# Copyright (c) OpenMMLab. All rights reserved. import argparse import os import os.path as osp import mmcv import numpy as np import torch from mmcv import Config, DictAction from mmcv.parallel import collate, scatter from mmaction.apis import init_recognizer from mmaction.datasets.pipelines import Compose from mmaction.utils import GradCAM def parse_args(): parser = argparse.ArgumentParser(description='MMAction2 GradCAM demo') parser.add_argument('config', help='test config file path') parser.add_argument('checkpoint', help='checkpoint file/url') parser.add_argument('video', help='video file/url or rawframes directory') parser.add_argument( '--use-frames', default=False, action='store_true', help='whether to use rawframes as input') parser.add_argument( '--device', type=str, default='cuda:0', help='CPU/CUDA device option') parser.add_argument( '--target-layer-name', type=str, default='backbone/layer4/1/relu', help='GradCAM target layer name') parser.add_argument('--out-filename', default=None, help='output filename') parser.add_argument('--fps', default=5, type=int) parser.add_argument( '--cfg-options', nargs='+', action=DictAction, default={}, help='override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file. For example, ' "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'") parser.add_argument( '--target-resolution', nargs=2, default=None, type=int, help='Target resolution (w, h) for resizing the frames when using a ' 'video as input. If either dimension is set to -1, the frames are ' 'resized by keeping the existing aspect ratio') parser.add_argument( '--resize-algorithm', default='bilinear', help='resize algorithm applied to generate video & gif') args = parser.parse_args() return args def build_inputs(model, video_path, use_frames=False): """build inputs for GradCAM. Note that, building inputs for GradCAM is exactly the same as building inputs for Recognizer test stage. Codes from `inference_recognizer`. Args: model (nn.Module): Recognizer model. video_path (str): video file/url or rawframes directory. use_frames (bool): whether to use rawframes as input. Returns: dict: Both GradCAM inputs and Recognizer test stage inputs, including two keys, ``imgs`` and ``label``. """ if not (osp.exists(video_path) or video_path.startswith('http')): raise RuntimeError(f"'{video_path}' is missing") if osp.isfile(video_path) and use_frames: raise RuntimeError( f"'{video_path}' is a video file, not a rawframe directory") if osp.isdir(video_path) and not use_frames: raise RuntimeError( f"'{video_path}' is a rawframe directory, not a video file") cfg = model.cfg device = next(model.parameters()).device # model device # build the data pipeline test_pipeline = cfg.data.test.pipeline test_pipeline = Compose(test_pipeline) # prepare data if use_frames: filename_tmpl = cfg.data.test.get('filename_tmpl', 'img_{:05}.jpg') modality = cfg.data.test.get('modality', 'RGB') start_index = cfg.data.test.get('start_index', 1) data = dict( frame_dir=video_path, total_frames=len(os.listdir(video_path)), label=-1, start_index=start_index, filename_tmpl=filename_tmpl, modality=modality) else: start_index = cfg.data.test.get('start_index', 0) data = dict( filename=video_path, label=-1, start_index=start_index, modality='RGB') data = test_pipeline(data) data = collate([data], samples_per_gpu=1) if next(model.parameters()).is_cuda: # scatter to specified GPU data = scatter(data, [device])[0] return data def _resize_frames(frame_list, scale, keep_ratio=True, interpolation='bilinear'): """resize frames according to given scale. Codes are modified from `mmaction2/datasets/pipelines/augmentation.py`, `Resize` class. Args: frame_list (list[np.ndarray]): frames to be resized. scale (tuple[int]): If keep_ratio is True, it serves as scaling factor or maximum size: the image will be rescaled as large as possible within the scale. Otherwise, it serves as (w, h) of output size. keep_ratio (bool): If set to True, Images will be resized without changing the aspect ratio. Otherwise, it will resize images to a given size. Default: True. interpolation (str): Algorithm used for interpolation: "nearest" | "bilinear". Default: "bilinear". Returns: list[np.ndarray]: Both GradCAM and Recognizer test stage inputs, including two keys, ``imgs`` and ``label``. """ if scale is None or (scale[0] == -1 and scale[1] == -1): return frame_list scale = tuple(scale) max_long_edge = max(scale) max_short_edge = min(scale) if max_short_edge == -1: scale = (np.inf, max_long_edge) img_h, img_w, _ = frame_list[0].shape if keep_ratio: new_w, new_h = mmcv.rescale_size((img_w, img_h), scale) else: new_w, new_h = scale frame_list = [ mmcv.imresize(img, (new_w, new_h), interpolation=interpolation) for img in frame_list ] return frame_list def main(): args = parse_args() # assign the desired device. device = torch.device(args.device) cfg = Config.fromfile(args.config) cfg.merge_from_dict(args.cfg_options) # build the recognizer from a config file and checkpoint file/url model = init_recognizer(cfg, args.checkpoint, device=device) inputs = build_inputs(model, args.video, use_frames=args.use_frames) gradcam = GradCAM(model, args.target_layer_name) results = gradcam(inputs) if args.out_filename is not None: try: from moviepy.editor import ImageSequenceClip except ImportError: raise ImportError('Please install moviepy to enable output file.') # frames_batches shape [B, T, H, W, 3], in RGB order frames_batches = (results[0] * 255.).numpy().astype(np.uint8) frames = frames_batches.reshape(-1, *frames_batches.shape[-3:]) frame_list = list(frames) frame_list = _resize_frames( frame_list, args.target_resolution, interpolation=args.resize_algorithm) video_clips = ImageSequenceClip(frame_list, fps=args.fps) out_type = osp.splitext(args.out_filename)[1][1:] if out_type == 'gif': video_clips.write_gif(args.out_filename) else: video_clips.write_videofile(args.out_filename, remove_temp=True) if __name__ == '__main__': main()