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optimize channelprune in ac (PaddlePaddle#1564)
* optimize prune in ac
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docs/zh_cn/api_cn/static/auto-compression/auto_compression_api.rst
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example/auto_compression/prune_sensitivity_analysis/README.md
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| # 结构化剪枝敏感度分析 | ||
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| 本示例将以自动压缩示例中MobileNetV1为例，介绍如何快速修改示例代码，进行结构化剪枝敏感度分析工具分析模型参数敏感度，从而设置合适的剪枝比例和要剪枝的参数，在保证剪枝后模型精度的前提下进行最大比例的模型剪枝。 | ||
| 图像分类除MobileNetV1模型外其他模型的结构化剪枝敏感度分析可以直接使用 [run.py](./run.py) 脚本，替换传入的 config_path 文件为其他模型的任一压缩yaml文件，即可对其他图像分类模型进行敏感度分析。 | ||
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| ## 计算通道剪枝敏感度 | ||
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| 以下为示例代码每一步的含义，如果您是ACT（自动压缩工具）的用户，加粗文字表示如何把一个自动压缩示例改为一个敏感度分析示例。 | ||
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| ### 1. 引入依赖 | ||
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| 引入一些需要的依赖，可以直接复用以下代码，如果您需要对其他场景下模型进行敏感度分析，需要把其他场景文件下中 ``run.py`` 文件中独有的依赖也导入进来。**或者把最后一个依赖放入自动压缩示例代码中。** | ||
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| ```python | ||
| import os | ||
| import sys | ||
| import argparse | ||
| import pickle | ||
| import functools | ||
| from functools import partial | ||
| import math | ||
| from tqdm import tqdm | ||
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| import numpy as np | ||
| import paddle | ||
| import paddle.nn as nn | ||
| from paddle.io import DataLoader | ||
| import paddleslim | ||
| from imagenet_reader import ImageNetDataset | ||
| from paddleslim.common import load_config as load_slim_config | ||
| from paddleslim.auto_compression.analysis import analysis_prune | ||
| ``` | ||
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| ### 2. 定义可传入参数 | ||
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| 定义一些可以通过指令传入的参数。**此段代码无论您想对任何场景的模型进行分析都无需修改，复制过去替换原本的指令即可** | ||
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| ```python | ||
| def argsparser(): | ||
| parser = argparse.ArgumentParser(description=__doc__) | ||
| parser.add_argument( | ||
| '--config_path', | ||
| type=str, | ||
| default=None, | ||
| help="path of compression strategy config.", | ||
| required=True) | ||
| parser.add_argument( | ||
| '--analysis_file', | ||
| type=str, | ||
| default='sensitivity_0.data', | ||
| help="directory to save compressed model.") | ||
| parser.add_argument( | ||
| '--pruned_ratios', | ||
| nargs='+', | ||
| type=float, | ||
| default=[0.1, 0.2, 0.3, 0.4], | ||
| help="The ratios to be pruned when compute sensitivity.") | ||
| parser.add_argument( | ||
| '--target_loss', | ||
| type=float, | ||
| default=0.2, | ||
| help="use the target loss to get prune ratio of each parameter") | ||
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| return parser | ||
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| ``` | ||
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| ### 3. 定义eval_function | ||
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| 需要定义完整的测试流程，可以直接使用对应场景文件夹下 ``run.py`` 文件中的测试流程即可，**把自动压缩示例代码中测试回调函数中下面这一行代码:** | ||
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| ```python | ||
| def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list): | ||
| ``` | ||
| **修改成：** | ||
| ```python | ||
| def eval_function(compiled_test_program, exe, test_feed_names, test_fetch_list): | ||
| ``` | ||
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| 最终的测试过程代码如下: | ||
| ```python | ||
| def eval_reader(data_dir, batch_size, crop_size, resize_size, place=None): | ||
| val_reader = ImageNetDataset( | ||
| mode='val', | ||
| data_dir=data_dir, | ||
| crop_size=crop_size, | ||
| resize_size=resize_size) | ||
| val_loader = DataLoader( | ||
| val_reader, | ||
| places=[place] if place is not None else None, | ||
| batch_size=global_config['batch_size'], | ||
| shuffle=False, | ||
| drop_last=False, | ||
| num_workers=0) | ||
| return val_loader | ||
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| def eval_function(compiled_test_program, exe, test_feed_names, test_fetch_list): | ||
| val_loader = eval_reader( | ||
| global_config['data_dir'], | ||
| batch_size=global_config['batch_size'], | ||
| crop_size=img_size, | ||
| resize_size=resize_size) | ||
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| results = [] | ||
| with tqdm( | ||
| total=len(val_loader), | ||
| bar_format='Evaluation stage, Run batch:|{bar}| {n_fmt}/{total_fmt}', | ||
| ncols=80) as t: | ||
| for batch_id, (image, label) in enumerate(val_loader): | ||
| # top1_acc, top5_acc | ||
| if len(test_feed_names) == 1: | ||
| image = np.array(image) | ||
| label = np.array(label).astype('int64') | ||
| pred = exe.run(compiled_test_program, | ||
| feed={test_feed_names[0]: image}, | ||
| fetch_list=test_fetch_list) | ||
| pred = np.array(pred[0]) | ||
| label = np.array(label) | ||
| sort_array = pred.argsort(axis=1) | ||
| top_1_pred = sort_array[:, -1:][:, ::-1] | ||
| top_1 = np.mean(label == top_1_pred) | ||
| top_5_pred = sort_array[:, -5:][:, ::-1] | ||
| acc_num = 0 | ||
| for i in range(len(label)): | ||
| if label[i][0] in top_5_pred[i]: | ||
| acc_num += 1 | ||
| top_5 = float(acc_num) / len(label) | ||
| results.append([top_1, top_5]) | ||
| else: | ||
| # eval "eval model", which inputs are image and label, output is top1 and top5 accuracy | ||
| image = np.array(image) | ||
| label = np.array(label).astype('int64') | ||
| result = exe.run(compiled_test_program, | ||
| feed={ | ||
| test_feed_names[0]: image, | ||
| test_feed_names[1]: label | ||
| }, | ||
| fetch_list=test_fetch_list) | ||
| result = [np.mean(r) for r in result] | ||
| results.append(result) | ||
| t.update() | ||
| result = np.mean(np.array(results), axis=0) | ||
| return result[0] | ||
| ``` | ||
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| ### 4. 加载配置文件 | ||
| 加载配置文件，获得文件中数据读取部分的相关配置。**使用原始的自动压缩示例代码中的即可** | ||
| ```python | ||
| global global_config | ||
| all_config = load_slim_config(args.config_path) | ||
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| assert "Global" in all_config, f"Key 'Global' not found in config file. \n{all_config}" | ||
| global_config = all_config["Global"] | ||
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| global img_size, resize_size | ||
| img_size = global_config['img_size'] if 'img_size' in global_config else 224 | ||
| resize_size = global_config[ | ||
| 'resize_size'] if 'resize_size' in global_config else 256 | ||
| ``` | ||
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| ### 4. 进行敏感度分析 | ||
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| 传入测试回调函数，配置（主要包括模型位置和模型名称等信息），分析文件保存的位置，要分析的裁剪比例和可以接受的精度目标损失。如果不传入可以接受的精度目标损失，则只返回敏感度分析情况。**把自动压缩代码中调用AutoCompression 和 ac.compress 的代码替换成以下代码即可** | ||
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| ```python | ||
| analysis_prune(eval_function, global_config['model_dir'], global_config['model_filename'], global_config['params_filename'], args.analysis_file, | ||
| args.pruned_ratios, args.target_loss) | ||
| ``` |
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