code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def adversarial_group_calibration(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
cali_type: str,
prop_type: str = "interval",
num_bins: int = 100,
num_group_bins: int = 10,
draw_with_replacement: bool = False,
num_trials: int = 10,
num_group_draws: int = 10,
verb... | Adversarial group calibration.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
cali_type: type of calibration error to ... | adversarial_group_calibration | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def miscalibration_area_from_proportions(
exp_proportions: np.ndarray, obs_proportions: np.ndarray
) -> float:
"""Miscalibration area from expected and observed proportions lists.
This function returns the same output as `miscalibration_area` directly from a list
of expected proportions (the proportion... | Miscalibration area from expected and observed proportions lists.
This function returns the same output as `miscalibration_area` directly from a list
of expected proportions (the proportion of data that you expect to observe within
prediction intervals) and a list of observed proportions (the proportion da... | miscalibration_area_from_proportions | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_proportion_lists_vectorized(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
num_bins: int = 100,
recal_model: Any = None,
prop_type: str = "interval",
) -> Tuple[np.ndarray, np.ndarray]:
"""Arrays of expected and observed proportions
Returns the expected proportions ... | Arrays of expected and observed proportions
Returns the expected proportions and observed proportion of points falling into
intervals corresponding to a range of quantiles.
Computations here are vectorized for faster execution, but this function is
not suited when there are memory constraints.
Arg... | get_proportion_lists_vectorized | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_proportion_lists(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
num_bins: int = 100,
recal_model: IsotonicRegression = None,
prop_type: str = "interval",
) -> Tuple[np.ndarray, np.ndarray]:
"""Arrays of expected and observed proportions
Return arrays of expected and... | Arrays of expected and observed proportions
Return arrays of expected and observed proportions of points falling into
intervals corresponding to a range of quantiles.
Computations here are not vectorized, in case there are memory constraints.
Args:
y_pred: 1D array of the predicted means for t... | get_proportion_lists | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_proportion_in_interval(
y_pred: np.ndarray, y_std: np.ndarray, y_true: np.ndarray, quantile: float
) -> float:
"""For a specified quantile, return the proportion of points falling into
an interval corresponding to that quantile.
Args:
y_pred: 1D array of the predicted means for the held... | For a specified quantile, return the proportion of points falling into
an interval corresponding to that quantile.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of t... | get_proportion_in_interval | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_proportion_under_quantile(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
quantile: float,
) -> float:
"""Get the proportion of data that are below the predicted quantile.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array... | Get the proportion of data that are below the predicted quantile.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
quant... | get_proportion_under_quantile | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_prediction_interval(
y_pred: np.ndarray,
y_std: np.ndarray,
quantile: np.ndarray,
recal_model: Optional[IsotonicRegression] = None,
) -> Namespace:
"""Return the centered predictional interval corresponding to a quantile.
For a specified quantile level q (must be a float, or a singleton... | Return the centered predictional interval corresponding to a quantile.
For a specified quantile level q (must be a float, or a singleton),
return the centered prediction interval corresponding
to the pair of quantiles at levels (0.5-q/2) and (0.5+q/2),
i.e. interval that has nominal coverage equal to q... | get_prediction_interval | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_quantile(
y_pred: np.ndarray,
y_std: np.ndarray,
quantile: np.ndarray,
recal_model: Optional[IsotonicRegression] = None,
) -> float:
"""Return the value corresponding with a quantile.
For a specified quantile level q (must be a float, or a singleton),
return the quantile prediction,... | Return the value corresponding with a quantile.
For a specified quantile level q (must be a float, or a singleton),
return the quantile prediction,
i.e. bound that has nominal coverage below the bound equal to q.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y... | get_quantile | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def nll_gaussian(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
scaled: bool = True,
) -> float:
"""Negative log likelihood for a gaussian.
The negative log likelihood for held out data (y_true) given predictive
uncertainty with mean (y_pred) and standard-deviation (y_std).
... | Negative log likelihood for a gaussian.
The negative log likelihood for held out data (y_true) given predictive
uncertainty with mean (y_pred) and standard-deviation (y_std).
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard d... | nll_gaussian | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_scoring_rule.py | MIT |
def crps_gaussian(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
scaled: bool = True,
) -> float:
"""The negatively oriented continuous ranked probability score for Gaussians.
Computes CRPS for held out data (y_true) given predictive uncertainty with mean
(y_pred) and standard-... | The negatively oriented continuous ranked probability score for Gaussians.
Computes CRPS for held out data (y_true) given predictive uncertainty with mean
(y_pred) and standard-deviation (y_std). Each test point is given equal weight
in the overall score over the test set.
Negatively oriented means a ... | crps_gaussian | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_scoring_rule.py | MIT |
def check_score(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
scaled: bool = True,
start_q: float = 0.01,
end_q: float = 0.99,
resolution: int = 99,
) -> float:
"""The negatively oriented check score.
Computes the negatively oriented check score for held out data (y_tr... | The negatively oriented check score.
Computes the negatively oriented check score for held out data (y_true)
given predictive uncertainty with mean (y_pred) and standard-deviation (y_std).
Each test point and each quantile is given equal weight in the overall score
over the test set and list of quantil... | check_score | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_scoring_rule.py | MIT |
def interval_score(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
scaled: bool = True,
start_p: float = 0.01,
end_p: float = 0.99,
resolution: int = 99,
) -> float:
"""The negatively oriented interval score.
Compute the negatively oriented interval score for held out da... | The negatively oriented interval score.
Compute the negatively oriented interval score for held out data (y_true)
given predictive uncertainty with mean (y_pred) and standard-deviation
(y_std). Each test point and each percentile is given equal weight in the
overall score over the test set and list of ... | interval_score | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_scoring_rule.py | MIT |
def get_q_idx(exp_props: np.ndarray, q: float) -> int:
"""Utility function which outputs the array index of an element.
Gets the (approximate) index of a specified probability value, q, in the expected proportions array.
Used as a utility function in isotonic regression recalibration.
Args:
ex... | Utility function which outputs the array index of an element.
Gets the (approximate) index of a specified probability value, q, in the expected proportions array.
Used as a utility function in isotonic regression recalibration.
Args:
exp_props: 1D array of expected probabilities.
q: a spec... | get_q_idx | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def iso_recal(
exp_props: np.ndarray,
obs_props: np.ndarray,
) -> IsotonicRegression:
"""Recalibration algorithm based on isotonic regression.
Fits and outputs an isotonic recalibration model that maps observed
probabilities to expected probabilities. This mapping provides
the necessary adjustm... | Recalibration algorithm based on isotonic regression.
Fits and outputs an isotonic recalibration model that maps observed
probabilities to expected probabilities. This mapping provides
the necessary adjustments to produce better calibrated outputs.
Args:
exp_props: 1D array of expected probabi... | iso_recal | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def optimize_recalibration_ratio(
y_mean: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
criterion: str = "ma_cal",
optimizer_bounds: Tuple[float, float] = (1e-2, 1e2),
) -> float:
"""Scale factor which uniformly recalibrates predicted standard deviations.
Searches via black-box optimiz... | Scale factor which uniformly recalibrates predicted standard deviations.
Searches via black-box optimization the standard deviation scale factor (opt_ratio)
which produces the best recalibration, i.e. updated standard deviation
can be written as opt_ratio * y_std.
Args:
y_mean: 1D array of the... | optimize_recalibration_ratio | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def get_std_recalibrator(
y_mean: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
criterion: str = "ma_cal",
optimizer_bounds: Tuple[float, float] = (1e-2, 1e2),
) -> Callable[[np.ndarray], np.ndarray]:
"""Standard deviation recalibrator.
Computes the standard deviation recalibration rat... | Standard deviation recalibrator.
Computes the standard deviation recalibration ratio and returns a function
which takes in an array of uncalibrated standard deviations and returns
an array of recalibrated standard deviations.
Args:
y_mean: 1D array of the predicted means for the recalibration ... | get_std_recalibrator | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def get_quantile_recalibrator(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
) -> Callable[[np.ndarray, np.ndarray, Union[float, np.ndarray]], np.ndarray]:
"""Quantile recalibrator.
Fits an isotonic regression recalibration model and returns a function
which takes in the mean and s... | Quantile recalibrator.
Fits an isotonic regression recalibration model and returns a function
which takes in the mean and standard deviation predictions and a specified
quantile level, and returns the recalibrated quantile.
Args:
y_pred: 1D array of the predicted means for the recalibration da... | get_quantile_recalibrator | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def get_interval_recalibrator(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
) -> Callable[[np.ndarray, np.ndarray, Union[float, np.ndarray]], np.ndarray]:
"""Prediction interval recalibrator.
Fits an isotonic regression recalibration model and returns a function
which takes in the... | Prediction interval recalibrator.
Fits an isotonic regression recalibration model and returns a function
which takes in the mean and standard deviation predictions and a specified
centered interval coverage level, and returns the recalibrated interval.
Args:
y_pred: 1D array of the predicted m... | get_interval_recalibrator | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def assert_is_flat_same_shape(*args: Any) -> Union[bool, NoReturn]:
"""Check if inputs are all same-length 1d numpy.ndarray.
Args:
args: the numpy arrays to check.
Returns:
True if all arrays are flat and the same shape, or else raises assertion error.
"""
assert len(args) > 0
... | Check if inputs are all same-length 1d numpy.ndarray.
Args:
args: the numpy arrays to check.
Returns:
True if all arrays are flat and the same shape, or else raises assertion error.
| assert_is_flat_same_shape | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/utils.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/utils.py | MIT |
def assert_is_positive(*args: Any) -> Union[bool, NoReturn]:
"""Assert that all numpy arrays are positive.
Args:
args: the numpy arrays to check.
Returns:
True if all elements in all arrays are positive values, or else raises assertion error.
"""
assert len(args) > 0
for arr in... | Assert that all numpy arrays are positive.
Args:
args: the numpy arrays to check.
Returns:
True if all elements in all arrays are positive values, or else raises assertion error.
| assert_is_positive | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/utils.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/utils.py | MIT |
def trapezoid_area(
xl: np.ndarray,
al: np.ndarray,
bl: np.ndarray,
xr: np.ndarray,
ar: np.ndarray,
br: np.ndarray,
absolute: bool = True,
) -> Numeric:
"""
Calculate the area of a vertical-sided trapezoid, formed connecting the following points:
(xl, al) - (xl, bl) - (xr, br... |
Calculate the area of a vertical-sided trapezoid, formed connecting the following points:
(xl, al) - (xl, bl) - (xr, br) - (xr, ar) - (xl, al)
This function considers the case that the edges of the trapezoid might cross,
and explicitly accounts for this.
Args:
xl: The x coordinate of ... | trapezoid_area | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/utils.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/utils.py | MIT |
def intersection(
p1: Tuple[Numeric, Numeric],
p2: Tuple[Numeric, Numeric],
p3: Tuple[Numeric, Numeric],
p4: Tuple[Numeric, Numeric],
) -> Tuple[Numeric, Numeric]:
"""
Calculate the intersection of two lines between four points, as defined in
https://en.wikipedia.org/wiki/Line%E2%80%93line_i... |
Calculate the intersection of two lines between four points, as defined in
https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection.
This is an array option and works can be used to calculate the intersections of
entire arrays of points at the same time.
Args:
p1: The point (x1, y1), ... | intersection | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/utils.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/utils.py | MIT |
def plot_xy(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
x: np.ndarray,
n_subset: Union[int, None] = None,
ylims: Union[Tuple[float, float], None] = None,
xlims: Union[Tuple[float, float], None] = None,
num_stds_confidence_bound: int = 2,
leg_loc: Union[int, str] = 3,
... | Plot one-dimensional inputs with associated predicted values, predictive
uncertainties, and true values.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true la... | plot_xy | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_intervals(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
ylims: Union[Tuple[float, float], None] = None,
num_stds_confidence_bound: int = 2,
ax: Union[matplotlib.axes.Axes, None] = None,
) -> matplotlib.axes.Axes:
"""Plot predictio... | Plot predictions and predictive intervals versus true values.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
n_subset:... | plot_intervals | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_intervals_ordered(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
ylims: Union[Tuple[float, float], None] = None,
num_stds_confidence_bound: int = 2,
ax: Union[matplotlib.axes.Axes, None] = None,
) -> matplotlib.axes.Axes:
"""Plot p... | Plot predictions and predictive intervals versus true values, with points ordered
by true value along x-axis.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the tr... | plot_intervals_ordered | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_calibration(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
curve_label: Union[str, None] = None,
vectorized: bool = True,
exp_props: Union[np.ndarray, None] = None,
obs_props: Union[np.ndarray, None] = None,
ax: Union[matplotli... | Plot the observed proportion vs prediction proportion of outputs falling into a
range of intervals, and display miscalibration area.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_t... | plot_calibration | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_adversarial_group_calibration(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
cali_type: str = "mean_abs",
curve_label: Union[str, None] = None,
group_size: Union[np.ndarray, None] = None,
score_mean: Union[np.ndarray, None] = None,... | Plot adversarial group calibration plots by varying group size from 0% to 100% of
dataset size and recording the worst calibration occurred for each group size.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the h... | plot_adversarial_group_calibration | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_sharpness(
y_std: np.ndarray,
n_subset: Union[int, None] = None,
ax: Union[matplotlib.axes.Axes, None] = None,
) -> matplotlib.axes.Axes:
"""Plot sharpness of the predictive uncertainties.
Args:
y_std: 1D array of the predicted standard deviations for the held out dataset.
... | Plot sharpness of the predictive uncertainties.
Args:
y_std: 1D array of the predicted standard deviations for the held out dataset.
n_subset: Number of points to plot after filtering.
ax: matplotlib.axes.Axes object.
Returns:
matplotlib.axes.Axes object with plot added.
| plot_sharpness | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_residuals_vs_stds(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
ax: Union[matplotlib.axes.Axes, None] = None,
) -> matplotlib.axes.Axes:
"""Plot absolute value of the prediction residuals versus standard deviations of the
predictive u... | Plot absolute value of the prediction residuals versus standard deviations of the
predictive uncertainties.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true... | plot_residuals_vs_stds | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def filter_subset(input_list: List[List[Any]], n_subset: int) -> List[List[Any]]:
"""Keep only n_subset random indices from all lists given in input_list.
Args:
input_list: list of lists.
n_subset: Number of points to plot after filtering.
Returns:
List of all input lists with size... | Keep only n_subset random indices from all lists given in input_list.
Args:
input_list: list of lists.
n_subset: Number of points to plot after filtering.
Returns:
List of all input lists with sizes reduced to n_subset.
| filter_subset | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def set_style(style_str: str = "default") -> NoReturn:
"""Set the matplotlib plotting style.
Args:
style_str: string for style file.
"""
if style_str == "default":
plt.style.use((pathlib.Path(__file__).parent / "matplotlibrc").resolve()) | Set the matplotlib plotting style.
Args:
style_str: string for style file.
| set_style | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def save_figure(
file_name: str = "figure",
ext_list: Union[list, str, None] = None,
white_background: bool = True,
) -> NoReturn:
"""Save matplotlib figure for all extensions in ext_list.
Args:
file_name: name of saved image file.
ext_list: list of strings (or single string) denoti... | Save matplotlib figure for all extensions in ext_list.
Args:
file_name: name of saved image file.
ext_list: list of strings (or single string) denoting file type.
white_background: set background of image to white if True.
| save_figure | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def setup(self) -> None:
"""Load the model into memory to make running multiple predictions efficient"""
self.enhancer = FaceEnhancement(
base_dir="checkpoints",
size=512,
model="GPEN-BFR-512",
use_sr=False,
sr_model="rrdb_realesrnet_psnr",
... | Load the model into memory to make running multiple predictions efficient | setup | python | OpenTalker/video-retalking | predict.py | https://github.com/OpenTalker/video-retalking/blob/master/predict.py | Apache-2.0 |
def predict(
self,
face: Path = Input(description="Input video file of a talking-head."),
input_audio: Path = Input(description="Input audio file."),
) -> Path:
"""Run a single prediction on the model"""
device = "cuda"
args = argparse.Namespace(
DNet_path... | Run a single prediction on the model | predict | python | OpenTalker/video-retalking | predict.py | https://github.com/OpenTalker/video-retalking/blob/master/predict.py | Apache-2.0 |
def image_transform(self, images, lm):
"""
param:
images: -- PIL image
lm: -- numpy array
"""
W,H = images.size
if np.mean(lm) == -1:
lm = (self.lm3d_std[:, :2]+1)/2.
lm = np.concatenate(
[lm[:... |
param:
images: -- PIL image
lm: -- numpy array
| image_transform | python | OpenTalker/video-retalking | third_part/face3d/coeff_detector.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/coeff_detector.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize the class; save the options in the class
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
self.opt = opt
# self.root = opt.dataroot
self.current_epoch = 0 | Initialize the class; save the options in the class
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
| __init__ | python | OpenTalker/video-retalking | third_part/face3d/data/base_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/base_dataset.py | Apache-2.0 |
def default_flist_reader(flist):
"""
flist format: impath label\nimpath label\n ...(same to caffe's filelist)
"""
imlist = []
with open(flist, 'r') as rf:
for line in rf.readlines():
impath = line.strip()
imlist.append(impath)
return imlist |
flist format: impath label
impath label
...(same to caffe's filelist)
| default_flist_reader | python | OpenTalker/video-retalking | third_part/face3d/data/flist_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/flist_dataset.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.lm3d_std = load_lm3d(opt.bfm_folder)
... | Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
| __init__ | python | OpenTalker/video-retalking | third_part/face3d/data/flist_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/flist_dataset.py | Apache-2.0 |
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index (int) -- a random integer for data indexing
Returns a dictionary that contains A, B, A_paths and B_paths
img (tensor) -- an image in the input domain
... | Return a data point and its metadata information.
Parameters:
index (int) -- a random integer for data indexing
Returns a dictionary that contains A, B, A_paths and B_paths
img (tensor) -- an image in the input domain
msk (tensor) -- its correspondi... | __getitem__ | python | OpenTalker/video-retalking | third_part/face3d/data/flist_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/flist_dataset.py | Apache-2.0 |
def modify_commandline_options(parser, is_train):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add tra... | Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
... | modify_commandline_options | python | OpenTalker/video-retalking | third_part/face3d/data/template_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/template_dataset.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
A few things can be done here.
- save the options (have been done in BaseDataset)
- get image paths an... | Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
A few things can be done here.
- save the options (have been done in BaseDataset)
- get image paths and meta information of the dataset.
... | __init__ | python | OpenTalker/video-retalking | third_part/face3d/data/template_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/template_dataset.py | Apache-2.0 |
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index -- a random integer for data indexing
Returns:
a dictionary of data with their names. It usually contains the data itself and its metadata information.
Step 1: ... | Return a data point and its metadata information.
Parameters:
index -- a random integer for data indexing
Returns:
a dictionary of data with their names. It usually contains the data itself and its metadata information.
Step 1: get a random image path: e.g., path = sel... | __getitem__ | python | OpenTalker/video-retalking | third_part/face3d/data/template_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/template_dataset.py | Apache-2.0 |
def find_dataset_using_name(dataset_name):
"""Import the module "data/[dataset_name]_dataset.py".
In the file, the class called DatasetNameDataset() will
be instantiated. It has to be a subclass of BaseDataset,
and it is case-insensitive.
"""
dataset_filename = "data." + dataset_name + "_datase... | Import the module "data/[dataset_name]_dataset.py".
In the file, the class called DatasetNameDataset() will
be instantiated. It has to be a subclass of BaseDataset,
and it is case-insensitive.
| find_dataset_using_name | python | OpenTalker/video-retalking | third_part/face3d/data/__init__.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/__init__.py | Apache-2.0 |
def create_dataset(opt, rank=0):
"""Create a dataset given the option.
This function wraps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from data import create_dataset
>>> dataset = create_dataset(opt)
... | Create a dataset given the option.
This function wraps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from data import create_dataset
>>> dataset = create_dataset(opt)
| create_dataset | python | OpenTalker/video-retalking | third_part/face3d/data/__init__.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/__init__.py | Apache-2.0 |
def __init__(self, opt, rank=0):
"""Initialize this class
Step 1: create a dataset instance given the name [dataset_mode]
Step 2: create a multi-threaded data loader.
"""
self.opt = opt
dataset_class = find_dataset_using_name(opt.dataset_mode)
self.dataset = data... | Initialize this class
Step 1: create a dataset instance given the name [dataset_mode]
Step 2: create a multi-threaded data loader.
| __init__ | python | OpenTalker/video-retalking | third_part/face3d/data/__init__.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/__init__.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize the BaseModel class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
When creating your custom class, you need to implement your own initialization.
In this fucntion, you should f... | Initialize the BaseModel class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
When creating your custom class, you need to implement your own initialization.
In this fucntion, you should first call <BaseModel.__init__(self, ... | __init__ | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def setup(self, opt):
"""Load and print networks; create schedulers
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
if self.isTrain:
self.schedulers = [networks.get_scheduler(optimizer, opt) for optimiz... | Load and print networks; create schedulers
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
| setup | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def update_learning_rate(self):
"""Update learning rates for all the networks; called at the end of every epoch"""
for scheduler in self.schedulers:
if self.opt.lr_policy == 'plateau':
scheduler.step(self.metric)
else:
scheduler.step()
lr ... | Update learning rates for all the networks; called at the end of every epoch | update_learning_rate | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def get_current_visuals(self):
"""Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
visual_ret = OrderedDict()
for name in self.visual_names:
if isinstance(name, str):
visual_ret[name] = getattr(self, name)[:... | Return visualization images. train.py will display these images with visdom, and save the images to a HTML | get_current_visuals | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def get_current_losses(self):
"""Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
errors_ret = OrderedDict()
for name in self.loss_names:
if isinstance(name, str):
errors_ret[name] = float(getattr(self, 'loss_... | Return traning losses / errors. train.py will print out these errors on console, and save them to a file | get_current_losses | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def save_networks(self, epoch):
"""Save all the networks to the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
if not os.path.isdir(self.save_dir):
os.makedirs(self.save_dir)
save_filename = 'epo... | Save all the networks to the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
| save_networks | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
"""Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
key = keys[i]
if i + 1 == len(keys): # at the end, pointing to a parameter/buffer
if module.__class__.__name__.startswith('InstanceNorm') and ... | Fix InstanceNorm checkpoints incompatibility (prior to 0.4) | __patch_instance_norm_state_dict | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def load_networks(self, epoch):
"""Load all the networks from the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
if self.opt.isTrain and self.opt.pretrained_name is not None:
load_dir = os.path.join(self.... | Load all the networks from the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
| load_networks | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def print_networks(self, verbose):
"""Print the total number of parameters in the network and (if verbose) network architecture
Parameters:
verbose (bool) -- if verbose: print the network architecture
"""
print('---------- Networks initialized -------------')
for nam... | Print the total number of parameters in the network and (if verbose) network architecture
Parameters:
verbose (bool) -- if verbose: print the network architecture
| print_networks | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def set_requires_grad(self, nets, requires_grad=False):
"""Set requies_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
"""
... | Set requies_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
| set_requires_grad | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def compute_shape(self, id_coeff, exp_coeff):
"""
Return:
face_shape -- torch.tensor, size (B, N, 3)
Parameters:
id_coeff -- torch.tensor, size (B, 80), identity coeffs
exp_coeff -- torch.tensor, size (B, 64), expression coeffs
""... |
Return:
face_shape -- torch.tensor, size (B, N, 3)
Parameters:
id_coeff -- torch.tensor, size (B, 80), identity coeffs
exp_coeff -- torch.tensor, size (B, 64), expression coeffs
| compute_shape | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def compute_texture(self, tex_coeff, normalize=True):
"""
Return:
face_texture -- torch.tensor, size (B, N, 3), in RGB order, range (0, 1.)
Parameters:
tex_coeff -- torch.tensor, size (B, 80)
"""
batch_size = tex_coeff.shape[0]
face_tex... |
Return:
face_texture -- torch.tensor, size (B, N, 3), in RGB order, range (0, 1.)
Parameters:
tex_coeff -- torch.tensor, size (B, 80)
| compute_texture | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def compute_norm(self, face_shape):
"""
Return:
vertex_norm -- torch.tensor, size (B, N, 3)
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
"""
v1 = face_shape[:, self.face_buf[:, 0]]
v2 = face_shape[:, self.face_buf[:, 1]]
... |
Return:
vertex_norm -- torch.tensor, size (B, N, 3)
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
| compute_norm | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def compute_color(self, face_texture, face_norm, gamma):
"""
Return:
face_color -- torch.tensor, size (B, N, 3), range (0, 1.)
Parameters:
face_texture -- torch.tensor, size (B, N, 3), from texture model, range (0, 1.)
face_norm -- torch.tens... |
Return:
face_color -- torch.tensor, size (B, N, 3), range (0, 1.)
Parameters:
face_texture -- torch.tensor, size (B, N, 3), from texture model, range (0, 1.)
face_norm -- torch.tensor, size (B, N, 3), rotated face normal
gamma ... | compute_color | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def compute_rotation(self, angles):
"""
Return:
rot -- torch.tensor, size (B, 3, 3) pts @ trans_mat
Parameters:
angles -- torch.tensor, size (B, 3), radian
"""
batch_size = angles.shape[0]
ones = torch.ones([batch_size, 1])... |
Return:
rot -- torch.tensor, size (B, 3, 3) pts @ trans_mat
Parameters:
angles -- torch.tensor, size (B, 3), radian
| compute_rotation | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def to_image(self, face_shape):
"""
Return:
face_proj -- torch.tensor, size (B, N, 2), y direction is opposite to v direction
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
"""
# to image_plane
face_proj = face_shape @ self.pe... |
Return:
face_proj -- torch.tensor, size (B, N, 2), y direction is opposite to v direction
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
| to_image | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def split_coeff(self, coeffs):
"""
Return:
coeffs_dict -- a dict of torch.tensors
Parameters:
coeffs -- torch.tensor, size (B, 256)
"""
id_coeffs = coeffs[:, :80]
exp_coeffs = coeffs[:, 80: 144]
tex_coeffs = coeffs[:, 144: 224... |
Return:
coeffs_dict -- a dict of torch.tensors
Parameters:
coeffs -- torch.tensor, size (B, 256)
| split_coeff | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def compute_for_render(self, coeffs):
"""
Return:
face_vertex -- torch.tensor, size (B, N, 3), in camera coordinate
face_color -- torch.tensor, size (B, N, 3), in RGB order
landmark -- torch.tensor, size (B, 68, 2), y direction is opposite to v directi... |
Return:
face_vertex -- torch.tensor, size (B, N, 3), in camera coordinate
face_color -- torch.tensor, size (B, N, 3), in RGB order
landmark -- torch.tensor, size (B, 68, 2), y direction is opposite to v direction
Parameters:
coeffs ... | compute_for_render | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def modify_commandline_options(parser, is_train=True):
""" Configures options specific for CUT model
"""
# net structure and parameters
parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='network structure')
parser... | Configures options specific for CUT model
| modify_commandline_options | python | OpenTalker/video-retalking | third_part/face3d/models/facerecon_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/facerecon_model.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize this model class.
Parameters:
opt -- training/test options
A few things can be done here.
- (required) call the initialization function of BaseModel
- define loss function, visualization images, model names, and optimizers
... | Initialize this model class.
Parameters:
opt -- training/test options
A few things can be done here.
- (required) call the initialization function of BaseModel
- define loss function, visualization images, model names, and optimizers
| __init__ | python | OpenTalker/video-retalking | third_part/face3d/models/facerecon_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/facerecon_model.py | Apache-2.0 |
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input: a dictionary that contains the data itself and its metadata information.
"""
self.input_img = input['imgs'].to(self.device)
self.atten... | Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input: a dictionary that contains the data itself and its metadata information.
| set_input | python | OpenTalker/video-retalking | third_part/face3d/models/facerecon_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/facerecon_model.py | Apache-2.0 |
def compute_losses(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
assert self.net_recog.training == False
trans_m = self.trans_m
if not self.opt.use_predef_M:
trans_m = estimate_norm_torch(self.pred_lm, self.input_img... | Calculate losses, gradients, and update network weights; called in every training iteration | compute_losses | python | OpenTalker/video-retalking | third_part/face3d/models/facerecon_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/facerecon_model.py | Apache-2.0 |
def forward(imageA, imageB, M):
"""
1 - cosine distance
Parameters:
imageA --torch.tensor (B, 3, H, W), range (0, 1) , RGB order
imageB --same as imageA
"""
imageA = self.preprocess(resize_n_crop(imageA, M, self.input_size))
imageB = s... |
1 - cosine distance
Parameters:
imageA --torch.tensor (B, 3, H, W), range (0, 1) , RGB order
imageB --same as imageA
| forward | python | OpenTalker/video-retalking | third_part/face3d/models/losses.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/losses.py | Apache-2.0 |
def photo_loss(imageA, imageB, mask, eps=1e-6):
"""
l2 norm (with sqrt, to ensure backward stabililty, use eps, otherwise Nan may occur)
Parameters:
imageA --torch.tensor (B, 3, H, W), range (0, 1), RGB order
imageB --same as imageA
"""
loss = torch.sqrt(eps + torch.sum(... |
l2 norm (with sqrt, to ensure backward stabililty, use eps, otherwise Nan may occur)
Parameters:
imageA --torch.tensor (B, 3, H, W), range (0, 1), RGB order
imageB --same as imageA
| photo_loss | python | OpenTalker/video-retalking | third_part/face3d/models/losses.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/losses.py | Apache-2.0 |
def landmark_loss(predict_lm, gt_lm, weight=None):
"""
weighted mse loss
Parameters:
predict_lm --torch.tensor (B, 68, 2)
gt_lm --torch.tensor (B, 68, 2)
weight --numpy.array (1, 68)
"""
if not weight:
weight = np.ones([68])
weight[28:31] = 2... |
weighted mse loss
Parameters:
predict_lm --torch.tensor (B, 68, 2)
gt_lm --torch.tensor (B, 68, 2)
weight --numpy.array (1, 68)
| landmark_loss | python | OpenTalker/video-retalking | third_part/face3d/models/losses.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/losses.py | Apache-2.0 |
def reg_loss(coeffs_dict, opt=None):
"""
l2 norm without the sqrt, from yu's implementation (mse)
tf.nn.l2_loss https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss
Parameters:
coeffs_dict -- a dict of torch.tensors , keys: id, exp, tex, angle, gamma, trans
"""
# coefficient re... |
l2 norm without the sqrt, from yu's implementation (mse)
tf.nn.l2_loss https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss
Parameters:
coeffs_dict -- a dict of torch.tensors , keys: id, exp, tex, angle, gamma, trans
| reg_loss | python | OpenTalker/video-retalking | third_part/face3d/models/losses.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/losses.py | Apache-2.0 |
def reflectance_loss(texture, mask):
"""
minimize texture variance (mse), albedo regularization to ensure an uniform skin albedo
Parameters:
texture --torch.tensor, (B, N, 3)
mask --torch.tensor, (N), 1 or 0
"""
mask = mask.reshape([1, mask.shape[0], 1])
texture_m... |
minimize texture variance (mse), albedo regularization to ensure an uniform skin albedo
Parameters:
texture --torch.tensor, (B, N, 3)
mask --torch.tensor, (N), 1 or 0
| reflectance_loss | python | OpenTalker/video-retalking | third_part/face3d/models/losses.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/losses.py | Apache-2.0 |
def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNeXt-50 32x4d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on Im... | ResNeXt-50 32x4d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
| resnext50_32x4d | python | OpenTalker/video-retalking | third_part/face3d/models/networks.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/networks.py | Apache-2.0 |
def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""ResNeXt-101 32x8d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ... | ResNeXt-101 32x8d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
| resnext101_32x8d | python | OpenTalker/video-retalking | third_part/face3d/models/networks.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/networks.py | Apache-2.0 |
def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""Wide ResNet-50-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every ... | Wide ResNet-50-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 h... | wide_resnet50_2 | python | OpenTalker/video-retalking | third_part/face3d/models/networks.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/networks.py | Apache-2.0 |
def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
r"""Wide ResNet-101-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in ever... | Wide ResNet-101-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 ... | wide_resnet101_2 | python | OpenTalker/video-retalking | third_part/face3d/models/networks.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/networks.py | Apache-2.0 |
def modify_commandline_options(parser, is_train=True):
"""Add new model-specific options and rewrite default values for existing options.
Parameters:
parser -- the option parser
is_train -- if it is training phase or test phase. You can use this flag to add training-specific or ... | Add new model-specific options and rewrite default values for existing options.
Parameters:
parser -- the option parser
is_train -- if it is training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified ... | modify_commandline_options | python | OpenTalker/video-retalking | third_part/face3d/models/template_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/template_model.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize this model class.
Parameters:
opt -- training/test options
A few things can be done here.
- (required) call the initialization function of BaseModel
- define loss function, visualization images, model names, and optimizers
... | Initialize this model class.
Parameters:
opt -- training/test options
A few things can be done here.
- (required) call the initialization function of BaseModel
- define loss function, visualization images, model names, and optimizers
| __init__ | python | OpenTalker/video-retalking | third_part/face3d/models/template_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/template_model.py | Apache-2.0 |
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input: a dictionary that contains the data itself and its metadata information.
"""
AtoB = self.opt.direction == 'AtoB' # use <direction> to swap dat... | Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input: a dictionary that contains the data itself and its metadata information.
| set_input | python | OpenTalker/video-retalking | third_part/face3d/models/template_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/template_model.py | Apache-2.0 |
def backward(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
# calculate the intermediate results if necessary; here self.output has been computed during function <forward>
# calculate loss given the input and intermediate results
... | Calculate losses, gradients, and update network weights; called in every training iteration | backward | python | OpenTalker/video-retalking | third_part/face3d/models/template_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/template_model.py | Apache-2.0 |
def optimize_parameters(self):
"""Update network weights; it will be called in every training iteration."""
self.forward() # first call forward to calculate intermediate results
self.optimizer.zero_grad() # clear network G's existing gradients
self.backward() ... | Update network weights; it will be called in every training iteration. | optimize_parameters | python | OpenTalker/video-retalking | third_part/face3d/models/template_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/template_model.py | Apache-2.0 |
def find_model_using_name(model_name):
"""Import the module "models/[model_name]_model.py".
In the file, the class called DatasetNameModel() will
be instantiated. It has to be a subclass of BaseModel,
and it is case-insensitive.
"""
model_filename = "face3d.models." + model_name + "_model"
... | Import the module "models/[model_name]_model.py".
In the file, the class called DatasetNameModel() will
be instantiated. It has to be a subclass of BaseModel,
and it is case-insensitive.
| find_model_using_name | python | OpenTalker/video-retalking | third_part/face3d/models/__init__.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/__init__.py | Apache-2.0 |
def create_model(opt):
"""Create a model given the option.
This function warps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from models import create_model
>>> model = create_model(opt)
"""
model = find... | Create a model given the option.
This function warps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from models import create_model
>>> model = create_model(opt)
| create_model | python | OpenTalker/video-retalking | third_part/face3d/models/__init__.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/__init__.py | Apache-2.0 |
def __init__(self, rank, local_rank, world_size, batch_size, resume,
margin_softmax, num_classes, sample_rate=1.0, embedding_size=512, prefix="./"):
"""
rank: int
Unique process(GPU) ID from 0 to world_size - 1.
local_rank: int
Unique process(GPU) ID with... |
rank: int
Unique process(GPU) ID from 0 to world_size - 1.
local_rank: int
Unique process(GPU) ID within the server from 0 to 7.
world_size: int
Number of GPU.
batch_size: int
Batch size on current rank(GPU).
resume: bool
... | __init__ | python | OpenTalker/video-retalking | third_part/face3d/models/arcface_torch/partial_fc.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/arcface_torch/partial_fc.py | Apache-2.0 |
def sample(self, total_label):
"""
Sample all positive class centers in each rank, and random select neg class centers to filling a fixed
`num_sample`.
total_label: tensor
Label after all gather, which cross all GPUs.
"""
index_positive = (self.class_start <=... |
Sample all positive class centers in each rank, and random select neg class centers to filling a fixed
`num_sample`.
total_label: tensor
Label after all gather, which cross all GPUs.
| sample | python | OpenTalker/video-retalking | third_part/face3d/models/arcface_torch/partial_fc.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/arcface_torch/partial_fc.py | Apache-2.0 |
def update(self):
""" Set updated weight and weight_mom to memory bank.
"""
self.weight_mom[self.index] = self.sub_weight_mom
self.weight[self.index] = self.sub_weight | Set updated weight and weight_mom to memory bank.
| update | python | OpenTalker/video-retalking | third_part/face3d/models/arcface_torch/partial_fc.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/arcface_torch/partial_fc.py | Apache-2.0 |
def prepare(self, label, optimizer):
"""
get sampled class centers for cal softmax.
label: tensor
Label tensor on each rank.
optimizer: opt
Optimizer for partial fc, which need to get weight mom.
"""
with torch.cuda.stream(self.stream):
... |
get sampled class centers for cal softmax.
label: tensor
Label tensor on each rank.
optimizer: opt
Optimizer for partial fc, which need to get weight mom.
| prepare | python | OpenTalker/video-retalking | third_part/face3d/models/arcface_torch/partial_fc.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/arcface_torch/partial_fc.py | Apache-2.0 |
def forward_backward(self, label, features, optimizer):
"""
Partial fc forward and backward with model parallel
label: tensor
Label tensor on each rank(GPU)
features: tensor
Features tensor on each rank(GPU)
optimizer: optimizer
Optimizer for ... |
Partial fc forward and backward with model parallel
label: tensor
Label tensor on each rank(GPU)
features: tensor
Features tensor on each rank(GPU)
optimizer: optimizer
Optimizer for partial fc
Returns:
--------
x_grad: tenso... | forward_backward | python | OpenTalker/video-retalking | third_part/face3d/models/arcface_torch/partial_fc.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/arcface_torch/partial_fc.py | Apache-2.0 |
def scale(self, outputs):
"""
Multiplies ('scales') a tensor or list of tensors by the scale factor.
Returns scaled outputs. If this instance of :class:`GradScaler` is not enabled, outputs are returned
unmodified.
Arguments:
outputs (Tensor or iterable of Tensors):... |
Multiplies ('scales') a tensor or list of tensors by the scale factor.
Returns scaled outputs. If this instance of :class:`GradScaler` is not enabled, outputs are returned
unmodified.
Arguments:
outputs (Tensor or iterable of Tensors): Outputs to scale.
| scale | python | OpenTalker/video-retalking | third_part/face3d/models/arcface_torch/utils/utils_amp.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/arcface_torch/utils/utils_amp.py | Apache-2.0 |
def __init__(self, cmd_line=None):
"""Reset the class; indicates the class hasn't been initialized"""
self.initialized = False
self.cmd_line = None
if cmd_line is not None:
self.cmd_line = cmd_line.split() | Reset the class; indicates the class hasn't been initialized | __init__ | python | OpenTalker/video-retalking | third_part/face3d/options/base_options.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/options/base_options.py | Apache-2.0 |
def initialize(self, parser):
"""Define the common options that are used in both training and test."""
# basic parameters
parser.add_argument('--name', type=str, default='face_recon', help='name of the experiment. It decides where to store samples and models')
parser.add_argument('--gpu_... | Define the common options that are used in both training and test. | initialize | python | OpenTalker/video-retalking | third_part/face3d/options/base_options.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/options/base_options.py | Apache-2.0 |
def gather_options(self):
"""Initialize our parser with basic options(only once).
Add additional model-specific and dataset-specific options.
These options are defined in the <modify_commandline_options> function
in model and dataset classes.
"""
if not self.initialized: ... | Initialize our parser with basic options(only once).
Add additional model-specific and dataset-specific options.
These options are defined in the <modify_commandline_options> function
in model and dataset classes.
| gather_options | python | OpenTalker/video-retalking | third_part/face3d/options/base_options.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/options/base_options.py | Apache-2.0 |
def print_options(self, opt):
"""Print and save options
It will print both current options and default values(if different).
It will save options into a text file / [checkpoints_dir] / opt.txt
"""
message = ''
message += '----------------- Options ---------------\n'
... | Print and save options
It will print both current options and default values(if different).
It will save options into a text file / [checkpoints_dir] / opt.txt
| print_options | python | OpenTalker/video-retalking | third_part/face3d/options/base_options.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/options/base_options.py | Apache-2.0 |
def parse(self):
"""Parse our options, create checkpoints directory suffix, and set up gpu device."""
opt = self.gather_options()
opt.isTrain = self.isTrain # train or test
# process opt.suffix
if opt.suffix:
suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.s... | Parse our options, create checkpoints directory suffix, and set up gpu device. | parse | python | OpenTalker/video-retalking | third_part/face3d/options/base_options.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/options/base_options.py | Apache-2.0 |
def __init__(self, web_dir, title, refresh=0):
"""Initialize the HTML classes
Parameters:
web_dir (str) -- a directory that stores the webpage. HTML file will be created at <web_dir>/index.html; images will be saved at <web_dir/images/
title (str) -- the webpage name
... | Initialize the HTML classes
Parameters:
web_dir (str) -- a directory that stores the webpage. HTML file will be created at <web_dir>/index.html; images will be saved at <web_dir/images/
title (str) -- the webpage name
refresh (int) -- how often the website refresh itself; ... | __init__ | python | OpenTalker/video-retalking | third_part/face3d/util/html.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/html.py | Apache-2.0 |
def add_images(self, ims, txts, links, width=400):
"""add images to the HTML file
Parameters:
ims (str list) -- a list of image paths
txts (str list) -- a list of image names shown on the website
links (str list) -- a list of hyperref links; when you click an ima... | add images to the HTML file
Parameters:
ims (str list) -- a list of image paths
txts (str list) -- a list of image names shown on the website
links (str list) -- a list of hyperref links; when you click an image, it will redirect you to a new page
| add_images | python | OpenTalker/video-retalking | third_part/face3d/util/html.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/html.py | Apache-2.0 |
def save(self):
"""save the current content to the HTML file"""
html_file = '%s/index.html' % self.web_dir
f = open(html_file, 'wt')
f.write(self.doc.render())
f.close() | save the current content to the HTML file | save | python | OpenTalker/video-retalking | third_part/face3d/util/html.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/html.py | Apache-2.0 |
def forward(self, vertex, tri, feat=None):
"""
Return:
mask -- torch.tensor, size (B, 1, H, W)
depth -- torch.tensor, size (B, 1, H, W)
features(optional) -- torch.tensor, size (B, C, H, W) if feat is not None
Parameters:
... |
Return:
mask -- torch.tensor, size (B, 1, H, W)
depth -- torch.tensor, size (B, 1, H, W)
features(optional) -- torch.tensor, size (B, C, H, W) if feat is not None
Parameters:
vertex -- torch.tensor, size (B, N, 3)
... | forward | python | OpenTalker/video-retalking | third_part/face3d/util/nvdiffrast.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/nvdiffrast.py | Apache-2.0 |
def align_img(img, lm, lm3D, mask=None, target_size=224., rescale_factor=102.):
"""
Return:
transparams --numpy.array (raw_W, raw_H, scale, tx, ty)
img_new --PIL.Image (target_size, target_size, 3)
lm_new --numpy.array (68, 2), y direction is opposite to ... |
Return:
transparams --numpy.array (raw_W, raw_H, scale, tx, ty)
img_new --PIL.Image (target_size, target_size, 3)
lm_new --numpy.array (68, 2), y direction is opposite to v direction
mask_new --PIL.Image (target_size, target_size)
... | align_img | python | OpenTalker/video-retalking | third_part/face3d/util/preprocess.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/preprocess.py | Apache-2.0 |
def estimate_norm(lm_68p, H):
# from https://github.com/deepinsight/insightface/blob/c61d3cd208a603dfa4a338bd743b320ce3e94730/recognition/common/face_align.py#L68
"""
Return:
trans_m --numpy.array (2, 3)
Parameters:
lm --numpy.array (68, 2), y direction is op... |
Return:
trans_m --numpy.array (2, 3)
Parameters:
lm --numpy.array (68, 2), y direction is opposite to v direction
H --int/float , image height
| estimate_norm | python | OpenTalker/video-retalking | third_part/face3d/util/preprocess.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/preprocess.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.