| |
| import argparse |
| import os |
|
|
| import mmcv |
| import numpy as np |
| import torch |
| from mmcv.parallel import MMDataParallel |
| from mmcv.parallel.scatter_gather import scatter_kwargs |
| from mmcv.runner import load_checkpoint, wrap_fp16_model |
| from PIL import Image |
|
|
| from mmseg.datasets import build_dataloader, build_dataset |
| from mmseg.models import build_segmentor |
|
|
|
|
| @torch.no_grad() |
| def main(args): |
|
|
| models = [] |
| gpu_ids = args.gpus |
| configs = args.config |
| ckpts = args.checkpoint |
|
|
| cfg = mmcv.Config.fromfile(configs[0]) |
|
|
| if args.aug_test: |
| cfg.data.test.pipeline[1].img_ratios = [ |
| 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0 |
| ] |
| cfg.data.test.pipeline[1].flip = True |
| else: |
| cfg.data.test.pipeline[1].img_ratios = [1.0] |
| cfg.data.test.pipeline[1].flip = False |
|
|
| torch.backends.cudnn.benchmark = True |
|
|
| |
| dataset = build_dataset(cfg.data.test) |
| data_loader = build_dataloader( |
| dataset, |
| samples_per_gpu=1, |
| workers_per_gpu=4, |
| dist=False, |
| shuffle=False, |
| ) |
|
|
| for idx, (config, ckpt) in enumerate(zip(configs, ckpts)): |
| cfg = mmcv.Config.fromfile(config) |
| cfg.model.pretrained = None |
| cfg.data.test.test_mode = True |
|
|
| model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg')) |
| if cfg.get('fp16', None): |
| wrap_fp16_model(model) |
| load_checkpoint(model, ckpt, map_location='cpu') |
| torch.cuda.empty_cache() |
| tmpdir = args.out |
| mmcv.mkdir_or_exist(tmpdir) |
| model = MMDataParallel(model, device_ids=[gpu_ids[idx % len(gpu_ids)]]) |
| model.eval() |
| models.append(model) |
|
|
| dataset = data_loader.dataset |
| prog_bar = mmcv.ProgressBar(len(dataset)) |
| loader_indices = data_loader.batch_sampler |
| for batch_indices, data in zip(loader_indices, data_loader): |
| result = [] |
|
|
| for model in models: |
| x, _ = scatter_kwargs( |
| inputs=data, kwargs=None, target_gpus=model.device_ids) |
| if args.aug_test: |
| logits = model.module.aug_test_logits(**x[0]) |
| else: |
| logits = model.module.simple_test_logits(**x[0]) |
| result.append(logits) |
|
|
| result_logits = 0 |
| for logit in result: |
| result_logits += logit |
|
|
| pred = result_logits.argmax(axis=1).squeeze() |
| img_info = dataset.img_infos[batch_indices[0]] |
| file_name = os.path.join( |
| tmpdir, img_info['ann']['seg_map'].split(os.path.sep)[-1]) |
| Image.fromarray(pred.astype(np.uint8)).save(file_name) |
| prog_bar.update() |
|
|
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser( |
| description='Model Ensemble with logits result') |
| parser.add_argument( |
| '--config', type=str, nargs='+', help='ensemble config files path') |
| parser.add_argument( |
| '--checkpoint', |
| type=str, |
| nargs='+', |
| help='ensemble checkpoint files path') |
| parser.add_argument( |
| '--aug-test', |
| action='store_true', |
| help='control ensemble aug-result or single-result (default)') |
| parser.add_argument( |
| '--out', type=str, default='results', help='the dir to save result') |
| parser.add_argument( |
| '--gpus', type=int, nargs='+', default=[0], help='id of gpu to use') |
|
|
| args = parser.parse_args() |
| assert len(args.config) == len(args.checkpoint), \ |
| f'len(config) must equal len(checkpoint), ' \ |
| f'but len(config) = {len(args.config)} and' \ |
| f'len(checkpoint) = {len(args.checkpoint)}' |
| assert args.out, "ensemble result out-dir can't be None" |
| return args |
|
|
|
|
| if __name__ == '__main__': |
| args = parse_args() |
| main(args) |
|
|
