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23,700 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | get_mnist_sym | def get_mnist_sym(output_op=None, num_hidden=400):
"""Get symbol of mnist"""
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='mnist_fc1', num_hidden=num_hidden)
net = mx.symbol.Activation(data=net, name='mnist_relu1', act_type="relu")
net = mx.symbol.FullyConnected(data=net, name='mnist_fc2', num_hidden=num_hidden)
net = mx.symbol.Activation(data=net, name='mnist_relu2', act_type="relu")
net = mx.symbol.FullyConnected(data=net, name='mnist_fc3', num_hidden=10)
if output_op is None:
net = mx.symbol.SoftmaxOutput(data=net, name='softmax')
else:
net = output_op(data=net, name='softmax')
return net | python | def get_mnist_sym(output_op=None, num_hidden=400):
"""Get symbol of mnist"""
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='mnist_fc1', num_hidden=num_hidden)
net = mx.symbol.Activation(data=net, name='mnist_relu1', act_type="relu")
net = mx.symbol.FullyConnected(data=net, name='mnist_fc2', num_hidden=num_hidden)
net = mx.symbol.Activation(data=net, name='mnist_relu2', act_type="relu")
net = mx.symbol.FullyConnected(data=net, name='mnist_fc3', num_hidden=10)
if output_op is None:
net = mx.symbol.SoftmaxOutput(data=net, name='softmax')
else:
net = output_op(data=net, name='softmax')
return net | [
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23,701 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | synthetic_grad | def synthetic_grad(X, theta, sigma1, sigma2, sigmax, rescale_grad=1.0, grad=None):
"""Get synthetic gradient value"""
if grad is None:
grad = nd.empty(theta.shape, theta.context)
theta1 = theta.asnumpy()[0]
theta2 = theta.asnumpy()[1]
v1 = sigma1 ** 2
v2 = sigma2 ** 2
vx = sigmax ** 2
denominator = numpy.exp(-(X - theta1) ** 2 / (2 * vx)) + numpy.exp(
-(X - theta1 - theta2) ** 2 / (2 * vx))
grad_npy = numpy.zeros(theta.shape)
grad_npy[0] = -rescale_grad * ((numpy.exp(-(X - theta1) ** 2 / (2 * vx)) * (X - theta1) / vx
+ numpy.exp(-(X - theta1 - theta2) ** 2 / (2 * vx)) *
(X - theta1 - theta2) / vx) / denominator).sum() + theta1 / v1
grad_npy[1] = -rescale_grad * ((numpy.exp(-(X - theta1 - theta2) ** 2 / (2 * vx)) *
(X - theta1 - theta2) / vx) / denominator).sum() + theta2 / v2
grad[:] = grad_npy
return grad | python | def synthetic_grad(X, theta, sigma1, sigma2, sigmax, rescale_grad=1.0, grad=None):
"""Get synthetic gradient value"""
if grad is None:
grad = nd.empty(theta.shape, theta.context)
theta1 = theta.asnumpy()[0]
theta2 = theta.asnumpy()[1]
v1 = sigma1 ** 2
v2 = sigma2 ** 2
vx = sigmax ** 2
denominator = numpy.exp(-(X - theta1) ** 2 / (2 * vx)) + numpy.exp(
-(X - theta1 - theta2) ** 2 / (2 * vx))
grad_npy = numpy.zeros(theta.shape)
grad_npy[0] = -rescale_grad * ((numpy.exp(-(X - theta1) ** 2 / (2 * vx)) * (X - theta1) / vx
+ numpy.exp(-(X - theta1 - theta2) ** 2 / (2 * vx)) *
(X - theta1 - theta2) / vx) / denominator).sum() + theta1 / v1
grad_npy[1] = -rescale_grad * ((numpy.exp(-(X - theta1 - theta2) ** 2 / (2 * vx)) *
(X - theta1 - theta2) / vx) / denominator).sum() + theta2 / v2
grad[:] = grad_npy
return grad | [
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23,702 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | get_toy_sym | def get_toy_sym(teacher=True, teacher_noise_precision=None):
"""Get toy symbol"""
if teacher:
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='teacher_fc1', num_hidden=100)
net = mx.symbol.Activation(data=net, name='teacher_relu1', act_type="relu")
net = mx.symbol.FullyConnected(data=net, name='teacher_fc2', num_hidden=1)
net = mx.symbol.LinearRegressionOutput(data=net, name='teacher_output',
grad_scale=teacher_noise_precision)
else:
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='student_fc1', num_hidden=100)
net = mx.symbol.Activation(data=net, name='student_relu1', act_type="relu")
student_mean = mx.symbol.FullyConnected(data=net, name='student_mean', num_hidden=1)
student_var = mx.symbol.FullyConnected(data=net, name='student_var', num_hidden=1)
net = mx.symbol.Group([student_mean, student_var])
return net | python | def get_toy_sym(teacher=True, teacher_noise_precision=None):
"""Get toy symbol"""
if teacher:
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='teacher_fc1', num_hidden=100)
net = mx.symbol.Activation(data=net, name='teacher_relu1', act_type="relu")
net = mx.symbol.FullyConnected(data=net, name='teacher_fc2', num_hidden=1)
net = mx.symbol.LinearRegressionOutput(data=net, name='teacher_output',
grad_scale=teacher_noise_precision)
else:
net = mx.symbol.Variable('data')
net = mx.symbol.FullyConnected(data=net, name='student_fc1', num_hidden=100)
net = mx.symbol.Activation(data=net, name='student_relu1', act_type="relu")
student_mean = mx.symbol.FullyConnected(data=net, name='student_mean', num_hidden=1)
student_var = mx.symbol.FullyConnected(data=net, name='student_var', num_hidden=1)
net = mx.symbol.Group([student_mean, student_var])
return net | [
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23,703 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_mnist_DistilledSGLD | def run_mnist_DistilledSGLD(num_training=50000, gpu_id=None):
"""Run DistilledSGLD on mnist dataset"""
X, Y, X_test, Y_test = load_mnist(num_training)
minibatch_size = 100
if num_training >= 10000:
num_hidden = 800
total_iter_num = 1000000
teacher_learning_rate = 1E-6
student_learning_rate = 0.0001
teacher_prior = 1
student_prior = 0.1
perturb_deviation = 0.1
else:
num_hidden = 400
total_iter_num = 20000
teacher_learning_rate = 4E-5
student_learning_rate = 0.0001
teacher_prior = 1
student_prior = 0.1
perturb_deviation = 0.001
teacher_net = get_mnist_sym(num_hidden=num_hidden)
logsoftmax = LogSoftmax()
student_net = get_mnist_sym(output_op=logsoftmax, num_hidden=num_hidden)
data_shape = (minibatch_size,) + X.shape[1::]
teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'softmax_label': nd.zeros((minibatch_size,), ctx=dev(gpu_id))}
student_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'softmax_label': nd.zeros((minibatch_size, 10), ctx=dev(gpu_id))}
teacher_initializer = BiasXavier(factor_type="in", magnitude=1)
student_initializer = BiasXavier(factor_type="in", magnitude=1)
student_exe, student_params, _ = \
DistilledSGLD(teacher_sym=teacher_net, student_sym=student_net,
teacher_data_inputs=teacher_data_inputs,
student_data_inputs=student_data_inputs,
X=X, Y=Y, X_test=X_test, Y_test=Y_test, total_iter_num=total_iter_num,
student_initializer=student_initializer,
teacher_initializer=teacher_initializer,
student_optimizing_algorithm="adam",
teacher_learning_rate=teacher_learning_rate,
student_learning_rate=student_learning_rate,
teacher_prior_precision=teacher_prior, student_prior_precision=student_prior,
perturb_deviation=perturb_deviation, minibatch_size=100, dev=dev(gpu_id)) | python | def run_mnist_DistilledSGLD(num_training=50000, gpu_id=None):
"""Run DistilledSGLD on mnist dataset"""
X, Y, X_test, Y_test = load_mnist(num_training)
minibatch_size = 100
if num_training >= 10000:
num_hidden = 800
total_iter_num = 1000000
teacher_learning_rate = 1E-6
student_learning_rate = 0.0001
teacher_prior = 1
student_prior = 0.1
perturb_deviation = 0.1
else:
num_hidden = 400
total_iter_num = 20000
teacher_learning_rate = 4E-5
student_learning_rate = 0.0001
teacher_prior = 1
student_prior = 0.1
perturb_deviation = 0.001
teacher_net = get_mnist_sym(num_hidden=num_hidden)
logsoftmax = LogSoftmax()
student_net = get_mnist_sym(output_op=logsoftmax, num_hidden=num_hidden)
data_shape = (minibatch_size,) + X.shape[1::]
teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'softmax_label': nd.zeros((minibatch_size,), ctx=dev(gpu_id))}
student_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'softmax_label': nd.zeros((minibatch_size, 10), ctx=dev(gpu_id))}
teacher_initializer = BiasXavier(factor_type="in", magnitude=1)
student_initializer = BiasXavier(factor_type="in", magnitude=1)
student_exe, student_params, _ = \
DistilledSGLD(teacher_sym=teacher_net, student_sym=student_net,
teacher_data_inputs=teacher_data_inputs,
student_data_inputs=student_data_inputs,
X=X, Y=Y, X_test=X_test, Y_test=Y_test, total_iter_num=total_iter_num,
student_initializer=student_initializer,
teacher_initializer=teacher_initializer,
student_optimizing_algorithm="adam",
teacher_learning_rate=teacher_learning_rate,
student_learning_rate=student_learning_rate,
teacher_prior_precision=teacher_prior, student_prior_precision=student_prior,
perturb_deviation=perturb_deviation, minibatch_size=100, dev=dev(gpu_id)) | [
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23,704 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_toy_SGLD | def run_toy_SGLD(gpu_id=None):
"""Run SGLD on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0 / 9.0
net = get_toy_sym(True, teacher_noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(gpu_id))}
initializer = mx.init.Uniform(0.07)
exe, params, _ = SGLD(sym=net,
data_inputs=data_inputs,
X=X,
Y=Y,
X_test=X_test,
Y_test=Y_test,
total_iter_num=50000,
initializer=initializer,
learning_rate=1E-4,
# lr_scheduler=mx.lr_scheduler.FactorScheduler(100000, 0.5),
prior_precision=0.1,
burn_in_iter_num=1000,
thin_interval=10,
task='regression',
minibatch_size=minibatch_size,
dev=dev(gpu_id)) | python | def run_toy_SGLD(gpu_id=None):
"""Run SGLD on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0 / 9.0
net = get_toy_sym(True, teacher_noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(gpu_id))}
initializer = mx.init.Uniform(0.07)
exe, params, _ = SGLD(sym=net,
data_inputs=data_inputs,
X=X,
Y=Y,
X_test=X_test,
Y_test=Y_test,
total_iter_num=50000,
initializer=initializer,
learning_rate=1E-4,
# lr_scheduler=mx.lr_scheduler.FactorScheduler(100000, 0.5),
prior_precision=0.1,
burn_in_iter_num=1000,
thin_interval=10,
task='regression',
minibatch_size=minibatch_size,
dev=dev(gpu_id)) | [
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23,705 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_toy_DistilledSGLD | def run_toy_DistilledSGLD(gpu_id):
"""Run DistilledSGLD on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0
teacher_net = get_toy_sym(True, teacher_noise_precision)
student_net = get_toy_sym(False)
data_shape = (minibatch_size,) + X.shape[1::]
teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(gpu_id))}
student_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id))}
teacher_initializer = mx.init.Uniform(0.07)
student_initializer = mx.init.Uniform(0.07)
student_grad_f = lambda student_outputs, teacher_pred: \
regression_student_grad(student_outputs, teacher_pred, teacher_noise_precision)
student_exe, student_params, _ = \
DistilledSGLD(teacher_sym=teacher_net, student_sym=student_net,
teacher_data_inputs=teacher_data_inputs,
student_data_inputs=student_data_inputs,
X=X, Y=Y, X_test=X_test, Y_test=Y_test, total_iter_num=80000,
teacher_initializer=teacher_initializer,
student_initializer=student_initializer,
teacher_learning_rate=1E-4, student_learning_rate=0.01,
# teacher_lr_scheduler=mx.lr_scheduler.FactorScheduler(100000, 0.5),
student_lr_scheduler=mx.lr_scheduler.FactorScheduler(8000, 0.8),
student_grad_f=student_grad_f,
teacher_prior_precision=0.1, student_prior_precision=0.001,
perturb_deviation=0.1, minibatch_size=minibatch_size, task='regression',
dev=dev(gpu_id)) | python | def run_toy_DistilledSGLD(gpu_id):
"""Run DistilledSGLD on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0
teacher_net = get_toy_sym(True, teacher_noise_precision)
student_net = get_toy_sym(False)
data_shape = (minibatch_size,) + X.shape[1::]
teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(gpu_id))}
student_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id))}
teacher_initializer = mx.init.Uniform(0.07)
student_initializer = mx.init.Uniform(0.07)
student_grad_f = lambda student_outputs, teacher_pred: \
regression_student_grad(student_outputs, teacher_pred, teacher_noise_precision)
student_exe, student_params, _ = \
DistilledSGLD(teacher_sym=teacher_net, student_sym=student_net,
teacher_data_inputs=teacher_data_inputs,
student_data_inputs=student_data_inputs,
X=X, Y=Y, X_test=X_test, Y_test=Y_test, total_iter_num=80000,
teacher_initializer=teacher_initializer,
student_initializer=student_initializer,
teacher_learning_rate=1E-4, student_learning_rate=0.01,
# teacher_lr_scheduler=mx.lr_scheduler.FactorScheduler(100000, 0.5),
student_lr_scheduler=mx.lr_scheduler.FactorScheduler(8000, 0.8),
student_grad_f=student_grad_f,
teacher_prior_precision=0.1, student_prior_precision=0.001,
perturb_deviation=0.1, minibatch_size=minibatch_size, task='regression',
dev=dev(gpu_id)) | [
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23,706 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_toy_HMC | def run_toy_HMC(gpu_id=None):
"""Run HMC on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = Y.shape[0]
noise_precision = 1 / 9.0
net = get_toy_sym(True, noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(gpu_id))}
initializer = mx.init.Uniform(0.07)
sample_pool = HMC(net, data_inputs=data_inputs, X=X, Y=Y, X_test=X_test, Y_test=Y_test,
sample_num=300000, initializer=initializer, prior_precision=1.0,
learning_rate=1E-3, L=10, dev=dev(gpu_id)) | python | def run_toy_HMC(gpu_id=None):
"""Run HMC on toy dataset"""
X, Y, X_test, Y_test = load_toy()
minibatch_size = Y.shape[0]
noise_precision = 1 / 9.0
net = get_toy_sym(True, noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(gpu_id)),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(gpu_id))}
initializer = mx.init.Uniform(0.07)
sample_pool = HMC(net, data_inputs=data_inputs, X=X, Y=Y, X_test=X_test, Y_test=Y_test,
sample_num=300000, initializer=initializer, prior_precision=1.0,
learning_rate=1E-3, L=10, dev=dev(gpu_id)) | [
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23,707 | apache/incubator-mxnet | example/bayesian-methods/bdk_demo.py | run_synthetic_SGLD | def run_synthetic_SGLD():
"""Run synthetic SGLD"""
theta1 = 0
theta2 = 1
sigma1 = numpy.sqrt(10)
sigma2 = 1
sigmax = numpy.sqrt(2)
X = load_synthetic(theta1=theta1, theta2=theta2, sigmax=sigmax, num=100)
minibatch_size = 1
total_iter_num = 1000000
lr_scheduler = SGLDScheduler(begin_rate=0.01, end_rate=0.0001, total_iter_num=total_iter_num,
factor=0.55)
optimizer = mx.optimizer.create('sgld',
learning_rate=None,
rescale_grad=1.0,
lr_scheduler=lr_scheduler,
wd=0)
updater = mx.optimizer.get_updater(optimizer)
theta = mx.random.normal(0, 1, (2,), mx.cpu())
grad = nd.empty((2,), mx.cpu())
samples = numpy.zeros((2, total_iter_num))
start = time.time()
for i in range(total_iter_num):
if (i + 1) % 100000 == 0:
end = time.time()
print("Iter:%d, Time spent: %f" % (i + 1, end - start))
start = time.time()
ind = numpy.random.randint(0, X.shape[0])
synthetic_grad(X[ind], theta, sigma1, sigma2, sigmax,
rescale_grad=X.shape[0] / float(minibatch_size), grad=grad)
updater('theta', grad, theta)
samples[:, i] = theta.asnumpy()
plt.hist2d(samples[0, :], samples[1, :], (200, 200), cmap=plt.cm.jet)
plt.colorbar()
plt.show() | python | def run_synthetic_SGLD():
"""Run synthetic SGLD"""
theta1 = 0
theta2 = 1
sigma1 = numpy.sqrt(10)
sigma2 = 1
sigmax = numpy.sqrt(2)
X = load_synthetic(theta1=theta1, theta2=theta2, sigmax=sigmax, num=100)
minibatch_size = 1
total_iter_num = 1000000
lr_scheduler = SGLDScheduler(begin_rate=0.01, end_rate=0.0001, total_iter_num=total_iter_num,
factor=0.55)
optimizer = mx.optimizer.create('sgld',
learning_rate=None,
rescale_grad=1.0,
lr_scheduler=lr_scheduler,
wd=0)
updater = mx.optimizer.get_updater(optimizer)
theta = mx.random.normal(0, 1, (2,), mx.cpu())
grad = nd.empty((2,), mx.cpu())
samples = numpy.zeros((2, total_iter_num))
start = time.time()
for i in range(total_iter_num):
if (i + 1) % 100000 == 0:
end = time.time()
print("Iter:%d, Time spent: %f" % (i + 1, end - start))
start = time.time()
ind = numpy.random.randint(0, X.shape[0])
synthetic_grad(X[ind], theta, sigma1, sigma2, sigmax,
rescale_grad=X.shape[0] / float(minibatch_size), grad=grad)
updater('theta', grad, theta)
samples[:, i] = theta.asnumpy()
plt.hist2d(samples[0, :], samples[1, :], (200, 200), cmap=plt.cm.jet)
plt.colorbar()
plt.show() | [
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23,708 | apache/incubator-mxnet | example/ssd/tools/prepare_dataset.py | load_pascal | def load_pascal(image_set, year, devkit_path, shuffle=False):
"""
wrapper function for loading pascal voc dataset
Parameters:
----------
image_set : str
train, trainval...
year : str
2007, 2012 or combinations splitted by comma
devkit_path : str
root directory of dataset
shuffle : bool
whether to shuffle initial list
Returns:
----------
Imdb
"""
image_set = [y.strip() for y in image_set.split(',')]
assert image_set, "No image_set specified"
year = [y.strip() for y in year.split(',')]
assert year, "No year specified"
# make sure (# sets == # years)
if len(image_set) > 1 and len(year) == 1:
year = year * len(image_set)
if len(image_set) == 1 and len(year) > 1:
image_set = image_set * len(year)
assert len(image_set) == len(year), "Number of sets and year mismatch"
imdbs = []
for s, y in zip(image_set, year):
imdbs.append(PascalVoc(s, y, devkit_path, shuffle, is_train=True))
if len(imdbs) > 1:
return ConcatDB(imdbs, shuffle)
else:
return imdbs[0] | python | def load_pascal(image_set, year, devkit_path, shuffle=False):
"""
wrapper function for loading pascal voc dataset
Parameters:
----------
image_set : str
train, trainval...
year : str
2007, 2012 or combinations splitted by comma
devkit_path : str
root directory of dataset
shuffle : bool
whether to shuffle initial list
Returns:
----------
Imdb
"""
image_set = [y.strip() for y in image_set.split(',')]
assert image_set, "No image_set specified"
year = [y.strip() for y in year.split(',')]
assert year, "No year specified"
# make sure (# sets == # years)
if len(image_set) > 1 and len(year) == 1:
year = year * len(image_set)
if len(image_set) == 1 and len(year) > 1:
image_set = image_set * len(year)
assert len(image_set) == len(year), "Number of sets and year mismatch"
imdbs = []
for s, y in zip(image_set, year):
imdbs.append(PascalVoc(s, y, devkit_path, shuffle, is_train=True))
if len(imdbs) > 1:
return ConcatDB(imdbs, shuffle)
else:
return imdbs[0] | [
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2007, 2012 or combinations splitted by comma
devkit_path : str
root directory of dataset
shuffle : bool
whether to shuffle initial list
Returns:
----------
Imdb | [
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23,709 | apache/incubator-mxnet | example/ssd/tools/prepare_dataset.py | load_coco | def load_coco(image_set, dirname, shuffle=False):
"""
wrapper function for loading ms coco dataset
Parameters:
----------
image_set : str
train2014, val2014, valminusminival2014, minival2014
dirname: str
root dir for coco
shuffle: boolean
initial shuffle
"""
anno_files = ['instances_' + y.strip() + '.json' for y in image_set.split(',')]
assert anno_files, "No image set specified"
imdbs = []
for af in anno_files:
af_path = os.path.join(dirname, 'annotations', af)
imdbs.append(Coco(af_path, dirname, shuffle=shuffle))
if len(imdbs) > 1:
return ConcatDB(imdbs, shuffle)
else:
return imdbs[0] | python | def load_coco(image_set, dirname, shuffle=False):
"""
wrapper function for loading ms coco dataset
Parameters:
----------
image_set : str
train2014, val2014, valminusminival2014, minival2014
dirname: str
root dir for coco
shuffle: boolean
initial shuffle
"""
anno_files = ['instances_' + y.strip() + '.json' for y in image_set.split(',')]
assert anno_files, "No image set specified"
imdbs = []
for af in anno_files:
af_path = os.path.join(dirname, 'annotations', af)
imdbs.append(Coco(af_path, dirname, shuffle=shuffle))
if len(imdbs) > 1:
return ConcatDB(imdbs, shuffle)
else:
return imdbs[0] | [
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initial shuffle | [
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23,710 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_transpose | def convert_transpose(net, node, module, builder):
"""Convert a transpose layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attrs(node)
axes = literal_eval(param['axes'])
builder.add_permute(name, axes, input_name, output_name) | python | def convert_transpose(net, node, module, builder):
"""Convert a transpose layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attrs(node)
axes = literal_eval(param['axes'])
builder.add_permute(name, axes, input_name, output_name) | [
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An module for MXNet
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A neural network builder object. | [
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23,711 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_flatten | def convert_flatten(net, node, module, builder):
"""Convert a flatten layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
mode = 0 # CHANNEL_FIRST
builder.add_flatten(name, mode, input_name, output_name) | python | def convert_flatten(net, node, module, builder):
"""Convert a flatten layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
mode = 0 # CHANNEL_FIRST
builder.add_flatten(name, mode, input_name, output_name) | [
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23,712 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_activation | def convert_activation(net, node, module, builder):
"""Convert an activation layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
mx_non_linearity = _get_attrs(node)['act_type']
#TODO add SCALED_TANH, SOFTPLUS, SOFTSIGN, SIGMOID_HARD, LEAKYRELU, PRELU, ELU, PARAMETRICSOFTPLUS, THRESHOLDEDRELU, LINEAR
if mx_non_linearity == 'relu':
non_linearity = 'RELU'
elif mx_non_linearity == 'tanh':
non_linearity = 'TANH'
elif mx_non_linearity == 'sigmoid':
non_linearity = 'SIGMOID'
else:
raise TypeError('Unknown activation type %s' % mx_non_linearity)
builder.add_activation(name = name,
non_linearity = non_linearity,
input_name = input_name,
output_name = output_name) | python | def convert_activation(net, node, module, builder):
"""Convert an activation layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
mx_non_linearity = _get_attrs(node)['act_type']
#TODO add SCALED_TANH, SOFTPLUS, SOFTSIGN, SIGMOID_HARD, LEAKYRELU, PRELU, ELU, PARAMETRICSOFTPLUS, THRESHOLDEDRELU, LINEAR
if mx_non_linearity == 'relu':
non_linearity = 'RELU'
elif mx_non_linearity == 'tanh':
non_linearity = 'TANH'
elif mx_non_linearity == 'sigmoid':
non_linearity = 'SIGMOID'
else:
raise TypeError('Unknown activation type %s' % mx_non_linearity)
builder.add_activation(name = name,
non_linearity = non_linearity,
input_name = input_name,
output_name = output_name) | [
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23,713 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_leakyrelu | def convert_leakyrelu(net, node, module, builder):
"""Convert a leakyrelu layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
args, _ = module.get_params()
mx_non_linearity = _get_attrs(node)['act_type']
if mx_non_linearity == 'elu':
non_linearity = 'ELU'
slope = _get_attrs(node)['slope'] if 'slope' in _get_attrs(node) else 0.25
params = slope
elif mx_non_linearity == 'leaky':
non_linearity = 'LEAKYRELU'
slope = _get_attrs(node)['slope'] if 'slope' in _get_attrs(node) else 0.25
params = [slope]
elif mx_non_linearity == 'prelu':
non_linearity = 'PRELU'
params = args[_get_node_name(net, inputs[1][0])].asnumpy()
else:
raise TypeError('Unknown activation type %s' % mx_non_linearity)
builder.add_activation(name = name,
non_linearity = non_linearity,
input_name = input_name,
output_name = output_name,
params = params) | python | def convert_leakyrelu(net, node, module, builder):
"""Convert a leakyrelu layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
args, _ = module.get_params()
mx_non_linearity = _get_attrs(node)['act_type']
if mx_non_linearity == 'elu':
non_linearity = 'ELU'
slope = _get_attrs(node)['slope'] if 'slope' in _get_attrs(node) else 0.25
params = slope
elif mx_non_linearity == 'leaky':
non_linearity = 'LEAKYRELU'
slope = _get_attrs(node)['slope'] if 'slope' in _get_attrs(node) else 0.25
params = [slope]
elif mx_non_linearity == 'prelu':
non_linearity = 'PRELU'
params = args[_get_node_name(net, inputs[1][0])].asnumpy()
else:
raise TypeError('Unknown activation type %s' % mx_non_linearity)
builder.add_activation(name = name,
non_linearity = non_linearity,
input_name = input_name,
output_name = output_name,
params = params) | [
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Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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23,714 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_elementwise_add | def convert_elementwise_add(net, node, module, builder):
"""Convert an elementwise add layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_names, output_name = _get_input_output_name(net, node, [0, 1])
name = node['name']
builder.add_elementwise(name, input_names, output_name, 'ADD') | python | def convert_elementwise_add(net, node, module, builder):
"""Convert an elementwise add layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_names, output_name = _get_input_output_name(net, node, [0, 1])
name = node['name']
builder.add_elementwise(name, input_names, output_name, 'ADD') | [
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Parameters
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network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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23,715 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_convolution | def convert_convolution(net, node, module, builder):
"""Convert a convolution layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attrs(node)
inputs = node['inputs']
args, _ = module.get_params()
if 'no_bias' in param.keys():
has_bias = not literal_eval(param['no_bias'])
else:
has_bias = True
if 'pad' in param.keys() and literal_eval(param['pad']) != (0, 0):
pad = literal_eval(param['pad'])
builder.add_padding(
name=name+"_pad",
left=pad[1],
right=pad[1],
top=pad[0],
bottom=pad[0],
value=0,
input_name=input_name,
output_name=name+"_pad_output")
input_name = name+"_pad_output"
border_mode = "valid"
n_filters = int(param['num_filter'])
n_groups = int(param['num_group']) if 'num_group' in param else 1
W = args[_get_node_name(net, inputs[1][0])].asnumpy()
if has_bias:
Wb = args[_get_node_name(net, inputs[2][0])].asnumpy()
else:
Wb = None
channels = W.shape[1]
stride_height = 1
stride_width = 1
if 'stride' in param.keys():
stride_height, stride_width = literal_eval(param['stride'])
kernel_height, kernel_width = literal_eval(param['kernel'])
W = W.transpose((2, 3, 1, 0))
builder.add_convolution(
name=name,
kernel_channels=channels,
output_channels=n_filters,
height=kernel_height,
width=kernel_width,
stride_height=stride_height,
stride_width=stride_width,
border_mode=border_mode,
groups=n_groups,
W=W,
b=Wb,
has_bias=has_bias,
is_deconv=False,
output_shape=None,
input_name=input_name,
output_name=output_name) | python | def convert_convolution(net, node, module, builder):
"""Convert a convolution layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attrs(node)
inputs = node['inputs']
args, _ = module.get_params()
if 'no_bias' in param.keys():
has_bias = not literal_eval(param['no_bias'])
else:
has_bias = True
if 'pad' in param.keys() and literal_eval(param['pad']) != (0, 0):
pad = literal_eval(param['pad'])
builder.add_padding(
name=name+"_pad",
left=pad[1],
right=pad[1],
top=pad[0],
bottom=pad[0],
value=0,
input_name=input_name,
output_name=name+"_pad_output")
input_name = name+"_pad_output"
border_mode = "valid"
n_filters = int(param['num_filter'])
n_groups = int(param['num_group']) if 'num_group' in param else 1
W = args[_get_node_name(net, inputs[1][0])].asnumpy()
if has_bias:
Wb = args[_get_node_name(net, inputs[2][0])].asnumpy()
else:
Wb = None
channels = W.shape[1]
stride_height = 1
stride_width = 1
if 'stride' in param.keys():
stride_height, stride_width = literal_eval(param['stride'])
kernel_height, kernel_width = literal_eval(param['kernel'])
W = W.transpose((2, 3, 1, 0))
builder.add_convolution(
name=name,
kernel_channels=channels,
output_channels=n_filters,
height=kernel_height,
width=kernel_width,
stride_height=stride_height,
stride_width=stride_width,
border_mode=border_mode,
groups=n_groups,
W=W,
b=Wb,
has_bias=has_bias,
is_deconv=False,
output_shape=None,
input_name=input_name,
output_name=output_name) | [
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Parameters
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network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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23,716 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_pooling | def convert_pooling(net, node, module, builder):
"""Convert a pooling layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attrs(node)
layer_type_mx = param['pool_type']
if layer_type_mx == 'max':
layer_type = 'MAX'
elif layer_type_mx == 'avg':
layer_type = 'AVERAGE'
else:
raise TypeError("Pooling type %s not supported" % layer_type_mx)
# Add padding if there is any
if 'pad' in param.keys() and literal_eval(param['pad']) != (0, 0):
pad = literal_eval(param['pad'])
builder.add_padding(
name=name+"_pad",
left=pad[1],
right=pad[1],
top=pad[0],
bottom=pad[0],
value=0,
input_name=input_name,
output_name=name+"_pad_output")
input_name = name+"_pad_output"
stride_height = 1
stride_width = 1
if 'stride' in param.keys():
stride_height, stride_width = literal_eval(param['stride'])
kernel_width, kernel_height = literal_eval(param['kernel'])
type_map = {'valid': 'VALID', 'full': 'INCLUDE_LAST_PIXEL'}
padding_type = param['pooling_convention'] if 'pooling_convention' in param else 'valid'
if padding_type not in type_map:
raise KeyError("%s type is not supported in this converter. It is a Github issue.")
padding_type = type_map[padding_type]
if 'global_pool' in param.keys():
is_global = literal_eval(param['global_pool'])
else:
is_global = False
# For reasons why we are not using the standard builder but having our own implementation,
# see the function documentation.
_add_pooling.add_pooling_with_padding_types(
builder=builder,
name=name,
height=kernel_height,
width=kernel_width,
stride_height=stride_height,
stride_width=stride_width,
layer_type=layer_type,
padding_type=padding_type,
exclude_pad_area=False,
is_global=is_global,
input_name=input_name,
output_name=output_name
) | python | def convert_pooling(net, node, module, builder):
"""Convert a pooling layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attrs(node)
layer_type_mx = param['pool_type']
if layer_type_mx == 'max':
layer_type = 'MAX'
elif layer_type_mx == 'avg':
layer_type = 'AVERAGE'
else:
raise TypeError("Pooling type %s not supported" % layer_type_mx)
# Add padding if there is any
if 'pad' in param.keys() and literal_eval(param['pad']) != (0, 0):
pad = literal_eval(param['pad'])
builder.add_padding(
name=name+"_pad",
left=pad[1],
right=pad[1],
top=pad[0],
bottom=pad[0],
value=0,
input_name=input_name,
output_name=name+"_pad_output")
input_name = name+"_pad_output"
stride_height = 1
stride_width = 1
if 'stride' in param.keys():
stride_height, stride_width = literal_eval(param['stride'])
kernel_width, kernel_height = literal_eval(param['kernel'])
type_map = {'valid': 'VALID', 'full': 'INCLUDE_LAST_PIXEL'}
padding_type = param['pooling_convention'] if 'pooling_convention' in param else 'valid'
if padding_type not in type_map:
raise KeyError("%s type is not supported in this converter. It is a Github issue.")
padding_type = type_map[padding_type]
if 'global_pool' in param.keys():
is_global = literal_eval(param['global_pool'])
else:
is_global = False
# For reasons why we are not using the standard builder but having our own implementation,
# see the function documentation.
_add_pooling.add_pooling_with_padding_types(
builder=builder,
name=name,
height=kernel_height,
width=kernel_width,
stride_height=stride_height,
stride_width=stride_width,
layer_type=layer_type,
padding_type=padding_type,
exclude_pad_area=False,
is_global=is_global,
input_name=input_name,
output_name=output_name
) | [
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Parameters
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network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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23,717 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_batchnorm | def convert_batchnorm(net, node, module, builder):
"""Convert a batchnorm layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
eps = 1e-3 # Default value of eps for MXNet.
use_global_stats = False # Default value of use_global_stats for MXNet.
fix_gamma = True # Default value of fix_gamma for MXNet.
attrs = _get_attrs(node)
if 'eps' in attrs:
eps = literal_eval(attrs['eps'])
if 'fix_gamma' in attrs:
fix_gamma = literal_eval(attrs['fix_gamma'])
args, aux = module.get_params()
gamma = args[_get_node_name(net, inputs[1][0])].asnumpy()
beta = args[_get_node_name(net, inputs[2][0])].asnumpy()
mean = aux[_get_node_name(net, inputs[3][0])].asnumpy()
variance = aux[_get_node_name(net, inputs[4][0])].asnumpy()
nb_channels = gamma.shape[0]
if fix_gamma:
gamma.fill(1.)
builder.add_batchnorm(
name=name,
channels=nb_channels,
gamma=gamma,
beta=beta,
mean=mean,
variance=variance,
input_name=input_name,
output_name=output_name,
epsilon=eps) | python | def convert_batchnorm(net, node, module, builder):
"""Convert a batchnorm layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
eps = 1e-3 # Default value of eps for MXNet.
use_global_stats = False # Default value of use_global_stats for MXNet.
fix_gamma = True # Default value of fix_gamma for MXNet.
attrs = _get_attrs(node)
if 'eps' in attrs:
eps = literal_eval(attrs['eps'])
if 'fix_gamma' in attrs:
fix_gamma = literal_eval(attrs['fix_gamma'])
args, aux = module.get_params()
gamma = args[_get_node_name(net, inputs[1][0])].asnumpy()
beta = args[_get_node_name(net, inputs[2][0])].asnumpy()
mean = aux[_get_node_name(net, inputs[3][0])].asnumpy()
variance = aux[_get_node_name(net, inputs[4][0])].asnumpy()
nb_channels = gamma.shape[0]
if fix_gamma:
gamma.fill(1.)
builder.add_batchnorm(
name=name,
channels=nb_channels,
gamma=gamma,
beta=beta,
mean=mean,
variance=variance,
input_name=input_name,
output_name=output_name,
epsilon=eps) | [
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network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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23,718 | apache/incubator-mxnet | tools/coreml/converter/_layers.py | convert_concat | def convert_concat(net, node, module, builder):
"""Convert concat layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_names, output_name = _get_input_output_name(net, node, 'all')
name = node['name']
mode = 'CONCAT'
builder.add_elementwise(name = name, input_names = input_names,
output_name = output_name, mode = mode) | python | def convert_concat(net, node, module, builder):
"""Convert concat layer from mxnet to coreml.
Parameters
----------
network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_names, output_name = _get_input_output_name(net, node, 'all')
name = node['name']
mode = 'CONCAT'
builder.add_elementwise(name = name, input_names = input_names,
output_name = output_name, mode = mode) | [
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network: net
A mxnet network object.
layer: node
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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23,719 | apache/incubator-mxnet | tools/launch.py | dmlc_opts | def dmlc_opts(opts):
"""convert from mxnet's opts to dmlc's opts
"""
args = ['--num-workers', str(opts.num_workers),
'--num-servers', str(opts.num_servers),
'--cluster', opts.launcher,
'--host-file', opts.hostfile,
'--sync-dst-dir', opts.sync_dst_dir]
# convert to dictionary
dopts = vars(opts)
for key in ['env_server', 'env_worker', 'env']:
for v in dopts[key]:
args.append('--' + key.replace("_","-"))
args.append(v)
args += opts.command
try:
from dmlc_tracker import opts
except ImportError:
print("Can't load dmlc_tracker package. Perhaps you need to run")
print(" git submodule update --init --recursive")
raise
dmlc_opts = opts.get_opts(args)
return dmlc_opts | python | def dmlc_opts(opts):
"""convert from mxnet's opts to dmlc's opts
"""
args = ['--num-workers', str(opts.num_workers),
'--num-servers', str(opts.num_servers),
'--cluster', opts.launcher,
'--host-file', opts.hostfile,
'--sync-dst-dir', opts.sync_dst_dir]
# convert to dictionary
dopts = vars(opts)
for key in ['env_server', 'env_worker', 'env']:
for v in dopts[key]:
args.append('--' + key.replace("_","-"))
args.append(v)
args += opts.command
try:
from dmlc_tracker import opts
except ImportError:
print("Can't load dmlc_tracker package. Perhaps you need to run")
print(" git submodule update --init --recursive")
raise
dmlc_opts = opts.get_opts(args)
return dmlc_opts | [
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23,720 | apache/incubator-mxnet | python/mxnet/gluon/rnn/rnn_layer.py | _RNNLayer._unfuse | def _unfuse(self):
"""Unfuses the fused RNN in to a stack of rnn cells."""
assert not self._projection_size, "_unfuse does not support projection layer yet!"
assert not self._lstm_state_clip_min and not self._lstm_state_clip_max, \
"_unfuse does not support state clipping yet!"
get_cell = {'rnn_relu': lambda **kwargs: rnn_cell.RNNCell(self._hidden_size,
activation='relu',
**kwargs),
'rnn_tanh': lambda **kwargs: rnn_cell.RNNCell(self._hidden_size,
activation='tanh',
**kwargs),
'lstm': lambda **kwargs: rnn_cell.LSTMCell(self._hidden_size,
**kwargs),
'gru': lambda **kwargs: rnn_cell.GRUCell(self._hidden_size,
**kwargs)}[self._mode]
stack = rnn_cell.HybridSequentialRNNCell(prefix=self.prefix, params=self.params)
with stack.name_scope():
ni = self._input_size
for i in range(self._num_layers):
kwargs = {'input_size': ni,
'i2h_weight_initializer': self._i2h_weight_initializer,
'h2h_weight_initializer': self._h2h_weight_initializer,
'i2h_bias_initializer': self._i2h_bias_initializer,
'h2h_bias_initializer': self._h2h_bias_initializer}
if self._dir == 2:
stack.add(rnn_cell.BidirectionalCell(
get_cell(prefix='l%d_'%i, **kwargs),
get_cell(prefix='r%d_'%i, **kwargs)))
else:
stack.add(get_cell(prefix='l%d_'%i, **kwargs))
if self._dropout > 0 and i != self._num_layers - 1:
stack.add(rnn_cell.DropoutCell(self._dropout))
ni = self._hidden_size * self._dir
return stack | python | def _unfuse(self):
"""Unfuses the fused RNN in to a stack of rnn cells."""
assert not self._projection_size, "_unfuse does not support projection layer yet!"
assert not self._lstm_state_clip_min and not self._lstm_state_clip_max, \
"_unfuse does not support state clipping yet!"
get_cell = {'rnn_relu': lambda **kwargs: rnn_cell.RNNCell(self._hidden_size,
activation='relu',
**kwargs),
'rnn_tanh': lambda **kwargs: rnn_cell.RNNCell(self._hidden_size,
activation='tanh',
**kwargs),
'lstm': lambda **kwargs: rnn_cell.LSTMCell(self._hidden_size,
**kwargs),
'gru': lambda **kwargs: rnn_cell.GRUCell(self._hidden_size,
**kwargs)}[self._mode]
stack = rnn_cell.HybridSequentialRNNCell(prefix=self.prefix, params=self.params)
with stack.name_scope():
ni = self._input_size
for i in range(self._num_layers):
kwargs = {'input_size': ni,
'i2h_weight_initializer': self._i2h_weight_initializer,
'h2h_weight_initializer': self._h2h_weight_initializer,
'i2h_bias_initializer': self._i2h_bias_initializer,
'h2h_bias_initializer': self._h2h_bias_initializer}
if self._dir == 2:
stack.add(rnn_cell.BidirectionalCell(
get_cell(prefix='l%d_'%i, **kwargs),
get_cell(prefix='r%d_'%i, **kwargs)))
else:
stack.add(get_cell(prefix='l%d_'%i, **kwargs))
if self._dropout > 0 and i != self._num_layers - 1:
stack.add(rnn_cell.DropoutCell(self._dropout))
ni = self._hidden_size * self._dir
return stack | [
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23,721 | apache/incubator-mxnet | python/mxnet/gluon/rnn/rnn_layer.py | _RNNLayer._forward_kernel | def _forward_kernel(self, F, inputs, states, **kwargs):
""" forward using CUDNN or CPU kenrel"""
if self._layout == 'NTC':
inputs = F.swapaxes(inputs, dim1=0, dim2=1)
if self._projection_size is None:
params = (kwargs['{}{}_{}_{}'.format(d, l, g, t)].reshape(-1)
for t in ['weight', 'bias']
for l in range(self._num_layers)
for d in ['l', 'r'][:self._dir]
for g in ['i2h', 'h2h'])
else:
params = (kwargs['{}{}_{}_{}'.format(d, l, g, t)].reshape(-1)
for t in ['weight', 'bias']
for l in range(self._num_layers)
for d in ['l', 'r'][:self._dir]
for g in ['i2h', 'h2h', 'h2r']
if g != 'h2r' or t != 'bias')
params = F._internal._rnn_param_concat(*params, dim=0)
rnn = F.RNN(inputs, params, *states, state_size=self._hidden_size,
projection_size=self._projection_size,
num_layers=self._num_layers, bidirectional=self._dir == 2,
p=self._dropout, state_outputs=True, mode=self._mode,
lstm_state_clip_min=self._lstm_state_clip_min,
lstm_state_clip_max=self._lstm_state_clip_max,
lstm_state_clip_nan=self._lstm_state_clip_nan)
if self._mode == 'lstm':
outputs, states = rnn[0], [rnn[1], rnn[2]]
else:
outputs, states = rnn[0], [rnn[1]]
if self._layout == 'NTC':
outputs = F.swapaxes(outputs, dim1=0, dim2=1)
return outputs, states | python | def _forward_kernel(self, F, inputs, states, **kwargs):
""" forward using CUDNN or CPU kenrel"""
if self._layout == 'NTC':
inputs = F.swapaxes(inputs, dim1=0, dim2=1)
if self._projection_size is None:
params = (kwargs['{}{}_{}_{}'.format(d, l, g, t)].reshape(-1)
for t in ['weight', 'bias']
for l in range(self._num_layers)
for d in ['l', 'r'][:self._dir]
for g in ['i2h', 'h2h'])
else:
params = (kwargs['{}{}_{}_{}'.format(d, l, g, t)].reshape(-1)
for t in ['weight', 'bias']
for l in range(self._num_layers)
for d in ['l', 'r'][:self._dir]
for g in ['i2h', 'h2h', 'h2r']
if g != 'h2r' or t != 'bias')
params = F._internal._rnn_param_concat(*params, dim=0)
rnn = F.RNN(inputs, params, *states, state_size=self._hidden_size,
projection_size=self._projection_size,
num_layers=self._num_layers, bidirectional=self._dir == 2,
p=self._dropout, state_outputs=True, mode=self._mode,
lstm_state_clip_min=self._lstm_state_clip_min,
lstm_state_clip_max=self._lstm_state_clip_max,
lstm_state_clip_nan=self._lstm_state_clip_nan)
if self._mode == 'lstm':
outputs, states = rnn[0], [rnn[1], rnn[2]]
else:
outputs, states = rnn[0], [rnn[1]]
if self._layout == 'NTC':
outputs = F.swapaxes(outputs, dim1=0, dim2=1)
return outputs, states | [
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23,722 | apache/incubator-mxnet | example/distributed_training/cifar10_dist.py | evaluate_accuracy | def evaluate_accuracy(data_iterator, network):
""" Measure the accuracy of ResNet
Parameters
----------
data_iterator: Iter
examples of dataset
network:
ResNet
Returns
----------
tuple of array element
"""
acc = mx.metric.Accuracy()
# Iterate through data and label
for i, (data, label) in enumerate(data_iterator):
# Get the data and label into the GPU
data = data.as_in_context(ctx[0])
label = label.as_in_context(ctx[0])
# Get network's output which is a probability distribution
# Apply argmax on the probability distribution to get network's classification.
output = network(data)
predictions = nd.argmax(output, axis=1)
# Give network's prediction and the correct label to update the metric
acc.update(preds=predictions, labels=label)
# Return the accuracy
return acc.get()[1] | python | def evaluate_accuracy(data_iterator, network):
""" Measure the accuracy of ResNet
Parameters
----------
data_iterator: Iter
examples of dataset
network:
ResNet
Returns
----------
tuple of array element
"""
acc = mx.metric.Accuracy()
# Iterate through data and label
for i, (data, label) in enumerate(data_iterator):
# Get the data and label into the GPU
data = data.as_in_context(ctx[0])
label = label.as_in_context(ctx[0])
# Get network's output which is a probability distribution
# Apply argmax on the probability distribution to get network's classification.
output = network(data)
predictions = nd.argmax(output, axis=1)
# Give network's prediction and the correct label to update the metric
acc.update(preds=predictions, labels=label)
# Return the accuracy
return acc.get()[1] | [
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23,723 | apache/incubator-mxnet | example/distributed_training/cifar10_dist.py | train_batch | def train_batch(batch_list, context, network, gluon_trainer):
""" Training with multiple GPUs
Parameters
----------
batch_list: List
list of dataset
context: List
a list of all GPUs to be used for training
network:
ResNet
gluon_trainer:
rain module of gluon
"""
# Split and load data into multiple GPUs
data = batch_list[0]
data = gluon.utils.split_and_load(data, context)
# Split and load label into multiple GPUs
label = batch_list[1]
label = gluon.utils.split_and_load(label, context)
# Run the forward and backward pass
forward_backward(network, data, label)
# Update the parameters
this_batch_size = batch_list[0].shape[0]
gluon_trainer.step(this_batch_size) | python | def train_batch(batch_list, context, network, gluon_trainer):
""" Training with multiple GPUs
Parameters
----------
batch_list: List
list of dataset
context: List
a list of all GPUs to be used for training
network:
ResNet
gluon_trainer:
rain module of gluon
"""
# Split and load data into multiple GPUs
data = batch_list[0]
data = gluon.utils.split_and_load(data, context)
# Split and load label into multiple GPUs
label = batch_list[1]
label = gluon.utils.split_and_load(label, context)
# Run the forward and backward pass
forward_backward(network, data, label)
# Update the parameters
this_batch_size = batch_list[0].shape[0]
gluon_trainer.step(this_batch_size) | [
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23,724 | apache/incubator-mxnet | python/mxnet/contrib/tensorrt.py | get_optimized_symbol | def get_optimized_symbol(executor):
"""
Take an executor's underlying symbol graph and return its generated optimized version.
Parameters
----------
executor :
An executor for which you want to see an optimized symbol. Getting an optimized symbol
is useful to compare and verify the work TensorRT has done against a legacy behaviour.
Returns
-------
symbol : nnvm::Symbol
The nnvm symbol optimized.
"""
handle = SymbolHandle()
try:
check_call(_LIB.MXExecutorGetOptimizedSymbol(executor.handle, ctypes.byref(handle)))
result = sym.Symbol(handle=handle)
return result
except MXNetError:
logging.error('Error while trying to fetch TRT optimized symbol for graph. Please ensure '
'build was compiled with MXNET_USE_TENSORRT enabled.')
raise | python | def get_optimized_symbol(executor):
"""
Take an executor's underlying symbol graph and return its generated optimized version.
Parameters
----------
executor :
An executor for which you want to see an optimized symbol. Getting an optimized symbol
is useful to compare and verify the work TensorRT has done against a legacy behaviour.
Returns
-------
symbol : nnvm::Symbol
The nnvm symbol optimized.
"""
handle = SymbolHandle()
try:
check_call(_LIB.MXExecutorGetOptimizedSymbol(executor.handle, ctypes.byref(handle)))
result = sym.Symbol(handle=handle)
return result
except MXNetError:
logging.error('Error while trying to fetch TRT optimized symbol for graph. Please ensure '
'build was compiled with MXNET_USE_TENSORRT enabled.')
raise | [
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23,725 | apache/incubator-mxnet | python/mxnet/contrib/tensorrt.py | tensorrt_bind | def tensorrt_bind(symbol, ctx, all_params, type_dict=None, stype_dict=None, group2ctx=None,
**kwargs):
"""Bind current symbol to get an optimized trt executor.
Parameters
----------
symbol : Symbol
The symbol you wish to bind, and optimize with TensorRT.
ctx : Context
The device context the generated executor to run on.
all_params : Dict of str->ndarray
A dictionary of mappings from parameter names to parameter NDArrays.
type_dict : Dict of str->numpy.dtype
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stype_dict : Dict of str->str
Input storage type dictionary, name->storage_type
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kwargs : Dict of str->shape
Input shape dictionary, name->shape
Returns
-------
executor : mxnet.Executor
An optimized TensorRT executor.
"""
kwargs['shared_buffer'] = all_params
return symbol.simple_bind(ctx, type_dict=type_dict, stype_dict=stype_dict,
group2ctx=group2ctx, **kwargs) | python | def tensorrt_bind(symbol, ctx, all_params, type_dict=None, stype_dict=None, group2ctx=None,
**kwargs):
"""Bind current symbol to get an optimized trt executor.
Parameters
----------
symbol : Symbol
The symbol you wish to bind, and optimize with TensorRT.
ctx : Context
The device context the generated executor to run on.
all_params : Dict of str->ndarray
A dictionary of mappings from parameter names to parameter NDArrays.
type_dict : Dict of str->numpy.dtype
Input type dictionary, name->dtype
stype_dict : Dict of str->str
Input storage type dictionary, name->storage_type
group2ctx : Dict of string to mx.Context
The dict mapping the `ctx_group` attribute to the context assignment.
kwargs : Dict of str->shape
Input shape dictionary, name->shape
Returns
-------
executor : mxnet.Executor
An optimized TensorRT executor.
"""
kwargs['shared_buffer'] = all_params
return symbol.simple_bind(ctx, type_dict=type_dict, stype_dict=stype_dict,
group2ctx=group2ctx, **kwargs) | [
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23,726 | apache/incubator-mxnet | example/ssd/detect/detector.py | Detector.detect_iter | def detect_iter(self, det_iter, show_timer=False):
"""
detect all images in iterator
Parameters:
----------
det_iter : DetIter
iterator for all testing images
show_timer : Boolean
whether to print out detection exec time
Returns:
----------
list of detection results
"""
num_images = det_iter._size
if not isinstance(det_iter, mx.io.PrefetchingIter):
det_iter = mx.io.PrefetchingIter(det_iter)
start = timer()
detections = self.mod.predict(det_iter).asnumpy()
time_elapsed = timer() - start
if show_timer:
logging.info("Detection time for {} images: {:.4f} sec".format(
num_images, time_elapsed))
result = Detector.filter_positive_detections(detections)
return result | python | def detect_iter(self, det_iter, show_timer=False):
"""
detect all images in iterator
Parameters:
----------
det_iter : DetIter
iterator for all testing images
show_timer : Boolean
whether to print out detection exec time
Returns:
----------
list of detection results
"""
num_images = det_iter._size
if not isinstance(det_iter, mx.io.PrefetchingIter):
det_iter = mx.io.PrefetchingIter(det_iter)
start = timer()
detections = self.mod.predict(det_iter).asnumpy()
time_elapsed = timer() - start
if show_timer:
logging.info("Detection time for {} images: {:.4f} sec".format(
num_images, time_elapsed))
result = Detector.filter_positive_detections(detections)
return result | [
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det_iter : DetIter
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show_timer : Boolean
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----------
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23,727 | apache/incubator-mxnet | example/ssd/detect/detector.py | Detector.im_detect | def im_detect(self, im_list, root_dir=None, extension=None, show_timer=False):
"""
wrapper for detecting multiple images
Parameters:
----------
im_list : list of str
image path or list of image paths
root_dir : str
directory of input images, optional if image path already
has full directory information
extension : str
image extension, eg. ".jpg", optional
Returns:
----------
list of detection results in format [det0, det1...], det is in
format np.array([id, score, xmin, ymin, xmax, ymax]...)
"""
test_db = TestDB(im_list, root_dir=root_dir, extension=extension)
test_iter = DetIter(test_db, 1, self.data_shape, self.mean_pixels,
is_train=False)
return self.detect_iter(test_iter, show_timer) | python | def im_detect(self, im_list, root_dir=None, extension=None, show_timer=False):
"""
wrapper for detecting multiple images
Parameters:
----------
im_list : list of str
image path or list of image paths
root_dir : str
directory of input images, optional if image path already
has full directory information
extension : str
image extension, eg. ".jpg", optional
Returns:
----------
list of detection results in format [det0, det1...], det is in
format np.array([id, score, xmin, ymin, xmax, ymax]...)
"""
test_db = TestDB(im_list, root_dir=root_dir, extension=extension)
test_iter = DetIter(test_db, 1, self.data_shape, self.mean_pixels,
is_train=False)
return self.detect_iter(test_iter, show_timer) | [
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directory of input images, optional if image path already
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image extension, eg. ".jpg", optional
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23,728 | apache/incubator-mxnet | example/ssd/detect/detector.py | Detector.visualize_detection | def visualize_detection(self, img, dets, classes=[], thresh=0.6):
"""
visualize detections in one image
Parameters:
----------
img : numpy.array
image, in bgr format
dets : numpy.array
ssd detections, numpy.array([[id, score, x1, y1, x2, y2]...])
each row is one object
classes : tuple or list of str
class names
thresh : float
score threshold
"""
import matplotlib.pyplot as plt
import random
plt.imshow(img)
height = img.shape[0]
width = img.shape[1]
colors = dict()
for det in dets:
(klass, score, x0, y0, x1, y1) = det
if score < thresh:
continue
cls_id = int(klass)
if cls_id not in colors:
colors[cls_id] = (random.random(), random.random(), random.random())
xmin = int(x0 * width)
ymin = int(y0 * height)
xmax = int(x1 * width)
ymax = int(y1 * height)
rect = plt.Rectangle((xmin, ymin), xmax - xmin,
ymax - ymin, fill=False,
edgecolor=colors[cls_id],
linewidth=3.5)
plt.gca().add_patch(rect)
class_name = str(cls_id)
if classes and len(classes) > cls_id:
class_name = classes[cls_id]
plt.gca().text(xmin, ymin - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor=colors[cls_id], alpha=0.5),
fontsize=12, color='white')
plt.show() | python | def visualize_detection(self, img, dets, classes=[], thresh=0.6):
"""
visualize detections in one image
Parameters:
----------
img : numpy.array
image, in bgr format
dets : numpy.array
ssd detections, numpy.array([[id, score, x1, y1, x2, y2]...])
each row is one object
classes : tuple or list of str
class names
thresh : float
score threshold
"""
import matplotlib.pyplot as plt
import random
plt.imshow(img)
height = img.shape[0]
width = img.shape[1]
colors = dict()
for det in dets:
(klass, score, x0, y0, x1, y1) = det
if score < thresh:
continue
cls_id = int(klass)
if cls_id not in colors:
colors[cls_id] = (random.random(), random.random(), random.random())
xmin = int(x0 * width)
ymin = int(y0 * height)
xmax = int(x1 * width)
ymax = int(y1 * height)
rect = plt.Rectangle((xmin, ymin), xmax - xmin,
ymax - ymin, fill=False,
edgecolor=colors[cls_id],
linewidth=3.5)
plt.gca().add_patch(rect)
class_name = str(cls_id)
if classes and len(classes) > cls_id:
class_name = classes[cls_id]
plt.gca().text(xmin, ymin - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor=colors[cls_id], alpha=0.5),
fontsize=12, color='white')
plt.show() | [
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dets : numpy.array
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each row is one object
classes : tuple or list of str
class names
thresh : float
score threshold | [
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23,729 | apache/incubator-mxnet | example/ssd/detect/detector.py | Detector.detect_and_visualize | def detect_and_visualize(self, im_list, root_dir=None, extension=None,
classes=[], thresh=0.6, show_timer=False):
"""
wrapper for im_detect and visualize_detection
Parameters:
----------
im_list : list of str or str
image path or list of image paths
root_dir : str or None
directory of input images, optional if image path already
has full directory information
extension : str or None
image extension, eg. ".jpg", optional
Returns:
----------
"""
dets = self.im_detect(im_list, root_dir, extension, show_timer=show_timer)
if not isinstance(im_list, list):
im_list = [im_list]
assert len(dets) == len(im_list)
for k, det in enumerate(dets):
img = cv2.imread(im_list[k])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
self.visualize_detection(img, det, classes, thresh) | python | def detect_and_visualize(self, im_list, root_dir=None, extension=None,
classes=[], thresh=0.6, show_timer=False):
"""
wrapper for im_detect and visualize_detection
Parameters:
----------
im_list : list of str or str
image path or list of image paths
root_dir : str or None
directory of input images, optional if image path already
has full directory information
extension : str or None
image extension, eg. ".jpg", optional
Returns:
----------
"""
dets = self.im_detect(im_list, root_dir, extension, show_timer=show_timer)
if not isinstance(im_list, list):
im_list = [im_list]
assert len(dets) == len(im_list)
for k, det in enumerate(dets):
img = cv2.imread(im_list[k])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
self.visualize_detection(img, det, classes, thresh) | [
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extension : str or None
image extension, eg. ".jpg", optional
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23,730 | apache/incubator-mxnet | tools/caffe_converter/caffe_proto_utils.py | process_network_proto | def process_network_proto(caffe_root, deploy_proto):
"""
Runs the caffe upgrade tool on the prototxt to create a prototxt in the latest format.
This enable us to work just with latest structures, instead of supporting all the variants
:param caffe_root: link to caffe root folder, where the upgrade tool is located
:param deploy_proto: name of the original prototxt file
:return: name of new processed prototxt file
"""
processed_deploy_proto = deploy_proto + ".processed"
from shutil import copyfile
copyfile(deploy_proto, processed_deploy_proto)
# run upgrade tool on new file name (same output file)
import os
upgrade_tool_command_line = caffe_root + '/build/tools/upgrade_net_proto_text.bin ' \
+ processed_deploy_proto + ' ' + processed_deploy_proto
os.system(upgrade_tool_command_line)
return processed_deploy_proto | python | def process_network_proto(caffe_root, deploy_proto):
"""
Runs the caffe upgrade tool on the prototxt to create a prototxt in the latest format.
This enable us to work just with latest structures, instead of supporting all the variants
:param caffe_root: link to caffe root folder, where the upgrade tool is located
:param deploy_proto: name of the original prototxt file
:return: name of new processed prototxt file
"""
processed_deploy_proto = deploy_proto + ".processed"
from shutil import copyfile
copyfile(deploy_proto, processed_deploy_proto)
# run upgrade tool on new file name (same output file)
import os
upgrade_tool_command_line = caffe_root + '/build/tools/upgrade_net_proto_text.bin ' \
+ processed_deploy_proto + ' ' + processed_deploy_proto
os.system(upgrade_tool_command_line)
return processed_deploy_proto | [
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23,731 | apache/incubator-mxnet | example/gluon/embedding_learning/model.py | get_distance | def get_distance(F, x):
"""Helper function for margin-based loss. Return a distance matrix given a matrix."""
n = x.shape[0]
square = F.sum(x ** 2.0, axis=1, keepdims=True)
distance_square = square + square.transpose() - (2.0 * F.dot(x, x.transpose()))
# Adding identity to make sqrt work.
return F.sqrt(distance_square + F.array(np.identity(n))) | python | def get_distance(F, x):
"""Helper function for margin-based loss. Return a distance matrix given a matrix."""
n = x.shape[0]
square = F.sum(x ** 2.0, axis=1, keepdims=True)
distance_square = square + square.transpose() - (2.0 * F.dot(x, x.transpose()))
# Adding identity to make sqrt work.
return F.sqrt(distance_square + F.array(np.identity(n))) | [
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23,732 | apache/incubator-mxnet | example/rnn/large_word_lm/model.py | cross_entropy_loss | def cross_entropy_loss(inputs, labels, rescale_loss=1):
""" cross entropy loss with a mask """
criterion = mx.gluon.loss.SoftmaxCrossEntropyLoss(weight=rescale_loss)
loss = criterion(inputs, labels)
mask = S.var('mask')
loss = loss * S.reshape(mask, shape=(-1,))
return S.make_loss(loss.mean()) | python | def cross_entropy_loss(inputs, labels, rescale_loss=1):
""" cross entropy loss with a mask """
criterion = mx.gluon.loss.SoftmaxCrossEntropyLoss(weight=rescale_loss)
loss = criterion(inputs, labels)
mask = S.var('mask')
loss = loss * S.reshape(mask, shape=(-1,))
return S.make_loss(loss.mean()) | [
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23,733 | apache/incubator-mxnet | example/rnn/large_word_lm/model.py | rnn | def rnn(bptt, vocab_size, num_embed, nhid, num_layers, dropout, num_proj, batch_size):
""" word embedding + LSTM Projected """
state_names = []
data = S.var('data')
weight = S.var("encoder_weight", stype='row_sparse')
embed = S.sparse.Embedding(data=data, weight=weight, input_dim=vocab_size,
output_dim=num_embed, name='embed', sparse_grad=True)
states = []
outputs = S.Dropout(embed, p=dropout)
for i in range(num_layers):
prefix = 'lstmp%d_' % i
init_h = S.var(prefix + 'init_h', shape=(batch_size, num_proj), init=mx.init.Zero())
init_c = S.var(prefix + 'init_c', shape=(batch_size, nhid), init=mx.init.Zero())
state_names += [prefix + 'init_h', prefix + 'init_c']
lstmp = mx.gluon.contrib.rnn.LSTMPCell(nhid, num_proj, prefix=prefix)
outputs, next_states = lstmp.unroll(bptt, outputs, begin_state=[init_h, init_c], \
layout='NTC', merge_outputs=True)
outputs = S.Dropout(outputs, p=dropout)
states += [S.stop_gradient(s) for s in next_states]
outputs = S.reshape(outputs, shape=(-1, num_proj))
trainable_lstm_args = []
for arg in outputs.list_arguments():
if 'lstmp' in arg and 'init' not in arg:
trainable_lstm_args.append(arg)
return outputs, states, trainable_lstm_args, state_names | python | def rnn(bptt, vocab_size, num_embed, nhid, num_layers, dropout, num_proj, batch_size):
""" word embedding + LSTM Projected """
state_names = []
data = S.var('data')
weight = S.var("encoder_weight", stype='row_sparse')
embed = S.sparse.Embedding(data=data, weight=weight, input_dim=vocab_size,
output_dim=num_embed, name='embed', sparse_grad=True)
states = []
outputs = S.Dropout(embed, p=dropout)
for i in range(num_layers):
prefix = 'lstmp%d_' % i
init_h = S.var(prefix + 'init_h', shape=(batch_size, num_proj), init=mx.init.Zero())
init_c = S.var(prefix + 'init_c', shape=(batch_size, nhid), init=mx.init.Zero())
state_names += [prefix + 'init_h', prefix + 'init_c']
lstmp = mx.gluon.contrib.rnn.LSTMPCell(nhid, num_proj, prefix=prefix)
outputs, next_states = lstmp.unroll(bptt, outputs, begin_state=[init_h, init_c], \
layout='NTC', merge_outputs=True)
outputs = S.Dropout(outputs, p=dropout)
states += [S.stop_gradient(s) for s in next_states]
outputs = S.reshape(outputs, shape=(-1, num_proj))
trainable_lstm_args = []
for arg in outputs.list_arguments():
if 'lstmp' in arg and 'init' not in arg:
trainable_lstm_args.append(arg)
return outputs, states, trainable_lstm_args, state_names | [
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23,734 | apache/incubator-mxnet | example/rnn/large_word_lm/model.py | sampled_softmax | def sampled_softmax(num_classes, num_samples, in_dim, inputs, weight, bias,
sampled_values, remove_accidental_hits=True):
""" Sampled softmax via importance sampling.
This under-estimates the full softmax and is only used for training.
"""
# inputs = (n, in_dim)
sample, prob_sample, prob_target = sampled_values
# (num_samples, )
sample = S.var('sample', shape=(num_samples,), dtype='float32')
# (n, )
label = S.var('label')
label = S.reshape(label, shape=(-1,), name="label_reshape")
# (num_samples+n, )
sample_label = S.concat(sample, label, dim=0)
# lookup weights and biases
# (num_samples+n, dim)
sample_target_w = S.sparse.Embedding(data=sample_label, weight=weight,
input_dim=num_classes, output_dim=in_dim,
sparse_grad=True)
# (num_samples+n, 1)
sample_target_b = S.sparse.Embedding(data=sample_label, weight=bias,
input_dim=num_classes, output_dim=1,
sparse_grad=True)
# (num_samples, dim)
sample_w = S.slice(sample_target_w, begin=(0, 0), end=(num_samples, None))
target_w = S.slice(sample_target_w, begin=(num_samples, 0), end=(None, None))
sample_b = S.slice(sample_target_b, begin=(0, 0), end=(num_samples, None))
target_b = S.slice(sample_target_b, begin=(num_samples, 0), end=(None, None))
# target
# (n, 1)
true_pred = S.sum(target_w * inputs, axis=1, keepdims=True) + target_b
# samples
# (n, num_samples)
sample_b = S.reshape(sample_b, (-1,))
sample_pred = S.FullyConnected(inputs, weight=sample_w, bias=sample_b,
num_hidden=num_samples)
# remove accidental hits
if remove_accidental_hits:
label_v = S.reshape(label, (-1, 1))
sample_v = S.reshape(sample, (1, -1))
neg = S.broadcast_equal(label_v, sample_v) * -1e37
sample_pred = sample_pred + neg
prob_sample = S.reshape(prob_sample, shape=(1, num_samples))
p_target = true_pred - S.log(prob_target)
p_sample = S.broadcast_sub(sample_pred, S.log(prob_sample))
# return logits and new_labels
# (n, 1+num_samples)
logits = S.concat(p_target, p_sample, dim=1)
new_targets = S.zeros_like(label)
return logits, new_targets | python | def sampled_softmax(num_classes, num_samples, in_dim, inputs, weight, bias,
sampled_values, remove_accidental_hits=True):
""" Sampled softmax via importance sampling.
This under-estimates the full softmax and is only used for training.
"""
# inputs = (n, in_dim)
sample, prob_sample, prob_target = sampled_values
# (num_samples, )
sample = S.var('sample', shape=(num_samples,), dtype='float32')
# (n, )
label = S.var('label')
label = S.reshape(label, shape=(-1,), name="label_reshape")
# (num_samples+n, )
sample_label = S.concat(sample, label, dim=0)
# lookup weights and biases
# (num_samples+n, dim)
sample_target_w = S.sparse.Embedding(data=sample_label, weight=weight,
input_dim=num_classes, output_dim=in_dim,
sparse_grad=True)
# (num_samples+n, 1)
sample_target_b = S.sparse.Embedding(data=sample_label, weight=bias,
input_dim=num_classes, output_dim=1,
sparse_grad=True)
# (num_samples, dim)
sample_w = S.slice(sample_target_w, begin=(0, 0), end=(num_samples, None))
target_w = S.slice(sample_target_w, begin=(num_samples, 0), end=(None, None))
sample_b = S.slice(sample_target_b, begin=(0, 0), end=(num_samples, None))
target_b = S.slice(sample_target_b, begin=(num_samples, 0), end=(None, None))
# target
# (n, 1)
true_pred = S.sum(target_w * inputs, axis=1, keepdims=True) + target_b
# samples
# (n, num_samples)
sample_b = S.reshape(sample_b, (-1,))
sample_pred = S.FullyConnected(inputs, weight=sample_w, bias=sample_b,
num_hidden=num_samples)
# remove accidental hits
if remove_accidental_hits:
label_v = S.reshape(label, (-1, 1))
sample_v = S.reshape(sample, (1, -1))
neg = S.broadcast_equal(label_v, sample_v) * -1e37
sample_pred = sample_pred + neg
prob_sample = S.reshape(prob_sample, shape=(1, num_samples))
p_target = true_pred - S.log(prob_target)
p_sample = S.broadcast_sub(sample_pred, S.log(prob_sample))
# return logits and new_labels
# (n, 1+num_samples)
logits = S.concat(p_target, p_sample, dim=1)
new_targets = S.zeros_like(label)
return logits, new_targets | [
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23,735 | apache/incubator-mxnet | example/rnn/large_word_lm/model.py | generate_samples | def generate_samples(label, num_splits, sampler):
""" Split labels into `num_splits` and
generate candidates based on log-uniform distribution.
"""
def listify(x):
return x if isinstance(x, list) else [x]
label_splits = listify(label.split(num_splits, axis=0))
prob_samples = []
prob_targets = []
samples = []
for label_split in label_splits:
label_split_2d = label_split.reshape((-1,1))
sampled_value = sampler.draw(label_split_2d)
sampled_classes, exp_cnt_true, exp_cnt_sampled = sampled_value
samples.append(sampled_classes.astype(np.float32))
prob_targets.append(exp_cnt_true.astype(np.float32).reshape((-1,1)))
prob_samples.append(exp_cnt_sampled.astype(np.float32))
return samples, prob_samples, prob_targets | python | def generate_samples(label, num_splits, sampler):
""" Split labels into `num_splits` and
generate candidates based on log-uniform distribution.
"""
def listify(x):
return x if isinstance(x, list) else [x]
label_splits = listify(label.split(num_splits, axis=0))
prob_samples = []
prob_targets = []
samples = []
for label_split in label_splits:
label_split_2d = label_split.reshape((-1,1))
sampled_value = sampler.draw(label_split_2d)
sampled_classes, exp_cnt_true, exp_cnt_sampled = sampled_value
samples.append(sampled_classes.astype(np.float32))
prob_targets.append(exp_cnt_true.astype(np.float32).reshape((-1,1)))
prob_samples.append(exp_cnt_sampled.astype(np.float32))
return samples, prob_samples, prob_targets | [
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23,736 | apache/incubator-mxnet | python/mxnet/gluon/model_zoo/vision/__init__.py | get_model | def get_model(name, **kwargs):
"""Returns a pre-defined model by name
Parameters
----------
name : str
Name of the model.
pretrained : bool
Whether to load the pretrained weights for model.
classes : int
Number of classes for the output layer.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
Returns
-------
HybridBlock
The model.
"""
models = {'resnet18_v1': resnet18_v1,
'resnet34_v1': resnet34_v1,
'resnet50_v1': resnet50_v1,
'resnet101_v1': resnet101_v1,
'resnet152_v1': resnet152_v1,
'resnet18_v2': resnet18_v2,
'resnet34_v2': resnet34_v2,
'resnet50_v2': resnet50_v2,
'resnet101_v2': resnet101_v2,
'resnet152_v2': resnet152_v2,
'vgg11': vgg11,
'vgg13': vgg13,
'vgg16': vgg16,
'vgg19': vgg19,
'vgg11_bn': vgg11_bn,
'vgg13_bn': vgg13_bn,
'vgg16_bn': vgg16_bn,
'vgg19_bn': vgg19_bn,
'alexnet': alexnet,
'densenet121': densenet121,
'densenet161': densenet161,
'densenet169': densenet169,
'densenet201': densenet201,
'squeezenet1.0': squeezenet1_0,
'squeezenet1.1': squeezenet1_1,
'inceptionv3': inception_v3,
'mobilenet1.0': mobilenet1_0,
'mobilenet0.75': mobilenet0_75,
'mobilenet0.5': mobilenet0_5,
'mobilenet0.25': mobilenet0_25,
'mobilenetv2_1.0': mobilenet_v2_1_0,
'mobilenetv2_0.75': mobilenet_v2_0_75,
'mobilenetv2_0.5': mobilenet_v2_0_5,
'mobilenetv2_0.25': mobilenet_v2_0_25
}
name = name.lower()
if name not in models:
raise ValueError(
'Model %s is not supported. Available options are\n\t%s' % (
name, '\n\t'.join(sorted(models.keys()))))
return models[name](**kwargs) | python | def get_model(name, **kwargs):
"""Returns a pre-defined model by name
Parameters
----------
name : str
Name of the model.
pretrained : bool
Whether to load the pretrained weights for model.
classes : int
Number of classes for the output layer.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
Returns
-------
HybridBlock
The model.
"""
models = {'resnet18_v1': resnet18_v1,
'resnet34_v1': resnet34_v1,
'resnet50_v1': resnet50_v1,
'resnet101_v1': resnet101_v1,
'resnet152_v1': resnet152_v1,
'resnet18_v2': resnet18_v2,
'resnet34_v2': resnet34_v2,
'resnet50_v2': resnet50_v2,
'resnet101_v2': resnet101_v2,
'resnet152_v2': resnet152_v2,
'vgg11': vgg11,
'vgg13': vgg13,
'vgg16': vgg16,
'vgg19': vgg19,
'vgg11_bn': vgg11_bn,
'vgg13_bn': vgg13_bn,
'vgg16_bn': vgg16_bn,
'vgg19_bn': vgg19_bn,
'alexnet': alexnet,
'densenet121': densenet121,
'densenet161': densenet161,
'densenet169': densenet169,
'densenet201': densenet201,
'squeezenet1.0': squeezenet1_0,
'squeezenet1.1': squeezenet1_1,
'inceptionv3': inception_v3,
'mobilenet1.0': mobilenet1_0,
'mobilenet0.75': mobilenet0_75,
'mobilenet0.5': mobilenet0_5,
'mobilenet0.25': mobilenet0_25,
'mobilenetv2_1.0': mobilenet_v2_1_0,
'mobilenetv2_0.75': mobilenet_v2_0_75,
'mobilenetv2_0.5': mobilenet_v2_0_5,
'mobilenetv2_0.25': mobilenet_v2_0_25
}
name = name.lower()
if name not in models:
raise ValueError(
'Model %s is not supported. Available options are\n\t%s' % (
name, '\n\t'.join(sorted(models.keys()))))
return models[name](**kwargs) | [
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Parameters
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name : str
Name of the model.
pretrained : bool
Whether to load the pretrained weights for model.
classes : int
Number of classes for the output layer.
ctx : Context, default CPU
The context in which to load the pretrained weights.
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Location for keeping the model parameters.
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23,737 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _new_alloc_handle | def _new_alloc_handle(stype, shape, ctx, delay_alloc, dtype, aux_types, aux_shapes=None):
"""Return a new handle with specified storage type, shape, dtype and context.
Empty handle is only used to hold results
Returns
-------
handle
A new empty ndarray handle
"""
hdl = NDArrayHandle()
for aux_t in aux_types:
if np.dtype(aux_t) != np.dtype("int64"):
raise NotImplementedError("only int64 is supported for aux types")
aux_type_ids = [int(_DTYPE_NP_TO_MX[np.dtype(aux_t).type]) for aux_t in aux_types]
aux_shapes = [(0,) for aux_t in aux_types] if aux_shapes is None else aux_shapes
aux_shape_lens = [len(aux_shape) for aux_shape in aux_shapes]
aux_shapes = py_sum(aux_shapes, ())
num_aux = mx_uint(len(aux_types))
check_call(_LIB.MXNDArrayCreateSparseEx(
ctypes.c_int(int(_STORAGE_TYPE_STR_TO_ID[stype])),
c_array_buf(mx_uint, native_array('I', shape)),
mx_uint(len(shape)),
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
ctypes.c_int(int(delay_alloc)),
ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
num_aux,
c_array_buf(ctypes.c_int, native_array('i', aux_type_ids)),
c_array_buf(mx_uint, native_array('I', aux_shape_lens)),
c_array_buf(mx_uint, native_array('I', aux_shapes)),
ctypes.byref(hdl)))
return hdl | python | def _new_alloc_handle(stype, shape, ctx, delay_alloc, dtype, aux_types, aux_shapes=None):
"""Return a new handle with specified storage type, shape, dtype and context.
Empty handle is only used to hold results
Returns
-------
handle
A new empty ndarray handle
"""
hdl = NDArrayHandle()
for aux_t in aux_types:
if np.dtype(aux_t) != np.dtype("int64"):
raise NotImplementedError("only int64 is supported for aux types")
aux_type_ids = [int(_DTYPE_NP_TO_MX[np.dtype(aux_t).type]) for aux_t in aux_types]
aux_shapes = [(0,) for aux_t in aux_types] if aux_shapes is None else aux_shapes
aux_shape_lens = [len(aux_shape) for aux_shape in aux_shapes]
aux_shapes = py_sum(aux_shapes, ())
num_aux = mx_uint(len(aux_types))
check_call(_LIB.MXNDArrayCreateSparseEx(
ctypes.c_int(int(_STORAGE_TYPE_STR_TO_ID[stype])),
c_array_buf(mx_uint, native_array('I', shape)),
mx_uint(len(shape)),
ctypes.c_int(ctx.device_typeid),
ctypes.c_int(ctx.device_id),
ctypes.c_int(int(delay_alloc)),
ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
num_aux,
c_array_buf(ctypes.c_int, native_array('i', aux_type_ids)),
c_array_buf(mx_uint, native_array('I', aux_shape_lens)),
c_array_buf(mx_uint, native_array('I', aux_shapes)),
ctypes.byref(hdl)))
return hdl | [
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23,738 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _prepare_src_array | def _prepare_src_array(source_array, dtype):
"""Prepare `source_array` so that it can be used to construct NDArray.
`source_array` is converted to a `np.ndarray` if it's neither an `NDArray` \
nor an `np.ndarray`.
"""
if not isinstance(source_array, NDArray) and not isinstance(source_array, np.ndarray):
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except:
raise TypeError('values must be array like object')
return source_array | python | def _prepare_src_array(source_array, dtype):
"""Prepare `source_array` so that it can be used to construct NDArray.
`source_array` is converted to a `np.ndarray` if it's neither an `NDArray` \
nor an `np.ndarray`.
"""
if not isinstance(source_array, NDArray) and not isinstance(source_array, np.ndarray):
try:
source_array = np.array(source_array, dtype=dtype)
except:
raise TypeError('values must be array like object')
return source_array | [
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23,739 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _prepare_default_dtype | def _prepare_default_dtype(src_array, dtype):
"""Prepare the value of dtype if `dtype` is None. If `src_array` is an NDArray, numpy.ndarray
or scipy.sparse.csr.csr_matrix, return src_array.dtype. float32 is returned otherwise."""
if dtype is None:
if isinstance(src_array, (NDArray, np.ndarray)):
dtype = src_array.dtype
elif spsp and isinstance(src_array, spsp.csr.csr_matrix):
dtype = src_array.dtype
else:
dtype = mx_real_t
return dtype | python | def _prepare_default_dtype(src_array, dtype):
"""Prepare the value of dtype if `dtype` is None. If `src_array` is an NDArray, numpy.ndarray
or scipy.sparse.csr.csr_matrix, return src_array.dtype. float32 is returned otherwise."""
if dtype is None:
if isinstance(src_array, (NDArray, np.ndarray)):
dtype = src_array.dtype
elif spsp and isinstance(src_array, spsp.csr.csr_matrix):
dtype = src_array.dtype
else:
dtype = mx_real_t
return dtype | [
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] | Prepare the value of dtype if `dtype` is None. If `src_array` is an NDArray, numpy.ndarray
or scipy.sparse.csr.csr_matrix, return src_array.dtype. float32 is returned otherwise. | [
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23,740 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _check_shape | def _check_shape(s1, s2):
"""check s1 == s2 if both are not None"""
if s1 and s2 and s1 != s2:
raise ValueError("Shape mismatch detected. " + str(s1) + " v.s. " + str(s2)) | python | def _check_shape(s1, s2):
"""check s1 == s2 if both are not None"""
if s1 and s2 and s1 != s2:
raise ValueError("Shape mismatch detected. " + str(s1) + " v.s. " + str(s2)) | [
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23,741 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _csr_matrix_from_definition | def _csr_matrix_from_definition(data, indices, indptr, shape=None, ctx=None,
dtype=None, indices_type=None, indptr_type=None):
"""Create a `CSRNDArray` based on data, indices and indptr"""
# pylint: disable= no-member, protected-access
storage_type = 'csr'
# context
ctx = current_context() if ctx is None else ctx
# types
dtype = _prepare_default_dtype(data, dtype)
indptr_type = _STORAGE_AUX_TYPES[storage_type][0] if indptr_type is None else indptr_type
indices_type = _STORAGE_AUX_TYPES[storage_type][1] if indices_type is None else indices_type
# prepare src array and types
data = _prepare_src_array(data, dtype)
indptr = _prepare_src_array(indptr, indptr_type)
indices = _prepare_src_array(indices, indices_type)
# TODO(junwu): Convert data, indptr, and indices to mxnet NDArrays
# if they are not for now. In the future, we should provide a c-api
# to accept np.ndarray types to copy from to result.data and aux_data
if not isinstance(data, NDArray):
data = _array(data, ctx, dtype)
if not isinstance(indptr, NDArray):
indptr = _array(indptr, ctx, indptr_type)
if not isinstance(indices, NDArray):
indices = _array(indices, ctx, indices_type)
if shape is None:
if indices.shape[0] == 0:
raise ValueError('invalid shape')
shape = (len(indptr) - 1, op.max(indices).asscalar() + 1)
# verify shapes
aux_shapes = [indptr.shape, indices.shape]
if data.ndim != 1 or indptr.ndim != 1 or indices.ndim != 1 or \
indptr.shape[0] == 0 or len(shape) != 2:
raise ValueError('invalid shape')
result = CSRNDArray(_new_alloc_handle(storage_type, shape, ctx, False, dtype,
[indptr_type, indices_type], aux_shapes))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, data.handle, ctypes.c_int(-1)))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, indptr.handle, ctypes.c_int(0)))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, indices.handle, ctypes.c_int(1)))
return result | python | def _csr_matrix_from_definition(data, indices, indptr, shape=None, ctx=None,
dtype=None, indices_type=None, indptr_type=None):
"""Create a `CSRNDArray` based on data, indices and indptr"""
# pylint: disable= no-member, protected-access
storage_type = 'csr'
# context
ctx = current_context() if ctx is None else ctx
# types
dtype = _prepare_default_dtype(data, dtype)
indptr_type = _STORAGE_AUX_TYPES[storage_type][0] if indptr_type is None else indptr_type
indices_type = _STORAGE_AUX_TYPES[storage_type][1] if indices_type is None else indices_type
# prepare src array and types
data = _prepare_src_array(data, dtype)
indptr = _prepare_src_array(indptr, indptr_type)
indices = _prepare_src_array(indices, indices_type)
# TODO(junwu): Convert data, indptr, and indices to mxnet NDArrays
# if they are not for now. In the future, we should provide a c-api
# to accept np.ndarray types to copy from to result.data and aux_data
if not isinstance(data, NDArray):
data = _array(data, ctx, dtype)
if not isinstance(indptr, NDArray):
indptr = _array(indptr, ctx, indptr_type)
if not isinstance(indices, NDArray):
indices = _array(indices, ctx, indices_type)
if shape is None:
if indices.shape[0] == 0:
raise ValueError('invalid shape')
shape = (len(indptr) - 1, op.max(indices).asscalar() + 1)
# verify shapes
aux_shapes = [indptr.shape, indices.shape]
if data.ndim != 1 or indptr.ndim != 1 or indices.ndim != 1 or \
indptr.shape[0] == 0 or len(shape) != 2:
raise ValueError('invalid shape')
result = CSRNDArray(_new_alloc_handle(storage_type, shape, ctx, False, dtype,
[indptr_type, indices_type], aux_shapes))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, data.handle, ctypes.c_int(-1)))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, indptr.handle, ctypes.c_int(0)))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, indices.handle, ctypes.c_int(1)))
return result | [
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23,742 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | row_sparse_array | def row_sparse_array(arg1, shape=None, ctx=None, dtype=None):
"""Creates a `RowSparseNDArray`, a multidimensional row sparse array with a set of \
tensor slices at given indices.
The RowSparseNDArray can be instantiated in several ways:
- row_sparse_array(D):
to construct a RowSparseNDArray with a dense ndarray ``D``
- **D** (*array_like*) - An object exposing the array interface, an object whose \
`__array__` method returns an array, or any (nested) sequence.
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is ``D.dtype`` if ``D`` is an NDArray or numpy.ndarray, \
float32 otherwise.
- row_sparse_array(S)
to construct a RowSparseNDArray with a sparse ndarray ``S``
- **S** (*RowSparseNDArray*) - A sparse ndarray.
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is ``S.dtype``.
- row_sparse_array((D0, D1 .. Dn))
to construct an empty RowSparseNDArray with shape ``(D0, D1, ... Dn)``
- **D0, D1 .. Dn** (*int*) - The shape of the ndarray
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is float32.
- row_sparse_array((data, indices))
to construct a RowSparseNDArray based on the definition of row sparse format \
using two separate arrays, \
where the `indices` stores the indices of the row slices with non-zeros,
while the values are stored in `data`. The corresponding NDArray ``dense``
represented by RowSparseNDArray ``rsp`` has \
``dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...]``
The row indices for are expected to be **sorted in ascending order.** \
- **data** (*array_like*) - An object exposing the array interface, which \
holds all the non-zero row slices of the array.
- **indices** (*array_like*) - An object exposing the array interface, which \
stores the row index for each row slice with non-zero elements.
- **shape** (*tuple of int, optional*) - The shape of the array. The default \
shape is inferred from the indices and indptr arrays.
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is float32.
Parameters
----------
arg1 : NDArray, numpy.ndarray, RowSparseNDArray, tuple of int or tuple of array_like
The argument to help instantiate the row sparse ndarray. See above for further details.
shape : tuple of int, optional
The shape of the row sparse ndarray. (Default value = None)
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. (Default value = None)
Returns
-------
RowSparseNDArray
An `RowSparseNDArray` with the `row_sparse` storage representation.
Examples
--------
>>> a = mx.nd.sparse.row_sparse_array(([[1, 2], [3, 4]], [1, 4]), shape=(6, 2))
>>> a.asnumpy()
array([[ 0., 0.],
[ 1., 2.],
[ 0., 0.],
[ 0., 0.],
[ 3., 4.],
[ 0., 0.]], dtype=float32)
See Also
--------
RowSparseNDArray : MXNet NDArray in row sparse format.
"""
# construct a row sparse array from (D0, D1 ..) or (data, indices)
if isinstance(arg1, tuple):
arg_len = len(arg1)
if arg_len < 2:
raise ValueError("Unexpected length of input tuple: " + str(arg_len))
elif arg_len > 2:
# empty ndarray with shape
_check_shape(arg1, shape)
return empty('row_sparse', arg1, ctx=ctx, dtype=dtype)
else:
# len(arg1) = 2, is either shape or (data, indices)
if isinstance(arg1[0], integer_types) and isinstance(arg1[1], integer_types):
# empty ndarray with shape
_check_shape(arg1, shape)
return empty('row_sparse', arg1, ctx=ctx, dtype=dtype)
else:
# data, indices, indptr
return _row_sparse_ndarray_from_definition(arg1[0], arg1[1], shape=shape,
ctx=ctx, dtype=dtype)
else:
# construct a row sparse ndarray from a dense / sparse array
if isinstance(arg1, RowSparseNDArray):
# construct a row sparse ndarray from RowSparseNDArray
_check_shape(arg1.shape, shape)
return array(arg1, ctx=ctx, dtype=dtype)
elif isinstance(arg1, CSRNDArray):
raise ValueError("Unexpected input type: CSRNDArray")
else:
# construct a csr matrix from a dense one
# prepare default dtype since mx.nd.array doesn't use default values
# based on source_array
dtype = _prepare_default_dtype(arg1, dtype)
# create dns array with provided dtype. ctx is not passed since copy across
# ctx requires dtype to be the same
dns = _array(arg1, dtype=dtype)
if ctx is not None and dns.context != ctx:
dns = dns.as_in_context(ctx)
_check_shape(dns.shape, shape)
return dns.tostype('row_sparse') | python | def row_sparse_array(arg1, shape=None, ctx=None, dtype=None):
"""Creates a `RowSparseNDArray`, a multidimensional row sparse array with a set of \
tensor slices at given indices.
The RowSparseNDArray can be instantiated in several ways:
- row_sparse_array(D):
to construct a RowSparseNDArray with a dense ndarray ``D``
- **D** (*array_like*) - An object exposing the array interface, an object whose \
`__array__` method returns an array, or any (nested) sequence.
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is ``D.dtype`` if ``D`` is an NDArray or numpy.ndarray, \
float32 otherwise.
- row_sparse_array(S)
to construct a RowSparseNDArray with a sparse ndarray ``S``
- **S** (*RowSparseNDArray*) - A sparse ndarray.
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is ``S.dtype``.
- row_sparse_array((D0, D1 .. Dn))
to construct an empty RowSparseNDArray with shape ``(D0, D1, ... Dn)``
- **D0, D1 .. Dn** (*int*) - The shape of the ndarray
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is float32.
- row_sparse_array((data, indices))
to construct a RowSparseNDArray based on the definition of row sparse format \
using two separate arrays, \
where the `indices` stores the indices of the row slices with non-zeros,
while the values are stored in `data`. The corresponding NDArray ``dense``
represented by RowSparseNDArray ``rsp`` has \
``dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...]``
The row indices for are expected to be **sorted in ascending order.** \
- **data** (*array_like*) - An object exposing the array interface, which \
holds all the non-zero row slices of the array.
- **indices** (*array_like*) - An object exposing the array interface, which \
stores the row index for each row slice with non-zero elements.
- **shape** (*tuple of int, optional*) - The shape of the array. The default \
shape is inferred from the indices and indptr arrays.
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is float32.
Parameters
----------
arg1 : NDArray, numpy.ndarray, RowSparseNDArray, tuple of int or tuple of array_like
The argument to help instantiate the row sparse ndarray. See above for further details.
shape : tuple of int, optional
The shape of the row sparse ndarray. (Default value = None)
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. (Default value = None)
Returns
-------
RowSparseNDArray
An `RowSparseNDArray` with the `row_sparse` storage representation.
Examples
--------
>>> a = mx.nd.sparse.row_sparse_array(([[1, 2], [3, 4]], [1, 4]), shape=(6, 2))
>>> a.asnumpy()
array([[ 0., 0.],
[ 1., 2.],
[ 0., 0.],
[ 0., 0.],
[ 3., 4.],
[ 0., 0.]], dtype=float32)
See Also
--------
RowSparseNDArray : MXNet NDArray in row sparse format.
"""
# construct a row sparse array from (D0, D1 ..) or (data, indices)
if isinstance(arg1, tuple):
arg_len = len(arg1)
if arg_len < 2:
raise ValueError("Unexpected length of input tuple: " + str(arg_len))
elif arg_len > 2:
# empty ndarray with shape
_check_shape(arg1, shape)
return empty('row_sparse', arg1, ctx=ctx, dtype=dtype)
else:
# len(arg1) = 2, is either shape or (data, indices)
if isinstance(arg1[0], integer_types) and isinstance(arg1[1], integer_types):
# empty ndarray with shape
_check_shape(arg1, shape)
return empty('row_sparse', arg1, ctx=ctx, dtype=dtype)
else:
# data, indices, indptr
return _row_sparse_ndarray_from_definition(arg1[0], arg1[1], shape=shape,
ctx=ctx, dtype=dtype)
else:
# construct a row sparse ndarray from a dense / sparse array
if isinstance(arg1, RowSparseNDArray):
# construct a row sparse ndarray from RowSparseNDArray
_check_shape(arg1.shape, shape)
return array(arg1, ctx=ctx, dtype=dtype)
elif isinstance(arg1, CSRNDArray):
raise ValueError("Unexpected input type: CSRNDArray")
else:
# construct a csr matrix from a dense one
# prepare default dtype since mx.nd.array doesn't use default values
# based on source_array
dtype = _prepare_default_dtype(arg1, dtype)
# create dns array with provided dtype. ctx is not passed since copy across
# ctx requires dtype to be the same
dns = _array(arg1, dtype=dtype)
if ctx is not None and dns.context != ctx:
dns = dns.as_in_context(ctx)
_check_shape(dns.shape, shape)
return dns.tostype('row_sparse') | [
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] | Creates a `RowSparseNDArray`, a multidimensional row sparse array with a set of \
tensor slices at given indices.
The RowSparseNDArray can be instantiated in several ways:
- row_sparse_array(D):
to construct a RowSparseNDArray with a dense ndarray ``D``
- **D** (*array_like*) - An object exposing the array interface, an object whose \
`__array__` method returns an array, or any (nested) sequence.
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is ``D.dtype`` if ``D`` is an NDArray or numpy.ndarray, \
float32 otherwise.
- row_sparse_array(S)
to construct a RowSparseNDArray with a sparse ndarray ``S``
- **S** (*RowSparseNDArray*) - A sparse ndarray.
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is ``S.dtype``.
- row_sparse_array((D0, D1 .. Dn))
to construct an empty RowSparseNDArray with shape ``(D0, D1, ... Dn)``
- **D0, D1 .. Dn** (*int*) - The shape of the ndarray
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is float32.
- row_sparse_array((data, indices))
to construct a RowSparseNDArray based on the definition of row sparse format \
using two separate arrays, \
where the `indices` stores the indices of the row slices with non-zeros,
while the values are stored in `data`. The corresponding NDArray ``dense``
represented by RowSparseNDArray ``rsp`` has \
``dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...]``
The row indices for are expected to be **sorted in ascending order.** \
- **data** (*array_like*) - An object exposing the array interface, which \
holds all the non-zero row slices of the array.
- **indices** (*array_like*) - An object exposing the array interface, which \
stores the row index for each row slice with non-zero elements.
- **shape** (*tuple of int, optional*) - The shape of the array. The default \
shape is inferred from the indices and indptr arrays.
- **ctx** (*Context, optional*) - Device context \
(default is the current default context).
- **dtype** (*str or numpy.dtype, optional*) - The data type of the output array. \
The default dtype is float32.
Parameters
----------
arg1 : NDArray, numpy.ndarray, RowSparseNDArray, tuple of int or tuple of array_like
The argument to help instantiate the row sparse ndarray. See above for further details.
shape : tuple of int, optional
The shape of the row sparse ndarray. (Default value = None)
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. (Default value = None)
Returns
-------
RowSparseNDArray
An `RowSparseNDArray` with the `row_sparse` storage representation.
Examples
--------
>>> a = mx.nd.sparse.row_sparse_array(([[1, 2], [3, 4]], [1, 4]), shape=(6, 2))
>>> a.asnumpy()
array([[ 0., 0.],
[ 1., 2.],
[ 0., 0.],
[ 0., 0.],
[ 3., 4.],
[ 0., 0.]], dtype=float32)
See Also
--------
RowSparseNDArray : MXNet NDArray in row sparse format. | [
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23,743 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | _row_sparse_ndarray_from_definition | def _row_sparse_ndarray_from_definition(data, indices, shape=None, ctx=None,
dtype=None, indices_type=None):
"""Create a `RowSparseNDArray` based on data and indices"""
storage_type = 'row_sparse'
# context
ctx = current_context() if ctx is None else ctx
# types
dtype = _prepare_default_dtype(data, dtype)
indices_type = _STORAGE_AUX_TYPES[storage_type][0] if indices_type is None else indices_type
# prepare src array and types
data = _prepare_src_array(data, dtype)
indices = _prepare_src_array(indices, indices_type)
# TODO(junwu): Convert data, indptr, and indices to mxnet NDArrays
# if they are not for now. In the future, we should provide a c-api
# to accept np.ndarray types to copy from to result.data and aux_data
if not isinstance(data, NDArray):
data = _array(data, ctx, dtype)
if not isinstance(indices, NDArray):
indices = _array(indices, ctx, indices_type)
if shape is None:
num_indices = indices.shape[0]
if num_indices == 0:
raise ValueError('invalid shape')
dim0 = indices[num_indices - 1].asscalar() + 1
shape = (dim0, ) + data.shape[1:]
# verify shapes
if data.ndim != len(shape) or indices.ndim != 1 or np.prod(shape[1:]) == 0:
raise ValueError("invalid shape")
result = RowSparseNDArray(_new_alloc_handle(storage_type, shape, ctx, False, dtype,
[indices_type], [indices.shape]))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, data.handle, ctypes.c_int(-1)))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, indices.handle, ctypes.c_int(0)))
return result | python | def _row_sparse_ndarray_from_definition(data, indices, shape=None, ctx=None,
dtype=None, indices_type=None):
"""Create a `RowSparseNDArray` based on data and indices"""
storage_type = 'row_sparse'
# context
ctx = current_context() if ctx is None else ctx
# types
dtype = _prepare_default_dtype(data, dtype)
indices_type = _STORAGE_AUX_TYPES[storage_type][0] if indices_type is None else indices_type
# prepare src array and types
data = _prepare_src_array(data, dtype)
indices = _prepare_src_array(indices, indices_type)
# TODO(junwu): Convert data, indptr, and indices to mxnet NDArrays
# if they are not for now. In the future, we should provide a c-api
# to accept np.ndarray types to copy from to result.data and aux_data
if not isinstance(data, NDArray):
data = _array(data, ctx, dtype)
if not isinstance(indices, NDArray):
indices = _array(indices, ctx, indices_type)
if shape is None:
num_indices = indices.shape[0]
if num_indices == 0:
raise ValueError('invalid shape')
dim0 = indices[num_indices - 1].asscalar() + 1
shape = (dim0, ) + data.shape[1:]
# verify shapes
if data.ndim != len(shape) or indices.ndim != 1 or np.prod(shape[1:]) == 0:
raise ValueError("invalid shape")
result = RowSparseNDArray(_new_alloc_handle(storage_type, shape, ctx, False, dtype,
[indices_type], [indices.shape]))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, data.handle, ctypes.c_int(-1)))
check_call(_LIB.MXNDArraySyncCopyFromNDArray(result.handle, indices.handle, ctypes.c_int(0)))
return result | [
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23,744 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | array | def array(source_array, ctx=None, dtype=None):
"""Creates a sparse array from any object exposing the array interface.
Parameters
----------
source_array : RowSparseNDArray, CSRNDArray or scipy.sparse.csr.csr_matrix
The source sparse array
ctx : Context, optional
The default context is ``source_array.context`` if ``source_array`` is an NDArray. \
The current default context otherwise.
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `numpy.ndarray` or `scipy.sparse.csr.csr_matrix`, \
`float32` otherwise.
Returns
-------
RowSparseNDArray or CSRNDArray
An array with the same contents as the `source_array`.
Examples
--------
>>> import scipy.sparse as spsp
>>> csr = spsp.csr_matrix((2, 100))
>>> mx.nd.sparse.array(csr)
<CSRNDArray 2x100 @cpu(0)>
>>> mx.nd.sparse.array(mx.nd.sparse.zeros('csr', (3, 2)))
<CSRNDArray 3x2 @cpu(0)>
>>> mx.nd.sparse.array(mx.nd.sparse.zeros('row_sparse', (3, 2)))
<RowSparseNDArray 3x2 @cpu(0)>
"""
ctx = current_context() if ctx is None else ctx
if isinstance(source_array, NDArray):
assert(source_array.stype != 'default'), \
"Please use `tostype` to create RowSparseNDArray or CSRNDArray from an NDArray"
# prepare dtype and ctx based on source_array, if not provided
dtype = _prepare_default_dtype(source_array, dtype)
# if both dtype and ctx are different from source_array, we cannot copy directly
if source_array.dtype != dtype and source_array.context != ctx:
arr = empty(source_array.stype, source_array.shape, dtype=dtype)
arr[:] = source_array
arr = arr.as_in_context(ctx)
else:
arr = empty(source_array.stype, source_array.shape, dtype=dtype, ctx=ctx)
arr[:] = source_array
return arr
elif spsp and isinstance(source_array, spsp.csr.csr_matrix):
# TODO(haibin) implement `_sync_copy_from` with scipy csr object to reduce a copy
# preprocess scipy csr to canonical form
csr = source_array.sorted_indices()
csr.sum_duplicates()
dtype = _prepare_default_dtype(source_array, dtype)
return csr_matrix((csr.data, csr.indices, csr.indptr), shape=csr.shape, \
dtype=dtype, ctx=ctx)
elif isinstance(source_array, (np.ndarray, np.generic)):
raise ValueError("Please use mx.nd.array to create an NDArray with source_array of type ",
type(source_array))
else:
raise ValueError("Unexpected source_array type: ", type(source_array)) | python | def array(source_array, ctx=None, dtype=None):
"""Creates a sparse array from any object exposing the array interface.
Parameters
----------
source_array : RowSparseNDArray, CSRNDArray or scipy.sparse.csr.csr_matrix
The source sparse array
ctx : Context, optional
The default context is ``source_array.context`` if ``source_array`` is an NDArray. \
The current default context otherwise.
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `numpy.ndarray` or `scipy.sparse.csr.csr_matrix`, \
`float32` otherwise.
Returns
-------
RowSparseNDArray or CSRNDArray
An array with the same contents as the `source_array`.
Examples
--------
>>> import scipy.sparse as spsp
>>> csr = spsp.csr_matrix((2, 100))
>>> mx.nd.sparse.array(csr)
<CSRNDArray 2x100 @cpu(0)>
>>> mx.nd.sparse.array(mx.nd.sparse.zeros('csr', (3, 2)))
<CSRNDArray 3x2 @cpu(0)>
>>> mx.nd.sparse.array(mx.nd.sparse.zeros('row_sparse', (3, 2)))
<RowSparseNDArray 3x2 @cpu(0)>
"""
ctx = current_context() if ctx is None else ctx
if isinstance(source_array, NDArray):
assert(source_array.stype != 'default'), \
"Please use `tostype` to create RowSparseNDArray or CSRNDArray from an NDArray"
# prepare dtype and ctx based on source_array, if not provided
dtype = _prepare_default_dtype(source_array, dtype)
# if both dtype and ctx are different from source_array, we cannot copy directly
if source_array.dtype != dtype and source_array.context != ctx:
arr = empty(source_array.stype, source_array.shape, dtype=dtype)
arr[:] = source_array
arr = arr.as_in_context(ctx)
else:
arr = empty(source_array.stype, source_array.shape, dtype=dtype, ctx=ctx)
arr[:] = source_array
return arr
elif spsp and isinstance(source_array, spsp.csr.csr_matrix):
# TODO(haibin) implement `_sync_copy_from` with scipy csr object to reduce a copy
# preprocess scipy csr to canonical form
csr = source_array.sorted_indices()
csr.sum_duplicates()
dtype = _prepare_default_dtype(source_array, dtype)
return csr_matrix((csr.data, csr.indices, csr.indptr), shape=csr.shape, \
dtype=dtype, ctx=ctx)
elif isinstance(source_array, (np.ndarray, np.generic)):
raise ValueError("Please use mx.nd.array to create an NDArray with source_array of type ",
type(source_array))
else:
raise ValueError("Unexpected source_array type: ", type(source_array)) | [
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Parameters
----------
source_array : RowSparseNDArray, CSRNDArray or scipy.sparse.csr.csr_matrix
The source sparse array
ctx : Context, optional
The default context is ``source_array.context`` if ``source_array`` is an NDArray. \
The current default context otherwise.
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `numpy.ndarray` or `scipy.sparse.csr.csr_matrix`, \
`float32` otherwise.
Returns
-------
RowSparseNDArray or CSRNDArray
An array with the same contents as the `source_array`.
Examples
--------
>>> import scipy.sparse as spsp
>>> csr = spsp.csr_matrix((2, 100))
>>> mx.nd.sparse.array(csr)
<CSRNDArray 2x100 @cpu(0)>
>>> mx.nd.sparse.array(mx.nd.sparse.zeros('csr', (3, 2)))
<CSRNDArray 3x2 @cpu(0)>
>>> mx.nd.sparse.array(mx.nd.sparse.zeros('row_sparse', (3, 2)))
<RowSparseNDArray 3x2 @cpu(0)> | [
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23,745 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray._aux_type | def _aux_type(self, i):
"""Data-type of the array's ith aux data.
Returns
-------
numpy.dtype
This BaseSparseNDArray's aux data type.
"""
aux_type = ctypes.c_int()
check_call(_LIB.MXNDArrayGetAuxType(self.handle, i, ctypes.byref(aux_type)))
return _DTYPE_MX_TO_NP[aux_type.value] | python | def _aux_type(self, i):
"""Data-type of the array's ith aux data.
Returns
-------
numpy.dtype
This BaseSparseNDArray's aux data type.
"""
aux_type = ctypes.c_int()
check_call(_LIB.MXNDArrayGetAuxType(self.handle, i, ctypes.byref(aux_type)))
return _DTYPE_MX_TO_NP[aux_type.value] | [
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23,746 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray._aux_types | def _aux_types(self):
"""The data types of the aux data for the BaseSparseNDArray.
"""
aux_types = []
num_aux = self._num_aux
for i in range(num_aux):
aux_types.append(self._aux_type(i))
return aux_types | python | def _aux_types(self):
"""The data types of the aux data for the BaseSparseNDArray.
"""
aux_types = []
num_aux = self._num_aux
for i in range(num_aux):
aux_types.append(self._aux_type(i))
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23,747 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray.astype | def astype(self, dtype, copy=True):
"""Return a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
allocated ndarray on the same context. If this is set to
`False`, and the dtype requested is the same as the ndarray's
dtype, the ndarray is returned instead of a copy.
Examples
--------
>>> x = mx.nd.sparse.zeros('row_sparse', (2,3), dtype='float32')
>>> y = x.astype('int32')
>>> y.dtype
<type 'numpy.int32'>
"""
if not copy and np.dtype(dtype) == self.dtype:
return self
res = zeros(shape=self.shape, ctx=self.context,
dtype=dtype, stype=self.stype)
self.copyto(res)
return res | python | def astype(self, dtype, copy=True):
"""Return a copy of the array after casting to a specified type.
Parameters
----------
dtype : numpy.dtype or str
The type of the returned array.
copy : bool
Default `True`. By default, astype always returns a newly
allocated ndarray on the same context. If this is set to
`False`, and the dtype requested is the same as the ndarray's
dtype, the ndarray is returned instead of a copy.
Examples
--------
>>> x = mx.nd.sparse.zeros('row_sparse', (2,3), dtype='float32')
>>> y = x.astype('int32')
>>> y.dtype
<type 'numpy.int32'>
"""
if not copy and np.dtype(dtype) == self.dtype:
return self
res = zeros(shape=self.shape, ctx=self.context,
dtype=dtype, stype=self.stype)
self.copyto(res)
return res | [
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Examples
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>>> x = mx.nd.sparse.zeros('row_sparse', (2,3), dtype='float32')
>>> y = x.astype('int32')
>>> y.dtype
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23,748 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray.check_format | def check_format(self, full_check=True):
"""Check whether the NDArray format is valid.
Parameters
----------
full_check : bool, optional
If `True`, rigorous check, O(N) operations. Otherwise
basic check, O(1) operations (default True).
"""
check_call(_LIB.MXNDArraySyncCheckFormat(self.handle, ctypes.c_bool(full_check))) | python | def check_format(self, full_check=True):
"""Check whether the NDArray format is valid.
Parameters
----------
full_check : bool, optional
If `True`, rigorous check, O(N) operations. Otherwise
basic check, O(1) operations (default True).
"""
check_call(_LIB.MXNDArraySyncCheckFormat(self.handle, ctypes.c_bool(full_check))) | [
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23,749 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray._data | def _data(self):
"""A deep copy NDArray of the data array associated with the BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetDataNDArray(self.handle, ctypes.byref(hdl)))
return NDArray(hdl) | python | def _data(self):
"""A deep copy NDArray of the data array associated with the BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetDataNDArray(self.handle, ctypes.byref(hdl)))
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23,750 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | BaseSparseNDArray._aux_data | def _aux_data(self, i):
""" Get a deep copy NDArray of the i-th aux data array associated with the
BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
hdl = NDArrayHandle()
check_call(_LIB.MXNDArrayGetAuxNDArray(self.handle, i, ctypes.byref(hdl)))
return NDArray(hdl) | python | def _aux_data(self, i):
""" Get a deep copy NDArray of the i-th aux data array associated with the
BaseSparseNDArray.
This function blocks. Do not use it in performance critical code.
"""
self.wait_to_read()
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23,751 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | CSRNDArray.asscipy | def asscipy(self):
"""Returns a ``scipy.sparse.csr.csr_matrix`` object with value copied from this array
Examples
--------
>>> x = mx.nd.sparse.zeros('csr', (2,3))
>>> y = x.asscipy()
>>> type(y)
<type 'scipy.sparse.csr.csr_matrix'>
>>> y
<2x3 sparse matrix of type '<type 'numpy.float32'>'
with 0 stored elements in Compressed Sparse Row format>
"""
data = self.data.asnumpy()
indices = self.indices.asnumpy()
indptr = self.indptr.asnumpy()
if not spsp:
raise ImportError("scipy is not available. \
Please check if the scipy python bindings are installed.")
return spsp.csr_matrix((data, indices, indptr), shape=self.shape, dtype=self.dtype) | python | def asscipy(self):
"""Returns a ``scipy.sparse.csr.csr_matrix`` object with value copied from this array
Examples
--------
>>> x = mx.nd.sparse.zeros('csr', (2,3))
>>> y = x.asscipy()
>>> type(y)
<type 'scipy.sparse.csr.csr_matrix'>
>>> y
<2x3 sparse matrix of type '<type 'numpy.float32'>'
with 0 stored elements in Compressed Sparse Row format>
"""
data = self.data.asnumpy()
indices = self.indices.asnumpy()
indptr = self.indptr.asnumpy()
if not spsp:
raise ImportError("scipy is not available. \
Please check if the scipy python bindings are installed.")
return spsp.csr_matrix((data, indices, indptr), shape=self.shape, dtype=self.dtype) | [
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23,752 | apache/incubator-mxnet | python/mxnet/ndarray/sparse.py | RowSparseNDArray.tostype | def tostype(self, stype):
"""Return a copy of the array with chosen storage type.
Returns
-------
NDArray or RowSparseNDArray
A copy of the array with the chosen storage stype
"""
# pylint: disable= no-member, protected-access
if stype == 'csr':
raise ValueError("cast_storage from row_sparse to csr is not supported")
return op.cast_storage(self, stype=stype) | python | def tostype(self, stype):
"""Return a copy of the array with chosen storage type.
Returns
-------
NDArray or RowSparseNDArray
A copy of the array with the chosen storage stype
"""
# pylint: disable= no-member, protected-access
if stype == 'csr':
raise ValueError("cast_storage from row_sparse to csr is not supported")
return op.cast_storage(self, stype=stype) | [
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23,753 | apache/incubator-mxnet | benchmark/python/sparse/memory_benchmark.py | bench_dot | def bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density,
rhs_density, dot_func, trans_lhs, lhs_stype,
rhs_stype, only_storage, distribution="uniform"):
""" Benchmarking both storage and dot
"""
lhs_nd = rand_ndarray((lhs_row_dim, lhs_col_dim), lhs_stype, density, distribution=distribution)
if not only_storage:
rhs_nd = rand_ndarray((lhs_col_dim, rhs_col_dim), rhs_stype,
density=rhs_density, distribution=distribution)
out = dot_func(lhs_nd, rhs_nd, trans_lhs)
mx.nd.waitall() | python | def bench_dot(lhs_row_dim, lhs_col_dim, rhs_col_dim, density,
rhs_density, dot_func, trans_lhs, lhs_stype,
rhs_stype, only_storage, distribution="uniform"):
""" Benchmarking both storage and dot
"""
lhs_nd = rand_ndarray((lhs_row_dim, lhs_col_dim), lhs_stype, density, distribution=distribution)
if not only_storage:
rhs_nd = rand_ndarray((lhs_col_dim, rhs_col_dim), rhs_stype,
density=rhs_density, distribution=distribution)
out = dot_func(lhs_nd, rhs_nd, trans_lhs)
mx.nd.waitall() | [
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23,754 | apache/incubator-mxnet | tools/caffe_converter/convert_mean.py | convert_mean | def convert_mean(binaryproto_fname, output=None):
"""Convert caffe mean
Parameters
----------
binaryproto_fname : str
Filename of the mean
output : str, optional
Save the mean into mxnet's format
Returns
-------
NDArray
Mean in ndarray
"""
mean_blob = caffe_parser.caffe_pb2.BlobProto()
with open(binaryproto_fname, 'rb') as f:
mean_blob.ParseFromString(f.read())
img_mean_np = np.array(mean_blob.data)
img_mean_np = img_mean_np.reshape(
mean_blob.channels, mean_blob.height, mean_blob.width
)
# swap channels from Caffe BGR to RGB
img_mean_np[[0, 2], :, :] = img_mean_np[[2, 0], :, :]
nd = mx.nd.array(img_mean_np)
if output is not None:
mx.nd.save(output, {"mean_image": nd})
return nd | python | def convert_mean(binaryproto_fname, output=None):
"""Convert caffe mean
Parameters
----------
binaryproto_fname : str
Filename of the mean
output : str, optional
Save the mean into mxnet's format
Returns
-------
NDArray
Mean in ndarray
"""
mean_blob = caffe_parser.caffe_pb2.BlobProto()
with open(binaryproto_fname, 'rb') as f:
mean_blob.ParseFromString(f.read())
img_mean_np = np.array(mean_blob.data)
img_mean_np = img_mean_np.reshape(
mean_blob.channels, mean_blob.height, mean_blob.width
)
# swap channels from Caffe BGR to RGB
img_mean_np[[0, 2], :, :] = img_mean_np[[2, 0], :, :]
nd = mx.nd.array(img_mean_np)
if output is not None:
mx.nd.save(output, {"mean_image": nd})
return nd | [
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Parameters
----------
binaryproto_fname : str
Filename of the mean
output : str, optional
Save the mean into mxnet's format
Returns
-------
NDArray
Mean in ndarray | [
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23,755 | apache/incubator-mxnet | example/ssd/symbol/symbol_builder.py | import_module | def import_module(module_name):
"""Helper function to import module"""
import sys, os
import importlib
sys.path.append(os.path.dirname(__file__))
return importlib.import_module(module_name) | python | def import_module(module_name):
"""Helper function to import module"""
import sys, os
import importlib
sys.path.append(os.path.dirname(__file__))
return importlib.import_module(module_name) | [
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23,756 | apache/incubator-mxnet | example/ssd/symbol/symbol_builder.py | get_symbol_train | def get_symbol_train(network, num_classes, from_layers, num_filters, strides, pads,
sizes, ratios, normalizations=-1, steps=[], min_filter=128,
nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs):
"""Build network symbol for training SSD
Parameters
----------
network : str
base network symbol name
num_classes : int
number of object classes not including background
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
sizes : list or list of list
[min_size, max_size] for all layers or [[], [], []...] for specific layers
ratios : list or list of list
[ratio1, ratio2...] for all layers or [[], [], ...] for specific layers
normalizations : int or list of int
use normalizations value for all layers or [...] for specific layers,
-1 indicate no normalizations and scales
steps : list
specify steps for each MultiBoxPrior layer, leave empty, it will calculate
according to layer dimensions
min_filter : int
minimum number of filters used in 1x1 convolution
nms_thresh : float
non-maximum suppression threshold
force_suppress : boolean
whether suppress different class objects
nms_topk : int
apply NMS to top K detections
Returns
-------
mx.Symbol
"""
label = mx.sym.Variable('label')
body = import_module(network).get_symbol(num_classes, **kwargs)
layers = multi_layer_feature(body, from_layers, num_filters, strides, pads,
min_filter=min_filter)
loc_preds, cls_preds, anchor_boxes = multibox_layer(layers, \
num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \
num_channels=num_filters, clip=False, interm_layer=0, steps=steps)
tmp = mx.symbol.contrib.MultiBoxTarget(
*[anchor_boxes, label, cls_preds], overlap_threshold=.5, \
ignore_label=-1, negative_mining_ratio=3, minimum_negative_samples=0, \
negative_mining_thresh=.5, variances=(0.1, 0.1, 0.2, 0.2),
name="multibox_target")
loc_target = tmp[0]
loc_target_mask = tmp[1]
cls_target = tmp[2]
cls_prob = mx.symbol.SoftmaxOutput(data=cls_preds, label=cls_target, \
ignore_label=-1, use_ignore=True, grad_scale=1., multi_output=True, \
normalization='valid', name="cls_prob")
loc_loss_ = mx.symbol.smooth_l1(name="loc_loss_", \
data=loc_target_mask * (loc_preds - loc_target), scalar=1.0)
loc_loss = mx.symbol.MakeLoss(loc_loss_, grad_scale=1., \
normalization='valid', name="loc_loss")
# monitoring training status
cls_label = mx.symbol.MakeLoss(data=cls_target, grad_scale=0, name="cls_label")
det = mx.symbol.contrib.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \
name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress,
variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk)
det = mx.symbol.MakeLoss(data=det, grad_scale=0, name="det_out")
# group output
out = mx.symbol.Group([cls_prob, loc_loss, cls_label, det])
return out | python | def get_symbol_train(network, num_classes, from_layers, num_filters, strides, pads,
sizes, ratios, normalizations=-1, steps=[], min_filter=128,
nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs):
"""Build network symbol for training SSD
Parameters
----------
network : str
base network symbol name
num_classes : int
number of object classes not including background
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
sizes : list or list of list
[min_size, max_size] for all layers or [[], [], []...] for specific layers
ratios : list or list of list
[ratio1, ratio2...] for all layers or [[], [], ...] for specific layers
normalizations : int or list of int
use normalizations value for all layers or [...] for specific layers,
-1 indicate no normalizations and scales
steps : list
specify steps for each MultiBoxPrior layer, leave empty, it will calculate
according to layer dimensions
min_filter : int
minimum number of filters used in 1x1 convolution
nms_thresh : float
non-maximum suppression threshold
force_suppress : boolean
whether suppress different class objects
nms_topk : int
apply NMS to top K detections
Returns
-------
mx.Symbol
"""
label = mx.sym.Variable('label')
body = import_module(network).get_symbol(num_classes, **kwargs)
layers = multi_layer_feature(body, from_layers, num_filters, strides, pads,
min_filter=min_filter)
loc_preds, cls_preds, anchor_boxes = multibox_layer(layers, \
num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \
num_channels=num_filters, clip=False, interm_layer=0, steps=steps)
tmp = mx.symbol.contrib.MultiBoxTarget(
*[anchor_boxes, label, cls_preds], overlap_threshold=.5, \
ignore_label=-1, negative_mining_ratio=3, minimum_negative_samples=0, \
negative_mining_thresh=.5, variances=(0.1, 0.1, 0.2, 0.2),
name="multibox_target")
loc_target = tmp[0]
loc_target_mask = tmp[1]
cls_target = tmp[2]
cls_prob = mx.symbol.SoftmaxOutput(data=cls_preds, label=cls_target, \
ignore_label=-1, use_ignore=True, grad_scale=1., multi_output=True, \
normalization='valid', name="cls_prob")
loc_loss_ = mx.symbol.smooth_l1(name="loc_loss_", \
data=loc_target_mask * (loc_preds - loc_target), scalar=1.0)
loc_loss = mx.symbol.MakeLoss(loc_loss_, grad_scale=1., \
normalization='valid', name="loc_loss")
# monitoring training status
cls_label = mx.symbol.MakeLoss(data=cls_target, grad_scale=0, name="cls_label")
det = mx.symbol.contrib.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \
name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress,
variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk)
det = mx.symbol.MakeLoss(data=det, grad_scale=0, name="det_out")
# group output
out = mx.symbol.Group([cls_prob, loc_loss, cls_label, det])
return out | [
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Parameters
----------
network : str
base network symbol name
num_classes : int
number of object classes not including background
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
sizes : list or list of list
[min_size, max_size] for all layers or [[], [], []...] for specific layers
ratios : list or list of list
[ratio1, ratio2...] for all layers or [[], [], ...] for specific layers
normalizations : int or list of int
use normalizations value for all layers or [...] for specific layers,
-1 indicate no normalizations and scales
steps : list
specify steps for each MultiBoxPrior layer, leave empty, it will calculate
according to layer dimensions
min_filter : int
minimum number of filters used in 1x1 convolution
nms_thresh : float
non-maximum suppression threshold
force_suppress : boolean
whether suppress different class objects
nms_topk : int
apply NMS to top K detections
Returns
-------
mx.Symbol | [
"Build",
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"symbol",
"for",
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"SSD"
] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/ssd/symbol/symbol_builder.py#L29-L116 |
23,757 | apache/incubator-mxnet | example/ssd/symbol/symbol_builder.py | get_symbol | def get_symbol(network, num_classes, from_layers, num_filters, sizes, ratios,
strides, pads, normalizations=-1, steps=[], min_filter=128,
nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs):
"""Build network for testing SSD
Parameters
----------
network : str
base network symbol name
num_classes : int
number of object classes not including background
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
sizes : list or list of list
[min_size, max_size] for all layers or [[], [], []...] for specific layers
ratios : list or list of list
[ratio1, ratio2...] for all layers or [[], [], ...] for specific layers
normalizations : int or list of int
use normalizations value for all layers or [...] for specific layers,
-1 indicate no normalizations and scales
steps : list
specify steps for each MultiBoxPrior layer, leave empty, it will calculate
according to layer dimensions
min_filter : int
minimum number of filters used in 1x1 convolution
nms_thresh : float
non-maximum suppression threshold
force_suppress : boolean
whether suppress different class objects
nms_topk : int
apply NMS to top K detections
Returns
-------
mx.Symbol
"""
body = import_module(network).get_symbol(num_classes, **kwargs)
layers = multi_layer_feature(body, from_layers, num_filters, strides, pads,
min_filter=min_filter)
loc_preds, cls_preds, anchor_boxes = multibox_layer(layers, \
num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \
num_channels=num_filters, clip=False, interm_layer=0, steps=steps)
cls_prob = mx.symbol.softmax(data=cls_preds, axis=1, name='cls_prob')
out = mx.symbol.contrib.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \
name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress,
variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk)
return out | python | def get_symbol(network, num_classes, from_layers, num_filters, sizes, ratios,
strides, pads, normalizations=-1, steps=[], min_filter=128,
nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs):
"""Build network for testing SSD
Parameters
----------
network : str
base network symbol name
num_classes : int
number of object classes not including background
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
sizes : list or list of list
[min_size, max_size] for all layers or [[], [], []...] for specific layers
ratios : list or list of list
[ratio1, ratio2...] for all layers or [[], [], ...] for specific layers
normalizations : int or list of int
use normalizations value for all layers or [...] for specific layers,
-1 indicate no normalizations and scales
steps : list
specify steps for each MultiBoxPrior layer, leave empty, it will calculate
according to layer dimensions
min_filter : int
minimum number of filters used in 1x1 convolution
nms_thresh : float
non-maximum suppression threshold
force_suppress : boolean
whether suppress different class objects
nms_topk : int
apply NMS to top K detections
Returns
-------
mx.Symbol
"""
body = import_module(network).get_symbol(num_classes, **kwargs)
layers = multi_layer_feature(body, from_layers, num_filters, strides, pads,
min_filter=min_filter)
loc_preds, cls_preds, anchor_boxes = multibox_layer(layers, \
num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \
num_channels=num_filters, clip=False, interm_layer=0, steps=steps)
cls_prob = mx.symbol.softmax(data=cls_preds, axis=1, name='cls_prob')
out = mx.symbol.contrib.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \
name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress,
variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk)
return out | [
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Parameters
----------
network : str
base network symbol name
num_classes : int
number of object classes not including background
from_layers : list of str
feature extraction layers, use '' for add extra layers
For example:
from_layers = ['relu4_3', 'fc7', '', '', '', '']
which means extract feature from relu4_3 and fc7, adding 4 extra layers
on top of fc7
num_filters : list of int
number of filters for extra layers, you can use -1 for extracted features,
however, if normalization and scale is applied, the number of filter for
that layer must be provided.
For example:
num_filters = [512, -1, 512, 256, 256, 256]
strides : list of int
strides for the 3x3 convolution appended, -1 can be used for extracted
feature layers
pads : list of int
paddings for the 3x3 convolution, -1 can be used for extracted layers
sizes : list or list of list
[min_size, max_size] for all layers or [[], [], []...] for specific layers
ratios : list or list of list
[ratio1, ratio2...] for all layers or [[], [], ...] for specific layers
normalizations : int or list of int
use normalizations value for all layers or [...] for specific layers,
-1 indicate no normalizations and scales
steps : list
specify steps for each MultiBoxPrior layer, leave empty, it will calculate
according to layer dimensions
min_filter : int
minimum number of filters used in 1x1 convolution
nms_thresh : float
non-maximum suppression threshold
force_suppress : boolean
whether suppress different class objects
nms_topk : int
apply NMS to top K detections
Returns
-------
mx.Symbol | [
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23,758 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | get_conv_out_grad | def get_conv_out_grad(net, image, class_id=None, conv_layer_name=None):
"""Get the output and gradients of output of a convolutional layer.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
class_id: int
Category ID this image belongs to. If not provided,
network's prediction will be used.
conv_layer_name: str
Name of the convolutional layer whose output and output's gradients need to be acptured."""
return _get_grad(net, image, class_id, conv_layer_name, image_grad=False) | python | def get_conv_out_grad(net, image, class_id=None, conv_layer_name=None):
"""Get the output and gradients of output of a convolutional layer.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
class_id: int
Category ID this image belongs to. If not provided,
network's prediction will be used.
conv_layer_name: str
Name of the convolutional layer whose output and output's gradients need to be acptured."""
return _get_grad(net, image, class_id, conv_layer_name, image_grad=False) | [
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Preprocessed image to use for visualization.
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23,759 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | get_image_grad | def get_image_grad(net, image, class_id=None):
"""Get the gradients of the image.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
class_id: int
Category ID this image belongs to. If not provided,
network's prediction will be used."""
return _get_grad(net, image, class_id, image_grad=True) | python | def get_image_grad(net, image, class_id=None):
"""Get the gradients of the image.
Parameters:
----------
net: Block
Network to use for visualization.
image: NDArray
Preprocessed image to use for visualization.
class_id: int
Category ID this image belongs to. If not provided,
network's prediction will be used."""
return _get_grad(net, image, class_id, image_grad=True) | [
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23,760 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | grad_to_image | def grad_to_image(gradient):
"""Convert gradients of image obtained using `get_image_grad`
into image. This shows parts of the image that is most strongly activating
the output neurons."""
gradient = gradient - gradient.min()
gradient /= gradient.max()
gradient = np.uint8(gradient * 255).transpose(1, 2, 0)
gradient = gradient[..., ::-1]
return gradient | python | def grad_to_image(gradient):
"""Convert gradients of image obtained using `get_image_grad`
into image. This shows parts of the image that is most strongly activating
the output neurons."""
gradient = gradient - gradient.min()
gradient /= gradient.max()
gradient = np.uint8(gradient * 255).transpose(1, 2, 0)
gradient = gradient[..., ::-1]
return gradient | [
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23,761 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | get_img_heatmap | def get_img_heatmap(orig_img, activation_map):
"""Draw a heatmap on top of the original image using intensities from activation_map"""
heatmap = cv2.applyColorMap(activation_map, cv2.COLORMAP_COOL)
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
img_heatmap = np.float32(heatmap) + np.float32(orig_img)
img_heatmap = img_heatmap / np.max(img_heatmap)
img_heatmap *= 255
return img_heatmap.astype(int) | python | def get_img_heatmap(orig_img, activation_map):
"""Draw a heatmap on top of the original image using intensities from activation_map"""
heatmap = cv2.applyColorMap(activation_map, cv2.COLORMAP_COOL)
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
img_heatmap = np.float32(heatmap) + np.float32(orig_img)
img_heatmap = img_heatmap / np.max(img_heatmap)
img_heatmap *= 255
return img_heatmap.astype(int) | [
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23,762 | apache/incubator-mxnet | docs/tutorial_utils/vision/cnn_visualization/gradcam.py | to_grayscale | def to_grayscale(cv2im):
"""Convert gradients to grayscale. This gives a saliency map."""
# How strongly does each position activate the output
grayscale_im = np.sum(np.abs(cv2im), axis=0)
# Normalize between min and 99th percentile
im_max = np.percentile(grayscale_im, 99)
im_min = np.min(grayscale_im)
grayscale_im = np.clip((grayscale_im - im_min) / (im_max - im_min), 0, 1)
grayscale_im = np.expand_dims(grayscale_im, axis=0)
return grayscale_im | python | def to_grayscale(cv2im):
"""Convert gradients to grayscale. This gives a saliency map."""
# How strongly does each position activate the output
grayscale_im = np.sum(np.abs(cv2im), axis=0)
# Normalize between min and 99th percentile
im_max = np.percentile(grayscale_im, 99)
im_min = np.min(grayscale_im)
grayscale_im = np.clip((grayscale_im - im_min) / (im_max - im_min), 0, 1)
grayscale_im = np.expand_dims(grayscale_im, axis=0)
return grayscale_im | [
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23,763 | apache/incubator-mxnet | python/mxnet/metric.py | check_label_shapes | def check_label_shapes(labels, preds, wrap=False, shape=False):
"""Helper function for checking shape of label and prediction
Parameters
----------
labels : list of `NDArray`
The labels of the data.
preds : list of `NDArray`
Predicted values.
wrap : boolean
If True, wrap labels/preds in a list if they are single NDArray
shape : boolean
If True, check the shape of labels and preds;
Otherwise only check their length.
"""
if not shape:
label_shape, pred_shape = len(labels), len(preds)
else:
label_shape, pred_shape = labels.shape, preds.shape
if label_shape != pred_shape:
raise ValueError("Shape of labels {} does not match shape of "
"predictions {}".format(label_shape, pred_shape))
if wrap:
if isinstance(labels, ndarray.ndarray.NDArray):
labels = [labels]
if isinstance(preds, ndarray.ndarray.NDArray):
preds = [preds]
return labels, preds | python | def check_label_shapes(labels, preds, wrap=False, shape=False):
"""Helper function for checking shape of label and prediction
Parameters
----------
labels : list of `NDArray`
The labels of the data.
preds : list of `NDArray`
Predicted values.
wrap : boolean
If True, wrap labels/preds in a list if they are single NDArray
shape : boolean
If True, check the shape of labels and preds;
Otherwise only check their length.
"""
if not shape:
label_shape, pred_shape = len(labels), len(preds)
else:
label_shape, pred_shape = labels.shape, preds.shape
if label_shape != pred_shape:
raise ValueError("Shape of labels {} does not match shape of "
"predictions {}".format(label_shape, pred_shape))
if wrap:
if isinstance(labels, ndarray.ndarray.NDArray):
labels = [labels]
if isinstance(preds, ndarray.ndarray.NDArray):
preds = [preds]
return labels, preds | [
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The labels of the data.
preds : list of `NDArray`
Predicted values.
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If True, wrap labels/preds in a list if they are single NDArray
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23,764 | apache/incubator-mxnet | python/mxnet/metric.py | create | def create(metric, *args, **kwargs):
"""Creates evaluation metric from metric names or instances of EvalMetric
or a custom metric function.
Parameters
----------
metric : str or callable
Specifies the metric to create.
This argument must be one of the below:
- Name of a metric.
- An instance of `EvalMetric`.
- A list, each element of which is a metric or a metric name.
- An evaluation function that computes custom metric for a given batch of
labels and predictions.
*args : list
Additional arguments to metric constructor.
Only used when metric is str.
**kwargs : dict
Additional arguments to metric constructor.
Only used when metric is str
Examples
--------
>>> def custom_metric(label, pred):
... return np.mean(np.abs(label - pred))
...
>>> metric1 = mx.metric.create('acc')
>>> metric2 = mx.metric.create(custom_metric)
>>> metric3 = mx.metric.create([metric1, metric2, 'rmse'])
"""
if callable(metric):
return CustomMetric(metric, *args, **kwargs)
elif isinstance(metric, list):
composite_metric = CompositeEvalMetric()
for child_metric in metric:
composite_metric.add(create(child_metric, *args, **kwargs))
return composite_metric
return _create(metric, *args, **kwargs) | python | def create(metric, *args, **kwargs):
"""Creates evaluation metric from metric names or instances of EvalMetric
or a custom metric function.
Parameters
----------
metric : str or callable
Specifies the metric to create.
This argument must be one of the below:
- Name of a metric.
- An instance of `EvalMetric`.
- A list, each element of which is a metric or a metric name.
- An evaluation function that computes custom metric for a given batch of
labels and predictions.
*args : list
Additional arguments to metric constructor.
Only used when metric is str.
**kwargs : dict
Additional arguments to metric constructor.
Only used when metric is str
Examples
--------
>>> def custom_metric(label, pred):
... return np.mean(np.abs(label - pred))
...
>>> metric1 = mx.metric.create('acc')
>>> metric2 = mx.metric.create(custom_metric)
>>> metric3 = mx.metric.create([metric1, metric2, 'rmse'])
"""
if callable(metric):
return CustomMetric(metric, *args, **kwargs)
elif isinstance(metric, list):
composite_metric = CompositeEvalMetric()
for child_metric in metric:
composite_metric.add(create(child_metric, *args, **kwargs))
return composite_metric
return _create(metric, *args, **kwargs) | [
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or a custom metric function.
Parameters
----------
metric : str or callable
Specifies the metric to create.
This argument must be one of the below:
- Name of a metric.
- An instance of `EvalMetric`.
- A list, each element of which is a metric or a metric name.
- An evaluation function that computes custom metric for a given batch of
labels and predictions.
*args : list
Additional arguments to metric constructor.
Only used when metric is str.
**kwargs : dict
Additional arguments to metric constructor.
Only used when metric is str
Examples
--------
>>> def custom_metric(label, pred):
... return np.mean(np.abs(label - pred))
...
>>> metric1 = mx.metric.create('acc')
>>> metric2 = mx.metric.create(custom_metric)
>>> metric3 = mx.metric.create([metric1, metric2, 'rmse']) | [
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23,765 | apache/incubator-mxnet | python/mxnet/metric.py | np | def np(numpy_feval, name=None, allow_extra_outputs=False):
"""Creates a custom evaluation metric that receives its inputs as numpy arrays.
Parameters
----------
numpy_feval : callable(label, pred)
Custom evaluation function that receives labels and predictions for a minibatch
as numpy arrays and returns the corresponding custom metric as a floating point number.
name : str, optional
Name of the custom metric.
allow_extra_outputs : bool, optional
Whether prediction output is allowed to have extra outputs. This is useful in cases
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in the next step. By default, extra outputs are not allowed.
Returns
-------
float
Custom metric corresponding to the provided labels and predictions.
Example
-------
>>> def custom_metric(label, pred):
... return np.mean(np.abs(label-pred))
...
>>> metric = mx.metric.np(custom_metric)
"""
def feval(label, pred):
"""Internal eval function."""
return numpy_feval(label, pred)
feval.__name__ = numpy_feval.__name__
return CustomMetric(feval, name, allow_extra_outputs) | python | def np(numpy_feval, name=None, allow_extra_outputs=False):
"""Creates a custom evaluation metric that receives its inputs as numpy arrays.
Parameters
----------
numpy_feval : callable(label, pred)
Custom evaluation function that receives labels and predictions for a minibatch
as numpy arrays and returns the corresponding custom metric as a floating point number.
name : str, optional
Name of the custom metric.
allow_extra_outputs : bool, optional
Whether prediction output is allowed to have extra outputs. This is useful in cases
like RNN where states are also part of output which can then be fed back to the RNN
in the next step. By default, extra outputs are not allowed.
Returns
-------
float
Custom metric corresponding to the provided labels and predictions.
Example
-------
>>> def custom_metric(label, pred):
... return np.mean(np.abs(label-pred))
...
>>> metric = mx.metric.np(custom_metric)
"""
def feval(label, pred):
"""Internal eval function."""
return numpy_feval(label, pred)
feval.__name__ = numpy_feval.__name__
return CustomMetric(feval, name, allow_extra_outputs) | [
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Name of the custom metric.
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Custom metric corresponding to the provided labels and predictions.
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...
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23,766 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.update_dict | def update_dict(self, label, pred):
"""Update the internal evaluation with named label and pred
Parameters
----------
labels : OrderedDict of str -> NDArray
name to array mapping for labels.
preds : OrderedDict of str -> NDArray
name to array mapping of predicted outputs.
"""
if self.output_names is not None:
pred = [pred[name] for name in self.output_names]
else:
pred = list(pred.values())
if self.label_names is not None:
label = [label[name] for name in self.label_names]
else:
label = list(label.values())
self.update(label, pred) | python | def update_dict(self, label, pred):
"""Update the internal evaluation with named label and pred
Parameters
----------
labels : OrderedDict of str -> NDArray
name to array mapping for labels.
preds : OrderedDict of str -> NDArray
name to array mapping of predicted outputs.
"""
if self.output_names is not None:
pred = [pred[name] for name in self.output_names]
else:
pred = list(pred.values())
if self.label_names is not None:
label = [label[name] for name in self.label_names]
else:
label = list(label.values())
self.update(label, pred) | [
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23,767 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.reset | def reset(self):
"""Resets the internal evaluation result to initial state."""
self.num_inst = 0
self.sum_metric = 0.0
self.global_num_inst = 0
self.global_sum_metric = 0.0 | python | def reset(self):
"""Resets the internal evaluation result to initial state."""
self.num_inst = 0
self.sum_metric = 0.0
self.global_num_inst = 0
self.global_sum_metric = 0.0 | [
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23,768 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.get | def get(self):
"""Gets the current evaluation result.
Returns
-------
names : list of str
Name of the metrics.
values : list of float
Value of the evaluations.
"""
if self.num_inst == 0:
return (self.name, float('nan'))
else:
return (self.name, self.sum_metric / self.num_inst) | python | def get(self):
"""Gets the current evaluation result.
Returns
-------
names : list of str
Name of the metrics.
values : list of float
Value of the evaluations.
"""
if self.num_inst == 0:
return (self.name, float('nan'))
else:
return (self.name, self.sum_metric / self.num_inst) | [
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23,769 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.get_global | def get_global(self):
"""Gets the current global evaluation result.
Returns
-------
names : list of str
Name of the metrics.
values : list of float
Value of the evaluations.
"""
if self._has_global_stats:
if self.global_num_inst == 0:
return (self.name, float('nan'))
else:
return (self.name, self.global_sum_metric / self.global_num_inst)
else:
return self.get() | python | def get_global(self):
"""Gets the current global evaluation result.
Returns
-------
names : list of str
Name of the metrics.
values : list of float
Value of the evaluations.
"""
if self._has_global_stats:
if self.global_num_inst == 0:
return (self.name, float('nan'))
else:
return (self.name, self.global_sum_metric / self.global_num_inst)
else:
return self.get() | [
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23,770 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.get_name_value | def get_name_value(self):
"""Returns zipped name and value pairs.
Returns
-------
list of tuples
A (name, value) tuple list.
"""
name, value = self.get()
if not isinstance(name, list):
name = [name]
if not isinstance(value, list):
value = [value]
return list(zip(name, value)) | python | def get_name_value(self):
"""Returns zipped name and value pairs.
Returns
-------
list of tuples
A (name, value) tuple list.
"""
name, value = self.get()
if not isinstance(name, list):
name = [name]
if not isinstance(value, list):
value = [value]
return list(zip(name, value)) | [
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23,771 | apache/incubator-mxnet | python/mxnet/metric.py | EvalMetric.get_global_name_value | def get_global_name_value(self):
"""Returns zipped name and value pairs for global results.
Returns
-------
list of tuples
A (name, value) tuple list.
"""
if self._has_global_stats:
name, value = self.get_global()
if not isinstance(name, list):
name = [name]
if not isinstance(value, list):
value = [value]
return list(zip(name, value))
else:
return self.get_name_value() | python | def get_global_name_value(self):
"""Returns zipped name and value pairs for global results.
Returns
-------
list of tuples
A (name, value) tuple list.
"""
if self._has_global_stats:
name, value = self.get_global()
if not isinstance(name, list):
name = [name]
if not isinstance(value, list):
value = [value]
return list(zip(name, value))
else:
return self.get_name_value() | [
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23,772 | apache/incubator-mxnet | python/mxnet/metric.py | _BinaryClassificationMetrics.matthewscc | def matthewscc(self, use_global=False):
"""
Calculate the Matthew's Correlation Coefficent
"""
if use_global:
if not self.global_total_examples:
return 0.
true_pos = float(self.global_true_positives)
false_pos = float(self.global_false_positives)
false_neg = float(self.global_false_negatives)
true_neg = float(self.global_true_negatives)
else:
if not self.total_examples:
return 0.
true_pos = float(self.true_positives)
false_pos = float(self.false_positives)
false_neg = float(self.false_negatives)
true_neg = float(self.true_negatives)
terms = [(true_pos + false_pos),
(true_pos + false_neg),
(true_neg + false_pos),
(true_neg + false_neg)]
denom = 1.
for t in filter(lambda t: t != 0., terms):
denom *= t
return ((true_pos * true_neg) - (false_pos * false_neg)) / math.sqrt(denom) | python | def matthewscc(self, use_global=False):
"""
Calculate the Matthew's Correlation Coefficent
"""
if use_global:
if not self.global_total_examples:
return 0.
true_pos = float(self.global_true_positives)
false_pos = float(self.global_false_positives)
false_neg = float(self.global_false_negatives)
true_neg = float(self.global_true_negatives)
else:
if not self.total_examples:
return 0.
true_pos = float(self.true_positives)
false_pos = float(self.false_positives)
false_neg = float(self.false_negatives)
true_neg = float(self.true_negatives)
terms = [(true_pos + false_pos),
(true_pos + false_neg),
(true_neg + false_pos),
(true_neg + false_neg)]
denom = 1.
for t in filter(lambda t: t != 0., terms):
denom *= t
return ((true_pos * true_neg) - (false_pos * false_neg)) / math.sqrt(denom) | [
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23,773 | apache/incubator-mxnet | python/mxnet/gluon/data/dataset.py | Dataset.transform | def transform(self, fn, lazy=True):
"""Returns a new dataset with each sample transformed by the
transformer function `fn`.
Parameters
----------
fn : callable
A transformer function that takes a sample as input and
returns the transformed sample.
lazy : bool, default True
If False, transforms all samples at once. Otherwise,
transforms each sample on demand. Note that if `fn`
is stochastic, you must set lazy to True or you will
get the same result on all epochs.
Returns
-------
Dataset
The transformed dataset.
"""
trans = _LazyTransformDataset(self, fn)
if lazy:
return trans
return SimpleDataset([i for i in trans]) | python | def transform(self, fn, lazy=True):
"""Returns a new dataset with each sample transformed by the
transformer function `fn`.
Parameters
----------
fn : callable
A transformer function that takes a sample as input and
returns the transformed sample.
lazy : bool, default True
If False, transforms all samples at once. Otherwise,
transforms each sample on demand. Note that if `fn`
is stochastic, you must set lazy to True or you will
get the same result on all epochs.
Returns
-------
Dataset
The transformed dataset.
"""
trans = _LazyTransformDataset(self, fn)
if lazy:
return trans
return SimpleDataset([i for i in trans]) | [
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transformer function `fn`.
Parameters
----------
fn : callable
A transformer function that takes a sample as input and
returns the transformed sample.
lazy : bool, default True
If False, transforms all samples at once. Otherwise,
transforms each sample on demand. Note that if `fn`
is stochastic, you must set lazy to True or you will
get the same result on all epochs.
Returns
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The transformed dataset. | [
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23,774 | apache/incubator-mxnet | example/ctc/ocr_predict.py | lstm_ocr_model.forward_ocr | def forward_ocr(self, img_):
"""Forward the image through the LSTM network model
Parameters
----------
img_: int of array
Returns
----------
label_list: string of list
"""
img_ = cv2.resize(img_, (80, 30))
img_ = img_.transpose(1, 0)
print(img_.shape)
img_ = img_.reshape((1, 80, 30))
print(img_.shape)
# img_ = img_.reshape((80 * 30))
img_ = np.multiply(img_, 1 / 255.0)
self.predictor.forward(data=img_, **self.init_state_dict)
prob = self.predictor.get_output(0)
label_list = []
for p in prob:
print(np.argsort(p))
max_index = np.argsort(p)[::-1][0]
label_list.append(max_index)
return self.__get_string(label_list) | python | def forward_ocr(self, img_):
"""Forward the image through the LSTM network model
Parameters
----------
img_: int of array
Returns
----------
label_list: string of list
"""
img_ = cv2.resize(img_, (80, 30))
img_ = img_.transpose(1, 0)
print(img_.shape)
img_ = img_.reshape((1, 80, 30))
print(img_.shape)
# img_ = img_.reshape((80 * 30))
img_ = np.multiply(img_, 1 / 255.0)
self.predictor.forward(data=img_, **self.init_state_dict)
prob = self.predictor.get_output(0)
label_list = []
for p in prob:
print(np.argsort(p))
max_index = np.argsort(p)[::-1][0]
label_list.append(max_index)
return self.__get_string(label_list) | [
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23,775 | apache/incubator-mxnet | tools/caffe_converter/caffe_parser.py | read_prototxt | def read_prototxt(fname):
"""Return a caffe_pb2.NetParameter object that defined in a prototxt file
"""
proto = caffe_pb2.NetParameter()
with open(fname, 'r') as f:
text_format.Merge(str(f.read()), proto)
return proto | python | def read_prototxt(fname):
"""Return a caffe_pb2.NetParameter object that defined in a prototxt file
"""
proto = caffe_pb2.NetParameter()
with open(fname, 'r') as f:
text_format.Merge(str(f.read()), proto)
return proto | [
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23,776 | apache/incubator-mxnet | tools/caffe_converter/caffe_parser.py | get_layers | def get_layers(proto):
"""Returns layers in a caffe_pb2.NetParameter object
"""
if len(proto.layer):
return proto.layer
elif len(proto.layers):
return proto.layers
else:
raise ValueError('Invalid proto file.') | python | def get_layers(proto):
"""Returns layers in a caffe_pb2.NetParameter object
"""
if len(proto.layer):
return proto.layer
elif len(proto.layers):
return proto.layers
else:
raise ValueError('Invalid proto file.') | [
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23,777 | apache/incubator-mxnet | tools/caffe_converter/caffe_parser.py | read_caffemodel | def read_caffemodel(prototxt_fname, caffemodel_fname):
"""Return a caffe_pb2.NetParameter object that defined in a binary
caffemodel file
"""
if use_caffe:
caffe.set_mode_cpu()
net = caffe.Net(prototxt_fname, caffemodel_fname, caffe.TEST)
layer_names = net._layer_names
layers = net.layers
return (layers, layer_names)
else:
proto = caffe_pb2.NetParameter()
with open(caffemodel_fname, 'rb') as f:
proto.ParseFromString(f.read())
return (get_layers(proto), None) | python | def read_caffemodel(prototxt_fname, caffemodel_fname):
"""Return a caffe_pb2.NetParameter object that defined in a binary
caffemodel file
"""
if use_caffe:
caffe.set_mode_cpu()
net = caffe.Net(prototxt_fname, caffemodel_fname, caffe.TEST)
layer_names = net._layer_names
layers = net.layers
return (layers, layer_names)
else:
proto = caffe_pb2.NetParameter()
with open(caffemodel_fname, 'rb') as f:
proto.ParseFromString(f.read())
return (get_layers(proto), None) | [
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23,778 | apache/incubator-mxnet | tools/caffe_converter/caffe_parser.py | layer_iter | def layer_iter(layers, layer_names):
"""Iterate over all layers"""
if use_caffe:
for layer_idx, layer in enumerate(layers):
layer_name = re.sub('[-/]', '_', layer_names[layer_idx])
layer_type = layer.type
layer_blobs = layer.blobs
yield (layer_name, layer_type, layer_blobs)
else:
for layer in layers:
layer_name = re.sub('[-/]', '_', layer.name)
layer_type = layer.type
layer_blobs = layer.blobs
yield (layer_name, layer_type, layer_blobs) | python | def layer_iter(layers, layer_names):
"""Iterate over all layers"""
if use_caffe:
for layer_idx, layer in enumerate(layers):
layer_name = re.sub('[-/]', '_', layer_names[layer_idx])
layer_type = layer.type
layer_blobs = layer.blobs
yield (layer_name, layer_type, layer_blobs)
else:
for layer in layers:
layer_name = re.sub('[-/]', '_', layer.name)
layer_type = layer.type
layer_blobs = layer.blobs
yield (layer_name, layer_type, layer_blobs) | [
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23,779 | apache/incubator-mxnet | python/mxnet/profiler.py | set_state | def set_state(state='stop', profile_process='worker'):
"""Set up the profiler state to 'run' or 'stop'.
Parameters
----------
state : string, optional
Indicates whether to run the profiler, can
be 'stop' or 'run'. Default is `stop`.
profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker`
"""
state2int = {'stop': 0, 'run': 1}
profile_process2int = {'worker': 0, 'server': 1}
check_call(_LIB.MXSetProcessProfilerState(ctypes.c_int(state2int[state]),
profile_process2int[profile_process],
profiler_kvstore_handle)) | python | def set_state(state='stop', profile_process='worker'):
"""Set up the profiler state to 'run' or 'stop'.
Parameters
----------
state : string, optional
Indicates whether to run the profiler, can
be 'stop' or 'run'. Default is `stop`.
profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker`
"""
state2int = {'stop': 0, 'run': 1}
profile_process2int = {'worker': 0, 'server': 1}
check_call(_LIB.MXSetProcessProfilerState(ctypes.c_int(state2int[state]),
profile_process2int[profile_process],
profiler_kvstore_handle)) | [
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23,780 | apache/incubator-mxnet | python/mxnet/profiler.py | dump | def dump(finished=True, profile_process='worker'):
"""Dump profile and stop profiler. Use this to save profile
in advance in case your program cannot exit normally.
Parameters
----------
finished : boolean
Indicates whether to stop statistic output (dumping) after this dump.
Default is True
profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker`
"""
fin = 1 if finished is True else 0
profile_process2int = {'worker': 0, 'server': 1}
check_call(_LIB.MXDumpProcessProfile(fin,
profile_process2int[profile_process],
profiler_kvstore_handle)) | python | def dump(finished=True, profile_process='worker'):
"""Dump profile and stop profiler. Use this to save profile
in advance in case your program cannot exit normally.
Parameters
----------
finished : boolean
Indicates whether to stop statistic output (dumping) after this dump.
Default is True
profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker`
"""
fin = 1 if finished is True else 0
profile_process2int = {'worker': 0, 'server': 1}
check_call(_LIB.MXDumpProcessProfile(fin,
profile_process2int[profile_process],
profiler_kvstore_handle)) | [
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23,781 | apache/incubator-mxnet | python/mxnet/profiler.py | dumps | def dumps(reset=False):
"""Return a printable string of aggregate profile stats.
Parameters
----------
reset: boolean
Indicates whether to clean aggeregate statistical data collected up to this point
"""
debug_str = ctypes.c_char_p()
do_reset = 1 if reset is True else 0
check_call(_LIB.MXAggregateProfileStatsPrint(ctypes.byref(debug_str), int(do_reset)))
return py_str(debug_str.value) | python | def dumps(reset=False):
"""Return a printable string of aggregate profile stats.
Parameters
----------
reset: boolean
Indicates whether to clean aggeregate statistical data collected up to this point
"""
debug_str = ctypes.c_char_p()
do_reset = 1 if reset is True else 0
check_call(_LIB.MXAggregateProfileStatsPrint(ctypes.byref(debug_str), int(do_reset)))
return py_str(debug_str.value) | [
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23,782 | apache/incubator-mxnet | python/mxnet/profiler.py | pause | def pause(profile_process='worker'):
"""Pause profiling.
Parameters
----------
profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker`
"""
profile_process2int = {'worker': 0, 'server': 1}
check_call(_LIB.MXProcessProfilePause(int(1),
profile_process2int[profile_process],
profiler_kvstore_handle)) | python | def pause(profile_process='worker'):
"""Pause profiling.
Parameters
----------
profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker`
"""
profile_process2int = {'worker': 0, 'server': 1}
check_call(_LIB.MXProcessProfilePause(int(1),
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Parameters
----------
profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker` | [
"Pause",
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23,783 | apache/incubator-mxnet | python/mxnet/profiler.py | resume | def resume(profile_process='worker'):
"""
Resume paused profiling.
Parameters
----------
profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker`
"""
profile_process2int = {'worker': 0, 'server': 1}
check_call(_LIB.MXProcessProfilePause(int(0),
profile_process2int[profile_process],
profiler_kvstore_handle)) | python | def resume(profile_process='worker'):
"""
Resume paused profiling.
Parameters
----------
profile_process : string
whether to profile kvstore `server` or `worker`.
server can only be profiled when kvstore is of type dist.
if this is not passed, defaults to `worker`
"""
profile_process2int = {'worker': 0, 'server': 1}
check_call(_LIB.MXProcessProfilePause(int(0),
profile_process2int[profile_process],
profiler_kvstore_handle)) | [
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23,784 | apache/incubator-mxnet | python/mxnet/profiler.py | Counter.set_value | def set_value(self, value):
"""Set counter value.
Parameters
----------
value : int
Value for the counter
"""
check_call(_LIB.MXProfileSetCounter(self.handle, int(value))) | python | def set_value(self, value):
"""Set counter value.
Parameters
----------
value : int
Value for the counter
"""
check_call(_LIB.MXProfileSetCounter(self.handle, int(value))) | [
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23,785 | apache/incubator-mxnet | python/mxnet/profiler.py | Counter.decrement | def decrement(self, delta=1):
"""Decrement counter value.
Parameters
----------
value_change : int
Amount by which to subtract from the counter
"""
check_call(_LIB.MXProfileAdjustCounter(self.handle, -int(delta))) | python | def decrement(self, delta=1):
"""Decrement counter value.
Parameters
----------
value_change : int
Amount by which to subtract from the counter
"""
check_call(_LIB.MXProfileAdjustCounter(self.handle, -int(delta))) | [
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23,786 | apache/incubator-mxnet | python/mxnet/profiler.py | Marker.mark | def mark(self, scope='process'):
"""Set up the profiler state to record operator.
Parameters
----------
scope : string, optional
Indicates what scope the marker should refer to.
Can be 'global', 'process', thread', task', and 'marker'
Default is `process`.
"""
check_call(_LIB.MXProfileSetMarker(self.domain.handle, c_str(self.name), c_str(scope))) | python | def mark(self, scope='process'):
"""Set up the profiler state to record operator.
Parameters
----------
scope : string, optional
Indicates what scope the marker should refer to.
Can be 'global', 'process', thread', task', and 'marker'
Default is `process`.
"""
check_call(_LIB.MXProfileSetMarker(self.domain.handle, c_str(self.name), c_str(scope))) | [
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23,787 | apache/incubator-mxnet | python/mxnet/rtc.py | CudaModule.get_kernel | def get_kernel(self, name, signature):
r"""Get CUDA kernel from compiled module.
Parameters
----------
name : str
String name of the kernel.
signature : str
Function signature for the kernel. For example, if a kernel is
declared as::
extern "C" __global__ void axpy(const float *x, double *y, int alpha)
Then its signature should be::
const float *x, double *y, int alpha
or::
const float *, double *, int
Note that `*` in signature marks an argument as array and
`const` marks an argument as constant (input) array.
Returns
-------
CudaKernel
CUDA kernels that can be launched on GPUs.
"""
hdl = CudaKernelHandle()
is_ndarray = []
is_const = []
dtypes = []
pattern = re.compile(r"""^\s*(const)?\s*([\w_]+)\s*(\*)?\s*([\w_]+)?\s*$""")
args = re.sub(r"\s+", " ", signature).split(",")
for arg in args:
match = pattern.match(arg)
if not match or match.groups()[1] == 'const':
raise ValueError(
'Invalid function prototype "%s". Must be in the '
'form of "(const) type (*) (name)"'%arg)
is_const.append(bool(match.groups()[0]))
dtype = match.groups()[1]
is_ndarray.append(bool(match.groups()[2]))
if dtype not in _DTYPE_CPP_TO_NP:
raise TypeError(
"Unsupported kernel argument type %s. Supported types are: %s."%(
arg, ','.join(_DTYPE_CPP_TO_NP.keys())))
dtypes.append(_DTYPE_NP_TO_MX[_DTYPE_CPP_TO_NP[dtype]])
check_call(_LIB.MXRtcCudaKernelCreate(
self.handle,
c_str(name),
len(dtypes),
c_array_buf(ctypes.c_int, array('i', is_ndarray)),
c_array_buf(ctypes.c_int, array('i', is_const)),
c_array_buf(ctypes.c_int, array('i', dtypes)),
ctypes.byref(hdl)))
return CudaKernel(hdl, name, is_ndarray, dtypes) | python | def get_kernel(self, name, signature):
r"""Get CUDA kernel from compiled module.
Parameters
----------
name : str
String name of the kernel.
signature : str
Function signature for the kernel. For example, if a kernel is
declared as::
extern "C" __global__ void axpy(const float *x, double *y, int alpha)
Then its signature should be::
const float *x, double *y, int alpha
or::
const float *, double *, int
Note that `*` in signature marks an argument as array and
`const` marks an argument as constant (input) array.
Returns
-------
CudaKernel
CUDA kernels that can be launched on GPUs.
"""
hdl = CudaKernelHandle()
is_ndarray = []
is_const = []
dtypes = []
pattern = re.compile(r"""^\s*(const)?\s*([\w_]+)\s*(\*)?\s*([\w_]+)?\s*$""")
args = re.sub(r"\s+", " ", signature).split(",")
for arg in args:
match = pattern.match(arg)
if not match or match.groups()[1] == 'const':
raise ValueError(
'Invalid function prototype "%s". Must be in the '
'form of "(const) type (*) (name)"'%arg)
is_const.append(bool(match.groups()[0]))
dtype = match.groups()[1]
is_ndarray.append(bool(match.groups()[2]))
if dtype not in _DTYPE_CPP_TO_NP:
raise TypeError(
"Unsupported kernel argument type %s. Supported types are: %s."%(
arg, ','.join(_DTYPE_CPP_TO_NP.keys())))
dtypes.append(_DTYPE_NP_TO_MX[_DTYPE_CPP_TO_NP[dtype]])
check_call(_LIB.MXRtcCudaKernelCreate(
self.handle,
c_str(name),
len(dtypes),
c_array_buf(ctypes.c_int, array('i', is_ndarray)),
c_array_buf(ctypes.c_int, array('i', is_const)),
c_array_buf(ctypes.c_int, array('i', dtypes)),
ctypes.byref(hdl)))
return CudaKernel(hdl, name, is_ndarray, dtypes) | [
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Parameters
----------
name : str
String name of the kernel.
signature : str
Function signature for the kernel. For example, if a kernel is
declared as::
extern "C" __global__ void axpy(const float *x, double *y, int alpha)
Then its signature should be::
const float *x, double *y, int alpha
or::
const float *, double *, int
Note that `*` in signature marks an argument as array and
`const` marks an argument as constant (input) array.
Returns
-------
CudaKernel
CUDA kernels that can be launched on GPUs. | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/rtc.py#L112-L171 |
23,788 | apache/incubator-mxnet | python/mxnet/rtc.py | CudaKernel.launch | def launch(self, args, ctx, grid_dims, block_dims, shared_mem=0):
"""Launch cuda kernel.
Parameters
----------
args : tuple of NDArray or numbers
List of arguments for kernel. NDArrays are expected for pointer
types (e.g. `float*`, `double*`) while numbers are expected for
non-pointer types (e.g. `int`, `float`).
ctx : Context
The context to launch kernel on. Must be GPU context.
grid_dims : tuple of 3 integers
Grid dimensions for CUDA kernel.
block_dims : tuple of 3 integers
Block dimensions for CUDA kernel.
shared_mem : integer, optional
Size of dynamically allocated shared memory. Defaults to 0.
"""
assert ctx.device_type == 'gpu', "Cuda kernel can only be launched on GPU"
assert len(grid_dims) == 3, "grid_dims must be a tuple of 3 integers"
assert len(block_dims) == 3, "grid_dims must be a tuple of 3 integers"
assert len(args) == len(self._dtypes), \
"CudaKernel(%s) expects %d arguments but got %d"%(
self._name, len(self._dtypes), len(args))
void_args = []
ref_holder = []
for i, (arg, is_nd, dtype) in enumerate(zip(args, self._is_ndarray, self._dtypes)):
if is_nd:
assert isinstance(arg, NDArray), \
"The %d-th argument is expected to be a NDArray but got %s"%(
i, type(arg))
void_args.append(arg.handle)
else:
assert isinstance(arg, numeric_types), \
"The %d-th argument is expected to be a number, but got %s"%(
i, type(arg))
ref_holder.append(np.array(arg, dtype=dtype))
void_args.append(ref_holder[-1].ctypes.data_as(ctypes.c_void_p))
check_call(_LIB.MXRtcCudaKernelCall(
self.handle,
ctx.device_id,
c_array(ctypes.c_void_p, void_args),
mx_uint(grid_dims[0]), mx_uint(grid_dims[1]), mx_uint(grid_dims[2]),
mx_uint(block_dims[0]), mx_uint(block_dims[1]), mx_uint(block_dims[2]),
mx_uint(shared_mem))) | python | def launch(self, args, ctx, grid_dims, block_dims, shared_mem=0):
"""Launch cuda kernel.
Parameters
----------
args : tuple of NDArray or numbers
List of arguments for kernel. NDArrays are expected for pointer
types (e.g. `float*`, `double*`) while numbers are expected for
non-pointer types (e.g. `int`, `float`).
ctx : Context
The context to launch kernel on. Must be GPU context.
grid_dims : tuple of 3 integers
Grid dimensions for CUDA kernel.
block_dims : tuple of 3 integers
Block dimensions for CUDA kernel.
shared_mem : integer, optional
Size of dynamically allocated shared memory. Defaults to 0.
"""
assert ctx.device_type == 'gpu', "Cuda kernel can only be launched on GPU"
assert len(grid_dims) == 3, "grid_dims must be a tuple of 3 integers"
assert len(block_dims) == 3, "grid_dims must be a tuple of 3 integers"
assert len(args) == len(self._dtypes), \
"CudaKernel(%s) expects %d arguments but got %d"%(
self._name, len(self._dtypes), len(args))
void_args = []
ref_holder = []
for i, (arg, is_nd, dtype) in enumerate(zip(args, self._is_ndarray, self._dtypes)):
if is_nd:
assert isinstance(arg, NDArray), \
"The %d-th argument is expected to be a NDArray but got %s"%(
i, type(arg))
void_args.append(arg.handle)
else:
assert isinstance(arg, numeric_types), \
"The %d-th argument is expected to be a number, but got %s"%(
i, type(arg))
ref_holder.append(np.array(arg, dtype=dtype))
void_args.append(ref_holder[-1].ctypes.data_as(ctypes.c_void_p))
check_call(_LIB.MXRtcCudaKernelCall(
self.handle,
ctx.device_id,
c_array(ctypes.c_void_p, void_args),
mx_uint(grid_dims[0]), mx_uint(grid_dims[1]), mx_uint(grid_dims[2]),
mx_uint(block_dims[0]), mx_uint(block_dims[1]), mx_uint(block_dims[2]),
mx_uint(shared_mem))) | [
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List of arguments for kernel. NDArrays are expected for pointer
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The context to launch kernel on. Must be GPU context.
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Grid dimensions for CUDA kernel.
block_dims : tuple of 3 integers
Block dimensions for CUDA kernel.
shared_mem : integer, optional
Size of dynamically allocated shared memory. Defaults to 0. | [
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23,789 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric.reset | def reset(self):
"""Clear the internal statistics to initial state."""
if getattr(self, 'num', None) is None:
self.num_inst = 0
self.sum_metric = 0.0
else:
self.num_inst = [0] * self.num
self.sum_metric = [0.0] * self.num
self.records = dict()
self.counts = dict() | python | def reset(self):
"""Clear the internal statistics to initial state."""
if getattr(self, 'num', None) is None:
self.num_inst = 0
self.sum_metric = 0.0
else:
self.num_inst = [0] * self.num
self.sum_metric = [0.0] * self.num
self.records = dict()
self.counts = dict() | [
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23,790 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric._update | def _update(self):
""" update num_inst and sum_metric """
aps = []
for k, v in self.records.items():
recall, prec = self._recall_prec(v, self.counts[k])
ap = self._average_precision(recall, prec)
aps.append(ap)
if self.num is not None and k < (self.num - 1):
self.sum_metric[k] = ap
self.num_inst[k] = 1
if self.num is None:
self.num_inst = 1
self.sum_metric = np.mean(aps)
else:
self.num_inst[-1] = 1
self.sum_metric[-1] = np.mean(aps) | python | def _update(self):
""" update num_inst and sum_metric """
aps = []
for k, v in self.records.items():
recall, prec = self._recall_prec(v, self.counts[k])
ap = self._average_precision(recall, prec)
aps.append(ap)
if self.num is not None and k < (self.num - 1):
self.sum_metric[k] = ap
self.num_inst[k] = 1
if self.num is None:
self.num_inst = 1
self.sum_metric = np.mean(aps)
else:
self.num_inst[-1] = 1
self.sum_metric[-1] = np.mean(aps) | [
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23,791 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric._recall_prec | def _recall_prec(self, record, count):
""" get recall and precision from internal records """
record = np.delete(record, np.where(record[:, 1].astype(int) == 0)[0], axis=0)
sorted_records = record[record[:,0].argsort()[::-1]]
tp = np.cumsum(sorted_records[:, 1].astype(int) == 1)
fp = np.cumsum(sorted_records[:, 1].astype(int) == 2)
if count <= 0:
recall = tp * 0.0
else:
recall = tp / float(count)
prec = tp.astype(float) / (tp + fp)
return recall, prec | python | def _recall_prec(self, record, count):
""" get recall and precision from internal records """
record = np.delete(record, np.where(record[:, 1].astype(int) == 0)[0], axis=0)
sorted_records = record[record[:,0].argsort()[::-1]]
tp = np.cumsum(sorted_records[:, 1].astype(int) == 1)
fp = np.cumsum(sorted_records[:, 1].astype(int) == 2)
if count <= 0:
recall = tp * 0.0
else:
recall = tp / float(count)
prec = tp.astype(float) / (tp + fp)
return recall, prec | [
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23,792 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric._average_precision | def _average_precision(self, rec, prec):
"""
calculate average precision
Params:
----------
rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
Returns:
----------
ap as float
"""
# append sentinel values at both ends
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute precision integration ladder
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# look for recall value changes
i = np.where(mrec[1:] != mrec[:-1])[0]
# sum (\delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap | python | def _average_precision(self, rec, prec):
"""
calculate average precision
Params:
----------
rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
Returns:
----------
ap as float
"""
# append sentinel values at both ends
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute precision integration ladder
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# look for recall value changes
i = np.where(mrec[1:] != mrec[:-1])[0]
# sum (\delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap | [
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rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
Returns:
----------
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23,793 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | MApMetric._insert | def _insert(self, key, records, count):
""" Insert records according to key """
if key not in self.records:
assert key not in self.counts
self.records[key] = records
self.counts[key] = count
else:
self.records[key] = np.vstack((self.records[key], records))
assert key in self.counts
self.counts[key] += count | python | def _insert(self, key, records, count):
""" Insert records according to key """
if key not in self.records:
assert key not in self.counts
self.records[key] = records
self.counts[key] = count
else:
self.records[key] = np.vstack((self.records[key], records))
assert key in self.counts
self.counts[key] += count | [
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23,794 | apache/incubator-mxnet | example/ssd/evaluate/eval_metric.py | VOC07MApMetric._average_precision | def _average_precision(self, rec, prec):
"""
calculate average precision, override the default one,
special 11-point metric
Params:
----------
rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
Returns:
----------
ap as float
"""
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap += p / 11.
return ap | python | def _average_precision(self, rec, prec):
"""
calculate average precision, override the default one,
special 11-point metric
Params:
----------
rec : numpy.array
cumulated recall
prec : numpy.array
cumulated precision
Returns:
----------
ap as float
"""
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap += p / 11.
return ap | [
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23,795 | apache/incubator-mxnet | python/mxnet/initializer.py | Initializer._verbose_print | def _verbose_print(self, desc, init, arr):
"""Internal verbose print function
Parameters
----------
desc : InitDesc or str
name of the array
init : str
initializer pattern
arr : NDArray
initialized array
"""
if self._verbose and self._print_func:
logging.info('Initialized %s as %s: %s', desc, init, self._print_func(arr)) | python | def _verbose_print(self, desc, init, arr):
"""Internal verbose print function
Parameters
----------
desc : InitDesc or str
name of the array
init : str
initializer pattern
arr : NDArray
initialized array
"""
if self._verbose and self._print_func:
logging.info('Initialized %s as %s: %s', desc, init, self._print_func(arr)) | [
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name of the array
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arr : NDArray
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23,796 | apache/incubator-mxnet | python/mxnet/initializer.py | Initializer._legacy_init | def _legacy_init(self, name, arr):
"""Legacy initialization method.
Parameters
----------
name : str
Name of corresponding NDArray.
arr : NDArray
NDArray to be initialized.
"""
warnings.warn(
"\033[91mCalling initializer with init(str, NDArray) has been deprecated." \
"please use init(mx.init.InitDesc(...), NDArray) instead.\033[0m",
DeprecationWarning, stacklevel=3)
if not isinstance(name, string_types):
raise TypeError('name must be string')
if not isinstance(arr, NDArray):
raise TypeError('arr must be NDArray')
if name.startswith('upsampling'):
self._init_bilinear(name, arr)
elif name.startswith('stn_loc') and name.endswith('weight'):
self._init_zero(name, arr)
elif name.startswith('stn_loc') and name.endswith('bias'):
self._init_loc_bias(name, arr)
elif name.endswith('bias'):
self._init_bias(name, arr)
elif name.endswith('gamma'):
self._init_gamma(name, arr)
elif name.endswith('beta'):
self._init_beta(name, arr)
elif name.endswith('weight'):
self._init_weight(name, arr)
elif name.endswith("moving_mean"):
self._init_zero(name, arr)
elif name.endswith("moving_var"):
self._init_one(name, arr)
elif name.endswith("moving_inv_var"):
self._init_zero(name, arr)
elif name.endswith("moving_avg"):
self._init_zero(name, arr)
elif name.endswith('min'):
self._init_zero(name, arr)
elif name.endswith('max'):
self._init_one(name, arr)
else:
self._init_default(name, arr) | python | def _legacy_init(self, name, arr):
"""Legacy initialization method.
Parameters
----------
name : str
Name of corresponding NDArray.
arr : NDArray
NDArray to be initialized.
"""
warnings.warn(
"\033[91mCalling initializer with init(str, NDArray) has been deprecated." \
"please use init(mx.init.InitDesc(...), NDArray) instead.\033[0m",
DeprecationWarning, stacklevel=3)
if not isinstance(name, string_types):
raise TypeError('name must be string')
if not isinstance(arr, NDArray):
raise TypeError('arr must be NDArray')
if name.startswith('upsampling'):
self._init_bilinear(name, arr)
elif name.startswith('stn_loc') and name.endswith('weight'):
self._init_zero(name, arr)
elif name.startswith('stn_loc') and name.endswith('bias'):
self._init_loc_bias(name, arr)
elif name.endswith('bias'):
self._init_bias(name, arr)
elif name.endswith('gamma'):
self._init_gamma(name, arr)
elif name.endswith('beta'):
self._init_beta(name, arr)
elif name.endswith('weight'):
self._init_weight(name, arr)
elif name.endswith("moving_mean"):
self._init_zero(name, arr)
elif name.endswith("moving_var"):
self._init_one(name, arr)
elif name.endswith("moving_inv_var"):
self._init_zero(name, arr)
elif name.endswith("moving_avg"):
self._init_zero(name, arr)
elif name.endswith('min'):
self._init_zero(name, arr)
elif name.endswith('max'):
self._init_one(name, arr)
else:
self._init_default(name, arr) | [
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Parameters
----------
name : str
Name of corresponding NDArray.
arr : NDArray
NDArray to be initialized. | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/python/mxnet/initializer.py#L171-L217 |
23,797 | apache/incubator-mxnet | example/ssd/dataset/imdb.py | Imdb.save_imglist | def save_imglist(self, fname=None, root=None, shuffle=False):
"""
save imglist to disk
Parameters:
----------
fname : str
saved filename
"""
def progress_bar(count, total, suffix=''):
import sys
bar_len = 24
filled_len = int(round(bar_len * count / float(total)))
percents = round(100.0 * count / float(total), 1)
bar = '=' * filled_len + '-' * (bar_len - filled_len)
sys.stdout.write('[%s] %s%s ...%s\r' % (bar, percents, '%', suffix))
sys.stdout.flush()
str_list = []
for index in range(self.num_images):
progress_bar(index, self.num_images)
label = self.label_from_index(index)
if label.size < 1:
continue
path = self.image_path_from_index(index)
if root:
path = osp.relpath(path, root)
str_list.append('\t'.join([str(index), str(2), str(label.shape[1])] \
+ ["{0:.4f}".format(x) for x in label.ravel()] + [path,]) + '\n')
if str_list:
if shuffle:
import random
random.shuffle(str_list)
if not fname:
fname = self.name + '.lst'
with open(fname, 'w') as f:
for line in str_list:
f.write(line)
else:
raise RuntimeError("No image in imdb") | python | def save_imglist(self, fname=None, root=None, shuffle=False):
"""
save imglist to disk
Parameters:
----------
fname : str
saved filename
"""
def progress_bar(count, total, suffix=''):
import sys
bar_len = 24
filled_len = int(round(bar_len * count / float(total)))
percents = round(100.0 * count / float(total), 1)
bar = '=' * filled_len + '-' * (bar_len - filled_len)
sys.stdout.write('[%s] %s%s ...%s\r' % (bar, percents, '%', suffix))
sys.stdout.flush()
str_list = []
for index in range(self.num_images):
progress_bar(index, self.num_images)
label = self.label_from_index(index)
if label.size < 1:
continue
path = self.image_path_from_index(index)
if root:
path = osp.relpath(path, root)
str_list.append('\t'.join([str(index), str(2), str(label.shape[1])] \
+ ["{0:.4f}".format(x) for x in label.ravel()] + [path,]) + '\n')
if str_list:
if shuffle:
import random
random.shuffle(str_list)
if not fname:
fname = self.name + '.lst'
with open(fname, 'w') as f:
for line in str_list:
f.write(line)
else:
raise RuntimeError("No image in imdb") | [
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Parameters:
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fname : str
saved filename | [
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] | 1af29e9c060a4c7d60eeaacba32afdb9a7775ba7 | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/ssd/dataset/imdb.py#L70-L110 |
23,798 | apache/incubator-mxnet | example/ssd/dataset/imdb.py | Imdb._load_class_names | def _load_class_names(self, filename, dirname):
"""
load class names from text file
Parameters:
----------
filename: str
file stores class names
dirname: str
file directory
"""
full_path = osp.join(dirname, filename)
classes = []
with open(full_path, 'r') as f:
classes = [l.strip() for l in f.readlines()]
return classes | python | def _load_class_names(self, filename, dirname):
"""
load class names from text file
Parameters:
----------
filename: str
file stores class names
dirname: str
file directory
"""
full_path = osp.join(dirname, filename)
classes = []
with open(full_path, 'r') as f:
classes = [l.strip() for l in f.readlines()]
return classes | [
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Parameters:
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23,799 | apache/incubator-mxnet | example/image-classification/train_mnist.py | read_data | def read_data(label, image):
"""
download and read data into numpy
"""
base_url = 'http://yann.lecun.com/exdb/mnist/'
with gzip.open(download_file(base_url+label, os.path.join('data',label))) as flbl:
magic, num = struct.unpack(">II", flbl.read(8))
label = np.fromstring(flbl.read(), dtype=np.int8)
with gzip.open(download_file(base_url+image, os.path.join('data',image)), 'rb') as fimg:
magic, num, rows, cols = struct.unpack(">IIII", fimg.read(16))
image = np.fromstring(fimg.read(), dtype=np.uint8).reshape(len(label), rows, cols)
return (label, image) | python | def read_data(label, image):
"""
download and read data into numpy
"""
base_url = 'http://yann.lecun.com/exdb/mnist/'
with gzip.open(download_file(base_url+label, os.path.join('data',label))) as flbl:
magic, num = struct.unpack(">II", flbl.read(8))
label = np.fromstring(flbl.read(), dtype=np.int8)
with gzip.open(download_file(base_url+image, os.path.join('data',image)), 'rb') as fimg:
magic, num, rows, cols = struct.unpack(">IIII", fimg.read(16))
image = np.fromstring(fimg.read(), dtype=np.uint8).reshape(len(label), rows, cols)
return (label, image) | [
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