fix bugs (PaddlePaddle#110)

lizexu123 · Feb 13, 2020 · a7b93e0 · a7b93e0
1 parent ea24d10
commit a7b93e0
Show file tree

Hide file tree

Showing 6 changed files with 76 additions and 42 deletions.
diff --git a/demo/nas/block_sa_nas_mobilenetv2.py b/demo/nas/block_sa_nas_mobilenetv2.py
@@ -16,13 +16,6 @@
 
 _logger = get_logger(__name__, level=logging.INFO)
 
-reduce_rate = 0.85
-init_temperature = 10.24
-max_flops = 321208544
-server_address = ""
-port = 8979
-retain_epoch = 5
-
 
 def create_data_loader(image_shape):
     data_shape = [None] + image_shape
@@ -71,17 +64,13 @@ def search_mobilenetv2_block(config, args, image_size):
     if args.is_server:
         sa_nas = SANAS(
             config,
-            server_addr=("", port),
-            init_temperature=init_temperature,
-            reduce_rate=reduce_rate,
+            server_addr=(args.server_address, args.port),
             search_steps=args.search_steps,
             is_server=True)
     else:
         sa_nas = SANAS(
             config,
-            server_addr=(server_address, port),
-            init_temperature=init_temperature,
-            reduce_rate=reduce_rate,
+            server_addr=(args.server_address, args.port),
             search_steps=args.search_steps,
             is_server=False)
 
@@ -140,7 +129,7 @@ def search_mobilenetv2_block(config, args, image_size):
 
         current_flops = flops(train_program)
         print('step: {}, current_flops: {}'.format(step, current_flops))
-        if current_flops > max_flops:
+        if current_flops > int(321208544):
             continue
 
         place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
@@ -178,7 +167,7 @@ def search_mobilenetv2_block(config, args, image_size):
         train_compiled_program = fluid.CompiledProgram(
             train_program).with_data_parallel(
                 loss_name=avg_cost.name, build_strategy=build_strategy)
-        for epoch_id in range(retain_epoch):
+        for epoch_id in range(args.retain_epoch):
             for batch_id, data in enumerate(train_loader()):
                 fetches = [avg_cost.name]
                 s_time = time.time()
@@ -243,6 +232,11 @@ def search_mobilenetv2_block(config, args, image_size):
         type=int,
         default=100,
         help='controller server number.')
+    parser.add_argument(
+        '--server_address', type=str, default="", help='server ip.')
+    parser.add_argument('--port', type=int, default=8881, help='server port')
+    parser.add_argument(
+        '--retain_epoch', type=int, default=5, help='epoch for each token.')
     parser.add_argument('--lr', type=float, default=0.1, help='learning rate.')
     args = parser.parse_args()
     print(args)
@@ -257,7 +251,7 @@ def search_mobilenetv2_block(config, args, image_size):
                 args.data))
 
     # block mask means block number, 1 mean downsample, 0 means the size of feature map don't change after this block
-    config_info = {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]}
+    config_info = {'block_mask': [0, 1, 1, 1, 0]}
     config = [('MobileNetV2BlockSpace', config_info)]
 
     search_mobilenetv2_block(config, args, image_size)
diff --git a/demo/nas/sa_nas_mobilenetv2.py b/demo/nas/sa_nas_mobilenetv2.py
@@ -18,13 +18,6 @@
 
 _logger = get_logger(__name__, level=logging.INFO)
 
-reduce_rate = 0.85
-init_temperature = 10.24
-max_flops = 321208544
-server_address = ""
-port = 8989
-retain_epoch = 5
-
 
 def create_data_loader(image_shape):
     data_shape = [None] + image_shape
@@ -66,18 +59,14 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
         ### start a server and a client
         sa_nas = SANAS(
             config,
-            server_addr=("", port),
-            init_temperature=init_temperature,
-            reduce_rate=reduce_rate,
+            server_addr=(args.server_address, args.port),
             search_steps=args.search_steps,
             is_server=True)
     else:
         ### start a client
         sa_nas = SANAS(
             config,
-            server_addr=(server_address, port),
-            init_temperature=init_temperature,
-            reduce_rate=reduce_rate,
+            server_addr=(args.server_address, args.port),
             search_steps=args.search_steps,
             is_server=False)
 
@@ -93,7 +82,7 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
 
         current_flops = flops(train_program)
         print('step: {}, current_flops: {}'.format(step, current_flops))
-        if current_flops > max_flops:
+        if current_flops > int(321208544):
             continue
 
         test_loader, test_avg_cost, test_acc_top1, test_acc_top5 = build_program(
@@ -139,7 +128,7 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
         train_compiled_program = fluid.CompiledProgram(
             train_program).with_data_parallel(
                 loss_name=avg_cost.name, build_strategy=build_strategy)
-        for epoch_id in range(retain_epoch):
+        for epoch_id in range(args.retain_epoch):
             for batch_id, data in enumerate(train_loader()):
                 fetches = [avg_cost.name]
                 s_time = time.time()
@@ -179,7 +168,7 @@ def search_mobilenetv2(config, args, image_size, is_server=True):
 def test_search_result(tokens, image_size, args, config):
     sa_nas = SANAS(
         config,
-        server_addr=("", 8887),
+        server_addr=(args.server_address, args.port),
         init_temperature=args.init_temperature,
         reduce_rate=args.reduce_rate,
         search_steps=args.search_steps,
@@ -234,7 +223,7 @@ def test_search_result(tokens, image_size, args, config):
     train_compiled_program = fluid.CompiledProgram(
         train_program).with_data_parallel(
             loss_name=avg_cost.name, build_strategy=build_strategy)
-    for epoch_id in range(retain_epoch):
+    for epoch_id in range(args.retain_epoch):
         for batch_id, data in enumerate(train_loader()):
             fetches = [avg_cost.name]
             s_time = time.time()
@@ -298,6 +287,11 @@ def test_search_result(tokens, image_size, args, config):
         type=int,
         default=100,
         help='controller server number.')
+    parser.add_argument(
+        '--server_address', type=str, default="", help='server ip.')
+    parser.add_argument('--port', type=int, default=8881, help='server port')
+    parser.add_argument(
+        '--retain_epoch', type=int, default=5, help='epoch for each token.')
     parser.add_argument('--lr', type=float, default=0.1, help='learning rate.')
     args = parser.parse_args()
     print(args)

diff --git a/docs/en/model_zoo_en.md b/docs/en/model_zoo_en.md
@@ -16,7 +16,7 @@ Dataset：ImageNet1000
 | MobileNetV2 | quant_aware |72.05%/90.63% (-0.1%/-0.02%)| 4.0 | - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_aware.tar) |
 |ResNet50|-|76.50%/93.00%| 99 | 2.71 | [model](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) |
 |ResNet50|quant_post|76.33%/93.02% (-0.17%/+0.02%)| 25.1| 1.19 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_post.tar) |
-|ResNet50|quant_aware|	76.48%/93.11% (-0.02%/+0.11%)| 25.1 | 1.17 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_awre.tar) |
+|ResNet50|quant_aware|    76.48%/93.11% (-0.02%/+0.11%)| 25.1 | 1.17 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_awre.tar) |
 
 PaddleLite latency(ms)
 
@@ -90,6 +90,13 @@ PaddleLite latency(ms)
     <a name="trans1">[1]</a>：The `_vd` suffix indicates that the pre-trained model uses Mixup. Please refer to the detailed introduction: [mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)
 
 
+### 1.4 NAS
+
+| Model | Method | Top-1/Top-5 Acc | Volume（MB） | GFLOPs | Download |
+|:--:|:---:|:--:|:--:|:--:|:--:|
+| MobileNetV2 |       -        |            72.15%/90.65%           |     15      |  0.59  | [model](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) |
+| MobileNetV2 |     SANAS      |  71.518%/90.208% (-0.632%/-0.442%) |     14      | 0.295  | [model](https://paddlemodels.cdn.bcebos.com/PaddleSlim/MobileNetV2_sanas.tar) |
+
 ## 2. Object Detection
 
 ### 2.1 Quantization
@@ -99,8 +106,8 @@ Dataset： COCO 2017
 |              Model              |  Method  | Dataset | Image/GPU | Input 608 Box AP | Input 416 Box AP | Input 320 Box AP | Model Size（MB） | TensorRT latency(V100, ms) |  Download  |
 | :----------------------------: | :---------: | :----: | :-------: | :------------: | :------------: | :------------: | :------------: | :----------: |:----------: |
 |      MobileNet-V1-YOLOv3       |      -      |  COCO  |     8     |      29.3      |      29.3      |      27.1      |       95       |  - | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) |
-|      MobileNet-V1-YOLOv3       | quant_post  |  COCO  |     8     |     27.9 (-1.4)|	28.0 (-1.3)	  |    26.0 (-1.0) |       25       | -  | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
-|      MobileNet-V1-YOLOv3       | quant_aware |  COCO  |     8     |     28.1 (-1.2)|  28.2 (-1.1)	  |    25.8 (-1.2) |       26.3     | -  | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_coco_quant_aware.tar) |
+|      MobileNet-V1-YOLOv3       | quant_post  |  COCO  |     8     |     27.9 (-1.4)|    28.0 (-1.3)      |    26.0 (-1.0) |       25       | -  | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
+|      MobileNet-V1-YOLOv3       | quant_aware |  COCO  |     8     |     28.1 (-1.2)|  28.2 (-1.1)      |    25.8 (-1.2) |       26.3     | -  | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_coco_quant_aware.tar) |
 |      R34-YOLOv3                |      -      |  COCO  |     8     |      36.2      |      34.3      |      31.4      |       162       |  - | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) |
 |      R34-YOLOv3                | quant_post  |  COCO  |     8     | 35.7 (-0.5)    |      -         |      -         |       42.7      |  - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_post.tar) |
 |      R34-YOLOv3                | quant_aware |  COCO  |     8     |  35.2 (-1.0)   | 33.3 (-1.0)    |     30.3 (-1.1)|       44       |  - | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) |
@@ -157,8 +164,22 @@ Dataset：Pasacl VOC & COCO 2017
 | MobileNet-V1-YOLOv3 | ResNet34-YOLOv3 distill |    COCO    |     8     |   31.4 (+2.1)    |   30.0 (+0.7)    |   27.1 (+0.1)    |        95        | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar) |
 
 
-## 3. Image Segmentation 
+### 2.4 NAS
+
+Dataset: WIDER-FACE
+
+|      Model      |  Method   | Image/GPU | Input size |        Easy/Medium/Hard         |  volume（KB） |    latency（ms）|                         Download                             |
+| :------------: | :---------: | :-------: | :------: | :-----------------------------: | :------------: | :------------: | :----------------------------------------------------------: |
+|   BlazeFace    |      -      |     8     |   640    |         91.5/89.2/79.7          |      815       |       71.862     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) |
+| BlazeFace-NAS  |      -      |     8     |   640    |         83.7/80.7/65.8          |      244       |       21.117     |[model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) |
+| BlazeFace-NAS1 |    SANAS    |     8     |   640    |         87.0/83.7/68.5          |      389       |       22.558     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) |
+
+!!! note "Note"
+
+    <a name="trans1">[1]</a>: latency is based on latency_855.txt, the file is test on 855 by PaddleLite。
+
 
+## 3. Image Segmentation
 Dataset：Cityscapes
 
 ### 3.1 Quantization

diff --git a/docs/zh_cn/api_cn/nas_api.rst b/docs/zh_cn/api_cn/nas_api.rst
@@ -128,7 +128,7 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火
    - **tokens(list):** - 一组tokens。tokens的长度和范围取决于搜索空间。
 
    **返回：**
-   根据传入的token得到一个模型结构实例。
+   根据传入的token得到一个模型结构实例列表。
 
    **示例代码：**
 
@@ -153,8 +153,10 @@ SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火
    **示例代码：**
 
    .. code-block:: python
+
       import paddle.fluid as fluid
       from paddleslim.nas import SANAS
       config = [('MobileNetV2Space')]
       sanas = SANAS(configs=config)
       print(sanas.current_info())
+
diff --git a/docs/zh_cn/model_zoo.md b/docs/zh_cn/model_zoo.md
@@ -16,7 +16,7 @@
 | MobileNetV2 | quant_aware |72.05%/90.63% (-0.1%/-0.02%)| 4.0 | - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_aware.tar) |
 |ResNet50|-|76.50%/93.00%| 99 | 2.71 | [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) |
 |ResNet50|quant_post|76.33%/93.02% (-0.17%/+0.02%)| 25.1| 1.19 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_post.tar) |
-|ResNet50|quant_aware|	76.48%/93.11% (-0.02%/+0.11%)| 25.1 | 1.17 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_awre.tar) |
+|ResNet50|quant_aware|    76.48%/93.11% (-0.02%/+0.11%)| 25.1 | 1.17 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_awre.tar) |
 
 分类模型Lite时延(ms)
 
@@ -89,6 +89,12 @@
 
     <a name="trans1">[1]</a>：带_vd后缀代表该预训练模型使用了Mixup，Mixup相关介绍参考[mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)
 
+### 1.4 搜索
+
+| 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积（MB） | GFLOPs | 下载 |
+|:--:|:---:|:--:|:--:|:--:|:--:|
+| MobileNetV2 |       -        |            72.15%/90.65%           |     15      |  0.59  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) |
+| MobileNetV2 |     SANAS      |  71.518%/90.208% (-0.632%/-0.442%) |     14      | 0.295  | [下载链接](https://paddlemodels.cdn.bcebos.com/PaddleSlim/MobileNetV2_sanas.tar) |
 
 ## 2. 目标检测
 
@@ -99,8 +105,8 @@
 |              模型              |  压缩方法   | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型体积（MB） |   TensorRT时延(V100, ms) |  下载     |
 | :----------------------------: | :---------: | :----: | :-------: | :------------: | :------------: | :------------: | :------------: | :----------: |:----------: |
 |      MobileNet-V1-YOLOv3       |      -      |  COCO  |     8     |      29.3      |      29.3      |      27.1      |       95       |  - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) |
-|      MobileNet-V1-YOLOv3       | quant_post  |  COCO  |     8     |     27.9 (-1.4)|	28.0 (-1.3)	  |    26.0 (-1.0) |       25       | -  | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
-|      MobileNet-V1-YOLOv3       | quant_aware |  COCO  |     8     |     28.1 (-1.2)|  28.2 (-1.1)	  |    25.8 (-1.2) |       26.3     | -  | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_coco_quant_aware.tar) |
+|      MobileNet-V1-YOLOv3       | quant_post  |  COCO  |     8     |     27.9 (-1.4)|    28.0 (-1.3)      |    26.0 (-1.0) |       25       | -  | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
+|      MobileNet-V1-YOLOv3       | quant_aware |  COCO  |     8     |     28.1 (-1.2)|  28.2 (-1.1)      |    25.8 (-1.2) |       26.3     | -  | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_coco_quant_aware.tar) |
 |      R34-YOLOv3                |      -      |  COCO  |     8     |      36.2      |      34.3      |      31.4      |       162       |  - | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) |
 |      R34-YOLOv3                | quant_post  |  COCO  |     8     | 35.7 (-0.5)    |      -         |      -         |       42.7      |  - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_post.tar) |
 |      R34-YOLOv3                | quant_aware |  COCO  |     8     |  35.2 (-1.0)   | 33.3 (-1.0)    |     30.3 (-1.1)|       44       |  - | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) |
@@ -157,6 +163,20 @@
 | MobileNet-V1-YOLOv3 | ResNet34-YOLOv3 distill |    COCO    |     8     |  31.4 (+2.1)   |  30.0 (+0.7)   |  27.1 (+0.1)   |       95       | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar) |
 
 
+### 2.4 搜索
+
+数据集：WIDER-FACE
+
+|      模型      |  压缩方法   | Image/GPU | 输入尺寸 |        Easy/Medium/Hard         | 模型体积（KB） |    硬件延时（ms）|                         下载                             |
+| :------------: | :---------: | :-------: | :------: | :-----------------------------: | :------------: | :------------: | :----------------------------------------------------------: |
+|   BlazeFace    |      -      |     8     |   640    |         91.5/89.2/79.7          |      815       |       71.862     | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) |
+| BlazeFace-NAS  |      -      |     8     |   640    |         83.7/80.7/65.8          |      244       |       21.117     |[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) |
+| BlazeFace-NAS1 |    SANAS    |     8     |   640    |         87.0/83.7/68.5          |      389       |       22.558     | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) |
+
+!!! note "Note"
+
+    <a name="trans1">[1]</a>: 硬件延时时间是利用提供的硬件延时表得到的，硬件延时表是在855芯片上基于PaddleLite测试的结果。
+
 ## 3. 图像分割
 
 数据集：Cityscapes

diff --git a/paddleslim/nas/sa_nas.py b/paddleslim/nas/sa_nas.py
@@ -190,7 +190,10 @@ def __init__(self,
             self._iter = 0
 
     def _get_host_ip(self):
-        return socket.gethostbyname(socket.gethostname())
+        if os.name == 'posix':
+            return socket.gethostbyname('localhost')
+        else:
+            return socket.gethostbyname(socket.gethostname())
 
     def tokens2arch(self, tokens):
         """