adapt rc for colorization and style transfer

demo/colorization/predict.py

@@ -2,7 +2,5 @@ import paddle
 import paddlehub as hub
 
 if __name__ == '__main__':
-    model = hub.Module(name='user_guided_colorization')
-    state_dict = paddle.load('img_colorization_ckpt')
-    model.set_dict(state_dict)
-    result = model.predict('house.png')
+    model = hub.Module(name='user_guided_colorization', load_checkpoint='/PATH/TO/CHECKPOINT')
+    result = model.predict(images='house.png')

demo/style_transfer/predict.py

@@ -2,7 +2,5 @@ import paddle
 import paddlehub as hub
 
 if __name__ == '__main__':
-    model = hub.Module(name='msgnet')
-    state_dict = paddle.load('img_style_transfer_ckpt')
-    model.set_dict(state_dict)
+    model = hub.Module(name='msgnet', load_checkpoint='/PATH/TO/CHECKPOINT')
     result = model.predict("venice-boat.jpg", "candy.jpg")

modules/image/colorization/user_guided_colorization/module.py

@@ -179,11 +179,7 @@ class UserGuidedColorization(nn.Layer):
             print("load custom checkpoint success")
         else:
             checkpoint = os.path.join(self.directory, 'user_guided.pdparams')
-            if not os.path.exists(checkpoint):
-                os.system('wget https://paddlehub.bj.bcebos.com/dygraph/image_colorization/user_guided.pdparams -O ' +
-                          checkpoint)
             model_dict = paddle.load(checkpoint)
-
             self.set_dict(model_dict)
             print("load pretrained checkpoint success")
 

modules/image/style_transfer/msgnet/module.py

@@ -314,9 +314,6 @@ class MSGNet(nn.Layer):
 
         else:
             checkpoint = os.path.join(self.directory, 'style_paddle.pdparams')
-            if not os.path.exists(checkpoint):
-                os.system('wget https://bj.bcebos.com/paddlehub/model/image/image_editing/style_paddle.pdparams -O ' +
-                          checkpoint)
             model_dict = paddle.load(checkpoint)
             model_dict_clone = model_dict.copy()
             for key, value in model_dict_clone.items():

paddlehub/module/cv_module.py

@@ -164,7 +164,7 @@ class ImageColorizeModule(RunModule, ImageServing):
         Returns:
             results(list[dict]) : The prediction result of each input image
         '''
-
+        self.eval()
         lab2rgb = T.LAB2RGB()
         process = T.ColorPostprocess()
         resize = T.Resize((256, 256))
@@ -239,16 +239,17 @@ class Yolov3Module(RunModule, ImageServing):
 
         for i, out in enumerate(outputs):
             anchor_mask = self.anchor_masks[i]
-            loss = F.yolov3_loss(x=out,
-                                 gt_box=gtbox,
-                                 gt_label=gtlabel,
-                                 gt_score=gtscore,
-                                 anchors=self.anchors,
-                                 anchor_mask=anchor_mask,
-                                 class_num=self.class_num,
-                                 ignore_thresh=self.ignore_thresh,
-                                 downsample_ratio=32,
-                                 use_label_smooth=False)
+            loss = F.yolov3_loss(
+                x=out,
+                gt_box=gtbox,
+                gt_label=gtlabel,
+                gt_score=gtscore,
+                anchors=self.anchors,
+                anchor_mask=anchor_mask,
+                class_num=self.class_num,
+                ignore_thresh=self.ignore_thresh,
+                downsample_ratio=32,
+                use_label_smooth=False)
             losses.append(paddle.mean(loss))
             self.downsample //= 2
 
@@ -269,6 +270,7 @@ class Yolov3Module(RunModule, ImageServing):
             scores(np.ndarray): Predict score.
             labels(np.ndarray): Predict labels.
         '''
+        self.eval()
         boxes = []
         scores = []
         self.downsample = 32
@@ -287,13 +289,14 @@ class Yolov3Module(RunModule, ImageServing):
                 mask_anchors.append((self.anchors[2 * m]))
                 mask_anchors.append(self.anchors[2 * m + 1])
 
-            box, score = F.yolo_box(x=out,
-                                    img_size=im_shape,
-                                    anchors=mask_anchors,
-                                    class_num=self.class_num,
-                                    conf_thresh=self.valid_thresh,
-                                    downsample_ratio=self.downsample,
-                                    name="yolo_box" + str(i))
+            box, score = F.yolo_box(
+                x=out,
+                img_size=im_shape,
+                anchors=mask_anchors,
+                class_num=self.class_num,
+                conf_thresh=self.valid_thresh,
+                downsample_ratio=self.downsample,
+                name="yolo_box" + str(i))
 
             boxes.append(box)
             scores.append(paddle.transpose(score, perm=[0, 2, 1]))
@@ -302,13 +305,14 @@ class Yolov3Module(RunModule, ImageServing):
         yolo_boxes = paddle.concat(boxes, axis=1)
         yolo_scores = paddle.concat(scores, axis=2)
 
-        pred = F.multiclass_nms(bboxes=yolo_boxes,
-                                scores=yolo_scores,
-                                score_threshold=self.valid_thresh,
-                                nms_top_k=self.nms_topk,
-                                keep_top_k=self.nms_posk,
-                                nms_threshold=self.nms_thresh,
-                                background_label=-1)
+        pred = F.multiclass_nms(
+            bboxes=yolo_boxes,
+            scores=yolo_scores,
+            score_threshold=self.valid_thresh,
+            nms_top_k=self.nms_topk,
+            keep_top_k=self.nms_posk,
+            nms_threshold=self.nms_thresh,
+            background_label=-1)
 
         bboxes = pred.numpy()
         labels = bboxes[:, 0].astype('int32')
@@ -388,6 +392,7 @@ class StyleTransferModule(RunModule, ImageServing):
         Returns:
             output(np.ndarray) : The style transformed images with bgr mode.
         '''
+        self.eval()
         content = paddle.to_tensor(self.transform(origin_path))
         style = paddle.to_tensor(self.transform(style_path))
         content = content.unsqueeze(0)