Add latency predictor function and doc (#905)

Author: ZichaoGuo

demo/analysis/latency_predictor.py

@@ -0,0 +1,63 @@
+import os
+import subprocess
+import argparse
+
+import paddle
+from paddleslim.analysis import TableLatencyPredictor
+
+from paddle.vision.models import mobilenet_v1, mobilenet_v2
+
+opt_tool = 'opt_ubuntu'  # use in linux
+# opt_tool = 'opt_M1_mac'     # use in mac with M1 chip
+# opt_tool = 'opt_intel_mac'  # use in mac with intel chip
+
+parser = argparse.ArgumentParser(description='latency predictor')
+parser.add_argument('--model', type=str, help='which model to test.')
+parser.add_argument('--data_type', type=str, default='fp32')
+
+args = parser.parse_args()
+
+if not os.path.exists(opt_tool):
+    subprocess.call(
+        f'wget https://paddle-slim-models.bj.bcebos.com/LatencyPredictor/{opt_tool}',
+        shell=True)
+    subprocess.call(f'chmod +x {opt_tool}', shell=True)
+
+
+def get_latency(model, data_type):
+    paddle.disable_static()
+    predictor = TableLatencyPredictor(
+        f'./{opt_tool}', hardware='845', threads=4, power_mode=3, batchsize=1)
+    latency = predictor.predict_latency(
+        model,
+        input_shape=[1, 3, 224, 224],
+        save_dir='./tmp_model',
+        data_type=data_type,
+        task_type='cls')
+    print('{} latency : {}'.format(data_type, latency))
+
+    subprocess.call('rm -rf ./tmp_model', shell=True)
+    paddle.disable_static()
+    return latency
+
+
+if __name__ == '__main__':
+    if args.model == 'mobilenet_v1':
+        model = mobilenet_v1()
+    elif args.model == 'mobilenet_v2':
+        model = mobilenet_v2()
+    else:
+        assert False, f'model should be mobilenet_v1 or mobilenet_v2'
+
+    latency = get_latency(model, args.data_type)
+
+    if args.model == 'mobilenet_v1' and args.data_type == 'fp32':
+        assert latency == 41.92806607483133
+    elif args.model == 'mobilenet_v1' and args.data_type == 'int8':
+        assert latency == 36.64814722993898
+    elif args.model == 'mobilenet_v2' and args.data_type == 'fp32':
+        assert latency == 27.847896889217566
+    elif args.model == 'mobilenet_v2' and args.data_type == 'int8':
+        assert latency == 23.967800360138803
+    else:
+        assert False, f'model or data_type wrong.'

docs/zh_cn/tutorials/analysis/dygraph/index.rst

@@ -0,0 +1,8 @@
+
+动态图
+==============
+
+.. toctree::
+   :maxdepth: 1
+
+   latency_predictor.md

docs/zh_cn/tutorials/analysis/dygraph/latency_predictor.md

@@ -0,0 +1,35 @@
+# LatencyPredictor使用教程
+
+LatencyPredictor主要功能是根据提供的op-latency映射表，预估神经网络网络在特定硬件设备上的实际耗时。它基于Paddle-Lite开发，适用于使用Paddle-Lite部署的模型。映射表以key-value的形式存储，key包含了神经网络模型经过Paddle-Lite图优化后的各种融合op信息，value则代表在特定硬件上的实际耗时。
+
+## 使用方法
+
+1. 下载或自行编译opt优化工具
+2. 构建LatencyPredictor
+3. 定义模型和预测
+
+### 1. 下载或自行编译opt优化工具
+1.1 下载提供的opt工具，可根据运行环境下载适用的opt，目前提供Mac平台([M1芯片](https://paddle-slim-models.bj.bcebos.com/LatencyPredictor/opt_M1_mac)，[Intel芯片](https://paddle-slim-models.bj.bcebos.com/LatencyPredictor/opt_intel_mac))和[Ubuntu](https://paddle-slim-models.bj.bcebos.com/LatencyPredictor/opt_ubuntu)平台的opt工具下载。
+1.2 也可以自行通过Paddle-Lite源码编译opt工具，具体请参考请参考Paddle-Lite[文档](https://paddle-lite.readthedocs.io/zh/latest/user_guides/model_optimize_tool.html)。编译时需要关闭Paddle-Lite的内存复用功能，即注释掉这[几行代码](https://github.com/PaddlePaddle/Paddle-Lite/blob/d76f45be989d3e01cebf2ac18e047cfd37d52666/lite/core/optimizer/optimizer.cc#L266-L268)。
+
+### 2. 构建LatencyPredictor
+
+提供opt工具路径，以及芯片和测试参数信息，LatencyPredictor会根据这些参数自动下载对应的映射表。如下所示，芯片为845芯片，测试线程数threads为4，测速模式power_mode为3，测试batchsize为1.
+```
+import paddleslim
+
+opt_path = {opt工具路径}
+predictor = paddleslim.TableLatencyPredictor(opt_path, hardware='845', threads=4, power_mode=3, batchsize=1)
+```
+
+### 3. 定义模型和预测
+
+定义model后可通过predict_latency函数直接预测模型推理耗时，其中，input_shape为输入大小，save_dir为中间pbmodel模型保存路径，data_type可选fp32或int8，task_type=‘cls'表示该模型为分类模型。
+```
+import paddle
+from paddle.vision.models import mobilenet_v1
+
+model = mobilenet_v1()
+latency = predictor.predict_latency(model, input_shape=[1,3,224,224], save_dir='./model', data_type='int8', task_type='cls')
+print('predicted latency = {}ms'.format(latency))
+```

paddleslim/analysis/__init__.py

@@ -14,8 +14,19 @@
 from .flops import flops, dygraph_flops
 from .model_size import model_size
 from .latency import LatencyEvaluator, TableLatencyEvaluator
+from .latency_predictor import LatencyPredictor, TableLatencyPredictor
+from ._utils import get_key_from_op, save_cls_model, save_det_model, save_seg_model
 
 __all__ = [
-    'flops', 'dygraph_flops', 'model_size', 'LatencyEvaluator',
-    'TableLatencyEvaluator'
+    'flops',
+    'dygraph_flops',
+    'model_size',
+    'LatencyEvaluator',
+    'TableLatencyEvaluator',
+    "LatencyPredictor",
+    "TableLatencyPredictor",
+    "get_key_from_op",
+    "save_cls_model",
+    "save_det_model",
+    "save_seg_model",
 ]