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mmagic.apis.inferencers.inference_functions 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import math

import mmcv
import numpy as np
import torch
from mmengine import Config
from mmengine.config import ConfigDict
from mmengine.fileio import get_file_backend
from mmengine.registry import init_default_scope
from mmengine.runner import load_checkpoint
from mmengine.runner import set_random_seed as set_random_seed_engine

from mmagic.registry import MODELS

[文档]VIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi')
[文档]FILE_CLIENT = get_file_backend(backend_args={'backend': 'local'})
[文档]def 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. Args: 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. """ set_random_seed_engine(seed, deterministic, use_rank_shift)
[文档]def delete_cfg(cfg, key='init_cfg'): """Delete key from config object. Args: cfg (str or :obj:`mmengine.Config`): Config object. key (str): Which key to delete. """ if key in cfg: cfg.pop(key) for _key in cfg.keys(): if isinstance(cfg[_key], ConfigDict): delete_cfg(cfg[_key], key)
[文档]def init_model(config, checkpoint=None, device='cuda:0'): """Initialize a model from config file. Args: config (str or :obj:`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: nn.Module: The constructed model. """ if isinstance(config, str): config = Config.fromfile(config) elif not isinstance(config, Config): raise TypeError('config must be a filename or Config object, ' f'but got {type(config)}') # config.test_cfg.metrics = None delete_cfg(config.model, 'init_cfg') init_default_scope(config.get('default_scope', 'mmagic')) model = MODELS.build(config.model) if checkpoint is not None: checkpoint = load_checkpoint(model, checkpoint) model.cfg = config # save the config in the model for convenience model.to(device) model.eval() return model
[文档]def pad_sequence(data, window_size): """Pad frame sequence data. Args: data (Tensor): The frame sequence data. window_size (int): The window size used in sliding-window framework. Returns: data (Tensor): The padded result. """ padding = window_size // 2 data = torch.cat([ data[:, 1 + padding:1 + 2 * padding].flip(1), data, data[:, -1 - 2 * padding:-1 - padding].flip(1) ], dim=1) return data
[文档]def read_image(filepath): """Read image from file. Args: filepath (str): File path. Returns: image (np.array): Image. """ img_bytes = FILE_CLIENT.get(filepath) image = mmcv.imfrombytes( img_bytes, flag='color', channel_order='rgb', backend='pillow') return image
[文档]def read_frames(source, start_index, num_frames, from_video, end_index): """Read frames from file or video. Args: 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 (np.array): Images. """ images = [] last_index = min(start_index + num_frames, end_index) # read frames from video if from_video: for index in range(start_index, last_index): if index >= source.frame_cnt: break images.append(np.flip(source.get_frame(index), axis=2)) else: files = source[start_index:last_index] images = [read_image(f) for f in files] return images
[文档]def 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. Args: 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: int: Calculated number of images per row. """ curr_ncol, curr_nrow = 1, num_batches curr_delta = curr_nrow * aspect_ratio - curr_ncol nrow = curr_nrow delta = curr_delta while curr_delta > 0: curr_ncol += 1 curr_nrow = math.ceil(num_batches / curr_ncol) curr_delta = curr_nrow * aspect_ratio - curr_ncol if curr_delta < delta and curr_delta >= 0: nrow, delta = curr_nrow, curr_delta return nrow
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