remove condition block && some todo (#2882)

deploy/python/visualize.py

@@ -38,10 +38,10 @@ def visualize_box_mask(im, results, labels, threshold=0.5):
         im = Image.open(im).convert('RGB')
     else:
         im = Image.fromarray(im)
-    if 'masks' in results and 'boxes' in results:
+    if 'masks' in results and 'boxes' in results and len(results['boxes']) > 0:
         im = draw_mask(
             im, results['boxes'], results['masks'], labels, threshold=threshold)
-    if 'boxes' in results:
+    if 'boxes' in results and len(results['boxes']) > 0:
         im = draw_box(im, results['boxes'], labels, threshold=threshold)
     if 'segm' in results:
         im = draw_segm(

ppdet/modeling/heads/solov2_head.py

@@ -446,9 +446,6 @@ class SOLOv2Head(nn.Layer):
         y = paddle.zeros(shape=paddle.shape(cate_preds), dtype='float32')
         inds = paddle.where(cate_preds > self.score_threshold, cate_preds, y)
         inds = paddle.nonzero(inds)
-        if paddle.shape(inds)[0] == 0:
-            out = paddle.full(shape=[1], fill_value=-1)
-            return out, out, out
         cate_preds = paddle.reshape(cate_preds, shape=[-1])
         # Prevent empty and increase fake data
         ind_a = paddle.cast(paddle.shape(kernel_preds)[0], 'int64')
@@ -530,6 +527,5 @@ class SOLOv2Head(nn.Layer):
                 align_corners=False,
                 align_mode=0),
             axis=[0])
-        # TODO: support bool type
-        seg_masks = paddle.cast(seg_masks > self.mask_threshold, 'int32')
+        seg_masks = paddle.cast(seg_masks > self.mask_threshold, 'uint8')
         return seg_masks, cate_labels, cate_scores

ppdet/modeling/post_process.py

@@ -60,14 +60,6 @@ class BBoxPostProcess(object):
         else:
             bbox_pred, bbox_num = self.decode(head_out, rois, im_shape,
                                               scale_factor)
-
-        # Prevent empty bbox_pred from decode or NMS.
-        # Bboxes and score before NMS may be empty due to the score threshold.
-        if bbox_pred.shape[0] == 0:
-            bbox_pred = paddle.to_tensor(
-                np.array(
-                    [[-1, 0.0, 0.0, 0.0, 0.0, 0.0]], dtype='float32'))
-            bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
         return bbox_pred, bbox_num
 
     def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
@@ -155,10 +147,6 @@ class MaskPostProcess(object):
 
         gx = paddle.expand(img_x, [N, img_y.shape[1], img_x.shape[2]])
         gy = paddle.expand(img_y, [N, img_y.shape[1], img_x.shape[2]])
-        # TODO: Because paddle.expand transform error when dygraph
-        # to static, use reshape to avoid mistakes.
-        gx = paddle.reshape(gx, [N, img_y.shape[1], img_x.shape[2]])
-        gy = paddle.reshape(gy, [N, img_y.shape[1], img_x.shape[2]])
         grid = paddle.stack([gx, gy], axis=3)
         img_masks = F.grid_sample(masks, grid, align_corners=False)
         return img_masks[:, 0]