[dev] fix deploy of ttfnet fcos (#3568)

* fix bs=2 of FCOS

* fix bs=2 of ttfnet

* fix cascade deploy

* speedup by paddle.expand

deploy/python/visualize.py

@@ -191,8 +191,10 @@ def draw_segm(im,
             color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
         idx = np.nonzero(mask)
         color_mask = np.array(color_mask)
-        im[idx[0], idx[1], :] *= 1.0 - alpha
-        im[idx[0], idx[1], :] += alpha * color_mask
+        idx0 = np.minimum(idx[0], im.shape[0] - 1)
+        idx1 = np.minimum(idx[1], im.shape[1] - 1)
+        im[idx0, idx1, :] *= 1.0 - alpha
+        im[idx0, idx1, :] += alpha * color_mask
         sum_x = np.sum(mask, axis=0)
         x = np.where(sum_x > 0.5)[0]
         sum_y = np.sum(mask, axis=1)

ppdet/modeling/heads/cascade_head.py

@@ -266,7 +266,9 @@ class CascadeHead(BBoxHead):
             proposals) > 1 else proposals[0]
         pred_bbox = delta2bbox(deltas, pred_proposals, weights)
         pred_bbox = paddle.reshape(pred_bbox, [-1, deltas.shape[-1]])
-        num_prop = [p.shape[0] for p in proposals]
+        num_prop = []
+        for p in proposals:
+            num_prop.append(p.shape[0])
         return pred_bbox.split(num_prop)
 
     def get_prediction(self, head_out_list):

ppdet/modeling/layers.py

@@ -386,7 +386,11 @@ class RCNNBox(object):
         scale_list = []
         origin_shape_list = []
 
-        batch_size = paddle.slice(paddle.shape(im_shape), [0], [0], [1])
+        batch_size = 1
+        if isinstance(roi, list):
+            batch_size = len(roi)
+        else:
+            batch_size = paddle.slice(paddle.shape(im_shape), [0], [0], [1])
         # bbox_pred.shape: [N, C*4]
         for idx in range(batch_size):
             roi_per_im = roi[idx]
@@ -755,6 +759,8 @@ class FCOSBox(object):
 
         # recover the location to original image
         im_scale = paddle.concat([scale_factor, scale_factor], axis=1)
+        im_scale = paddle.expand(im_scale, [box_reg_decoding.shape[0], 4])
+        im_scale = paddle.reshape(im_scale, [box_reg_decoding.shape[0], -1, 4])
         box_reg_decoding = box_reg_decoding / im_scale
         box_cls_ch_last = box_cls_ch_last * box_ctn_ch_last
         return box_cls_ch_last, box_reg_decoding
@@ -822,7 +828,7 @@ class TTFBox(object):
 
         return topk_score, topk_inds, topk_clses, topk_ys, topk_xs
 
-    def __call__(self, hm, wh, im_shape, scale_factor):
+    def _decode(self, hm, wh, im_shape, scale_factor):
         heatmap = F.sigmoid(hm)
         heat = self._simple_nms(heatmap)
         scores, inds, clses, ys, xs = self._topk(heat)
@@ -861,6 +867,19 @@ class TTFBox(object):
         results = paddle.gather(results, valid_ind)
         return results, paddle.shape(results)[0:1]
 
+    def __call__(self, hm, wh, im_shape, scale_factor):
+        results = []
+        results_num = []
+        for i in range(scale_factor.shape[0]):
+            result, num = self._decode(hm[i:i + 1, ], wh[i:i + 1, ],
+                                       im_shape[i:i + 1, ],
+                                       scale_factor[i:i + 1, ])
+            results.append(result)
+            results_num.append(num)
+        results = paddle.concat(results, axis=0)
+        results_num = paddle.concat(results_num, axis=0)
+        return results, results_num
+
 
 @register
 @serializable