COCO dataset for SSD and update README.md

fluid/object_detection/README.md

@@ -6,7 +6,7 @@ The minimum PaddlePaddle version needed for the code sample in this directory is
 
 ### Introduction
 
-[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325) framework for object detection is based on a feed-forward convolutional network. The early network is a standard convolutional architecture for image classification, such as VGG, ResNet, or MobileNet, which is als called base network. In this tutorial we used [MobileNet](https://arxiv.org/abs/1704.04861).
+[Single Shot MultiBox Detector (SSD)](https://arxiv.org/abs/1512.02325) framework for object detection is based on a feed-forward convolutional network. The early network is a standard convolutional architecture for image classification, such as VGG, ResNet, or MobileNet, which is also called base network. In this tutorial we used [MobileNet](https://arxiv.org/abs/1704.04861).
 
 ### Data Preparation
 
@@ -52,39 +52,68 @@ Declaration: the MobileNet-v1 SSD model is converted by [TensorFlow model](https
 #### Train on PASCAL VOC
   - Train on one device (/GPU).
   ```python
-  env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --data='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/'
+  env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --dataset='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/'
   ```
   - Train on multi devices (/GPUs).
 
   ```python
-  env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --data='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/'
+  env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --dataset='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/'
   ```
 
 #### Train on MS-COCO
   - Train on one device (/GPU).
   ```python
-  env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --data='coco' --pretrained_model='pretrained/mobilenet_imagenet/'
+  env CUDA_VISIABLE_DEVICES=0 python -u train.py --parallel=False --dataset='coco2014' --pretrained_model='pretrained/mobilenet_imagenet/'
   ```
   - Train on multi devices (/GPUs).
   ```python
-  env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --data='coco' --pretrained_model='pretrained/mobilenet_imagenet/'
+  env CUDA_VISIABLE_DEVICES=0,1 python -u train.py --batch_size=64 --dataset='coco2014' --pretrained_model='pretrained/mobilenet_imagenet/'
   ```
 
 TBD
 
 ### Evaluate
 
+You can evaluate your trained model in different metric like 11point, integral on both PASCAL VOC and COCO dataset. Moreover, we provide eval_coco_map.py which uses a COCO-specific mAP metric defined by [COCO committee](http://cocodataset.org/#detections-eval). To use this eval_coco_map.py, [cocoapi](https://github.com/cocodataset/cocoapi) is needed.
+Install the cocoapi:
+```
+# COCOAPI=/path/to/clone/cocoapi
+git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
+cd $COCOAPI/PythonAPI
+# Install into global site-packages
+make install
+# Alternatively, if you do not have permissions or prefer
+# not to install the COCO API into global site-packages
+python2 setup.py install --user
+```
+Note we set the defualt test list to the dataset's test/val list, you can use your own test list by setting test_list args.
+
+#### Evaluate on PASCAL VOC
+```python
+env CUDA_VISIABLE_DEVICES=0 python eval.py --dataset='pascalvoc' --model_dir='train_pascal_model/90' --data_dir='data/pascalvoc' --test_list='test.txt' --ap_version='11point'
+```
+
+#### Evaluate on MS-COCO
 ```python
-env CUDA_VISIABLE_DEVICES=0 python eval.py --model='model/90' --test_list=''
+env CUDA_VISIABLE_DEVICES=0 python eval.py --dataset='coco2014' --nms_threshold=0.5 --model_dir='train_coco_model/40' --test_list='annotations/instances_minival2014.json' --ap_version='integral'
+env CUDA_VISIABLE_DEVICES=0 python eval_coco_map.py --dataset='coco2017' --nms_threshold=0.5 --model_dir='train_coco_model/40' --test_list='annotations/instances_minival2017.json'
 ```
 
 TBD
 
 ### Infer and Visualize
 
 ```python
-env CUDA_VISIABLE_DEVICES=0 python infer.py --batch_size=2 --model='model/90' --test_list=''
+env CUDA_VISIABLE_DEVICES=0 python infer.py --model_dir='train_coco_model/20' --image_path='./data/coco/val2014/COCO_val2014_000000000139.jpg'
 ```
+Below is the examples after running python infer.py to inference and visualize the model result.
+<p align="center">
+<img src="images/COCO_val2014_000000000139.jpg" height=300 width=400 hspace='10'/>
+<img src="images/COCO_val2014_000000000785.jpg" height=300 width=400 hspace='10'/>
+<img src="images/COCO_val2014_000000142324.jpg" height=300 width=400 hspace='10'/>
+<img src="images/COCO_val2014_000000144003.jpg" height=300 width=400 hspace='10'/> <br />
+MobileNet-SSD300x300 Visualization Examples
+</p>
 
 TBD
 

fluid/object_detection/data/coco/download.sh

@@ -0,0 +1,20 @@
+DIR="$( cd "$(dirname "$0")" ; pwd -P )"
+cd "$DIR"
+
+# Download the data.
+echo "Downloading..."
+wget http://images.cocodataset.org/zips/train2014.zip
+wget http://images.cocodataset.org/zips/val2014.zip
+wget http://images.cocodataset.org/zips/train2017.zip
+wget http://images.cocodataset.org/zips/val2017.zip
+wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip
+wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+# Extract the data.
+echo "Extractint..."
+unzip train2014.tar
+unzip val2014.tar
+unzip train2017.tar
+unzip val2017.tar
+unzip annotations_trainval2014.tar
+unzip annotations_trainval2017.tar
+

fluid/object_detection/eval.py

@@ -13,27 +13,27 @@
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
-add_arg('dataset',          str, 'pascalvoc', "coco or pascalvoc.")
+add_arg('dataset',          str,   'pascalvoc',  "coco2014, coco2017, and pascalvoc.")
 add_arg('batch_size',       int,   32,        "Minibatch size.")
-add_arg('use_gpu',          bool,  True,      "Whether to use GPU or not.")
+add_arg('use_gpu',          bool,  True,      "Whether use GPU.")
 add_arg('data_dir',         str,   '',        "The data root path.")
 add_arg('test_list',        str,   '',        "The testing data lists.")
-add_arg('label_file',       str,   '',        "The label file, which save the real name and is only used for Pascal VOC.")
-add_arg('model_dir',        str,   '',        "The model path.")
-add_arg('ap_version',       str,  '11point',  "11point or integral")
-add_arg('resize_h',         int,  300,         "The resized image height.")
-add_arg('resize_w',         int,  300,         "The resized image width.")
-add_arg('mean_value_B',     float, 127.5,      "mean value for B channel which will be subtracted")  #123.68
-add_arg('mean_value_G',     float, 127.5,      "mean value for G channel which will be subtracted")  #116.78
-add_arg('mean_value_R',     float, 127.5,      "mean value for R channel which will be subtracted")  #103.94
+add_arg('model_dir',        str,   '',     "The model path.")
+add_arg('nms_threshold',    float, 0.45,   "NMS threshold.")
+add_arg('ap_version',       str,   '11point',   "integral, 11point.")
+add_arg('resize_h',         int,   300,    "The resized image height.")
+add_arg('resize_w',         int,   300,    "The resized image height.")
+add_arg('mean_value_B',     float, 127.5,  "Mean value for B channel which will be subtracted.")  #123.68
+add_arg('mean_value_G',     float, 127.5,  "Mean value for G channel which will be subtracted.")  #116.78
+add_arg('mean_value_R',     float, 127.5,  "Mean value for R channel which will be subtracted.")  #103.94
 # yapf: enable
 
 
 def eval(args, data_args, test_list, batch_size, model_dir=None):
     image_shape = [3, data_args.resize_h, data_args.resize_w]
-    if data_args.dataset == 'coco':
-        num_classes = 81
-    elif data_args.dataset == 'pascalvoc':
+    if 'coco' in data_args.dataset:
+        num_classes = 91
+    elif 'pascalvoc' in data_args.dataset:
         num_classes = 21
 
     image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
@@ -46,61 +46,77 @@ def eval(args, data_args, test_list, batch_size, model_dir=None):
 
     locs, confs, box, box_var = mobile_net(num_classes, image, image_shape)
     nmsed_out = fluid.layers.detection_output(
-        locs, confs, box, box_var, nms_threshold=0.45)
+        locs, confs, box, box_var, nms_threshold=args.nms_threshold)
     loss = fluid.layers.ssd_loss(locs, confs, gt_box, gt_label, box, box_var)
     loss = fluid.layers.reduce_sum(loss)
 
-    test_program = fluid.default_main_program().clone(for_test=True)
-    with fluid.program_guard(test_program):
-        map_eval = fluid.evaluator.DetectionMAP(
-            nmsed_out,
-            gt_label,
-            gt_box,
-            difficult,
-            num_classes,
-            overlap_threshold=0.5,
-            evaluate_difficult=False,
-            ap_version=args.ap_version)
-
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
-
+    # yapf: disable
     if model_dir:
-
         def if_exist(var):
             return os.path.exists(os.path.join(model_dir, var.name))
-
         fluid.io.load_vars(exe, model_dir, predicate=if_exist)
-
+    # yapf: enable
     test_reader = paddle.batch(
         reader.test(data_args, test_list), batch_size=batch_size)
     feeder = fluid.DataFeeder(
         place=place, feed_list=[image, gt_box, gt_label, difficult])
 
-    _, accum_map = map_eval.get_map_var()
-    map_eval.reset(exe)
-    for idx, data in enumerate(test_reader()):
-        test_map = exe.run(test_program,
-                           feed=feeder.feed(data),
-                           fetch_list=[accum_map])
-        if idx % 50 == 0:
-            print("Batch {0}, map {1}".format(idx, test_map[0]))
-    print("Test model {0}, map {1}".format(model_dir, test_map[0]))
+    def test():
+        test_program = fluid.default_main_program().clone(for_test=True)
+        with fluid.program_guard(test_program):
+            map_eval = fluid.evaluator.DetectionMAP(
+                nmsed_out,
+                gt_label,
+                gt_box,
+                difficult,
+                num_classes,
+                overlap_threshold=0.5,
+                evaluate_difficult=False,
+                ap_version=args.ap_version)
+
+        _, accum_map = map_eval.get_map_var()
+        map_eval.reset(exe)
+        for batch_id, data in enumerate(test_reader()):
+            test_map = exe.run(test_program,
+                               feed=feeder.feed(data),
+                               fetch_list=[accum_map])
+            if batch_id % 20 == 0:
+                print("Batch {0}, map {1}".format(batch_id, test_map[0]))
+        print("Test model {0}, map {1}".format(model_dir, test_map[0]))
+
+    test()
 
 
 if __name__ == '__main__':
     args = parser.parse_args()
     print_arguments(args)
+
+    data_dir = 'data/pascalvoc'
+    test_list = 'test.txt'
+    label_file = 'label_list'
+    if 'coco' in args.dataset:
+        data_dir = './data/coco'
+        if '2014' in args.dataset:
+            test_list = 'annotations/instances_minival2014.json'
+        elif '2017' in args.dataset:
+            test_list = 'annotations/instances_val2017.json'
+
     data_args = reader.Settings(
         dataset=args.dataset,
-        data_dir=args.data_dir,
-        label_file=args.label_file,
+        data_dir=args.data_dir if len(args.data_dir) > 0 else data_dir,
+        label_file=label_file,
         resize_h=args.resize_h,
         resize_w=args.resize_w,
-        mean_value=[args.mean_value_B, args.mean_value_G, args.mean_value_R])
+        mean_value=[args.mean_value_B, args.mean_value_G, args.mean_value_R],
+        apply_distort=False,
+        apply_expand=False,
+        ap_version=args.ap_version,
+        toy=0)
     eval(
         args,
-        test_list=args.test_list,
         data_args=data_args,
+        test_list=args.test_list if len(args.test_list) > 0 else test_list,
         batch_size=args.batch_size,
         model_dir=args.model_dir)