merge tem update

.gitignore

@@ -29,3 +29,4 @@ dist/
 /datasets
 /models
 /output
+local_config.py
\ No newline at end of file

demo/webcam.py

@@ -61,7 +61,10 @@ def img_to_three_color_map(source_path, target_path):
         masks_per_dim=args.masks_per_dim,
         min_image_size=args.min_image_size,
     )
-
+    source_path = source_path[3:]
+    target_path = target_path[3:]
+    print(target_path)
+    print(source_path)
     img = cv2.imread(source_path)
     composite = coco_demo.run_on_opencv_image(img)
     if composite is not None:

work.py → demo/work.py

@@ -13,18 +13,23 @@ def write_redis(source_path, target_path):
 
 
 def main():
-    rs = os.walk('../fashionbase/images/dataset_0520/weiyi')
+    rs = os.walk('../../fashionbase/images/dataset_0520/xifu')
     for a, b, files in rs:
+        print(a)
         for file in files:
-            dir = source_path.replace('dataset_0520', 'dataset_0520_10000')
-            fold = os.exit(dir)
+            dir = '../../fashionbase/images/dataset_0520/xifu'.replace('dataset_0520', 'dataset_0520_10000')
+            fold = os.path.exists(dir)
             if not fold:
                 os.mkdir(dir)
-            source_path = '../fashionbase/images/dataset_0520/weiyi/' + file
+            source_path = '../../fashionbase/images/dataset_0520/xifu/' + file
             target_path = source_path.replace('dataset_0520', 'dataset_0520_10000')
+
+            # source_path = source_path[3:]
+            # target_path = target_path[3:]
             print(source_path)
             print(target_path)
-            #write_redis(source_path, target_path)
+            # img_reproce.delay(source_path, target_path)
+            write_redis(source_path, target_path)
 
 
 if __name__ == '__main__':

maskrcnn_benchmark/utils/comm.py

@@ -63,8 +63,9 @@ def all_gather(data):
     tensor = torch.ByteTensor(storage).to("cuda")
 
     # obtain Tensor size of each rank
-    local_size = torch.IntTensor([tensor.numel()]).to("cuda")
-    size_list = [torch.IntTensor([0]).to("cuda") for _ in range(world_size)]
+    local_size = torch.LongTensor([tensor.numel()]).to("cuda")
+    size_list = [torch.LongTensor([0]).to("cuda") for _ in range(world_size)]
+
     dist.all_gather(size_list, local_size)
     size_list = [int(size.item()) for size in size_list]
     max_size = max(size_list)

proj/config.py

@@ -10,4 +10,9 @@ CELERY_RESULT_SERIALIZER = 'json' # 读取任务结果一般性能要求不高
 
 CELERY_TASK_RESULT_EXPIRES = 60 * 60 * 24 # 任务过期时间，不建议直接写86400，应该让这样的magic数字表述更明显
 
-CELERY_ACCEPT_CONTENT = ['json', 'msgpack'] # 指定接受的内容类型
\ No newline at end of file
+CELERY_ACCEPT_CONTENT = ['json', 'msgpack'] # 指定接受的内容类型
+
+try:
+    from .config_local import *  # noqa
+except ImportError:
+    pass