mmagic.apis.inferencers.inference_functions

Module Contents

Functions

set_random_seed(seed[, deterministic, use_rank_shift])	Set random seed.
delete_cfg(cfg[, key])	Delete key from config object.
init_model(config[, checkpoint, device])	Initialize a model from config file.
pad_sequence(data, window_size)	Pad frame sequence data.
read_image(filepath)	Read image from file.
read_frames(source, start_index, num_frames, ...)	Read frames from file or video.
calculate_grid_size(→ int)	Calculate the number of images per row (nrow) to make the grid closer to

Attributes

VIDEO_EXTENSIONS
FILE_CLIENT

mmagic.apis.inferencers.inference_functions.VIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi')

mmagic.apis.inferencers.inference_functions.FILE_CLIENT

mmagic.apis.inferencers.inference_functions.set_random_seed(seed, deterministic=False, use_rank_shift=True)

Set random seed.

In this function, we just modify the default behavior of the similar function defined in MMCV.

Parameters

• seed (int) – Seed to be used.

• deterministic (bool) – Whether to set the deterministic option for CUDNN backend, i.e., set torch.backends.cudnn.deterministic to True and torch.backends.cudnn.benchmark to False. Default: False.

• rank_shift (bool) – Whether to add rank number to the random seed to have different random seed in different threads. Default: True.

mmagic.apis.inferencers.inference_functions.delete_cfg(cfg, key='init_cfg')

Delete key from config object.

Parameters

• cfg (str or mmengine.Config) – Config object.

• key (str) – Which key to delete.

mmagic.apis.inferencers.inference_functions.init_model(config, checkpoint=None, device='cuda:0')

Initialize a model from config file.

Parameters

• config (str or mmengine.Config) – Config file path or the config object.

• checkpoint (str, optional) – Checkpoint path. If left as None, the model will not load any weights.

• device (str) – Which device the model will deploy. Default: ‘cuda:0’.

Returns

The constructed model.

Return type

nn.Module

mmagic.apis.inferencers.inference_functions.pad_sequence(data, window_size)

Pad frame sequence data.

Parameters

• data (Tensor) – The frame sequence data.

• window_size (int) – The window size used in sliding-window framework.

Returns

The padded result.

Return type

data (Tensor)

mmagic.apis.inferencers.inference_functions.read_image(filepath)

Read image from file.

Parameters

filepath (str) – File path.

Returns

Image.

Return type

image (np.array)

mmagic.apis.inferencers.inference_functions.read_frames(source, start_index, num_frames, from_video, end_index)

Read frames from file or video.

Parameters

• source (list | mmcv.VideoReader) – Source of frames.

• start_index (int) – Start index of frames.

• num_frames (int) – frames number to be read.

• from_video (bool) – Weather read frames from video.

• end_index (int) – The end index of frames.

Returns

Images.

Return type

images (np.array)

mmagic.apis.inferencers.inference_functions.calculate_grid_size(num_batches: int = 1, aspect_ratio: int = 1) → int

Calculate the number of images per row (nrow) to make the grid closer to square when formatting a batch of images to grid.

Parameters

• num_batches (int, optional) – Number of images per batch. Defaults to 1.

• aspect_ratio (int, optional) – The aspect ratio (width / height) of each image sample. Defaults to 1.

Returns

Calculated number of images per row.

Return type

int