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train | COCODetection.load | Args:
add_gt: whether to add ground truth bounding box annotations to the dicts
add_mask: whether to also add ground truth mask
Returns:
a list of dict, each has keys including:
'image_id', 'file_name',
and (if add_gt is True) 'boxes', 'class'... | examples/FasterRCNN/dataset.py | def load(self, add_gt=True, add_mask=False):
"""
Args:
add_gt: whether to add ground truth bounding box annotations to the dicts
add_mask: whether to also add ground truth mask
Returns:
a list of dict, each has keys including:
'image_id', 'fil... | def load(self, add_gt=True, add_mask=False):
"""
Args:
add_gt: whether to add ground truth bounding box annotations to the dicts
add_mask: whether to also add ground truth mask
Returns:
a list of dict, each has keys including:
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train | COCODetection._use_absolute_file_name | Change relative filename to abosolute file name. | examples/FasterRCNN/dataset.py | def _use_absolute_file_name(self, img):
"""
Change relative filename to abosolute file name.
"""
img['file_name'] = os.path.join(
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assert os.path.isfile(img['file_name']), img['file_name'] | def _use_absolute_file_name(self, img):
"""
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img['file_name'] = os.path.join(
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train | COCODetection._add_detection_gt | 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. | examples/FasterRCNN/dataset.py | 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.... | 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.
"""
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train | COCODetection.load_many | Load and merges several instance files together.
Returns the same format as :meth:`COCODetection.load`. | examples/FasterRCNN/dataset.py | 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)):
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for n in name... | 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)):
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train | DetectionDataset.load_training_roidbs | Args:
names (list[str]): name of the training datasets, e.g. ['train2014', 'valminusminival2014']
Returns:
roidbs (list[dict]):
Produce "roidbs" as a list of dict, each dict corresponds to one image with k>=0 instances.
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Args:
names (list[str]): name of the training datasets, e.g. ['train2014', 'valminusminival2014']
Returns:
roidbs (list[dict]):
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names (list[str]): name of the training datasets, e.g. ['train2014', 'valminusminival2014']
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train | DetectionDataset.load_inference_roidbs | Args:
name (str): name of one inference dataset, e.g. 'minival2014'
Returns:
roidbs (list[dict]):
Each dict corresponds to one image to run inference on. The
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file_name (str): full path to the image
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"""
Args:
name (str): name of one inference dataset, e.g. 'minival2014'
Returns:
roidbs (list[dict]):
Each dict corresponds to one image to run inference on. The
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"""
Args:
name (str): name of one inference dataset, e.g. 'minival2014'
Returns:
roidbs (list[dict]):
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train | DetectionDataset.eval_or_save_inference_results | Args:
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Each dict corresponds to one __instance__. It contains the following keys:
image_id (str): the id that matches `load_inference_roidbs`.
category_id (int): the category prediction, in range [1, #categ... | examples/FasterRCNN/dataset.py | def eval_or_save_inference_results(self, results, dataset, output=None):
"""
Args:
results (list[dict]): the inference results as dicts.
Each dict corresponds to one __instance__. It contains the following keys:
image_id (str): the id that matches `load_infer... | def eval_or_save_inference_results(self, results, dataset, output=None):
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train | timed_operation | 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:: pytho... | tensorpack/utils/timer.py | 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... | def timed_operation(msg, log_start=False):
"""
Surround a context with a timer.
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train | total_timer | A context which add the time spent inside to TotalTimer. | tensorpack/utils/timer.py | def total_timer(msg):
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start = timer()
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train | print_total_timer | Print the content of the TotalTimer, if it's not empty. This function will automatically get
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"""
Print the content of the TotalTimer, if it's not empty. This function will automatically get
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"""
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Print the content of the TotalTimer, if it's not empty. This function will automatically get
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train | AugmentorList.reset_state | Will reset state of each augmentor | tensorpack/dataflow/imgaug/base.py | def reset_state(self):
""" Will reset state of each augmentor """
super(AugmentorList, self).reset_state()
for a in self.augmentors:
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""" Will reset state of each augmentor """
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train | ensure_proc_terminate | Make sure processes terminate when main process exit.
Args:
proc (multiprocessing.Process or list) | tensorpack/utils/concurrency.py | 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... | def ensure_proc_terminate(proc):
"""
Make sure processes terminate when main process exit.
Args:
proc (multiprocessing.Process or list)
"""
if isinstance(proc, list):
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ensure_proc_terminate(p)
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train | enable_death_signal | Set the "death signal" of the current process, so that
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in case the parent dies accidentally. | tensorpack/utils/concurrency.py | def enable_death_signal(_warn=True):
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"""
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Set the "death signal" of the current process, so that
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if platform.system() != 'Linux':
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train | mask_sigint | Returns:
If called in main thread, returns a context where ``SIGINT`` is ignored, and yield True.
Otherwise yield False. | tensorpack/utils/concurrency.py | def mask_sigint():
"""
Returns:
If called in main thread, returns a context where ``SIGINT`` is ignored, and yield True.
Otherwise yield False.
"""
if is_main_thread():
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yield True
signal.signal(signal.S... | def mask_sigint():
"""
Returns:
If called in main thread, returns a context where ``SIGINT`` is ignored, and yield True.
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"""
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train | start_proc_mask_signal | Start process(es) with SIGINT ignored.
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Note:
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"""
Start process(es) with SIGINT ignored.
Args:
proc: (mp.Process or list)
Note:
The signal mask is only applied when called from main thread.
"""
if not isinstance(proc, list):
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Start process(es) with SIGINT ignored.
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train | StoppableThread.queue_get_stoppable | Take obj from queue, but will give up when the thread is stopped | tensorpack/utils/concurrency.py | def queue_get_stoppable(self, q):
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rank(int): rank of th element. All elements must have different ranks.
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train | visualize_conv_weights | Visualize use weights in convolution filters.
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filters: tensor containing the weights [H,W,Cin,Cout]
name: label for tensorboard
Returns:
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filters: tensor containing the weights [H,W,Cin,Cout]
name: label for tensorboard
Returns:
image of all weight
"""
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Args:
filters: tensor containing the weights [H,W,Cin,Cout]
name: label for tensorboard
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image of all weight
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train | visualize_conv_activations | Visualize activations for convolution layers.
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name: label for tensorboard
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activation: tensor with the activation [B,H,W,C]
name: label for tensorboard
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train | entropy_from_samples | Estimate H(x|s) ~= -E_{x \sim P(x|s)}[\log Q(x|s)], where x are samples, and Q is parameterized by vec. | examples/GAN/InfoGAN-mnist.py | def entropy_from_samples(samples, vec):
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train | sample_prior | OpenAI official code actually models the "uniform" latent code as
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train | Model.build_graph | Mutual information between x (i.e. zc in this case) and some
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= H(x) + E[\log P(x|s)]
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train | DynamicConvFilter | see "Dynamic Filter Networks" (NIPS 2016)
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Remarks:
This is the convolution version of a dynamic filter.
Args:
inputs : unfiltered input [b, h, w, 1] only grayscale images.
filters : learned filters of [b, k, k, ... | examples/DynamicFilterNetwork/steering-filter.py | def DynamicConvFilter(inputs, filters, out_channel,
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stride=1,
padding='SAME'):
""" see "Dynamic Filter Networks" (NIPS 2016)
by Bert De Brabandere*, Xu Jia*, Tinne Tuytelaars and Luc Van Gool
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kernel_shape,
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Args:
theta: angle of filter
kernel_shape: size of each filter
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learned filter as [B, k, k, 1] | examples/DynamicFilterNetwork/steering-filter.py | def _parameter_net(self, theta, kernel_shape=9):
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Args:
theta: angle of filter
kernel_shape: size of each filter
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kernel_shape: size of each filter
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train | ThetaImages.filter_with_theta | 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
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"""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
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"""Implements a steerable Gaussian filter.
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train | GANModelDesc.collect_variables | Assign `self.g_vars` to the parameters under scope `g_scope`,
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train | GANTrainer._build_gan_trainer | We need to set tower_func because it's a TowerTrainer,
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"""
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If we don't care about inference during training, using tower_func is
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train | regularize_cost | Apply a regularizer on trainable variables matching the regex, and print
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In replicated mode, it will only regularize variables within the current tower.
If called under a TowerContext with `is_training==False`, this function returns a zero co... | tensorpack/models/regularize.py | def regularize_cost(regex, func, name='regularize_cost'):
"""
Apply a regularizer on trainable variables matching the regex, and print
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In replicated mode, it will only regularize variables within the current tower.
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Apply a regularizer on trainable variables matching the regex, and print
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name (str): the name of the returned tensor
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tf.Tensor: a scalar, the total regularization cost. | tensorpack/models/regularize.py | def regularize_cost_from_collection(name='regularize_cost'):
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train | Dropout | Same as `tf.layers.dropout`.
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Explicitly use `rate=` keyword arguments to ensure things are consistent. | tensorpack/models/regularize.py | def Dropout(x, *args, **kwargs):
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train | BackgroundFiller.fill | Return a proper background image of background_shape, given img.
Args:
background_shape (tuple): a shape (h, w)
img: an image
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a background image | tensorpack/dataflow/imgaug/paste.py | 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
"""
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Return a proper background image of background_shape, given img.
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train | LinearWrap.apply | Apply a function on the wrapped tensor.
Returns:
LinearWrap: ``LinearWrap(func(self.tensor(), *args, **kwargs))``. | tensorpack/models/linearwrap.py | 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) | 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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Returns:
LinearWrap: ``LinearWrap(func(args[0], self.tensor(), *args[1:], **kwa... | tensorpack/models/linearwrap.py | def apply2(self, func, *args, **kwargs):
"""
Apply a function on the wrapped tensor. The tensor
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train | guided_relu | Returns:
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guided back-propagation, as described in the paper:
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<https://arxiv.org/abs/1412.6806>`_ | examples/Saliency/saliency-maps.py | def guided_relu():
"""
Returns:
A context where the gradient of :meth:`tf.nn.relu` is replaced by
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`Striving for Simplicity: The All Convolutional Net
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"""
from tensorflow.python.ops imp... | def guided_relu():
"""
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train | saliency_map | Produce a saliency map as described in the paper:
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<https://arxiv.org/abs/1312.6034>`_.
The saliency map is the gradient of the max element in output w.r.t input.
Returns:
tf.Tensor: the saliency map. ... | examples/Saliency/saliency-maps.py | def saliency_map(output, input, name="saliency_map"):
"""
Produce a saliency map as described in the paper:
`Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
<https://arxiv.org/abs/1312.6034>`_.
The saliency map is the gradient of the max element in outpu... | def saliency_map(output, input, name="saliency_map"):
"""
Produce a saliency map as described in the paper:
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activation=None,
use_bias=True,
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train | GraphVarParam.setup_graph | Will setup the assign operator for that variable. | tensorpack/callbacks/param.py | def setup_graph(self):
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train | ScheduledHyperParamSetter._get_value_to_set_at_point | Using schedule, compute the value to be set at a given point. | tensorpack/callbacks/param.py | def _get_value_to_set_at_point(self, point):
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train | Model.build_graph | This function should build the model which takes the input variables
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"""This function should build the model which takes the input variables
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train | name_conversion | Convert a caffe parameter name to a tensorflow parameter name as
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""" Convert a caffe parameter name to a tensorflow parameter name as
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# beginning & end mapping
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model_desc (str): path to caffe model description file (.prototxt).
model_file (str): path to caffe model parameter file (.caffemodel).
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Args:
model_desc (str): path to caffe model description file (.prototxt).
model_file (str): path to caffe model parameter file (.caffemodel).
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model_desc (str): path to caffe model description file (.prototxt).
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train | get_caffe_pb | Get caffe protobuf.
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"""
Get caffe protobuf.
Returns:
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Get caffe protobuf.
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train | finalize_configs | Run some sanity checks, and populate some configs from others | examples/FasterRCNN/config.py | def finalize_configs(is_training):
"""
Run some sanity checks, and populate some configs from others
"""
_C.freeze(False) # populate new keys now
_C.DATA.NUM_CLASS = _C.DATA.NUM_CATEGORY + 1 # +1 background
_C.DATA.BASEDIR = os.path.expanduser(_C.DATA.BASEDIR)
if isinstance(_C.DATA.VAL, si... | def finalize_configs(is_training):
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Run some sanity checks, and populate some configs from others
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train | AttrDict.to_dict | Convert to a nested dict. | examples/FasterRCNN/config.py | def to_dict(self):
"""Convert to a nested dict. """
return {k: v.to_dict() if isinstance(v, AttrDict) else v
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train | AttrDict.update_args | Update from command line args. | examples/FasterRCNN/config.py | def update_args(self, args):
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train | get_model_loader | Get a corresponding model loader by looking at the file name.
Returns:
SessInit: either a :class:`DictRestore` (if name ends with 'npy/npz') or
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train | SaverRestore._read_checkpoint_vars | return a set of strings | tensorpack/tfutils/sessinit.py | def _read_checkpoint_vars(model_path):
""" return a set of strings """
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train | argscope | Args:
layers (list or layer): layer or list of layers to apply the arguments.
Returns:
a context where all appearance of these layer will by default have the
arguments specified by kwargs.
Example:
.. code-block:: python
with argscope(Conv2D, kernel_shape=3, nl=tf.... | tensorpack/tfutils/argscope.py | def argscope(layers, **kwargs):
"""
Args:
layers (list or layer): layer or list of layers to apply the arguments.
Returns:
a context where all appearance of these layer will by default have the
arguments specified by kwargs.
Example:
.. code-block:: python
... | def argscope(layers, **kwargs):
"""
Args:
layers (list or layer): layer or list of layers to apply the arguments.
Returns:
a context where all appearance of these layer will by default have the
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Example:
.. code-block:: python
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train | enable_argscope_for_function | Decorator for function to support argscope
Example:
.. code-block:: python
from mylib import myfunc
myfunc = enable_argscope_for_function(myfunc)
Args:
func: A function mapping one or multiple tensors to one or multiple
tensors.
log_shape (bool): S... | tensorpack/tfutils/argscope.py | def enable_argscope_for_function(func, log_shape=True):
"""Decorator for function to support argscope
Example:
.. code-block:: python
from mylib import myfunc
myfunc = enable_argscope_for_function(myfunc)
Args:
func: A function mapping one or multiple tensors to o... | def enable_argscope_for_function(func, log_shape=True):
"""Decorator for function to support argscope
Example:
.. code-block:: python
from mylib import myfunc
myfunc = enable_argscope_for_function(myfunc)
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train | enable_argscope_for_module | Overwrite all functions of a given module to support argscope.
Note that this function monkey-patches the module and therefore could
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It has been only tested to work well with ``tf.layers`` module.
Example:
.. code-block:: python
import tensorflow as t... | tensorpack/tfutils/argscope.py | def enable_argscope_for_module(module, log_shape=True):
"""
Overwrite all functions of a given module to support argscope.
Note that this function monkey-patches the module and therefore could
have unexpected consequences.
It has been only tested to work well with ``tf.layers`` module.
Example:... | def enable_argscope_for_module(module, log_shape=True):
"""
Overwrite all functions of a given module to support argscope.
Note that this function monkey-patches the module and therefore could
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It has been only tested to work well with ``tf.layers`` module.
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train | visualize_tensors | Generate tensor for TensorBoard (casting, clipping)
Args:
name: name for visualization operation
*imgs: multiple tensors as list
scale_func: scale input tensors to fit range [0, 255]
Example:
visualize_tensors('viz1', [img1])
visualize_tensors('viz2', [img1, img2, img3]... | examples/GAN/Image2Image.py | def visualize_tensors(name, imgs, scale_func=lambda x: (x + 1.) * 128., max_outputs=1):
"""Generate tensor for TensorBoard (casting, clipping)
Args:
name: name for visualization operation
*imgs: multiple tensors as list
scale_func: scale input tensors to fit range [0, 255]
Example:... | def visualize_tensors(name, imgs, scale_func=lambda x: (x + 1.) * 128., max_outputs=1):
"""Generate tensor for TensorBoard (casting, clipping)
Args:
name: name for visualization operation
*imgs: multiple tensors as list
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train | split_input | img: an RGB image of shape (s, 2s, 3).
:return: [input, output] | examples/GAN/Image2Image.py | def split_input(img):
"""
img: an RGB image of shape (s, 2s, 3).
:return: [input, output]
"""
# split the image into left + right pairs
s = img.shape[0]
assert img.shape[1] == 2 * s
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train | Model.discriminator | return a (b, 1) logits | examples/GAN/Image2Image.py | def discriminator(self, inputs, outputs):
""" return a (b, 1) logits"""
l = tf.concat([inputs, outputs], 3)
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""" return a (b, 1) logits"""
l = tf.concat([inputs, outputs], 3)
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train | print_stat | A simple print Op that might be easier to use than :meth:`tf.Print`.
Use it like: ``x = print_stat(x, message='This is x')``. | tensorpack/tfutils/symbolic_functions.py | def print_stat(x, message=None):
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Use it like: ``x = print_stat(x, message='This is x')``.
"""
if message is None:
message = x.op.name
lst = [tf.shape(x), tf.reduce_mean(x)]
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"""
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train | rms | Returns:
root mean square of tensor x. | tensorpack/tfutils/symbolic_functions.py | def rms(x, name=None):
"""
Returns:
root mean square of tensor x.
"""
if name is None:
name = x.op.name + '/rms'
with tfv1.name_scope(None): # name already contains the scope
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return tf.sqrt(tf.reduce_mean(t... | def rms(x, name=None):
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Returns:
root mean square of tensor x.
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if name is None:
name = x.op.name + '/rms'
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train | psnr | `Peek Signal to Noise Ratio <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`_.
.. math::
PSNR = 20 \cdot \log_{10}(MAX_p) - 10 \cdot \log_{10}(MSE)
Args:
prediction: a :class:`tf.Tensor` representing the prediction signal.
ground_truth: another :class:`tf.Tensor` with the s... | tensorpack/tfutils/symbolic_functions.py | def psnr(prediction, ground_truth, maxp=None, name='psnr'):
"""`Peek Signal to Noise Ratio <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`_.
.. math::
PSNR = 20 \cdot \log_{10}(MAX_p) - 10 \cdot \log_{10}(MSE)
Args:
prediction: a :class:`tf.Tensor` representing the prediction ... | def psnr(prediction, ground_truth, maxp=None, name='psnr'):
"""`Peek Signal to Noise Ratio <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`_.
.. math::
PSNR = 20 \cdot \log_{10}(MAX_p) - 10 \cdot \log_{10}(MSE)
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train | GaussianMap.get_gaussian_weight | Args:
anchor: coordinate of the center | tensorpack/dataflow/imgaug/deform.py | def get_gaussian_weight(self, anchor):
"""
Args:
anchor: coordinate of the center
"""
ret = np.zeros(self.shape, dtype='float32')
y, x = np.mgrid[:self.shape[0], :self.shape[1]]
y = y.astype('float32') / ret.shape[0] - anchor[0]
x = x.astype('float32'... | def get_gaussian_weight(self, anchor):
"""
Args:
anchor: coordinate of the center
"""
ret = np.zeros(self.shape, dtype='float32')
y, x = np.mgrid[:self.shape[0], :self.shape[1]]
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train | pad | Pad tensor in H, W
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Args:
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Args:
x (tf.tensor): incoming tensor
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TensorFlow uses "ceil(input_spatial_shape[i] / strides[i])" rather than explicit padding
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train | correlation | Correlation Cost Volume computation.
This is a fallback Python-only implementation, specialized just for FlowNet2.
It takes a lot of memory and is slow.
If you know to compile a custom op yourself, it's better to use the cuda implementation here:
https://github.com/PatWie/tensorflow-recipes/tree/maste... | examples/OpticalFlow/flownet_models.py | def correlation(ina, inb,
kernel_size, max_displacement,
stride_1, stride_2,
pad, data_format):
"""
Correlation Cost Volume computation.
This is a fallback Python-only implementation, specialized just for FlowNet2.
It takes a lot of memory and is slow.
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Correlation Cost Volume computation.
This is a fallback Python-only implementation, specialized just for FlowNet2.
It takes a lot of memory and is slow.
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train | resize | Resize input tensor with unkown input-shape by a factor
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Note:
Differences here against Caffe have huge impacts on the
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Returns:
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Args:
x (tf.Tensor): tensor NCHW
factor (int, optional): resize factor for H, W
Note:
Differences here against Caffe have huge impacts on the
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Returns:
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x (tf.Tensor): tensor NCHW
factor (int, optional): resize factor for H, W
Note:
Differences here against Caffe have huge impacts on the
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train | FlowNet2.flownet2_fusion | Architecture in Table 4 of FlowNet 2.0.
Args:
x: NCHW tensor, where C=11 is the concatenation of 7 items of [3, 2, 2, 1, 1, 1, 1] channels. | examples/OpticalFlow/flownet_models.py | def flownet2_fusion(self, x):
"""
Architecture in Table 4 of FlowNet 2.0.
Args:
x: NCHW tensor, where C=11 is the concatenation of 7 items of [3, 2, 2, 1, 1, 1, 1] channels.
"""
with argscope([tf.layers.conv2d], activation=lambda x: tf.nn.leaky_relu(x, 0.1),
... | def flownet2_fusion(self, x):
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Architecture in Table 4 of FlowNet 2.0.
Args:
x: NCHW tensor, where C=11 is the concatenation of 7 items of [3, 2, 2, 1, 1, 1, 1] channels.
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train | FlowNet2.flownet2_sd | Architecture in Table 3 of FlowNet 2.0.
Args:
x: concatenation of two inputs, of shape [1, 2xC, H, W] | examples/OpticalFlow/flownet_models.py | def flownet2_sd(self, x):
"""
Architecture in Table 3 of FlowNet 2.0.
Args:
x: concatenation of two inputs, of shape [1, 2xC, H, W]
"""
with argscope([tf.layers.conv2d], activation=lambda x: tf.nn.leaky_relu(x, 0.1),
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"""
Architecture in Table 3 of FlowNet 2.0.
Args:
x: concatenation of two inputs, of shape [1, 2xC, H, W]
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train | FlowNet2S.graph_structure | Architecture of FlowNetSimple in Figure 2 of FlowNet 1.0.
Args:
x: 2CHW if standalone==True, else NCHW where C=12 is a concatenation
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standalone: If True, this model is used to predict flow from two inputs.
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"""
Architecture of FlowNetSimple in Figure 2 of FlowNet 1.0.
Args:
x: 2CHW if standalone==True, else NCHW where C=12 is a concatenation
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standalone: If True, this mod... | def graph_structure(self, x, standalone=True):
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Architecture of FlowNetSimple in Figure 2 of FlowNet 1.0.
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train | FlowNet2C.graph_structure | Architecture of FlowNetCorr in Figure 2 of FlowNet 1.0.
Args:
x: 2CHW. | examples/OpticalFlow/flownet_models.py | def graph_structure(self, x1x2):
"""
Architecture of FlowNetCorr in Figure 2 of FlowNet 1.0.
Args:
x: 2CHW.
"""
with argscope([tf.layers.conv2d], activation=lambda x: tf.nn.leaky_relu(x, 0.1),
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Architecture of FlowNetCorr in Figure 2 of FlowNet 1.0.
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train | draw_annotation | Will not modify img | examples/FasterRCNN/viz.py | def draw_annotation(img, boxes, klass, is_crowd=None):
"""Will not modify img"""
labels = []
assert len(boxes) == len(klass)
if is_crowd is not None:
assert len(boxes) == len(is_crowd)
for cls, crd in zip(klass, is_crowd):
clsname = cfg.DATA.CLASS_NAMES[cls]
if cr... | def draw_annotation(img, boxes, klass, is_crowd=None):
"""Will not modify img"""
labels = []
assert len(boxes) == len(klass)
if is_crowd is not None:
assert len(boxes) == len(is_crowd)
for cls, crd in zip(klass, is_crowd):
clsname = cfg.DATA.CLASS_NAMES[cls]
if cr... | [
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train | draw_proposal_recall | Draw top3 proposals for each gt.
Args:
proposals: NPx4
proposal_scores: NP
gt_boxes: NG | examples/FasterRCNN/viz.py | def draw_proposal_recall(img, proposals, proposal_scores, gt_boxes):
"""
Draw top3 proposals for each gt.
Args:
proposals: NPx4
proposal_scores: NP
gt_boxes: NG
"""
box_ious = np_iou(gt_boxes, proposals) # ng x np
box_ious_argsort = np.argsort(-box_ious, axis=1)
go... | def draw_proposal_recall(img, proposals, proposal_scores, gt_boxes):
"""
Draw top3 proposals for each gt.
Args:
proposals: NPx4
proposal_scores: NP
gt_boxes: NG
"""
box_ious = np_iou(gt_boxes, proposals) # ng x np
box_ious_argsort = np.argsort(-box_ious, axis=1)
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train | draw_predictions | Args:
boxes: kx4
scores: kxC | examples/FasterRCNN/viz.py | def draw_predictions(img, boxes, scores):
"""
Args:
boxes: kx4
scores: kxC
"""
if len(boxes) == 0:
return img
labels = scores.argmax(axis=1)
scores = scores.max(axis=1)
tags = ["{},{:.2f}".format(cfg.DATA.CLASS_NAMES[lb], score) for lb, score in zip(labels, scores)]
... | def draw_predictions(img, boxes, scores):
"""
Args:
boxes: kx4
scores: kxC
"""
if len(boxes) == 0:
return img
labels = scores.argmax(axis=1)
scores = scores.max(axis=1)
tags = ["{},{:.2f}".format(cfg.DATA.CLASS_NAMES[lb], score) for lb, score in zip(labels, scores)]
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train | draw_final_outputs | Args:
results: [DetectionResult] | examples/FasterRCNN/viz.py | def draw_final_outputs(img, results):
"""
Args:
results: [DetectionResult]
"""
if len(results) == 0:
return img
# Display in largest to smallest order to reduce occlusion
boxes = np.asarray([r.box for r in results])
areas = np_area(boxes)
sorted_inds = np.argsort(-areas)... | def draw_final_outputs(img, results):
"""
Args:
results: [DetectionResult]
"""
if len(results) == 0:
return img
# Display in largest to smallest order to reduce occlusion
boxes = np.asarray([r.box for r in results])
areas = np_area(boxes)
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train | draw_mask | Overlay a mask on top of the image.
Args:
im: a 3-channel uint8 image in BGR
mask: a binary 1-channel image of the same size
color: if None, will choose automatically | examples/FasterRCNN/viz.py | def draw_mask(im, mask, alpha=0.5, color=None):
"""
Overlay a mask on top of the image.
Args:
im: a 3-channel uint8 image in BGR
mask: a binary 1-channel image of the same size
color: if None, will choose automatically
"""
if color is None:
color = PALETTE_RGB[np.ran... | def draw_mask(im, mask, alpha=0.5, color=None):
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Overlay a mask on top of the image.
Args:
im: a 3-channel uint8 image in BGR
mask: a binary 1-channel image of the same size
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train | send_dataflow_zmq | Run DataFlow and send data to a ZMQ socket addr.
It will serialize and send each datapoint to this address with a PUSH socket.
This function never returns.
Args:
df (DataFlow): Will infinitely loop over the DataFlow.
addr: a ZMQ socket endpoint.
hwm (int): ZMQ high-water mark (buffe... | tensorpack/dataflow/remote.py | def send_dataflow_zmq(df, addr, hwm=50, format=None, bind=False):
"""
Run DataFlow and send data to a ZMQ socket addr.
It will serialize and send each datapoint to this address with a PUSH socket.
This function never returns.
Args:
df (DataFlow): Will infinitely loop over the DataFlow.
... | def send_dataflow_zmq(df, addr, hwm=50, format=None, bind=False):
"""
Run DataFlow and send data to a ZMQ socket addr.
It will serialize and send each datapoint to this address with a PUSH socket.
This function never returns.
Args:
df (DataFlow): Will infinitely loop over the DataFlow.
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train | dump_dataflow_to_process_queue | Convert a DataFlow to a :class:`multiprocessing.Queue`.
The DataFlow will only be reset in the spawned process.
Args:
df (DataFlow): the DataFlow to dump.
size (int): size of the queue
nr_consumer (int): number of consumer of the queue.
The producer will add this many of ``D... | tensorpack/dataflow/remote.py | def dump_dataflow_to_process_queue(df, size, nr_consumer):
"""
Convert a DataFlow to a :class:`multiprocessing.Queue`.
The DataFlow will only be reset in the spawned process.
Args:
df (DataFlow): the DataFlow to dump.
size (int): size of the queue
nr_consumer (int): number of co... | def dump_dataflow_to_process_queue(df, size, nr_consumer):
"""
Convert a DataFlow to a :class:`multiprocessing.Queue`.
The DataFlow will only be reset in the spawned process.
Args:
df (DataFlow): the DataFlow to dump.
size (int): size of the queue
nr_consumer (int): number of co... | [
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train | AtariPlayer._grab_raw_image | :returns: the current 3-channel image | examples/DeepQNetwork/atari.py | def _grab_raw_image(self):
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:returns: the current 3-channel image
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train | AtariPlayer._current_state | :returns: a gray-scale (h, w) uint8 image | examples/DeepQNetwork/atari.py | def _current_state(self):
"""
:returns: a gray-scale (h, w) uint8 image
"""
ret = self._grab_raw_image()
# max-pooled over the last screen
ret = np.maximum(ret, self.last_raw_screen)
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train | clip_boxes | Args:
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"""
Args:
boxes: nx4, xyxy
window: [h, w]
"""
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m = tf.tile(tf.reverse(window, [0]), [2]) # (4,)
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window: [h, w]
"""
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m = tf.tile(tf.reverse(window, [0]), [2]) # (4,)
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box_decoded: (..., 4), float32. With the same shape.
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anchors: (..., 4), floatbox. Must have the same shape
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train | encode_bbox_target | Args:
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anchors: (..., 4), float32
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train | crop_and_resize | Aligned version of tf.image.crop_and_resize, following our definition of floating point boxes.
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image: NCHW
boxes: nx4, x1y1x2y2
box_ind: (n,)
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Aligned version of tf.image.crop_and_resize, following our definition of floating point boxes.
Args:
image: NCHW
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crop_size (int):
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n,C,size,size... | def crop_and_resize(image, boxes, box_ind, crop_size, pad_border=True):
"""
Aligned version of tf.image.crop_and_resize, following our definition of floating point boxes.
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train | roi_align | Args:
featuremap: 1xCxHxW
boxes: Nx4 floatbox
resolution: output spatial resolution
Returns:
NxCx res x res | examples/FasterRCNN/model_box.py | def roi_align(featuremap, boxes, resolution):
"""
Args:
featuremap: 1xCxHxW
boxes: Nx4 floatbox
resolution: output spatial resolution
Returns:
NxCx res x res
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# sample 4 locations per roi bin
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featuremap: 1xCxHxW
boxes: Nx4 floatbox
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NxCx res x res
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train | RPNAnchors.narrow_to | Slice anchors to the spatial size of this featuremap. | examples/FasterRCNN/model_box.py | def narrow_to(self, featuremap):
"""
Slice anchors to the spatial size of this featuremap.
"""
shape2d = tf.shape(featuremap)[2:] # h,w
slice3d = tf.concat([shape2d, [-1]], axis=0)
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Slice anchors to the spatial size of this featuremap.
"""
shape2d = tf.shape(featuremap)[2:] # h,w
slice3d = tf.concat([shape2d, [-1]], axis=0)
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train | colorize | img: bgr, [0,255]
heatmap: [0,1] | examples/CaffeModels/load-cpm.py | def colorize(img, heatmap):
""" img: bgr, [0,255]
heatmap: [0,1]
"""
heatmap = viz.intensity_to_rgb(heatmap, cmap='jet')[:, :, ::-1]
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heatmap: [0,1]
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train | Rotation._get_augment_params | The correct center is shape*0.5-0.5. This can be verified by:
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arr = np.random.rand(SHAPE, SHAPE)
orig = arr
c = SHAPE * 0.5 - 0.5
c = (c, c)
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deg = self._rand_range(-self.max_deg, self.max_deg)
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train | RotationAndCropValid.largest_rotated_rect | Get largest rectangle after rotation.
http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders | tensorpack/dataflow/imgaug/geometry.py | def largest_rotated_rect(w, h, angle):
"""
Get largest rectangle after rotation.
http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
"""
angle = angle / 180.0 * math.pi
if w <= 0 or h <= 0:
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"""
Get largest rectangle after rotation.
http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
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angle = angle / 180.0 * math.pi
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train | map_arg | Apply a mapping on certain argument before calling the original function.
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"""
Apply a mapping on certain argument before calling the original function.
Args:
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"""
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train | graph_memoized | Like memoized, but keep one cache per default graph. | tensorpack/utils/argtools.py | def graph_memoized(func):
"""
Like memoized, but keep one cache per default graph.
"""
# TODO it keeps the graph alive
from ..compat import tfv1
GRAPH_ARG_NAME = '__IMPOSSIBLE_NAME_FOR_YOU__'
@memoized
def func_with_graph_arg(*args, **kwargs):
kwargs.pop(GRAPH_ARG_NAME)
... | def graph_memoized(func):
"""
Like memoized, but keep one cache per default graph.
"""
# TODO it keeps the graph alive
from ..compat import tfv1
GRAPH_ARG_NAME = '__IMPOSSIBLE_NAME_FOR_YOU__'
@memoized
def func_with_graph_arg(*args, **kwargs):
kwargs.pop(GRAPH_ARG_NAME)
... | [
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train | memoized_ignoreargs | A decorator. It performs memoization ignoring the arguments used to call
the function. | tensorpack/utils/argtools.py | def memoized_ignoreargs(func):
"""
A decorator. It performs memoization ignoring the arguments used to call
the function.
"""
def wrapper(*args, **kwargs):
if func not in _MEMOIZED_NOARGS:
res = func(*args, **kwargs)
_MEMOIZED_NOARGS[func] = res
return res... | def memoized_ignoreargs(func):
"""
A decorator. It performs memoization ignoring the arguments used to call
the function.
"""
def wrapper(*args, **kwargs):
if func not in _MEMOIZED_NOARGS:
res = func(*args, **kwargs)
_MEMOIZED_NOARGS[func] = res
return res... | [
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train | shape2d | Ensure a 2D shape.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 2. if ``a`` is a int, return ``[a, a]``. | tensorpack/utils/argtools.py | def shape2d(a):
"""
Ensure a 2D shape.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 2. if ``a`` is a int, return ``[a, a]``.
"""
if type(a) == int:
return [a, a]
if isinstance(a, (list, tuple)):
assert len(a) == 2
return list(a)
... | def shape2d(a):
"""
Ensure a 2D shape.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 2. if ``a`` is a int, return ``[a, a]``.
"""
if type(a) == int:
return [a, a]
if isinstance(a, (list, tuple)):
assert len(a) == 2
return list(a)
... | [
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] | tensorpack/tensorpack | python | https://github.com/tensorpack/tensorpack/blob/d7a13cb74c9066bc791d7aafc3b744b60ee79a9f/tensorpack/utils/argtools.py#L89-L104 | [
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train | shape4d | Ensuer a 4D shape, to use with 4D symbolic functions.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 4. if ``a`` is a int, return ``[1, a, a, 1]``
or ``[1, 1, a, a]`` depending on data_format. | tensorpack/utils/argtools.py | def shape4d(a, data_format='NHWC'):
"""
Ensuer a 4D shape, to use with 4D symbolic functions.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 4. if ``a`` is a int, return ``[1, a, a, 1]``
or ``[1, 1, a, a]`` depending on data_format.
"""
s2d = shap... | def shape4d(a, data_format='NHWC'):
"""
Ensuer a 4D shape, to use with 4D symbolic functions.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 4. if ``a`` is a int, return ``[1, a, a, 1]``
or ``[1, 1, a, a]`` depending on data_format.
"""
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] | tensorpack/tensorpack | python | https://github.com/tensorpack/tensorpack/blob/d7a13cb74c9066bc791d7aafc3b744b60ee79a9f/tensorpack/utils/argtools.py#L118-L133 | [
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train | call_only_once | Decorate a method or property of a class, so that this method can only
be called once for every instance.
Calling it more than once will result in exception. | tensorpack/utils/argtools.py | def call_only_once(func):
"""
Decorate a method or property of a class, so that this method can only
be called once for every instance.
Calling it more than once will result in exception.
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
self = args[0]
# cannot use has... | def call_only_once(func):
"""
Decorate a method or property of a class, so that this method can only
be called once for every instance.
Calling it more than once will result in exception.
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
self = args[0]
# cannot use has... | [
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] | tensorpack/tensorpack | python | https://github.com/tensorpack/tensorpack/blob/d7a13cb74c9066bc791d7aafc3b744b60ee79a9f/tensorpack/utils/argtools.py#L149-L178 | [
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train | memoized_method | A decorator that performs memoization on methods. It stores the cache on the object instance itself. | tensorpack/utils/argtools.py | def memoized_method(func):
"""
A decorator that performs memoization on methods. It stores the cache on the object instance itself.
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
self = args[0]
assert func.__name__ in dir(self), "memoized_method can only be used on method!... | def memoized_method(func):
"""
A decorator that performs memoization on methods. It stores the cache on the object instance itself.
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
self = args[0]
assert func.__name__ in dir(self), "memoized_method can only be used on method!... | [
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] | tensorpack/tensorpack | python | https://github.com/tensorpack/tensorpack/blob/d7a13cb74c9066bc791d7aafc3b744b60ee79a9f/tensorpack/utils/argtools.py#L181-L204 | [
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... | d7a13cb74c9066bc791d7aafc3b744b60ee79a9f |
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