277 lines
9.9 KiB
Python
277 lines
9.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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import time
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from functools import partial
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from typing import Optional
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from mmengine.config import Config, DictAction
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from mmengine.data import pseudo_collate, worker_init_fn
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from mmengine.dist import get_rank, init_dist
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from mmengine.logging import MMLogger
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from mmengine.model.wrappers import MMDistributedDataParallel, is_model_wrapper
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from mmengine.runner import load_checkpoint
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from mmengine.utils import mkdir_or_exist
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from sklearn.manifold import TSNE
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from torch.utils.data import DataLoader
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from mmselfsup.models.utils import Extractor
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from mmselfsup.registry import DATA_SAMPLERS, DATASETS, MODELS
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from mmselfsup.utils import register_all_modules
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def parse_args():
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parser = argparse.ArgumentParser(description='t-SNE visualization')
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parser.add_argument('config', help='train config file path')
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parser.add_argument('--checkpoint', default=None, help='checkpoint file')
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parser.add_argument('--work-dir', help='the dir to save logs and models')
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='job launcher')
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parser.add_argument(
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'--dataset-config',
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default='configs/benchmarks/classification/tsne_imagenet.py',
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help='extract dataset config file path')
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parser.add_argument(
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'--max-num-class',
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type=int,
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default=20,
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help='the maximum number of classes to apply t-SNE algorithms, now the'
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'function supports maximum 20 classes')
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parser.add_argument('--seed', type=int, default=0, help='random seed')
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parser.add_argument(
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'--deterministic',
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action='store_true',
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help='whether to set deterministic options for CUDNN backend.')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. If the value to '
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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'Note that the quotation marks are necessary and that no white space '
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'is allowed.')
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# t-SNE settings
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parser.add_argument(
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'--n-components', type=int, default=2, help='the dimension of results')
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parser.add_argument(
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'--perplexity',
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type=float,
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default=30.0,
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help='The perplexity is related to the number of nearest neighbors'
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'that is used in other manifold learning algorithms.')
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parser.add_argument(
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'--early-exaggeration',
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type=float,
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default=12.0,
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help='Controls how tight natural clusters in the original space are in'
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'the embedded space and how much space will be between them.')
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parser.add_argument(
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'--learning-rate',
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type=float,
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default=200.0,
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help='The learning rate for t-SNE is usually in the range'
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'[10.0, 1000.0]. If the learning rate is too high, the data may look'
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'like a ball with any point approximately equidistant from its nearest'
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'neighbours. If the learning rate is too low, most points may look'
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'compressed in a dense cloud with few outliers.')
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parser.add_argument(
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'--n-iter',
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type=int,
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default=1000,
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help='Maximum number of iterations for the optimization. Should be at'
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'least 250.')
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parser.add_argument(
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'--n-iter-without-progress',
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type=int,
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default=300,
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help='Maximum number of iterations without progress before we abort'
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'the optimization.')
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parser.add_argument(
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'--init', type=str, default='random', help='The init method')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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# register all modules in mmselfsup into the registries
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register_all_modules()
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cfg = Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# set cudnn_benchmark
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if cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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# work_dir is determined in this priority: CLI > segment in file > filename
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if args.work_dir is not None:
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# update configs according to CLI args if args.work_dir is not None
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cfg.work_dir = args.work_dir
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elif cfg.get('work_dir', None) is None:
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# use config filename as default work_dir if cfg.work_dir is None
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work_type = args.config.split('/')[1]
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cfg.work_dir = osp.join('./work_dirs', work_type,
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osp.splitext(osp.basename(args.config))[0])
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# init distributed env first, since logger depends on the dist info.
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if args.launcher == 'none':
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distributed = False
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else:
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distributed = True
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init_dist(args.launcher)
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# create work_dir
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timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
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tsne_work_dir = osp.join(cfg.work_dir, f'tsne_{timestamp}/')
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mkdir_or_exist(osp.abspath(tsne_work_dir))
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# init the logger before other steps
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log_file = osp.join(tsne_work_dir, 'extract.log')
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logger = MMLogger.get_instance(
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'mmselfsup',
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logger_name='mmselfsup',
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log_file=log_file,
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log_level=cfg.log_level)
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# build the dataset
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dataset_cfg = Config.fromfile(args.dataset_config)
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extract_dataloader_cfg = dataset_cfg.get('extract_dataloader')
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extract_dataset_cfg = extract_dataloader_cfg.pop('dataset')
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if isinstance(extract_dataset_cfg, dict):
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dataset = DATASETS.build(extract_dataset_cfg)
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if hasattr(dataset, 'full_init'):
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dataset.full_init()
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# compress dataset, select that the label is less then max_num_class
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subset_idx_list = []
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for i in range(len(dataset)):
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if dataset.get_data_info(i)['gt_label'] < args.max_num_class:
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subset_idx_list.append(i)
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dataset.get_subset_(subset_idx_list)
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logger.info(f'Apply t-SNE to visualize {len(subset_idx_list)} samples.')
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# build sampler
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sampler_cfg = extract_dataloader_cfg.pop('sampler')
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if isinstance(sampler_cfg, dict):
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sampler = DATA_SAMPLERS.build(
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sampler_cfg, default_args=dict(dataset=dataset, seed=args.seed))
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# build dataloader
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init_fn: Optional[partial]
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if args.seed is not None:
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init_fn = partial(
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worker_init_fn,
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num_workers=extract_dataloader_cfg.get('num_workers'),
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rank=get_rank(),
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seed=args.seed)
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else:
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init_fn = None
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tsne_dataloader = DataLoader(
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dataset=dataset,
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sampler=sampler,
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collate_fn=pseudo_collate,
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worker_init_fn=init_fn,
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**extract_dataloader_cfg)
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# build the model
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# get backbone out_indices from pool_cfg in tsne_dataset_config
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cfg.model.backbone.out_indices = dataset_cfg.pool_cfg.get(
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'in_indices', [4])
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logger.info(
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f'The output indices of backbone is {cfg.model.backbone.out_indices}.')
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model = MODELS.build(cfg.model)
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model.init_weights()
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# model is determined in this priority: init_cfg > checkpoint > random
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if hasattr(cfg.model.backbone, 'init_cfg'):
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if getattr(cfg.model.backbone.init_cfg, 'type', None) == 'Pretrained':
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logger.info(
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f'Use pretrained model: '
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f'{cfg.model.backbone.init_cfg.checkpoint} to extract features'
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)
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elif args.checkpoint is not None:
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logger.info(f'Use checkpoint: {args.checkpoint} to extract features')
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load_checkpoint(model, args.checkpoint, map_location='cpu')
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else:
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logger.info('No pretrained or checkpoint is given, use random init.')
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if torch.cuda.is_available():
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model = model.cuda()
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if distributed:
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model = MMDistributedDataParallel(
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module=model.cuda(),
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device_ids=[torch.cuda.current_device()],
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broadcast_buffers=False)
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if is_model_wrapper(model):
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model = model.module
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# build extractor and extract features
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extractor = Extractor(
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extract_dataloader=tsne_dataloader,
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seed=args.seed,
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pool_cfg=dataset_cfg.pool_cfg,
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dist_mode=distributed)
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features = extractor(model)
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labels = tsne_dataloader.dataset.get_gt_labels()
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# save features
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mkdir_or_exist(f'{tsne_work_dir}features/')
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logger.info(f'Save features to {tsne_work_dir}features/')
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if distributed:
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rank = get_rank()
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if rank == 0:
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for key, val in features.items():
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output_file = \
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f'{tsne_work_dir}features/{dataset_cfg.name}_{key}.npy'
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np.save(output_file, val)
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else:
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for key, val in features.items():
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output_file = \
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f'{tsne_work_dir}features/{dataset_cfg.name}_{key}.npy'
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np.save(output_file, val)
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# build t-SNE model
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tsne_model = TSNE(
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n_components=args.n_components,
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perplexity=args.perplexity,
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early_exaggeration=args.early_exaggeration,
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learning_rate=args.learning_rate,
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n_iter=args.n_iter,
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n_iter_without_progress=args.n_iter_without_progress,
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init=args.init)
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# run and get results
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mkdir_or_exist(f'{tsne_work_dir}saved_pictures/')
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logger.info('Running t-SNE......')
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for key, val in features.items():
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result = tsne_model.fit_transform(val)
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res_min, res_max = result.min(0), result.max(0)
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res_norm = (result - res_min) / (res_max - res_min)
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plt.figure(figsize=(10, 10))
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plt.scatter(
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res_norm[:, 0],
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res_norm[:, 1],
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alpha=1.0,
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s=15,
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c=labels,
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cmap='tab20')
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plt.savefig(f'{tsne_work_dir}saved_pictures/{key}.png')
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logger.info(f'Saved results to {tsne_work_dir}saved_pictures/')
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if __name__ == '__main__':
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main()
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