def import_onnx():
"""Import the onnx model into mxnet"""
model_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_resolution.onnx'
download(model_url, 'super_resolution.onnx')
LOGGER.info("Converting onnx format to mxnet's symbol and params...")
sym, arg_params, aux_params = onnx_mxnet.import_model('super_resolution.onnx')
LOGGER.info("Successfully Converted onnx format to mxnet's symbol and params...")
return sym, arg_params, aux_params
def get_test_image():
"""Download and process the test image"""
# Load test image
input_image_dim = 224
img_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_res_input.jpg'
download(img_url, 'super_res_input.jpg')
img = Image.open('super_res_input.jpg').resize((input_image_dim, input_image_dim))
img_ycbcr = img.convert("YCbCr")
img_y, img_cb, img_cr = img_ycbcr.split()
input_image = np.array(img_y)[np.newaxis, np.newaxis, :, :]
return input_image, img_cb, img_cr
def download_sick(dirpath):
if os.path.exists(dirpath):
print('Found SICK dataset - skip')
return
else:
os.makedirs(dirpath)
train_url = 'http://alt.qcri.org/semeval2014/task1/data/uploads/sick_train.zip'
trial_url = 'http://alt.qcri.org/semeval2014/task1/data/uploads/sick_trial.zip'
test_url = 'http://alt.qcri.org/semeval2014/task1/data/uploads/sick_test_annotated.zip'
unzip(download(train_url, dirname=dirpath))
unzip(download(trial_url, dirname=dirpath))
unzip(download(test_url, dirname=dirpath))
def download_sick(dirpath):
if os.path.exists(dirpath):
print('Found SICK dataset - skip')
return
else:
os.makedirs(dirpath)
train_url = 'http://alt.qcri.org/semeval2014/task1/data/uploads/sick_train.zip'
trial_url = 'http://alt.qcri.org/semeval2014/task1/data/uploads/sick_trial.zip'
test_url = 'http://alt.qcri.org/semeval2014/task1/data/uploads/sick_test_annotated.zip'
unzip(download(train_url, dirname=dirpath))
unzip(download(trial_url, dirname=dirpath))
unzip(download(test_url, dirname=dirpath))
def get_test_image():
"""Download and process the test image"""
# Load test image
input_image_dim = 224
img_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_res_input.jpg'
download(img_url, 'super_res_input.jpg')
img = Image.open('super_res_input.jpg').resize(
(input_image_dim, input_image_dim))
img_ycbcr = img.convert("YCbCr")
img_y, img_cb, img_cr = img_ycbcr.split()
input_image = np.array(img_y)[np.newaxis, np.newaxis, :, :]
return input_image, img_cb, img_cr
def get_Dataset():
url_format = 'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/pikachu/{}'
hashes = {
'train.rec': 'e6bcb6ffba1ac04ff8a9b1115e650af56ee969c8',
'train.idx': 'dcf7318b2602c06428b9988470c731621716c393',
'val.rec': 'd6c33f799b4d058e82f2cb5bd9a976f69d72d520'
}
for k, v in hashes.items():
fname = k
target = osp.join('data', fname)
url = url_format.format(k)
if not osp.exists(target) or not verified(target, v):
print('Downloading', target, url)
download(url, fname=fname, dirname='data', overwrite=True)
def get_test_files(name):
"""Extract tar file and returns model path and input, output data"""
tar_name = download(URLS.get(name), dirname=CURR_PATH.__str__())
# extract tar file
tar_path = os.path.join(CURR_PATH, tar_name)
tar = tarfile.open(tar_path.__str__(), "r:*")
tar.extractall(path=CURR_PATH.__str__())
tar.close()
data_dir = os.path.join(CURR_PATH, name)
model_path = os.path.join(data_dir, 'model.onnx')
inputs = []
outputs = []
# get test files
for test_file in os.listdir(data_dir):
case_dir = os.path.join(data_dir, test_file)
# skip the non-dir files
if not os.path.isdir(case_dir):
continue
input_file = os.path.join(case_dir, 'input_0.pb')
input_tensor = TensorProto()
with open(input_file, 'rb') as proto_file:
input_tensor.ParseFromString(proto_file.read())
inputs.append(numpy_helper.to_array(input_tensor))
output_tensor = TensorProto()
output_file = os.path.join(case_dir, 'output_0.pb')
with open(output_file, 'rb') as proto_file:
output_tensor.ParseFromString(proto_file.read())
outputs.append(numpy_helper.to_array(output_tensor))
return model_path, inputs, outputs
def get_mnist_iterator(rank):
data_dir = "data-%d" % rank
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
zip_file_path = download('http://data.mxnet.io/mxnet/data/mnist.zip',
dirname=data_dir)
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall(data_dir)
input_shape = (1, 28, 28)
batch_size = args.batch_size
train_iter = mx.io.MNISTIter(
image="%s/train-images-idx3-ubyte" % data_dir,
label="%s/train-labels-idx1-ubyte" % data_dir,
input_shape=input_shape,
batch_size=batch_size,
shuffle=True,
flat=False,
num_parts=hvd.size(),
part_index=hvd.rank()
)
val_iter = mx.io.MNISTIter(
image="%s/t10k-images-idx3-ubyte" % data_dir,
label="%s/t10k-labels-idx1-ubyte" % data_dir,
input_shape=input_shape,
batch_size=batch_size,
flat=False,
num_parts=hvd.size(),
part_index=hvd.rank()
)
return train_iter, val_iter
def get_mnist_iterator(rank):
data_dir = "data-%d" % rank
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
zip_file_path = download('http://data.mxnet.io/mxnet/data/mnist.zip',
dirname=data_dir)
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall(data_dir)
input_shape = (1, 28, 28)
batch_size = args.batch_size
train_iter = mx.io.MNISTIter(
image="%s/train-images-idx3-ubyte" % data_dir,
label="%s/train-labels-idx1-ubyte" % data_dir,
input_shape=input_shape,
batch_size=batch_size,
shuffle=True,
flat=False,
num_parts=hvd.size(),
part_index=hvd.rank()
)
val_iter = mx.io.MNISTIter(
image="%s/t10k-images-idx3-ubyte" % data_dir,
label="%s/t10k-labels-idx1-ubyte" % data_dir,
input_shape=input_shape,
batch_size=batch_size,
flat=False,
)
return train_iter, val_iter
def get_test_files(name):
"""Extract tar file and returns model path and input, output data"""
tar_name = download(URLS.get(name), dirname=CURR_PATH.__str__())
# extract tar file
tar_path = os.path.join(CURR_PATH, tar_name)
tar = tarfile.open(tar_path.__str__(), "r:*")
tar.extractall(path=CURR_PATH.__str__())
tar.close()
data_dir = os.path.join(CURR_PATH, name)
model_path = os.path.join(data_dir, 'model.onnx')
inputs = []
outputs = []
# get test files
for test_file in os.listdir(data_dir):
case_dir = os.path.join(data_dir, test_file)
# skip the non-dir files
if not os.path.isdir(case_dir):
continue
input_file = os.path.join(case_dir, 'input_0.pb')
input_tensor = TensorProto()
with open(input_file, 'rb') as proto_file:
input_tensor.ParseFromString(proto_file.read())
inputs.append(numpy_helper.to_array(input_tensor))
output_tensor = TensorProto()
output_file = os.path.join(case_dir, 'output_0.pb')
with open(output_file, 'rb') as proto_file:
output_tensor.ParseFromString(proto_file.read())
outputs.append(numpy_helper.to_array(output_tensor))
return model_path, inputs, outputs
def get_dataset(prefetch=False):
image_path = os.path.join(dataset_path, "BSDS300/images")
if not os.path.exists(image_path):
os.makedirs(dataset_path)
file_name = download(dataset_url)
with tarfile.open(file_name) as tar:
for item in tar:
tar.extract(item, dataset_path)
os.remove(file_name)
crop_size = 256
crop_size -= crop_size % upscale_factor
input_crop_size = crop_size // upscale_factor
input_transform = [
CenterCropAug((crop_size, crop_size)),
ResizeAug(input_crop_size)
]
target_transform = [CenterCropAug((crop_size, crop_size))]
iters = (ImagePairIter(os.path.join(image_path, "train"),
(input_crop_size, input_crop_size),
(crop_size, crop_size), batch_size, color_flag,
input_transform, target_transform),
ImagePairIter(os.path.join(image_path, "test"),
(input_crop_size, input_crop_size),
(crop_size, crop_size), test_batch_size, color_flag,
input_transform, target_transform))
return [PrefetchingIter(i) for i in iters] if prefetch else iters
def get_dataset(prefetch=False):
image_path = os.path.join(dataset_path, "BSDS300/images")
if not os.path.exists(image_path):
os.makedirs(dataset_path)
file_name = download(dataset_url)
with tarfile.open(file_name) as tar:
for item in tar:
tar.extract(item, dataset_path)
os.remove(file_name)
crop_size = 256
crop_size -= crop_size % upscale_factor
input_crop_size = crop_size // upscale_factor
input_transform = [CenterCropAug((crop_size, crop_size)), ResizeAug(input_crop_size)]
target_transform = [CenterCropAug((crop_size, crop_size))]
iters = (ImagePairIter(os.path.join(image_path, "train"),
(input_crop_size, input_crop_size),
(crop_size, crop_size),
batch_size, color_flag, input_transform, target_transform),
ImagePairIter(os.path.join(image_path, "test"),
(input_crop_size, input_crop_size),
(crop_size, crop_size),
test_batch_size, color_flag,
input_transform, target_transform))
return [PrefetchingIter(i) for i in iters] if prefetch else iters
def download_wordvecs(dirpath):
if os.path.exists(dirpath):
print('Found Glove vectors - skip')
return
else:
os.makedirs(dirpath)
url = 'http://www-nlp.stanford.edu/data/glove.840B.300d.zip'
unzip(download(url, dirname=dirpath))
def download_wordvecs(dirpath):
if os.path.exists(dirpath):
print('Found Glove vectors - skip')
return
else:
os.makedirs(dirpath)
url = 'http://www-nlp.stanford.edu/data/glove.840B.300d.zip'
unzip(download(url, dirname=dirpath))
def download_model(model_name, model_path):
# reference: https://github.com/mlperf/inference/tree/master/v0.5/classification_and_detection
if model_name == 'resnet50-v1.5':
model_url = 'https://zenodo.org/record/2592612/files/resnet50_v1.onnx'
data_shape = (1, 3, 224, 224)
else:
raise ValueError('Model: {} not implemented.'.format(model_name))
if not os.path.exists(model_path):
os.mkdir(model_path)
onnx_model_file = os.path.join(model_path, model_name + '.onnx')
print(onnx_model_file)
if not os.path.exists(onnx_model_file):
print("Downloading ONNX model from: {}".format(model_url))
download(model_url, onnx_model_file)
return onnx_model_file, data_shape
def test_nodims_import():
# Download test model without dims mentioned in params
test_model = download(test_model_path, dirname=CURR_PATH.__str__())
input_data = np.array([0.2, 0.5])
nd_data = mx.nd.array(input_data).expand_dims(0)
sym, arg_params, aux_params = onnx_mxnet.import_model(test_model)
model_metadata = onnx_mxnet.get_model_metadata(test_model)
input_names = [inputs[0] for inputs in model_metadata.get('input_tensor_data')]
output_data = forward_pass(sym, arg_params, aux_params, input_names, nd_data)
assert(output_data.shape == (1,1))
def GetMNIST_ubyte():
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/train-images-idx3-ubyte')) or \
(not os.path.exists('data/train-labels-idx1-ubyte')) or \
(not os.path.exists('data/t10k-images-idx3-ubyte')) or \
(not os.path.exists('data/t10k-labels-idx1-ubyte')):
zip_file_path = download('http://data.mxnet.io/mxnet/data/mnist.zip',
dirname='data')
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall('data')
def GetCifar10():
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/cifar/train.rec')) or \
(not os.path.exists('data/cifar/test.rec')) or \
(not os.path.exists('data/cifar/train.lst')) or \
(not os.path.exists('data/cifar/test.lst')):
zip_file_path = download('http://data.mxnet.io/mxnet/data/cifar10.zip',
dirname='data')
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall('data')
def GetCifar10():
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/cifar/train.rec')) or \
(not os.path.exists('data/cifar/test.rec')) or \
(not os.path.exists('data/cifar/train.lst')) or \
(not os.path.exists('data/cifar/test.lst')):
zip_file_path = download('http://data.mxnet.io/mxnet/data/cifar10.zip',
dirname='data')
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall('data')
def GetMNIST_ubyte():
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/train-images-idx3-ubyte')) or \
(not os.path.exists('data/train-labels-idx1-ubyte')) or \
(not os.path.exists('data/t10k-images-idx3-ubyte')) or \
(not os.path.exists('data/t10k-labels-idx1-ubyte')):
zip_file_path = download('http://data.mxnet.io/mxnet/data/mnist.zip',
dirname='data')
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall('data')
def download_parser(dirpath):
parser_dir = 'stanford-parser'
if os.path.exists(os.path.join(dirpath, parser_dir)):
print('Found Stanford Parser - skip')
return
url = 'http://nlp.stanford.edu/software/stanford-parser-full-2015-01-29.zip'
filepath = download(url, dirname=dirpath)
zip_dir = ''
with zipfile.ZipFile(filepath) as zf:
zip_dir = zf.namelist()[0]
zf.extractall(dirpath)
os.remove(filepath)
os.rename(os.path.join(dirpath, zip_dir), os.path.join(dirpath, parser_dir))
def download_training_data():
print('downloading training data...')
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/train.rec')) or \
(not os.path.exists('data/test.rec')) or \
(not os.path.exists('data/train.lst')) or \
(not os.path.exists('data/test.lst')):
zip_file_path = download('http://data.mxnet.io/mxnet/data/cifar10.zip')
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall()
os.rename('cifar', 'data')
print('done')
def download_training_data():
print('downloading training data...')
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/train.rec')) or \
(not os.path.exists('data/test.rec')) or \
(not os.path.exists('data/train.lst')) or \
(not os.path.exists('data/test.lst')):
zip_file_path = download('http://data.mxnet.io/mxnet/data/cifar10.zip')
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall()
os.rename('cifar', 'data')
print('done')
def download_training_data(fileName):
print('downloading training data...')
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/train.rec')) or \
(not os.path.exists('data/test.rec')) or \
(not os.path.exists('data/train.lst')) or \
(not os.path.exists('data/test.lst')):
zip_file_path = download('https://sagemaker-crops-corn.s3.amazonaws.com/' + str(fileName)) #'http://data.mxnet.io/mxnet/data/cifar10.zip')
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall()
os.rename('cifar', 'data')
print('done')
def get_cifar10(dir="data"):
"""Downloads CIFAR10 dataset into a directory in the current directory with the name `data`,
and then extracts all files into the directory `data/cifar`.
"""
if not os.path.isdir(dir):
os.makedirs(dir)
if (not os.path.exists(os.path.join(dir, 'cifar', 'train.rec'))) or \
(not os.path.exists(os.path.join(dir, 'cifar', 'test.rec'))) or \
(not os.path.exists(os.path.join(dir, 'cifar', 'train.lst'))) or \
(not os.path.exists(os.path.join(dir, 'cifar', 'test.lst'))):
zip_file_path = download('http://data.mxnet.io/mxnet/data/cifar10.zip',
dirname=dir)
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall(dir)
def download_parser(dirpath):
parser_dir = 'stanford-parser'
if os.path.exists(os.path.join(dirpath, parser_dir)):
print('Found Stanford Parser - skip')
return
url = 'http://nlp.stanford.edu/software/stanford-parser-full-2015-01-29.zip'
filepath = download(url, dirname=dirpath)
zip_dir = ''
with zipfile.ZipFile(filepath) as zf:
zip_dir = zf.namelist()[0]
zf.extractall(dirpath)
os.remove(filepath)
os.rename(os.path.join(dirpath, zip_dir),
os.path.join(dirpath, parser_dir))
def download_training_data():
print("downloading training data...")
if not os.path.isdir("data"):
os.makedirs("data")
if (
(not os.path.exists("data/train.rec"))
or (not os.path.exists("data/test.rec"))
or (not os.path.exists("data/train.lst"))
or (not os.path.exists("data/test.lst"))
):
zip_file_path = download("http://data.mxnet.io/mxnet/data/cifar10.zip")
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall()
os.rename("cifar", "data")
print("done")
def download_voc(path, overwrite=False):
_DOWNLOAD_URLS = [
('http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
'34ed68851bce2a36e2a223fa52c661d592c66b3c'),
('http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',
'41a8d6e12baa5ab18ee7f8f8029b9e11805b4ef1'),
('http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar',
'4e443f8a2eca6b1dac8a6c57641b67dd40621a49')
]
makedirs(path)
for url, checksum in _DOWNLOAD_URLS:
# try:
# filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
# except:
filename = download(url, dirname=path)
# extract
with tarfile.open(filename) as tar:
tar.extractall(path=path)
matched = sha1.hexdigest() == sha1hash
if not matched:
print('Found hash mismatch in file {}, possibly due to incomplete download.'.format(file_path))
return matched
url_format = 'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/dataset/pikachu/{}'
hashes = {'train.rec': 'e6bcb6ffba1ac04ff8a9b1115e650af56ee969c8',
'train.idx': 'dcf7318b2602c06428b9988470c731621716c393',
'val.rec': 'd6c33f799b4d058e82f2cb5bd9a976f69d72d520'}
for k, v in hashes.items():
fname = 'pikachu_' + k
target = osp.join('data', fname)
url = url_format.format(k)
if not osp.exists(target) or not verified(target, v):
print('Downloading', target, url)
download(url, fname=fname, dirname='data', overwrite=True)
import mxnet.image as image
data_shape = 256
batch_size = 32
def get_iterators(data_shape, batch_size):
class_names = ['pikachu']
num_class = len(class_names)
train_iter = image.ImageDetIter(
batch_size=batch_size,
data_shape=(3, data_shape, data_shape),
path_imgrec='./data/pikachu_train.rec',
path_imgidx='./data/pikachu_train.idx',
shuffle=True,
mean=True,
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import zipfile
import shutil
from mxnet.test_utils import download
zip_file_path = 'models/msgnet_21styles.zip'
download('https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/models/msgnet_21styles-2cb88353.zip', zip_file_path)
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall()
os.remove(zip_file_path)
shutil.move('msgnet_21styles-2cb88353.params', 'models/21styles.params')
def get_dataset(prefetch=False):
"""Download the BSDS500 dataset and return train and test iters."""
if path.exists(data_dir):
print(
"Directory {} already exists, skipping.\n"
"To force download and extraction, delete the directory and re-run."
"".format(data_dir),
file=sys.stderr,
)
else:
print("Downloading dataset...", file=sys.stderr)
downloaded_file = download(dataset_url, dirname=datasets_tmpdir)
print("done", file=sys.stderr)
print("Extracting files...", end="", file=sys.stderr)
os.makedirs(data_dir)
os.makedirs(tmp_dir)
with zipfile.ZipFile(downloaded_file) as archive:
archive.extractall(tmp_dir)
shutil.rmtree(datasets_tmpdir)
shutil.copytree(
path.join(tmp_dir, "BSDS500-master", "BSDS500", "data", "images"),
path.join(data_dir, "images"),
)
shutil.copytree(
path.join(tmp_dir, "BSDS500-master", "BSDS500", "data", "groundTruth"),
path.join(data_dir, "groundTruth"),
)
shutil.rmtree(tmp_dir)
print("done", file=sys.stderr)
crop_size = 256
crop_size -= crop_size % upscale_factor
input_crop_size = crop_size // upscale_factor
input_transform = [CenterCropAug((crop_size, crop_size)), ResizeAug(input_crop_size)]
target_transform = [CenterCropAug((crop_size, crop_size))]
iters = (
ImagePairIter(
path.join(data_dir, "images", "train"),
(input_crop_size, input_crop_size),
(crop_size, crop_size),
batch_size,
color_flag,
input_transform,
target_transform,
),
ImagePairIter(
path.join(data_dir, "images", "test"),
(input_crop_size, input_crop_size),
(crop_size, crop_size),
test_batch_size,
color_flag,
input_transform,
target_transform,
),
)
return [PrefetchingIter(i) for i in iters] if prefetch else iters
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
"""Testing super_resolution model conversion"""
from __future__ import absolute_import as _abs
from __future__ import print_function
from collections import namedtuple
import mxnet as mx
from mxnet.test_utils import download
import numpy as np
from PIL import Image
import onnx_mxnet
model_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_resolution.onnx'
download(model_url, 'super_resolution.onnx')
print("Converting onnx format to mxnet's symbol and params...")
sym, params = onnx_mxnet.import_model('super_resolution.onnx')
# Load test image
input_image_dim = 224
output_image_dim = 672
img_url = 'https://s3.amazonaws.com/onnx-mxnet/examples/super_res_input.jpg'
download(img_url, 'super_res_input.jpg')
img = Image.open('super_res_input.jpg').resize(
(input_image_dim, input_image_dim))
img_ycbcr = img.convert("YCbCr")
img_y, img_cb, img_cr = img_ycbcr.split()
x = np.array(img_y)[np.newaxis, np.newaxis, :, :]
def get_dataset(prefetch=False):
"""Download the BSDS500 dataset and return train and test iters."""
if path.exists(data_dir):
print(
"Directory {} already exists, skipping.\n"
"To force download and extraction, delete the directory and re-run."
"".format(data_dir),
file=sys.stderr,
)
else:
print("Downloading dataset...", file=sys.stderr)
downloaded_file = download(dataset_url, dirname=datasets_tmpdir)
print("done", file=sys.stderr)
print("Extracting files...", end="", file=sys.stderr)
os.makedirs(data_dir)
os.makedirs(tmp_dir)
with zipfile.ZipFile(downloaded_file) as archive:
archive.extractall(tmp_dir)
shutil.rmtree(datasets_tmpdir)
shutil.copytree(
path.join(tmp_dir, "BSDS500-master", "BSDS500", "data", "images"),
path.join(data_dir, "images"),
)
shutil.copytree(
path.join(tmp_dir, "BSDS500-master", "BSDS500", "data",
"groundTruth"),
path.join(data_dir, "groundTruth"),
)
shutil.rmtree(tmp_dir)
print("done", file=sys.stderr)
crop_size = 256
crop_size -= crop_size % upscale_factor
input_crop_size = crop_size // upscale_factor
input_transform = [
CenterCropAug((crop_size, crop_size)),
ResizeAug(input_crop_size)
]
target_transform = [CenterCropAug((crop_size, crop_size))]
iters = (
ImagePairIter(
path.join(data_dir, "images", "train"),
(input_crop_size, input_crop_size),
(crop_size, crop_size),
batch_size,
color_flag,
input_transform,
target_transform,
),
ImagePairIter(
path.join(data_dir, "images", "test"),
(input_crop_size, input_crop_size),
(crop_size, crop_size),
test_batch_size,
color_flag,
input_transform,
target_transform,
),
)
return [PrefetchingIter(i) for i in iters] if prefetch else iters
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import zipfile
import shutil
from mxnet.test_utils import download
zip_file_path = 'models/msgnet_21styles.zip'
download(
'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/models/msgnet_21styles-2cb88353.zip',
zip_file_path)
with zipfile.ZipFile(zip_file_path) as zf:
zf.extractall()
os.remove(zip_file_path)
shutil.move('msgnet_21styles-2cb88353.params', 'models/21styles.params')
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os, zipfile
import mxnet
from mxnet.test_utils import download
def unzip_file(filename, outpath):
fh = open(filename, 'rb')
z = zipfile.ZipFile(fh)
for name in z.namelist():
z.extract(name, outpath)
fh.close()
# Dataset from COCO 2014: http://cocodataset.org/#download
# The dataset annotations and site are Copyright COCO Consortium and licensed CC BY 4.0 Attribution.
# The images within the dataset are available under the Flickr Terms of Use.
# See http://cocodataset.org/#termsofuse for details
download('http://msvocds.blob.core.windows.net/coco2014/train2014.zip', 'dataset/train2014.zip')
download('http://msvocds.blob.core.windows.net/coco2014/val2014.zip', 'dataset/val2014.zip')
unzip_file('dataset/train2014.zip', 'dataset')
unzip_file('dataset/val2014.zip', 'dataset')
#!/usr/bin/env python
#-*- coding:utf-8 -*-
from mxnet.test_utils import download
import os.path as osp
def verified(file_path, sha1hash):
import hashlib
sha1 = hashlib.sha1()
with open(file_path, 'rb') as f:
while True:
data = f.read(1048576)
if not data:
break
sha1.update(data)
matched = sha1.hexdigest() == sha1hash
if not matched:
print('Found hash mismatch in file {}, possibly due to incomplete download.'.format(file_path))
return matched
url_format = 'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/datasets/pikachu/{}'
hashes = {'train.rec': 'e6bcb6ffba1ac04ff8a9b1115e650af56ee969c8',
'train.idx': 'dcf7318b2602c06428b9988470c731621716c393',
'val.rec': 'd6c33f799b4d058e82f2cb5bd9a976f69d72d520'}
for k, v in hashes.items():##键 值
fname = 'pikachu_' + k
target = osp.join('data', fname)#新建数据集文件夹
url = url_format.format(k)
if not osp.exists(target) or not verified(target, v):
print('Downloading', target, url)
download(url, fname=fname, dirname='data', overwrite=True)
def GetMNIST_pkl():
if not os.path.isdir("data"):
os.makedirs('data')
if not os.path.exists('data/mnist.pkl.gz'):
download('http://deeplearning.net/data/mnist/mnist.pkl.gz',
dirname='data')
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from mxnet.test_utils import download
download(
'https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/models/21styles-32f7205c.params',
'models/21styles.params')
def GetMNIST_pkl():
if not os.path.isdir("data"):
os.makedirs('data')
if not os.path.exists('data/mnist.pkl.gz'):
download('http://deeplearning.net/data/mnist/mnist.pkl.gz',
dirname='data')
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os, zipfile
import mxnet
from mxnet.test_utils import download
def unzip_file(filename, outpath):
fh = open(filename, 'rb')
z = zipfile.ZipFile(fh)
for name in z.namelist():
z.extract(name, outpath)
fh.close()
download('http://msvocds.blob.core.windows.net/coco2014/train2014.zip', 'dataset/train2014.zip')
download('http://msvocds.blob.core.windows.net/coco2014/val2014.zip', 'dataset/val2014.zip')
unzip_file('dataset/train2014.zip', 'dataset')
unzip_file('dataset/val2014.zip', 'dataset')
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from mxnet.test_utils import download
download('https://apache-mxnet.s3-accelerate.amazonaws.com/gluon/models/21styles-32f7205c.params', 'models/21styles.params')