Fix some bugs

examples/basic_tutorials/load_pytorch_parameters_to_tensorlayerx.py

@@ -88,7 +88,7 @@ def def_torch_weight_reshape(weight):
     # Step1: save pytorch model parameters to a.pth
     # On the first run, uncomment lines 90 and 91.
     # b = B()
-    # torch.save(a.state_dict(), 'a.pth')
+    # torch.save(b.state_dict(), 'a.pth')
 
     a = A()
     # Step2: Converts pytorch a.pth to the model parameter format of tensorlayerx

tensorlayerx/backend/ops/paddle_nn.py

@@ -496,10 +496,10 @@ class Conv2D(object):
 
     def __init__(self, strides, padding, data_format='NHWC', dilations=None, out_channel=None, k_size=None):
         self.data_format, self.padding = preprocess_2d_format(data_format, padding)
-        if self.data_format is 'NHWC':
+        if self.data_format == 'NHWC':
             self._stride = (strides[1], strides[2])
             self._dilation = (dilations[1], dilations[2])
-        elif self.data_format is 'NCHW':
+        elif self.data_format == 'NCHW':
             self._stride = (strides[2], strides[3])
             self._dilation = (dilations[2], dilations[3])
 
@@ -537,10 +537,10 @@ def conv2d(input, filters, strides, padding, data_format='NCHW', dilations=None)
         A Tensor. Has the same type as input.
     """
     data_format, padding = preprocess_2d_format(data_format, padding)
-    if data_format is 'NHWC':
+    if data_format == 'NHWC':
         _stride = (strides[1], strides[2])
         _dilation = (dilations[1], dilations[2])
-    elif data_format is 'NCHW':
+    elif data_format == 'NCHW':
         _stride = (strides[2], strides[3])
         _dilation = (dilations[2], dilations[3])
     outputs = F.conv2d(
@@ -553,10 +553,10 @@ class Conv3D(object):
 
     def __init__(self, strides, padding, data_format='NDHWC', dilations=None, out_channel=None, k_size=None):
         self.data_format, self.padding = preprocess_3d_format(data_format, padding)
-        if self.data_format is 'NDHWC':
+        if self.data_format == 'NDHWC':
             self._strides = (strides[1], strides[2], strides[3])
             self._dilations = (dilations[1], dilations[2], dilations[3])
-        elif self.data_format is 'NCDHW':
+        elif self.data_format == 'NCDHW':
             self._strides = (strides[2], strides[3], strides[4])
             self._dilations = (dilations[2], dilations[3], dilations[4])
 
@@ -603,10 +603,10 @@ def conv3d(input, filters, strides, padding, data_format='NDHWC', dilations=None
         A Tensor. Has the same type as input.
     """
     data_format, padding = preprocess_3d_format(data_format, padding)
-    if data_format is 'NDHWC':
+    if data_format == 'NDHWC':
         _strides = (strides[1], strides[2], strides[3])
         _dilations = (dilations[1], dilations[2], dilations[3])
-    elif data_format is 'NCDHW':
+    elif data_format == 'NCDHW':
         _strides = (strides[2], strides[3], strides[4])
         _dilations = (dilations[2], dilations[3], dilations[4])
     outputs = F.conv3d(
@@ -1195,10 +1195,10 @@ def __init__(self, strides, padding, data_format, dilations, out_channel, k_size
         self.k_size = k_size
         self.groups = groups
         self.data_format, self.padding = preprocess_2d_format(data_format, padding)
-        if self.data_format is 'NHWC':
+        if self.data_format == 'NHWC':
             self.strides = (strides[1], strides[2])
             self.dilations = (dilations[1], dilations[2])
-        elif self.data_format is 'NCHW':
+        elif self.data_format == 'NCHW':
             self.strides = (strides[2], strides[3])
             self.dilations = (dilations[2], dilations[3])
 
@@ -1241,10 +1241,10 @@ def __init__(self, strides, padding, data_format, dilations, out_channel, k_size
         self.in_channel = int(in_channel)
         self.depth_multiplier = depth_multiplier
         self.data_format, self.padding = preprocess_2d_format(data_format, padding)
-        if self.data_format is 'NHWC':
+        if self.data_format == 'NHWC':
             self.strides = (strides[1], strides[2])
             self.dilations = (dilations[1], dilations[2])
-        elif self.data_format is 'NCHW':
+        elif self.data_format == 'NCHW':
             self.strides = (strides[2], strides[3])
             self.dilations = (dilations[2], dilations[3])
 

tensorlayerx/files/dataset_loaders/mnist_dataset.py

@@ -31,6 +31,6 @@ def load_mnist_dataset(shape=(-1, 784), path='data'):
     """
     logging.info("If can't download this dataset automatically, "
                   "please download it from the official website manually."
-                  "mnist Dataset <http://yann.lecun.com/exdb/mnist/>."
+                  "mnist Dataset <https://ossci-datasets.s3.amazonaws.com/mnist/>."
                   "Please place dataset under 'data/mnist/' by default.")
-    return _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/exdb/mnist/')
+    return _load_mnist_dataset(shape, path, name='mnist', url='https://ossci-datasets.s3.amazonaws.com/mnist/')

tensorlayerx/files/dataset_loaders/mnist_utils.py

@@ -12,7 +12,7 @@
 __all__ = ["_load_mnist_dataset"]
 
 
-def _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/exdb/mnist/'):
+def _load_mnist_dataset(shape, path, name='mnist', url='https://ossci-datasets.s3.amazonaws.com/mnist/'):
     """A generic function to load mnist-like dataset.
 
     Parameters:
@@ -24,7 +24,7 @@ def _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/ex
     name : str
         The dataset name you want to use(the default is 'mnist').
     url : str
-        The url of dataset(the default is 'http://yann.lecun.com/exdb/mnist/').
+        The url of dataset(the default is 'https://ossci-datasets.s3.amazonaws.com/mnist/').
     """
     path = os.path.join(path, name)
 

tensorlayerx/files/utils.py

@@ -280,7 +280,7 @@ def load_mnist_dataset(shape=(-1, 784), path='data'):
     >>> X_train, y_train, X_val, y_val, X_test, y_test = tlx.files.load_mnist_dataset(shape=(-1,784), path='datasets')
     >>> X_train, y_train, X_val, y_val, X_test, y_test = tlx.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
     """
-    return _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/exdb/mnist/')
+    return _load_mnist_dataset(shape, path, name='mnist', url='https://ossci-datasets.s3.amazonaws.com/mnist/')
 
 
 def load_fashion_mnist_dataset(shape=(-1, 784), path='data'):
@@ -310,7 +310,7 @@ def load_fashion_mnist_dataset(shape=(-1, 784), path='data'):
     )
 
 
-def _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/exdb/mnist/'):
+def _load_mnist_dataset(shape, path, name='mnist', url='https://ossci-datasets.s3.amazonaws.com/mnist/'):
     """A generic function to load mnist-like dataset.
 
     Parameters:
@@ -322,7 +322,7 @@ def _load_mnist_dataset(shape, path, name='mnist', url='http://yann.lecun.com/ex
     name : str
         The dataset name you want to use(the default is 'mnist').
     url : str
-        The url of dataset(the default is 'http://yann.lecun.com/exdb/mnist/').
+        The url of dataset(the default is 'https://ossci-datasets.s3.amazonaws.com/mnist/').
     """
     path = os.path.join(path, name)
 
@@ -2375,7 +2375,7 @@ def maybe_download_and_extract(filename, working_directory, url_source, extract=
     --------
     >>> down_file = tlx.files.maybe_download_and_extract(filename='train-images-idx3-ubyte.gz',
     ...                                            working_directory='data/',
-    ...                                            url_source='http://yann.lecun.com/exdb/mnist/')
+    ...                                            url_source='https://ossci-datasets.s3.amazonaws.com/mnist/')
     >>> tlx.files.maybe_download_and_extract(filename='ADEChallengeData2016.zip',
     ...                                             working_directory='data/',
     ...                                             url_source='http://sceneparsing.csail.mit.edu/data/',