fix multi-inputs

ppocr/modeling/backbones/kie_unet_sdmgr.py

@@ -167,20 +167,12 @@ class Kie_backbone(nn.Layer):
                     gt_bboxes[i, :num, ...], dtype='float32'))
         return img, temp_relations, temp_texts, temp_gt_bboxes
 
-    def forward(self, inputs):
-        img, relations, texts, gt_bboxes, tag, img_size = inputs[0], inputs[
-            1], inputs[2], inputs[3], inputs[5], inputs[-1]
+    def forward(self, images, inputs):
+        img = images
+        relations, texts, gt_bboxes, tag, img_size = inputs[0], inputs[
+            1], inputs[2], inputs[4], inputs[-1]
         img, relations, texts, gt_bboxes = self.pre_process(
             img, relations, texts, gt_bboxes, tag, img_size)
-        # for i in range(4):
-        #     img_t = (img[i].numpy().transpose([1, 2, 0]) * 255.0).astype('uint8')
-        #     img_t = img_t.copy()
-        #     gt_bboxes_t = gt_bboxes[i].cpu().numpy()
-        #     box = gt_bboxes_t.astype(np.int32).reshape((-1, 1, 2))
-        #     cv2.polylines(img_t, [box], True, color=(255, 255, 0), thickness=1)
-        #     cv2.imwrite("/Users/hongyongjie/project/PaddleOCR/output/{}.png".format(i), img_t)
-        #     # cv2.imwrite("/Users/hongyongjie/project/PaddleOCR/output/{}.png".format(i), img_t * 255.0)
-        # exit()
         x = self.img_feat(img)
         boxes, rois_num = self.bbox2roi(gt_bboxes)
         feats = paddle.fluid.layers.roi_align(

tools/infer_kie.py

@@ -80,7 +80,8 @@ def draw_kie_result(batch, node, idx_to_cls, count):
     vis_img = np.ones((h, w * 3, 3), dtype=np.uint8) * 255
     vis_img[:, :w] = img
     vis_img[:, w:] = pred_img
-    save_kie_path = os.path.dirname(config['Global']['save_res_path']) + "/kie_results/"
+    save_kie_path = os.path.dirname(config['Global'][
+        'save_res_path']) + "/kie_results/"
     if not os.path.exists(save_kie_path):
         os.makedirs(save_kie_path)
     save_path = os.path.join(save_kie_path, str(count) + ".png")
@@ -128,7 +129,8 @@ def main():
                     batch_pred[i] = paddle.to_tensor(
                         np.expand_dims(
                             batch[i], axis=0))
-                node, edge = model(batch_pred)
+
+                node, edge = model(batch[0], batch[1:])
                 node = F.softmax(node, -1)
                 draw_kie_result(batch, node, idx_to_cls, index)
     logger.info("success!")

tools/program.py

@@ -197,7 +197,7 @@ def train(config,
 
     use_srn = config['Architecture']['algorithm'] == "SRN"
     extra_input = config['Architecture'][
-        'algorithm'] in ["SRN", "NRTR", "SAR", "SEED"]
+        'algorithm'] in ["SRN", "NRTR", "SAR", "SEED", "SDMGR"]
     try:
         model_type = config['Architecture']['model_type']
     except:
@@ -230,7 +230,7 @@ def train(config,
             if model_type == 'table' or extra_input:
                 preds = model(images, data=batch[1:])
             else:
-                preds = model(batch)
+                preds = model(images)
             loss = loss_class(preds, batch)
             avg_loss = loss['loss']
             avg_loss.backward()
@@ -379,7 +379,7 @@ def eval(model,
             if model_type == 'table' or extra_input:
                 preds = model(images, data=batch[1:])
             else:
-                preds = model(batch)
+                preds = model(images)
             batch = [item.numpy() for item in batch]
             # Obtain usable results from post-processing methods
             total_time += time.time() - start