Refine eval.py and update pyramidbox parameter init method (#1091)

* refine eval and update pyramidbox parameter init

fluid/face_detection/pyramidbox.py

@@ -145,7 +145,6 @@ class PyramidBox(object):
                 upsampling = fluid.layers.resize_bilinear(
                     conv1, out_shape=up_to.shape[2:])
 
-            b_attr = ParamAttr(learning_rate=2., regularizer=L2Decay(0.))
             conv2 = fluid.layers.conv2d(
                 up_to, ch, 1, act='relu', bias_attr=b_attr)
             if self.is_infer:
@@ -220,10 +219,13 @@ class PyramidBox(object):
 
         def permute_and_reshape(input, last_dim):
             trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1])
-            new_shape = [
+            compile_shape = [
                 trans.shape[0], np.prod(trans.shape[1:]) / last_dim, last_dim
             ]
-            return fluid.layers.reshape(trans, shape=new_shape)
+            run_shape = fluid.layers.assign(
+                np.array([0, -1, last_dim]).astype("int32"))
+            return fluid.layers.reshape(
+                trans, shape=compile_shape, actual_shape=run_shape)
 
         face_locs, face_confs = [], []
         head_locs, head_confs = [], []
@@ -288,10 +290,13 @@ class PyramidBox(object):
 
         def permute_and_reshape(input, last_dim):
             trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1])
-            new_shape = [
+            compile_shape = [
                 trans.shape[0], np.prod(trans.shape[1:]) / last_dim, last_dim
             ]
-            return fluid.layers.reshape(trans, shape=new_shape)
+            run_shape = fluid.layers.assign(
+                np.array([0, -1, last_dim]).astype("int32"))
+            return fluid.layers.reshape(
+                trans, shape=compile_shape, actual_shape=run_shape)
 
         locs, confs = [], []
         boxes, vars = [], []

fluid/face_detection/train.py

@@ -16,14 +16,14 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
 add_arg('parallel',         bool,  True,            "Whether use multi-GPU/threads or not.")
 add_arg('learning_rate',    float, 0.001,           "The start learning rate.")
-add_arg('batch_size',       int,   12,              "Minibatch size.")
+add_arg('batch_size',       int,   16,              "Minibatch size.")
 add_arg('num_passes',       int,   160,             "Epoch number.")
 add_arg('use_gpu',          bool,  True,            "Whether use GPU.")
 add_arg('use_pyramidbox',   bool,  True,            "Whether use PyramidBox model.")
 add_arg('model_save_dir',   str,   'output',        "The path to save model.")
 add_arg('resize_h',         int,   640,             "The resized image height.")
 add_arg('resize_w',         int,   640,             "The resized image width.")
-add_arg('with_mem_opt',     bool,  False,            "Whether to use memory optimization or not.")
+add_arg('with_mem_opt',     bool,  True,            "Whether to use memory optimization or not.")
 add_arg('pretrained_model', str,   './vgg_ilsvrc_16_fc_reduced/', "The init model path.")
 #yapf: enable
 

fluid/face_detection/widerface_eval.py

@@ -8,33 +8,35 @@ from PIL import Image
 import paddle.fluid as fluid
 import reader
 from pyramidbox import PyramidBox
+from visualize import draw_bboxes
 from utility import add_arguments, print_arguments
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 
 # yapf: disable
-add_arg('use_gpu',        bool, True,                              "Whether use GPU or not.")
-add_arg('use_pyramidbox', bool, True,                              "Whether use PyramidBox model.")
-add_arg('data_dir',       str,  'data/WIDER_val/images/',          "The validation dataset path.")
-add_arg('model_dir',      str,  '',                                "The model path.")
-add_arg('pred_dir',       str,  'pred',                            "The path to save the evaluation results.")
-add_arg('file_list',      str,  'data/wider_face_split/wider_face_val_bbx_gt.txt', "The validation dataset path.")
+add_arg('use_gpu',         bool,  True,                              "Whether use GPU or not.")
+add_arg('use_pyramidbox',  bool,  True,                              "Whether use PyramidBox model.")
+add_arg('data_dir',        str,   'data/WIDER_val/images/',          "The validation dataset path.")
+add_arg('model_dir',       str,   '',                                "The model path.")
+add_arg('pred_dir',        str,   'pred',                            "The path to save the evaluation results.")
+add_arg('file_list',       str,   'data/wider_face_split/wider_face_val_bbx_gt.txt', "The validation dataset path.")
+add_arg('infer',           bool,  False,                             "Whether do infer or eval.")
+add_arg('confs_threshold', float, 0.15,                              "Confidence threshold to draw bbox.")
+add_arg('image_path',      str,   '',                                "The image used to inference and visualize.")
 # yapf: enable
 
 
 def infer(args, config):
-    batch_size = 1
     model_dir = args.model_dir
-    data_dir = args.data_dir
-    file_list = args.file_list
     pred_dir = args.pred_dir
-
     if not os.path.exists(model_dir):
         raise ValueError("The model path [%s] does not exist." % (model_dir))
 
-    test_reader = reader.test(config, file_list)
-
-    for image, image_path in test_reader():
+    if args.infer:
+        image_path = args.image_path
+        image = Image.open(image_path)
+        if image.mode == 'L':
+            image = img.convert('RGB')
         shrink, max_shrink = get_shrink(image.size[1], image.size[0])
 
         det0 = detect_face(image, shrink)
@@ -44,9 +46,24 @@ def infer(args, config):
         det = np.row_stack((det0, det1, det2, det3, det4))
         dets = bbox_vote(det)
 
-        save_widerface_bboxes(image_path, dets, pred_dir)
+        keep_index = np.where(dets[:, 4] >= args.confs_threshold)[0]
+        dets = dets[keep_index, :]
+        draw_bboxes(image_path, dets[:, 0:4])
+    else:
+        test_reader = reader.test(config, args.file_list)
+        for image, image_path in test_reader():
+            shrink, max_shrink = get_shrink(image.size[1], image.size[0])
+
+            det0 = detect_face(image, shrink)
+            det1 = flip_test(image, shrink)
+            [det2, det3] = multi_scale_test(image, max_shrink)
+            det4 = multi_scale_test_pyramid(image, max_shrink)
+            det = np.row_stack((det0, det1, det2, det3, det4))
+            dets = bbox_vote(det)
 
-    print("Finish evaluation.")
+            save_widerface_bboxes(image_path, dets, pred_dir)
+
+        print("Finish evaluation.")
 
 
 def save_widerface_bboxes(image_path, bboxes_scores, output_dir):
@@ -97,25 +114,11 @@ def detect_face(image, shrink):
     img = [img]
     img = np.array(img)
 
-    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    main_program = fluid.Program()
-    startup_program = fluid.Program()
-
-    with fluid.unique_name.guard():
-        with fluid.program_guard(main_program, startup_program):
-            network = PyramidBox(
-                image_shape, sub_network=args.use_pyramidbox, is_infer=True)
-            infer_program, nmsed_out = network.infer(main_program)
-            fetches = [nmsed_out]
-            fluid.io.load_persistables(
-                exe, args.model_dir, main_program=main_program)
-
-            detection, = exe.run(infer_program,
-                                 feed={'image': img},
-                                 fetch_list=fetches,
-                                 return_numpy=False)
-            detection = np.array(detection)
+    detection, = exe.run(infer_program,
+                         feed={'image': img},
+                         fetch_list=fetches,
+                         return_numpy=False)
+    detection = np.array(detection)
     # layout: xmin, ymin, xmax. ymax, score
     if detection.shape == (1, ):
         print("No face detected")
@@ -290,4 +293,18 @@ if __name__ == '__main__':
     args = parser.parse_args()
     print_arguments(args)
     config = reader.Settings(data_dir=args.data_dir)
+
+    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    main_program = fluid.Program()
+    startup_program = fluid.Program()
+    image_shape = [3, 1024, 1024]
+    with fluid.program_guard(main_program, startup_program):
+        network = PyramidBox(
+            image_shape, sub_network=args.use_pyramidbox, is_infer=True)
+        infer_program, nmsed_out = network.infer(main_program)
+        fetches = [nmsed_out]
+        fluid.io.load_persistables(
+            exe, args.model_dir, main_program=main_program)
+
     infer(args, config)