Incorporated CR feedback
This commit is contained in:
REDMOND\sayanpa
Родитель
156028a4dc
Коммит
97782a2098
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@ -10,6 +10,29 @@ from shutil import copyfile
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envvar = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'
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# Generic helper to read data from 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' to local machine
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# One should use this helper when copying code is needed
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# TODO: Update the other data loaders to reuse this code
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def _data_copier(src_files, dst_files):
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src_files = [os.path.normpath(os.path.join((os.environ[envvar]), \
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*src_file.split("/"))) for src_file in src_files]
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dst_files = [os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(os.getcwd())), \
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*dst_file.split("/"))) for dst_file in dst_files]
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if not len(src_files) == len(dst_files):
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raise Exception('The length of src and dst should be same')
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for src_dst_file in zip(src_files, dst_files):
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# Note index 0 is the source and index 1 is destination
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if not os.path.isfile(src_dst_file[1]):
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# copy from backup location
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print("Copying file from: ", src_dst_file[0])
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print("Copying file to: ", src_dst_file[1])
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copyfile( src_dst_file[0], src_dst_file[1])
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else:
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print("Reusing cached file", src_dst_file[1])
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def prepare_CIFAR10_data():
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base_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
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*"../../../../Examples/Image/DataSets/CIFAR-10".split("/"))
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@ -18,7 +41,7 @@ def prepare_CIFAR10_data():
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# If {train,test}_map.txt don't exist locally, copy to local location
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if not (os.path.isfile(os.path.join(base_path, 'train_map.txt')) and os.path.isfile(os.path.join(base_path, 'test_map.txt'))):
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# copy from backup location
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base_path_bak = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
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base_path_bak = os.path.join(os.environ[envvar],
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*"Image/CIFAR/v0/cifar-10-batches-py".split("/"))
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base_path_bak = os.path.normpath(base_path_bak)
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@ -40,7 +63,7 @@ def prepare_ImageNet_data():
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# If val1024_map.txt don't exist locally, copy to local location
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if not (os.path.isfile(os.path.join(base_path, 'train_map.txt')) and os.path.isfile(os.path.join(base_path, 'val_map.txt'))):
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# copy from backup location
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base_path_bak = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
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base_path_bak = os.path.join(os.environ[envvar],
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*"Image/ImageNet/2012/v0".split("/"))
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base_path_bak = os.path.normpath(base_path_bak)
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@ -57,7 +80,7 @@ def prepare_Grocery_data():
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# If val1024_map.txt don't exist locally, copy to local location
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if not os.path.isfile(os.path.join(base_path, 'test.txt')):
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# copy from backup location
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base_path_bak = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
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base_path_bak = os.path.join(os.environ[envvar],
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*"Image/Grocery".split("/"))
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base_path_bak = os.path.normpath(base_path_bak)
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@ -81,26 +104,6 @@ def cmudict_dataset_directory():
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base_path = os.path.normpath(base_path)
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return base_path
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# Generic helper to read data from 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' to local machine
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def _data_copier(src_files, dst_files):
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src_files = [os.path.normpath(os.path.join((os.environ[envvar]), \
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*src_file.split("/"))) for src_file in src_files]
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dst_files = [os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(os.getcwd())), \
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*dst_file.split("/"))) for dst_file in dst_files]
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if not len(src_files) == len(dst_files):
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raise Exception('The length of src and dst should be same')
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for idx, dst in enumerate(dst_files):
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if not os.path.isfile(dst):
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# copy from backup location
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print("Copying file from: ", src_files[idx])
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print("Copying file to: ", dst)
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copyfile( src_files[idx], dst)
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else:
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print("Reusing cached file", dst)
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# Read the flower and animal data set file
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def prepare_resnet_v1_model():
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@ -126,32 +129,3 @@ def prepare_animals_data():
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dst_file = "Examples/Image/DataSets/Animals/Animals.zip"
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_data_copier([src_file], [dst_file])
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def prepare_ResNetmodel_and_Flower_Animals_data():
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src_files = ["PreTrainedModels/ResNet/v1/ResNet_18.model",
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"Image/Flowers/102flowers.tgz",
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"Image/Flowers/imagelabels.mat",
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"Image/Flowers/imagelabels.mat",
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"Image/Animals/Animals.zip"]
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src_files = [os.path.normpath(os.path.join((os.environ[envvar]), \
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*src_file.split("/"))) for src_file in src_files]
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dst_files = ["Examples/Image/PretrainedModels/ResNet_18.model",
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"Examples/Image/DataSets/Flowers/102flowers.tgz",
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"Examples/Image/DataSets/Flowers/imagelabels.mat",
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"Examples/Image/DataSets/Flowers/imagelabels.mat",
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"Examples/Image/DataSets/Animals/Animals.zip"]
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dst_files = [os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(os.getcwd())), \
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*dst_file.split("/"))) for dst_file in dst_files]
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for idx, dst in enumerate(dst_files):
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if not os.path.isfile(dst):
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# copy from backup location
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print("Copying file from: ", src_files[idx])
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print("Copying file to: ", dst)
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copyfile( src_files[idx], dst)
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else:
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print("Reusing cached file", dst)
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@ -35,7 +35,7 @@
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"category": ["Image"],
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"name": "Transfer Learning",
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"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_301_Image_Recognition_with_Deep_Transfer_Learning.ipynb",
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"description": "Recognize flowers and animal in natural scene images using deep transfer learning with pre-trained ResNet data.",
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"description": "Recognize flowers and animals in natural scene images using deep transfer learning with pre-trained ResNet data.",
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"language": ["Python"],
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"type": ["Tutorial", "Recipe"]
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},
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Различия файлов скрыты, потому что одна или несколько строк слишком длинны
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@ -45,7 +45,7 @@ Tutorials
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Try these notebooks pre-installed on `CNTK Azure Notebooks`_ for free.
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For our Japanese users, you can find some of the `tutorials in Japanese`_.
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For our Japanese users, you can find some of the `tutorials in Japanese`_ (unsupported).
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.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/blob/v2.0.beta15.0/Tutorials/CNTK_101_LogisticRegression.ipynb
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.. _`Feed Forward network`: https://github.com/Microsoft/CNTK/blob/v2.0.beta15.0/Tutorials/CNTK_102_FeedForward.ipynb
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@ -61,8 +61,9 @@ For our Japanese users, you can find some of the `tutorials in Japanese`_.
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.. _`Sequence to sequence basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta15.0/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
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.. _`Artistic Style Transfer`: https://github.com/Microsoft/CNTK/blob/v2.0.beta15.0/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb
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.. _`Basic Generative Adversarial Networks (GAN)`: https://github.com/Microsoft/CNTK/blob/v2.0.beta15.0/Tutorials/CNTK_206_Basic_GAN.ipynb
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.. _`Deep Convolutional GAN`: https://github.com/Microsoft/CNTK/blob/v2.0.beta15.0/Tutorials/CNTK_206B_DCGAN.ipynb
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.. _`Training with Sampled Softmax`: https://github.com/Microsoft/CNTK/blob/v2.0.beta15.0/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb
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.. _`Deep transfer learning with pre-trained ResNet model`: https://github.com/Microsoft/CNTK/blob/v2.0.beta12.0/Tutorials/CNTK_301_Image_Recognition_with_Deep_Transfer_Learning.ipynb
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.. _`Deep transfer learning with pre-trained ResNet model`: https://github.com/Microsoft/CNTK/blob/v2.0.beta15.0/Tutorials/CNTK_301_Image_Recognition_with_Deep_Transfer_Learning.ipynb
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.. _`CNTK Azure Notebooks`: https://notebooks.azure.com/cntk/libraries/tutorials
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.. _`tutorials in Japanese`: https://notebooks.azure.com/library/cntkbeta2_ja
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