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tensorflow/cleverhans | cleverhans/utils_tfe.py | train | def train(model, X_train=None, Y_train=None, save=False,
predictions_adv=None, evaluate=None,
args=None, rng=None, var_list=None,
attack=None, attack_args=None):
"""
Train a TF Eager model
:param model: cleverhans.model.Model
:param X_train: numpy array with training inputs
:para... | python | def train(model, X_train=None, Y_train=None, save=False,
predictions_adv=None, evaluate=None,
args=None, rng=None, var_list=None,
attack=None, attack_args=None):
"""
Train a TF Eager model
:param model: cleverhans.model.Model
:param X_train: numpy array with training inputs
:para... | [
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:param save: boolean controlling the save operation
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tensorflow/cleverhans | cleverhans/utils_tfe.py | model_eval | def model_eval(model, X_test=None, Y_test=None, args=None,
attack=None, attack_args=None):
"""
Compute the accuracy of a TF Eager model on some data
:param model: instance of cleverhans.model.Model_Eager
with pretrained weights for evaluation.
:param X_test: numpy array with tra... | python | def model_eval(model, X_test=None, Y_test=None, args=None,
attack=None, attack_args=None):
"""
Compute the accuracy of a TF Eager model on some data
:param model: instance of cleverhans.model.Model_Eager
with pretrained weights for evaluation.
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:param Y_test: numpy array with training outputs
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tensorflow/cleverhans | cleverhans/utils_tfe.py | model_argmax | def model_argmax(model, samples):
"""
Helper function that computes the current class prediction
:param samples: numpy array with input samples (dims must match x)
:return: the argmax output of predictions, i.e. the current predicted class
"""
tfe = tf.contrib.eager
tf_samples = tfe.Variable(samples)
pr... | python | def model_argmax(model, samples):
"""
Helper function that computes the current class prediction
:param samples: numpy array with input samples (dims must match x)
:return: the argmax output of predictions, i.e. the current predicted class
"""
tfe = tf.contrib.eager
tf_samples = tfe.Variable(samples)
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tensorflow/cleverhans | cleverhans/attacks/noise.py | Noise.generate | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
if self.ord != np.inf:
rai... | python | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
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tensorflow/cleverhans | cleverhans/experimental/certification/utils.py | initialize_dual | def initialize_dual(neural_net_params_object, init_dual_file=None,
random_init_variance=0.01, init_nu=200.0):
"""Function to initialize the dual variables of the class.
Args:
neural_net_params_object: Object with the neural net weights, biases
and types
init_dual_file: Path to fil... | python | def initialize_dual(neural_net_params_object, init_dual_file=None,
random_init_variance=0.01, init_nu=200.0):
"""Function to initialize the dual variables of the class.
Args:
neural_net_params_object: Object with the neural net weights, biases
and types
init_dual_file: Path to fil... | [
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tensorflow/cleverhans | cleverhans/experimental/certification/utils.py | eig_one_step | def eig_one_step(current_vector, learning_rate, vector_prod_fn):
"""Function that performs one step of gd (variant) for min eigen value.
Args:
current_vector: current estimate of the eigen vector with minimum eigen
value.
learning_rate: learning rate.
vector_prod_fn: function which returns produc... | python | def eig_one_step(current_vector, learning_rate, vector_prod_fn):
"""Function that performs one step of gd (variant) for min eigen value.
Args:
current_vector: current estimate of the eigen vector with minimum eigen
value.
learning_rate: learning rate.
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current_vector: current estimate of the eigen vector with minimum eigen
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learning_rate: learning rate.
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tensorflow/cleverhans | cleverhans/experimental/certification/utils.py | minimum_eigen_vector | def minimum_eigen_vector(x, num_steps, learning_rate, vector_prod_fn):
"""Computes eigenvector which corresponds to minimum eigenvalue.
Args:
x: initial value of eigenvector.
num_steps: number of optimization steps.
learning_rate: learning rate.
vector_prod_fn: function which takes x and returns pr... | python | def minimum_eigen_vector(x, num_steps, learning_rate, vector_prod_fn):
"""Computes eigenvector which corresponds to minimum eigenvalue.
Args:
x: initial value of eigenvector.
num_steps: number of optimization steps.
learning_rate: learning rate.
vector_prod_fn: function which takes x and returns pr... | [
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tensorflow/cleverhans | cleverhans/experimental/certification/utils.py | tf_lanczos_smallest_eigval | def tf_lanczos_smallest_eigval(vector_prod_fn,
matrix_dim,
initial_vector,
num_iter=1000,
max_iter=1000,
collapse_tol=1e-9,
dtype=tf.f... | python | def tf_lanczos_smallest_eigval(vector_prod_fn,
matrix_dim,
initial_vector,
num_iter=1000,
max_iter=1000,
collapse_tol=1e-9,
dtype=tf.f... | [
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tensorflow/cleverhans | cleverhans/serial.py | NoRefModel.get_vars | def get_vars(self):
"""
Provides access to the model's Variables.
This may include Variables that are not parameters, such as batch
norm running moments.
:return: A list of all Variables defining the model.
"""
# Catch eager execution and assert function overload.
try:
if tf.execu... | python | def get_vars(self):
"""
Provides access to the model's Variables.
This may include Variables that are not parameters, such as batch
norm running moments.
:return: A list of all Variables defining the model.
"""
# Catch eager execution and assert function overload.
try:
if tf.execu... | [
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tensorflow/cleverhans | cleverhans/picklable_model.py | Dropout.fprop | def fprop(self, x, dropout=False, dropout_dict=None, **kwargs):
"""
Forward propagation as either no-op or dropping random units.
:param x: The input to the layer
:param dropout: bool specifying whether to drop units
:param dropout_dict: dict
This dictionary is usually not needed.
In... | python | def fprop(self, x, dropout=False, dropout_dict=None, **kwargs):
"""
Forward propagation as either no-op or dropping random units.
:param x: The input to the layer
:param dropout: bool specifying whether to drop units
:param dropout_dict: dict
This dictionary is usually not needed.
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tensorflow/cleverhans | cleverhans/attacks/carlini_wagner_l2.py | CarliniWagnerL2.generate | def generate(self, x, **kwargs):
"""
Return a tensor that constructs adversarial examples for the given
input. Generate uses tf.py_func in order to operate over tensors.
:param x: A tensor with the inputs.
:param kwargs: See `parse_params`
"""
assert self.sess is not None, \
'Cannot... | python | def generate(self, x, **kwargs):
"""
Return a tensor that constructs adversarial examples for the given
input. Generate uses tf.py_func in order to operate over tensors.
:param x: A tensor with the inputs.
:param kwargs: See `parse_params`
"""
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tensorflow/cleverhans | cleverhans/attacks/carlini_wagner_l2.py | CarliniWagnerL2.parse_params | def parse_params(self,
y=None,
y_target=None,
batch_size=1,
confidence=0,
learning_rate=5e-3,
binary_search_steps=5,
max_iterations=1000,
abort_early=True,
... | python | def parse_params(self,
y=None,
y_target=None,
batch_size=1,
confidence=0,
learning_rate=5e-3,
binary_search_steps=5,
max_iterations=1000,
abort_early=True,
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tensorflow/cleverhans | cleverhans/attacks/carlini_wagner_l2.py | CWL2.attack | def attack(self, imgs, targets):
"""
Perform the L_2 attack on the given instance for the given targets.
If self.targeted is true, then the targets represents the target labels
If self.targeted is false, then targets are the original class labels
"""
r = []
for i in range(0, len(imgs), sel... | python | def attack(self, imgs, targets):
"""
Perform the L_2 attack on the given instance for the given targets.
If self.targeted is true, then the targets represents the target labels
If self.targeted is false, then targets are the original class labels
"""
r = []
for i in range(0, len(imgs), sel... | [
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tensorflow/cleverhans | cleverhans/attacks/carlini_wagner_l2.py | CWL2.attack_batch | def attack_batch(self, imgs, labs):
"""
Run the attack on a batch of instance and labels.
"""
def compare(x, y):
if not isinstance(x, (float, int, np.int64)):
x = np.copy(x)
if self.TARGETED:
x[y] -= self.CONFIDENCE
else:
x[y] += self.CONFIDENCE
... | python | def attack_batch(self, imgs, labs):
"""
Run the attack on a batch of instance and labels.
"""
def compare(x, y):
if not isinstance(x, (float, int, np.int64)):
x = np.copy(x)
if self.TARGETED:
x[y] -= self.CONFIDENCE
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x[y] += self.CONFIDENCE
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tensorflow/cleverhans | examples/RL-attack/train.py | maybe_load_model | def maybe_load_model(savedir, container):
"""Load model if present at the specified path."""
if savedir is None:
return
state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip'))
if container is not None:
logger.log("Attempting to download model from Azure")
found_model = container.... | python | def maybe_load_model(savedir, container):
"""Load model if present at the specified path."""
if savedir is None:
return
state_path = os.path.join(os.path.join(savedir, 'training_state.pkl.zip'))
if container is not None:
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tensorflow/cleverhans | cleverhans_tutorials/__init__.py | check_installation | def check_installation(cur_file):
"""Warn user if running cleverhans from a different directory than tutorial."""
cur_dir = os.path.split(os.path.dirname(os.path.abspath(cur_file)))[0]
ch_dir = os.path.split(cleverhans.__path__[0])[0]
if cur_dir != ch_dir:
warnings.warn("It appears that you have at least tw... | python | def check_installation(cur_file):
"""Warn user if running cleverhans from a different directory than tutorial."""
cur_dir = os.path.split(os.path.dirname(os.path.abspath(cur_file)))[0]
ch_dir = os.path.split(cleverhans.__path__[0])[0]
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dataset/download_images.py | parse_args | def parse_args():
"""Parses command line arguments."""
parser = argparse.ArgumentParser(
description='Tool to download dataset images.')
parser.add_argument('--input_file', required=True,
help='Location of dataset.csv')
parser.add_argument('--output_dir', required=True,
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"""Parses command line arguments."""
parser = argparse.ArgumentParser(
description='Tool to download dataset images.')
parser.add_argument('--input_file', required=True,
help='Location of dataset.csv')
parser.add_argument('--output_dir', required=True,
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dataset/download_images.py | get_image | def get_image(row, output_dir):
"""Downloads the image that corresponds to the given row.
Prints a notification if the download fails."""
if not download_image(image_id=row[0],
url=row[1],
x1=float(row[2]),
y1=float(row[3]),
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"""Downloads the image that corresponds to the given row.
Prints a notification if the download fails."""
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dataset/download_images.py | download_image | def download_image(image_id, url, x1, y1, x2, y2, output_dir):
"""Downloads one image, crops it, resizes it and saves it locally."""
output_filename = os.path.join(output_dir, image_id + '.png')
if os.path.exists(output_filename):
# Don't download image if it's already there
return True
try:
# Downl... | python | def download_image(image_id, url, x1, y1, x2, y2, output_dir):
"""Downloads one image, crops it, resizes it and saves it locally."""
output_filename = os.path.join(output_dir, image_id + '.png')
if os.path.exists(output_filename):
# Don't download image if it's already there
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tensorflow/cleverhans | examples/robust_vision_benchmark/cleverhans_attack_example/utils.py | py_func_grad | def py_func_grad(func, inp, Tout, stateful=True, name=None, grad=None):
"""Custom py_func with gradient support
"""
# Need to generate a unique name to avoid duplicates:
rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
tf.RegisterGradient(rnd_name)(grad)
g = tf.get_default_graph()
with g.gradie... | python | def py_func_grad(func, inp, Tout, stateful=True, name=None, grad=None):
"""Custom py_func with gradient support
"""
# Need to generate a unique name to avoid duplicates:
rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
tf.RegisterGradient(rnd_name)(grad)
g = tf.get_default_graph()
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tensorflow/cleverhans | cleverhans_tutorials/tutorial_models_tfe.py | ModelBasicCNNTFE.fprop | def fprop(self, x):
"""
Forward propagation throught the network
:return: dictionary with layer names mapping to activation values.
"""
# Feed forward through the network layers
for layer_name in self.layer_names:
if layer_name == 'input':
prev_layer_act = x
continue
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Forward propagation throught the network
:return: dictionary with layer names mapping to activation values.
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# Feed forward through the network layers
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if layer_name == 'input':
prev_layer_act = x
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tensorflow/cleverhans | cleverhans_tutorials/tutorial_models_tfe.py | ModelBasicCNNTFE.get_layer_params | def get_layer_params(self, layer_name):
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eager mode.
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"""
Provides access to the parameters of the given layer.
Works arounds the non-availability of graph collections in
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tensorflow/cleverhans | cleverhans_tutorials/tutorial_models_tfe.py | ModelBasicCNNTFE.get_params | def get_params(self):
"""
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Works arounds the non-availability of graph collections in
eager mode.
:return: A list of all Variables defining the model parameters.
"""
assert tf.executing_eagerly()
out = []
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"""
Provides access to the model's parameters.
Works arounds the non-availability of graph collections in
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assert tf.executing_eagerly()
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tensorflow/cleverhans | cleverhans/plot/pyplot_image.py | pair_visual | def pair_visual(original, adversarial, figure=None):
"""
This function displays two images: the original and the adversarial sample
:param original: the original input
:param adversarial: the input after perturbations have been applied
:param figure: if we've already displayed images, use the same plot
:ret... | python | def pair_visual(original, adversarial, figure=None):
"""
This function displays two images: the original and the adversarial sample
:param original: the original input
:param adversarial: the input after perturbations have been applied
:param figure: if we've already displayed images, use the same plot
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tensorflow/cleverhans | cleverhans/plot/pyplot_image.py | grid_visual | def grid_visual(data):
"""
This function displays a grid of images to show full misclassification
:param data: grid data of the form;
[nb_classes : nb_classes : img_rows : img_cols : nb_channels]
:return: if necessary, the matplot figure to reuse
"""
import matplotlib.pyplot as plt
# Ensure interac... | python | def grid_visual(data):
"""
This function displays a grid of images to show full misclassification
:param data: grid data of the form;
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:return: if necessary, the matplot figure to reuse
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tensorflow/cleverhans | cleverhans/plot/pyplot_image.py | get_logits_over_interval | def get_logits_over_interval(sess, model, x_data, fgsm_params,
min_epsilon=-10., max_epsilon=10.,
num_points=21):
"""Get logits when the input is perturbed in an interval in adv direction.
Args:
sess: Tf session
model: Model for which we wish to... | python | def get_logits_over_interval(sess, model, x_data, fgsm_params,
min_epsilon=-10., max_epsilon=10.,
num_points=21):
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tensorflow/cleverhans | cleverhans/plot/pyplot_image.py | linear_extrapolation_plot | def linear_extrapolation_plot(log_prob_adv_array, y, file_name,
min_epsilon=-10, max_epsilon=10,
num_points=21):
"""Generate linear extrapolation plot.
Args:
log_prob_adv_array: Numpy array containing log probabilities
y: Tf placeholder for th... | python | def linear_extrapolation_plot(log_prob_adv_array, y, file_name,
min_epsilon=-10, max_epsilon=10,
num_points=21):
"""Generate linear extrapolation plot.
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log_prob_adv_array: Numpy array containing log probabilities
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backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool._send_cmd | def _send_cmd(self, cmd: str):
"""Encode IQFeed API messages."""
self._sock.sendall(cmd.encode(encoding='latin-1', errors='strict')) | python | def _send_cmd(self, cmd: str):
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backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool.iq_query | def iq_query(self, message: str):
"""Send data query to IQFeed API."""
end_msg = '!ENDMSG!'
recv_buffer = 4096
# Send the historical data request message and buffer the data
self._send_cmd(message)
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"""Send data query to IQFeed API."""
end_msg = '!ENDMSG!'
recv_buffer = 4096
# Send the historical data request message and buffer the data
self._send_cmd(message)
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backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool.get_historical_minute_data | def get_historical_minute_data(self, ticker: str):
"""Request historical 5 minute data from DTN."""
start = self._start
stop = self._stop
if len(stop) > 4:
stop = stop[:4]
if len(start) > 4:
start = start[:4]
for year in range(int(start), int(st... | python | def get_historical_minute_data(self, ticker: str):
"""Request historical 5 minute data from DTN."""
start = self._start
stop = self._stop
if len(stop) > 4:
stop = stop[:4]
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start = start[:4]
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backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool.add_data_to_df | def add_data_to_df(self, data: np.array):
"""Build Pandas Dataframe in memory"""
col_names = ['high_p', 'low_p', 'open_p', 'close_p', 'volume', 'oi']
data = np.array(data).reshape(-1, len(col_names) + 1)
df = pd.DataFrame(data=data[:, 1:], index=data[:, 0],
co... | python | def add_data_to_df(self, data: np.array):
"""Build Pandas Dataframe in memory"""
col_names = ['high_p', 'low_p', 'open_p', 'close_p', 'volume', 'oi']
data = np.array(data).reshape(-1, len(col_names) + 1)
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backtrader/backtrader | contrib/utils/iqfeed-to-influxdb.py | IQFeedTool.get_tickers_from_file | def get_tickers_from_file(self, filename):
"""Load ticker list from txt file"""
if not os.path.exists(filename):
log.error("Ticker List file does not exist: %s", filename)
tickers = []
with io.open(filename, 'r') as fd:
for ticker in fd:
tickers.a... | python | def get_tickers_from_file(self, filename):
"""Load ticker list from txt file"""
if not os.path.exists(filename):
log.error("Ticker List file does not exist: %s", filename)
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backtrader/backtrader | contrib/utils/influxdb-import.py | InfluxDBTool.write_dataframe_to_idb | def write_dataframe_to_idb(self, ticker):
"""Write Pandas Dataframe to InfluxDB database"""
cachepath = self._cache
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cachepath = self._cache
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backtrader/backtrader | backtrader/plot/multicursor.py | MultiCursor.connect | def connect(self):
"""connect events"""
self._cidmotion = self.canvas.mpl_connect('motion_notify_event',
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self._ciddraw = self.canvas.mpl_connect('draw_event', self.clear) | python | def connect(self):
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AirtestProject/Airtest | playground/win_ide.py | WindowsInIDE.connect | def connect(self, **kwargs):
"""
Connect to window and set it foreground
Args:
**kwargs: optional arguments
Returns:
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"""
self.app = self._app.connect(**kwargs)
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"""
Connect to window and set it foreground
Args:
**kwargs: optional arguments
Returns:
None
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Get rectangle of app or desktop resolution
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AirtestProject/Airtest | playground/win_ide.py | WindowsInIDE.snapshot | def snapshot(self, filename="tmp.png"):
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Take a screenshot and save it to `tmp.png` filename by default
Args:
filename: name of file where to store the screenshot
Returns:
display the screenshot
"""
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Take a screenshot and save it to `tmp.png` filename by default
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display the screenshot
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AirtestProject/Airtest | benchmark/plot.py | PlotResult.extract_data | def extract_data(self):
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self.mem_axis = []
self.timestamp_list = []
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# 按照时间分割线，划分成几段数据，取其中的最值
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"""从数据中获取到绘图相关的有用信息."""
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AirtestProject/Airtest | benchmark/plot.py | PlotResult.get_each_method_maximun_cpu_mem | def get_each_method_maximun_cpu_mem(self):
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"""获取每个方法中的cpu和内存耗费最值点."""
# 本函数用于丰富self.method_exec_info的信息:存入cpu、mem最值点
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AirtestProject/Airtest | benchmark/plot.py | PlotResult._get_graph_title | def _get_graph_title(self):
"""获取图像的title."""
start_time = datetime.fromtimestamp(int(self.timestamp_list[0]))
end_time = datetime.fromtimestamp(int(self.timestamp_list[-1]))
end_time = end_time.strftime('%H:%M:%S')
title = "Timespan: %s —— %s" % (start_time, end_time)
r... | python | def _get_graph_title(self):
"""获取图像的title."""
start_time = datetime.fromtimestamp(int(self.timestamp_list[0]))
end_time = datetime.fromtimestamp(int(self.timestamp_list[-1]))
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AirtestProject/Airtest | benchmark/plot.py | PlotResult.plot_cpu_mem_keypoints | def plot_cpu_mem_keypoints(self):
"""绘制CPU/Mem/特征点数量."""
plt.figure(1)
# 开始绘制子图:
plt.subplot(311)
title = self._get_graph_title()
plt.title(title, loc="center") # 设置绘图的标题
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"""绘制CPU/Mem/特征点数量."""
plt.figure(1)
# 开始绘制子图:
plt.subplot(311)
title = self._get_graph_title()
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AirtestProject/Airtest | benchmark/profile_recorder.py | CheckKeypointResult.refresh_method_objects | def refresh_method_objects(self):
"""初始化方法对象."""
self.method_object_dict = {}
for key, method in self.MATCHING_METHODS.items():
method_object = method(self.im_search, self.im_source, self.threshold, self.rgb)
self.method_object_dict.update({key: method_object}) | python | def refresh_method_objects(self):
"""初始化方法对象."""
self.method_object_dict = {}
for key, method in self.MATCHING_METHODS.items():
method_object = method(self.im_search, self.im_source, self.threshold, self.rgb)
self.method_object_dict.update({key: method_object}) | [
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AirtestProject/Airtest | benchmark/profile_recorder.py | CheckKeypointResult._get_result | def _get_result(self, method_name="kaze"):
"""获取特征点."""
method_object = self.method_object_dict.get(method_name)
# 提取结果和特征点:
try:
result = method_object.find_best_result()
except Exception:
import traceback
traceback.print_exc()
ret... | python | def _get_result(self, method_name="kaze"):
"""获取特征点."""
method_object = self.method_object_dict.get(method_name)
# 提取结果和特征点:
try:
result = method_object.find_best_result()
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import traceback
traceback.print_exc()
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AirtestProject/Airtest | benchmark/profile_recorder.py | CheckKeypointResult.get_and_plot_keypoints | def get_and_plot_keypoints(self, method_name, plot=False):
"""获取并且绘制出特征点匹配结果."""
if method_name not in self.method_object_dict.keys():
print("'%s' is not in MATCHING_METHODS" % method_name)
return None
kp_sch, kp_src, good, result = self._get_result(method_name)
... | python | def get_and_plot_keypoints(self, method_name, plot=False):
"""获取并且绘制出特征点匹配结果."""
if method_name not in self.method_object_dict.keys():
print("'%s' is not in MATCHING_METHODS" % method_name)
return None
kp_sch, kp_src, good, result = self._get_result(method_name)
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AirtestProject/Airtest | benchmark/profile_recorder.py | RecordThread.run | def run(self):
"""开始线程."""
while not self.stop_flag:
timestamp = time.time()
cpu_percent = self.process.cpu_percent() / self.cpu_num
# mem_percent = mem = self.process.memory_percent()
mem_info = dict(self.process.memory_info()._asdict())
mem_g... | python | def run(self):
"""开始线程."""
while not self.stop_flag:
timestamp = time.time()
cpu_percent = self.process.cpu_percent() / self.cpu_num
# mem_percent = mem = self.process.memory_percent()
mem_info = dict(self.process.memory_info()._asdict())
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AirtestProject/Airtest | benchmark/profile_recorder.py | ProfileRecorder.load_images | def load_images(self, search_file, source_file):
"""加载待匹配图片."""
self.search_file, self.source_file = search_file, source_file
self.im_search, self.im_source = imread(self.search_file), imread(self.source_file)
# 初始化对象
self.check_macthing_object = CheckKeypointResult(self.im_searc... | python | def load_images(self, search_file, source_file):
"""加载待匹配图片."""
self.search_file, self.source_file = search_file, source_file
self.im_search, self.im_source = imread(self.search_file), imread(self.source_file)
# 初始化对象
self.check_macthing_object = CheckKeypointResult(self.im_searc... | [
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AirtestProject/Airtest | benchmark/profile_recorder.py | ProfileRecorder.profile_methods | def profile_methods(self, method_list):
"""帮助函数执行时记录数据."""
self.method_exec_info = []
# 开始数据记录进程
self.record_thread.stop_flag = False
self.record_thread.start()
for name in method_list:
if name not in self.check_macthing_object.MATCHING_METHODS.keys():
... | python | def profile_methods(self, method_list):
"""帮助函数执行时记录数据."""
self.method_exec_info = []
# 开始数据记录进程
self.record_thread.stop_flag = False
self.record_thread.start()
for name in method_list:
if name not in self.check_macthing_object.MATCHING_METHODS.keys():
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AirtestProject/Airtest | benchmark/profile_recorder.py | ProfileRecorder.wite_to_json | def wite_to_json(self, dir_path="", file_name=""):
"""将性能数据写入文件."""
# 提取数据
data = {
"plot_data": self.record_thread.profile_data,
"method_exec_info": self.method_exec_info,
"search_file": self.search_file,
"source_file": self.source_file}
#... | python | def wite_to_json(self, dir_path="", file_name=""):
"""将性能数据写入文件."""
# 提取数据
data = {
"plot_data": self.record_thread.profile_data,
"method_exec_info": self.method_exec_info,
"search_file": self.search_file,
"source_file": self.source_file}
#... | [
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AirtestProject/Airtest | playground/poco.py | PocoReport.translate_poco_step | def translate_poco_step(self, step):
"""
处理poco的相关操作，参数与airtest的不同，由一个截图和一个操作构成，需要合成一个步骤
Parameters
----------
step 一个完整的操作，如click
prev_step 前一个步骤，应该是截图
Returns
-------
"""
ret = {}
prev_step = self._steps[-1]
if prev_step... | python | def translate_poco_step(self, step):
"""
处理poco的相关操作，参数与airtest的不同，由一个截图和一个操作构成，需要合成一个步骤
Parameters
----------
step 一个完整的操作，如click
prev_step 前一个步骤，应该是截图
Returns
-------
"""
ret = {}
prev_step = self._steps[-1]
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AirtestProject/Airtest | playground/poco.py | PocoReport.func_desc_poco | def func_desc_poco(self, step):
""" 把对应的poco操作显示成中文"""
desc = {
"touch": u"点击UI组件 {name}".format(name=step.get("text", "")),
}
if step['type'] in desc:
return desc.get(step['type'])
else:
return self._translate_desc(step) | python | def func_desc_poco(self, step):
""" 把对应的poco操作显示成中文"""
desc = {
"touch": u"点击UI组件 {name}".format(name=step.get("text", "")),
}
if step['type'] in desc:
return desc.get(step['type'])
else:
return self._translate_desc(step) | [
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AirtestProject/Airtest | benchmark/benchmark.py | profile_different_methods | def profile_different_methods(search_file, screen_file, method_list, dir_path, file_name):
"""对指定的图片进行性能测试."""
profiler = ProfileRecorder(0.05)
# 加载图片
profiler.load_images(search_file, screen_file)
# 传入待测试的方法列表
profiler.profile_methods(method_list)
# 将性能数据写入文件
profiler.wite_to_json(dir_p... | python | def profile_different_methods(search_file, screen_file, method_list, dir_path, file_name):
"""对指定的图片进行性能测试."""
profiler = ProfileRecorder(0.05)
# 加载图片
profiler.load_images(search_file, screen_file)
# 传入待测试的方法列表
profiler.profile_methods(method_list)
# 将性能数据写入文件
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AirtestProject/Airtest | benchmark/benchmark.py | plot_profiled_all_images_table | def plot_profiled_all_images_table(method_list):
"""绘制多个图片的结果."""
high_dpi_dir_path, high_dpi_file_name = "result", "high_dpi.json"
rich_texture_dir_path, rich_texture_file_name = "result", "rich_texture.json"
text_dir_path, text_file_name = "result", "text.json"
image_list = ['high_dpi', 'rich_tex... | python | def plot_profiled_all_images_table(method_list):
"""绘制多个图片的结果."""
high_dpi_dir_path, high_dpi_file_name = "result", "high_dpi.json"
rich_texture_dir_path, rich_texture_file_name = "result", "rich_texture.json"
text_dir_path, text_file_name = "result", "text.json"
image_list = ['high_dpi', 'rich_tex... | [
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AirtestProject/Airtest | benchmark/benchmark.py | get_color_list | def get_color_list(method_list):
"""获取method对应的color列表."""
color_list = []
for method in method_list:
color = tuple([random() for _ in range(3)]) # 随机颜色画线
color_list.append(color)
return color_list | python | def get_color_list(method_list):
"""获取method对应的color列表."""
color_list = []
for method in method_list:
color = tuple([random() for _ in range(3)]) # 随机颜色画线
color_list.append(color)
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AirtestProject/Airtest | benchmark/benchmark.py | plot_compare_table | def plot_compare_table(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111):
"""绘制了对比表格."""
row_labels = image_list
# 写入值：
table_vals = []
for i in range(len(row_labels)):
row_vals = []
for method in method_list:
row_vals.append(compare_dict[method... | python | def plot_compare_table(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111):
"""绘制了对比表格."""
row_labels = image_list
# 写入值：
table_vals = []
for i in range(len(row_labels)):
row_vals = []
for method in method_list:
row_vals.append(compare_dict[method... | [
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AirtestProject/Airtest | benchmark/benchmark.py | plot_compare_curves | def plot_compare_curves(image_list, method_list, color_list, compare_dict, fig_name="", fig_num=111):
"""绘制对比曲线."""
plt.subplot(fig_num)
plt.title(fig_name, loc="center") # 设置绘图的标题
mix_ins = []
for index, method in enumerate(method_list):
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"""绘制对比曲线."""
plt.subplot(fig_num)
plt.title(fig_name, loc="center") # 设置绘图的标题
mix_ins = []
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | ReadTag | def ReadTag(buffer, pos):
"""Read a tag from the buffer, and return a (tag_bytes, new_pos) tuple.
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"""Read a tag from the buffer, and return a (tag_bytes, new_pos) tuple.
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _SimpleDecoder | def _SimpleDecoder(wire_type, decode_value):
"""Return a constructor for a decoder for fields of a particular type.
Args:
wire_type: The field's wire type.
decode_value: A function which decodes an individual value, e.g.
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"""
def SpecificDecoder(field_number, is_repeated, ... | python | def _SimpleDecoder(wire_type, decode_value):
"""Return a constructor for a decoder for fields of a particular type.
Args:
wire_type: The field's wire type.
decode_value: A function which decodes an individual value, e.g.
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _ModifiedDecoder | def _ModifiedDecoder(wire_type, decode_value, modify_value):
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _StructPackDecoder | def _StructPackDecoder(wire_type, format):
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wire_type: The field's wire type.
format: The format string to pass to struct.unpack().
"""
value_size = struct.calcsize(format)
local_unpack = struct.unpack
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"""Return a constructor for a decoder for a fixed-width field.
Args:
wire_type: The field's wire type.
format: The format string to pass to struct.unpack().
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _FloatDecoder | def _FloatDecoder():
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _DoubleDecoder | def _DoubleDecoder():
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | StringDecoder | def StringDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a string field."""
local_DecodeVarint = _DecodeVarint
local_unicode = six.text_type
def _ConvertToUnicode(byte_str):
try:
return local_unicode(byte_str, 'utf-8')
except UnicodeDecodeError as e:
... | python | def StringDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a string field."""
local_DecodeVarint = _DecodeVarint
local_unicode = six.text_type
def _ConvertToUnicode(byte_str):
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | BytesDecoder | def BytesDecoder(field_number, is_repeated, is_packed, key, new_default):
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | GroupDecoder | def GroupDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a group field."""
end_tag_bytes = encoder.TagBytes(field_number,
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end_tag_len = len(end_tag_bytes)
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"""Returns a decoder for a group field."""
end_tag_bytes = encoder.TagBytes(field_number,
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end_tag_len = len(end_tag_bytes)
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | MapDecoder | def MapDecoder(field_descriptor, new_default, is_message_map):
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key = field_descriptor
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key = field_descriptor
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _SkipVarint | def _SkipVarint(buffer, pos, end):
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# Previously ord(buffer[pos]) raised IndexError when pos is out of range.
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _SkipLengthDelimited | def _SkipLengthDelimited(buffer, pos, end):
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raise _DecodeError('Truncated message.')
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _SkipGroup | def _SkipGroup(buffer, pos, end):
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/decoder.py | _FieldSkipper | def _FieldSkipper():
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wiretype_mask = wire_format.TAG_TYPE_M... | python | def _FieldSkipper():
"""Constructs the SkipField function."""
WIRETYPE_TO_SKIPPER = [
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apple/turicreate | src/external/xgboost/python-package/xgboost/plotting.py | _parse_node | def _parse_node(graph, text):
"""parse dumped node"""
match = _NODEPAT.match(text)
if match is not None:
node = match.group(1)
graph.node(node, label=match.group(2), shape='circle')
return node
match = _LEAFPAT.match(text)
if match is not None:
node = match.group(1)
... | python | def _parse_node(graph, text):
"""parse dumped node"""
match = _NODEPAT.match(text)
if match is not None:
node = match.group(1)
graph.node(node, label=match.group(2), shape='circle')
return node
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apple/turicreate | src/external/xgboost/python-package/xgboost/plotting.py | plot_tree | def plot_tree(booster, num_trees=0, rankdir='UT', ax=None, **kwargs):
"""Plot specified tree.
Parameters
----------
booster : Booster, XGBModel
Booster or XGBModel instance
num_trees : int, default 0
Specify the ordinal number of target tree
rankdir : str, default "UT"
P... | python | def plot_tree(booster, num_trees=0, rankdir='UT', ax=None, **kwargs):
"""Plot specified tree.
Parameters
----------
booster : Booster, XGBModel
Booster or XGBModel instance
num_trees : int, default 0
Specify the ordinal number of target tree
rankdir : str, default "UT"
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/manager.py | Manager.construct | def construct (self, properties = [], targets = []):
""" Constructs the dependency graph.
properties: the build properties.
targets: the targets to consider. If none is specified, uses all.
"""
if not targets:
for name, project in self.projects ().project... | python | def construct (self, properties = [], targets = []):
""" Constructs the dependency graph.
properties: the build properties.
targets: the targets to consider. If none is specified, uses all.
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apple/turicreate | src/unity/python/turicreate/toolkits/classifier/decision_tree_classifier.py | DecisionTreeClassifier.evaluate | def evaluate(self, dataset, metric='auto', missing_value_action='auto'):
"""
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these to actual values.
Parameters
----------
dataset : SFrame
Dataset of new observations. Must include colum... | python | def evaluate(self, dataset, metric='auto', missing_value_action='auto'):
"""
Evaluate the model by making predictions of target values and comparing
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apple/turicreate | src/unity/python/turicreate/toolkits/classifier/decision_tree_classifier.py | DecisionTreeClassifier.predict | def predict(self, dataset, output_type='class', missing_value_action='auto'):
"""
A flexible and advanced prediction API.
The target column is provided during
:func:`~turicreate.decision_tree.create`. If the target column is in the
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Paramete... | python | def predict(self, dataset, output_type='class', missing_value_action='auto'):
"""
A flexible and advanced prediction API.
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apple/turicreate | src/unity/python/turicreate/toolkits/classifier/decision_tree_classifier.py | DecisionTreeClassifier.predict_topk | def predict_topk(self, dataset, output_type="probability", k=3, missing_value_action='auto'):
"""
Return top-k predictions for the ``dataset``, using the trained model.
Predictions are returned as an SFrame with three columns: `id`,
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apple/turicreate | src/unity/python/turicreate/toolkits/classifier/decision_tree_classifier.py | DecisionTreeClassifier.classify | def classify(self, dataset, missing_value_action='auto'):
"""
Return a classification, for each example in the ``dataset``, using the
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(0 or 1) and probabilities associated with the the example.
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... | python | def classify(self, dataset, missing_value_action='auto'):
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apple/turicreate | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | Tracker.slave_envs | def slave_envs(self):
"""
get enviroment variables for slaves
can be passed in as args or envs
"""
if self.hostIP == 'dns':
host = socket.gethostname()
elif self.hostIP == 'ip':
host = socket.gethostbyname(socket.getfqdn())
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"""
get enviroment variables for slaves
can be passed in as args or envs
"""
if self.hostIP == 'dns':
host = socket.gethostname()
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apple/turicreate | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | Tracker.find_share_ring | def find_share_ring(self, tree_map, parent_map, r):
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get a ring structure that tends to share nodes with the tree
return a list starting from r
"""
nset = set(tree_map[r])
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rlst... | python | def find_share_ring(self, tree_map, parent_map, r):
"""
get a ring structure that tends to share nodes with the tree
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nset = set(tree_map[r])
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apple/turicreate | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | Tracker.get_ring | def get_ring(self, tree_map, parent_map):
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get a ring connection used to recover local data
"""
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get a ring connection used to recover local data
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apple/turicreate | src/external/xgboost/subtree/rabit/tracker/rabit_tracker.py | Tracker.get_link_map | def get_link_map(self, nslave):
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/tools/msvc.py | maybe_rewrite_setup | def maybe_rewrite_setup(toolset, setup_script, setup_options, version, rewrite_setup='off'):
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Helper rule to generate a faster alternative to MSVC setup scripts.
We used to call MSVC setup scripts directly in every action, however in
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apple/turicreate | src/unity/python/turicreate/toolkits/classifier/_classifier.py | create | def create(dataset, target, features=None, validation_set = 'auto',
verbose=True):
"""
Automatically create a suitable classifier model based on the provided
training data.
To use specific options of a desired model, use the ``create`` function
of the corresponding model.
Parameters
... | python | def create(dataset, target, features=None, validation_set = 'auto',
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apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.add_column | def add_column(self, data, column_name="", inplace=False):
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Adds the specified column to this SFrame. The number of elements in
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apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.add_columns | def add_columns(self, data, column_names=None, inplace=False):
"""
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If inplace == False (default) this operation does not modify the
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apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.remove_column | def remove_column(self, column_name, inplace=False):
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apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.swap_columns | def swap_columns(self, column_name_1, column_name_2, inplace=False):
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... | python | def swap_columns(self, column_name_1, column_name_2, inplace=False):
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apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.rename | def rename(self, names, inplace=False):
"""
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"""
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apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.num_rows | def num_rows(self):
"""
Returns the number of rows.
Returns
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out : int
Number of rows in the SFrame.
"""
if self._is_vertex_frame():
return self.__graph__.summary()['num_vertices']
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ret... | python | def num_rows(self):
"""
Returns the number of rows.
Returns
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out : int
Number of rows in the SFrame.
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return self.__graph__.summary()['num_vertices']
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apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.column_names | def column_names(self):
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Column names of the SFrame.
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apple/turicreate | src/unity/python/turicreate/data_structures/gframe.py | GFrame.column_types | def column_types(self):
"""
Returns the column types.
Returns
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out : list[type]
Column types of the SFrame.
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if self.__type__ == VERTEX_GFRAME:
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out : list[type]
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apple/turicreate | src/unity/python/turicreate/toolkits/regression/_regression.py | create | def create(dataset, target, features=None, validation_set = 'auto',
verbose=True):
"""
Automatically create a suitable regression model based on the provided
training data.
To use specific options of a desired model, use the ``create`` function
of the corresponding model.
Parameters
... | python | def create(dataset, target, features=None, validation_set = 'auto',
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"""
Automatically create a suitable regression model based on the provided
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apple/turicreate | src/unity/python/turicreate/meta/asttools/mutators/prune_mutator.py | removable | def removable(self, node):
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ret... | python | def removable(self, node):
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apple/turicreate | src/unity/python/turicreate/meta/asttools/mutators/prune_mutator.py | PruneVisitor.reduce | def reduce(self, body):
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remove nodes from a list
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i = 0
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remove nodes from a list
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apple/turicreate | src/unity/python/turicreate/toolkits/audio_analysis/audio_analysis.py | load_audio | def load_audio(path, with_path=True, recursive=True, ignore_failure=True, random_order=False):
"""
Loads WAV file(s) from a path.
Parameters
----------
path : str
Path to WAV files to be loaded.
with_path : bool, optional
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path : str
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/symbol_database.py | SymbolDatabase.RegisterMessage | def RegisterMessage(self, message):
"""Registers the given message type in the local database.
Calls to GetSymbol() and GetMessages() will return messages registered here.
Args:
message: a message.Message, to be registered.
Returns:
The provided message.
"""
desc = message.DESCRI... | python | def RegisterMessage(self, message):
"""Registers the given message type in the local database.
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message: a message.Message, to be registered.
Returns:
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/symbol_database.py | SymbolDatabase.GetMessages | def GetMessages(self, files):
# TODO(amauryfa): Fix the differences with MessageFactory.
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apple/turicreate | src/unity/python/turicreate/toolkits/object_detector/util/_visualization.py | _string_hash | def _string_hash(s):
"""String hash (djb2) with consistency between py2/py3 and persistency between runs (unlike `hash`)."""
h = 5381
for c in s:
h = h * 33 + ord(c)
return h | python | def _string_hash(s):
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apple/turicreate | src/unity/python/turicreate/toolkits/object_detector/util/_visualization.py | draw_bounding_boxes | def draw_bounding_boxes(images, annotations, confidence_threshold=0):
"""
Visualizes bounding boxes (ground truth or predictions) by
returning annotated copies of the images.
Parameters
----------
images: SArray or Image
An `SArray` of type `Image`. A single `Image` instance may also be... | python | def draw_bounding_boxes(images, annotations, confidence_threshold=0):
"""
Visualizes bounding boxes (ground truth or predictions) by
returning annotated copies of the images.
Parameters
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images: SArray or Image
An `SArray` of type `Image`. A single `Image` instance may also be... | [
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Parameters
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images: SArray or Image
An `SArray` of type `Image`. A single `Image` instance may also be
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annotations: SArray or list
An `SArray` of annotation... | [
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apple/turicreate | src/unity/python/turicreate/toolkits/_supervised_learning.py | create | def create(dataset, target, model_name, features=None,
validation_set='auto', distributed='auto',
verbose=True, seed=None, **kwargs):
"""
Create a :class:`~turicreate.toolkits.SupervisedLearningModel`,
This is generic function that allows you to create any model that
implements Su... | python | def create(dataset, target, model_name, features=None,
validation_set='auto', distributed='auto',
verbose=True, seed=None, **kwargs):
"""
Create a :class:`~turicreate.toolkits.SupervisedLearningModel`,
This is generic function that allows you to create any model that
implements Su... | [
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This is generic function that allows you to create any model that
implements SupervisedLearningModel This function is normally not called, call
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Parameters
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dataset : SFrame
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