no padding for eval (#1263)

fluid/faster_rcnn/data_utils.py

@@ -74,8 +74,7 @@ def prep_im_for_blob(im, pixel_means, target_size, max_size):
         fy=im_scale,
         interpolation=cv2.INTER_LINEAR)
     im_height, im_width, channel = im.shape
-    padding_im = np.zeros((max_size, max_size, im_shape[2]), dtype=np.float32)
-    padding_im[:im_height, :im_width, :] = im
+    padding_im = im
     #print(padding_im[10:, 10:, :])
     channel_swap = (2, 0, 1)  #(batch, channel, height, width)
     #im = im.transpose(channel_swap)

fluid/faster_rcnn/eval_coco_map.py

@@ -72,7 +72,8 @@ def eval(args):
         is_train=False,
         use_random=False)
     model.build_model(image_shape)
-    rpn_rois, confs, locs = model.eval_out()
+    rpn_rois, scores, locs = model.eval_out()
+    confs = fluid.layers.softmax(scores, use_cudnn=False)
     place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
     # yapf: disable
@@ -90,12 +91,23 @@ def eval(args):
         start = time.time()
         #image, gt_box, gt_label, is_crowd, im_info, im_id = data[0]
         im_info = []
+        image = []
         for i in range(len(data)):
             im_info.append(data[i][4])
+            image.append(data[i][0])
+        image_t = fluid.core.LoDTensor()
+        image_t.set(image, place)
 
+        im_info_t = fluid.core.LoDTensor()
+        im_info_t.set(im_info, place)
+
+        feeding = {}
+        feeding['image'] = image_t
+        feeding['im_info'] = im_info_t
         rpn_rois_v, confs_v, locs_v = exe.run(
             fetch_list=[v.name for v in fetch_list],
-            feed=feeder.feed(data),
+            #feed=feeder.feed(data),
+            feed=feeding,
             return_numpy=False)
         new_lod, nmsed_out = get_nmsed_box(args, rpn_rois_v, confs_v, locs_v,
                                            class_nums, im_info,

fluid/faster_rcnn/train.py

@@ -65,7 +65,7 @@ def train(cfg):
         add_conv_body_func=resnet.add_ResNet50_conv4_body,
         add_roi_box_head_func=resnet.add_ResNet_roi_conv5_head,
         use_pyreader=cfg.use_pyreader,
-        use_random=False)
+        use_random=True)
     model.build_model(image_shape)
     loss_cls, loss_bbox, rpn_cls_loss, rpn_reg_loss = model.loss()
     loss_cls.persistable=True