BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def harmonics_2d(harmonic_out, x, freqs, h_range, kind='linear', fill_value=0, axis=0): '''Populate a harmonic tensor from a time-frequency representation with time-varying frequencies. Parameters ---------- harmonic_out : np.ndarray The output array to store harmonics ...
def load(path, sr=22050, mono=True, offset=0.0, duration=None, dtype=np.float32, res_type='kaiser_best'): """Load an audio file as a floating point time series. Audio will be automatically resampled to the given rate (default `sr=22050`). To preserve the native sampling rate of the file, use ...
def __audioread_load(path, offset, duration, dtype): '''Load an audio buffer using audioread. This loads one block at a time, and then concatenates the results. ''' y = [] with audioread.audio_open(path) as input_file: sr_native = input_file.samplerate n_channels = input_file.chann...
def to_mono(y): '''Force an audio signal down to mono. Parameters ---------- y : np.ndarray [shape=(2,n) or shape=(n,)] audio time series, either stereo or mono Returns ------- y_mono : np.ndarray [shape=(n,)] `y` as a monophonic time-series Notes ----- This fu...
def resample(y, orig_sr, target_sr, res_type='kaiser_best', fix=True, scale=False, **kwargs): """Resample a time series from orig_sr to target_sr Parameters ---------- y : np.ndarray [shape=(n,) or shape=(2, n)] audio time series. Can be mono or stereo. orig_sr : number > 0 [scalar] ...
def get_duration(y=None, sr=22050, S=None, n_fft=2048, hop_length=512, center=True, filename=None): """Compute the duration (in seconds) of an audio time series, feature matrix, or filename. Examples -------- >>> # Load the example audio file >>> y, sr = librosa.load(librosa.ut...
def autocorrelate(y, max_size=None, axis=-1): """Bounded auto-correlation Parameters ---------- y : np.ndarray array to autocorrelate max_size : int > 0 or None maximum correlation lag. If unspecified, defaults to `y.shape[axis]` (unbounded) axis : int The axi...
def lpc(y, order): """Linear Prediction Coefficients via Burg's method This function applies Burg's method to estimate coefficients of a linear filter on `y` of order `order`. Burg's method is an extension to the Yule-Walker approach, which are both sometimes referred to as LPC parameter estimatio...
def zero_crossings(y, threshold=1e-10, ref_magnitude=None, pad=True, zero_pos=True, axis=-1): '''Find the zero-crossings of a signal `y`: indices `i` such that `sign(y[i]) != sign(y[j])`. If `y` is multi-dimensional, then zero-crossings are computed along the specified `axis`. ...
def clicks(times=None, frames=None, sr=22050, hop_length=512, click_freq=1000.0, click_duration=0.1, click=None, length=None): """Returns a signal with the signal `click` placed at each specified time Parameters ---------- times : np.ndarray or None times to place clicks, in seconds ...
def tone(frequency, sr=22050, length=None, duration=None, phi=None): """Returns a pure tone signal. The signal generated is a cosine wave. Parameters ---------- frequency : float > 0 frequency sr : number > 0 desired sampling rate of the output signal length : int > 0 ...
def chirp(fmin, fmax, sr=22050, length=None, duration=None, linear=False, phi=None): """Returns a chirp signal that goes from frequency `fmin` to frequency `fmax` Parameters ---------- fmin : float > 0 initial frequency fmax : float > 0 final frequency sr : number > 0 ...
def tempogram(y=None, sr=22050, onset_envelope=None, hop_length=512, win_length=384, center=True, window='hann', norm=np.inf): '''Compute the tempogram: local autocorrelation of the onset strength envelope. [1]_ .. [1] Grosche, Peter, Meinard Müller, and Frank Kurth. "Cyclic tempogram - A...
def find_files(directory, ext=None, recurse=True, case_sensitive=False, limit=None, offset=0): '''Get a sorted list of (audio) files in a directory or directory sub-tree. Examples -------- >>> # Get all audio files in a directory sub-tree >>> files = librosa.util.find_files('~/Music'...
def __get_files(dir_name, extensions): '''Helper function to get files in a single directory''' # Expand out the directory dir_name = os.path.abspath(os.path.expanduser(dir_name)) myfiles = set() for sub_ext in extensions: globstr = os.path.join(dir_name, '*' + os.path.extsep + sub_ext) ...
def stretch_demo(input_file, output_file, speed): '''Phase-vocoder time stretch demo function. :parameters: - input_file : str path to input audio - output_file : str path to save output (wav) - speed : float > 0 speed up by this factor ''' # 1. Load the...
def process_arguments(args): '''Argparse function to get the program parameters''' parser = argparse.ArgumentParser(description='Time stretching example') parser.add_argument('input_file', action='store', help='path to the input file (wav, mp3, etc)') p...
def hpss_demo(input_file, output_harmonic, output_percussive): '''HPSS demo function. :parameters: - input_file : str path to input audio - output_harmonic : str path to save output harmonic (wav) - output_percussive : str path to save output harmonic (wav) '...
def beat_track(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120.0, tightness=100, trim=True, bpm=None, units='frames'): r'''Dynamic programming beat tracker. Beats are detected in three stages, following the method of [1]_: 1. Measure onset strength ...
def tempo(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120, std_bpm=1.0, ac_size=8.0, max_tempo=320.0, aggregate=np.mean): """Estimate the tempo (beats per minute) Parameters ---------- y : np.ndarray [shape=(n,)] or None audio time series sr : number > 0 [sca...
def __beat_tracker(onset_envelope, bpm, fft_res, tightness, trim): """Internal function that tracks beats in an onset strength envelope. Parameters ---------- onset_envelope : np.ndarray [shape=(n,)] onset strength envelope bpm : float [scalar] tempo estimate fft_res : float ...
def __normalize_onsets(onsets): '''Maps onset strength function into the range [0, 1]''' norm = onsets.std(ddof=1) if norm > 0: onsets = onsets / norm return onsets
def __beat_local_score(onset_envelope, period): '''Construct the local score for an onset envlope and given period''' window = np.exp(-0.5 * (np.arange(-period, period+1)32.0/period)*2) return scipy.signal.convolve(__normalize_onsets(onset_envelope), window, ...
def __beat_track_dp(localscore, period, tightness): """Core dynamic program for beat tracking""" backlink = np.zeros_like(localscore, dtype=int) cumscore = np.zeros_like(localscore) # Search range for previous beat window = np.arange(-2 * period, -np.round(period / 2) + 1, dtype=int) # Make a...
def __last_beat(cumscore): """Get the last beat from the cumulative score array""" maxes = util.localmax(cumscore) med_score = np.median(cumscore[np.argwhere(maxes)]) # The last of these is the last beat (since score generally increases) return np.argwhere((cumscore * maxes * 2 > med_score)).max()
def __trim_beats(localscore, beats, trim): """Final post-processing: throw out spurious leading/trailing beats""" smooth_boe = scipy.signal.convolve(localscore[beats], scipy.signal.hann(5), 'same') if trim: threshold = 0...
def recurrence_matrix(data, k=None, width=1, metric='euclidean', sym=False, sparse=False, mode='connectivity', bandwidth=None, self=False, axis=-1): '''Compute a recurrence matrix from a data matrix. `rec[i, j]` is non-zero if (`data[:, i]`, `data[:, j]`) are k-n...
def recurrence_to_lag(rec, pad=True, axis=-1): '''Convert a recurrence matrix into a lag matrix. `lag[i, j] == rec[i+j, j]` Parameters ---------- rec : np.ndarray, or scipy.sparse.spmatrix [shape=(n, n)] A (binary) recurrence matrix, as returned by `recurrence_matrix` pad : bool ...
def lag_to_recurrence(lag, axis=-1): '''Convert a lag matrix into a recurrence matrix. Parameters ---------- lag : np.ndarray or scipy.sparse.spmatrix A lag matrix, as produced by `recurrence_to_lag` axis : int The axis corresponding to the time dimension. The alternate axi...
def timelag_filter(function, pad=True, index=0): '''Filtering in the time-lag domain. This is primarily useful for adapting image filters to operate on `recurrence_to_lag` output. Using `timelag_filter` is equivalent to the following sequence of operations: >>> data_tl = librosa.segment.recur...
def subsegment(data, frames, n_segments=4, axis=-1): '''Sub-divide a segmentation by feature clustering. Given a set of frame boundaries (`frames`), and a data matrix (`data`), each successive interval defined by `frames` is partitioned into `n_segments` by constrained agglomerative clustering. .....
def agglomerative(data, k, clusterer=None, axis=-1): """Bottom-up temporal segmentation. Use a temporally-constrained agglomerative clustering routine to partition `data` into `k` contiguous segments. Parameters ---------- data : np.ndarray data to cluster k : int > 0 [...
def path_enhance(R, n, window='hann', max_ratio=2.0, min_ratio=None, n_filters=7, zero_mean=False, clip=True, **kwargs): '''Multi-angle path enhancement for self- and cross-similarity matrices. This function convolves multiple diagonal smoothing filters with a self-similarity (or recurrenc...
def onset_detect(input_file, output_csv): '''Onset detection function :parameters: - input_file : str Path to input audio file (wav, mp3, m4a, flac, etc.) - output_file : str Path to save onset timestamps as a CSV file ''' # 1. load the wav file and resample to 22.050 ...
def frame(y, frame_length=2048, hop_length=512): '''Slice a time series into overlapping frames. This implementation uses low-level stride manipulation to avoid redundant copies of the time series data. Parameters ---------- y : np.ndarray [shape=(n,)] Time series to frame. Must be one...
def valid_audio(y, mono=True): '''Validate whether a variable contains valid, mono audio data. Parameters ---------- y : np.ndarray The input data to validate mono : bool Whether or not to force monophonic audio Returns ------- valid : bool True if all tests pass ...
def valid_int(x, cast=None): '''Ensure that an input value is integer-typed. This is primarily useful for ensuring integrable-valued array indices. Parameters ---------- x : number A scalar value to be cast to int cast : function [optional] A function to modify `x` before c...
def valid_intervals(intervals): '''Ensure that an array is a valid representation of time intervals: - intervals.ndim == 2 - intervals.shape[1] == 2 - intervals[i, 0] <= intervals[i, 1] for all i Parameters ---------- intervals : np.ndarray [shape=(n, 2)] set of time in...
def pad_center(data, size, axis=-1, **kwargs): '''Wrapper for np.pad to automatically center an array prior to padding. This is analogous to `str.center()` Examples -------- >>> # Generate a vector >>> data = np.ones(5) >>> librosa.util.pad_center(data, 10, mode='constant') array([ 0., ...
def fix_length(data, size, axis=-1, **kwargs): '''Fix the length an array `data` to exactly `size`. If `data.shape[axis] < n`, pad according to the provided kwargs. By default, `data` is padded with trailing zeros. Examples -------- >>> y = np.arange(7) >>> # Default: pad with zeros >>...
def fix_frames(frames, x_min=0, x_max=None, pad=True): '''Fix a list of frames to lie within [x_min, x_max] Examples -------- >>> # Generate a list of frame indices >>> frames = np.arange(0, 1000.0, 50) >>> frames array([ 0., 50., 100., 150., 200., 250., 300., 350., 40...
def axis_sort(S, axis=-1, index=False, value=None): '''Sort an array along its rows or columns. Examples -------- Visualize NMF output for a spectrogram S >>> # Sort the columns of W by peak frequency bin >>> y, sr = librosa.load(librosa.util.example_audio_file()) >>> S = np.abs(librosa.st...
def normalize(S, norm=np.inf, axis=0, threshold=None, fill=None): '''Normalize an array along a chosen axis. Given a norm (described below) and a target axis, the input array is scaled so that `norm(S, axis=axis) == 1` For example, `axis=0` normalizes each column of a 2-d array by aggrega...
def localmax(x, axis=0): """Find local maxima in an array `x`. An element `x[i]` is considered a local maximum if the following conditions are met: - `x[i] > x[i-1]` - `x[i] >= x[i+1]` Note that the first condition is strict, and that the first element `x[0]` will never be considered as a...
def peak_pick(x, pre_max, post_max, pre_avg, post_avg, delta, wait): '''Uses a flexible heuristic to pick peaks in a signal. A sample n is selected as an peak if the corresponding x[n] fulfills the following three conditions: 1. `x[n] == max(x[n - pre_max:n + post_max])` 2. `x[n] >= mean(x[n - pre...
def sparsify_rows(x, quantile=0.01): ''' Return a row-sparse matrix approximating the input `x`. Parameters ---------- x : np.ndarray [ndim <= 2] The input matrix to sparsify. quantile : float in [0, 1.0) Percentage of magnitude to discard in each row of `x` Returns --...
def roll_sparse(x, shift, axis=0): '''Sparse matrix roll This operation is equivalent to ``numpy.roll``, but operates on sparse matrices. Parameters ---------- x : scipy.sparse.spmatrix or np.ndarray The sparse matrix input shift : int The number of positions to roll the speci...
def buf_to_float(x, n_bytes=2, dtype=np.float32): """Convert an integer buffer to floating point values. This is primarily useful when loading integer-valued wav data into numpy arrays. See Also -------- buf_to_float Parameters ---------- x : np.ndarray [dtype=int] The inte...
def index_to_slice(idx, idx_min=None, idx_max=None, step=None, pad=True): '''Generate a slice array from an index array. Parameters ---------- idx : list-like Array of index boundaries idx_min : None or int idx_max : None or int Minimum and maximum allowed indices step : N...
def sync(data, idx, aggregate=None, pad=True, axis=-1): """Synchronous aggregation of a multi-dimensional array between boundaries .. note:: In order to ensure total coverage, boundary points may be added to `idx`. If synchronizing a feature matrix against beat tracker output, ensure ...
def softmask(X, X_ref, power=1, split_zeros=False): '''Robustly compute a softmask operation. `M = Xpower / (Xpower + X_ref**power)` Parameters ---------- X : np.ndarray The (non-negative) input array corresponding to the positive mask elements X_ref : np.ndarray The ...
def tiny(x): '''Compute the tiny-value corresponding to an input's data type. This is the smallest "usable" number representable in `x`'s data type (e.g., float32). This is primarily useful for determining a threshold for numerical underflow in division or multiplication operations. Parameter...
def fill_off_diagonal(x, radius, value=0): """Sets all cells of a matrix to a given ``value`` if they lie outside a constraint region. In this case, the constraint region is the Sakoe-Chiba band which runs with a fixed ``radius`` along the main diagonal. When ``x.shape[0] != x.shape[1]``, the ra...
def frames2video(frame_dir, video_file, fps=30, fourcc='XVID', filename_tmpl='{:06d}.jpg', start=0, end=0, show_progress=True): """Read the frame images from a directory and join them as a video ...
def read(self): """Read the next frame. If the next frame have been decoded before and in the cache, then return it directly, otherwise decode, cache and return it. Returns: ndarray or None: Return the frame if successful, otherwise None. """ # pos = self._p...
def get_frame(self, frame_id): """Get frame by index. Args: frame_id (int): Index of the expected frame, 0-based. Returns: ndarray or None: Return the frame if successful, otherwise None. """ if frame_id < 0 or frame_id >= self._frame_cnt: ra...
def cvt2frames(self, frame_dir, file_start=0, filename_tmpl='{:06d}.jpg', start=0, max_num=0, show_progress=True): """Convert a video to frame images Args: frame_dir (str): Outp...
def track_progress(func, tasks, bar_width=50, **kwargs): """Track the progress of tasks execution with a progress bar. Tasks are done with a simple for-loop. Args: func (callable): The function to be applied to each task. tasks (list or tuple[Iterable, int]): A list of tasks or ...
def track_parallel_progress(func, tasks, nproc, initializer=None, initargs=None, bar_width=50, chunksize=1, skip_first=False...
def imflip(img, direction='horizontal'): """Flip an image horizontally or vertically. Args: img (ndarray): Image to be flipped. direction (str): The flip direction, either "horizontal" or "vertical". Returns: ndarray: The flipped image. """ assert direction in ['horizontal'...
def imrotate(img, angle, center=None, scale=1.0, border_value=0, auto_bound=False): """Rotate an image. Args: img (ndarray): Image to be rotated. angle (float): Rotation angle in degrees, positive values mean clockwise...
def bbox_clip(bboxes, img_shape): """Clip bboxes to fit the image shape. Args: bboxes (ndarray): Shape (..., 4*k) img_shape (tuple): (height, width) of the image. Returns: ndarray: Clipped bboxes. """ assert bboxes.shape[-1] % 4 == 0 clipped_bboxes = np.empty_like(bboxe...
def bbox_scaling(bboxes, scale, clip_shape=None): """Scaling bboxes w.r.t the box center. Args: bboxes (ndarray): Shape(..., 4). scale (float): Scaling factor. clip_shape (tuple, optional): If specified, bboxes that exceed the boundary will be clipped according to the given ...
def imcrop(img, bboxes, scale=1.0, pad_fill=None): """Crop image patches. 3 steps: scale the bboxes -> clip bboxes -> crop and pad. Args: img (ndarray): Image to be cropped. bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes. scale (float, optional): Scale ratio of...
def impad(img, shape, pad_val=0): """Pad an image to a certain shape. Args: img (ndarray): Image to be padded. shape (tuple): Expected padding shape. pad_val (number or sequence): Values to be filled in padding areas. Returns: ndarray: The padded image. """ if not i...
def impad_to_multiple(img, divisor, pad_val=0): """Pad an image to ensure each edge to be multiple to some number. Args: img (ndarray): Image to be padded. divisor (int): Padded image edges will be multiple to divisor. pad_val (number or sequence): Same as :func:`impad`. Returns: ...
def _scale_size(size, scale): """Rescale a size by a ratio. Args: size (tuple): w, h. scale (float): Scaling factor. Returns: tuple[int]: scaled size. """ w, h = size return int(w * float(scale) + 0.5), int(h * float(scale) + 0.5)
def imresize(img, size, return_scale=False, interpolation='bilinear'): """Resize image to a given size. Args: img (ndarray): The input image. size (tuple): Target (w, h). return_scale (bool): Whether to return `w_scale` and `h_scale`. interpolation (str): Interpolation method, a...
def imresize_like(img, dst_img, return_scale=False, interpolation='bilinear'): """Resize image to the same size of a given image. Args: img (ndarray): The input image. dst_img (ndarray): The target image. return_scale (bool): Whether to return `w_scale` and `h_scale`. interpolat...
def imrescale(img, scale, return_scale=False, interpolation='bilinear'): """Resize image while keeping the aspect ratio. Args: img (ndarray): The input image. scale (float or tuple[int]): The scaling factor or maximum size. If it is a float number, then the image will be rescaled by...
def load(file, file_format=None, **kwargs): """Load data from json/yaml/pickle files. This method provides a unified api for loading data from serialized files. Args: file (str or file-like object): Filename or a file-like object. file_format (str, optional): If not specified, the file for...
def dump(obj, file=None, file_format=None, **kwargs): """Dump data to json/yaml/pickle strings or files. This method provides a unified api for dumping data as strings or to files, and also supports custom arguments for each file format. Args: obj (any): The python object to be dumped. ...
def _register_handler(handler, file_formats): """Register a handler for some file extensions. Args: handler (:obj:`BaseFileHandler`): Handler to be registered. file_formats (str or list[str]): File formats to be handled by this handler. """ if not isinstance(handler, BaseFil...
def get_priority(priority): """Get priority value. Args: priority (int or str or :obj:`Priority`): Priority. Returns: int: The priority value. """ if isinstance(priority, int): if priority < 0 or priority > 100: raise ValueError('priority must be between 0 and 1...
def quantize(arr, min_val, max_val, levels, dtype=np.int64): """Quantize an array of (-inf, inf) to [0, levels-1]. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clipped. max_val (scalar): Maximum value to be clipped. levels (int): Quantization levels. ...
def dequantize(arr, min_val, max_val, levels, dtype=np.float64): """Dequantize an array. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clipped. max_val (scalar): Maximum value to be clipped. levels (int): Quantization levels. dtype (np.type): Th...
def auto_argparser(description=None): """Generate argparser from config file automatically (experimental) """ partial_parser = ArgumentParser(description=description) partial_parser.add_argument('config', help='config file path') cfg_file = partial_parser.parse_known_args()[0].co...
def collate(batch, samples_per_gpu=1): """Puts each data field into a tensor/DataContainer with outer dimension batch size. Extend default_collate to add support for :type:`~mmcv.parallel.DataContainer`. There are 3 cases. 1. cpu_only = True, e.g., meta data 2. cpu_only = False, stack = True, ...
def imshow(img, win_name='', wait_time=0): """Show an image. Args: img (str or ndarray): The image to be displayed. win_name (str): The window name. wait_time (int): Value of waitKey param. """ cv2.imshow(win_name, imread(img)) cv2.waitKey(wait_time)
def imshow_bboxes(img, bboxes, colors='green', top_k=-1, thickness=1, show=True, win_name='', wait_time=0, out_file=None): """Draw bboxes on an image. Args: im...
def imshow_det_bboxes(img, bboxes, labels, class_names=None, score_thr=0, bbox_color='green', text_color='green', thickness=1, font_scale=0.5, ...
def flowread(flow_or_path, quantize=False, concat_axis=0, args, *kwargs): """Read an optical flow map. Args: flow_or_path (ndarray or str): A flow map or filepath. quantize (bool): whether to read quantized pair, if set to True, remaining args will be passed to :func:`dequantize_f...
def flowwrite(flow, filename, quantize=False, concat_axis=0, args, *kwargs): """Write optical flow to file. If the flow is not quantized, it will be saved as a .flo file losslessly, otherwise a jpeg image which is lossy but of much smaller size. (dx and dy will be concatenated horizontally into a sin...
def quantize_flow(flow, max_val=0.02, norm=True): """Quantize flow to [0, 255]. After this step, the size of flow will be much smaller, and can be dumped as jpeg images. Args: flow (ndarray): (h, w, 2) array of optical flow. max_val (float): Maximum value of flow, values beyond ...
def dequantize_flow(dx, dy, max_val=0.02, denorm=True): """Recover from quantized flow. Args: dx (ndarray): Quantized dx. dy (ndarray): Quantized dy. max_val (float): Maximum value used when quantizing. denorm (bool): Whether to multiply flow values with width/height. Retur...
def load_state_dict(module, state_dict, strict=False, logger=None): """Load state_dict to a module. This method is modified from :meth:`torch.nn.Module.load_state_dict`. Default value for ``strict`` is set to ``False`` and the message for param mismatch will be shown even if strict is False. Args:...
def load_checkpoint(model, filename, map_location=None, strict=False, logger=None): """Load checkpoint from a file or URI. Args: model (Module): Module to load checkpoint. filename (str): Either a filepath or URL or...
def weights_to_cpu(state_dict): """Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on GPU. """ state_dict_cpu = OrderedDict() for key, val in state_dict.items(): state_dict_cpu[key] = val.cpu() ...
def save_checkpoint(model, filename, optimizer=None, meta=None): """Save checkpoint to file. The checkpoint will have 3 fields: ``meta``, ``state_dict`` and ``optimizer``. By default ``meta`` will contain version and time info. Args: model (Module): Module whose params are to be saved. ...
def init_optimizer(self, optimizer): """Init the optimizer. Args: optimizer (dict or :obj:`~torch.optim.Optimizer`): Either an optimizer object or a dict used for constructing the optimizer. Returns: :obj:`~torch.optim.Optimizer`: An optimizer object. ...
def init_logger(self, log_dir=None, level=logging.INFO): """Init the logger. Args: log_dir(str, optional): Log file directory. If not specified, no log file will be used. level (int or str): See the built-in python logging module. Returns: :o...
def current_lr(self): """Get current learning rates. Returns: list: Current learning rate of all param groups. """ if self.optimizer is None: raise RuntimeError( 'lr is not applicable because optimizer does not exist.') return [group['lr']...
def register_hook(self, hook, priority='NORMAL'): """Register a hook into the hook list. Args: hook (:obj:`Hook`): The hook to be registered. priority (int or str or :obj:`Priority`): Hook priority. Lower value means higher priority. """ assert is...
def run(self, data_loaders, workflow, max_epochs, **kwargs): """Start running. Args: data_loaders (list[:obj:`DataLoader`]): Dataloaders for training and validation. workflow (list[tuple]): A list of (phase, epochs) to specify the running order an...
def register_training_hooks(self, lr_config, optimizer_config=None, checkpoint_config=None, log_config=None): """Register default hooks for training. Default hooks include: ...
def convert_video(in_file, out_file, print_cmd=False, pre_options='', **kwargs): """Convert a video with ffmpeg. This provides a general api to ffmpeg, the executed command is:: `ffmpeg -y <pre_options> -i <in_file> <options> <out_file>` Options(kwargs) are mapped to ffmpeg comm...
def resize_video(in_file, out_file, size=None, ratio=None, keep_ar=False, log_level='info', print_cmd=False, **kwargs): """Resize a video. Args: in_file (str): Input video filename. ...
def cut_video(in_file, out_file, start=None, end=None, vcodec=None, acodec=None, log_level='info', print_cmd=False, **kwargs): """Cut a clip from a video. Args: in_file (str): Input video fil...
def concat_video(video_list, out_file, vcodec=None, acodec=None, log_level='info', print_cmd=False, **kwargs): """Concatenate multiple videos into a single one. Args: video_list (list): A list of video...
def list_from_file(filename, prefix='', offset=0, max_num=0): """Load a text file and parse the content as a list of strings. Args: filename (str): Filename. prefix (str): The prefix to be inserted to the begining of each item. offset (int): The offset of lines. max_num (int): T...

def harmonics_2d(harmonic_out, x, freqs, h_range, kind='linear', fill_value=0, axis=0): '''Populate a harmonic tensor from a time-frequency representation with time-varying frequencies. Parameters ---------- harmonic_out : np.ndarray The output array to store harmonics ...

def load(path, sr=22050, mono=True, offset=0.0, duration=None, dtype=np.float32, res_type='kaiser_best'): """Load an audio file as a floating point time series. Audio will be automatically resampled to the given rate (default `sr=22050`). To preserve the native sampling rate of the file, use ...

def __audioread_load(path, offset, duration, dtype): '''Load an audio buffer using audioread. This loads one block at a time, and then concatenates the results. ''' y = [] with audioread.audio_open(path) as input_file: sr_native = input_file.samplerate n_channels = input_file.chann...

def to_mono(y): '''Force an audio signal down to mono. Parameters ---------- y : np.ndarray [shape=(2,n) or shape=(n,)] audio time series, either stereo or mono Returns ------- y_mono : np.ndarray [shape=(n,)] `y` as a monophonic time-series Notes ----- This fu...

def resample(y, orig_sr, target_sr, res_type='kaiser_best', fix=True, scale=False, **kwargs): """Resample a time series from orig_sr to target_sr Parameters ---------- y : np.ndarray [shape=(n,) or shape=(2, n)] audio time series. Can be mono or stereo. orig_sr : number > 0 [scalar] ...

def get_duration(y=None, sr=22050, S=None, n_fft=2048, hop_length=512, center=True, filename=None): """Compute the duration (in seconds) of an audio time series, feature matrix, or filename. Examples -------- >>> # Load the example audio file >>> y, sr = librosa.load(librosa.ut...

def autocorrelate(y, max_size=None, axis=-1): """Bounded auto-correlation Parameters ---------- y : np.ndarray array to autocorrelate max_size : int > 0 or None maximum correlation lag. If unspecified, defaults to `y.shape[axis]` (unbounded) axis : int The axi...

def lpc(y, order): """Linear Prediction Coefficients via Burg's method This function applies Burg's method to estimate coefficients of a linear filter on `y` of order `order`. Burg's method is an extension to the Yule-Walker approach, which are both sometimes referred to as LPC parameter estimatio...

def zero_crossings(y, threshold=1e-10, ref_magnitude=None, pad=True, zero_pos=True, axis=-1): '''Find the zero-crossings of a signal `y`: indices `i` such that `sign(y[i]) != sign(y[j])`. If `y` is multi-dimensional, then zero-crossings are computed along the specified `axis`. ...

def clicks(times=None, frames=None, sr=22050, hop_length=512, click_freq=1000.0, click_duration=0.1, click=None, length=None): """Returns a signal with the signal `click` placed at each specified time Parameters ---------- times : np.ndarray or None times to place clicks, in seconds ...

def tone(frequency, sr=22050, length=None, duration=None, phi=None): """Returns a pure tone signal. The signal generated is a cosine wave. Parameters ---------- frequency : float > 0 frequency sr : number > 0 desired sampling rate of the output signal length : int > 0 ...

def chirp(fmin, fmax, sr=22050, length=None, duration=None, linear=False, phi=None): """Returns a chirp signal that goes from frequency `fmin` to frequency `fmax` Parameters ---------- fmin : float > 0 initial frequency fmax : float > 0 final frequency sr : number > 0 ...

def tempogram(y=None, sr=22050, onset_envelope=None, hop_length=512, win_length=384, center=True, window='hann', norm=np.inf): '''Compute the tempogram: local autocorrelation of the onset strength envelope. [1]_ .. [1] Grosche, Peter, Meinard Müller, and Frank Kurth. "Cyclic tempogram - A...

def find_files(directory, ext=None, recurse=True, case_sensitive=False, limit=None, offset=0): '''Get a sorted list of (audio) files in a directory or directory sub-tree. Examples -------- >>> # Get all audio files in a directory sub-tree >>> files = librosa.util.find_files('~/Music'...

def __get_files(dir_name, extensions): '''Helper function to get files in a single directory''' # Expand out the directory dir_name = os.path.abspath(os.path.expanduser(dir_name)) myfiles = set() for sub_ext in extensions: globstr = os.path.join(dir_name, '*' + os.path.extsep + sub_ext) ...

def stretch_demo(input_file, output_file, speed): '''Phase-vocoder time stretch demo function. :parameters: - input_file : str path to input audio - output_file : str path to save output (wav) - speed : float > 0 speed up by this factor ''' # 1. Load the...

def process_arguments(args): '''Argparse function to get the program parameters''' parser = argparse.ArgumentParser(description='Time stretching example') parser.add_argument('input_file', action='store', help='path to the input file (wav, mp3, etc)') p...

def hpss_demo(input_file, output_harmonic, output_percussive): '''HPSS demo function. :parameters: - input_file : str path to input audio - output_harmonic : str path to save output harmonic (wav) - output_percussive : str path to save output harmonic (wav) '...

def beat_track(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120.0, tightness=100, trim=True, bpm=None, units='frames'): r'''Dynamic programming beat tracker. Beats are detected in three stages, following the method of [1]_: 1. Measure onset strength ...

def tempo(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120, std_bpm=1.0, ac_size=8.0, max_tempo=320.0, aggregate=np.mean): """Estimate the tempo (beats per minute) Parameters ---------- y : np.ndarray [shape=(n,)] or None audio time series sr : number > 0 [sca...

def __beat_tracker(onset_envelope, bpm, fft_res, tightness, trim): """Internal function that tracks beats in an onset strength envelope. Parameters ---------- onset_envelope : np.ndarray [shape=(n,)] onset strength envelope bpm : float [scalar] tempo estimate fft_res : float ...

def __normalize_onsets(onsets): '''Maps onset strength function into the range [0, 1]''' norm = onsets.std(ddof=1) if norm > 0: onsets = onsets / norm return onsets

def __beat_local_score(onset_envelope, period): '''Construct the local score for an onset envlope and given period''' window = np.exp(-0.5 * (np.arange(-period, period+1)*32.0/period)**2) return scipy.signal.convolve(__normalize_onsets(onset_envelope), window, ...

def __beat_track_dp(localscore, period, tightness): """Core dynamic program for beat tracking""" backlink = np.zeros_like(localscore, dtype=int) cumscore = np.zeros_like(localscore) # Search range for previous beat window = np.arange(-2 * period, -np.round(period / 2) + 1, dtype=int) # Make a...

def __last_beat(cumscore): """Get the last beat from the cumulative score array""" maxes = util.localmax(cumscore) med_score = np.median(cumscore[np.argwhere(maxes)]) # The last of these is the last beat (since score generally increases) return np.argwhere((cumscore * maxes * 2 > med_score)).max()

def __trim_beats(localscore, beats, trim): """Final post-processing: throw out spurious leading/trailing beats""" smooth_boe = scipy.signal.convolve(localscore[beats], scipy.signal.hann(5), 'same') if trim: threshold = 0...

def recurrence_matrix(data, k=None, width=1, metric='euclidean', sym=False, sparse=False, mode='connectivity', bandwidth=None, self=False, axis=-1): '''Compute a recurrence matrix from a data matrix. `rec[i, j]` is non-zero if (`data[:, i]`, `data[:, j]`) are k-n...

def recurrence_to_lag(rec, pad=True, axis=-1): '''Convert a recurrence matrix into a lag matrix. `lag[i, j] == rec[i+j, j]` Parameters ---------- rec : np.ndarray, or scipy.sparse.spmatrix [shape=(n, n)] A (binary) recurrence matrix, as returned by `recurrence_matrix` pad : bool ...

def lag_to_recurrence(lag, axis=-1): '''Convert a lag matrix into a recurrence matrix. Parameters ---------- lag : np.ndarray or scipy.sparse.spmatrix A lag matrix, as produced by `recurrence_to_lag` axis : int The axis corresponding to the time dimension. The alternate axi...

def timelag_filter(function, pad=True, index=0): '''Filtering in the time-lag domain. This is primarily useful for adapting image filters to operate on `recurrence_to_lag` output. Using `timelag_filter` is equivalent to the following sequence of operations: >>> data_tl = librosa.segment.recur...

def subsegment(data, frames, n_segments=4, axis=-1): '''Sub-divide a segmentation by feature clustering. Given a set of frame boundaries (`frames`), and a data matrix (`data`), each successive interval defined by `frames` is partitioned into `n_segments` by constrained agglomerative clustering. .....

def agglomerative(data, k, clusterer=None, axis=-1): """Bottom-up temporal segmentation. Use a temporally-constrained agglomerative clustering routine to partition `data` into `k` contiguous segments. Parameters ---------- data : np.ndarray data to cluster k : int > 0 [...

def path_enhance(R, n, window='hann', max_ratio=2.0, min_ratio=None, n_filters=7, zero_mean=False, clip=True, **kwargs): '''Multi-angle path enhancement for self- and cross-similarity matrices. This function convolves multiple diagonal smoothing filters with a self-similarity (or recurrenc...

def onset_detect(input_file, output_csv): '''Onset detection function :parameters: - input_file : str Path to input audio file (wav, mp3, m4a, flac, etc.) - output_file : str Path to save onset timestamps as a CSV file ''' # 1. load the wav file and resample to 22.050 ...

def frame(y, frame_length=2048, hop_length=512): '''Slice a time series into overlapping frames. This implementation uses low-level stride manipulation to avoid redundant copies of the time series data. Parameters ---------- y : np.ndarray [shape=(n,)] Time series to frame. Must be one...

def valid_audio(y, mono=True): '''Validate whether a variable contains valid, mono audio data. Parameters ---------- y : np.ndarray The input data to validate mono : bool Whether or not to force monophonic audio Returns ------- valid : bool True if all tests pass ...

def valid_int(x, cast=None): '''Ensure that an input value is integer-typed. This is primarily useful for ensuring integrable-valued array indices. Parameters ---------- x : number A scalar value to be cast to int cast : function [optional] A function to modify `x` before c...

def valid_intervals(intervals): '''Ensure that an array is a valid representation of time intervals: - intervals.ndim == 2 - intervals.shape[1] == 2 - intervals[i, 0] <= intervals[i, 1] for all i Parameters ---------- intervals : np.ndarray [shape=(n, 2)] set of time in...

def pad_center(data, size, axis=-1, **kwargs): '''Wrapper for np.pad to automatically center an array prior to padding. This is analogous to `str.center()` Examples -------- >>> # Generate a vector >>> data = np.ones(5) >>> librosa.util.pad_center(data, 10, mode='constant') array([ 0., ...

def fix_length(data, size, axis=-1, **kwargs): '''Fix the length an array `data` to exactly `size`. If `data.shape[axis] < n`, pad according to the provided kwargs. By default, `data` is padded with trailing zeros. Examples -------- >>> y = np.arange(7) >>> # Default: pad with zeros >>...

def fix_frames(frames, x_min=0, x_max=None, pad=True): '''Fix a list of frames to lie within [x_min, x_max] Examples -------- >>> # Generate a list of frame indices >>> frames = np.arange(0, 1000.0, 50) >>> frames array([ 0., 50., 100., 150., 200., 250., 300., 350., 40...

def axis_sort(S, axis=-1, index=False, value=None): '''Sort an array along its rows or columns. Examples -------- Visualize NMF output for a spectrogram S >>> # Sort the columns of W by peak frequency bin >>> y, sr = librosa.load(librosa.util.example_audio_file()) >>> S = np.abs(librosa.st...

def normalize(S, norm=np.inf, axis=0, threshold=None, fill=None): '''Normalize an array along a chosen axis. Given a norm (described below) and a target axis, the input array is scaled so that `norm(S, axis=axis) == 1` For example, `axis=0` normalizes each column of a 2-d array by aggrega...

def localmax(x, axis=0): """Find local maxima in an array `x`. An element `x[i]` is considered a local maximum if the following conditions are met: - `x[i] > x[i-1]` - `x[i] >= x[i+1]` Note that the first condition is strict, and that the first element `x[0]` will never be considered as a...

def peak_pick(x, pre_max, post_max, pre_avg, post_avg, delta, wait): '''Uses a flexible heuristic to pick peaks in a signal. A sample n is selected as an peak if the corresponding x[n] fulfills the following three conditions: 1. `x[n] == max(x[n - pre_max:n + post_max])` 2. `x[n] >= mean(x[n - pre...

def sparsify_rows(x, quantile=0.01): ''' Return a row-sparse matrix approximating the input `x`. Parameters ---------- x : np.ndarray [ndim <= 2] The input matrix to sparsify. quantile : float in [0, 1.0) Percentage of magnitude to discard in each row of `x` Returns --...

def roll_sparse(x, shift, axis=0): '''Sparse matrix roll This operation is equivalent to ``numpy.roll``, but operates on sparse matrices. Parameters ---------- x : scipy.sparse.spmatrix or np.ndarray The sparse matrix input shift : int The number of positions to roll the speci...

def buf_to_float(x, n_bytes=2, dtype=np.float32): """Convert an integer buffer to floating point values. This is primarily useful when loading integer-valued wav data into numpy arrays. See Also -------- buf_to_float Parameters ---------- x : np.ndarray [dtype=int] The inte...

def index_to_slice(idx, idx_min=None, idx_max=None, step=None, pad=True): '''Generate a slice array from an index array. Parameters ---------- idx : list-like Array of index boundaries idx_min : None or int idx_max : None or int Minimum and maximum allowed indices step : N...

def sync(data, idx, aggregate=None, pad=True, axis=-1): """Synchronous aggregation of a multi-dimensional array between boundaries .. note:: In order to ensure total coverage, boundary points may be added to `idx`. If synchronizing a feature matrix against beat tracker output, ensure ...

def softmask(X, X_ref, power=1, split_zeros=False): '''Robustly compute a softmask operation. `M = X**power / (X**power + X_ref**power)` Parameters ---------- X : np.ndarray The (non-negative) input array corresponding to the positive mask elements X_ref : np.ndarray The ...

def tiny(x): '''Compute the tiny-value corresponding to an input's data type. This is the smallest "usable" number representable in `x`'s data type (e.g., float32). This is primarily useful for determining a threshold for numerical underflow in division or multiplication operations. Parameter...

def fill_off_diagonal(x, radius, value=0): """Sets all cells of a matrix to a given ``value`` if they lie outside a constraint region. In this case, the constraint region is the Sakoe-Chiba band which runs with a fixed ``radius`` along the main diagonal. When ``x.shape[0] != x.shape[1]``, the ra...

def frames2video(frame_dir, video_file, fps=30, fourcc='XVID', filename_tmpl='{:06d}.jpg', start=0, end=0, show_progress=True): """Read the frame images from a directory and join them as a video ...

def read(self): """Read the next frame. If the next frame have been decoded before and in the cache, then return it directly, otherwise decode, cache and return it. Returns: ndarray or None: Return the frame if successful, otherwise None. """ # pos = self._p...

def get_frame(self, frame_id): """Get frame by index. Args: frame_id (int): Index of the expected frame, 0-based. Returns: ndarray or None: Return the frame if successful, otherwise None. """ if frame_id < 0 or frame_id >= self._frame_cnt: ra...

def cvt2frames(self, frame_dir, file_start=0, filename_tmpl='{:06d}.jpg', start=0, max_num=0, show_progress=True): """Convert a video to frame images Args: frame_dir (str): Outp...

def track_progress(func, tasks, bar_width=50, **kwargs): """Track the progress of tasks execution with a progress bar. Tasks are done with a simple for-loop. Args: func (callable): The function to be applied to each task. tasks (list or tuple[Iterable, int]): A list of tasks or ...

def track_parallel_progress(func, tasks, nproc, initializer=None, initargs=None, bar_width=50, chunksize=1, skip_first=False...

def imflip(img, direction='horizontal'): """Flip an image horizontally or vertically. Args: img (ndarray): Image to be flipped. direction (str): The flip direction, either "horizontal" or "vertical". Returns: ndarray: The flipped image. """ assert direction in ['horizontal'...

def imrotate(img, angle, center=None, scale=1.0, border_value=0, auto_bound=False): """Rotate an image. Args: img (ndarray): Image to be rotated. angle (float): Rotation angle in degrees, positive values mean clockwise...

def bbox_clip(bboxes, img_shape): """Clip bboxes to fit the image shape. Args: bboxes (ndarray): Shape (..., 4*k) img_shape (tuple): (height, width) of the image. Returns: ndarray: Clipped bboxes. """ assert bboxes.shape[-1] % 4 == 0 clipped_bboxes = np.empty_like(bboxe...

def bbox_scaling(bboxes, scale, clip_shape=None): """Scaling bboxes w.r.t the box center. Args: bboxes (ndarray): Shape(..., 4). scale (float): Scaling factor. clip_shape (tuple, optional): If specified, bboxes that exceed the boundary will be clipped according to the given ...

def imcrop(img, bboxes, scale=1.0, pad_fill=None): """Crop image patches. 3 steps: scale the bboxes -> clip bboxes -> crop and pad. Args: img (ndarray): Image to be cropped. bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes. scale (float, optional): Scale ratio of...

def impad(img, shape, pad_val=0): """Pad an image to a certain shape. Args: img (ndarray): Image to be padded. shape (tuple): Expected padding shape. pad_val (number or sequence): Values to be filled in padding areas. Returns: ndarray: The padded image. """ if not i...

def impad_to_multiple(img, divisor, pad_val=0): """Pad an image to ensure each edge to be multiple to some number. Args: img (ndarray): Image to be padded. divisor (int): Padded image edges will be multiple to divisor. pad_val (number or sequence): Same as :func:`impad`. Returns: ...

def _scale_size(size, scale): """Rescale a size by a ratio. Args: size (tuple): w, h. scale (float): Scaling factor. Returns: tuple[int]: scaled size. """ w, h = size return int(w * float(scale) + 0.5), int(h * float(scale) + 0.5)

def imresize(img, size, return_scale=False, interpolation='bilinear'): """Resize image to a given size. Args: img (ndarray): The input image. size (tuple): Target (w, h). return_scale (bool): Whether to return `w_scale` and `h_scale`. interpolation (str): Interpolation method, a...

def imresize_like(img, dst_img, return_scale=False, interpolation='bilinear'): """Resize image to the same size of a given image. Args: img (ndarray): The input image. dst_img (ndarray): The target image. return_scale (bool): Whether to return `w_scale` and `h_scale`. interpolat...

def imrescale(img, scale, return_scale=False, interpolation='bilinear'): """Resize image while keeping the aspect ratio. Args: img (ndarray): The input image. scale (float or tuple[int]): The scaling factor or maximum size. If it is a float number, then the image will be rescaled by...

def load(file, file_format=None, **kwargs): """Load data from json/yaml/pickle files. This method provides a unified api for loading data from serialized files. Args: file (str or file-like object): Filename or a file-like object. file_format (str, optional): If not specified, the file for...

def dump(obj, file=None, file_format=None, **kwargs): """Dump data to json/yaml/pickle strings or files. This method provides a unified api for dumping data as strings or to files, and also supports custom arguments for each file format. Args: obj (any): The python object to be dumped. ...

def _register_handler(handler, file_formats): """Register a handler for some file extensions. Args: handler (:obj:`BaseFileHandler`): Handler to be registered. file_formats (str or list[str]): File formats to be handled by this handler. """ if not isinstance(handler, BaseFil...

def get_priority(priority): """Get priority value. Args: priority (int or str or :obj:`Priority`): Priority. Returns: int: The priority value. """ if isinstance(priority, int): if priority < 0 or priority > 100: raise ValueError('priority must be between 0 and 1...

def quantize(arr, min_val, max_val, levels, dtype=np.int64): """Quantize an array of (-inf, inf) to [0, levels-1]. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clipped. max_val (scalar): Maximum value to be clipped. levels (int): Quantization levels. ...

def dequantize(arr, min_val, max_val, levels, dtype=np.float64): """Dequantize an array. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clipped. max_val (scalar): Maximum value to be clipped. levels (int): Quantization levels. dtype (np.type): Th...

def auto_argparser(description=None): """Generate argparser from config file automatically (experimental) """ partial_parser = ArgumentParser(description=description) partial_parser.add_argument('config', help='config file path') cfg_file = partial_parser.parse_known_args()[0].co...

def collate(batch, samples_per_gpu=1): """Puts each data field into a tensor/DataContainer with outer dimension batch size. Extend default_collate to add support for :type:`~mmcv.parallel.DataContainer`. There are 3 cases. 1. cpu_only = True, e.g., meta data 2. cpu_only = False, stack = True, ...

def imshow(img, win_name='', wait_time=0): """Show an image. Args: img (str or ndarray): The image to be displayed. win_name (str): The window name. wait_time (int): Value of waitKey param. """ cv2.imshow(win_name, imread(img)) cv2.waitKey(wait_time)

def imshow_bboxes(img, bboxes, colors='green', top_k=-1, thickness=1, show=True, win_name='', wait_time=0, out_file=None): """Draw bboxes on an image. Args: im...

def imshow_det_bboxes(img, bboxes, labels, class_names=None, score_thr=0, bbox_color='green', text_color='green', thickness=1, font_scale=0.5, ...

def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs): """Read an optical flow map. Args: flow_or_path (ndarray or str): A flow map or filepath. quantize (bool): whether to read quantized pair, if set to True, remaining args will be passed to :func:`dequantize_f...

def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs): """Write optical flow to file. If the flow is not quantized, it will be saved as a .flo file losslessly, otherwise a jpeg image which is lossy but of much smaller size. (dx and dy will be concatenated horizontally into a sin...

def quantize_flow(flow, max_val=0.02, norm=True): """Quantize flow to [0, 255]. After this step, the size of flow will be much smaller, and can be dumped as jpeg images. Args: flow (ndarray): (h, w, 2) array of optical flow. max_val (float): Maximum value of flow, values beyond ...

def dequantize_flow(dx, dy, max_val=0.02, denorm=True): """Recover from quantized flow. Args: dx (ndarray): Quantized dx. dy (ndarray): Quantized dy. max_val (float): Maximum value used when quantizing. denorm (bool): Whether to multiply flow values with width/height. Retur...

def load_state_dict(module, state_dict, strict=False, logger=None): """Load state_dict to a module. This method is modified from :meth:`torch.nn.Module.load_state_dict`. Default value for ``strict`` is set to ``False`` and the message for param mismatch will be shown even if strict is False. Args:...

def load_checkpoint(model, filename, map_location=None, strict=False, logger=None): """Load checkpoint from a file or URI. Args: model (Module): Module to load checkpoint. filename (str): Either a filepath or URL or...

def weights_to_cpu(state_dict): """Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on GPU. """ state_dict_cpu = OrderedDict() for key, val in state_dict.items(): state_dict_cpu[key] = val.cpu() ...

def save_checkpoint(model, filename, optimizer=None, meta=None): """Save checkpoint to file. The checkpoint will have 3 fields: ``meta``, ``state_dict`` and ``optimizer``. By default ``meta`` will contain version and time info. Args: model (Module): Module whose params are to be saved. ...

def init_optimizer(self, optimizer): """Init the optimizer. Args: optimizer (dict or :obj:`~torch.optim.Optimizer`): Either an optimizer object or a dict used for constructing the optimizer. Returns: :obj:`~torch.optim.Optimizer`: An optimizer object. ...

def init_logger(self, log_dir=None, level=logging.INFO): """Init the logger. Args: log_dir(str, optional): Log file directory. If not specified, no log file will be used. level (int or str): See the built-in python logging module. Returns: :o...

def current_lr(self): """Get current learning rates. Returns: list: Current learning rate of all param groups. """ if self.optimizer is None: raise RuntimeError( 'lr is not applicable because optimizer does not exist.') return [group['lr']...

def register_hook(self, hook, priority='NORMAL'): """Register a hook into the hook list. Args: hook (:obj:`Hook`): The hook to be registered. priority (int or str or :obj:`Priority`): Hook priority. Lower value means higher priority. """ assert is...

def run(self, data_loaders, workflow, max_epochs, **kwargs): """Start running. Args: data_loaders (list[:obj:`DataLoader`]): Dataloaders for training and validation. workflow (list[tuple]): A list of (phase, epochs) to specify the running order an...

def register_training_hooks(self, lr_config, optimizer_config=None, checkpoint_config=None, log_config=None): """Register default hooks for training. Default hooks include: ...

def convert_video(in_file, out_file, print_cmd=False, pre_options='', **kwargs): """Convert a video with ffmpeg. This provides a general api to ffmpeg, the executed command is:: `ffmpeg -y <pre_options> -i <in_file> <options> <out_file>` Options(kwargs) are mapped to ffmpeg comm...

def resize_video(in_file, out_file, size=None, ratio=None, keep_ar=False, log_level='info', print_cmd=False, **kwargs): """Resize a video. Args: in_file (str): Input video filename. ...

def cut_video(in_file, out_file, start=None, end=None, vcodec=None, acodec=None, log_level='info', print_cmd=False, **kwargs): """Cut a clip from a video. Args: in_file (str): Input video fil...

def concat_video(video_list, out_file, vcodec=None, acodec=None, log_level='info', print_cmd=False, **kwargs): """Concatenate multiple videos into a single one. Args: video_list (list): A list of video...

def list_from_file(filename, prefix='', offset=0, max_num=0): """Load a text file and parse the content as a list of strings. Args: filename (str): Filename. prefix (str): The prefix to be inserted to the begining of each item. offset (int): The offset of lines. max_num (int): T...