结构化稀疏的联合训练，首先要配置 config 文件，包含蒸馏、稀疏和训练三部分参数。
+
+- 蒸馏参数
+
+蒸馏参数主要设置蒸馏节点（``distill_node_pair``）和教师网络测预测模型路径。蒸馏节点需包含教师网络节点和对应的学生网络节点，其中教师网络节点名称将在程序中自动添加 “teacher_” 前缀，如下所示。
+```yaml
+Distillation:
+  distill_lambda: 1.0
+  distill_loss: l2_loss
+  distill_node_pair:
+  - teacher_relu_30.tmp_0
+  - relu_30.tmp_0
+  merge_feed: true
+  teacher_model_dir: ./inference_model
+  teacher_model_filename: model.pdmodel
+  teacher_params_filename: model.pdiparams
+```
+- 稀疏参数
+
+稀疏参数设置如下所示，其中参数含义详见[非结构化稀疏API文档](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/api_cn/dygraph/pruners/unstructured_pruner.rst)。
+```yaml
+UnstructurePrune:
+  prune_strategy: gmp
+  prune_mode: ratio
+  pruned_ratio: 0.75
+  gmp_config:
+    stable_iterations: 0
+    pruning_iterations: 4500
+    tunning_iterations: 4500
+    resume_iteration: -1
+    pruning_steps: 100
+    initial_ratio: 0.15
+  prune_params_type: conv1x1_only
+  local_sparsity: True
+```
+
+- 训练参数
+
+训练参数主要设置学习率、训练次数（epochs）和优化器等。
+```yaml
+TrainConfig:
+  epochs: 14
+  eval_iter: 400
+  learning_rate: 5.0e-03
+  optimizer: SGD
+  optim_args:
+    weight_decay: 0.0005
+```
+#### 3.1.2 准备 dataloader 和测试函数
+准备好数据集后，需将训练数据封装成 dict 类型传入自动压缩接口，可参考以下函数进行封装。测试函数用于测试模型精度，需在静态图模式下实现。
+```python
+def reader_wrapper(reader):
+    def gen():
+        for i, data in enumerate(reader()):
+            imgs = np.array(data[0])
+            yield {"x": imgs}
+    return gen
+```
+> 注：该dict类型的key值要和保存预测模型时的输入名称保持一致。
+
+#### 3.1.3 开启训练
+
+将训练数据集 dataloader 和测试函数传入接口 ``paddleslim.auto_compression.AutoCompression``，对模型进行非结构化稀疏训练。运行指令如下：
+```shell
+python run.py \
+    --model_dir='inference_model' \
+    --model_filename='inference.pdmodel' \
+    --params_filename='./inference.pdiparams' \
+    --save_dir='./save_model' \
+    --config_path='configs/humanseg_sparse_dis.yaml'
+```
+
+### 3.2 进行蒸馏量化压缩
+#### 3.2.1 配置config
+使用自动压缩进行量化训练，首先要配置config文件，包含蒸馏、量化和训练三部分参数。其中蒸馏和训练参数与稀疏训练类似，下面主要介绍量化参数的设置。
+- 量化参数
+
+量化参数主要设置量化比特数和量化op类型，其中量化op包含卷积层（conv2d, depthwise_conv2d）和全连接层（matmul）。以下为只量化卷积层的示例：
+```yaml
+Quantization:
+  activation_bits: 8
+  weight_bits: 8
+  is_full_quantize: false
+  not_quant_pattern:
+  - skip_quant
+  quantize_op_types:
+  - conv2d
+  - depthwise_conv2d
+```
+#### 3.2.2 开启训练
+将数据集 dataloader 和测试函数（``eval_function``）传入接口``paddleslim.auto_compression.AutoCompression``，对模型进行量化训练。运行指令如下：
+```
+python run.py \
+    --model_dir='inference_model' \
+    --model_filename='inference.pdmodel' \
+    --params_filename='./inference.pdiparams' \
+    --save_dir='./save_model' \
+    --config_path='configs/humanseg_quant_dis.yaml'
+```

demo/auto_compression/pp-humanseg/configs/humanseg_quant_dis.yaml

+Distillation:
+  distill_lambda: 1.0
+  distill_loss: l2_loss
+  distill_node_pair:
+  - teacher_reshape2_1.tmp_0 #
+  - reshape2_1.tmp_0
+  - teacher_reshape2_3.tmp_0 #
+  - reshape2_3.tmp_0
+  - teacher_reshape2_5.tmp_0 #
+  - reshape2_5.tmp_0
+  - teacher_reshape2_7.tmp_0 #block1
+  - reshape2_7.tmp_0
+  - teacher_reshape2_9.tmp_0 #
+  - reshape2_9.tmp_0
+  - teacher_reshape2_11.tmp_0 #
+  - reshape2_11.tmp_0
+  - teacher_reshape2_13.tmp_0 #
+  - reshape2_13.tmp_0
+  - teacher_reshape2_15.tmp_0 #
+  - reshape2_15.tmp_0
+  - teacher_reshape2_17.tmp_0 #
+  - reshape2_17.tmp_0
+  - teacher_reshape2_19.tmp_0 #
+  - reshape2_19.tmp_0
+  - teacher_reshape2_21.tmp_0 #
+  - reshape2_21.tmp_0
+  - teacher_depthwise_conv2d_14.tmp_0 # block2
+  - depthwise_conv2d_14.tmp_0
+  - teacher_depthwise_conv2d_15.tmp_0
+  - depthwise_conv2d_15.tmp_0
+  - teacher_reshape2_23.tmp_0 #block1
+  - reshape2_23.tmp_0
+  - teacher_relu_30.tmp_0 # final_conv
+  - relu_30.tmp_0 
+  - teacher_bilinear_interp_v2_1.tmp_0
+  - bilinear_interp_v2_1.tmp_0
+  merge_feed: true
+  teacher_model_dir: ./inference_model
+  teacher_model_filename: inference.pdmodel
+  teacher_params_filename: inference.pdiparams
+Quantization:
+  activation_bits: 8
+  is_full_quantize: false
+  not_quant_pattern:
+  - skip_quant
+  quantize_op_types:
+  - conv2d
+  - depthwise_conv2d
+  weight_bits: 8
+TrainConfig:
+  epochs: 1
+  eval_iter: 400
+  learning_rate: 0.0005
+  optimizer: SGD
+  optim_args:
+    weight_decay: 4.0e-05
\ No newline at end of file

demo/auto_compression/pp-humanseg/configs/humanseg_sparse_dis.yaml

+Distillation:
+  distill_lambda: 1.0
+  distill_loss: l2_loss
+  distill_node_pair: 
+  - teacher_reshape2_1.tmp_0 
+  - reshape2_1.tmp_0
+  - teacher_reshape2_3.tmp_0 
+  - reshape2_3.tmp_0
+  - teacher_reshape2_5.tmp_0 
+  - reshape2_5.tmp_0
+  - teacher_reshape2_7.tmp_0 #block1
+  - reshape2_7.tmp_0
+  - teacher_reshape2_9.tmp_0 
+  - reshape2_9.tmp_0
+  - teacher_reshape2_11.tmp_0 
+  - reshape2_11.tmp_0
+  - teacher_reshape2_13.tmp_0 
+  - reshape2_13.tmp_0
+  - teacher_reshape2_15.tmp_0 
+  - reshape2_15.tmp_0
+  - teacher_reshape2_17.tmp_0 
+  - reshape2_17.tmp_0
+  - teacher_reshape2_19.tmp_0 
+  - reshape2_19.tmp_0
+  - teacher_reshape2_21.tmp_0 
+  - reshape2_21.tmp_0
+  - teacher_depthwise_conv2d_14.tmp_0 # block2
+  - depthwise_conv2d_14.tmp_0
+  - teacher_depthwise_conv2d_15.tmp_0
+  - depthwise_conv2d_15.tmp_0
+  - teacher_reshape2_23.tmp_0 #block1
+  - reshape2_23.tmp_0
+  - teacher_relu_30.tmp_0 # final_conv
+  - relu_30.tmp_0 
+  - teacher_bilinear_interp_v2_1.tmp_0
+  - bilinear_interp_v2_1.tmp_0
+  merge_feed: true
+  teacher_model_dir: ./inference_model
+  teacher_model_filename: inference.pdmodel
+  teacher_params_filename: inference.pdiparams
+UnstructurePrune:
+  prune_strategy: gmp
+  prune_mode: ratio
+  pruned_ratio: 0.75
+  gmp_config: 
+    stable_iterations: 0
+    pruning_iterations: 4500
+    tunning_iterations: 4500
+    resume_iteration: -1
+    pruning_steps: 100
+    initial_ratio: 0.15
+  prune_params_type: conv1x1_only
+  local_sparsity: True
+TrainConfig:
+  epochs: 14
+  eval_iter: 400
+  learning_rate: 5.0e-03
+  optim_args:
+    weight_decay: 0.0005
+  optimizer: SGD
+  
\ No newline at end of file

demo/auto_compression/pp-humanseg/run.py

+import os
+import argparse
+import random
+import paddle
+import numpy as np
+import paddleseg.transforms as T
+from paddleseg.datasets import Dataset
+from paddleseg.utils import worker_init_fn
+from paddleslim.auto_compression.config_helpers import load_config
+from paddleslim.auto_compression import AutoCompression
+
+from paddleseg.core.infer import reverse_transform
+from paddleseg.utils import metrics
+import paddle.nn.functional as F
+import cv2
+import paddle.fluid as fluid
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Model training')
+    parser.add_argument(
+        '--model_dir',
+        type=str,
+        default=None,
+        help="inference model directory.")
+    parser.add_argument(
+        '--model_filename',
+        type=str,
+        default=None,
+        help="inference model filename.")
+    parser.add_argument(
+        '--params_filename',
+        type=str,
+        default=None,
+        help="inference params filename.")
+    parser.add_argument(
+        '--save_dir',
+        type=str,
+        default=None,
+        help="directory to save compressed model.")
+    parser.add_argument(
+        '--config_path',
+        type=str,
+        default=None,
+        help="path of compression strategy config.")
+    return parser.parse_args()
+
+
+def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
+
+    nranks = paddle.distributed.ParallelEnv().local_rank
+
+    batch_sampler = paddle.io.DistributedBatchSampler(
+        eval_dataset, batch_size=1, shuffle=False, drop_last=False)
+    loader = paddle.io.DataLoader(
+        eval_dataset,
+        batch_sampler=batch_sampler,
+        num_workers=1,
+        return_list=True, )
+
+    total_iters = len(loader)
+    intersect_area_all = 0
+    pred_area_all = 0
+    label_area_all = 0
+
+    print("Start evaluating (total_samples: {}, total_iters: {})...".format(
+        len(eval_dataset), total_iters))
+    print("nranks:", nranks)
+
+    for iter, (image, label) in enumerate(loader):
+        paddle.enable_static()
+
+        label = np.array(label).astype('int64')
+        ori_shape = np.array(label).shape[-2:]
+
+        image = np.array(image)
+        logits = exe.run(compiled_test_program,
+                         feed={test_feed_names[0]: image},
+                         fetch_list=test_fetch_list,
+                         return_numpy=True)
+
+        paddle.disable_static()
+        logit = logits[0]
+
+        logit = reverse_transform(
+            paddle.to_tensor(logit),
+            ori_shape,
+            eval_dataset.transforms.transforms,
+            mode='bilinear')
+
+        pred = paddle.argmax(
+            paddle.to_tensor(logit), axis=1, keepdim=True, dtype='int32')
+
+        intersect_area, pred_area, label_area = metrics.calculate_area(
+            pred,
+            paddle.to_tensor(label),
+            eval_dataset.num_classes,
+            ignore_index=eval_dataset.ignore_index)
+
+        if nranks > 1:
+            intersect_area_list = []
+            pred_area_list = []
+            label_area_list = []
+            paddle.distributed.all_gather(intersect_area_list, intersect_area)
+            paddle.distributed.all_gather(pred_area_list, pred_area)
+            paddle.distributed.all_gather(label_area_list, label_area)
+
+            # Some image has been evaluated and should be eliminated in last iter
+            if (iter + 1) * nranks > len(eval_dataset):
+                valid = len(eval_dataset) - iter * nranks
+                intersect_area_list = intersect_area_list[:valid]
+                pred_area_list = pred_area_list[:valid]
+                label_area_list = label_area_list[:valid]
+
+            for i in range(len(intersect_area_list)):
+                intersect_area_all = intersect_area_all + intersect_area_list[i]
+                pred_area_all = pred_area_all + pred_area_list[i]
+                label_area_all = label_area_all + label_area_list[i]
+        else:
+            intersect_area_all = intersect_area_all + intersect_area
+            pred_area_all = pred_area_all + pred_area
+            label_area_all = label_area_all + label_area
+
+    class_iou, miou = metrics.mean_iou(intersect_area_all, pred_area_all,
+                                       label_area_all)
+    class_acc, acc = metrics.accuracy(intersect_area_all, pred_area_all)
+    kappa = metrics.kappa(intersect_area_all, pred_area_all, label_area_all)
+    class_dice, mdice = metrics.dice(intersect_area_all, pred_area_all,
+                                     label_area_all)
+
+    infor = "[EVAL] #Images: {} mIoU: {:.4f} Acc: {:.4f} Kappa: {:.4f} Dice: {:.4f}".format(
+        len(eval_dataset), miou, acc, kappa, mdice)
+    print(infor)
+
+    paddle.enable_static()
+    return miou
+
+
+def reader_wrapper(reader):
+    def gen():
+        for i, data in enumerate(reader()):
+            imgs = np.array(data[0])
+            yield {"x": imgs}
+
+    return gen
+
+
+if __name__ == '__main__':
+
+    args = parse_args()
+
+    transforms = [T.RandomPaddingCrop(crop_size=(512, 512)), T.Normalize()]
+    train_dataset = Dataset(
+        transforms=transforms,
+        dataset_root='dataset_root',  # Need to fill in
+        num_classes=2,
+        train_path='train_path',  # Need to fill in
+        mode='train')
+    eval_dataset = Dataset(
+        transforms=transforms,
+        dataset_root='dataset_root',  # Need to fill in
+        num_classes=2,
+        train_path='val_path',  # Need to fill in
+        mode='val')
+
+    batch_sampler = paddle.io.DistributedBatchSampler(
+        train_dataset, batch_size=128, shuffle=True, drop_last=True)
+
+    train_loader = paddle.io.DataLoader(
+        train_dataset,
+        batch_sampler=batch_sampler,
+        num_workers=2,
+        return_list=True,
+        worker_init_fn=worker_init_fn, )
+    train_dataloader = reader_wrapper(train_loader)
+
+    # set auto_compression
+    compress_config, train_config = load_config(args.config_path)
+
+    ac = AutoCompression(
+        model_dir=args.model_dir,
+        model_filename=args.model_filename,
+        params_filename=args.param_filename,
+        save_dir=args.save_dir,
+        strategy_config=compress_config,
+        train_config=train_config,
+        train_dataloader=train_dataloader,
+        eval_callback=eval_function)
+
+    ac.compress()