Add infer scripts.

fluid/face_detection/infer.py

@@ -0,0 +1,105 @@
+import os
+import time
+import numpy as np
+import argparse
+import functools
+from PIL import Image
+from PIL import ImageDraw
+
+import paddle
+import paddle.fluid as fluid
+import reader
+from pyramidbox import PyramidBox
+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.")
+add_arg('use_pyramidbox',   bool,  False, "Whether use PyramidBox model.")
+add_arg('confs_threshold',  float, 0.2,    "Confidence threshold to draw bbox.")
+add_arg('image_path',       str,   '',        "The data root path.")
+add_arg('model_dir',        str,   '',     "The model path.")
+add_arg('resize_h',         int,   0,    "The resized image height.")
+add_arg('resize_w',         int,   0,    "The resized image height.")
+# yapf: enable
+
+
+def draw_bounding_box_on_image(image_path, nms_out, confs_threshold):
+    image = Image.open(image_path)
+    draw = ImageDraw.Draw(image)
+    im_width, im_height = image.size
+
+    for dt in nms_out:
+        category_id, score, xmin, ymin, xmax, ymax = dt.tolist()
+        if score < confs_threshold:
+            continue
+        bbox = dt[2:]
+        xmin, ymin, xmax, ymax = bbox
+        (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
+                                      ymin * im_height, ymax * im_height)
+        draw.line(
+            [(left, top), (left, bottom), (right, bottom), (right, top),
+             (left, top)],
+            width=4,
+            fill='red')
+    image_name = image_path.split('/')[-1]
+    print("image with bbox drawed saved as {}".format(image_name))
+    image.save(image_name)
+
+
+def infer(args, data_args):
+    num_classes = 2
+    infer_reader = reader.infer(data_args, args.image_path)
+    data = infer_reader()
+
+    # TODO(qingqing): support variable-length
+    if args.resize_h and args.resize_w:
+        image_shape = [3, args.resize_h, args.resize_w]
+    else:
+        image_shape = data.shape[1:]
+
+    fetches = []
+
+    network = PyramidBox(
+        image_shape, sub_network=args.use_pyramidbox, is_infer=True)
+    infer_program, nmsed_out = network.infer()
+    fetches = [nmsed_out]
+
+    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    model_dir = args.model_dir
+    if not os.path.exists(model_dir):
+        raise ValueError("The model path [%s] does not exist." % (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)
+
+    feed = {'image': fluid.create_lod_tensor(data, [], place)}
+    predict, = exe.run(infer_program,
+                       feed=feed,
+                       fetch_list=fetches,
+                       return_numpy=False)
+    predict = np.array(predict)
+    draw_bounding_box_on_image(args.image_path, predict, args.confs_threshold)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    print_arguments(args)
+
+    data_dir = 'data/WIDERFACE/WIDER_val/images/'
+    file_list = 'label/val_gt_widerface.res'
+
+    data_args = reader.Settings(
+        data_dir=data_dir,
+        resize_h=args.resize_h,
+        resize_w=args.resize_w,
+        mean_value=[104., 117., 123],
+        apply_distort=False,
+        apply_expand=False,
+        ap_version='11point')
+    infer(args, data_args=data_args)

fluid/face_detection/pyramidbox.py

@@ -45,12 +45,17 @@ def conv_block(input, groups, filters, ksizes, strides=None, with_pool=True):
 
 
 class PyramidBox(object):
-    def __init__(self, data_shape, is_infer=False, sub_network=False):
+    def __init__(self,
+                 data_shape,
+                 num_classes,
+                 is_infer=False,
+                 sub_network=False):
         self.data_shape = data_shape
         self.min_sizes = [16., 32., 64., 128., 256., 512.]
         self.steps = [4., 8., 16., 32., 64., 128.]
         self.is_infer = is_infer
         self.sub_network = sub_network
+        self.num_classes = num_classes
 
         # the base network is VGG with atrous layers
         self._input()
@@ -59,6 +64,8 @@ def __init__(self, data_shape, is_infer=False, sub_network=False):
             self._low_level_fpn()
             self._cpm_module()
             self._pyramidbox()
+        else:
+            self._vgg_ssd()
 
     def feeds(self):
         if self.is_infer:
@@ -188,9 +195,10 @@ def _pyramidbox(self):
         """
         Get prior-boxes and pyramid-box
         """
-        self.ssh_conv3_norm = self._l2_norm_scale(self.ssh_conv3)
-        self.ssh_conv4_norm = self._l2_norm_scale(self.ssh_conv4)
-        self.ssh_conv5_norm = self._l2_norm_scale(self.ssh_conv5)
+        self.ssh_conv3_norm = self._l2_norm_scale(
+            self.ssh_conv3, init_scale=10.)
+        self.ssh_conv4_norm = self._l2_norm_scale(self.ssh_conv4, init_scale=8.)
+        self.ssh_conv5_norm = self._l2_norm_scale(self.ssh_conv5, init_scale=5.)
 
         def permute_and_reshape(input, last_dim):
             trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1])
@@ -253,34 +261,41 @@ def permute_and_reshape(input, last_dim):
         self.prior_boxes = fluid.layers.concat(boxes)
         self.box_vars = fluid.layers.concat(vars)
 
-    def vgg_ssd(self, num_classes, image_shape):
-        self.conv3_norm = self._l2_norm_scale(self.conv3)
-        self.conv4_norm = self._l2_norm_scale(self.conv4)
-        self.conv5_norm = self._l2_norm_scale(self.conv5)
+    def _vgg_ssd(self):
+        self.conv3_norm = self._l2_norm_scale(self.conv3, init_scale=10.)
+        self.conv4_norm = self._l2_norm_scale(self.conv4, init_scale=8.)
+        self.conv5_norm = self._l2_norm_scale(self.conv5, init_scale=5.)
 
         mbox_locs, mbox_confs, box, box_var = fluid.layers.multi_box_head(
             inputs=[
                 self.conv3_norm, self.conv4_norm, self.conv5_norm, self.conv6,
                 self.conv7, self.conv8
             ],
             image=self.image,
-            num_classes=num_classes,
-            # min_ratio=20,
-            # max_ratio=90,
+            num_classes=self.num_classes,
             min_sizes=[16.0, 32.0, 64.0, 128.0, 256.0, 512.0],
             max_sizes=[[], [], [], [], [], []],
-            # max_sizes=[[], 150.0, 195.0, 240.0, 285.0, 300.0],
             aspect_ratios=[[1.], [1.], [1.], [1.], [1.], [1.]],
             steps=[4.0, 8.0, 16.0, 32.0, 64.0, 128.0],
-            base_size=image_shape[2],
+            base_size=self.data_shape[2],
             offset=0.5,
             flip=False)
 
-        # locs, confs, box, box_var = vgg_extra_net(num_classes, image, image_shape)
-        # nmsed_out = fluid.layers.detection_output(
-        # locs, confs, box, box_var, nms_threshold=args.nms_threshold)
-        loss = fluid.layers.ssd_loss(mbox_locs, mbox_confs, self.face_box,
-                                     self.gt_label, box, box_var)
+        self.face_mbox_loc = mbox_locs
+        self.face_mbox_conf = mbox_confs
+        self.prior_boxes = box
+        self.box_vars = box_var
+
+    def vgg_ssd_loss(self):
+        loss = fluid.layers.ssd_loss(
+            self.face_mbox_loc,
+            self.face_mbox_conf,
+            self.face_box,
+            self.gt_label,
+            self.prior_boxes,
+            self.box_vars,
+            overlap_threshold=0.35,
+            neg_overlap=0.35)
         loss = fluid.layers.reduce_sum(loss)
 
         return loss
@@ -297,7 +312,7 @@ def train(self):
         total_loss = face_loss + head_loss
         return face_loss, head_loss, total_loss
 
-    def test(self):
+    def infer(self):
         test_program = fluid.default_main_program().clone(for_test=True)
         with fluid.program_guard(test_program):
             face_nmsed_out = fluid.layers.detection_output(
@@ -306,24 +321,4 @@ def test(self):
                 self.prior_boxes,
                 self.box_vars,
                 nms_threshold=0.45)
-            head_nmsed_out = fluid.layers.detection_output(
-                self.head_mbox_loc,
-                self.head_mbox_conf,
-                self.prior_boxes,
-                self.box_vars,
-                nms_threshold=0.45)
-            face_map_eval = fluid.evaluator.DetectionMAP(
-                face_nmsed_out,
-                self.gt_label,
-                self.face_box,
-                class_num=2,
-                overlap_threshold=0.5,
-                ap_version='11point')
-            head_map_eval = fluid.evaluator.DetectionMAP(
-                head_nmsed_out,
-                self.gt_label,
-                self.head_box,
-                class_num=2,
-                overlap_threshold=0.5,
-                ap_version='11point')
-        return test_program, face_map_eval, head_map_eval
+        return test_program, face_nmsed_out

fluid/face_detection/reader.py

@@ -272,3 +272,29 @@ def reader():
 
 def train(settings, file_list, shuffle=True):
     return pyramidbox(settings, file_list, 'train', shuffle)
+
+
+def infer(settings, image_path):
+    def batch_reader():
+        img = Image.open(image_path)
+        if img.mode == 'L':
+            img = im.convert('RGB')
+        im_width, im_height = img.size
+        if settings.resize_w and settings.resize_h:
+            img = img.resize((settings.resize_w, settings.resize_h),
+                             Image.ANTIALIAS)
+        img = np.array(img)
+        # HWC to CHW
+        if len(img.shape) == 3:
+            img = np.swapaxes(img, 1, 2)
+            img = np.swapaxes(img, 1, 0)
+        # RBG to BGR
+        img = img[[2, 1, 0], :, :]
+        img = img.astype('float32')
+        img -= settings.img_mean
+        img = img * 0.007843
+        img = [img]
+        img = np.array(img)
+        return img
+
+    return batch_reader

fluid/face_detection/train.py

@@ -40,13 +40,13 @@ def train(args, data_args, learning_rate, batch_size, pretrained_model,
     image_shape = [3, data_args.resize_h, data_args.resize_w]
 
     fetches = []
+    network = PyramidBox(image_shape, num_classes,
+                         sub_network=args.use_pyramidbox)
     if args.use_pyramidbox:
-        network = PyramidBox(image_shape, sub_network=args.use_pyramidbox)
         face_loss, head_loss, loss = network.train()
         fetches = [face_loss, head_loss]
     else:
-        network = PyramidBox(image_shape, sub_network=args.use_pyramidbox)
-        loss = network.vgg_ssd(num_classes, image_shape)
+        loss = network.vgg_ssd_loss()
         fetches = [loss]
 
     epocs = 12880 / batch_size
@@ -126,7 +126,7 @@ def save_model(postfix):
                            batch_id, fetch_vars[0], fetch_vars[1],
                            start_time - prev_start_time))
 
-        if pass_id % 10 == 0 or pass_id == num_passes - 1:
+        if pass_id % 1 == 0 or pass_id == num_passes - 1:
             save_model(str(pass_id))