check gpu for ppdet/yolov3 and update ppdet MODEL_ZOO (#2730)

* check gpu for ppdet/yolov3

* add random shape descp in MODEL_ZOO

* refine doc

* not use core

PaddleCV/PaddleDetection/README.md

@@ -56,6 +56,8 @@ Advanced Features:
 - [x] **Modulated Deformable Convolution**: pretrained models to be released.
 - [x] **Deformable PSRoI Pooling**: pretrained models to be released.
 
+**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
+
 
 ## Model zoo
 

PaddleCV/PaddleDetection/docs/MODEL_ZOO.md

@@ -67,8 +67,10 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
 | ResNet34     | 416  |    8    |   270e  |  34.3  | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) |
 | ResNet34     | 320  |    8    |   270e  |  31.4  | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) |
 
-**NOTE**: Yolo v3 trained in 8 GPU with total batch size as 64 and trained 270 epoches. Yolo v3 training data augmentations: mixup,
-randomly color distortion, randomly cropping, randomly expansion, randomly interpolation method, randomly flippling.
+**NOTE**: Yolo v3 is trained in 8 GPU with total batch size as 64 and trained 270 epoches. Yolo v3 training data augmentations: mixup,
+randomly color distortion, randomly cropping, randomly expansion, randomly interpolation method, randomly flippling. Yolo v3 used randomly
+reshaped minibatch in training, inferences can be performed on different image sizes with the same model weights, and we provided evaluation
+results of image size 608/416/320 above.
 
 ### RetinaNet
 
@@ -85,5 +87,5 @@ randomly color distortion, randomly cropping, randomly expansion, randomly inter
 | :----------- | :--: | :-----: | :-----: | :----: | :-------: |
 | MobileNet v1 | 300  |    32   |   120e  |  73.2  | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar) |
 
-**NOTE**: SSD trained in 2 GPU with totoal batch size as 64 and trained 120 epoches. SSD training data augmentations: randomly color distortion,
+**NOTE**: SSD is trained in 2 GPU with totoal batch size as 64 and trained 120 epoches. SSD training data augmentations: randomly color distortion,
 randomly cropping, randomly expansion, randomly flipping.

PaddleCV/PaddleDetection/ppdet/utils/check.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import sys
+
+import paddle.fluid as fluid
+
+import logging
+logger = logging.getLogger(__name__)
+
+__all__ = ['check_gpu']
+
+
+def check_gpu(use_gpu):
+    """
+    Log error and exit when set use_gpu=true in paddlepaddle
+    cpu version.
+    """
+    err = "Config use_gpu cannot be set as true while you are " \
+          "using paddlepaddle cpu version ! \nPlease try: \n" \
+          "\t1. Install paddlepaddle-gpu to run model on GPU \n" \
+          "\t2. Set use_gpu as false in config file to run " \
+          "model on CPU"
+
+    try:
+        if use_gpu and not fluid.is_compiled_with_cuda():
+            logger.error(err)
+            sys.exit(1)
+    except Exception as e:
+        pass
+

PaddleCV/PaddleDetection/tools/eval.py

@@ -24,6 +24,7 @@ import paddle.fluid as fluid
 from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results
 import ppdet.utils.checkpoint as checkpoint
 from ppdet.utils.cli import ArgsParser
+from ppdet.utils.check import check_gpu
 from ppdet.modeling.model_input import create_feed
 from ppdet.data.data_feed import create_reader
 from ppdet.core.workspace import load_config, merge_config, create
@@ -46,6 +47,9 @@ def main():
 
     merge_config(FLAGS.opt)
 
+    # check if set use_gpu=True in paddlepaddle cpu version
+    check_gpu(cfg.use_gpu)
+
     if cfg.use_gpu:
         devices_num = fluid.core.get_cuda_device_count()
     else:

PaddleCV/PaddleDetection/tools/infer.py

@@ -30,6 +30,7 @@ from ppdet.data.data_feed import create_reader
 
 from ppdet.utils.eval_utils import parse_fetches
 from ppdet.utils.cli import ArgsParser
+from ppdet.utils.check import check_gpu
 from ppdet.utils.visualizer import visualize_results
 import ppdet.utils.checkpoint as checkpoint
 
@@ -109,6 +110,9 @@ def main():
 
     merge_config(FLAGS.opt)
 
+    # check if set use_gpu=True in paddlepaddle cpu version
+    check_gpu(cfg.use_gpu)
+
     if 'test_feed' not in cfg:
         test_feed = create(main_arch + 'TestFeed')
     else:

PaddleCV/PaddleDetection/tools/train.py

@@ -43,6 +43,7 @@ from ppdet.data.data_feed import create_reader
 from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results
 from ppdet.utils.stats import TrainingStats
 from ppdet.utils.cli import ArgsParser
+from ppdet.utils.check import check_gpu
 import ppdet.utils.checkpoint as checkpoint
 from ppdet.modeling.model_input import create_feed
 
@@ -62,6 +63,9 @@ def main():
 
     merge_config(FLAGS.opt)
 
+    # check if set use_gpu=True in paddlepaddle cpu version
+    check_gpu(cfg.use_gpu)
+
     if cfg.use_gpu:
         devices_num = fluid.core.get_cuda_device_count()
     else:

PaddleCV/yolov3/README.md

@@ -111,6 +111,8 @@ dataset/coco/
 *  学习率采用warmup算法，前4000轮学习率从0.0线性增加至0.001。在400000，450000轮时使用0.1,0.01乘子进行学习率衰减，最大训练500000轮。
 *  通过设置`--syncbn=True`可以开启Synchronized batch normalization，该模式下精度会提高
 
+**注意：** Synchronized batch normalization只能用于多GPU训练，不能用于CPU训练和单GPU训练。
+
 下图为模型训练结果：
 <p align="center">
 <img src="image/train_loss.png" height="400" width="550" hspace="10"/><br />

PaddleCV/yolov3/README_en.md

@@ -112,6 +112,8 @@ Please make sure that pre-trained model is downloaded and loaded correctly, othe
 *  In first 4000 iteration, the learning rate increases linearly from 0.0 to 0.001. Then lr is decayed at 400000, 450000 iteration with multiplier 0.1, 0.01. The maximum iteration is 500200.
 *  Synchronized batch normalization can be set by `--syncbn=True`, which can produce a higher performance.
 
+**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
+
 Training losses is shown as below：
 <p align="center">
 <img src="image/train_loss.png" height="500" width="650" hspace="10"/><br />

PaddleCV/yolov3/eval.py

@@ -23,13 +23,16 @@ import paddle
 import paddle.fluid as fluid
 import reader
 from models.yolov3 import YOLOv3
-from utility import print_arguments, parse_args
+from utility import print_arguments, parse_args, check_gpu
 from pycocotools.coco import COCO
 from pycocotools.cocoeval import COCOeval, Params
 from config import cfg
 
 
 def eval():
+    # check if set use_gpu=True in paddlepaddle cpu version
+    check_gpu(cfg.use_gpu)
+
     if '2014' in cfg.dataset:
         test_list = 'annotations/instances_val2014.json'
     elif '2017' in cfg.dataset:

PaddleCV/yolov3/infer.py

@@ -19,7 +19,7 @@ import paddle
 import paddle.fluid as fluid
 import box_utils
 import reader
-from utility import print_arguments, parse_args
+from utility import print_arguments, parse_args, check_gpu
 from models.yolov3 import YOLOv3
 from pycocotools.coco import COCO
 from pycocotools.cocoeval import COCOeval, Params
@@ -28,6 +28,9 @@ from config import cfg
 
 def infer():
 
+    # check if set use_gpu=True in paddlepaddle cpu version
+    check_gpu(cfg.use_gpu)
+
     if not os.path.exists('output'):
         os.mkdir('output')
 

PaddleCV/yolov3/train.py

@@ -35,7 +35,8 @@ import numpy as np
 import random
 import time
 import shutil
-from utility import parse_args, print_arguments, SmoothedValue
+from utility import (parse_args, print_arguments, 
+                     SmoothedValue, check_gpu)
 
 import paddle
 import paddle.fluid as fluid
@@ -61,6 +62,9 @@ def get_device_num():
 
 def train():
 
+    # check if set use_gpu=True in paddlepaddle cpu version
+    check_gpu(cfg.use_gpu)
+
     if cfg.debug or args.enable_ce:
         fluid.default_startup_program().random_seed = 1000
         fluid.default_main_program().random_seed = 1000

PaddleCV/yolov3/utility.py

@@ -23,7 +23,7 @@ import distutils.util
 import numpy as np
 import six
 from collections import deque
-from paddle.fluid import core
+import paddle.fluid as fluid
 import argparse
 import functools
 from config import *
@@ -87,6 +87,24 @@ class SmoothedValue(object):
         return self.loss_sum / self.iter_cnt
 
 
+def check_gpu(use_gpu):
+    """
+    Log error and exit when set use_gpu=True in paddlepaddle
+    cpu version.
+    """
+    err = "Config use_gpu cannot be set as True while you are " \
+          "using paddlepaddle cpu version ! \nPlease try: \n" \
+          "\t1. Install paddlepaddle-gpu to run model on GPU \n" \
+          "\t2. Set --use_gpu=False to run model on CPU"
+
+    try:
+        if use_gpu and not fluid.is_compiled_with_cuda():
+            print(err)
+            sys.exit(1)
+    except Exception as e:
+        pass
+
+
 def parse_args():
     """return all args
     """