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pyup-botJonathan DEKHTIAR
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Jonathan DEKHTIAR
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PyUP - Dependency Update Scheduled daily dependency update on thursday (#823)
* Pin imageio to latest version 2.4.0 * Pin pytest to latest version 3.8.0 * Update requirements_test.txt * Update requirements.txt * Update CHANGELOG.md * YAPF Applied * YAPF Corrections Applied
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‎CHANGELOG.md‎

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@@ -70,13 +70,15 @@ To release a new version, please update the changelog as followed:
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## [Unreleased]
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### Added
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- unittest `tests\test_timeout.py` has been added to ensure the network creation process does not freeze.
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### Changed
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- remove 'tensorboard' param, replaced by 'tensorboard_dir' in `tensorlayer/utils.py` with customizable tensorboard directory (PR #819)
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### Deprecated
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### Removed
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- TL Graph API removed. Memory Leaks Issues with this API, will be fixed and integrated in TL 2.0 (PR #818)
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### Fixed
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@@ -85,11 +87,13 @@ To release a new version, please update the changelog as followed:
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### Dependencies Update
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- autopep8>=1.3,<1.4 => autopep8>=1.3,<1.5 (PR #815)
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- pytest-cov>=2.5,<2.6 => pytest-cov>=2.5,<2.7 (PR #820)
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- pytest>=3.6,<3.8 => pytest>=3.6,<3.9 (PR #823)
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- imageio>=2.3,<2.4 => imageio>=2.3,<2.5 (PR #823)
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### Contributors
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- @DEKHTIARJonathan: #815 #818 #820 #823
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- @ndiy: #819
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- @DEKHTIARJonathan: #815 #820
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- @zsdonghao: #818
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## [1.10.0] - 2018-09-02
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‎requirements/requirements.txt‎

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@@ -1,4 +1,4 @@
1-
imageio>=2.3,<2.4
1+
imageio>=2.3,<2.5
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lxml>=4.2,<4.3
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matplotlib>=2.2,<2.3
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numpy>=1.14,<1.16
@@ -8,4 +8,4 @@ scikit-learn>=0.19,<0.20
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scikit-image>=0.14,<0.15
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scipy>=1.1,<1.2
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tqdm>=4.23,<4.26
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wrapt>=1.10,<1.11
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wrapt>=1.10,<1.11

‎requirements/requirements_test.txt‎

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@@ -1,7 +1,7 @@
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keras>=2.2,<2.3
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pycodestyle>=2.0,<2.5
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pydocstyle>=2.1,<2.2
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pytest>=3.6,<3.8
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pytest>=3.6,<3.9
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pytest-cache>=1.0,<1.1
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pytest-cov>=2.5,<2.7
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pytest-xdist>=1.22,<1.24

‎tensorlayer/cost.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -259,7 +259,7 @@ def dice_coe(output, target, loss_type='jaccard', axis=(1, 2, 3), smooth=1e-5):
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# epsilon = 1e-5
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# dice = tf.clip_by_value(dice, 0, 1.0-epsilon) # if all empty, dice = 1
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# new haodong
262-
dice = (2. * inse + smooth) / (l +r+ smooth)
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dice = (2. * inse + smooth) / (l + r + smooth)
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##
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dice = tf.reduce_mean(dice, name='dice_coe')
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return dice
@@ -298,7 +298,7 @@ def dice_hard_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5):
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# epsilon = 1e-5
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# hard_dice = tf.clip_by_value(hard_dice, 0, 1.0-epsilon)
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# new haodong
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hard_dice = (2. * inse + smooth) / (l +r+ smooth)
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hard_dice = (2. * inse + smooth) / (l + r + smooth)
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##
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hard_dice = tf.reduce_mean(hard_dice, name='hard_dice')
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return hard_dice

‎tensorlayer/db.py‎

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Original file line numberDiff line numberDiff line change
@@ -219,9 +219,8 @@ def find_top_model(self, sess, sort=None, model_name='model', **kwargs):
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pc = self.db.Model.find(kwargs)
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print(
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"[Database] Find one model SUCCESS. kwargs:{} sort:{} save time:{} took: {}s".format(
223-
kwargs, sort, _datetime, round(time.time() - s, 2)
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)
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"[Database] Find one model SUCCESS. kwargs:{} sort:{} save time:{} took: {}s".
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format(kwargs, sort, _datetime, round(time.time() - s, 2))
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)
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# put all informations of model into the TL layer
@@ -656,10 +655,9 @@ def run_top_task(self, task_name=None, sort=None, **kwargs):
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}}, return_document=pymongo.ReturnDocument.AFTER
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)
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logging.info(
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"[Database] Finished Task: task_name - {} sort: {} push time: {} took: {}s".format(
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task_name, sort, _datetime,
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time.time() - s
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)
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"[Database] Finished Task: task_name - {} sort: {} push time: {} took: {}s".
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format(task_name, sort, _datetime,
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time.time() - s)
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)
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return True
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except Exception as e:

‎tensorlayer/decorators/deprecated.py‎

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@@ -44,8 +44,8 @@ def wrapper(wrapped, instance=None, args=None, kwargs=None):
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'%s: `%s.%s` (in file: %s) is deprecated and will be removed %s.\n'
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'Instructions for updating: %s\n' % (
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"Class" if inspect.isclass(wrapped) else "Function", wrapped.__module__, class_or_func_name,
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wrapped.__code__.co_filename, 'in a future version'
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if date is None else ('after %s' % date), instructions
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wrapped.__code__.co_filename, 'in a future version' if date is None else
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('after %s' % date), instructions
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)
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)
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‎tensorlayer/distributed.py‎

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@@ -398,9 +398,9 @@ def create_task_spec_def():
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task_data = env.get('task', None) or {'type': 'master', 'index': 0}
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cluster_data = env.get('cluster', None) or {'ps': None, 'worker': None, 'master': None}
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return TaskSpecDef(
401-
task_type=task_data['type'], index=task_data['index'], trial=task_data['trial'] if 'trial' in task_data else
402-
None, ps_hosts=cluster_data['ps'], worker_hosts=cluster_data['worker'], master=cluster_data['master']
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if 'master' in cluster_data else None
401+
task_type=task_data['type'], index=task_data['index'],
402+
trial=task_data['trial'] if 'trial' in task_data else None, ps_hosts=cluster_data['ps'],
403+
worker_hosts=cluster_data['worker'], master=cluster_data['master'] if 'master' in cluster_data else None
404404
)
405405
elif 'JOB_NAME' in os.environ:
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# JOB_NAME, TASK_INDEX, PS_HOSTS, WORKER_HOSTS and MASTER_HOST are used in TensorPort

‎tensorlayer/files/dataset_loaders/mpii_dataset.py‎

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@@ -157,7 +157,7 @@ def save_joints():
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vis = None
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159159
# if len(joint_pos) == 16:
160-
if ((is_16_pos_only == True) and (len(joint_pos) == 16)) or (is_16_pos_only == False):
160+
if ((is_16_pos_only ==True) and (len(joint_pos) == 16)) or (is_16_pos_only == False):
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# only use image with 16 key points / or use all
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data = {
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'filename': img_fn,

‎tensorlayer/files/utils.py‎

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@@ -1462,7 +1462,7 @@ def save_joints():
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vis = None
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# if len(joint_pos) == 16:
1465-
if ((is_16_pos_only == True) and (len(joint_pos) == 16)) or (is_16_pos_only == False):
1465+
if ((is_16_pos_only ==True) and (len(joint_pos) == 16)) or (is_16_pos_only == False):
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# only use image with 16 key points / or use all
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data = {
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'filename': img_fn,

‎tensorlayer/iterate.py‎

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@@ -65,7 +65,7 @@ def minibatches(inputs=None, targets=None, batch_size=None, allow_dynamic_batch_
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excerpt = indices[start_idx:end_idx]
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else:
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excerpt = slice(start_idx, end_idx)
68-
if (isinstance(inputs, list) or isinstance(targets, list)) and (shuffle == True):
68+
if (isinstance(inputs, list) or isinstance(targets, list)) and (shuffle ==True):
6969
# zsdonghao: for list indexing when shuffle==True
7070
yield [inputs[i] for i in excerpt], [targets[i] for i in excerpt]
7171
else:

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