mmedit.apis.inferencers¶
Package Contents¶
Classes¶
Class to assign task to different inferencers. |
Functions¶
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Calculate the number of images per row (nrow) to make the grid closer to |
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Inference image with the model. |
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Delete key from config object. |
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Initialize a model from config file. |
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Inference image with the model. |
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Inference image(s) with the model. |
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Inference image with the model. |
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Inference image with the model. |
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Inference image with the model. |
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Sampling from conditional models. |
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Sampling from translation models. |
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Sampling from unconditional models. |
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Set random seed. |
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Inference image with the model. |
- mmedit.apis.inferencers.calculate_grid_size(num_batches: int = 1, aspect_ratio: int = 1) int[source]¶
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
- mmedit.apis.inferencers.colorization_inference(model, img)[source]¶
Inference image with the model.
- Parameters
model (nn.Module) – The loaded model.
img (str) – Image file path.
- Returns
The predicted colorization result.
- Return type
Tensor
- mmedit.apis.inferencers.delete_cfg(cfg, key='init_cfg')[source]¶
Delete key from config object.
- Parameters
cfg (str or
mmengine.Config) – Config object.key (str) – Which key to delete.
- mmedit.apis.inferencers.init_model(config, checkpoint=None, device='cuda:0')[source]¶
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
- mmedit.apis.inferencers.inpainting_inference(model, masked_img, mask)[source]¶
Inference image with the model.
- Parameters
model (nn.Module) – The loaded model.
masked_img (str) – File path of image with mask.
mask (str) – Mask file path.
- Returns
The predicted inpainting result.
- Return type
Tensor
- mmedit.apis.inferencers.matting_inference(model, img, trimap)[source]¶
Inference image(s) with the model.
- Parameters
model (nn.Module) – The loaded model.
img (str) – Image file path.
trimap (str) – Trimap file path.
- Returns
The predicted alpha matte.
- Return type
np.ndarray
- mmedit.apis.inferencers.restoration_face_inference(model, img, upscale_factor=1, face_size=1024)[source]¶
Inference image with the model.
- Parameters
model (nn.Module) – The loaded model.
img (str) – File path of input image.
upscale_factor (int, optional) – The number of times the input image is upsampled. Default: 1.
face_size (int, optional) – The size of the cropped and aligned faces. Default: 1024.
- Returns
The predicted restoration result.
- Return type
Tensor
- mmedit.apis.inferencers.restoration_inference(model, img, ref=None)[source]¶
Inference image with the model.
- Parameters
model (nn.Module) – The loaded model.
img (str) – File path of input image.
ref (str | None) – File path of reference image. Default: None.
- Returns
The predicted restoration result.
- Return type
Tensor
- mmedit.apis.inferencers.restoration_video_inference(model, img_dir, window_size, start_idx, filename_tmpl, max_seq_len=None)[source]¶
Inference image with the model.
- Parameters
model (nn.Module) – The loaded model.
img_dir (str) – Directory of the input video.
window_size (int) – The window size used in sliding-window framework. This value should be set according to the settings of the network. A value smaller than 0 means using recurrent framework.
start_idx (int) – The index corresponds to the first frame in the sequence.
filename_tmpl (str) – Template for file name.
max_seq_len (int | None) – The maximum sequence length that the model processes. If the sequence length is larger than this number, the sequence is split into multiple segments. If it is None, the entire sequence is processed at once.
- Returns
The predicted restoration result.
- Return type
Tensor
- mmedit.apis.inferencers.sample_conditional_model(model, num_samples=16, num_batches=4, sample_model='ema', label=None, **kwargs)[source]¶
Sampling from conditional models.
- Parameters
model (nn.Module) – Conditional models in MMGeneration.
num_samples (int, optional) – The total number of samples. Defaults to 16.
num_batches (int, optional) – The number of batch size for inference. Defaults to 4.
sample_model (str, optional) – Which model you want to use. [‘ema’, ‘orig’]. Defaults to ‘ema’.
label (int | torch.Tensor | list[int], optional) – Labels used to generate images. Default to None.,
- Returns
Generated image tensor.
- Return type
Tensor
- mmedit.apis.inferencers.sample_img2img_model(model, image_path, target_domain=None, **kwargs)[source]¶
Sampling from translation models.
- Parameters
model (nn.Module) – The loaded model.
image_path (str) – File path of input image.
style (str) – Target style of output image.
- Returns
Translated image tensor.
- Return type
Tensor
- mmedit.apis.inferencers.sample_unconditional_model(model, num_samples=16, num_batches=4, sample_model='ema', **kwargs)[source]¶
Sampling from unconditional models.
- Parameters
model (nn.Module) – Unconditional models in MMGeneration.
num_samples (int, optional) – The total number of samples. Defaults to 16.
num_batches (int, optional) – The number of batch size for inference. Defaults to 4.
sample_model (str, optional) – Which model you want to use. [‘ema’, ‘orig’]. Defaults to ‘ema’.
- Returns
Generated image tensor.
- Return type
Tensor
- mmedit.apis.inferencers.set_random_seed(seed, deterministic=False, use_rank_shift=True)[source]¶
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.
- mmedit.apis.inferencers.video_interpolation_inference(model, input_dir, output_dir, start_idx=0, end_idx=None, batch_size=4, fps_multiplier=0, fps=0, filename_tmpl='{:08d}.png')[source]¶
Inference image with the model.
- Parameters
model (nn.Module) – The loaded model.
input_dir (str) – Directory of the input video.
output_dir (str) – Directory of the output video.
start_idx (int) – The index corresponding to the first frame in the sequence. Default: 0
end_idx (int | None) – The index corresponding to the last interpolated frame in the sequence. If it is None, interpolate to the last frame of video or sequence. Default: None
batch_size (int) – Batch size. Default: 4
fps_multiplier (float) – multiply the fps based on the input video. Default: 0.
fps (float) – frame rate of the output video. Default: 0.
filename_tmpl (str) – template of the file names. Default: ‘{:08d}.png’
- class mmedit.apis.inferencers.MMEditInferencer(task: Optional[str] = None, config: Optional[Union[mmedit.utils.ConfigType, str]] = None, ckpt: Optional[str] = None, device: torch.device = None, extra_parameters: Optional[Dict] = None, seed: int = 2022)[source]¶
Class to assign task to different inferencers.
- Parameters
task (str) – Inferencer task.
config (str or ConfigType) – Model config or the path to it.
ckpt (str, optional) – Path to the checkpoint.
device (str, optional) – Device to run inference. If None, the best device will be automatically used.
seed (int) – The random seed used in inference. Defaults to 2022.