set allow_pickle=True in np.load()

CHANGELOG.md

@@ -86,6 +86,8 @@ To release a new version, please update the changelog as followed:
 ### Fixed
 - Fix `tf.models.Model._construct_graph` for list of outputs, e.g. STN case (PR #1010)
 - Enable better `in_channels` exception raise. (pR #1015)
+- Set allow_pickle=True in np.load() (#PR 1021)
+
 ### Removed
 
 ### Security
@@ -94,7 +96,7 @@ To release a new version, please update the changelog as followed:
 
 - @zsdonghao
 - @ChrisWu1997: #1010 #1015
-- @warshallrho: #1017
+- @warshallrho: #1017 #1021
 
 ## [2.1.0]
 

examples/tutorial_work_with_onnx.py

@@ -105,7 +105,7 @@
 >>>model = onnx.load('mnist.onnx')
 >>>tf_rep = prepare(model)
 >>>#Image Path
->>>img = np.load("./assets/image.npz")
+>>>img = np.load("./assets/image.npz", allow_pickle=True)
 >>>output = tf_rep.run(img.reshape([1, 784]))
 >>>print "The digit is classified as ", np.argmax(output)
 
@@ -317,7 +317,7 @@ def convert_onnx_to_model(onnx_input_path):
     model = onnx.load(onnx_input_path)
     tf_rep = prepare(model)
     # Image Path
-    img = np.load("./assets/image.npz")
+    img = np.load("./assets/image.npz", allow_pickle=True)
     output = tf_rep.run(img.reshape([1, 784]))
     print("The digit is classified as ", np.argmax(output))
 

tensorlayer/files/utils.py

@@ -689,7 +689,7 @@ def load_cropped_svhn(path='data', include_extra=True):
         np.savez(np_file, X=X_train, y=y_train)
         del_file(filepath)
     else:
-        v = np.load(np_file)
+        v = np.load(np_file, allow_pickle=True)
         X_train = v['X']
         y_train = v['y']
     logging.info("  n_train: {}".format(len(y_train)))
@@ -706,7 +706,7 @@ def load_cropped_svhn(path='data', include_extra=True):
         np.savez(np_file, X=X_test, y=y_test)
         del_file(filepath)
     else:
-        v = np.load(np_file)
+        v = np.load(np_file, allow_pickle=True)
         X_test = v['X']
         y_test = v['y']
     logging.info("  n_test: {}".format(len(y_test)))
@@ -726,7 +726,7 @@ def load_cropped_svhn(path='data', include_extra=True):
             np.savez(np_file, X=X_extra, y=y_extra)
             del_file(filepath)
         else:
-            v = np.load(np_file)
+            v = np.load(np_file, allow_pickle=True)
             X_extra = v['X']
             y_extra = v['y']
         # print(X_train.shape, X_extra.shape)
@@ -2096,7 +2096,7 @@ def load_and_assign_npz_dict(name='model.npz', network=None, skip=False):
         logging.error("file {} doesn't exist.".format(name))
         return False
 
-    weights = np.load(name)
+    weights = np.load(name, allow_pickle=True)
     if len(weights.keys()) != len(set(weights.keys())):
         raise Exception("Duplication in model npz_dict %s" % name)
 
@@ -2288,9 +2288,9 @@ def load_npy_to_any(path='', name='file.npy'):
     """
     file_path = os.path.join(path, name)
     try:
-        return np.load(file_path).item()
+        return np.load(file_path, allow_pickle=True).item()
     except Exception:
-        return np.load(file_path)
+        return np.load(file_path, allow_pickle=True)
     raise Exception("[!] Fail to load %s" % file_path)
 
 

tensorlayer/models/vgg.py

@@ -57,14 +57,16 @@
 cfg = {
     'A': [[64], 'M', [128], 'M', [256, 256], 'M', [512, 512], 'M', [512, 512], 'M', 'F', 'fc1', 'fc2', 'O'],
     'B': [[64, 64], 'M', [128, 128], 'M', [256, 256], 'M', [512, 512], 'M', [512, 512], 'M', 'F', 'fc1', 'fc2', 'O'],
-    'D': [
-        [64, 64], 'M', [128, 128], 'M', [256, 256, 256], 'M', [512, 512, 512], 'M', [512, 512, 512], 'M', 'F', 'fc1',
-        'fc2', 'O'
-    ],
-    'E': [
-        [64, 64], 'M', [128, 128], 'M', [256, 256, 256, 256], 'M', [512, 512, 512, 512], 'M', [512, 512, 512, 512], 'M',
-        'F', 'fc1', 'fc2', 'O'
-    ],
+    'D':
+        [
+            [64, 64], 'M', [128, 128], 'M', [256, 256, 256], 'M', [512, 512, 512], 'M', [512, 512, 512], 'M', 'F',
+            'fc1', 'fc2', 'O'
+        ],
+    'E':
+        [
+            [64, 64], 'M', [128, 128], 'M', [256, 256, 256, 256], 'M', [512, 512, 512, 512], 'M', [512, 512, 512, 512],
+            'M', 'F', 'fc1', 'fc2', 'O'
+        ],
 }
 
 mapped_cfg = {
@@ -158,15 +160,15 @@ def restore_model(model, layer_type):
     maybe_download_and_extract(model_saved_name[layer_type], 'models', model_urls[layer_type])
     weights = []
     if layer_type == 'vgg16':
-        npz = np.load(os.path.join('models', model_saved_name[layer_type]))
+        npz = np.load(os.path.join('models', model_saved_name[layer_type]), allow_pickle=True)
         # get weight list
         for val in sorted(npz.items()):
             logging.info("  Loading weights %s in %s" % (str(val[1].shape), val[0]))
             weights.append(val[1])
             if len(model.all_weights) == len(weights):
                 break
     elif layer_type == 'vgg19':
-        npz = np.load(os.path.join('models', model_saved_name[layer_type]), encoding='latin1').item()
+        npz = np.load(os.path.join('models', model_saved_name[layer_type]), allow_pickle=True, encoding='latin1').item()
         # get weight list
         for val in sorted(npz.items()):
             logging.info("  Loading %s in %s" % (str(val[1][0].shape), val[0]))