INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
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Decorator for easily setting custom gradients for TensorFlow functions.
* DO NOT use this function if you need to serialize your graph.
* This function will cause the decorated function to run slower.
Example:
def _foo_grad(op, grad): ...
@use_gradient(_foo_grad)
def foo(x1, x2, x3): ...
Args:
... | def use_gradient(grad_f):
"""Decorator for easily setting custom gradients for TensorFlow functions.
* DO NOT use this function if you need to serialize your graph.
* This function will cause the decorated function to run slower.
Example:
def _foo_grad(op, grad): ...
@use_gradient(_foo_grad)
def... |
A naive, pixel-based image parameterization.
Defaults to a random initialization, but can take a supplied init_val argument
instead.
Args:
shape: shape of resulting image, [batch, width, height, channels].
sd: standard deviation of param initialization noise.
init_val: an initial value to... | def pixel_image(shape, sd=None, init_val=None):
"""A naive, pixel-based image parameterization.
Defaults to a random initialization, but can take a supplied init_val argument
instead.
Args:
shape: shape of resulting image, [batch, width, height, channels].
sd: standard deviation of param in... |
Computes 2D spectrum frequencies. | def rfft2d_freqs(h, w):
"""Computes 2D spectrum frequencies."""
fy = np.fft.fftfreq(h)[:, None]
# when we have an odd input dimension we need to keep one additional
# frequency and later cut off 1 pixel
if w % 2 == 1:
fx = np.fft.fftfreq(w)[: w // 2 + 2]
else:
fx = np.fft.fftfre... |
An image paramaterization using 2D Fourier coefficients. | def fft_image(shape, sd=None, decay_power=1):
"""An image paramaterization using 2D Fourier coefficients."""
sd = sd or 0.01
batch, h, w, ch = shape
freqs = rfft2d_freqs(h, w)
init_val_size = (2, ch) + freqs.shape
images = []
for _ in range(batch):
# Create a random variable holdin... |
Simple laplacian pyramid paramaterization of an image.
For more flexibility, use a sum of lowres_tensor()s.
Args:
shape: shape of resulting image, [batch, width, height, channels].
n_levels: number of levels of laplacian pyarmid.
sd: standard deviation of param initialization.
Returns:
... | def laplacian_pyramid_image(shape, n_levels=4, sd=None):
"""Simple laplacian pyramid paramaterization of an image.
For more flexibility, use a sum of lowres_tensor()s.
Args:
shape: shape of resulting image, [batch, width, height, channels].
n_levels: number of levels of laplacian pyarmid.
... |
Build bilinear texture sampling graph.
Coordinate transformation rules match OpenGL GL_REPEAT wrapping and GL_LINEAR
interpolation modes.
Args:
texture: [tex_h, tex_w, channel_n] tensor.
uv: [frame_h, frame_h, 2] tensor with per-pixel UV coordinates in range [0..1]
Returns:
[frame_h... | def bilinearly_sampled_image(texture, uv):
"""Build bilinear texture sampling graph.
Coordinate transformation rules match OpenGL GL_REPEAT wrapping and GL_LINEAR
interpolation modes.
Args:
texture: [tex_h, tex_w, channel_n] tensor.
uv: [frame_h, frame_h, 2] tensor with per-pixel UV coordi... |
Multiply input by sqrt of emperical (ImageNet) color correlation matrix.
If you interpret t's innermost dimension as describing colors in a
decorrelated version of the color space (which is a very natural way to
describe colors -- see discussion in Feature Visualization article) the way
to map back to normal... | def _linear_decorelate_color(t):
"""Multiply input by sqrt of emperical (ImageNet) color correlation matrix.
If you interpret t's innermost dimension as describing colors in a
decorrelated version of the color space (which is a very natural way to
describe colors -- see discussion in Feature Visualization ar... |
Transform inner dimension of t to valid rgb colors.
In practice this consistes of two parts:
(1) If requested, transform the colors from a decorrelated color space to RGB.
(2) Constrain the color channels to be in [0,1], either using a sigmoid
function or clipping.
Args:
t: input tensor, innerm... | def to_valid_rgb(t, decorrelate=False, sigmoid=True):
"""Transform inner dimension of t to valid rgb colors.
In practice this consistes of two parts:
(1) If requested, transform the colors from a decorrelated color space to RGB.
(2) Constrain the color channels to be in [0,1], either using a sigmoid
f... |
Add Inception bottlenecks and their pre-Relu versions to the graph. | def _populate_inception_bottlenecks(scope):
"""Add Inception bottlenecks and their pre-Relu versions to the graph."""
graph = tf.get_default_graph()
for op in graph.get_operations():
if op.name.startswith(scope+'/') and 'Concat' in op.type:
name = op.name.split('/')[1]
pre_relus = []
for tow... |
Decorator for creating Objective factories.
Changes f from the closure: (args) => () => TF Tensor
into an Obejective factory: (args) => Objective
while perserving function name, arg info, docs... for interactive python. | def wrap_objective(f, *args, **kwds):
"""Decorator for creating Objective factories.
Changes f from the closure: (args) => () => TF Tensor
into an Obejective factory: (args) => Objective
while perserving function name, arg info, docs... for interactive python.
"""
objective_func = f(*args, **kwds)
objec... |
Visualize a single neuron of a single channel.
Defaults to the center neuron. When width and height are even numbers, we
choose the neuron in the bottom right of the center 2x2 neurons.
Odd width & height: Even width & height:
+---+---+---+ +---+---+---+---+
| | | | ... | def neuron(layer_name, channel_n, x=None, y=None, batch=None):
"""Visualize a single neuron of a single channel.
Defaults to the center neuron. When width and height are even numbers, we
choose the neuron in the bottom right of the center 2x2 neurons.
Odd width & height: Even width & height:
... |
Visualize a single channel | def channel(layer, n_channel, batch=None):
"""Visualize a single channel"""
if batch is None:
return lambda T: tf.reduce_mean(T(layer)[..., n_channel])
else:
return lambda T: tf.reduce_mean(T(layer)[batch, ..., n_channel]) |
Visualize a direction | def direction(layer, vec, batch=None, cossim_pow=0):
"""Visualize a direction"""
if batch is None:
vec = vec[None, None, None]
return lambda T: _dot_cossim(T(layer), vec)
else:
vec = vec[None, None]
return lambda T: _dot_cossim(T(layer)[batch], vec) |
Visualize a single (x, y) position along the given direction | def direction_neuron(layer_name, vec, batch=None, x=None, y=None, cossim_pow=0):
"""Visualize a single (x, y) position along the given direction"""
def inner(T):
layer = T(layer_name)
shape = tf.shape(layer)
x_ = shape[1] // 2 if x is None else x
y_ = shape[2] // 2 if y is None else y
if batch i... |
Visualize a direction (cossine similarity) | def direction_cossim(layer, vec, batch=None):
"""Visualize a direction (cossine similarity)"""
def inner(T):
act_mags = tf.sqrt(tf.reduce_sum(T(layer)**2, -1, keepdims=True))
vec_mag = tf.sqrt(tf.reduce_sum(vec**2))
mags = act_mags * vec_mag
if batch is None:
return tf.reduce_mean(T(layer) * v... |
L1 norm of layer. Generally used as penalty. | def L1(layer="input", constant=0, batch=None):
"""L1 norm of layer. Generally used as penalty."""
if batch is None:
return lambda T: tf.reduce_sum(tf.abs(T(layer) - constant))
else:
return lambda T: tf.reduce_sum(tf.abs(T(layer)[batch] - constant)) |
L2 norm of layer. Generally used as penalty. | def L2(layer="input", constant=0, epsilon=1e-6, batch=None):
"""L2 norm of layer. Generally used as penalty."""
if batch is None:
return lambda T: tf.sqrt(epsilon + tf.reduce_sum((T(layer) - constant) ** 2))
else:
return lambda T: tf.sqrt(epsilon + tf.reduce_sum((T(layer)[batch] - constant) ** 2)) |
Interpolate between layer1, n_channel1 and layer2, n_channel2.
Optimize for a convex combination of layer1, n_channel1 and
layer2, n_channel2, transitioning across the batch.
Args:
layer1: layer to optimize 100% at batch=0.
n_channel1: neuron index to optimize 100% at batch=0.
layer2: layer to optim... | def channel_interpolate(layer1, n_channel1, layer2, n_channel2):
"""Interpolate between layer1, n_channel1 and layer2, n_channel2.
Optimize for a convex combination of layer1, n_channel1 and
layer2, n_channel2, transitioning across the batch.
Args:
layer1: layer to optimize 100% at batch=0.
n_channel1... |
Minimizing this objective is equivelant to blurring input each step.
Optimizing (-k)*blur_input_each_step() is equivelant to:
input <- (1-k)*input + k*blur(input)
An operation that was used in early feature visualization work.
See Nguyen, et al., 2015. | def blur_input_each_step():
"""Minimizing this objective is equivelant to blurring input each step.
Optimizing (-k)*blur_input_each_step() is equivelant to:
input <- (1-k)*input + k*blur(input)
An operation that was used in early feature visualization work.
See Nguyen, et al., 2015.
"""
def inner(T):... |
Encourage the boundaries of an image to have less variation and of color C.
Args:
shp: shape of T("input") because this may not be known.
w: width of boundary to penalize. Ignored if mask is set.
mask: mask describing what area should be penalized.
Returns:
Objective. | def penalize_boundary_complexity(shp, w=20, mask=None, C=0.5):
"""Encourage the boundaries of an image to have less variation and of color C.
Args:
shp: shape of T("input") because this may not be known.
w: width of boundary to penalize. Ignored if mask is set.
mask: mask describing what area should be... |
Encourage neighboring images to be similar.
When visualizing the interpolation between two objectives, it's often
desireable to encourage analagous boejcts to be drawn in the same position,
to make them more comparable.
This term penalizes L2 distance between neighboring images, as evaluated at
layer.
In... | def alignment(layer, decay_ratio=2):
"""Encourage neighboring images to be similar.
When visualizing the interpolation between two objectives, it's often
desireable to encourage analagous boejcts to be drawn in the same position,
to make them more comparable.
This term penalizes L2 distance between neighbor... |
Encourage diversity between each batch element.
A neural net feature often responds to multiple things, but naive feature
visualization often only shows us one. If you optimize a batch of images,
this objective will encourage them all to be different.
In particular, it caculuates the correlation matrix of act... | def diversity(layer):
"""Encourage diversity between each batch element.
A neural net feature often responds to multiple things, but naive feature
visualization often only shows us one. If you optimize a batch of images,
this objective will encourage them all to be different.
In particular, it caculuates th... |
Average L2 difference between optimized image and orig_img.
This objective is usually mutliplied by a negative number and used as a
penalty in making advarsarial counterexamples. | def input_diff(orig_img):
"""Average L2 difference between optimized image and orig_img.
This objective is usually mutliplied by a negative number and used as a
penalty in making advarsarial counterexamples.
"""
def inner(T):
diff = T("input") - orig_img
return tf.sqrt(tf.reduce_mean(diff**2))
retu... |
Like channel, but for softmax layers.
Args:
layer: A layer name string.
label: Either a string (refering to a label in model.labels) or an int
label position.
Returns:
Objective maximizing a logit. | def class_logit(layer, label):
"""Like channel, but for softmax layers.
Args:
layer: A layer name string.
label: Either a string (refering to a label in model.labels) or an int
label position.
Returns:
Objective maximizing a logit.
"""
def inner(T):
if isinstance(label, int):
cla... |
Convert obj into Objective class.
Strings of the form "layer:n" become the Objective channel(layer, n).
Objectives are returned unchanged.
Args:
obj: string or Objective.
Returns:
Objective | def as_objective(obj):
"""Convert obj into Objective class.
Strings of the form "layer:n" become the Objective channel(layer, n).
Objectives are returned unchanged.
Args:
obj: string or Objective.
Returns:
Objective
"""
if isinstance(obj, Objective):
return obj
elif callable(obj):
ret... |
Gradient for constrained optimization on an L2 unit ball.
This function projects the gradient onto the ball if you are on the boundary
(or outside!), but leaves it untouched if you are inside the ball.
Args:
op: the tensorflow op we're computing the gradient for.
grad: gradient we need to backprop
Re... | def _constrain_L2_grad(op, grad):
"""Gradient for constrained optimization on an L2 unit ball.
This function projects the gradient onto the ball if you are on the boundary
(or outside!), but leaves it untouched if you are inside the ball.
Args:
op: the tensorflow op we're computing the gradient for.
g... |
A tensorflow variable tranfomed to be constrained in a L2 unit ball.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code. | def unit_ball_L2(shape):
"""A tensorflow variable tranfomed to be constrained in a L2 unit ball.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this code.
"""
x = tf.Variable(tf.zeros(shape))
return constrain_L2(x) |
A tensorflow variable tranfomed to be constrained in a L_inf unit ball.
Note that this code also preconditions the gradient to go in the L_inf
direction of steepest descent.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they are strong attacks. We are not yet confident in this ... | def unit_ball_L_inf(shape, precondition=True):
"""A tensorflow variable tranfomed to be constrained in a L_inf unit ball.
Note that this code also preconditions the gradient to go in the L_inf
direction of steepest descent.
EXPERIMENTAL: Do not use for adverserial examples if you need to be confident
they a... |
Flexible optimization-base feature vis.
There's a lot of ways one might wish to customize otpimization-based
feature visualization. It's hard to create an abstraction that stands up
to all the things one might wish to try.
This function probably can't do *everything* you want, but it's much more
flexible th... | def render_vis(model, objective_f, param_f=None, optimizer=None,
transforms=None, thresholds=(512,), print_objectives=None,
verbose=True, relu_gradient_override=True, use_fixed_seed=False):
"""Flexible optimization-base feature vis.
There's a lot of ways one might wish to customize ot... |
Even more flexible optimization-base feature vis.
This function is the inner core of render_vis(), and can be used
when render_vis() isn't flexible enough. Unfortunately, it's a bit more
tedious to use:
> with tf.Graph().as_default() as graph, tf.Session() as sess:
>
> T = make_vis_T(model, "mixed4a_p... | def make_vis_T(model, objective_f, param_f=None, optimizer=None,
transforms=None, relu_gradient_override=False):
"""Even more flexible optimization-base feature vis.
This function is the inner core of render_vis(), and can be used
when render_vis() isn't flexible enough. Unfortunately, it's a bit ... |
layout: numpy arrays x, y
metadata: user-defined numpy arrays with metadata
n_layer: number of cells in the layer (squared)
n_tile: number of cells in the tile (squared) | def grid(metadata, layout, params):
"""
layout: numpy arrays x, y
metadata: user-defined numpy arrays with metadata
n_layer: number of cells in the layer (squared)
n_tile: number of cells in the tile (squared)
"""
x = layout["x"]
y = layout["y"]
x_min = np.min(x)
x_max = np.max(x)
y_min = np.min(y... |
Write a file for each tile | def write_grid_local(tiles, params):
"""
Write a file for each tile
"""
# TODO: this isn't being used right now, will need to be
# ported to gfile if we want to keep it
for ti,tj,tile in enumerate_tiles(tiles):
filename = "{directory}/{name}/tile_{n_layer}_{n_tile}_{ti}_{tj}".format(ti=ti, tj=tj, **para... |
Convenience | def enumerate_tiles(tiles):
"""
Convenience
"""
enumerated = []
for key in tiles.keys():
enumerated.append((key[0], key[1], tiles[key]))
return enumerated |
Load image file as numpy array. | def _load_img(handle, target_dtype=np.float32, size=None, **kwargs):
"""Load image file as numpy array."""
image_pil = PIL.Image.open(handle, **kwargs)
# resize the image to the requested size, if one was specified
if size is not None:
if len(size) > 2:
size = size[:2]
... |
Load and decode a string. | def _load_text(handle, split=False, encoding="utf-8"):
"""Load and decode a string."""
string = handle.read().decode(encoding)
return string.splitlines() if split else string |
Load GraphDef from a binary proto file. | def _load_graphdef_protobuf(handle, **kwargs):
"""Load GraphDef from a binary proto file."""
# as_graph_def
graph_def = tf.GraphDef.FromString(handle.read())
# check if this is a lucid-saved model
# metadata = modelzoo.util.extract_metadata(graph_def)
# if metadata is not None:
# url = ha... |
Load a file.
File format is inferred from url. File retrieval strategy is inferred from
URL. Returned object type is inferred from url extension.
Args:
url_or_handle: a (reachable) URL, or an already open file handle
Raises:
RuntimeError: If file extension or URL is not supported. | def load(url_or_handle, cache=None, **kwargs):
"""Load a file.
File format is inferred from url. File retrieval strategy is inferred from
URL. Returned object type is inferred from url extension.
Args:
url_or_handle: a (reachable) URL, or an already open file handle
Raises:
RuntimeErr... |
Ensures the specified spatial shape by either padding or cropping.
Meant to be used as a last transform for architectures insisting on a specific
spatial shape of their inputs. | def crop_or_pad_to(height, width):
"""Ensures the specified spatial shape by either padding or cropping.
Meant to be used as a last transform for architectures insisting on a specific
spatial shape of their inputs.
"""
def inner(t_image):
return tf.image.resize_image_with_crop_or_pad(t_image... |
Given an arbitrary rank-3 NumPy array, produce one representing an image.
This ensures the resulting array has a dtype of uint8 and a domain of 0-255.
Args:
array: NumPy array representing the image
domain: expected range of values in array,
defaults to (0, 1), if explicitly set to None will use the... | def _normalize_array(array, domain=(0, 1)):
"""Given an arbitrary rank-3 NumPy array, produce one representing an image.
This ensures the resulting array has a dtype of uint8 and a domain of 0-255.
Args:
array: NumPy array representing the image
domain: expected range of values in array,
defaults ... |
Given a normalized array, returns byte representation of image encoding.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO bu... | def _serialize_normalized_array(array, fmt='png', quality=70):
"""Given a normalized array, returns byte representation of image encoding.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from... |
Given an arbitrary rank-3 NumPy array,
returns the byte representation of the encoded image.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
domain: expected range of values in array, see `_normalize_array()`
fmt: string describing desired file format, defaults to 'png'
quality: specifie... | def serialize_array(array, domain=(0, 1), fmt='png', quality=70):
"""Given an arbitrary rank-3 NumPy array,
returns the byte representation of the encoded image.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
domain: expected range of values in array, see `_normalize_array()`
fmt: string ... |
Serialize 1d NumPy array to JS TypedArray.
Data is serialized to base64-encoded string, which is much faster
and memory-efficient than json list serialization.
Args:
array: 1d NumPy array, dtype must be one of JS_ARRAY_TYPES.
Returns:
JS code that evaluates to a TypedArray as string.
Raises:
T... | def array_to_jsbuffer(array):
"""Serialize 1d NumPy array to JS TypedArray.
Data is serialized to base64-encoded string, which is much faster
and memory-efficient than json list serialization.
Args:
array: 1d NumPy array, dtype must be one of JS_ARRAY_TYPES.
Returns:
JS code that evaluates to a Typ... |
Utility for applying f to inner dimension of acts.
Flattens acts into a 2D tensor, applies f, then unflattens so that all
dimesnions except innermost are unchanged. | def _apply_flat(cls, f, acts):
"""Utility for applying f to inner dimension of acts.
Flattens acts into a 2D tensor, applies f, then unflattens so that all
dimesnions except innermost are unchanged.
"""
orig_shape = acts.shape
acts_flat = acts.reshape([-1, acts.shape[-1]])
new_flat = f(acts... |
Set target style variables.
Expected usage:
style_loss = StyleLoss(style_layers)
...
init_op = tf.global_variables_initializer()
init_op.run()
feeds = {... session.run() 'feeds' argument that will make 'style_layers'
tensors evaluate to activation values of ... | def set_style(self, input_feeds):
"""Set target style variables.
Expected usage:
style_loss = StyleLoss(style_layers)
...
init_op = tf.global_variables_initializer()
init_op.run()
feeds = {... session.run() 'feeds' argument that will make 'style_layers'
... |
Create a data URL representing an image from a PIL.Image.
Args:
image: a numpy
mode: presently only supports "data" for data URL
Returns:
URL representing image | def _image_url(array, fmt='png', mode="data", quality=90, domain=None):
"""Create a data URL representing an image from a PIL.Image.
Args:
image: a numpy
mode: presently only supports "data" for data URL
Returns:
URL representing image
"""
supported_modes = ("data")
if mode not in supported_mo... |
Display an image.
Args:
array: NumPy array representing the image
fmt: Image format e.g. png, jpeg
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor interpolation.
size unchanged if None | def image(array, domain=None, width=None, format='png', **kwargs):
"""Display an image.
Args:
array: NumPy array representing the image
fmt: Image format e.g. png, jpeg
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nearest neighbor int... |
Display a list of images with optional labels.
Args:
arrays: A list of NumPy arrays representing images
labels: A list of strings to label each image.
Defaults to show index if None
domain: Domain of pixel values, inferred from min & max values if None
w: width of output image, scaled using nea... | def images(arrays, labels=None, domain=None, w=None):
"""Display a list of images with optional labels.
Args:
arrays: A list of NumPy arrays representing images
labels: A list of strings to label each image.
Defaults to show index if None
domain: Domain of pixel values, inferred from min & max va... |
Display a nupmy array without having to specify what it represents.
This module will attempt to infer how to display your tensor based on its
rank, shape and dtype. rank 4 tensors will be displayed as image grids, rank
2 and 3 tensors as images. | def show(thing, domain=(0, 1), **kwargs):
"""Display a nupmy array without having to specify what it represents.
This module will attempt to infer how to display your tensor based on its
rank, shape and dtype. rank 4 tensors will be displayed as image grids, rank
2 and 3 tensors as images.
"""
if isinstanc... |
Strip large constant values from graph_def.
This is mostly a utility function for graph(), and also originates here:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb | def _strip_consts(graph_def, max_const_size=32):
"""Strip large constant values from graph_def.
This is mostly a utility function for graph(), and also originates here:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
"""
strip_def = tf.Gr... |
Visualize a TensorFlow graph.
This function was originally found in this notebook (also Apache licensed):
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb | def graph(graph_def, max_const_size=32):
"""Visualize a TensorFlow graph.
This function was originally found in this notebook (also Apache licensed):
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
"""
if hasattr(graph_def, 'as_graph_def'... |
Resize an ndarray image of rank 3 or 4.
target_size can be a tuple `(width, height)` or scalar `width`. | def resize(image, target_size, **kwargs):
"""Resize an ndarray image of rank 3 or 4.
target_size can be a tuple `(width, height)` or scalar `width`."""
if isinstance(target_size, int):
target_size = (target_size, target_size)
if not isinstance(target_size, (list, tuple, np.ndarray)):
m... |
Takes two images and composites them. | def composite(
background_image,
foreground_image,
foreground_width_ratio=0.25,
foreground_position=(0.0, 0.0),
):
"""Takes two images and composites them."""
if foreground_width_ratio <= 0:
return background_image
composite = background_image.copy()
width = int(foreground_widt... |
Create an intractive TensorFlow session.
Helper function that creates TF session that uses growing GPU memory
allocation and opration timeout. 'allow_growth' flag prevents TF
from allocating the whole GPU memory an once, which is useful
when having multiple python sessions sharing the same GPU. | def create_session(target='', timeout_sec=10):
'''Create an intractive TensorFlow session.
Helper function that creates TF session that uses growing GPU memory
allocation and opration timeout. 'allow_growth' flag prevents TF
from allocating the whole GPU memory an once, which is useful
when having multiple p... |
Produces a tensor paramaterized by a interpolated lower resolution tensor.
This is like what is done in a laplacian pyramid, but a bit more general. It
can be a powerful way to describe images.
Args:
shape: desired shape of resulting tensor
underlying_shape: shape of the tensor being resized into final ... | def lowres_tensor(shape, underlying_shape, offset=None, sd=None):
"""Produces a tensor paramaterized by a interpolated lower resolution tensor.
This is like what is done in a laplacian pyramid, but a bit more general. It
can be a powerful way to describe images.
Args:
shape: desired shape of resulting t... |
Read from any URL.
Internally differentiates between URLs supported by tf.gfile, such as URLs
with the Google Cloud Storage scheme ('gs://...') or local paths, and HTTP
URLs. This way users don't need to know about the underlying fetch mechanism.
Args:
url: a URL including scheme or a local pa... | def read(url, encoding=None, cache=None, mode="rb"):
"""Read from any URL.
Internally differentiates between URLs supported by tf.gfile, such as URLs
with the Google Cloud Storage scheme ('gs://...') or local paths, and HTTP
URLs. This way users don't need to know about the underlying fetch mechanism.
... |
Read from any URL with a file handle.
Use this to get a handle to a file rather than eagerly load the data:
```
with read_handle(url) as handle:
result = something.load(handle)
result.do_something()
```
When program execution leaves this `with` block, the handle will be closed
autom... | def read_handle(url, cache=None, mode="rb"):
"""Read from any URL with a file handle.
Use this to get a handle to a file rather than eagerly load the data:
```
with read_handle(url) as handle:
result = something.load(handle)
result.do_something()
```
When program execution leaves th... |
Returns the path that remote_url would be cached at locally. | def local_cache_path(remote_url):
"""Returns the path that remote_url would be cached at locally."""
local_name = RESERVED_PATH_CHARS.sub("_", remote_url)
return os.path.join(gettempdir(), local_name) |
Compositional Pattern Producing Network
Args:
width: width of resulting image, equals height
batch: batch dimension of output, note that all params share the same weights!
num_output_channels:
num_hidden_channels:
num_layers:
activation_func:
normalize:
Returns:
... | def cppn(
width,
batch=1,
num_output_channels=3,
num_hidden_channels=24,
num_layers=8,
activation_func=_composite_activation,
normalize=False,
):
"""Compositional Pattern Producing Network
Args:
width: width of resulting image, equals height
batch: batch dimension of out... |
Returns a model instance such as `model = vision_models.InceptionV1()`.
In the future may be expanded to filter by additional criteria, such as
architecture, dataset, and task the model was trained on.
Args:
name: The name of the model, as given by the class name in vision_models.
Returns:
A... | def get_model(name):
"""Returns a model instance such as `model = vision_models.InceptionV1()`.
In the future may be expanded to filter by additional criteria, such as
architecture, dataset, and task the model was trained on.
Args:
name: The name of the model, as given by the class name in vision_... |
Renders an Activation Atlas of the given model's layer. | def activation_atlas(
model,
layer,
grid_size=10,
icon_size=96,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
icon_batch_size=32,
verbose=False,
):
"""Renders an Activation Atlas of the given model's layer."""
activations = layer.activations[:number_activations, ...]
layout, =... |
Renders two aligned Activation Atlases of the given models' layers.
Returns a generator of the two atlasses, and a nested generator for intermediate
atlasses while they're being rendered. | def aligned_activation_atlas(
model1,
layer1,
model2,
layer2,
grid_size=10,
icon_size=80,
num_steps=1024,
whiten_layers=True,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
icon_batch_size=32,
verbose=False,
):
"""Renders two aligned Activation Atlases of the given model... |
Given two layers, combines their activations according to mode.
ActivationTranslation.ONE_TO_TWO:
Translate activations of layer1 into the space of layer2, and return a tuple of
the translated activations and the original layer2 activations.
ActivationTranslation.BIDIRECTIONAL:
Translate act... | def _combine_activations(
layer1,
layer2,
activations1=None,
activations2=None,
mode=ActivationTranslation.BIDIRECTIONAL,
number_activations=NUMBER_OF_AVAILABLE_SAMPLES,
):
"""Given two layers, combines their activations according to mode.
ActivationTranslation.ONE_TO_TWO:
Transla... |
Given a layout and activations, overlays a grid on the layout and returns
averaged activations for each grid cell. If a cell contains less than `threshold`
activations it will be discarded, so the number of returned data is variable. | def bin_laid_out_activations(layout, activations, grid_size, threshold=5):
"""Given a layout and activations, overlays a grid on the layout and returns
averaged activations for each grid cell. If a cell contains less than `threshold`
activations it will be discarded, so the number of returned data is variab... |
Load GraphDef from a binary proto file. | def load_graphdef(model_url, reset_device=True):
"""Load GraphDef from a binary proto file."""
graph_def = load(model_url)
if reset_device:
for n in graph_def.node:
n.device = ""
return graph_def |
Ignore sizes of dimensions (1, 2) of a 4d tensor in shape inference.
This allows using smaller input sizes, which create an invalid graph at higher
layers (for example because a spatial dimension becomes smaller than a conv
filter) when we only use early parts of it. | def forget_xy(t):
"""Ignore sizes of dimensions (1, 2) of a 4d tensor in shape inference.
This allows using smaller input sizes, which create an invalid graph at higher
layers (for example because a spatial dimension becomes smaller than a conv
filter) when we only use early parts of it.
"""
shape = (t.sha... |
Return frozen and simplified graph_def of default graph. | def frozen_default_graph_def(input_node_names, output_node_names):
"""Return frozen and simplified graph_def of default graph."""
sess = tf.get_default_session()
input_graph_def = tf.get_default_graph().as_graph_def()
pruned_graph = tf.graph_util.remove_training_nodes(
input_graph_def, protected_nodes=(... |
Embed meta data as a string constant in a TF graph.
This function takes info, converts it into json, and embeds
it in graph_def as a constant op called `__lucid_metadata_json`. | def infuse_metadata(graph_def, info):
"""Embed meta data as a string constant in a TF graph.
This function takes info, converts it into json, and embeds
it in graph_def as a constant op called `__lucid_metadata_json`.
"""
temp_graph = tf.Graph()
with temp_graph.as_default():
tf.constant(json.dumps(info... |
Attempt to extract meta data hidden in graph_def.
Looks for a `__lucid_metadata_json` constant string op.
If present, extract it's content and convert it from json to python.
If not, returns None. | def extract_metadata(graph_def):
"""Attempt to extract meta data hidden in graph_def.
Looks for a `__lucid_metadata_json` constant string op.
If present, extract it's content and convert it from json to python.
If not, returns None.
"""
meta_matches = [n for n in graph_def.node if n.name==metadata_node_nam... |
Am I really handcoding graph traversal please no | def neighborhood(self, node, degree=4):
"""Am I really handcoding graph traversal please no"""
assert self.by_name[node.name] == node
already_visited = frontier = set([node.name])
for _ in range(degree):
neighbor_names = set()
for node_name in frontier:
outgoing = set(n.name for n in... |
Retrieve messages using before parameter. | async def _retrieve_messages_before_strategy(self, retrieve):
"""Retrieve messages using before parameter."""
before = self.before.id if self.before else None
data = await self.logs_from(self.channel.id, retrieve, before=before)
if len(data):
if self.limit is not None:
... |
Retrieve messages using after parameter. | async def _retrieve_messages_after_strategy(self, retrieve):
"""Retrieve messages using after parameter."""
after = self.after.id if self.after else None
data = await self.logs_from(self.channel.id, retrieve, after=after)
if len(data):
if self.limit is not None:
... |
Retrieve messages using around parameter. | async def _retrieve_messages_around_strategy(self, retrieve):
"""Retrieve messages using around parameter."""
if self.around:
around = self.around.id if self.around else None
data = await self.logs_from(self.channel.id, retrieve, around=around)
self.around = None
... |
Retrieve guilds using before parameter. | async def _retrieve_guilds_before_strategy(self, retrieve):
"""Retrieve guilds using before parameter."""
before = self.before.id if self.before else None
data = await self.get_guilds(retrieve, before=before)
if len(data):
if self.limit is not None:
self.limit... |
Retrieve guilds using after parameter. | async def _retrieve_guilds_after_strategy(self, retrieve):
"""Retrieve guilds using after parameter."""
after = self.after.id if self.after else None
data = await self.get_guilds(retrieve, after=after)
if len(data):
if self.limit is not None:
self.limit -= ret... |
|coro|
Retrieves an :class:`Invite` from a invite URL or ID.
This is the same as :meth:`Client.get_invite`; the invite
code is abstracted away.
Parameters
-----------
with_counts: :class:`bool`
Whether to include count information in the invite. This fills t... | async def fetch_invite(self, *, with_counts=True):
"""|coro|
Retrieves an :class:`Invite` from a invite URL or ID.
This is the same as :meth:`Client.get_invite`; the invite
code is abstracted away.
Parameters
-----------
with_counts: :class:`bool`
Wh... |
Constructs a :class:`Colour` from an HSV tuple. | def from_hsv(cls, h, s, v):
"""Constructs a :class:`Colour` from an HSV tuple."""
rgb = colorsys.hsv_to_rgb(h, s, v)
return cls.from_rgb(*(int(x * 255) for x in rgb)) |
:class:`str`: Returns the cog's description, typically the cleaned docstring. | def description(self):
""":class:`str`: Returns the cog's description, typically the cleaned docstring."""
try:
return self.__cog_cleaned_doc__
except AttributeError:
self.__cog_cleaned_doc__ = cleaned = inspect.getdoc(self)
return cleaned |
An iterator that recursively walks through this cog's commands and subcommands. | def walk_commands(self):
"""An iterator that recursively walks through this cog's commands and subcommands."""
from .core import GroupMixin
for command in self.__cog_commands__:
if command.parent is None:
yield command
if isinstance(command, GroupMixin... |
Returns a :class:`list` of (name, function) listener pairs that are defined in this cog. | def get_listeners(self):
"""Returns a :class:`list` of (name, function) listener pairs that are defined in this cog."""
return [(name, getattr(self, method_name)) for name, method_name in self.__cog_listeners__] |
A decorator that marks a function as a listener.
This is the cog equivalent of :meth:`.Bot.listen`.
Parameters
------------
name: :class:`str`
The name of the event being listened to. If not provided, it
defaults to the function's name.
Raises
-... | def listener(cls, name=None):
"""A decorator that marks a function as a listener.
This is the cog equivalent of :meth:`.Bot.listen`.
Parameters
------------
name: :class:`str`
The name of the event being listened to. If not provided, it
defaults to the f... |
Sets the author for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the author.
url: :class:`str`
The URL for the author.
icon_url: :cla... | def set_author(self, *, name, url=EmptyEmbed, icon_url=EmptyEmbed):
"""Sets the author for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the author.
... |
Sets the footer for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
text: :class:`str`
The footer text.
icon_url: :class:`str`
The URL of the footer icon. Only HTTP(S) is supp... | def set_footer(self, *, text=EmptyEmbed, icon_url=EmptyEmbed):
"""Sets the footer for the embed content.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
text: :class:`str`
The footer text.
icon_url:... |
Adds a field to the embed object.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the field.
value: :class:`str`
The value of the field.
inline: :class:`bo... | def add_field(self, *, name, value, inline=True):
"""Adds a field to the embed object.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
name: :class:`str`
The name of the field.
value: :class:`str`
... |
Modifies a field to the embed object.
The index must point to a valid pre-existing field.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
index: :class:`int`
The index of the field to modify.
name:... | def set_field_at(self, index, *, name, value, inline=True):
"""Modifies a field to the embed object.
The index must point to a valid pre-existing field.
This function returns the class instance to allow for fluent-style
chaining.
Parameters
-----------
index: :... |
Converts this embed object into a dict. | def to_dict(self):
"""Converts this embed object into a dict."""
# add in the raw data into the dict
result = {
key[1:]: getattr(self, key)
for key in self.__slots__
if key[0] == '_' and hasattr(self, key)
}
# deal with basic convenience wrap... |
Returns a friendly URL version of the avatar the user has.
If the user does not have a traditional avatar, their default
avatar URL is returned instead.
The format must be one of 'webp', 'jpeg', 'jpg', 'png' or 'gif', and
'gif' is only valid for animated avatars. The size must be a pow... | def avatar_url_as(self, *, format=None, static_format='webp', size=1024):
"""Returns a friendly URL version of the avatar the user has.
If the user does not have a traditional avatar, their default
avatar URL is returned instead.
The format must be one of 'webp', 'jpeg', 'jpg', 'png' o... |
Checks if the user is mentioned in the specified message.
Parameters
-----------
message: :class:`Message`
The message to check if you're mentioned in. | def mentioned_in(self, message):
"""Checks if the user is mentioned in the specified message.
Parameters
-----------
message: :class:`Message`
The message to check if you're mentioned in.
"""
if message.mention_everyone:
return True
for ... |
r"""Returns a :class:`list` of :class:`User`\s that the user is friends with.
.. note::
This only applies to non-bot accounts. | def friends(self):
r"""Returns a :class:`list` of :class:`User`\s that the user is friends with.
.. note::
This only applies to non-bot accounts.
"""
return [r.user for r in self._relationships.values() if r.type is RelationshipType.friend] |
r"""Returns a :class:`list` of :class:`User`\s that the user has blocked.
.. note::
This only applies to non-bot accounts. | def blocked(self):
r"""Returns a :class:`list` of :class:`User`\s that the user has blocked.
.. note::
This only applies to non-bot accounts.
"""
return [r.user for r in self._relationships.values() if r.type is RelationshipType.blocked] |
|coro|
Edits the current profile of the client.
If a bot account is used then a password field is optional,
otherwise it is required.
Note
-----
To upload an avatar, a :term:`py:bytes-like object` must be passed in that
represents the image being uploaded. If t... | async def edit(self, **fields):
"""|coro|
Edits the current profile of the client.
If a bot account is used then a password field is optional,
otherwise it is required.
Note
-----
To upload an avatar, a :term:`py:bytes-like object` must be passed in that
... |
r"""|coro|
Creates a group direct message with the recipients
provided. These recipients must be have a relationship
of type :attr:`RelationshipType.friend`.
.. note::
This only applies to non-bot accounts.
Parameters
-----------
\*recipients: :cla... | async def create_group(self, *recipients):
r"""|coro|
Creates a group direct message with the recipients
provided. These recipients must be have a relationship
of type :attr:`RelationshipType.friend`.
.. note::
This only applies to non-bot accounts.
Parame... |
|coro|
Edits the client user's settings.
.. note::
This only applies to non-bot accounts.
Parameters
-------
afk_timeout: :class:`int`
How long (in seconds) the user needs to be AFK until Discord
sends push notifications to your mobile devi... | async def edit_settings(self, **kwargs):
"""|coro|
Edits the client user's settings.
.. note::
This only applies to non-bot accounts.
Parameters
-------
afk_timeout: :class:`int`
How long (in seconds) the user needs to be AFK until Discord
... |
Creates a :class:`DMChannel` with this user.
This should be rarely called, as this is done transparently for most
people. | async def create_dm(self):
"""Creates a :class:`DMChannel` with this user.
This should be rarely called, as this is done transparently for most
people.
"""
found = self.dm_channel
if found is not None:
return found
state = self._state
data = ... |
|coro|
Gets all mutual friends of this user.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to get mutual friends of this user.
HTTPException
Getting mutual friends failed.
Returns
... | async def mutual_friends(self):
"""|coro|
Gets all mutual friends of this user.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to get mutual friends of this user.
HTTPException
Getting ... |
:class:`bool`: Checks if the user is your friend.
.. note::
This only applies to non-bot accounts. | def is_friend(self):
""":class:`bool`: Checks if the user is your friend.
.. note::
This only applies to non-bot accounts.
"""
r = self.relationship
if r is None:
return False
return r.type is RelationshipType.friend |
:class:`bool`: Checks if the user is blocked.
.. note::
This only applies to non-bot accounts. | def is_blocked(self):
""":class:`bool`: Checks if the user is blocked.
.. note::
This only applies to non-bot accounts.
"""
r = self.relationship
if r is None:
return False
return r.type is RelationshipType.blocked |
|coro|
Blocks the user.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to block this user.
HTTPException
Blocking the user failed. | async def block(self):
"""|coro|
Blocks the user.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to block this user.
HTTPException
Blocking the user failed.
"""
await s... |
|coro|
Sends the user a friend request.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to send a friend request to the user.
HTTPException
Sending the friend request failed. | async def send_friend_request(self):
"""|coro|
Sends the user a friend request.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to send a friend request to the user.
HTTPException
Sendin... |
|coro|
Gets the user's profile.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to fetch profiles.
HTTPException
Fetching the profile failed.
Returns
--------
:class:`Pr... | async def profile(self):
"""|coro|
Gets the user's profile.
.. note::
This only applies to non-bot accounts.
Raises
-------
Forbidden
Not allowed to fetch profiles.
HTTPException
Fetching the profile failed.
Returns... |
Returns a numeric snowflake pretending to be created at the given date.
When using as the lower end of a range, use time_snowflake(high=False) - 1 to be inclusive, high=True to be exclusive
When using as the higher end of a range, use time_snowflake(high=True) + 1 to be inclusive, high=False to be exclusive
... | def time_snowflake(datetime_obj, high=False):
"""Returns a numeric snowflake pretending to be created at the given date.
When using as the lower end of a range, use time_snowflake(high=False) - 1 to be inclusive, high=True to be exclusive
When using as the higher end of a range, use time_snowflake(high=Tru... |
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