INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
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Args: add_gt: whether to add ground truth bounding box annotations to the dicts add_mask: whether to also add ground truth mask | 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... |
Change relative filename to abosolute 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'] |
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. | 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.... |
Load and merges several instance files together. | 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 name... |
Args: names ( list [ str ] ): name of the training datasets e. g. [ train2014 valminusminival2014 ] | def load_training_roidbs(self, names):
"""
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... |
Args: name ( str ): name of one inference dataset e. g. minival2014 | def load_inference_roidbs(self, name):
"""
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
following keys in the dict are expected:
... |
Args: results ( list [ dict ] ): the inference results as dicts. Each dict corresponds to one __instance__. It contains the following keys: | 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... |
Surround a context with a timer. | 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... |
A context which add the time spent inside to TotalTimer. | 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) |
Print the content of the TotalTimer if it s not empty. This function will automatically get called when program exits. | 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,... |
Will reset state of each augmentor | def reset_state(self):
""" Will reset state of each augmentor """
super(AugmentorList, self).reset_state()
for a in self.augmentors:
a.reset_state() |
Make sure processes terminate when main process exit. | 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... |
Set the death signal of the current process so that the current process will be cleaned with guarantee in case the parent dies accidentally. | 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... |
Returns: If called in main thread returns a context where SIGINT is ignored and yield True. Otherwise yield False. | 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():
sigint_handler = signal.signal(signal.SIGINT, signal.SIG_IGN)
yield True
signal.signal(signal.S... |
Start process ( es ) with SIGINT ignored. | def start_proc_mask_signal(proc):
"""
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):
proc = [proc]
with mask_sigint():
for p in p... |
Execute a command with timeout and return STDOUT and STDERR | 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 = s... |
Put obj to queue but will give up when the thread is stopped | 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 |
Take obj from queue but will give up when the thread is stopped | 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 |
Args: rank ( int ): rank of th element. All elements must have different ranks. val: an object | def put(self, rank, val):
"""
Args:
rank(int): rank of th element. All elements must have different ranks.
val: an object
"""
idx = bisect.bisect(self.ranks, rank)
self.ranks.insert(idx, rank)
self.data.insert(idx, val) |
Visualize use weights in convolution filters. | 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 = ... |
Visualize activations for convolution layers. | 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 al... |
Make the static shape of a tensor less specific. | 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 ... |
Estimate H ( x|s ) ~ = - E_ { x \ sim P ( x|s ) } [ \ log Q ( x|s ) ] where x are samples and Q is parameterized by vec. | def entropy_from_samples(samples, vec):
"""
Estimate H(x|s) ~= -E_{x \sim P(x|s)}[\log Q(x|s)], where x are samples, and Q is parameterized by vec.
"""
samples_cat = tf.argmax(samples[:, :NUM_CLASS], axis=1, output_type=tf.int32)
samples_uniform = samples[:, NUM_CLASS:]
cat, uniform = get_distri... |
OpenAI official code actually models the uniform latent code as a Gaussian distribution but obtain the samples from a uniform distribution. | 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 ... |
Mutual information between x ( i. e. zc in this case ) and some information s ( the generated samples in this case ): | def build_graph(self, real_sample):
real_sample = tf.expand_dims(real_sample, -1)
# sample the latent code:
zc = shapeless_placeholder(sample_prior(BATCH), 0, name='z_code')
z_noise = shapeless_placeholder(
tf.random_uniform([BATCH, NOISE_DIM], -1, 1), 0, name='z_noise')
... |
see Dynamic Filter Networks ( NIPS 2016 ) by Bert De Brabandere * Xu Jia * Tinne Tuytelaars and Luc Van Gool | def DynamicConvFilter(inputs, filters, out_channel,
kernel_shape,
stride=1,
padding='SAME'):
""" see "Dynamic Filter Networks" (NIPS 2016)
by Bert De Brabandere*, Xu Jia*, Tinne Tuytelaars and Luc Van Gool
Remarks:
This is the co... |
Estimate filters for convolution layers | 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.leak... |
Implements a steerable Gaussian filter. | 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
... |
Assign self. g_vars to the parameters under scope g_scope and same with self. d_vars. | 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_v... |
Build standard GAN loss and set self. g_loss and self. d_loss. | 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... |
We need to set tower_func because it s a TowerTrainer and only TowerTrainer supports automatic graph creation for inference during training. | 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 ca... |
After applying this decorator: 1. data_format becomes tf. layers style 2. nl becomes activation 3. initializers are renamed 4. positional args are transformed to corresponding kwargs according to args_names 5. kwargs are mapped to tf. layers names if needed by name_mapping | def convert_to_tflayer_args(args_names, name_mapping):
"""
After applying this decorator:
1. data_format becomes tf.layers style
2. nl becomes activation
3. initializers are renamed
4. positional args are transformed to corresponding kwargs, according to args_names
5. kwargs are mapped to tf... |
Args: mapping ( dict ): an old - > new mapping for variable basename. e. g. { kernel: W } | def rename_get_variable(mapping):
"""
Args:
mapping(dict): an old -> new mapping for variable basename. e.g. {'kernel': 'W'}
Returns:
A context where the variables are renamed.
"""
def custom_getter(getter, name, *args, **kwargs):
splits = name.split('/')
basename = ... |
Apply a regularizer on trainable variables matching the regex and print the matched variables ( only print once in multi - tower training ). In replicated mode it will only regularize variables within the current tower. | def regularize_cost(regex, func, name='regularize_cost'):
"""
Apply a regularizer on trainable variables matching the regex, and print
the matched variables (only print once in multi-tower training).
In replicated mode, it will only regularize variables within the current tower.
If called under a T... |
Get the cost from the regularizers in tf. GraphKeys. REGULARIZATION_LOSSES. If in replicated mode will only regularize variables created within the current tower. | 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:
... |
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. | 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:
... |
Return a proper background image of background_shape given img. | 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(backgroun... |
Apply a function on the wrapped tensor. | 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) |
Apply a function on the wrapped tensor. The tensor will be the second argument of func. | 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:
LinearWra... |
Returns: A context where the gradient of: meth: tf. nn. relu is replaced by guided back - propagation as described in the paper: Striving for Simplicity: The All Convolutional Net <https:// arxiv. org/ abs/ 1412. 6806 > _ | def guided_relu():
"""
Returns:
A context where the gradient of :meth:`tf.nn.relu` is replaced by
guided back-propagation, as described in the paper:
`Striving for Simplicity: The All Convolutional Net
<https://arxiv.org/abs/1412.6806>`_
"""
from tensorflow.python.ops imp... |
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 output w. r. t input. | 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... |
A wrapper around tf. layers. Conv2D. Some differences to maintain backward - compatibility: | def Conv2D(
inputs,
filters,
kernel_size,
strides=(1, 1),
padding='same',
data_format='channels_last',
dilation_rate=(1, 1),
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.zeros_initializer(),
k... |
A wrapper around tf. layers. Conv2DTranspose. Some differences to maintain backward - compatibility: | def Conv2DTranspose(
inputs,
filters,
kernel_size,
strides=(1, 1),
padding='same',
data_format='channels_last',
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.zeros_initializer(),
kernel_regularizer=Non... |
Will setup the assign operator for that variable. | 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("{... |
Returns: The value to assign to the variable. | def get_value_to_set(self):
"""
Returns:
The value to assign to the variable.
Note:
Subclasses will implement the abstract method
:meth:`_get_value_to_set`, which should return a new value to
set, or return None to do nothing.
"""
... |
Using schedule compute the value to be set at a given 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... |
This function should build the model which takes the input variables and return cost at the end | def build_graph(self, image, label):
"""This function should build the model which takes the input variables
and return cost at the end"""
# In tensorflow, inputs to convolution function are assumed to be
# NHWC. Add a single channel here.
image = tf.expand_dims(image, 3)
... |
Convert a caffe parameter name to a tensorflow parameter name as defined in the above model | 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': ... |
Args: custom_getter: the same as in: func: tf. get_variable | def custom_getter_scope(custom_getter):
"""
Args:
custom_getter: the same as in :func:`tf.get_variable`
Returns:
The current variable scope with a custom_getter.
"""
scope = tf.get_variable_scope()
if get_tf_version_tuple() >= (1, 5):
with tf.variable_scope(
... |
Use fn to map the output of any variable getter. | 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 varreplac... |
Return a context to freeze variables by wrapping tf. get_variable with a custom getter. It works by either applying tf. stop_gradient on the variables or by keeping them out of the TRAINABLE_VARIABLES collection or both. | def freeze_variables(stop_gradient=True, skip_collection=False):
"""
Return a context to freeze variables,
by wrapping ``tf.get_variable`` with a custom getter.
It works by either applying ``tf.stop_gradient`` on the variables,
or by keeping them out of the ``TRAINABLE_VARIABLES`` collection, or
... |
Load a caffe model. You must be able to import caffe to use this function. Args: model_desc ( str ): path to caffe model description file (. prototxt ). model_file ( str ): path to caffe model parameter file (. caffemodel ). Returns: dict: the parameters. | def load_caffe(model_desc, model_file):
"""
Load a caffe model. You must be able to ``import caffe`` to use this
function.
Args:
model_desc (str): path to caffe model description file (.prototxt).
model_file (str): path to caffe model parameter file (.caffemodel).
Returns:
di... |
Get caffe protobuf. Returns: The imported caffe protobuf module. | def get_caffe_pb():
"""
Get caffe protobuf.
Returns:
The imported caffe protobuf module.
"""
dir = get_dataset_path('caffe')
caffe_pb_file = os.path.join(dir, 'caffe_pb2.py')
if not os.path.isfile(caffe_pb_file):
download(CAFFE_PROTO_URL, dir)
assert os.path.isfile(os... |
Run some sanity checks and populate some configs from others | 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... |
Convert to a nested dict. | def to_dict(self):
"""Convert to a nested dict. """
return {k: v.to_dict() if isinstance(v, AttrDict) else v
for k, v in self.__dict__.items() if not k.startswith('_')} |
Update from command line args. | def update_args(self, args):
"""Update from command line args. """
for cfg in args:
keys, v = cfg.split('=', maxsplit=1)
keylist = keys.split('.')
dic = self
for i, k in enumerate(keylist[:-1]):
assert k in dir(dic), "Unknown config key: {... |
Get a corresponding model loader by looking at the file name. | def get_model_loader(filename):
"""
Get a corresponding model loader by looking at the file name.
Returns:
SessInit: either a :class:`DictRestore` (if name ends with 'npy/npz') or
:class:`SaverRestore` (otherwise).
"""
assert isinstance(filename, six.string_types), filename
file... |
return a set of strings | def _read_checkpoint_vars(model_path):
""" return a set of strings """
reader = tf.train.NewCheckpointReader(model_path)
reader = CheckpointReaderAdapter(reader) # use an adapter to standardize the name
ckpt_vars = reader.get_variable_to_shape_map().keys()
return reader, set(c... |
Args: layers ( list or layer ): layer or list of layers to apply the arguments. | 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
... |
Decorator for function to support argscope | 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... |
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. | 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:... |
Generate tensor for TensorBoard ( casting clipping ) | 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:... |
img: an RGB image of shape ( s 2s 3 ).: return: [ input output ] | 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
input, output = img[:, :s, :], img[:, s:, :]
if args.mode == 'BtoA':
input, output = output, i... |
return a ( b 1 ) logits | def discriminator(self, inputs, outputs):
""" return a (b, 1) logits"""
l = tf.concat([inputs, outputs], 3)
with argscope(Conv2D, kernel_size=4, strides=2, activation=BNLReLU):
l = (LinearWrap(l)
.Conv2D('conv0', NF, activation=tf.nn.leaky_relu)
.Con... |
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 ). | def print_stat(x, message=None):
""" 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')``.
"""
if message is None:
message = x.op.name
lst = [tf.shape(x), tf.reduce_mean(x)]
if x.dtype.is_floating:
lst.ap... |
Returns: root mean square of tensor x. | 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
return tf.sqrt(tf.reduce_mean(tf.square(x)), name=name)
return tf.sqrt(tf.reduce_mean(t... |
Peek Signal to Noise Ratio <https:// en. wikipedia. org/ wiki/ Peak_signal - to - noise_ratio > _. | 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 ... |
Args: anchor: coordinate of the center | 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'... |
Pad tensor in H W | def pad(x, p=3):
"""Pad tensor in H, W
Remarks:
TensorFlow uses "ceil(input_spatial_shape[i] / strides[i])" rather than explicit padding
like Caffe, pyTorch does. Hence, we need to pad here beforehand.
Args:
x (tf.tensor): incoming tensor
p (int, optional): padding for H, W... |
Correlation Cost Volume computation. | 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.
... |
Resize input tensor with unkown input - shape by a factor | def resize(x, mode, factor=4):
"""Resize input tensor with unkown input-shape by a factor
Args:
x (tf.Tensor): tensor NCHW
factor (int, optional): resize factor for H, W
Note:
Differences here against Caffe have huge impacts on the
quality of the predictions.
Returns:
... |
Architecture in Table 4 of FlowNet 2. 0. | 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),
... |
Architecture in Table 3 of FlowNet 2. 0. | 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),
padding='valid', strides=2, ... |
Architecture of FlowNetSimple in Figure 2 of FlowNet 1. 0. | def graph_structure(self, x, standalone=True):
"""
Architecture of FlowNetSimple in Figure 2 of FlowNet 1.0.
Args:
x: 2CHW if standalone==True, else NCHW where C=12 is a concatenation
of 5 tensors of [3, 3, 3, 2, 1] channels.
standalone: If True, this mod... |
Architecture of FlowNetCorr in Figure 2 of FlowNet 1. 0. Args: x: 2CHW. | 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),
padding='valid', strides=2, kernel_size=3,
... |
Will not modify img | 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... |
Draw top3 proposals for each gt. Args: proposals: NPx4 proposal_scores: NP gt_boxes: NG | 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... |
Args: boxes: kx4 scores: kxC | 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)]
... |
Args: results: [ DetectionResult ] | 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)... |
Overlay a mask on top of the image. | 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... |
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. | 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.
... |
Convert a DataFlow to a: class: multiprocessing. Queue. The DataFlow will only be reset in the spawned process. | 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... |
: returns: the current 3 - channel image | def _grab_raw_image(self):
"""
:returns: the current 3-channel image
"""
m = self.ale.getScreenRGB()
return m.reshape((self.height, self.width, 3)) |
: returns: a gray - scale ( h w ) uint8 image | 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)
if self.viz:
if isinstance(self.viz, float):
cv2.imsh... |
Args: boxes: nx4 xyxy window: [ h w ] | def clip_boxes(boxes, window, name=None):
"""
Args:
boxes: nx4, xyxy
window: [h, w]
"""
boxes = tf.maximum(boxes, 0.0)
m = tf.tile(tf.reverse(window, [0]), [2]) # (4,)
boxes = tf.minimum(boxes, tf.cast(m, tf.float32), name=name)
return boxes |
Args: box_predictions: (... 4 ) logits anchors: (... 4 ) floatbox. Must have the same shape | def decode_bbox_target(box_predictions, anchors):
"""
Args:
box_predictions: (..., 4), logits
anchors: (..., 4), floatbox. Must have the same shape
Returns:
box_decoded: (..., 4), float32. With the same shape.
"""
orig_shape = tf.shape(anchors)
box_pred_txtytwth = tf.res... |
Args: boxes: (... 4 ) float32 anchors: (... 4 ) float32 | def encode_bbox_target(boxes, anchors):
"""
Args:
boxes: (..., 4), float32
anchors: (..., 4), float32
Returns:
box_encoded: (..., 4), float32 with the same shape.
"""
anchors_x1y1x2y2 = tf.reshape(anchors, (-1, 2, 2))
anchors_x1y1, anchors_x2y2 = tf.split(anchors_x1y1x2y... |
Aligned version of tf. image. crop_and_resize following our definition of floating point boxes. | 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.
Args:
image: NCHW
boxes: nx4, x1y1x2y2
box_ind: (n,)
crop_size (int):
Returns:
n,C,size,size... |
Args: featuremap: 1xCxHxW boxes: Nx4 floatbox resolution: output spatial resolution | def roi_align(featuremap, boxes, resolution):
"""
Args:
featuremap: 1xCxHxW
boxes: Nx4 floatbox
resolution: output spatial resolution
Returns:
NxCx res x res
"""
# sample 4 locations per roi bin
ret = crop_and_resize(
featuremap, boxes,
tf.zeros([... |
Slice anchors to the spatial size of this featuremap. | 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)
slice4d = tf.concat([shape2d, [-1, -1]], axis=0)
boxes = tf.slice(self.boxes, [0, ... |
img: bgr [ 0 255 ] heatmap: [ 0 1 ] | def colorize(img, heatmap):
""" img: bgr, [0,255]
heatmap: [0,1]
"""
heatmap = viz.intensity_to_rgb(heatmap, cmap='jet')[:, :, ::-1]
return img * 0.5 + heatmap * 0.5 |
The correct center is shape * 0. 5 - 0. 5. This can be verified by: | def _get_augment_params(self, img):
center = img.shape[1::-1] * self._rand_range(
self.center_range[0], self.center_range[1], (2,))
deg = self._rand_range(-self.max_deg, self.max_deg)
if self.step_deg:
deg = deg // self.step_deg * self.step_deg
"""
The cor... |
Get largest rectangle after rotation. http:// stackoverflow. com/ questions/ 16702966/ rotate - image - and - crop - out - black - borders | 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:
return 0, 0
width_is_longer =... |
Apply a mapping on certain argument before calling the original function. | def map_arg(**maps):
"""
Apply a mapping on certain argument before calling the original function.
Args:
maps (dict): {argument_name: map_func}
"""
def deco(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
if six.PY2:
argmap = inspect.g... |
Like memoized but keep one cache per default graph. | 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)
... |
A decorator. It performs memoization ignoring the arguments used to call the function. | 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... |
Ensure a 2D shape. | 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)
... |
Ensuer a 4D shape to use with 4D symbolic functions. | 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... |
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. | 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... |
A decorator that performs memoization on methods. It stores the cache on the object instance itself. | 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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