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27,200 | Microsoft/nni | tools/nni_cmd/tensorboard_utils.py | start_tensorboard_process | def start_tensorboard_process(args, nni_config, path_list, temp_nni_path):
'''call cmds to start tensorboard process in local machine'''
if detect_port(args.port):
print_error('Port %s is used by another process, please reset port!' % str(args.port))
exit(1)
stdout_file = open(os.path.join(temp_nni_path, 'tensorboard_stdout'), 'a+')
stderr_file = open(os.path.join(temp_nni_path, 'tensorboard_stderr'), 'a+')
cmds = ['tensorboard', '--logdir', format_tensorboard_log_path(path_list), '--port', str(args.port)]
tensorboard_process = Popen(cmds, stdout=stdout_file, stderr=stderr_file)
url_list = get_local_urls(args.port)
print_normal(COLOR_GREEN_FORMAT % 'Start tensorboard success!\n' + 'Tensorboard urls: ' + ' '.join(url_list))
tensorboard_process_pid_list = nni_config.get_config('tensorboardPidList')
if tensorboard_process_pid_list is None:
tensorboard_process_pid_list = [tensorboard_process.pid]
else:
tensorboard_process_pid_list.append(tensorboard_process.pid)
nni_config.set_config('tensorboardPidList', tensorboard_process_pid_list) | python | def start_tensorboard_process(args, nni_config, path_list, temp_nni_path):
'''call cmds to start tensorboard process in local machine'''
if detect_port(args.port):
print_error('Port %s is used by another process, please reset port!' % str(args.port))
exit(1)
stdout_file = open(os.path.join(temp_nni_path, 'tensorboard_stdout'), 'a+')
stderr_file = open(os.path.join(temp_nni_path, 'tensorboard_stderr'), 'a+')
cmds = ['tensorboard', '--logdir', format_tensorboard_log_path(path_list), '--port', str(args.port)]
tensorboard_process = Popen(cmds, stdout=stdout_file, stderr=stderr_file)
url_list = get_local_urls(args.port)
print_normal(COLOR_GREEN_FORMAT % 'Start tensorboard success!\n' + 'Tensorboard urls: ' + ' '.join(url_list))
tensorboard_process_pid_list = nni_config.get_config('tensorboardPidList')
if tensorboard_process_pid_list is None:
tensorboard_process_pid_list = [tensorboard_process.pid]
else:
tensorboard_process_pid_list.append(tensorboard_process.pid)
nni_config.set_config('tensorboardPidList', tensorboard_process_pid_list) | [
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27,201 | Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | _ratio_scores | def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad):
'''
The ratio is smaller the better
'''
ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
sigma = 0
return ratio, sigma | python | def _ratio_scores(parameters_value, clusteringmodel_gmm_good, clusteringmodel_gmm_bad):
'''
The ratio is smaller the better
'''
ratio = clusteringmodel_gmm_good.score([parameters_value]) / clusteringmodel_gmm_bad.score([parameters_value])
sigma = 0
return ratio, sigma | [
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27,202 | Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | selection | def selection(x_bounds,
x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
minimize_starting_points,
minimize_constraints_fun=None):
'''
Select the lowest mu value
'''
results = lib_acquisition_function.next_hyperparameter_lowest_mu(\
_ratio_scores, [clusteringmodel_gmm_good, clusteringmodel_gmm_bad],\
x_bounds, x_types, minimize_starting_points, \
minimize_constraints_fun=minimize_constraints_fun)
return results | python | def selection(x_bounds,
x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
minimize_starting_points,
minimize_constraints_fun=None):
'''
Select the lowest mu value
'''
results = lib_acquisition_function.next_hyperparameter_lowest_mu(\
_ratio_scores, [clusteringmodel_gmm_good, clusteringmodel_gmm_bad],\
x_bounds, x_types, minimize_starting_points, \
minimize_constraints_fun=minimize_constraints_fun)
return results | [
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27,203 | Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py | _minimize_constraints_fun_summation | def _minimize_constraints_fun_summation(x):
'''
Minimize constraints fun summation
'''
summation = sum([x[i] for i in CONSTRAINT_PARAMS_IDX])
return CONSTRAINT_UPPERBOUND >= summation >= CONSTRAINT_LOWERBOUND | python | def _minimize_constraints_fun_summation(x):
'''
Minimize constraints fun summation
'''
summation = sum([x[i] for i in CONSTRAINT_PARAMS_IDX])
return CONSTRAINT_UPPERBOUND >= summation >= CONSTRAINT_LOWERBOUND | [
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27,204 | Microsoft/nni | examples/trials/sklearn/classification/main.py | load_data | def load_data():
'''Load dataset, use 20newsgroups dataset'''
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25)
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
return X_train, X_test, y_train, y_test | python | def load_data():
'''Load dataset, use 20newsgroups dataset'''
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=99, test_size=0.25)
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
return X_train, X_test, y_train, y_test | [
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27,205 | Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | Bracket.get_hyperparameter_configurations | def get_hyperparameter_configurations(self, num, r, config_generator):
"""generate num hyperparameter configurations from search space using Bayesian optimization
Parameters
----------
num: int
the number of hyperparameter configurations
Returns
-------
list
a list of hyperparameter configurations. Format: [[key1, value1], [key2, value2], ...]
"""
global _KEY
assert self.i == 0
hyperparameter_configs = dict()
for _ in range(num):
params_id = create_bracket_parameter_id(self.s, self.i)
params = config_generator.get_config(r)
params[_KEY] = r
hyperparameter_configs[params_id] = params
self._record_hyper_configs(hyperparameter_configs)
return [[key, value] for key, value in hyperparameter_configs.items()] | python | def get_hyperparameter_configurations(self, num, r, config_generator):
"""generate num hyperparameter configurations from search space using Bayesian optimization
Parameters
----------
num: int
the number of hyperparameter configurations
Returns
-------
list
a list of hyperparameter configurations. Format: [[key1, value1], [key2, value2], ...]
"""
global _KEY
assert self.i == 0
hyperparameter_configs = dict()
for _ in range(num):
params_id = create_bracket_parameter_id(self.s, self.i)
params = config_generator.get_config(r)
params[_KEY] = r
hyperparameter_configs[params_id] = params
self._record_hyper_configs(hyperparameter_configs)
return [[key, value] for key, value in hyperparameter_configs.items()] | [
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27,206 | Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_initialize | def handle_initialize(self, data):
"""Initialize Tuner, including creating Bayesian optimization-based parametric models
and search space formations
Parameters
----------
data: search space
search space of this experiment
Raises
------
ValueError
Error: Search space is None
"""
logger.info('start to handle_initialize')
# convert search space jason to ConfigSpace
self.handle_update_search_space(data)
# generate BOHB config_generator using Bayesian optimization
if self.search_space:
self.cg = CG_BOHB(configspace=self.search_space,
min_points_in_model=self.min_points_in_model,
top_n_percent=self.top_n_percent,
num_samples=self.num_samples,
random_fraction=self.random_fraction,
bandwidth_factor=self.bandwidth_factor,
min_bandwidth=self.min_bandwidth)
else:
raise ValueError('Error: Search space is None')
# generate first brackets
self.generate_new_bracket()
send(CommandType.Initialized, '') | python | def handle_initialize(self, data):
"""Initialize Tuner, including creating Bayesian optimization-based parametric models
and search space formations
Parameters
----------
data: search space
search space of this experiment
Raises
------
ValueError
Error: Search space is None
"""
logger.info('start to handle_initialize')
# convert search space jason to ConfigSpace
self.handle_update_search_space(data)
# generate BOHB config_generator using Bayesian optimization
if self.search_space:
self.cg = CG_BOHB(configspace=self.search_space,
min_points_in_model=self.min_points_in_model,
top_n_percent=self.top_n_percent,
num_samples=self.num_samples,
random_fraction=self.random_fraction,
bandwidth_factor=self.bandwidth_factor,
min_bandwidth=self.min_bandwidth)
else:
raise ValueError('Error: Search space is None')
# generate first brackets
self.generate_new_bracket()
send(CommandType.Initialized, '') | [
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Parameters
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data: search space
search space of this experiment
Raises
------
ValueError
Error: Search space is None | [
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27,207 | Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.generate_new_bracket | def generate_new_bracket(self):
"""generate a new bracket"""
logger.debug(
'start to create a new SuccessiveHalving iteration, self.curr_s=%d', self.curr_s)
if self.curr_s < 0:
logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate new round")
self.curr_s = self.s_max
self.brackets[self.curr_s] = Bracket(s=self.curr_s, s_max=self.s_max, eta=self.eta,
max_budget=self.max_budget, optimize_mode=self.optimize_mode)
next_n, next_r = self.brackets[self.curr_s].get_n_r()
logger.debug(
'new SuccessiveHalving iteration, next_n=%d, next_r=%d', next_n, next_r)
# rewrite with TPE
generated_hyper_configs = self.brackets[self.curr_s].get_hyperparameter_configurations(
next_n, next_r, self.cg)
self.generated_hyper_configs = generated_hyper_configs.copy() | python | def generate_new_bracket(self):
"""generate a new bracket"""
logger.debug(
'start to create a new SuccessiveHalving iteration, self.curr_s=%d', self.curr_s)
if self.curr_s < 0:
logger.info("s < 0, Finish this round of Hyperband in BOHB. Generate new round")
self.curr_s = self.s_max
self.brackets[self.curr_s] = Bracket(s=self.curr_s, s_max=self.s_max, eta=self.eta,
max_budget=self.max_budget, optimize_mode=self.optimize_mode)
next_n, next_r = self.brackets[self.curr_s].get_n_r()
logger.debug(
'new SuccessiveHalving iteration, next_n=%d, next_r=%d', next_n, next_r)
# rewrite with TPE
generated_hyper_configs = self.brackets[self.curr_s].get_hyperparameter_configurations(
next_n, next_r, self.cg)
self.generated_hyper_configs = generated_hyper_configs.copy() | [
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27,208 | Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_request_trial_jobs | def handle_request_trial_jobs(self, data):
"""recerive the number of request and generate trials
Parameters
----------
data: int
number of trial jobs that nni manager ask to generate
"""
# Receive new request
self.credit += data
for _ in range(self.credit):
self._request_one_trial_job() | python | def handle_request_trial_jobs(self, data):
"""recerive the number of request and generate trials
Parameters
----------
data: int
number of trial jobs that nni manager ask to generate
"""
# Receive new request
self.credit += data
for _ in range(self.credit):
self._request_one_trial_job() | [
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Parameters
----------
data: int
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27,209 | Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_trial_end | def handle_trial_end(self, data):
"""receive the information of trial end and generate next configuaration.
Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
event: the job's state
hyper_params: the hyperparameters (a string) generated and returned by tuner
"""
logger.debug('Tuner handle trial end, result is %s', data)
hyper_params = json_tricks.loads(data['hyper_params'])
s, i, _ = hyper_params['parameter_id'].split('_')
hyper_configs = self.brackets[int(s)].inform_trial_end(int(i))
if hyper_configs is not None:
logger.debug(
'bracket %s next round %s, hyper_configs: %s', s, i, hyper_configs)
self.generated_hyper_configs = self.generated_hyper_configs + hyper_configs
for _ in range(self.credit):
self._request_one_trial_job()
# Finish this bracket and generate a new bracket
elif self.brackets[int(s)].no_more_trial:
self.curr_s -= 1
self.generate_new_bracket()
for _ in range(self.credit):
self._request_one_trial_job() | python | def handle_trial_end(self, data):
"""receive the information of trial end and generate next configuaration.
Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
event: the job's state
hyper_params: the hyperparameters (a string) generated and returned by tuner
"""
logger.debug('Tuner handle trial end, result is %s', data)
hyper_params = json_tricks.loads(data['hyper_params'])
s, i, _ = hyper_params['parameter_id'].split('_')
hyper_configs = self.brackets[int(s)].inform_trial_end(int(i))
if hyper_configs is not None:
logger.debug(
'bracket %s next round %s, hyper_configs: %s', s, i, hyper_configs)
self.generated_hyper_configs = self.generated_hyper_configs + hyper_configs
for _ in range(self.credit):
self._request_one_trial_job()
# Finish this bracket and generate a new bracket
elif self.brackets[int(s)].no_more_trial:
self.curr_s -= 1
self.generate_new_bracket()
for _ in range(self.credit):
self._request_one_trial_job() | [
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Parameters
----------
data: dict()
it has three keys: trial_job_id, event, hyper_params
trial_job_id: the id generated by training service
event: the job's state
hyper_params: the hyperparameters (a string) generated and returned by tuner | [
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27,210 | Microsoft/nni | src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py | BOHB.handle_report_metric_data | def handle_report_metric_data(self, data):
"""reveice the metric data and update Bayesian optimization with final result
Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
ValueError
Data type not supported
"""
logger.debug('handle report metric data = %s', data)
assert 'value' in data
value = extract_scalar_reward(data['value'])
if self.optimize_mode is OptimizeMode.Maximize:
reward = -value
else:
reward = value
assert 'parameter_id' in data
s, i, _ = data['parameter_id'].split('_')
logger.debug('bracket id = %s, metrics value = %s, type = %s', s, value, data['type'])
s = int(s)
assert 'type' in data
if data['type'] == 'FINAL':
# and PERIODICAL metric are independent, thus, not comparable.
assert 'sequence' in data
self.brackets[s].set_config_perf(
int(i), data['parameter_id'], sys.maxsize, value)
self.completed_hyper_configs.append(data)
_parameters = self.parameters[data['parameter_id']]
_parameters.pop(_KEY)
# update BO with loss, max_s budget, hyperparameters
self.cg.new_result(loss=reward, budget=data['sequence'], parameters=_parameters, update_model=True)
elif data['type'] == 'PERIODICAL':
self.brackets[s].set_config_perf(
int(i), data['parameter_id'], data['sequence'], value)
else:
raise ValueError(
'Data type not supported: {}'.format(data['type'])) | python | def handle_report_metric_data(self, data):
"""reveice the metric data and update Bayesian optimization with final result
Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
ValueError
Data type not supported
"""
logger.debug('handle report metric data = %s', data)
assert 'value' in data
value = extract_scalar_reward(data['value'])
if self.optimize_mode is OptimizeMode.Maximize:
reward = -value
else:
reward = value
assert 'parameter_id' in data
s, i, _ = data['parameter_id'].split('_')
logger.debug('bracket id = %s, metrics value = %s, type = %s', s, value, data['type'])
s = int(s)
assert 'type' in data
if data['type'] == 'FINAL':
# and PERIODICAL metric are independent, thus, not comparable.
assert 'sequence' in data
self.brackets[s].set_config_perf(
int(i), data['parameter_id'], sys.maxsize, value)
self.completed_hyper_configs.append(data)
_parameters = self.parameters[data['parameter_id']]
_parameters.pop(_KEY)
# update BO with loss, max_s budget, hyperparameters
self.cg.new_result(loss=reward, budget=data['sequence'], parameters=_parameters, update_model=True)
elif data['type'] == 'PERIODICAL':
self.brackets[s].set_config_perf(
int(i), data['parameter_id'], data['sequence'], value)
else:
raise ValueError(
'Data type not supported: {}'.format(data['type'])) | [
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Parameters
----------
data:
it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
Raises
------
ValueError
Data type not supported | [
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27,211 | Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | data_transforms_cifar10 | def data_transforms_cifar10(args):
""" data_transforms for cifar10 dataset
"""
cifar_mean = [0.49139968, 0.48215827, 0.44653124]
cifar_std = [0.24703233, 0.24348505, 0.26158768]
train_transform = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(cifar_mean, cifar_std),
]
)
if args.cutout:
train_transform.transforms.append(Cutout(args.cutout_length))
valid_transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(cifar_mean, cifar_std)]
)
return train_transform, valid_transform | python | def data_transforms_cifar10(args):
""" data_transforms for cifar10 dataset
"""
cifar_mean = [0.49139968, 0.48215827, 0.44653124]
cifar_std = [0.24703233, 0.24348505, 0.26158768]
train_transform = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(cifar_mean, cifar_std),
]
)
if args.cutout:
train_transform.transforms.append(Cutout(args.cutout_length))
valid_transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(cifar_mean, cifar_std)]
)
return train_transform, valid_transform | [
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27,212 | Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | data_transforms_mnist | def data_transforms_mnist(args, mnist_mean=None, mnist_std=None):
""" data_transforms for mnist dataset
"""
if mnist_mean is None:
mnist_mean = [0.5]
if mnist_std is None:
mnist_std = [0.5]
train_transform = transforms.Compose(
[
transforms.RandomCrop(28, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mnist_mean, mnist_std),
]
)
if args.cutout:
train_transform.transforms.append(Cutout(args.cutout_length))
valid_transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mnist_mean, mnist_std)]
)
return train_transform, valid_transform | python | def data_transforms_mnist(args, mnist_mean=None, mnist_std=None):
""" data_transforms for mnist dataset
"""
if mnist_mean is None:
mnist_mean = [0.5]
if mnist_std is None:
mnist_std = [0.5]
train_transform = transforms.Compose(
[
transforms.RandomCrop(28, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mnist_mean, mnist_std),
]
)
if args.cutout:
train_transform.transforms.append(Cutout(args.cutout_length))
valid_transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mnist_mean, mnist_std)]
)
return train_transform, valid_transform | [
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27,213 | Microsoft/nni | examples/trials/network_morphism/FashionMNIST/utils.py | get_mean_and_std | def get_mean_and_std(dataset):
"""Compute the mean and std value of dataset."""
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=1, shuffle=True, num_workers=2
)
mean = torch.zeros(3)
std = torch.zeros(3)
print("==> Computing mean and std..")
for inputs, _ in dataloader:
for i in range(3):
mean[i] += inputs[:, i, :, :].mean()
std[i] += inputs[:, i, :, :].std()
mean.div_(len(dataset))
std.div_(len(dataset))
return mean, std | python | def get_mean_and_std(dataset):
"""Compute the mean and std value of dataset."""
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=1, shuffle=True, num_workers=2
)
mean = torch.zeros(3)
std = torch.zeros(3)
print("==> Computing mean and std..")
for inputs, _ in dataloader:
for i in range(3):
mean[i] += inputs[:, i, :, :].mean()
std[i] += inputs[:, i, :, :].std()
mean.div_(len(dataset))
std.div_(len(dataset))
return mean, std | [
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27,214 | Microsoft/nni | src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py | check_feasibility | def check_feasibility(x_bounds, lowerbound, upperbound):
'''
This can have false positives.
For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7.
'''
# x_bounds should be sorted, so even for "discrete_int" type,
# the smallest and the largest number should the first and the last element
x_bounds_lowerbound = sum([x_bound[0] for x_bound in x_bounds])
x_bounds_upperbound = sum([x_bound[-1] for x_bound in x_bounds])
# return ((x_bounds_lowerbound <= lowerbound) and (x_bounds_upperbound >= lowerbound)) or \
# ((x_bounds_lowerbound <= upperbound) and (x_bounds_upperbound >= upperbound))
return (x_bounds_lowerbound <= lowerbound <= x_bounds_upperbound) or \
(x_bounds_lowerbound <= upperbound <= x_bounds_upperbound) | python | def check_feasibility(x_bounds, lowerbound, upperbound):
'''
This can have false positives.
For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7.
'''
# x_bounds should be sorted, so even for "discrete_int" type,
# the smallest and the largest number should the first and the last element
x_bounds_lowerbound = sum([x_bound[0] for x_bound in x_bounds])
x_bounds_upperbound = sum([x_bound[-1] for x_bound in x_bounds])
# return ((x_bounds_lowerbound <= lowerbound) and (x_bounds_upperbound >= lowerbound)) or \
# ((x_bounds_lowerbound <= upperbound) and (x_bounds_upperbound >= upperbound))
return (x_bounds_lowerbound <= lowerbound <= x_bounds_upperbound) or \
(x_bounds_lowerbound <= upperbound <= x_bounds_upperbound) | [
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27,215 | Microsoft/nni | src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py | rand | def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
'''
Key idea is that we try to move towards upperbound, by randomly choose one
value for each parameter. However, for the last parameter,
we need to make sure that its value can help us get above lowerbound
'''
outputs = None
if check_feasibility(x_bounds, lowerbound, upperbound) is True:
# Order parameters by their range size. We want the smallest range first,
# because the corresponding parameter has less numbers to choose from
x_idx_sorted = []
for i, _ in enumerate(x_bounds):
if x_types[i] == "discrete_int":
x_idx_sorted.append([i, len(x_bounds[i])])
elif (x_types[i] == "range_int") or (x_types[i] == "range_continuous"):
x_idx_sorted.append([i, math.floor(x_bounds[i][1] - x_bounds[i][0])])
x_idx_sorted = sorted(x_idx_sorted, key=itemgetter(1))
for _ in range(max_retries):
budget_allocated = 0
outputs = [None] * len(x_bounds)
for i, _ in enumerate(x_idx_sorted):
x_idx = x_idx_sorted[i][0]
# The amount of unallocated space that we have
budget_max = upperbound - budget_allocated
# NOT the Last x that we need to assign a random number
if i < (len(x_idx_sorted) - 1):
if x_bounds[x_idx][0] <= budget_max:
if x_types[x_idx] == "discrete_int":
# Note the valid integer
temp = []
for j in x_bounds[x_idx]:
if j <= budget_max:
temp.append(j)
# Randomly pick a number from the integer array
if temp:
outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
elif (x_types[x_idx] == "range_int") or \
(x_types[x_idx] == "range_continuous"):
outputs[x_idx] = random.randint(x_bounds[x_idx][0],
min(x_bounds[x_idx][-1], budget_max))
else:
# The last x that we need to assign a random number
randint_lowerbound = lowerbound - budget_allocated
randint_lowerbound = 0 if randint_lowerbound < 0 else randint_lowerbound
# This check:
# is our smallest possible value going to overflow the available budget space,
# and is our largest possible value going to underflow the lower bound
if (x_bounds[x_idx][0] <= budget_max) and \
(x_bounds[x_idx][-1] >= randint_lowerbound):
if x_types[x_idx] == "discrete_int":
temp = []
for j in x_bounds[x_idx]:
# if (j <= budget_max) and (j >= randint_lowerbound):
if randint_lowerbound <= j <= budget_max:
temp.append(j)
if temp:
outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
elif (x_types[x_idx] == "range_int") or \
(x_types[x_idx] == "range_continuous"):
outputs[x_idx] = random.randint(randint_lowerbound,
min(x_bounds[x_idx][1], budget_max))
if outputs[x_idx] is None:
break
else:
budget_allocated += outputs[x_idx]
if None not in outputs:
break
return outputs | python | def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
'''
Key idea is that we try to move towards upperbound, by randomly choose one
value for each parameter. However, for the last parameter,
we need to make sure that its value can help us get above lowerbound
'''
outputs = None
if check_feasibility(x_bounds, lowerbound, upperbound) is True:
# Order parameters by their range size. We want the smallest range first,
# because the corresponding parameter has less numbers to choose from
x_idx_sorted = []
for i, _ in enumerate(x_bounds):
if x_types[i] == "discrete_int":
x_idx_sorted.append([i, len(x_bounds[i])])
elif (x_types[i] == "range_int") or (x_types[i] == "range_continuous"):
x_idx_sorted.append([i, math.floor(x_bounds[i][1] - x_bounds[i][0])])
x_idx_sorted = sorted(x_idx_sorted, key=itemgetter(1))
for _ in range(max_retries):
budget_allocated = 0
outputs = [None] * len(x_bounds)
for i, _ in enumerate(x_idx_sorted):
x_idx = x_idx_sorted[i][0]
# The amount of unallocated space that we have
budget_max = upperbound - budget_allocated
# NOT the Last x that we need to assign a random number
if i < (len(x_idx_sorted) - 1):
if x_bounds[x_idx][0] <= budget_max:
if x_types[x_idx] == "discrete_int":
# Note the valid integer
temp = []
for j in x_bounds[x_idx]:
if j <= budget_max:
temp.append(j)
# Randomly pick a number from the integer array
if temp:
outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
elif (x_types[x_idx] == "range_int") or \
(x_types[x_idx] == "range_continuous"):
outputs[x_idx] = random.randint(x_bounds[x_idx][0],
min(x_bounds[x_idx][-1], budget_max))
else:
# The last x that we need to assign a random number
randint_lowerbound = lowerbound - budget_allocated
randint_lowerbound = 0 if randint_lowerbound < 0 else randint_lowerbound
# This check:
# is our smallest possible value going to overflow the available budget space,
# and is our largest possible value going to underflow the lower bound
if (x_bounds[x_idx][0] <= budget_max) and \
(x_bounds[x_idx][-1] >= randint_lowerbound):
if x_types[x_idx] == "discrete_int":
temp = []
for j in x_bounds[x_idx]:
# if (j <= budget_max) and (j >= randint_lowerbound):
if randint_lowerbound <= j <= budget_max:
temp.append(j)
if temp:
outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
elif (x_types[x_idx] == "range_int") or \
(x_types[x_idx] == "range_continuous"):
outputs[x_idx] = random.randint(randint_lowerbound,
min(x_bounds[x_idx][1], budget_max))
if outputs[x_idx] is None:
break
else:
budget_allocated += outputs[x_idx]
if None not in outputs:
break
return outputs | [
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27,216 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | expand_path | def expand_path(experiment_config, key):
'''Change '~' to user home directory'''
if experiment_config.get(key):
experiment_config[key] = os.path.expanduser(experiment_config[key]) | python | def expand_path(experiment_config, key):
'''Change '~' to user home directory'''
if experiment_config.get(key):
experiment_config[key] = os.path.expanduser(experiment_config[key]) | [
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27,217 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | parse_relative_path | def parse_relative_path(root_path, experiment_config, key):
'''Change relative path to absolute path'''
if experiment_config.get(key) and not os.path.isabs(experiment_config.get(key)):
absolute_path = os.path.join(root_path, experiment_config.get(key))
print_normal('expand %s: %s to %s ' % (key, experiment_config[key], absolute_path))
experiment_config[key] = absolute_path | python | def parse_relative_path(root_path, experiment_config, key):
'''Change relative path to absolute path'''
if experiment_config.get(key) and not os.path.isabs(experiment_config.get(key)):
absolute_path = os.path.join(root_path, experiment_config.get(key))
print_normal('expand %s: %s to %s ' % (key, experiment_config[key], absolute_path))
experiment_config[key] = absolute_path | [
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27,218 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | parse_time | def parse_time(time):
'''Change the time to seconds'''
unit = time[-1]
if unit not in ['s', 'm', 'h', 'd']:
print_error('the unit of time could only from {s, m, h, d}')
exit(1)
time = time[:-1]
if not time.isdigit():
print_error('time format error!')
exit(1)
parse_dict = {'s':1, 'm':60, 'h':3600, 'd':86400}
return int(time) * parse_dict[unit] | python | def parse_time(time):
'''Change the time to seconds'''
unit = time[-1]
if unit not in ['s', 'm', 'h', 'd']:
print_error('the unit of time could only from {s, m, h, d}')
exit(1)
time = time[:-1]
if not time.isdigit():
print_error('time format error!')
exit(1)
parse_dict = {'s':1, 'm':60, 'h':3600, 'd':86400}
return int(time) * parse_dict[unit] | [
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27,219 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | parse_path | def parse_path(experiment_config, config_path):
'''Parse path in config file'''
expand_path(experiment_config, 'searchSpacePath')
if experiment_config.get('trial'):
expand_path(experiment_config['trial'], 'codeDir')
if experiment_config.get('tuner'):
expand_path(experiment_config['tuner'], 'codeDir')
if experiment_config.get('assessor'):
expand_path(experiment_config['assessor'], 'codeDir')
if experiment_config.get('advisor'):
expand_path(experiment_config['advisor'], 'codeDir')
#if users use relative path, convert it to absolute path
root_path = os.path.dirname(config_path)
if experiment_config.get('searchSpacePath'):
parse_relative_path(root_path, experiment_config, 'searchSpacePath')
if experiment_config.get('trial'):
parse_relative_path(root_path, experiment_config['trial'], 'codeDir')
if experiment_config.get('tuner'):
parse_relative_path(root_path, experiment_config['tuner'], 'codeDir')
if experiment_config.get('assessor'):
parse_relative_path(root_path, experiment_config['assessor'], 'codeDir')
if experiment_config.get('advisor'):
parse_relative_path(root_path, experiment_config['advisor'], 'codeDir')
if experiment_config.get('machineList'):
for index in range(len(experiment_config['machineList'])):
parse_relative_path(root_path, experiment_config['machineList'][index], 'sshKeyPath') | python | def parse_path(experiment_config, config_path):
'''Parse path in config file'''
expand_path(experiment_config, 'searchSpacePath')
if experiment_config.get('trial'):
expand_path(experiment_config['trial'], 'codeDir')
if experiment_config.get('tuner'):
expand_path(experiment_config['tuner'], 'codeDir')
if experiment_config.get('assessor'):
expand_path(experiment_config['assessor'], 'codeDir')
if experiment_config.get('advisor'):
expand_path(experiment_config['advisor'], 'codeDir')
#if users use relative path, convert it to absolute path
root_path = os.path.dirname(config_path)
if experiment_config.get('searchSpacePath'):
parse_relative_path(root_path, experiment_config, 'searchSpacePath')
if experiment_config.get('trial'):
parse_relative_path(root_path, experiment_config['trial'], 'codeDir')
if experiment_config.get('tuner'):
parse_relative_path(root_path, experiment_config['tuner'], 'codeDir')
if experiment_config.get('assessor'):
parse_relative_path(root_path, experiment_config['assessor'], 'codeDir')
if experiment_config.get('advisor'):
parse_relative_path(root_path, experiment_config['advisor'], 'codeDir')
if experiment_config.get('machineList'):
for index in range(len(experiment_config['machineList'])):
parse_relative_path(root_path, experiment_config['machineList'][index], 'sshKeyPath') | [
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27,220 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_search_space_content | def validate_search_space_content(experiment_config):
'''Validate searchspace content,
if the searchspace file is not json format or its values does not contain _type and _value which must be specified,
it will not be a valid searchspace file'''
try:
search_space_content = json.load(open(experiment_config.get('searchSpacePath'), 'r'))
for value in search_space_content.values():
if not value.get('_type') or not value.get('_value'):
print_error('please use _type and _value to specify searchspace!')
exit(1)
except:
print_error('searchspace file is not a valid json format!')
exit(1) | python | def validate_search_space_content(experiment_config):
'''Validate searchspace content,
if the searchspace file is not json format or its values does not contain _type and _value which must be specified,
it will not be a valid searchspace file'''
try:
search_space_content = json.load(open(experiment_config.get('searchSpacePath'), 'r'))
for value in search_space_content.values():
if not value.get('_type') or not value.get('_value'):
print_error('please use _type and _value to specify searchspace!')
exit(1)
except:
print_error('searchspace file is not a valid json format!')
exit(1) | [
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27,221 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_kubeflow_operators | def validate_kubeflow_operators(experiment_config):
'''Validate whether the kubeflow operators are valid'''
if experiment_config.get('kubeflowConfig'):
if experiment_config.get('kubeflowConfig').get('operator') == 'tf-operator':
if experiment_config.get('trial').get('master') is not None:
print_error('kubeflow with tf-operator can not set master')
exit(1)
if experiment_config.get('trial').get('worker') is None:
print_error('kubeflow with tf-operator must set worker')
exit(1)
elif experiment_config.get('kubeflowConfig').get('operator') == 'pytorch-operator':
if experiment_config.get('trial').get('ps') is not None:
print_error('kubeflow with pytorch-operator can not set ps')
exit(1)
if experiment_config.get('trial').get('master') is None:
print_error('kubeflow with pytorch-operator must set master')
exit(1)
if experiment_config.get('kubeflowConfig').get('storage') == 'nfs':
if experiment_config.get('kubeflowConfig').get('nfs') is None:
print_error('please set nfs configuration!')
exit(1)
elif experiment_config.get('kubeflowConfig').get('storage') == 'azureStorage':
if experiment_config.get('kubeflowConfig').get('azureStorage') is None:
print_error('please set azureStorage configuration!')
exit(1)
elif experiment_config.get('kubeflowConfig').get('storage') is None:
if experiment_config.get('kubeflowConfig').get('azureStorage'):
print_error('please set storage type!')
exit(1) | python | def validate_kubeflow_operators(experiment_config):
'''Validate whether the kubeflow operators are valid'''
if experiment_config.get('kubeflowConfig'):
if experiment_config.get('kubeflowConfig').get('operator') == 'tf-operator':
if experiment_config.get('trial').get('master') is not None:
print_error('kubeflow with tf-operator can not set master')
exit(1)
if experiment_config.get('trial').get('worker') is None:
print_error('kubeflow with tf-operator must set worker')
exit(1)
elif experiment_config.get('kubeflowConfig').get('operator') == 'pytorch-operator':
if experiment_config.get('trial').get('ps') is not None:
print_error('kubeflow with pytorch-operator can not set ps')
exit(1)
if experiment_config.get('trial').get('master') is None:
print_error('kubeflow with pytorch-operator must set master')
exit(1)
if experiment_config.get('kubeflowConfig').get('storage') == 'nfs':
if experiment_config.get('kubeflowConfig').get('nfs') is None:
print_error('please set nfs configuration!')
exit(1)
elif experiment_config.get('kubeflowConfig').get('storage') == 'azureStorage':
if experiment_config.get('kubeflowConfig').get('azureStorage') is None:
print_error('please set azureStorage configuration!')
exit(1)
elif experiment_config.get('kubeflowConfig').get('storage') is None:
if experiment_config.get('kubeflowConfig').get('azureStorage'):
print_error('please set storage type!')
exit(1) | [
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27,222 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_common_content | def validate_common_content(experiment_config):
'''Validate whether the common values in experiment_config is valid'''
if not experiment_config.get('trainingServicePlatform') or \
experiment_config.get('trainingServicePlatform') not in ['local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller']:
print_error('Please set correct trainingServicePlatform!')
exit(1)
schema_dict = {
'local': LOCAL_CONFIG_SCHEMA,
'remote': REMOTE_CONFIG_SCHEMA,
'pai': PAI_CONFIG_SCHEMA,
'kubeflow': KUBEFLOW_CONFIG_SCHEMA,
'frameworkcontroller': FRAMEWORKCONTROLLER_CONFIG_SCHEMA
}
separate_schema_dict = {
'tuner': tuner_schema_dict,
'advisor': advisor_schema_dict,
'assessor': assessor_schema_dict
}
separate_builtInName_dict = {
'tuner': 'builtinTunerName',
'advisor': 'builtinAdvisorName',
'assessor': 'builtinAssessorName'
}
try:
schema_dict.get(experiment_config['trainingServicePlatform']).validate(experiment_config)
for separate_key in separate_schema_dict.keys():
if experiment_config.get(separate_key):
if experiment_config[separate_key].get(separate_builtInName_dict[separate_key]):
validate = False
for key in separate_schema_dict[separate_key].keys():
if key.__contains__(experiment_config[separate_key][separate_builtInName_dict[separate_key]]):
Schema({**separate_schema_dict[separate_key][key]}).validate(experiment_config[separate_key])
validate = True
break
if not validate:
print_error('%s %s error!' % (separate_key, separate_builtInName_dict[separate_key]))
exit(1)
else:
Schema({**separate_schema_dict[separate_key]['customized']}).validate(experiment_config[separate_key])
except SchemaError as error:
print_error('Your config file is not correct, please check your config file content!')
if error.__str__().__contains__('Wrong key'):
print_error(' '.join(error.__str__().split()[:3]))
else:
print_error(error)
exit(1)
#set default value
if experiment_config.get('maxExecDuration') is None:
experiment_config['maxExecDuration'] = '999d'
if experiment_config.get('maxTrialNum') is None:
experiment_config['maxTrialNum'] = 99999
if experiment_config['trainingServicePlatform'] == 'remote':
for index in range(len(experiment_config['machineList'])):
if experiment_config['machineList'][index].get('port') is None:
experiment_config['machineList'][index]['port'] = 22 | python | def validate_common_content(experiment_config):
'''Validate whether the common values in experiment_config is valid'''
if not experiment_config.get('trainingServicePlatform') or \
experiment_config.get('trainingServicePlatform') not in ['local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller']:
print_error('Please set correct trainingServicePlatform!')
exit(1)
schema_dict = {
'local': LOCAL_CONFIG_SCHEMA,
'remote': REMOTE_CONFIG_SCHEMA,
'pai': PAI_CONFIG_SCHEMA,
'kubeflow': KUBEFLOW_CONFIG_SCHEMA,
'frameworkcontroller': FRAMEWORKCONTROLLER_CONFIG_SCHEMA
}
separate_schema_dict = {
'tuner': tuner_schema_dict,
'advisor': advisor_schema_dict,
'assessor': assessor_schema_dict
}
separate_builtInName_dict = {
'tuner': 'builtinTunerName',
'advisor': 'builtinAdvisorName',
'assessor': 'builtinAssessorName'
}
try:
schema_dict.get(experiment_config['trainingServicePlatform']).validate(experiment_config)
for separate_key in separate_schema_dict.keys():
if experiment_config.get(separate_key):
if experiment_config[separate_key].get(separate_builtInName_dict[separate_key]):
validate = False
for key in separate_schema_dict[separate_key].keys():
if key.__contains__(experiment_config[separate_key][separate_builtInName_dict[separate_key]]):
Schema({**separate_schema_dict[separate_key][key]}).validate(experiment_config[separate_key])
validate = True
break
if not validate:
print_error('%s %s error!' % (separate_key, separate_builtInName_dict[separate_key]))
exit(1)
else:
Schema({**separate_schema_dict[separate_key]['customized']}).validate(experiment_config[separate_key])
except SchemaError as error:
print_error('Your config file is not correct, please check your config file content!')
if error.__str__().__contains__('Wrong key'):
print_error(' '.join(error.__str__().split()[:3]))
else:
print_error(error)
exit(1)
#set default value
if experiment_config.get('maxExecDuration') is None:
experiment_config['maxExecDuration'] = '999d'
if experiment_config.get('maxTrialNum') is None:
experiment_config['maxTrialNum'] = 99999
if experiment_config['trainingServicePlatform'] == 'remote':
for index in range(len(experiment_config['machineList'])):
if experiment_config['machineList'][index].get('port') is None:
experiment_config['machineList'][index]['port'] = 22 | [
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27,223 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | parse_assessor_content | def parse_assessor_content(experiment_config):
'''Validate whether assessor in experiment_config is valid'''
if experiment_config.get('assessor'):
if experiment_config['assessor'].get('builtinAssessorName'):
experiment_config['assessor']['className'] = experiment_config['assessor']['builtinAssessorName']
else:
validate_customized_file(experiment_config, 'assessor') | python | def parse_assessor_content(experiment_config):
'''Validate whether assessor in experiment_config is valid'''
if experiment_config.get('assessor'):
if experiment_config['assessor'].get('builtinAssessorName'):
experiment_config['assessor']['className'] = experiment_config['assessor']['builtinAssessorName']
else:
validate_customized_file(experiment_config, 'assessor') | [
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27,224 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_pai_trial_conifg | def validate_pai_trial_conifg(experiment_config):
'''validate the trial config in pai platform'''
if experiment_config.get('trainingServicePlatform') == 'pai':
if experiment_config.get('trial').get('shmMB') and \
experiment_config['trial']['shmMB'] > experiment_config['trial']['memoryMB']:
print_error('shmMB should be no more than memoryMB!')
exit(1) | python | def validate_pai_trial_conifg(experiment_config):
'''validate the trial config in pai platform'''
if experiment_config.get('trainingServicePlatform') == 'pai':
if experiment_config.get('trial').get('shmMB') and \
experiment_config['trial']['shmMB'] > experiment_config['trial']['memoryMB']:
print_error('shmMB should be no more than memoryMB!')
exit(1) | [
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27,225 | Microsoft/nni | tools/nni_cmd/launcher_utils.py | validate_all_content | def validate_all_content(experiment_config, config_path):
'''Validate whether experiment_config is valid'''
parse_path(experiment_config, config_path)
validate_common_content(experiment_config)
validate_pai_trial_conifg(experiment_config)
experiment_config['maxExecDuration'] = parse_time(experiment_config['maxExecDuration'])
if experiment_config.get('advisor'):
if experiment_config.get('assessor') or experiment_config.get('tuner'):
print_error('advisor could not be set with assessor or tuner simultaneously!')
exit(1)
parse_advisor_content(experiment_config)
validate_annotation_content(experiment_config, 'advisor', 'builtinAdvisorName')
else:
if not experiment_config.get('tuner'):
raise Exception('Please provide tuner spec!')
parse_tuner_content(experiment_config)
parse_assessor_content(experiment_config)
validate_annotation_content(experiment_config, 'tuner', 'builtinTunerName') | python | def validate_all_content(experiment_config, config_path):
'''Validate whether experiment_config is valid'''
parse_path(experiment_config, config_path)
validate_common_content(experiment_config)
validate_pai_trial_conifg(experiment_config)
experiment_config['maxExecDuration'] = parse_time(experiment_config['maxExecDuration'])
if experiment_config.get('advisor'):
if experiment_config.get('assessor') or experiment_config.get('tuner'):
print_error('advisor could not be set with assessor or tuner simultaneously!')
exit(1)
parse_advisor_content(experiment_config)
validate_annotation_content(experiment_config, 'advisor', 'builtinAdvisorName')
else:
if not experiment_config.get('tuner'):
raise Exception('Please provide tuner spec!')
parse_tuner_content(experiment_config)
parse_assessor_content(experiment_config)
validate_annotation_content(experiment_config, 'tuner', 'builtinTunerName') | [
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27,226 | Microsoft/nni | tools/nni_cmd/url_utils.py | get_local_urls | def get_local_urls(port):
'''get urls of local machine'''
url_list = []
for name, info in psutil.net_if_addrs().items():
for addr in info:
if AddressFamily.AF_INET == addr.family:
url_list.append('http://{}:{}'.format(addr.address, port))
return url_list | python | def get_local_urls(port):
'''get urls of local machine'''
url_list = []
for name, info in psutil.net_if_addrs().items():
for addr in info:
if AddressFamily.AF_INET == addr.family:
url_list.append('http://{}:{}'.format(addr.address, port))
return url_list | [
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27,227 | Microsoft/nni | tools/nni_annotation/code_generator.py | convert_args_to_dict | def convert_args_to_dict(call, with_lambda=False):
"""Convert all args to a dict such that every key and value in the dict is the same as the value of the arg.
Return the AST Call node with only one arg that is the dictionary
"""
keys, values = list(), list()
for arg in call.args:
if type(arg) in [ast.Str, ast.Num]:
arg_value = arg
else:
# if arg is not a string or a number, we use its source code as the key
arg_value = astor.to_source(arg).strip('\n"')
arg_value = ast.Str(str(arg_value))
arg = make_lambda(arg) if with_lambda else arg
keys.append(arg_value)
values.append(arg)
del call.args[:]
call.args.append(ast.Dict(keys=keys, values=values))
return call | python | def convert_args_to_dict(call, with_lambda=False):
"""Convert all args to a dict such that every key and value in the dict is the same as the value of the arg.
Return the AST Call node with only one arg that is the dictionary
"""
keys, values = list(), list()
for arg in call.args:
if type(arg) in [ast.Str, ast.Num]:
arg_value = arg
else:
# if arg is not a string or a number, we use its source code as the key
arg_value = astor.to_source(arg).strip('\n"')
arg_value = ast.Str(str(arg_value))
arg = make_lambda(arg) if with_lambda else arg
keys.append(arg_value)
values.append(arg)
del call.args[:]
call.args.append(ast.Dict(keys=keys, values=values))
return call | [
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27,228 | Microsoft/nni | src/sdk/pynni/nni/__main__.py | main | def main():
'''
main function.
'''
args = parse_args()
if args.multi_thread:
enable_multi_thread()
if args.advisor_class_name:
# advisor is enabled and starts to run
if args.multi_phase:
raise AssertionError('multi_phase has not been supported in advisor')
if args.advisor_class_name in AdvisorModuleName:
dispatcher = create_builtin_class_instance(
args.advisor_class_name,
args.advisor_args, True)
else:
dispatcher = create_customized_class_instance(
args.advisor_directory,
args.advisor_class_filename,
args.advisor_class_name,
args.advisor_args)
if dispatcher is None:
raise AssertionError('Failed to create Advisor instance')
try:
dispatcher.run()
except Exception as exception:
logger.exception(exception)
raise
else:
# tuner (and assessor) is enabled and starts to run
tuner = None
assessor = None
if args.tuner_class_name in ModuleName:
tuner = create_builtin_class_instance(
args.tuner_class_name,
args.tuner_args)
else:
tuner = create_customized_class_instance(
args.tuner_directory,
args.tuner_class_filename,
args.tuner_class_name,
args.tuner_args)
if tuner is None:
raise AssertionError('Failed to create Tuner instance')
if args.assessor_class_name:
if args.assessor_class_name in ModuleName:
assessor = create_builtin_class_instance(
args.assessor_class_name,
args.assessor_args)
else:
assessor = create_customized_class_instance(
args.assessor_directory,
args.assessor_class_filename,
args.assessor_class_name,
args.assessor_args)
if assessor is None:
raise AssertionError('Failed to create Assessor instance')
if args.multi_phase:
dispatcher = MultiPhaseMsgDispatcher(tuner, assessor)
else:
dispatcher = MsgDispatcher(tuner, assessor)
try:
dispatcher.run()
tuner._on_exit()
if assessor is not None:
assessor._on_exit()
except Exception as exception:
logger.exception(exception)
tuner._on_error()
if assessor is not None:
assessor._on_error()
raise | python | def main():
'''
main function.
'''
args = parse_args()
if args.multi_thread:
enable_multi_thread()
if args.advisor_class_name:
# advisor is enabled and starts to run
if args.multi_phase:
raise AssertionError('multi_phase has not been supported in advisor')
if args.advisor_class_name in AdvisorModuleName:
dispatcher = create_builtin_class_instance(
args.advisor_class_name,
args.advisor_args, True)
else:
dispatcher = create_customized_class_instance(
args.advisor_directory,
args.advisor_class_filename,
args.advisor_class_name,
args.advisor_args)
if dispatcher is None:
raise AssertionError('Failed to create Advisor instance')
try:
dispatcher.run()
except Exception as exception:
logger.exception(exception)
raise
else:
# tuner (and assessor) is enabled and starts to run
tuner = None
assessor = None
if args.tuner_class_name in ModuleName:
tuner = create_builtin_class_instance(
args.tuner_class_name,
args.tuner_args)
else:
tuner = create_customized_class_instance(
args.tuner_directory,
args.tuner_class_filename,
args.tuner_class_name,
args.tuner_args)
if tuner is None:
raise AssertionError('Failed to create Tuner instance')
if args.assessor_class_name:
if args.assessor_class_name in ModuleName:
assessor = create_builtin_class_instance(
args.assessor_class_name,
args.assessor_args)
else:
assessor = create_customized_class_instance(
args.assessor_directory,
args.assessor_class_filename,
args.assessor_class_name,
args.assessor_args)
if assessor is None:
raise AssertionError('Failed to create Assessor instance')
if args.multi_phase:
dispatcher = MultiPhaseMsgDispatcher(tuner, assessor)
else:
dispatcher = MsgDispatcher(tuner, assessor)
try:
dispatcher.run()
tuner._on_exit()
if assessor is not None:
assessor._on_exit()
except Exception as exception:
logger.exception(exception)
tuner._on_error()
if assessor is not None:
assessor._on_error()
raise | [
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27,229 | Microsoft/nni | tools/nni_cmd/common_utils.py | get_yml_content | def get_yml_content(file_path):
'''Load yaml file content'''
try:
with open(file_path, 'r') as file:
return yaml.load(file, Loader=yaml.Loader)
except yaml.scanner.ScannerError as err:
print_error('yaml file format error!')
exit(1)
except Exception as exception:
print_error(exception)
exit(1) | python | def get_yml_content(file_path):
'''Load yaml file content'''
try:
with open(file_path, 'r') as file:
return yaml.load(file, Loader=yaml.Loader)
except yaml.scanner.ScannerError as err:
print_error('yaml file format error!')
exit(1)
except Exception as exception:
print_error(exception)
exit(1) | [
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27,230 | Microsoft/nni | tools/nni_cmd/common_utils.py | detect_port | def detect_port(port):
'''Detect if the port is used'''
socket_test = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
try:
socket_test.connect(('127.0.0.1', int(port)))
socket_test.close()
return True
except:
return False | python | def detect_port(port):
'''Detect if the port is used'''
socket_test = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
try:
socket_test.connect(('127.0.0.1', int(port)))
socket_test.close()
return True
except:
return False | [
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27,231 | Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GMM/CreateModel.py | create_model | def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
'''
Create the Gaussian Mixture Model
'''
samples = [samples_x[i] + [samples_y_aggregation[i]] for i in range(0, len(samples_x))]
# Sorts so that we can get the top samples
samples = sorted(samples, key=itemgetter(-1))
samples_goodbatch_size = int(len(samples) * percentage_goodbatch)
samples_goodbatch = samples[0:samples_goodbatch_size]
samples_badbatch = samples[samples_goodbatch_size:]
samples_x_goodbatch = [sample_goodbatch[0:-1] for sample_goodbatch in samples_goodbatch]
#samples_y_goodbatch = [sample_goodbatch[-1] for sample_goodbatch in samples_goodbatch]
samples_x_badbatch = [sample_badbatch[0:-1] for sample_badbatch in samples_badbatch]
# === Trains GMM clustering models === #
#sys.stderr.write("[%s] Train GMM's GMM model\n" % (os.path.basename(__file__)))
bgmm_goodbatch = mm.BayesianGaussianMixture(n_components=max(1, samples_goodbatch_size - 1))
bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
bgmm_goodbatch.fit(samples_x_goodbatch)
bgmm_badbatch.fit(samples_x_badbatch)
model = {}
model['clusteringmodel_good'] = bgmm_goodbatch
model['clusteringmodel_bad'] = bgmm_badbatch
return model | python | def create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
'''
Create the Gaussian Mixture Model
'''
samples = [samples_x[i] + [samples_y_aggregation[i]] for i in range(0, len(samples_x))]
# Sorts so that we can get the top samples
samples = sorted(samples, key=itemgetter(-1))
samples_goodbatch_size = int(len(samples) * percentage_goodbatch)
samples_goodbatch = samples[0:samples_goodbatch_size]
samples_badbatch = samples[samples_goodbatch_size:]
samples_x_goodbatch = [sample_goodbatch[0:-1] for sample_goodbatch in samples_goodbatch]
#samples_y_goodbatch = [sample_goodbatch[-1] for sample_goodbatch in samples_goodbatch]
samples_x_badbatch = [sample_badbatch[0:-1] for sample_badbatch in samples_badbatch]
# === Trains GMM clustering models === #
#sys.stderr.write("[%s] Train GMM's GMM model\n" % (os.path.basename(__file__)))
bgmm_goodbatch = mm.BayesianGaussianMixture(n_components=max(1, samples_goodbatch_size - 1))
bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
bgmm_goodbatch.fit(samples_x_goodbatch)
bgmm_badbatch.fit(samples_x_badbatch)
model = {}
model['clusteringmodel_good'] = bgmm_goodbatch
model['clusteringmodel_bad'] = bgmm_badbatch
return model | [
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27,232 | Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/Selection.py | selection_r | def selection_r(acquisition_function,
samples_y_aggregation,
x_bounds,
x_types,
regressor_gp,
num_starting_points=100,
minimize_constraints_fun=None):
'''
Selecte R value
'''
minimize_starting_points = [lib_data.rand(x_bounds, x_types) \
for i in range(0, num_starting_points)]
outputs = selection(acquisition_function, samples_y_aggregation,
x_bounds, x_types, regressor_gp,
minimize_starting_points,
minimize_constraints_fun=minimize_constraints_fun)
return outputs | python | def selection_r(acquisition_function,
samples_y_aggregation,
x_bounds,
x_types,
regressor_gp,
num_starting_points=100,
minimize_constraints_fun=None):
'''
Selecte R value
'''
minimize_starting_points = [lib_data.rand(x_bounds, x_types) \
for i in range(0, num_starting_points)]
outputs = selection(acquisition_function, samples_y_aggregation,
x_bounds, x_types, regressor_gp,
minimize_starting_points,
minimize_constraints_fun=minimize_constraints_fun)
return outputs | [
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27,233 | Microsoft/nni | examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py | get_args | def get_args():
""" get args from command line
"""
parser = argparse.ArgumentParser("FashionMNIST")
parser.add_argument("--batch_size", type=int, default=128, help="batch size")
parser.add_argument("--optimizer", type=str, default="SGD", help="optimizer")
parser.add_argument("--epochs", type=int, default=200, help="epoch limit")
parser.add_argument(
"--learning_rate", type=float, default=0.001, help="learning rate"
)
parser.add_argument("--cutout", action="store_true", default=False, help="use cutout")
parser.add_argument("--cutout_length", type=int, default=8, help="cutout length")
parser.add_argument(
"--model_path", type=str, default="./", help="Path to save the destination model"
)
return parser.parse_args() | python | def get_args():
""" get args from command line
"""
parser = argparse.ArgumentParser("FashionMNIST")
parser.add_argument("--batch_size", type=int, default=128, help="batch size")
parser.add_argument("--optimizer", type=str, default="SGD", help="optimizer")
parser.add_argument("--epochs", type=int, default=200, help="epoch limit")
parser.add_argument(
"--learning_rate", type=float, default=0.001, help="learning rate"
)
parser.add_argument("--cutout", action="store_true", default=False, help="use cutout")
parser.add_argument("--cutout_length", type=int, default=8, help="cutout length")
parser.add_argument(
"--model_path", type=str, default="./", help="Path to save the destination model"
)
return parser.parse_args() | [
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27,234 | Microsoft/nni | examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py | build_graph_from_json | def build_graph_from_json(ir_model_json):
"""build model from json representation
"""
graph = json_to_graph(ir_model_json)
logging.debug(graph.operation_history)
model = graph.produce_torch_model()
return model | python | def build_graph_from_json(ir_model_json):
"""build model from json representation
"""
graph = json_to_graph(ir_model_json)
logging.debug(graph.operation_history)
model = graph.produce_torch_model()
return model | [
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] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py#L75-L81 |
27,235 | Microsoft/nni | examples/trials/network_morphism/FashionMNIST/FashionMNIST_pytorch.py | train | def train(epoch):
""" train model on each epoch in trainset
"""
global trainloader
global testloader
global net
global criterion
global optimizer
logger.debug("Epoch: %d", epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
acc = 100.0 * correct / total
logger.debug(
"Loss: %.3f | Acc: %.3f%% (%d/%d)",
train_loss / (batch_idx + 1),
100.0 * correct / total,
correct,
total,
)
return acc | python | def train(epoch):
""" train model on each epoch in trainset
"""
global trainloader
global testloader
global net
global criterion
global optimizer
logger.debug("Epoch: %d", epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
acc = 100.0 * correct / total
logger.debug(
"Loss: %.3f | Acc: %.3f%% (%d/%d)",
train_loss / (batch_idx + 1),
100.0 * correct / total,
correct,
total,
)
return acc | [
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27,236 | Microsoft/nni | examples/trials/kaggle-tgs-salt/models.py | UNetResNetV4.freeze_bn | def freeze_bn(self):
'''Freeze BatchNorm layers.'''
for layer in self.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.eval() | python | def freeze_bn(self):
'''Freeze BatchNorm layers.'''
for layer in self.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.eval() | [
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27,237 | tensorpack/tensorpack | tensorpack/utils/nvml.py | NvidiaDevice.memory | def memory(self):
"""Memory information in bytes
Example:
>>> print(ctx.device(0).memory())
{'total': 4238016512L, 'used': 434831360L, 'free': 3803185152L}
Returns:
total/used/free memory in bytes
"""
class GpuMemoryInfo(Structure):
_fields_ = [
('total', c_ulonglong),
('free', c_ulonglong),
('used', c_ulonglong),
]
c_memory = GpuMemoryInfo()
_check_return(_NVML.get_function(
"nvmlDeviceGetMemoryInfo")(self.hnd, byref(c_memory)))
return {'total': c_memory.total, 'free': c_memory.free, 'used': c_memory.used} | python | def memory(self):
"""Memory information in bytes
Example:
>>> print(ctx.device(0).memory())
{'total': 4238016512L, 'used': 434831360L, 'free': 3803185152L}
Returns:
total/used/free memory in bytes
"""
class GpuMemoryInfo(Structure):
_fields_ = [
('total', c_ulonglong),
('free', c_ulonglong),
('used', c_ulonglong),
]
c_memory = GpuMemoryInfo()
_check_return(_NVML.get_function(
"nvmlDeviceGetMemoryInfo")(self.hnd, byref(c_memory)))
return {'total': c_memory.total, 'free': c_memory.free, 'used': c_memory.used} | [
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Example:
>>> print(ctx.device(0).memory())
{'total': 4238016512L, 'used': 434831360L, 'free': 3803185152L}
Returns:
total/used/free memory in bytes | [
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27,238 | tensorpack/tensorpack | tensorpack/utils/nvml.py | NvidiaDevice.utilization | def utilization(self):
"""Percent of time over the past second was utilized.
Details:
Percent of time over the past second during which one or more kernels was executing on the GPU.
Percent of time over the past second during which global (device) memory was being read or written
Example:
>>> print(ctx.device(0).utilization())
{'gpu': 4L, 'memory': 6L}
"""
class GpuUtilizationInfo(Structure):
_fields_ = [
('gpu', c_uint),
('memory', c_uint),
]
c_util = GpuUtilizationInfo()
_check_return(_NVML.get_function(
"nvmlDeviceGetUtilizationRates")(self.hnd, byref(c_util)))
return {'gpu': c_util.gpu, 'memory': c_util.memory} | python | def utilization(self):
"""Percent of time over the past second was utilized.
Details:
Percent of time over the past second during which one or more kernels was executing on the GPU.
Percent of time over the past second during which global (device) memory was being read or written
Example:
>>> print(ctx.device(0).utilization())
{'gpu': 4L, 'memory': 6L}
"""
class GpuUtilizationInfo(Structure):
_fields_ = [
('gpu', c_uint),
('memory', c_uint),
]
c_util = GpuUtilizationInfo()
_check_return(_NVML.get_function(
"nvmlDeviceGetUtilizationRates")(self.hnd, byref(c_util)))
return {'gpu': c_util.gpu, 'memory': c_util.memory} | [
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Percent of time over the past second during which global (device) memory was being read or written
Example:
>>> print(ctx.device(0).utilization())
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27,239 | tensorpack/tensorpack | tensorpack/utils/nvml.py | NVMLContext.num_devices | def num_devices(self):
"""Get number of devices """
c_count = c_uint()
_check_return(_NVML.get_function(
"nvmlDeviceGetCount_v2")(byref(c_count)))
return c_count.value | python | def num_devices(self):
"""Get number of devices """
c_count = c_uint()
_check_return(_NVML.get_function(
"nvmlDeviceGetCount_v2")(byref(c_count)))
return c_count.value | [
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27,240 | tensorpack/tensorpack | tensorpack/utils/nvml.py | NVMLContext.device | def device(self, idx):
"""Get a specific GPU device
Args:
idx: index of device
Returns:
NvidiaDevice: single GPU device
"""
class GpuDevice(Structure):
pass
c_nvmlDevice_t = POINTER(GpuDevice)
c_index = c_uint(idx)
device = c_nvmlDevice_t()
_check_return(_NVML.get_function(
"nvmlDeviceGetHandleByIndex_v2")(c_index, byref(device)))
return NvidiaDevice(device) | python | def device(self, idx):
"""Get a specific GPU device
Args:
idx: index of device
Returns:
NvidiaDevice: single GPU device
"""
class GpuDevice(Structure):
pass
c_nvmlDevice_t = POINTER(GpuDevice)
c_index = c_uint(idx)
device = c_nvmlDevice_t()
_check_return(_NVML.get_function(
"nvmlDeviceGetHandleByIndex_v2")(c_index, byref(device)))
return NvidiaDevice(device) | [
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27,241 | tensorpack/tensorpack | tensorpack/dataflow/dataset/cifar.py | maybe_download_and_extract | def maybe_download_and_extract(dest_directory, cifar_classnum):
"""Download and extract the tarball from Alex's website. Copied from tensorflow example """
assert cifar_classnum == 10 or cifar_classnum == 100
if cifar_classnum == 10:
cifar_foldername = 'cifar-10-batches-py'
else:
cifar_foldername = 'cifar-100-python'
if os.path.isdir(os.path.join(dest_directory, cifar_foldername)):
logger.info("Found cifar{} data in {}.".format(cifar_classnum, dest_directory))
return
else:
DATA_URL = DATA_URL_CIFAR_10 if cifar_classnum == 10 else DATA_URL_CIFAR_100
filename = DATA_URL[0].split('/')[-1]
filepath = os.path.join(dest_directory, filename)
download(DATA_URL[0], dest_directory, expect_size=DATA_URL[1])
tarfile.open(filepath, 'r:gz').extractall(dest_directory) | python | def maybe_download_and_extract(dest_directory, cifar_classnum):
"""Download and extract the tarball from Alex's website. Copied from tensorflow example """
assert cifar_classnum == 10 or cifar_classnum == 100
if cifar_classnum == 10:
cifar_foldername = 'cifar-10-batches-py'
else:
cifar_foldername = 'cifar-100-python'
if os.path.isdir(os.path.join(dest_directory, cifar_foldername)):
logger.info("Found cifar{} data in {}.".format(cifar_classnum, dest_directory))
return
else:
DATA_URL = DATA_URL_CIFAR_10 if cifar_classnum == 10 else DATA_URL_CIFAR_100
filename = DATA_URL[0].split('/')[-1]
filepath = os.path.join(dest_directory, filename)
download(DATA_URL[0], dest_directory, expect_size=DATA_URL[1])
tarfile.open(filepath, 'r:gz').extractall(dest_directory) | [
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27,242 | tensorpack/tensorpack | tensorpack/graph_builder/model_desc.py | build_or_reuse_placeholder | def build_or_reuse_placeholder(tensor_spec):
"""
Build a tf.placeholder from the metadata in the given tensor spec, or return an existing one.
Args:
tensor_spec (tf.TensorSpec):
Returns:
tf.Tensor:
"""
g = tfv1.get_default_graph()
name = tensor_spec.name
try:
tensor = g.get_tensor_by_name(name + ':0')
assert "Placeholder" in tensor.op.type, "Tensor {} exists but is not a placeholder!".format(name)
assert tensor_spec.is_compatible_with(tensor), \
"Tensor {} exists but is not compatible with the signature!".format(tensor)
return tensor
except KeyError:
with tfv1.name_scope(None): # clear any name scope it might get called in
ret = tfv1.placeholder(
tensor_spec.dtype, shape=tensor_spec.shape, name=tensor_spec.name)
return ret | python | def build_or_reuse_placeholder(tensor_spec):
"""
Build a tf.placeholder from the metadata in the given tensor spec, or return an existing one.
Args:
tensor_spec (tf.TensorSpec):
Returns:
tf.Tensor:
"""
g = tfv1.get_default_graph()
name = tensor_spec.name
try:
tensor = g.get_tensor_by_name(name + ':0')
assert "Placeholder" in tensor.op.type, "Tensor {} exists but is not a placeholder!".format(name)
assert tensor_spec.is_compatible_with(tensor), \
"Tensor {} exists but is not compatible with the signature!".format(tensor)
return tensor
except KeyError:
with tfv1.name_scope(None): # clear any name scope it might get called in
ret = tfv1.placeholder(
tensor_spec.dtype, shape=tensor_spec.shape, name=tensor_spec.name)
return ret | [
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27,243 | tensorpack/tensorpack | tensorpack/tfutils/dependency.py | dependency_of_targets | def dependency_of_targets(targets, op):
"""
Check that op is in the subgraph induced by the dependencies of targets.
The result is memoized.
This is useful if some SessionRunHooks should be run only together with certain ops.
Args:
targets: a tuple of ops or tensors. The targets to find dependencies of.
op (tf.Operation or tf.Tensor):
Returns:
bool: True if any one of `targets` depend on `op`.
"""
# TODO tensorarray? sparsetensor?
if isinstance(op, tf.Tensor):
op = op.op
assert isinstance(op, tf.Operation), op
from tensorflow.contrib.graph_editor import get_backward_walk_ops
# alternative implementation can use graph_util.extract_sub_graph
dependent_ops = get_backward_walk_ops(targets, control_inputs=True)
return op in dependent_ops | python | def dependency_of_targets(targets, op):
"""
Check that op is in the subgraph induced by the dependencies of targets.
The result is memoized.
This is useful if some SessionRunHooks should be run only together with certain ops.
Args:
targets: a tuple of ops or tensors. The targets to find dependencies of.
op (tf.Operation or tf.Tensor):
Returns:
bool: True if any one of `targets` depend on `op`.
"""
# TODO tensorarray? sparsetensor?
if isinstance(op, tf.Tensor):
op = op.op
assert isinstance(op, tf.Operation), op
from tensorflow.contrib.graph_editor import get_backward_walk_ops
# alternative implementation can use graph_util.extract_sub_graph
dependent_ops = get_backward_walk_ops(targets, control_inputs=True)
return op in dependent_ops | [
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Args:
targets: a tuple of ops or tensors. The targets to find dependencies of.
op (tf.Operation or tf.Tensor):
Returns:
bool: True if any one of `targets` depend on `op`. | [
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27,244 | tensorpack/tensorpack | tensorpack/tfutils/dependency.py | dependency_of_fetches | def dependency_of_fetches(fetches, op):
"""
Check that op is in the subgraph induced by the dependencies of fetches.
fetches may have more general structure.
Args:
fetches: An argument to `sess.run`. Nested structure will affect performance.
op (tf.Operation or tf.Tensor):
Returns:
bool: True if any of `fetches` depend on `op`.
"""
try:
from tensorflow.python.client.session import _FetchHandler as FetchHandler
# use the graph of the op, so that this function can be called without being under a default graph
handler = FetchHandler(op.graph, fetches, {})
targets = tuple(handler.fetches() + handler.targets())
except ImportError:
if isinstance(fetches, list):
targets = tuple(fetches)
elif isinstance(fetches, dict):
raise ValueError("Don't know how to parse dictionary to fetch list! "
"This is a bug of tensorpack.")
else:
targets = (fetches, )
return dependency_of_targets(targets, op) | python | def dependency_of_fetches(fetches, op):
"""
Check that op is in the subgraph induced by the dependencies of fetches.
fetches may have more general structure.
Args:
fetches: An argument to `sess.run`. Nested structure will affect performance.
op (tf.Operation or tf.Tensor):
Returns:
bool: True if any of `fetches` depend on `op`.
"""
try:
from tensorflow.python.client.session import _FetchHandler as FetchHandler
# use the graph of the op, so that this function can be called without being under a default graph
handler = FetchHandler(op.graph, fetches, {})
targets = tuple(handler.fetches() + handler.targets())
except ImportError:
if isinstance(fetches, list):
targets = tuple(fetches)
elif isinstance(fetches, dict):
raise ValueError("Don't know how to parse dictionary to fetch list! "
"This is a bug of tensorpack.")
else:
targets = (fetches, )
return dependency_of_targets(targets, op) | [
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op (tf.Operation or tf.Tensor):
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27,245 | tensorpack/tensorpack | tensorpack/tfutils/summary.py | add_tensor_summary | def add_tensor_summary(x, types, name=None, collections=None,
main_tower_only=True):
"""
Summarize a tensor by different methods.
Args:
x (tf.Tensor): a tensor to summarize
types (list[str]): summary types, can be scalar/histogram/sparsity/mean/rms
name (str): summary name. Defaults to be the op name.
collections (list[str]): collections of the summary ops.
main_tower_only (bool): Only run under main training tower. If
set to True, calling this function under other TowerContext
has no effect.
Example:
.. code-block:: python
with tf.name_scope('mysummaries'): # to not mess up tensorboard
add_tensor_summary(
tensor, ['histogram', 'rms', 'sparsity'], name='mytensor')
"""
types = set(types)
if name is None:
name = x.op.name
ctx = get_current_tower_context()
if main_tower_only and ctx is not None and not ctx.is_main_training_tower:
return
SUMMARY_TYPES_DIC = {
'scalar': lambda: tf.summary.scalar(name + '-summary', x, collections=collections),
'histogram': lambda: tf.summary.histogram(name + '-histogram', x, collections=collections),
'sparsity': lambda: tf.summary.scalar(
name + '-sparsity', tf.nn.zero_fraction(x),
collections=collections),
'mean': lambda: tf.summary.scalar(
name + '-mean', tf.reduce_mean(x),
collections=collections),
'rms': lambda: tf.summary.scalar(
name + '-rms', rms(x), collections=collections)
}
for typ in types:
SUMMARY_TYPES_DIC[typ]() | python | def add_tensor_summary(x, types, name=None, collections=None,
main_tower_only=True):
"""
Summarize a tensor by different methods.
Args:
x (tf.Tensor): a tensor to summarize
types (list[str]): summary types, can be scalar/histogram/sparsity/mean/rms
name (str): summary name. Defaults to be the op name.
collections (list[str]): collections of the summary ops.
main_tower_only (bool): Only run under main training tower. If
set to True, calling this function under other TowerContext
has no effect.
Example:
.. code-block:: python
with tf.name_scope('mysummaries'): # to not mess up tensorboard
add_tensor_summary(
tensor, ['histogram', 'rms', 'sparsity'], name='mytensor')
"""
types = set(types)
if name is None:
name = x.op.name
ctx = get_current_tower_context()
if main_tower_only and ctx is not None and not ctx.is_main_training_tower:
return
SUMMARY_TYPES_DIC = {
'scalar': lambda: tf.summary.scalar(name + '-summary', x, collections=collections),
'histogram': lambda: tf.summary.histogram(name + '-histogram', x, collections=collections),
'sparsity': lambda: tf.summary.scalar(
name + '-sparsity', tf.nn.zero_fraction(x),
collections=collections),
'mean': lambda: tf.summary.scalar(
name + '-mean', tf.reduce_mean(x),
collections=collections),
'rms': lambda: tf.summary.scalar(
name + '-rms', rms(x), collections=collections)
}
for typ in types:
SUMMARY_TYPES_DIC[typ]() | [
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Args:
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name (str): summary name. Defaults to be the op name.
collections (list[str]): collections of the summary ops.
main_tower_only (bool): Only run under main training tower. If
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has no effect.
Example:
.. code-block:: python
with tf.name_scope('mysummaries'): # to not mess up tensorboard
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27,246 | tensorpack/tensorpack | tensorpack/tfutils/summary.py | add_param_summary | def add_param_summary(*summary_lists, **kwargs):
"""
Add summary ops for all trainable variables matching the regex, under a
reused 'param-summary' name scope.
This function is a no-op if not calling from main training tower.
Args:
summary_lists (list): each is (regex, [list of summary type]).
Summary type is defined in :func:`add_tensor_summary`.
collections (list[str]): collections of the summary ops.
Example:
.. code-block:: python
add_param_summary(
('.*/W', ['histogram', 'rms']),
('.*/gamma', ['scalar']),
)
"""
collections = kwargs.pop('collections', None)
assert len(kwargs) == 0, "Unknown kwargs: " + str(kwargs)
ctx = get_current_tower_context()
if ctx is not None and not ctx.is_main_training_tower:
return
params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
with cached_name_scope('param-summary'):
for p in params:
name = p.op.name
for rgx, actions in summary_lists:
if not rgx.endswith('$'):
rgx = rgx + '$'
if re.match(rgx, name):
add_tensor_summary(p, actions, name=name, collections=collections) | python | def add_param_summary(*summary_lists, **kwargs):
"""
Add summary ops for all trainable variables matching the regex, under a
reused 'param-summary' name scope.
This function is a no-op if not calling from main training tower.
Args:
summary_lists (list): each is (regex, [list of summary type]).
Summary type is defined in :func:`add_tensor_summary`.
collections (list[str]): collections of the summary ops.
Example:
.. code-block:: python
add_param_summary(
('.*/W', ['histogram', 'rms']),
('.*/gamma', ['scalar']),
)
"""
collections = kwargs.pop('collections', None)
assert len(kwargs) == 0, "Unknown kwargs: " + str(kwargs)
ctx = get_current_tower_context()
if ctx is not None and not ctx.is_main_training_tower:
return
params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
with cached_name_scope('param-summary'):
for p in params:
name = p.op.name
for rgx, actions in summary_lists:
if not rgx.endswith('$'):
rgx = rgx + '$'
if re.match(rgx, name):
add_tensor_summary(p, actions, name=name, collections=collections) | [
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Args:
summary_lists (list): each is (regex, [list of summary type]).
Summary type is defined in :func:`add_tensor_summary`.
collections (list[str]): collections of the summary ops.
Example:
.. code-block:: python
add_param_summary(
('.*/W', ['histogram', 'rms']),
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27,247 | tensorpack/tensorpack | tensorpack/tfutils/summary.py | add_moving_summary | def add_moving_summary(*args, **kwargs):
"""
Summarize the moving average for scalar tensors.
This function is a no-op if not calling from main training tower.
Args:
args: scalar tensors to summarize
decay (float): the decay rate. Defaults to 0.95.
collection (str or None): the name of the collection to add EMA-maintaining ops.
The default will work together with the default
:class:`MovingAverageSummary` callback.
summary_collections ([str]): the names of collections to add the
summary op. Default is TF's default (`tf.GraphKeys.SUMMARIES`).
Returns:
[tf.Tensor]: list of tensors returned by assign_moving_average,
which can be used to maintain the EMA.
"""
decay = kwargs.pop('decay', 0.95)
coll = kwargs.pop('collection', MOVING_SUMMARY_OPS_KEY)
summ_coll = kwargs.pop('summary_collections', None)
assert len(kwargs) == 0, "Unknown arguments: " + str(kwargs)
ctx = get_current_tower_context()
# allow ctx to be none
if ctx is not None and not ctx.is_main_training_tower:
return []
graph = tf.get_default_graph()
try:
control_flow_ctx = graph._get_control_flow_context()
# XLA does not support summaries anyway
# However, this function will generate unnecessary dependency edges,
# which makes the tower function harder to compile under XLA, so we skip it
if control_flow_ctx is not None and control_flow_ctx.IsXLAContext():
return
except Exception:
pass
if tf.get_variable_scope().reuse is True:
logger.warn("add_moving_summary() called under reuse=True scope, ignored.")
return []
for x in args:
assert isinstance(x, (tf.Tensor, tf.Variable)), x
assert x.get_shape().ndims == 0, \
"add_moving_summary() only accepts scalar tensor! Got one with {}".format(x.get_shape())
ema_ops = []
for c in args:
name = re.sub('tower[0-9]+/', '', c.op.name)
with tf.name_scope(None):
if not c.dtype.is_floating:
c = tf.cast(c, tf.float32)
# assign_moving_average creates variables with op names, therefore clear ns first.
with _enter_vs_reuse_ns('EMA') as vs:
ema_var = tf.get_variable(name, shape=c.shape, dtype=c.dtype,
initializer=tf.constant_initializer(),
trainable=False)
ns = vs.original_name_scope
with tf.name_scope(ns): # reuse VS&NS so that EMA_1 won't appear
ema_op = moving_averages.assign_moving_average(
ema_var, c, decay,
zero_debias=True, name=name + '_EMA_apply')
ema_ops.append(ema_op)
with tf.name_scope(None):
tf.summary.scalar(
name + '-summary', ema_op,
collections=summ_coll) # write the EMA value as a summary
if coll is not None:
for op in ema_ops:
tf.add_to_collection(coll, op)
return ema_ops | python | def add_moving_summary(*args, **kwargs):
"""
Summarize the moving average for scalar tensors.
This function is a no-op if not calling from main training tower.
Args:
args: scalar tensors to summarize
decay (float): the decay rate. Defaults to 0.95.
collection (str or None): the name of the collection to add EMA-maintaining ops.
The default will work together with the default
:class:`MovingAverageSummary` callback.
summary_collections ([str]): the names of collections to add the
summary op. Default is TF's default (`tf.GraphKeys.SUMMARIES`).
Returns:
[tf.Tensor]: list of tensors returned by assign_moving_average,
which can be used to maintain the EMA.
"""
decay = kwargs.pop('decay', 0.95)
coll = kwargs.pop('collection', MOVING_SUMMARY_OPS_KEY)
summ_coll = kwargs.pop('summary_collections', None)
assert len(kwargs) == 0, "Unknown arguments: " + str(kwargs)
ctx = get_current_tower_context()
# allow ctx to be none
if ctx is not None and not ctx.is_main_training_tower:
return []
graph = tf.get_default_graph()
try:
control_flow_ctx = graph._get_control_flow_context()
# XLA does not support summaries anyway
# However, this function will generate unnecessary dependency edges,
# which makes the tower function harder to compile under XLA, so we skip it
if control_flow_ctx is not None and control_flow_ctx.IsXLAContext():
return
except Exception:
pass
if tf.get_variable_scope().reuse is True:
logger.warn("add_moving_summary() called under reuse=True scope, ignored.")
return []
for x in args:
assert isinstance(x, (tf.Tensor, tf.Variable)), x
assert x.get_shape().ndims == 0, \
"add_moving_summary() only accepts scalar tensor! Got one with {}".format(x.get_shape())
ema_ops = []
for c in args:
name = re.sub('tower[0-9]+/', '', c.op.name)
with tf.name_scope(None):
if not c.dtype.is_floating:
c = tf.cast(c, tf.float32)
# assign_moving_average creates variables with op names, therefore clear ns first.
with _enter_vs_reuse_ns('EMA') as vs:
ema_var = tf.get_variable(name, shape=c.shape, dtype=c.dtype,
initializer=tf.constant_initializer(),
trainable=False)
ns = vs.original_name_scope
with tf.name_scope(ns): # reuse VS&NS so that EMA_1 won't appear
ema_op = moving_averages.assign_moving_average(
ema_var, c, decay,
zero_debias=True, name=name + '_EMA_apply')
ema_ops.append(ema_op)
with tf.name_scope(None):
tf.summary.scalar(
name + '-summary', ema_op,
collections=summ_coll) # write the EMA value as a summary
if coll is not None:
for op in ema_ops:
tf.add_to_collection(coll, op)
return ema_ops | [
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] | Summarize the moving average for scalar tensors.
This function is a no-op if not calling from main training tower.
Args:
args: scalar tensors to summarize
decay (float): the decay rate. Defaults to 0.95.
collection (str or None): the name of the collection to add EMA-maintaining ops.
The default will work together with the default
:class:`MovingAverageSummary` callback.
summary_collections ([str]): the names of collections to add the
summary op. Default is TF's default (`tf.GraphKeys.SUMMARIES`).
Returns:
[tf.Tensor]: list of tensors returned by assign_moving_average,
which can be used to maintain the EMA. | [
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27,248 | tensorpack/tensorpack | examples/basics/export-model.py | export_serving | def export_serving(model_path):
"""Export trained model to use it in TensorFlow Serving or cloudML. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=InferenceOnlyModel(),
input_names=['input_img_bytes'],
output_names=['prediction_img_bytes'])
ModelExporter(pred_config).export_serving('/tmp/exported') | python | def export_serving(model_path):
"""Export trained model to use it in TensorFlow Serving or cloudML. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=InferenceOnlyModel(),
input_names=['input_img_bytes'],
output_names=['prediction_img_bytes'])
ModelExporter(pred_config).export_serving('/tmp/exported') | [
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27,249 | tensorpack/tensorpack | examples/basics/export-model.py | export_compact | def export_compact(model_path):
"""Export trained model to use it as a frozen and pruned inference graph in
mobile applications. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=Model(),
input_names=['input_img'],
output_names=['prediction_img'])
ModelExporter(pred_config).export_compact('/tmp/compact_graph.pb') | python | def export_compact(model_path):
"""Export trained model to use it as a frozen and pruned inference graph in
mobile applications. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=Model(),
input_names=['input_img'],
output_names=['prediction_img'])
ModelExporter(pred_config).export_compact('/tmp/compact_graph.pb') | [
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27,250 | tensorpack/tensorpack | examples/basics/export-model.py | apply | def apply(model_path):
"""Run inference from a training model checkpoint. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=Model(),
input_names=['input_img'],
output_names=['prediction_img'])
pred = OfflinePredictor(pred_config)
img = cv2.imread('lena.png')
prediction = pred([img])[0]
cv2.imwrite('applied_default.jpg', prediction[0]) | python | def apply(model_path):
"""Run inference from a training model checkpoint. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=Model(),
input_names=['input_img'],
output_names=['prediction_img'])
pred = OfflinePredictor(pred_config)
img = cv2.imread('lena.png')
prediction = pred([img])[0]
cv2.imwrite('applied_default.jpg', prediction[0]) | [
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27,251 | tensorpack/tensorpack | examples/basics/export-model.py | apply_inference_graph | def apply_inference_graph(model_path):
"""Run inference from a different graph, which receives encoded images buffers. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=InferenceOnlyModel(),
input_names=['input_img_bytes'],
output_names=['prediction_img_bytes'])
pred = OfflinePredictor(pred_config)
buf = open('lena.png', 'rb').read()
prediction = pred([buf])[0]
with open('applied_inference_graph.png', 'wb') as f:
f.write(prediction[0]) | python | def apply_inference_graph(model_path):
"""Run inference from a different graph, which receives encoded images buffers. """
pred_config = PredictConfig(
session_init=get_model_loader(model_path),
model=InferenceOnlyModel(),
input_names=['input_img_bytes'],
output_names=['prediction_img_bytes'])
pred = OfflinePredictor(pred_config)
buf = open('lena.png', 'rb').read()
prediction = pred([buf])[0]
with open('applied_inference_graph.png', 'wb') as f:
f.write(prediction[0]) | [
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27,252 | tensorpack/tensorpack | examples/basics/export-model.py | apply_compact | def apply_compact(graph_path):
"""Run the pruned and frozen inference graph. """
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# Note, we just load the graph and do *not* need to initialize anything.
with tf.gfile.GFile(graph_path, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def)
input_img = sess.graph.get_tensor_by_name('import/input_img:0')
prediction_img = sess.graph.get_tensor_by_name('import/prediction_img:0')
prediction = sess.run(prediction_img, {input_img: cv2.imread('lena.png')[None, ...]})
cv2.imwrite('applied_compact.png', prediction[0]) | python | def apply_compact(graph_path):
"""Run the pruned and frozen inference graph. """
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# Note, we just load the graph and do *not* need to initialize anything.
with tf.gfile.GFile(graph_path, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def)
input_img = sess.graph.get_tensor_by_name('import/input_img:0')
prediction_img = sess.graph.get_tensor_by_name('import/prediction_img:0')
prediction = sess.run(prediction_img, {input_img: cv2.imread('lena.png')[None, ...]})
cv2.imwrite('applied_compact.png', prediction[0]) | [
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27,253 | tensorpack/tensorpack | tensorpack/dataflow/common.py | PrintData._analyze_input_data | def _analyze_input_data(self, entry, k, depth=1, max_depth=3, max_list=3):
"""
Gather useful debug information from a datapoint.
Args:
entry: the datapoint component
k (int): index of this component in current datapoint
depth (int, optional): recursion depth
max_depth, max_list: same as in :meth:`__init__`.
Returns:
string: debug message
"""
class _elementInfo(object):
def __init__(self, el, pos, depth=0, max_list=3):
self.shape = ""
self.type = type(el).__name__
self.dtype = ""
self.range = ""
self.sub_elements = []
self.ident = " " * (depth * 2)
self.pos = pos
numpy_scalar_types = list(itertools.chain(*np.sctypes.values()))
if isinstance(el, (int, float, bool)):
self.range = " with value {}".format(el)
elif type(el) is np.ndarray:
self.shape = " of shape {}".format(el.shape)
self.dtype = ":{}".format(str(el.dtype))
self.range = " in range [{}, {}]".format(el.min(), el.max())
elif type(el) in numpy_scalar_types:
self.range = " with value {}".format(el)
elif isinstance(el, (list)):
self.shape = " of len {}".format(len(el))
if depth < max_depth:
for k, subel in enumerate(el):
if k < max_list:
self.sub_elements.append(_elementInfo(subel, k, depth + 1, max_list))
else:
self.sub_elements.append(" " * ((depth + 1) * 2) + '...')
break
else:
if len(el) > 0:
self.sub_elements.append(" " * ((depth + 1) * 2) + ' ...')
def __str__(self):
strings = []
vals = (self.ident, self.pos, self.type, self.dtype, self.shape, self.range)
strings.append("{}{}: {}{}{}{}".format(*vals))
for k, el in enumerate(self.sub_elements):
strings.append(str(el))
return "\n".join(strings)
return str(_elementInfo(entry, k, depth, max_list)) | python | def _analyze_input_data(self, entry, k, depth=1, max_depth=3, max_list=3):
"""
Gather useful debug information from a datapoint.
Args:
entry: the datapoint component
k (int): index of this component in current datapoint
depth (int, optional): recursion depth
max_depth, max_list: same as in :meth:`__init__`.
Returns:
string: debug message
"""
class _elementInfo(object):
def __init__(self, el, pos, depth=0, max_list=3):
self.shape = ""
self.type = type(el).__name__
self.dtype = ""
self.range = ""
self.sub_elements = []
self.ident = " " * (depth * 2)
self.pos = pos
numpy_scalar_types = list(itertools.chain(*np.sctypes.values()))
if isinstance(el, (int, float, bool)):
self.range = " with value {}".format(el)
elif type(el) is np.ndarray:
self.shape = " of shape {}".format(el.shape)
self.dtype = ":{}".format(str(el.dtype))
self.range = " in range [{}, {}]".format(el.min(), el.max())
elif type(el) in numpy_scalar_types:
self.range = " with value {}".format(el)
elif isinstance(el, (list)):
self.shape = " of len {}".format(len(el))
if depth < max_depth:
for k, subel in enumerate(el):
if k < max_list:
self.sub_elements.append(_elementInfo(subel, k, depth + 1, max_list))
else:
self.sub_elements.append(" " * ((depth + 1) * 2) + '...')
break
else:
if len(el) > 0:
self.sub_elements.append(" " * ((depth + 1) * 2) + ' ...')
def __str__(self):
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depth (int, optional): recursion depth
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27,254 | tensorpack/tensorpack | tensorpack/tfutils/optimizer.py | apply_grad_processors | def apply_grad_processors(opt, gradprocs):
"""
Wrapper around optimizers to apply gradient processors.
Args:
opt (tf.train.Optimizer):
gradprocs (list[GradientProcessor]): gradient processors to add to the
optimizer.
Returns:
a :class:`tf.train.Optimizer` instance which runs the gradient
processors before updating the variables.
"""
assert isinstance(gradprocs, (list, tuple)), gradprocs
for gp in gradprocs:
assert isinstance(gp, GradientProcessor), gp
class _ApplyGradientProcessor(ProxyOptimizer):
def __init__(self, opt, gradprocs):
self._gradprocs = gradprocs[:]
super(_ApplyGradientProcessor, self).__init__(opt)
def apply_gradients(self, grads_and_vars,
global_step=None, name=None):
g = self._apply(grads_and_vars)
return self._opt.apply_gradients(g, global_step, name)
def _apply(self, g):
for proc in self._gradprocs:
g = proc.process(g)
return g
return _ApplyGradientProcessor(opt, gradprocs) | python | def apply_grad_processors(opt, gradprocs):
"""
Wrapper around optimizers to apply gradient processors.
Args:
opt (tf.train.Optimizer):
gradprocs (list[GradientProcessor]): gradient processors to add to the
optimizer.
Returns:
a :class:`tf.train.Optimizer` instance which runs the gradient
processors before updating the variables.
"""
assert isinstance(gradprocs, (list, tuple)), gradprocs
for gp in gradprocs:
assert isinstance(gp, GradientProcessor), gp
class _ApplyGradientProcessor(ProxyOptimizer):
def __init__(self, opt, gradprocs):
self._gradprocs = gradprocs[:]
super(_ApplyGradientProcessor, self).__init__(opt)
def apply_gradients(self, grads_and_vars,
global_step=None, name=None):
g = self._apply(grads_and_vars)
return self._opt.apply_gradients(g, global_step, name)
def _apply(self, g):
for proc in self._gradprocs:
g = proc.process(g)
return g
return _ApplyGradientProcessor(opt, gradprocs) | [
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gradprocs (list[GradientProcessor]): gradient processors to add to the
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Returns:
a :class:`tf.train.Optimizer` instance which runs the gradient
processors before updating the variables. | [
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27,255 | tensorpack/tensorpack | examples/FasterRCNN/eval.py | multithread_predict_dataflow | def multithread_predict_dataflow(dataflows, model_funcs):
"""
Running multiple `predict_dataflow` in multiple threads, and aggregate the results.
Args:
dataflows: a list of DataFlow to be used in :func:`predict_dataflow`
model_funcs: a list of callable to be used in :func:`predict_dataflow`
Returns:
list of dict, in the format used by
`DetectionDataset.eval_or_save_inference_results`
"""
num_worker = len(model_funcs)
assert len(dataflows) == num_worker
if num_worker == 1:
return predict_dataflow(dataflows[0], model_funcs[0])
kwargs = {'thread_name_prefix': 'EvalWorker'} if sys.version_info.minor >= 6 else {}
with ThreadPoolExecutor(max_workers=num_worker, **kwargs) as executor, \
tqdm.tqdm(total=sum([df.size() for df in dataflows])) as pbar:
futures = []
for dataflow, pred in zip(dataflows, model_funcs):
futures.append(executor.submit(predict_dataflow, dataflow, pred, pbar))
all_results = list(itertools.chain(*[fut.result() for fut in futures]))
return all_results | python | def multithread_predict_dataflow(dataflows, model_funcs):
"""
Running multiple `predict_dataflow` in multiple threads, and aggregate the results.
Args:
dataflows: a list of DataFlow to be used in :func:`predict_dataflow`
model_funcs: a list of callable to be used in :func:`predict_dataflow`
Returns:
list of dict, in the format used by
`DetectionDataset.eval_or_save_inference_results`
"""
num_worker = len(model_funcs)
assert len(dataflows) == num_worker
if num_worker == 1:
return predict_dataflow(dataflows[0], model_funcs[0])
kwargs = {'thread_name_prefix': 'EvalWorker'} if sys.version_info.minor >= 6 else {}
with ThreadPoolExecutor(max_workers=num_worker, **kwargs) as executor, \
tqdm.tqdm(total=sum([df.size() for df in dataflows])) as pbar:
futures = []
for dataflow, pred in zip(dataflows, model_funcs):
futures.append(executor.submit(predict_dataflow, dataflow, pred, pbar))
all_results = list(itertools.chain(*[fut.result() for fut in futures]))
return all_results | [
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model_funcs: a list of callable to be used in :func:`predict_dataflow`
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27,256 | tensorpack/tensorpack | tensorpack/models/fc.py | batch_flatten | def batch_flatten(x):
"""
Flatten the tensor except the first dimension.
"""
shape = x.get_shape().as_list()[1:]
if None not in shape:
return tf.reshape(x, [-1, int(np.prod(shape))])
return tf.reshape(x, tf.stack([tf.shape(x)[0], -1])) | python | def batch_flatten(x):
"""
Flatten the tensor except the first dimension.
"""
shape = x.get_shape().as_list()[1:]
if None not in shape:
return tf.reshape(x, [-1, int(np.prod(shape))])
return tf.reshape(x, tf.stack([tf.shape(x)[0], -1])) | [
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27,257 | tensorpack/tensorpack | tensorpack/predict/concurrency.py | MultiProcessPredictWorker._init_runtime | def _init_runtime(self):
""" Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll
have workers that run on multiGPUs
"""
if self.idx != 0:
from tensorpack.models.registry import disable_layer_logging
disable_layer_logging()
self.predictor = OfflinePredictor(self.config)
if self.idx == 0:
with self.predictor.graph.as_default():
describe_trainable_vars() | python | def _init_runtime(self):
""" Call _init_runtime under different CUDA_VISIBLE_DEVICES, you'll
have workers that run on multiGPUs
"""
if self.idx != 0:
from tensorpack.models.registry import disable_layer_logging
disable_layer_logging()
self.predictor = OfflinePredictor(self.config)
if self.idx == 0:
with self.predictor.graph.as_default():
describe_trainable_vars() | [
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27,258 | tensorpack/tensorpack | tensorpack/predict/concurrency.py | PredictorWorkerThread.fetch_batch | def fetch_batch(self):
""" Fetch a batch of data without waiting"""
inp, f = self.queue.get()
nr_input_var = len(inp)
batched, futures = [[] for _ in range(nr_input_var)], []
for k in range(nr_input_var):
batched[k].append(inp[k])
futures.append(f)
while len(futures) < self.batch_size:
try:
inp, f = self.queue.get_nowait()
for k in range(nr_input_var):
batched[k].append(inp[k])
futures.append(f)
except queue.Empty:
break # do not wait
for k in range(nr_input_var):
batched[k] = np.asarray(batched[k])
return batched, futures | python | def fetch_batch(self):
""" Fetch a batch of data without waiting"""
inp, f = self.queue.get()
nr_input_var = len(inp)
batched, futures = [[] for _ in range(nr_input_var)], []
for k in range(nr_input_var):
batched[k].append(inp[k])
futures.append(f)
while len(futures) < self.batch_size:
try:
inp, f = self.queue.get_nowait()
for k in range(nr_input_var):
batched[k].append(inp[k])
futures.append(f)
except queue.Empty:
break # do not wait
for k in range(nr_input_var):
batched[k] = np.asarray(batched[k])
return batched, futures | [
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27,259 | tensorpack/tensorpack | examples/GAN/DCGAN.py | Model.generator | def generator(self, z):
""" return an image generated from z"""
nf = 64
l = FullyConnected('fc0', z, nf * 8 * 4 * 4, activation=tf.identity)
l = tf.reshape(l, [-1, 4, 4, nf * 8])
l = BNReLU(l)
with argscope(Conv2DTranspose, activation=BNReLU, kernel_size=4, strides=2):
l = Conv2DTranspose('deconv1', l, nf * 4)
l = Conv2DTranspose('deconv2', l, nf * 2)
l = Conv2DTranspose('deconv3', l, nf)
l = Conv2DTranspose('deconv4', l, 3, activation=tf.identity)
l = tf.tanh(l, name='gen')
return l | python | def generator(self, z):
""" return an image generated from z"""
nf = 64
l = FullyConnected('fc0', z, nf * 8 * 4 * 4, activation=tf.identity)
l = tf.reshape(l, [-1, 4, 4, nf * 8])
l = BNReLU(l)
with argscope(Conv2DTranspose, activation=BNReLU, kernel_size=4, strides=2):
l = Conv2DTranspose('deconv1', l, nf * 4)
l = Conv2DTranspose('deconv2', l, nf * 2)
l = Conv2DTranspose('deconv3', l, nf)
l = Conv2DTranspose('deconv4', l, 3, activation=tf.identity)
l = tf.tanh(l, name='gen')
return l | [
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27,260 | tensorpack/tensorpack | tensorpack/models/nonlin.py | BNReLU | def BNReLU(x, name=None):
"""
A shorthand of BatchNormalization + ReLU.
"""
x = BatchNorm('bn', x)
x = tf.nn.relu(x, name=name)
return x | python | def BNReLU(x, name=None):
"""
A shorthand of BatchNormalization + ReLU.
"""
x = BatchNorm('bn', x)
x = tf.nn.relu(x, name=name)
return x | [
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27,261 | tensorpack/tensorpack | tensorpack/utils/develop.py | create_dummy_class | def create_dummy_class(klass, dependency):
"""
When a dependency of a class is not available, create a dummy class which throws ImportError when used.
Args:
klass (str): name of the class.
dependency (str): name of the dependency.
Returns:
class: a class object
"""
assert not building_rtfd()
class _DummyMetaClass(type):
# throw error on class attribute access
def __getattr__(_, __):
raise AttributeError("Cannot import '{}', therefore '{}' is not available".format(dependency, klass))
@six.add_metaclass(_DummyMetaClass)
class _Dummy(object):
# throw error on constructor
def __init__(self, *args, **kwargs):
raise ImportError("Cannot import '{}', therefore '{}' is not available".format(dependency, klass))
return _Dummy | python | def create_dummy_class(klass, dependency):
"""
When a dependency of a class is not available, create a dummy class which throws ImportError when used.
Args:
klass (str): name of the class.
dependency (str): name of the dependency.
Returns:
class: a class object
"""
assert not building_rtfd()
class _DummyMetaClass(type):
# throw error on class attribute access
def __getattr__(_, __):
raise AttributeError("Cannot import '{}', therefore '{}' is not available".format(dependency, klass))
@six.add_metaclass(_DummyMetaClass)
class _Dummy(object):
# throw error on constructor
def __init__(self, *args, **kwargs):
raise ImportError("Cannot import '{}', therefore '{}' is not available".format(dependency, klass))
return _Dummy | [
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klass (str): name of the class.
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27,262 | tensorpack/tensorpack | tensorpack/utils/develop.py | create_dummy_func | def create_dummy_func(func, dependency):
"""
When a dependency of a function is not available, create a dummy function which throws ImportError when used.
Args:
func (str): name of the function.
dependency (str or list[str]): name(s) of the dependency.
Returns:
function: a function object
"""
assert not building_rtfd()
if isinstance(dependency, (list, tuple)):
dependency = ','.join(dependency)
def _dummy(*args, **kwargs):
raise ImportError("Cannot import '{}', therefore '{}' is not available".format(dependency, func))
return _dummy | python | def create_dummy_func(func, dependency):
"""
When a dependency of a function is not available, create a dummy function which throws ImportError when used.
Args:
func (str): name of the function.
dependency (str or list[str]): name(s) of the dependency.
Returns:
function: a function object
"""
assert not building_rtfd()
if isinstance(dependency, (list, tuple)):
dependency = ','.join(dependency)
def _dummy(*args, **kwargs):
raise ImportError("Cannot import '{}', therefore '{}' is not available".format(dependency, func))
return _dummy | [
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dependency (str or list[str]): name(s) of the dependency.
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27,263 | tensorpack/tensorpack | tensorpack/utils/develop.py | log_deprecated | def log_deprecated(name="", text="", eos=""):
"""
Log deprecation warning.
Args:
name (str): name of the deprecated item.
text (str, optional): information about the deprecation.
eos (str, optional): end of service date such as "YYYY-MM-DD".
"""
assert name or text
if eos:
eos = "after " + datetime(*map(int, eos.split("-"))).strftime("%d %b")
if name:
if eos:
warn_msg = "%s will be deprecated %s. %s" % (name, eos, text)
else:
warn_msg = "%s was deprecated. %s" % (name, text)
else:
warn_msg = text
if eos:
warn_msg += " Legacy period ends %s" % eos
logger.warn("[Deprecated] " + warn_msg) | python | def log_deprecated(name="", text="", eos=""):
"""
Log deprecation warning.
Args:
name (str): name of the deprecated item.
text (str, optional): information about the deprecation.
eos (str, optional): end of service date such as "YYYY-MM-DD".
"""
assert name or text
if eos:
eos = "after " + datetime(*map(int, eos.split("-"))).strftime("%d %b")
if name:
if eos:
warn_msg = "%s will be deprecated %s. %s" % (name, eos, text)
else:
warn_msg = "%s was deprecated. %s" % (name, text)
else:
warn_msg = text
if eos:
warn_msg += " Legacy period ends %s" % eos
logger.warn("[Deprecated] " + warn_msg) | [
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27,264 | tensorpack/tensorpack | tensorpack/input_source/input_source.py | QueueInput._create_ema_callback | def _create_ema_callback(self):
"""
Create a hook-only callback which maintain EMA of the queue size.
Also tf.summary.scalar the EMA.
"""
with self.cached_name_scope():
# in TF there is no API to get queue capacity, so we can only summary the size
size = tf.cast(self.queue.size(), tf.float32, name='queue_size')
size_ema_op = add_moving_summary(size, collection=None, decay=0.5)[0].op
ret = RunOp(
lambda: size_ema_op,
run_before=False,
run_as_trigger=False,
run_step=True)
ret.name_scope = "InputSource/EMA"
return ret | python | def _create_ema_callback(self):
"""
Create a hook-only callback which maintain EMA of the queue size.
Also tf.summary.scalar the EMA.
"""
with self.cached_name_scope():
# in TF there is no API to get queue capacity, so we can only summary the size
size = tf.cast(self.queue.size(), tf.float32, name='queue_size')
size_ema_op = add_moving_summary(size, collection=None, decay=0.5)[0].op
ret = RunOp(
lambda: size_ema_op,
run_before=False,
run_as_trigger=False,
run_step=True)
ret.name_scope = "InputSource/EMA"
return ret | [
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27,265 | tensorpack/tensorpack | tensorpack/input_source/input_source.py | BatchQueueInput._setup | def _setup(self, inputs):
logger.info("Setting up the queue for CPU prefetching ...")
self.input_placehdrs = [build_or_reuse_placeholder(v) for v in inputs]
assert len(self.input_placehdrs) > 0, \
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# prepare placeholders without the first dimension
placehdrs_nobatch = []
for p in self.input_placehdrs:
placehdrs_nobatch.append(tfv1.placeholder(
dtype=p.dtype, shape=p.get_shape().as_list()[1:],
name=get_op_tensor_name(p.name)[0] + '-nobatch'))
# dequeue_many requires fully-defined shapes
shape_err = "Use of BatchQueueInput requires inputs to have fully-defined "
"shapes except for the batch dimension"
shapes = []
for p in placehdrs_nobatch:
assert p.get_shape().is_fully_defined(), shape_err
shapes.append(p.get_shape())
with self.cached_name_scope():
if self.queue is None:
self.queue = tf.FIFOQueue(
3000, [x.dtype for x in self.input_placehdrs],
shapes=shapes,
name='input_queue')
for shp in self.queue.shapes:
assert shp.is_fully_defined(), shape_err
self.thread = EnqueueThread(self.queue, self._inf_ds, placehdrs_nobatch) | python | def _setup(self, inputs):
logger.info("Setting up the queue for CPU prefetching ...")
self.input_placehdrs = [build_or_reuse_placeholder(v) for v in inputs]
assert len(self.input_placehdrs) > 0, \
"BatchQueueInput has to be used with some input signature!"
# prepare placeholders without the first dimension
placehdrs_nobatch = []
for p in self.input_placehdrs:
placehdrs_nobatch.append(tfv1.placeholder(
dtype=p.dtype, shape=p.get_shape().as_list()[1:],
name=get_op_tensor_name(p.name)[0] + '-nobatch'))
# dequeue_many requires fully-defined shapes
shape_err = "Use of BatchQueueInput requires inputs to have fully-defined "
"shapes except for the batch dimension"
shapes = []
for p in placehdrs_nobatch:
assert p.get_shape().is_fully_defined(), shape_err
shapes.append(p.get_shape())
with self.cached_name_scope():
if self.queue is None:
self.queue = tf.FIFOQueue(
3000, [x.dtype for x in self.input_placehdrs],
shapes=shapes,
name='input_queue')
for shp in self.queue.shapes:
assert shp.is_fully_defined(), shape_err
self.thread = EnqueueThread(self.queue, self._inf_ds, placehdrs_nobatch) | [
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27,266 | tensorpack/tensorpack | tensorpack/input_source/input_source.py | TFDatasetInput.dataflow_to_dataset | def dataflow_to_dataset(df, types):
"""
Wrap a dataflow to tf.data.Dataset.
This function will also reset the dataflow.
If the dataflow itself is finite, the returned dataset is also finite.
Therefore, if used for training, you'll need to add `.repeat()` on the returned
dataset.
Args:
df (DataFlow): a dataflow which produces lists
types([tf.DType]): list of types
Returns:
(tf.data.Dataset)
"""
# TODO theoretically it can support dict
assert isinstance(df, DataFlow), df
assert isinstance(types, (list, tuple)), types
df = MapData(df, lambda dp: tuple(dp))
df.reset_state()
ds = tf.data.Dataset.from_generator(
df.get_data, tuple(types))
return ds | python | def dataflow_to_dataset(df, types):
"""
Wrap a dataflow to tf.data.Dataset.
This function will also reset the dataflow.
If the dataflow itself is finite, the returned dataset is also finite.
Therefore, if used for training, you'll need to add `.repeat()` on the returned
dataset.
Args:
df (DataFlow): a dataflow which produces lists
types([tf.DType]): list of types
Returns:
(tf.data.Dataset)
"""
# TODO theoretically it can support dict
assert isinstance(df, DataFlow), df
assert isinstance(types, (list, tuple)), types
df = MapData(df, lambda dp: tuple(dp))
df.reset_state()
ds = tf.data.Dataset.from_generator(
df.get_data, tuple(types))
return ds | [
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27,267 | tensorpack/tensorpack | examples/FasterRCNN/utils/np_box_ops.py | ioa | def ioa(boxes1, boxes2):
"""Computes pairwise intersection-over-area between box collections.
Intersection-over-area (ioa) between two boxes box1 and box2 is defined as
their intersection area over box2's area. Note that ioa is not symmetric,
that is, IOA(box1, box2) != IOA(box2, box1).
Args:
boxes1: a numpy array with shape [N, 4] holding N boxes.
boxes2: a numpy array with shape [M, 4] holding N boxes.
Returns:
a numpy array with shape [N, M] representing pairwise ioa scores.
"""
intersect = intersection(boxes1, boxes2)
inv_areas = np.expand_dims(1.0 / area(boxes2), axis=0)
return intersect * inv_areas | python | def ioa(boxes1, boxes2):
"""Computes pairwise intersection-over-area between box collections.
Intersection-over-area (ioa) between two boxes box1 and box2 is defined as
their intersection area over box2's area. Note that ioa is not symmetric,
that is, IOA(box1, box2) != IOA(box2, box1).
Args:
boxes1: a numpy array with shape [N, 4] holding N boxes.
boxes2: a numpy array with shape [M, 4] holding N boxes.
Returns:
a numpy array with shape [N, M] representing pairwise ioa scores.
"""
intersect = intersection(boxes1, boxes2)
inv_areas = np.expand_dims(1.0 / area(boxes2), axis=0)
return intersect * inv_areas | [
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27,268 | tensorpack/tensorpack | tensorpack/dataflow/dataset/caltech101.py | maybe_download | def maybe_download(url, work_directory):
"""Download the data from Marlin's website, unless it's already here."""
filename = url.split("/")[-1]
filepath = os.path.join(work_directory, filename)
if not os.path.exists(filepath):
logger.info("Downloading to {}...".format(filepath))
download(url, work_directory)
return filepath | python | def maybe_download(url, work_directory):
"""Download the data from Marlin's website, unless it's already here."""
filename = url.split("/")[-1]
filepath = os.path.join(work_directory, filename)
if not os.path.exists(filepath):
logger.info("Downloading to {}...".format(filepath))
download(url, work_directory)
return filepath | [
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27,269 | tensorpack/tensorpack | tensorpack/dataflow/dataset/ilsvrc.py | ILSVRCMeta.guess_dir_structure | def guess_dir_structure(dir):
"""
Return the directory structure of "dir".
Args:
dir(str): something like '/path/to/imagenet/val'
Returns:
either 'train' or 'original'
"""
subdir = os.listdir(dir)[0]
# find a subdir starting with 'n'
if subdir.startswith('n') and \
os.path.isdir(os.path.join(dir, subdir)):
dir_structure = 'train'
else:
dir_structure = 'original'
logger.info(
"[ILSVRC12] Assuming directory {} has '{}' structure.".format(
dir, dir_structure))
return dir_structure | python | def guess_dir_structure(dir):
"""
Return the directory structure of "dir".
Args:
dir(str): something like '/path/to/imagenet/val'
Returns:
either 'train' or 'original'
"""
subdir = os.listdir(dir)[0]
# find a subdir starting with 'n'
if subdir.startswith('n') and \
os.path.isdir(os.path.join(dir, subdir)):
dir_structure = 'train'
else:
dir_structure = 'original'
logger.info(
"[ILSVRC12] Assuming directory {} has '{}' structure.".format(
dir, dir_structure))
return dir_structure | [
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27,270 | tensorpack/tensorpack | examples/FasterRCNN/dataset.py | COCODetection._use_absolute_file_name | def _use_absolute_file_name(self, img):
"""
Change relative filename to abosolute file name.
"""
img['file_name'] = os.path.join(
self._imgdir, img['file_name'])
assert os.path.isfile(img['file_name']), img['file_name'] | python | def _use_absolute_file_name(self, img):
"""
Change relative filename to abosolute file name.
"""
img['file_name'] = os.path.join(
self._imgdir, img['file_name'])
assert os.path.isfile(img['file_name']), img['file_name'] | [
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27,271 | tensorpack/tensorpack | examples/FasterRCNN/dataset.py | COCODetection._add_detection_gt | def _add_detection_gt(self, img, add_mask):
"""
Add 'boxes', 'class', 'is_crowd' of this image to the dict, used by detection.
If add_mask is True, also add 'segmentation' in coco poly format.
"""
# ann_ids = self.coco.getAnnIds(imgIds=img['image_id'])
# objs = self.coco.loadAnns(ann_ids)
objs = self.coco.imgToAnns[img['image_id']] # equivalent but faster than the above two lines
# clean-up boxes
valid_objs = []
width = img.pop('width')
height = img.pop('height')
for objid, obj in enumerate(objs):
if obj.get('ignore', 0) == 1:
continue
x1, y1, w, h = obj['bbox']
# bbox is originally in float
# x1/y1 means upper-left corner and w/h means true w/h. This can be verified by segmentation pixels.
# But we do make an assumption here that (0.0, 0.0) is upper-left corner of the first pixel
x1 = np.clip(float(x1), 0, width)
y1 = np.clip(float(y1), 0, height)
w = np.clip(float(x1 + w), 0, width) - x1
h = np.clip(float(y1 + h), 0, height) - y1
# Require non-zero seg area and more than 1x1 box size
if obj['area'] > 1 and w > 0 and h > 0 and w * h >= 4:
obj['bbox'] = [x1, y1, x1 + w, y1 + h]
valid_objs.append(obj)
if add_mask:
segs = obj['segmentation']
if not isinstance(segs, list):
assert obj['iscrowd'] == 1
obj['segmentation'] = None
else:
valid_segs = [np.asarray(p).reshape(-1, 2).astype('float32') for p in segs if len(p) >= 6]
if len(valid_segs) == 0:
logger.error("Object {} in image {} has no valid polygons!".format(objid, img['file_name']))
elif len(valid_segs) < len(segs):
logger.warn("Object {} in image {} has invalid polygons!".format(objid, img['file_name']))
obj['segmentation'] = valid_segs
# all geometrically-valid boxes are returned
boxes = np.asarray([obj['bbox'] for obj in valid_objs], dtype='float32') # (n, 4)
cls = np.asarray([
self.COCO_id_to_category_id[obj['category_id']]
for obj in valid_objs], dtype='int32') # (n,)
is_crowd = np.asarray([obj['iscrowd'] for obj in valid_objs], dtype='int8')
# add the keys
img['boxes'] = boxes # nx4
img['class'] = cls # n, always >0
img['is_crowd'] = is_crowd # n,
if add_mask:
# also required to be float32
img['segmentation'] = [
obj['segmentation'] for obj in valid_objs] | python | def _add_detection_gt(self, img, add_mask):
"""
Add 'boxes', 'class', 'is_crowd' of this image to the dict, used by detection.
If add_mask is True, also add 'segmentation' in coco poly format.
"""
# ann_ids = self.coco.getAnnIds(imgIds=img['image_id'])
# objs = self.coco.loadAnns(ann_ids)
objs = self.coco.imgToAnns[img['image_id']] # equivalent but faster than the above two lines
# clean-up boxes
valid_objs = []
width = img.pop('width')
height = img.pop('height')
for objid, obj in enumerate(objs):
if obj.get('ignore', 0) == 1:
continue
x1, y1, w, h = obj['bbox']
# bbox is originally in float
# x1/y1 means upper-left corner and w/h means true w/h. This can be verified by segmentation pixels.
# But we do make an assumption here that (0.0, 0.0) is upper-left corner of the first pixel
x1 = np.clip(float(x1), 0, width)
y1 = np.clip(float(y1), 0, height)
w = np.clip(float(x1 + w), 0, width) - x1
h = np.clip(float(y1 + h), 0, height) - y1
# Require non-zero seg area and more than 1x1 box size
if obj['area'] > 1 and w > 0 and h > 0 and w * h >= 4:
obj['bbox'] = [x1, y1, x1 + w, y1 + h]
valid_objs.append(obj)
if add_mask:
segs = obj['segmentation']
if not isinstance(segs, list):
assert obj['iscrowd'] == 1
obj['segmentation'] = None
else:
valid_segs = [np.asarray(p).reshape(-1, 2).astype('float32') for p in segs if len(p) >= 6]
if len(valid_segs) == 0:
logger.error("Object {} in image {} has no valid polygons!".format(objid, img['file_name']))
elif len(valid_segs) < len(segs):
logger.warn("Object {} in image {} has invalid polygons!".format(objid, img['file_name']))
obj['segmentation'] = valid_segs
# all geometrically-valid boxes are returned
boxes = np.asarray([obj['bbox'] for obj in valid_objs], dtype='float32') # (n, 4)
cls = np.asarray([
self.COCO_id_to_category_id[obj['category_id']]
for obj in valid_objs], dtype='int32') # (n,)
is_crowd = np.asarray([obj['iscrowd'] for obj in valid_objs], dtype='int8')
# add the keys
img['boxes'] = boxes # nx4
img['class'] = cls # n, always >0
img['is_crowd'] = is_crowd # n,
if add_mask:
# also required to be float32
img['segmentation'] = [
obj['segmentation'] for obj in valid_objs] | [
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If add_mask is True, also add 'segmentation' in coco poly format. | [
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27,272 | tensorpack/tensorpack | examples/FasterRCNN/dataset.py | COCODetection.load_many | def load_many(basedir, names, add_gt=True, add_mask=False):
"""
Load and merges several instance files together.
Returns the same format as :meth:`COCODetection.load`.
"""
if not isinstance(names, (list, tuple)):
names = [names]
ret = []
for n in names:
coco = COCODetection(basedir, n)
ret.extend(coco.load(add_gt, add_mask=add_mask))
return ret | python | def load_many(basedir, names, add_gt=True, add_mask=False):
"""
Load and merges several instance files together.
Returns the same format as :meth:`COCODetection.load`.
"""
if not isinstance(names, (list, tuple)):
names = [names]
ret = []
for n in names:
coco = COCODetection(basedir, n)
ret.extend(coco.load(add_gt, add_mask=add_mask))
return ret | [
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27,273 | tensorpack/tensorpack | tensorpack/utils/timer.py | timed_operation | def timed_operation(msg, log_start=False):
"""
Surround a context with a timer.
Args:
msg(str): the log to print.
log_start(bool): whether to print also at the beginning.
Example:
.. code-block:: python
with timed_operation('Good Stuff'):
time.sleep(1)
Will print:
.. code-block:: python
Good stuff finished, time:1sec.
"""
assert len(msg)
if log_start:
logger.info('Start {} ...'.format(msg))
start = timer()
yield
msg = msg[0].upper() + msg[1:]
logger.info('{} finished, time:{:.4f} sec.'.format(
msg, timer() - start)) | python | def timed_operation(msg, log_start=False):
"""
Surround a context with a timer.
Args:
msg(str): the log to print.
log_start(bool): whether to print also at the beginning.
Example:
.. code-block:: python
with timed_operation('Good Stuff'):
time.sleep(1)
Will print:
.. code-block:: python
Good stuff finished, time:1sec.
"""
assert len(msg)
if log_start:
logger.info('Start {} ...'.format(msg))
start = timer()
yield
msg = msg[0].upper() + msg[1:]
logger.info('{} finished, time:{:.4f} sec.'.format(
msg, timer() - start)) | [
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27,274 | tensorpack/tensorpack | tensorpack/utils/timer.py | total_timer | def total_timer(msg):
""" A context which add the time spent inside to TotalTimer. """
start = timer()
yield
t = timer() - start
_TOTAL_TIMER_DATA[msg].feed(t) | python | def total_timer(msg):
""" A context which add the time spent inside to TotalTimer. """
start = timer()
yield
t = timer() - start
_TOTAL_TIMER_DATA[msg].feed(t) | [
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27,275 | tensorpack/tensorpack | tensorpack/utils/timer.py | print_total_timer | def print_total_timer():
"""
Print the content of the TotalTimer, if it's not empty. This function will automatically get
called when program exits.
"""
if len(_TOTAL_TIMER_DATA) == 0:
return
for k, v in six.iteritems(_TOTAL_TIMER_DATA):
logger.info("Total Time: {} -> {:.2f} sec, {} times, {:.3g} sec/time".format(
k, v.sum, v.count, v.average)) | python | def print_total_timer():
"""
Print the content of the TotalTimer, if it's not empty. This function will automatically get
called when program exits.
"""
if len(_TOTAL_TIMER_DATA) == 0:
return
for k, v in six.iteritems(_TOTAL_TIMER_DATA):
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27,276 | tensorpack/tensorpack | tensorpack/dataflow/imgaug/base.py | AugmentorList.reset_state | def reset_state(self):
""" Will reset state of each augmentor """
super(AugmentorList, self).reset_state()
for a in self.augmentors:
a.reset_state() | python | def reset_state(self):
""" Will reset state of each augmentor """
super(AugmentorList, self).reset_state()
for a in self.augmentors:
a.reset_state() | [
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27,277 | tensorpack/tensorpack | tensorpack/utils/concurrency.py | ensure_proc_terminate | def ensure_proc_terminate(proc):
"""
Make sure processes terminate when main process exit.
Args:
proc (multiprocessing.Process or list)
"""
if isinstance(proc, list):
for p in proc:
ensure_proc_terminate(p)
return
def stop_proc_by_weak_ref(ref):
proc = ref()
if proc is None:
return
if not proc.is_alive():
return
proc.terminate()
proc.join()
assert isinstance(proc, mp.Process)
atexit.register(stop_proc_by_weak_ref, weakref.ref(proc)) | python | def ensure_proc_terminate(proc):
"""
Make sure processes terminate when main process exit.
Args:
proc (multiprocessing.Process or list)
"""
if isinstance(proc, list):
for p in proc:
ensure_proc_terminate(p)
return
def stop_proc_by_weak_ref(ref):
proc = ref()
if proc is None:
return
if not proc.is_alive():
return
proc.terminate()
proc.join()
assert isinstance(proc, mp.Process)
atexit.register(stop_proc_by_weak_ref, weakref.ref(proc)) | [
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27,278 | tensorpack/tensorpack | tensorpack/utils/concurrency.py | enable_death_signal | def enable_death_signal(_warn=True):
"""
Set the "death signal" of the current process, so that
the current process will be cleaned with guarantee
in case the parent dies accidentally.
"""
if platform.system() != 'Linux':
return
try:
import prctl # pip install python-prctl
except ImportError:
if _warn:
log_once('"import prctl" failed! Install python-prctl so that processes can be cleaned with guarantee.',
'warn')
return
else:
assert hasattr(prctl, 'set_pdeathsig'), \
"prctl.set_pdeathsig does not exist! Note that you need to install 'python-prctl' instead of 'prctl'."
# is SIGHUP a good choice?
prctl.set_pdeathsig(signal.SIGHUP) | python | def enable_death_signal(_warn=True):
"""
Set the "death signal" of the current process, so that
the current process will be cleaned with guarantee
in case the parent dies accidentally.
"""
if platform.system() != 'Linux':
return
try:
import prctl # pip install python-prctl
except ImportError:
if _warn:
log_once('"import prctl" failed! Install python-prctl so that processes can be cleaned with guarantee.',
'warn')
return
else:
assert hasattr(prctl, 'set_pdeathsig'), \
"prctl.set_pdeathsig does not exist! Note that you need to install 'python-prctl' instead of 'prctl'."
# is SIGHUP a good choice?
prctl.set_pdeathsig(signal.SIGHUP) | [
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27,279 | tensorpack/tensorpack | tensorpack/utils/concurrency.py | subproc_call | def subproc_call(cmd, timeout=None):
"""
Execute a command with timeout, and return STDOUT and STDERR
Args:
cmd(str): the command to execute.
timeout(float): timeout in seconds.
Returns:
output(bytes), retcode(int). If timeout, retcode is -1.
"""
try:
output = subprocess.check_output(
cmd, stderr=subprocess.STDOUT,
shell=True, timeout=timeout)
return output, 0
except subprocess.TimeoutExpired as e:
logger.warn("Command '{}' timeout!".format(cmd))
logger.warn(e.output.decode('utf-8'))
return e.output, -1
except subprocess.CalledProcessError as e:
logger.warn("Command '{}' failed, return code={}".format(cmd, e.returncode))
logger.warn(e.output.decode('utf-8'))
return e.output, e.returncode
except Exception:
logger.warn("Command '{}' failed to run.".format(cmd))
return "", -2 | python | def subproc_call(cmd, timeout=None):
"""
Execute a command with timeout, and return STDOUT and STDERR
Args:
cmd(str): the command to execute.
timeout(float): timeout in seconds.
Returns:
output(bytes), retcode(int). If timeout, retcode is -1.
"""
try:
output = subprocess.check_output(
cmd, stderr=subprocess.STDOUT,
shell=True, timeout=timeout)
return output, 0
except subprocess.TimeoutExpired as e:
logger.warn("Command '{}' timeout!".format(cmd))
logger.warn(e.output.decode('utf-8'))
return e.output, -1
except subprocess.CalledProcessError as e:
logger.warn("Command '{}' failed, return code={}".format(cmd, e.returncode))
logger.warn(e.output.decode('utf-8'))
return e.output, e.returncode
except Exception:
logger.warn("Command '{}' failed to run.".format(cmd))
return "", -2 | [
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27,280 | tensorpack/tensorpack | tensorpack/utils/concurrency.py | StoppableThread.queue_put_stoppable | def queue_put_stoppable(self, q, obj):
""" Put obj to queue, but will give up when the thread is stopped"""
while not self.stopped():
try:
q.put(obj, timeout=5)
break
except queue.Full:
pass | python | def queue_put_stoppable(self, q, obj):
""" Put obj to queue, but will give up when the thread is stopped"""
while not self.stopped():
try:
q.put(obj, timeout=5)
break
except queue.Full:
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27,281 | tensorpack/tensorpack | tensorpack/utils/concurrency.py | StoppableThread.queue_get_stoppable | def queue_get_stoppable(self, q):
""" Take obj from queue, but will give up when the thread is stopped"""
while not self.stopped():
try:
return q.get(timeout=5)
except queue.Empty:
pass | python | def queue_get_stoppable(self, q):
""" Take obj from queue, but will give up when the thread is stopped"""
while not self.stopped():
try:
return q.get(timeout=5)
except queue.Empty:
pass | [
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27,282 | tensorpack/tensorpack | examples/basics/mnist-visualizations.py | visualize_conv_weights | def visualize_conv_weights(filters, name):
"""Visualize use weights in convolution filters.
Args:
filters: tensor containing the weights [H,W,Cin,Cout]
name: label for tensorboard
Returns:
image of all weight
"""
with tf.name_scope('visualize_w_' + name):
filters = tf.transpose(filters, (3, 2, 0, 1)) # [h, w, cin, cout] -> [cout, cin, h, w]
filters = tf.unstack(filters) # --> cout * [cin, h, w]
filters = tf.concat(filters, 1) # --> [cin, cout * h, w]
filters = tf.unstack(filters) # --> cin * [cout * h, w]
filters = tf.concat(filters, 1) # --> [cout * h, cin * w]
filters = tf.expand_dims(filters, 0)
filters = tf.expand_dims(filters, -1)
tf.summary.image('visualize_w_' + name, filters) | python | def visualize_conv_weights(filters, name):
"""Visualize use weights in convolution filters.
Args:
filters: tensor containing the weights [H,W,Cin,Cout]
name: label for tensorboard
Returns:
image of all weight
"""
with tf.name_scope('visualize_w_' + name):
filters = tf.transpose(filters, (3, 2, 0, 1)) # [h, w, cin, cout] -> [cout, cin, h, w]
filters = tf.unstack(filters) # --> cout * [cin, h, w]
filters = tf.concat(filters, 1) # --> [cin, cout * h, w]
filters = tf.unstack(filters) # --> cin * [cout * h, w]
filters = tf.concat(filters, 1) # --> [cout * h, cin * w]
filters = tf.expand_dims(filters, 0)
filters = tf.expand_dims(filters, -1)
tf.summary.image('visualize_w_' + name, filters) | [
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27,283 | tensorpack/tensorpack | examples/basics/mnist-visualizations.py | visualize_conv_activations | def visualize_conv_activations(activation, name):
"""Visualize activations for convolution layers.
Remarks:
This tries to place all activations into a square.
Args:
activation: tensor with the activation [B,H,W,C]
name: label for tensorboard
Returns:
image of almost all activations
"""
import math
with tf.name_scope('visualize_act_' + name):
_, h, w, c = activation.get_shape().as_list()
rows = []
c_per_row = int(math.sqrt(c))
for y in range(0, c - c_per_row, c_per_row):
row = activation[:, :, :, y:y + c_per_row] # [?, H, W, 32] --> [?, H, W, 5]
cols = tf.unstack(row, axis=3) # [?, H, W, 5] --> 5 * [?, H, W]
row = tf.concat(cols, 1)
rows.append(row)
viz = tf.concat(rows, 2)
tf.summary.image('visualize_act_' + name, tf.expand_dims(viz, -1)) | python | def visualize_conv_activations(activation, name):
"""Visualize activations for convolution layers.
Remarks:
This tries to place all activations into a square.
Args:
activation: tensor with the activation [B,H,W,C]
name: label for tensorboard
Returns:
image of almost all activations
"""
import math
with tf.name_scope('visualize_act_' + name):
_, h, w, c = activation.get_shape().as_list()
rows = []
c_per_row = int(math.sqrt(c))
for y in range(0, c - c_per_row, c_per_row):
row = activation[:, :, :, y:y + c_per_row] # [?, H, W, 32] --> [?, H, W, 5]
cols = tf.unstack(row, axis=3) # [?, H, W, 5] --> 5 * [?, H, W]
row = tf.concat(cols, 1)
rows.append(row)
viz = tf.concat(rows, 2)
tf.summary.image('visualize_act_' + name, tf.expand_dims(viz, -1)) | [
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27,284 | tensorpack/tensorpack | examples/GAN/InfoGAN-mnist.py | shapeless_placeholder | def shapeless_placeholder(x, axis, name):
"""
Make the static shape of a tensor less specific.
If you want to feed to a tensor, the shape of the feed value must match
the tensor's static shape. This function creates a placeholder which
defaults to x if not fed, but has a less specific static shape than x.
See also `tensorflow#5680 <https://github.com/tensorflow/tensorflow/issues/5680>`_.
Args:
x: a tensor
axis(int or list of ints): these axes of ``x.get_shape()`` will become
None in the output.
name(str): name of the output tensor
Returns:
a tensor equal to x, but shape information is partially cleared.
"""
shp = x.get_shape().as_list()
if not isinstance(axis, list):
axis = [axis]
for a in axis:
if shp[a] is None:
raise ValueError("Axis {} of shape {} is already unknown!".format(a, shp))
shp[a] = None
x = tf.placeholder_with_default(x, shape=shp, name=name)
return x | python | def shapeless_placeholder(x, axis, name):
"""
Make the static shape of a tensor less specific.
If you want to feed to a tensor, the shape of the feed value must match
the tensor's static shape. This function creates a placeholder which
defaults to x if not fed, but has a less specific static shape than x.
See also `tensorflow#5680 <https://github.com/tensorflow/tensorflow/issues/5680>`_.
Args:
x: a tensor
axis(int or list of ints): these axes of ``x.get_shape()`` will become
None in the output.
name(str): name of the output tensor
Returns:
a tensor equal to x, but shape information is partially cleared.
"""
shp = x.get_shape().as_list()
if not isinstance(axis, list):
axis = [axis]
for a in axis:
if shp[a] is None:
raise ValueError("Axis {} of shape {} is already unknown!".format(a, shp))
shp[a] = None
x = tf.placeholder_with_default(x, shape=shp, name=name)
return x | [
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See also `tensorflow#5680 <https://github.com/tensorflow/tensorflow/issues/5680>`_.
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27,285 | tensorpack/tensorpack | examples/GAN/InfoGAN-mnist.py | sample_prior | def sample_prior(batch_size):
cat, _ = get_distributions(DIST_PRIOR_PARAM[:NUM_CLASS], DIST_PRIOR_PARAM[NUM_CLASS:])
sample_cat = tf.one_hot(cat.sample(batch_size), NUM_CLASS)
"""
OpenAI official code actually models the "uniform" latent code as
a Gaussian distribution, but obtain the samples from a uniform distribution.
"""
sample_uni = tf.random_uniform([batch_size, NUM_UNIFORM], -1, 1)
samples = tf.concat([sample_cat, sample_uni], axis=1)
return samples | python | def sample_prior(batch_size):
cat, _ = get_distributions(DIST_PRIOR_PARAM[:NUM_CLASS], DIST_PRIOR_PARAM[NUM_CLASS:])
sample_cat = tf.one_hot(cat.sample(batch_size), NUM_CLASS)
"""
OpenAI official code actually models the "uniform" latent code as
a Gaussian distribution, but obtain the samples from a uniform distribution.
"""
sample_uni = tf.random_uniform([batch_size, NUM_UNIFORM], -1, 1)
samples = tf.concat([sample_cat, sample_uni], axis=1)
return samples | [
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27,286 | tensorpack/tensorpack | examples/DynamicFilterNetwork/steering-filter.py | Model._parameter_net | def _parameter_net(self, theta, kernel_shape=9):
"""Estimate filters for convolution layers
Args:
theta: angle of filter
kernel_shape: size of each filter
Returns:
learned filter as [B, k, k, 1]
"""
with argscope(FullyConnected, nl=tf.nn.leaky_relu):
net = FullyConnected('fc1', theta, 64)
net = FullyConnected('fc2', net, 128)
pred_filter = FullyConnected('fc3', net, kernel_shape ** 2, nl=tf.identity)
pred_filter = tf.reshape(pred_filter, [BATCH, kernel_shape, kernel_shape, 1], name="pred_filter")
logger.info('Parameter net output: {}'.format(pred_filter.get_shape().as_list()))
return pred_filter | python | def _parameter_net(self, theta, kernel_shape=9):
"""Estimate filters for convolution layers
Args:
theta: angle of filter
kernel_shape: size of each filter
Returns:
learned filter as [B, k, k, 1]
"""
with argscope(FullyConnected, nl=tf.nn.leaky_relu):
net = FullyConnected('fc1', theta, 64)
net = FullyConnected('fc2', net, 128)
pred_filter = FullyConnected('fc3', net, kernel_shape ** 2, nl=tf.identity)
pred_filter = tf.reshape(pred_filter, [BATCH, kernel_shape, kernel_shape, 1], name="pred_filter")
logger.info('Parameter net output: {}'.format(pred_filter.get_shape().as_list()))
return pred_filter | [
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27,287 | tensorpack/tensorpack | examples/DynamicFilterNetwork/steering-filter.py | ThetaImages.filter_with_theta | def filter_with_theta(image, theta, sigma=1., filter_size=9):
"""Implements a steerable Gaussian filter.
This function can be used to evaluate the first
directional derivative of an image, using the
method outlined in
W. T. Freeman and E. H. Adelson, "The Design
and Use of Steerable Filters", IEEE PAMI, 1991.
It evaluates the directional derivative of the input
image I, oriented at THETA degrees with respect to the
image rows. The standard deviation of the Gaussian kernel
is given by SIGMA (assumed to be equal to unity by default).
Args:
image: any input image (only one channel)
theta: orientation of filter [0, 2 * pi]
sigma (float, optional): standard derivation of Gaussian
filter_size (int, optional): filter support
Returns:
filtered image and the filter
"""
x = np.arange(-filter_size // 2 + 1, filter_size // 2 + 1)
# 1D Gaussian
g = np.array([np.exp(-(x**2) / (2 * sigma**2))])
# first-derivative of 1D Gaussian
gp = np.array([-(x / sigma) * np.exp(-(x**2) / (2 * sigma**2))])
ix = convolve2d(image, -gp, mode='same', boundary='fill', fillvalue=0)
ix = convolve2d(ix, g.T, mode='same', boundary='fill', fillvalue=0)
iy = convolve2d(image, g, mode='same', boundary='fill', fillvalue=0)
iy = convolve2d(iy, -gp.T, mode='same', boundary='fill', fillvalue=0)
output = np.cos(theta) * ix + np.sin(theta) * iy
# np.cos(theta) * np.matmul(g.T, gp) + np.sin(theta) * np.matmul(gp.T, g)
gt_filter = np.matmul(g.T, gp)
gt_filter = np.cos(theta) * gt_filter + np.sin(theta) * gt_filter.T
return output, gt_filter | python | def filter_with_theta(image, theta, sigma=1., filter_size=9):
"""Implements a steerable Gaussian filter.
This function can be used to evaluate the first
directional derivative of an image, using the
method outlined in
W. T. Freeman and E. H. Adelson, "The Design
and Use of Steerable Filters", IEEE PAMI, 1991.
It evaluates the directional derivative of the input
image I, oriented at THETA degrees with respect to the
image rows. The standard deviation of the Gaussian kernel
is given by SIGMA (assumed to be equal to unity by default).
Args:
image: any input image (only one channel)
theta: orientation of filter [0, 2 * pi]
sigma (float, optional): standard derivation of Gaussian
filter_size (int, optional): filter support
Returns:
filtered image and the filter
"""
x = np.arange(-filter_size // 2 + 1, filter_size // 2 + 1)
# 1D Gaussian
g = np.array([np.exp(-(x**2) / (2 * sigma**2))])
# first-derivative of 1D Gaussian
gp = np.array([-(x / sigma) * np.exp(-(x**2) / (2 * sigma**2))])
ix = convolve2d(image, -gp, mode='same', boundary='fill', fillvalue=0)
ix = convolve2d(ix, g.T, mode='same', boundary='fill', fillvalue=0)
iy = convolve2d(image, g, mode='same', boundary='fill', fillvalue=0)
iy = convolve2d(iy, -gp.T, mode='same', boundary='fill', fillvalue=0)
output = np.cos(theta) * ix + np.sin(theta) * iy
# np.cos(theta) * np.matmul(g.T, gp) + np.sin(theta) * np.matmul(gp.T, g)
gt_filter = np.matmul(g.T, gp)
gt_filter = np.cos(theta) * gt_filter + np.sin(theta) * gt_filter.T
return output, gt_filter | [
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This function can be used to evaluate the first
directional derivative of an image, using the
method outlined in
W. T. Freeman and E. H. Adelson, "The Design
and Use of Steerable Filters", IEEE PAMI, 1991.
It evaluates the directional derivative of the input
image I, oriented at THETA degrees with respect to the
image rows. The standard deviation of the Gaussian kernel
is given by SIGMA (assumed to be equal to unity by default).
Args:
image: any input image (only one channel)
theta: orientation of filter [0, 2 * pi]
sigma (float, optional): standard derivation of Gaussian
filter_size (int, optional): filter support
Returns:
filtered image and the filter | [
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] | d7a13cb74c9066bc791d7aafc3b744b60ee79a9f | https://github.com/tensorpack/tensorpack/blob/d7a13cb74c9066bc791d7aafc3b744b60ee79a9f/examples/DynamicFilterNetwork/steering-filter.py#L162-L204 |
27,288 | tensorpack/tensorpack | examples/GAN/GAN.py | GANModelDesc.collect_variables | def collect_variables(self, g_scope='gen', d_scope='discrim'):
"""
Assign `self.g_vars` to the parameters under scope `g_scope`,
and same with `self.d_vars`.
"""
self.g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, g_scope)
assert self.g_vars
self.d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, d_scope)
assert self.d_vars | python | def collect_variables(self, g_scope='gen', d_scope='discrim'):
"""
Assign `self.g_vars` to the parameters under scope `g_scope`,
and same with `self.d_vars`.
"""
self.g_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, g_scope)
assert self.g_vars
self.d_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, d_scope)
assert self.d_vars | [
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27,289 | tensorpack/tensorpack | examples/GAN/GAN.py | GANModelDesc.build_losses | def build_losses(self, logits_real, logits_fake):
"""
Build standard GAN loss and set `self.g_loss` and `self.d_loss`.
D and G play two-player minimax game with value function V(G,D)
min_G max _D V(D, G) = IE_{x ~ p_data} [log D(x)] + IE_{z ~ p_fake} [log (1 - D(G(z)))]
Args:
logits_real (tf.Tensor): discrim logits from real samples
logits_fake (tf.Tensor): discrim logits from fake samples produced by generator
"""
with tf.name_scope("GAN_loss"):
score_real = tf.sigmoid(logits_real)
score_fake = tf.sigmoid(logits_fake)
tf.summary.histogram('score-real', score_real)
tf.summary.histogram('score-fake', score_fake)
with tf.name_scope("discrim"):
d_loss_pos = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_real, labels=tf.ones_like(logits_real)), name='loss_real')
d_loss_neg = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=tf.zeros_like(logits_fake)), name='loss_fake')
d_pos_acc = tf.reduce_mean(tf.cast(score_real > 0.5, tf.float32), name='accuracy_real')
d_neg_acc = tf.reduce_mean(tf.cast(score_fake < 0.5, tf.float32), name='accuracy_fake')
d_accuracy = tf.add(.5 * d_pos_acc, .5 * d_neg_acc, name='accuracy')
self.d_loss = tf.add(.5 * d_loss_pos, .5 * d_loss_neg, name='loss')
with tf.name_scope("gen"):
self.g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=tf.ones_like(logits_fake)), name='loss')
g_accuracy = tf.reduce_mean(tf.cast(score_fake > 0.5, tf.float32), name='accuracy')
add_moving_summary(self.g_loss, self.d_loss, d_accuracy, g_accuracy) | python | def build_losses(self, logits_real, logits_fake):
"""
Build standard GAN loss and set `self.g_loss` and `self.d_loss`.
D and G play two-player minimax game with value function V(G,D)
min_G max _D V(D, G) = IE_{x ~ p_data} [log D(x)] + IE_{z ~ p_fake} [log (1 - D(G(z)))]
Args:
logits_real (tf.Tensor): discrim logits from real samples
logits_fake (tf.Tensor): discrim logits from fake samples produced by generator
"""
with tf.name_scope("GAN_loss"):
score_real = tf.sigmoid(logits_real)
score_fake = tf.sigmoid(logits_fake)
tf.summary.histogram('score-real', score_real)
tf.summary.histogram('score-fake', score_fake)
with tf.name_scope("discrim"):
d_loss_pos = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_real, labels=tf.ones_like(logits_real)), name='loss_real')
d_loss_neg = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=tf.zeros_like(logits_fake)), name='loss_fake')
d_pos_acc = tf.reduce_mean(tf.cast(score_real > 0.5, tf.float32), name='accuracy_real')
d_neg_acc = tf.reduce_mean(tf.cast(score_fake < 0.5, tf.float32), name='accuracy_fake')
d_accuracy = tf.add(.5 * d_pos_acc, .5 * d_neg_acc, name='accuracy')
self.d_loss = tf.add(.5 * d_loss_pos, .5 * d_loss_neg, name='loss')
with tf.name_scope("gen"):
self.g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=tf.ones_like(logits_fake)), name='loss')
g_accuracy = tf.reduce_mean(tf.cast(score_fake > 0.5, tf.float32), name='accuracy')
add_moving_summary(self.g_loss, self.d_loss, d_accuracy, g_accuracy) | [
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Args:
logits_real (tf.Tensor): discrim logits from real samples
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27,290 | tensorpack/tensorpack | examples/GAN/GAN.py | GANTrainer._build_gan_trainer | def _build_gan_trainer(self, input, model):
"""
We need to set tower_func because it's a TowerTrainer,
and only TowerTrainer supports automatic graph creation for inference during training.
If we don't care about inference during training, using tower_func is
not needed. Just calling model.build_graph directly is OK.
"""
# Build the graph
self.tower_func = TowerFuncWrapper(model.build_graph, model.get_input_signature())
with TowerContext('', is_training=True):
self.tower_func(*input.get_input_tensors())
opt = model.get_optimizer()
# Define the training iteration
# by default, run one d_min after one g_min
with tf.name_scope('optimize'):
g_min = opt.minimize(model.g_loss, var_list=model.g_vars, name='g_op')
with tf.control_dependencies([g_min]):
d_min = opt.minimize(model.d_loss, var_list=model.d_vars, name='d_op')
self.train_op = d_min | python | def _build_gan_trainer(self, input, model):
"""
We need to set tower_func because it's a TowerTrainer,
and only TowerTrainer supports automatic graph creation for inference during training.
If we don't care about inference during training, using tower_func is
not needed. Just calling model.build_graph directly is OK.
"""
# Build the graph
self.tower_func = TowerFuncWrapper(model.build_graph, model.get_input_signature())
with TowerContext('', is_training=True):
self.tower_func(*input.get_input_tensors())
opt = model.get_optimizer()
# Define the training iteration
# by default, run one d_min after one g_min
with tf.name_scope('optimize'):
g_min = opt.minimize(model.g_loss, var_list=model.g_vars, name='g_op')
with tf.control_dependencies([g_min]):
d_min = opt.minimize(model.d_loss, var_list=model.d_vars, name='d_op')
self.train_op = d_min | [
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27,291 | tensorpack/tensorpack | tensorpack/models/regularize.py | regularize_cost_from_collection | def regularize_cost_from_collection(name='regularize_cost'):
"""
Get the cost from the regularizers in ``tf.GraphKeys.REGULARIZATION_LOSSES``.
If in replicated mode, will only regularize variables created within the current tower.
Args:
name (str): the name of the returned tensor
Returns:
tf.Tensor: a scalar, the total regularization cost.
"""
ctx = get_current_tower_context()
if not ctx.is_training:
# TODO Currently cannot build the wd_cost correctly at inference,
# because ths vs_name used in inference can be '', therefore the
# variable filter will fail
return tf.constant(0, dtype=tf.float32, name='empty_' + name)
# NOTE: this collection doesn't always grow with towers.
# It only grows with actual variable creation, but not get_variable call.
if ctx.has_own_variables: # be careful of the first tower (name='')
losses = ctx.get_collection_in_tower(tfv1.GraphKeys.REGULARIZATION_LOSSES)
else:
losses = tfv1.get_collection(tfv1.GraphKeys.REGULARIZATION_LOSSES)
if len(losses) > 0:
logger.info("regularize_cost_from_collection() found {} regularizers "
"in REGULARIZATION_LOSSES collection.".format(len(losses)))
def maploss(l):
assert l.dtype.is_floating, l
if l.dtype != tf.float32:
l = tf.cast(l, tf.float32)
return l
losses = [maploss(l) for l in losses]
reg_loss = tf.add_n(losses, name=name)
return reg_loss
else:
return tf.constant(0, dtype=tf.float32, name='empty_' + name) | python | def regularize_cost_from_collection(name='regularize_cost'):
"""
Get the cost from the regularizers in ``tf.GraphKeys.REGULARIZATION_LOSSES``.
If in replicated mode, will only regularize variables created within the current tower.
Args:
name (str): the name of the returned tensor
Returns:
tf.Tensor: a scalar, the total regularization cost.
"""
ctx = get_current_tower_context()
if not ctx.is_training:
# TODO Currently cannot build the wd_cost correctly at inference,
# because ths vs_name used in inference can be '', therefore the
# variable filter will fail
return tf.constant(0, dtype=tf.float32, name='empty_' + name)
# NOTE: this collection doesn't always grow with towers.
# It only grows with actual variable creation, but not get_variable call.
if ctx.has_own_variables: # be careful of the first tower (name='')
losses = ctx.get_collection_in_tower(tfv1.GraphKeys.REGULARIZATION_LOSSES)
else:
losses = tfv1.get_collection(tfv1.GraphKeys.REGULARIZATION_LOSSES)
if len(losses) > 0:
logger.info("regularize_cost_from_collection() found {} regularizers "
"in REGULARIZATION_LOSSES collection.".format(len(losses)))
def maploss(l):
assert l.dtype.is_floating, l
if l.dtype != tf.float32:
l = tf.cast(l, tf.float32)
return l
losses = [maploss(l) for l in losses]
reg_loss = tf.add_n(losses, name=name)
return reg_loss
else:
return tf.constant(0, dtype=tf.float32, name='empty_' + name) | [
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] | Get the cost from the regularizers in ``tf.GraphKeys.REGULARIZATION_LOSSES``.
If in replicated mode, will only regularize variables created within the current tower.
Args:
name (str): the name of the returned tensor
Returns:
tf.Tensor: a scalar, the total regularization cost. | [
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27,292 | tensorpack/tensorpack | tensorpack/models/regularize.py | Dropout | def Dropout(x, *args, **kwargs):
"""
Same as `tf.layers.dropout`.
However, for historical reasons, the first positional argument is
interpreted as keep_prob rather than drop_prob.
Explicitly use `rate=` keyword arguments to ensure things are consistent.
"""
if 'is_training' in kwargs:
kwargs['training'] = kwargs.pop('is_training')
if len(args) > 0:
if args[0] != 0.5:
logger.warn(
"The first positional argument to tensorpack.Dropout is the probability to keep, rather than to drop. "
"This is different from the rate argument in tf.layers.Dropout due to historical reasons. "
"To mimic tf.layers.Dropout, explicitly use keyword argument 'rate' instead")
rate = 1 - args[0]
elif 'keep_prob' in kwargs:
assert 'rate' not in kwargs, "Cannot set both keep_prob and rate!"
rate = 1 - kwargs.pop('keep_prob')
elif 'rate' in kwargs:
rate = kwargs.pop('rate')
else:
rate = 0.5
if kwargs.get('training', None) is None:
kwargs['training'] = get_current_tower_context().is_training
if get_tf_version_tuple() <= (1, 12):
return tf.layers.dropout(x, rate=rate, **kwargs)
else:
return tf.nn.dropout(x, rate=rate if kwargs['training'] else 0.) | python | def Dropout(x, *args, **kwargs):
"""
Same as `tf.layers.dropout`.
However, for historical reasons, the first positional argument is
interpreted as keep_prob rather than drop_prob.
Explicitly use `rate=` keyword arguments to ensure things are consistent.
"""
if 'is_training' in kwargs:
kwargs['training'] = kwargs.pop('is_training')
if len(args) > 0:
if args[0] != 0.5:
logger.warn(
"The first positional argument to tensorpack.Dropout is the probability to keep, rather than to drop. "
"This is different from the rate argument in tf.layers.Dropout due to historical reasons. "
"To mimic tf.layers.Dropout, explicitly use keyword argument 'rate' instead")
rate = 1 - args[0]
elif 'keep_prob' in kwargs:
assert 'rate' not in kwargs, "Cannot set both keep_prob and rate!"
rate = 1 - kwargs.pop('keep_prob')
elif 'rate' in kwargs:
rate = kwargs.pop('rate')
else:
rate = 0.5
if kwargs.get('training', None) is None:
kwargs['training'] = get_current_tower_context().is_training
if get_tf_version_tuple() <= (1, 12):
return tf.layers.dropout(x, rate=rate, **kwargs)
else:
return tf.nn.dropout(x, rate=rate if kwargs['training'] else 0.) | [
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27,293 | tensorpack/tensorpack | tensorpack/dataflow/imgaug/paste.py | BackgroundFiller.fill | def fill(self, background_shape, img):
"""
Return a proper background image of background_shape, given img.
Args:
background_shape (tuple): a shape (h, w)
img: an image
Returns:
a background image
"""
background_shape = tuple(background_shape)
return self._fill(background_shape, img) | python | def fill(self, background_shape, img):
"""
Return a proper background image of background_shape, given img.
Args:
background_shape (tuple): a shape (h, w)
img: an image
Returns:
a background image
"""
background_shape = tuple(background_shape)
return self._fill(background_shape, img) | [
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27,294 | tensorpack/tensorpack | tensorpack/models/linearwrap.py | LinearWrap.apply | def apply(self, func, *args, **kwargs):
"""
Apply a function on the wrapped tensor.
Returns:
LinearWrap: ``LinearWrap(func(self.tensor(), *args, **kwargs))``.
"""
ret = func(self._t, *args, **kwargs)
return LinearWrap(ret) | python | def apply(self, func, *args, **kwargs):
"""
Apply a function on the wrapped tensor.
Returns:
LinearWrap: ``LinearWrap(func(self.tensor(), *args, **kwargs))``.
"""
ret = func(self._t, *args, **kwargs)
return LinearWrap(ret) | [
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27,295 | tensorpack/tensorpack | tensorpack/models/linearwrap.py | LinearWrap.apply2 | def apply2(self, func, *args, **kwargs):
"""
Apply a function on the wrapped tensor. The tensor
will be the second argument of func.
This is because many symbolic functions
(such as tensorpack's layers) takes 'scope' as the first argument.
Returns:
LinearWrap: ``LinearWrap(func(args[0], self.tensor(), *args[1:], **kwargs))``.
"""
ret = func(args[0], self._t, *(args[1:]), **kwargs)
return LinearWrap(ret) | python | def apply2(self, func, *args, **kwargs):
"""
Apply a function on the wrapped tensor. The tensor
will be the second argument of func.
This is because many symbolic functions
(such as tensorpack's layers) takes 'scope' as the first argument.
Returns:
LinearWrap: ``LinearWrap(func(args[0], self.tensor(), *args[1:], **kwargs))``.
"""
ret = func(args[0], self._t, *(args[1:]), **kwargs)
return LinearWrap(ret) | [
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27,296 | tensorpack/tensorpack | tensorpack/callbacks/param.py | GraphVarParam.setup_graph | def setup_graph(self):
""" Will setup the assign operator for that variable. """
all_vars = tfv1.global_variables() + tfv1.local_variables()
for v in all_vars:
if v.name == self.var_name:
self.var = v
break
else:
raise ValueError("{} is not a variable in the graph!".format(self.var_name)) | python | def setup_graph(self):
""" Will setup the assign operator for that variable. """
all_vars = tfv1.global_variables() + tfv1.local_variables()
for v in all_vars:
if v.name == self.var_name:
self.var = v
break
else:
raise ValueError("{} is not a variable in the graph!".format(self.var_name)) | [
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27,297 | tensorpack/tensorpack | tensorpack/callbacks/param.py | ScheduledHyperParamSetter._get_value_to_set_at_point | def _get_value_to_set_at_point(self, point):
"""
Using schedule, compute the value to be set at a given point.
"""
laste, lastv = None, None
for e, v in self.schedule:
if e == point:
return v # meet the exact boundary, return directly
if e > point:
break
laste, lastv = e, v
if laste is None or laste == e:
# hasn't reached the first scheduled point, or reached the end of all scheduled points
return None
if self.interp is None:
# If no interpolation, nothing to do.
return None
v = (point - laste) * 1. / (e - laste) * (v - lastv) + lastv
return v | python | def _get_value_to_set_at_point(self, point):
"""
Using schedule, compute the value to be set at a given point.
"""
laste, lastv = None, None
for e, v in self.schedule:
if e == point:
return v # meet the exact boundary, return directly
if e > point:
break
laste, lastv = e, v
if laste is None or laste == e:
# hasn't reached the first scheduled point, or reached the end of all scheduled points
return None
if self.interp is None:
# If no interpolation, nothing to do.
return None
v = (point - laste) * 1. / (e - laste) * (v - lastv) + lastv
return v | [
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27,298 | tensorpack/tensorpack | examples/ResNet/load-resnet.py | name_conversion | def name_conversion(caffe_layer_name):
""" Convert a caffe parameter name to a tensorflow parameter name as
defined in the above model """
# beginning & end mapping
NAME_MAP = {'bn_conv1/beta': 'conv0/bn/beta',
'bn_conv1/gamma': 'conv0/bn/gamma',
'bn_conv1/mean/EMA': 'conv0/bn/mean/EMA',
'bn_conv1/variance/EMA': 'conv0/bn/variance/EMA',
'conv1/W': 'conv0/W', 'conv1/b': 'conv0/b',
'fc1000/W': 'linear/W', 'fc1000/b': 'linear/b'}
if caffe_layer_name in NAME_MAP:
return NAME_MAP[caffe_layer_name]
s = re.search('([a-z]+)([0-9]+)([a-z]+)_', caffe_layer_name)
if s is None:
s = re.search('([a-z]+)([0-9]+)([a-z]+)([0-9]+)_', caffe_layer_name)
layer_block_part1 = s.group(3)
layer_block_part2 = s.group(4)
assert layer_block_part1 in ['a', 'b']
layer_block = 0 if layer_block_part1 == 'a' else int(layer_block_part2)
else:
layer_block = ord(s.group(3)) - ord('a')
layer_type = s.group(1)
layer_group = s.group(2)
layer_branch = int(re.search('_branch([0-9])', caffe_layer_name).group(1))
assert layer_branch in [1, 2]
if layer_branch == 2:
layer_id = re.search('_branch[0-9]([a-z])/', caffe_layer_name).group(1)
layer_id = ord(layer_id) - ord('a') + 1
TYPE_DICT = {'res': 'conv{}', 'bn': 'conv{}/bn'}
layer_type = TYPE_DICT[layer_type].format(layer_id if layer_branch == 2 else 'shortcut')
tf_name = caffe_layer_name[caffe_layer_name.index('/'):]
tf_name = 'group{}/block{}/{}'.format(
int(layer_group) - 2, layer_block, layer_type) + tf_name
return tf_name | python | def name_conversion(caffe_layer_name):
""" Convert a caffe parameter name to a tensorflow parameter name as
defined in the above model """
# beginning & end mapping
NAME_MAP = {'bn_conv1/beta': 'conv0/bn/beta',
'bn_conv1/gamma': 'conv0/bn/gamma',
'bn_conv1/mean/EMA': 'conv0/bn/mean/EMA',
'bn_conv1/variance/EMA': 'conv0/bn/variance/EMA',
'conv1/W': 'conv0/W', 'conv1/b': 'conv0/b',
'fc1000/W': 'linear/W', 'fc1000/b': 'linear/b'}
if caffe_layer_name in NAME_MAP:
return NAME_MAP[caffe_layer_name]
s = re.search('([a-z]+)([0-9]+)([a-z]+)_', caffe_layer_name)
if s is None:
s = re.search('([a-z]+)([0-9]+)([a-z]+)([0-9]+)_', caffe_layer_name)
layer_block_part1 = s.group(3)
layer_block_part2 = s.group(4)
assert layer_block_part1 in ['a', 'b']
layer_block = 0 if layer_block_part1 == 'a' else int(layer_block_part2)
else:
layer_block = ord(s.group(3)) - ord('a')
layer_type = s.group(1)
layer_group = s.group(2)
layer_branch = int(re.search('_branch([0-9])', caffe_layer_name).group(1))
assert layer_branch in [1, 2]
if layer_branch == 2:
layer_id = re.search('_branch[0-9]([a-z])/', caffe_layer_name).group(1)
layer_id = ord(layer_id) - ord('a') + 1
TYPE_DICT = {'res': 'conv{}', 'bn': 'conv{}/bn'}
layer_type = TYPE_DICT[layer_type].format(layer_id if layer_branch == 2 else 'shortcut')
tf_name = caffe_layer_name[caffe_layer_name.index('/'):]
tf_name = 'group{}/block{}/{}'.format(
int(layer_group) - 2, layer_block, layer_type) + tf_name
return tf_name | [
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defined in the above model | [
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27,299 | tensorpack/tensorpack | tensorpack/tfutils/varreplace.py | remap_variables | def remap_variables(fn):
"""
Use fn to map the output of any variable getter.
Args:
fn (tf.Variable -> tf.Tensor)
Returns:
The current variable scope with a custom_getter that maps
all the variables by fn.
Example:
.. code-block:: python
with varreplace.remap_variables(lambda var: quantize(var)):
x = FullyConnected('fc', x, 1000) # fc/{W,b} will be quantized
"""
def custom_getter(getter, *args, **kwargs):
v = getter(*args, **kwargs)
return fn(v)
return custom_getter_scope(custom_getter) | python | def remap_variables(fn):
"""
Use fn to map the output of any variable getter.
Args:
fn (tf.Variable -> tf.Tensor)
Returns:
The current variable scope with a custom_getter that maps
all the variables by fn.
Example:
.. code-block:: python
with varreplace.remap_variables(lambda var: quantize(var)):
x = FullyConnected('fc', x, 1000) # fc/{W,b} will be quantized
"""
def custom_getter(getter, *args, **kwargs):
v = getter(*args, **kwargs)
return fn(v)
return custom_getter_scope(custom_getter) | [
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.. code-block:: python
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