revise colorize for image size
@@ -187,11 +187,14 @@ class ImageColorizeModule(RunModule, ImageServing):
visual_ret['fake_reg'] = resize(process(fake))
if visualization:
+ img = Image.open(images)
+ w, h = img.size[0], img.size[1]
fake_name = "fake_" + str(time.time()) + ".png"
if not os.path.exists(save_path):
os.mkdir(save_path)
fake_path = os.path.join(save_path, fake_name)
visual_gray = Image.fromarray(visual_ret['fake_reg'])
+ visual_gray = visual_gray.resize((w, h), Image.BILINEAR)
visual_gray.save(fake_path)
mse = np.mean((visual_ret['real'] * 1.0 - visual_ret['fake_reg'] * 1.0)**2)
@@ -236,8 +239,7 @@ class Yolov3Module(RunModule, ImageServing):
for i, out in enumerate(outputs):
anchor_mask = self.anchor_masks[i]
- loss = F.yolov3_loss(
- x=out,
+ loss = F.yolov3_loss(x=out,
gt_box=gtbox,
gt_label=gtlabel,
gt_score=gtscore,
@@ -285,8 +287,7 @@ 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,
+ box, score = F.yolo_box(x=out,
img_size=im_shape,
anchors=mask_anchors,
class_num=self.class_num,
@@ -301,8 +302,7 @@ 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,
+ pred = F.multiclass_nms(bboxes=yolo_boxes,
scores=yolo_scores,
score_threshold=self.valid_thresh,
nms_top_k=self.nms_topk,
