fix kie infer and eval bug

ppocr/modeling/backbones/kie_unet_sdmgr.py

@@ -167,10 +167,10 @@ class Kie_backbone(nn.Layer):
                     gt_bboxes[i, :num, ...], dtype='float32'))
         return img, temp_relations, temp_texts, temp_gt_bboxes
 
-    def forward(self, images, inputs):
-        img = images
-        relations, texts, gt_bboxes, tag, img_size = inputs[0], inputs[
-            1], inputs[2], inputs[4], inputs[-1]
+    def forward(self, inputs):
+        img = inputs[0]
+        relations, texts, gt_bboxes, tag, img_size = inputs[1], inputs[
+            2], inputs[3], inputs[5], inputs[-1]
         img, relations, texts, gt_bboxes = self.pre_process(
             img, relations, texts, gt_bboxes, tag, img_size)
         x = self.img_feat(img)

ppocr/modeling/heads/kie_sdmgr_head.py

@@ -49,7 +49,7 @@ class SDMGRHead(nn.Layer):
         self.node_cls = nn.Linear(node_embed, num_classes)
         self.edge_cls = nn.Linear(edge_embed, 2)
 
-    def forward(self, input):
+    def forward(self, input, targets):
         relations, texts, x = input
         node_nums, char_nums = [], []
         for text in texts:

tools/eval.py

@@ -54,7 +54,7 @@ def main():
             config['Architecture']["Head"]['out_channels'] = char_num
 
     model = build_model(config['Architecture'])
-    extra_input = config['Architecture']['algorithm'] in ["SRN", "SAR"]
+    extra_input = config['Architecture']['algorithm'] in  ["SRN", "NRTR", "SAR", "SEED"] 
     if "model_type" in config['Architecture'].keys():
         model_type = config['Architecture']['model_type']
     else:
@@ -68,7 +68,7 @@ def main():
 
     # build metric
     eval_class = build_metric(config['Metric'])
-
+    logger.info(f"extra_inputs: {extra_input}")
     # start eval
     metric = program.eval(model, valid_dataloader, post_process_class,
                           eval_class, model_type, extra_input)

tools/infer_kie.py

@@ -80,8 +80,7 @@ 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")
@@ -129,8 +128,7 @@ def main():
                     batch_pred[i] = paddle.to_tensor(
                         np.expand_dims(
                             batch[i], axis=0))
-
-                node, edge = model(batch[0], batch[1:])
+                node, edge = model(batch_pred)
                 node = F.softmax(node, -1)
                 draw_kie_result(batch, node, idx_to_cls, index)
     logger.info("success!")

tools/program.py

@@ -196,7 +196,7 @@ def train(config,
 
     use_srn = config['Architecture']['algorithm'] == "SRN"
     extra_input = config['Architecture'][
-        'algorithm'] in ["SRN", "NRTR", "SAR", "SEED", "SDMGR"]
+        'algorithm'] in ["SRN", "NRTR", "SAR", "SEED"]
     try:
         model_type = config['Architecture']['model_type']
     except:
@@ -228,6 +228,8 @@ def train(config,
                 model_average = True
             if model_type == 'table' or extra_input:
                 preds = model(images, data=batch[1:])
+            if model_type == "kie":
+                preds = model(batch)
             else:
                 preds = model(images)
             loss = loss_class(preds, batch)
@@ -249,7 +251,7 @@ def train(config,
 
             if cal_metric_during_train:  # only rec and cls need
                 batch = [item.numpy() for item in batch]
-                if model_type == 'table':
+                if model_type in ['table', 'kie']:
                     eval_class(preds, batch)
                 else:
                     post_result = post_process_class(preds, batch[1])
@@ -377,13 +379,15 @@ def eval(model,
             start = time.time()
             if model_type == 'table' or extra_input:
                 preds = model(images, data=batch[1:])
+            if model_type == "kie":
+                preds = model(batch)
             else:
                 preds = model(images)
             batch = [item.numpy() for item in batch]
             # Obtain usable results from post-processing methods
             total_time += time.time() - start
             # Evaluate the results of the current batch
-            if model_type == 'table':
+            if model_type in ['table', 'kie']:
                 eval_class(preds, batch)
             else:
                 post_result = post_process_class(preds, batch[1])