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How to use cfg_snapshot method in yandex-tank

Best Python code snippet using yandex-tank

retrieval_evaluation.py

Source:retrieval_evaluation.py

1import argparse2import torch3from copy import deepcopy4from pprint import pprint5try:6    from apex.parallel import DistributedDataParallel as DDP7except ImportError:8    pass9from torch.nn.parallel import DistributedDataParallel as torch_DDP10from zerovl.core import init_device, cfg, update_cfg11from zerovl.datasets.clip.clip_dataset import build_torch_valid_loader12from zerovl.models import PIPELINE13from zerovl.utils import build_from_cfg, ENV, logger, all_gather14from zerovl.core.hooks.checkpoint import get_dist_state_dict15from zerovl.tasks.clip.hooks.utils import RetrievalMetric, IndexedEmbInfo16from zerovl.tasks.clip.config import task_cfg_init_fn, update_clip_config17@ENV.root_only18def calcaulate_retrieval_metrics_and_log(collection_dict, cuda_eval = True):19    retrieval = RetrievalMetric()20    index = collection_dict['image_id'] if collection_dict['dataset_name'] != 'imagenet' else collection_dict['caption_id']21    image_embedding = collection_dict['image_embeddings']22    text_embedding = collection_dict['text_embeddings']23    if not cuda_eval:24        index = index.cpu()25        image_embedding = image_embedding.cpu()26        text_embedding = text_embedding.cpu()27    if collection_dict["dataset_name"] != 'imagenet':28        img_emb = IndexedEmbInfo(emb_name='image',group_idx=index,emb_mat=image_embedding).unique()29        text_emb = IndexedEmbInfo(emb_name='text',group_idx=index,emb_mat=text_embedding)30    else:31        img_emb = IndexedEmbInfo(emb_name='image',group_idx=index,emb_mat=image_embedding)32        text_emb = IndexedEmbInfo(emb_name='text',group_idx=index,emb_mat=text_embedding).unique()33    logger.info('{} validation: image emb shape: {}, text emb shape: {}'.format(collection_dict['dataset_name'], img_emb.emb_mat.shape, text_emb.emb_mat.shape))34    i2t = retrieval(img_emb, text_emb)35    t2i = retrieval(text_emb, img_emb)36    37    i2t.update(t2i)38    summary_dict = {}39    for k, v in i2t.items():40        k = k.replace('[image] to [text]', 'I2T')41        k = k.replace('[text] to [image]', 'T2I')42        k = k.replace(': ', '-')43        summary_dict[k] = v * 100.044    summary_dict['RSUM'] = sum(list(summary_dict.values()))45    summary_dict = {'{}_{}'.format(collection_dict['dataset_name'], k): v for k, v in summary_dict.items()}46    logger.emph('-------------- {} Evaluation --------------'.format(collection_dict['dataset_name']))47    pprint(summary_dict)48    logger.emph('-------------- {} Evaluation --------------\n'.format(collection_dict['dataset_name']))49def evaluate_benchmark(loader, model, name):50    collection_keys = ['image_embeddings', 'text_embeddings', 'image_id', 'caption_id']51    epoch_state = {}52    for key in collection_keys:53        epoch_state[key] = []54    for batch in loader:55        batch_dict = {}56        batch_dict['image'], batch_dict['input_ids'], batch_dict['attention_mask'], \57                batch_dict['caption'], batch_dict['image_id'], batch_dict['caption_id'] = batch58        batch_dict = {k: v.cuda(ENV.device, non_blocking=True) for k,v in batch_dict.items() if k not in ['caption']}59        image_embeddings, text_embeddings = model(batch_dict, embeddings='all')60        output = {'image_embeddings': image_embeddings,61                    'text_embeddings': text_embeddings,62                    'image_id': batch_dict['image_id'],63                    'caption_id': batch_dict['caption_id']}64        for key in collection_keys:65            epoch_state[key].append(output[key])66    collection_dict = {}67    for key in collection_keys:68        value = torch.cat(epoch_state[key], 0)69        value = torch.cat(all_gather(value), 0)70        collection_dict[key] = value71    valid_index = collection_dict['image_id'] > -172    collection_dict = {k: v[valid_index] for k,v in collection_dict.items()}73    collection_dict['dataset_name'] = name74    calcaulate_retrieval_metrics_and_log(collection_dict)75def parse_args():76    # Parse args with argparse tool77    parser = argparse.ArgumentParser(description='ZeroVL Evaluation')78    parser.add_argument('--cfg', type=str, required=True,79                        help='experiment configure file name')80    parser.add_argument("--local_rank", type=int, default=0)  # Compatibility with torch launch.py81    parser.add_argument("--ckpt_path", type=str, default='')  82    args, cfg_overrided = parser.parse_known_args()83    # Update config from yaml and argv for override84    update_cfg(task_cfg_init_fn, args.cfg, cfg_overrided, preprocess_fn=update_clip_config)85    # Record the global config and its snapshot (for easy experiment reproduction)86    ENV.cfg = cfg87    ENV.cfg_snapshot = deepcopy(cfg)88    ENV.local_rank = args.local_rank89    return args90def main():91    # Configuration: user config updating and global config generating92    args = parse_args()93    # Initialization: set device, generate global config and inform the user library94    init_device(cfg)95    # Build model96    model = build_from_cfg(cfg.model.name, cfg, PIPELINE).to(ENV.device)97    if cfg.dist.name == 'apex':98        model = DDP(model, delay_allreduce=False)99    elif cfg.dist.name == 'torch':100        model = torch_DDP(model,101                        device_ids=[ENV.local_rank],102                        output_device=ENV.local_rank,103                        find_unused_parameters=False)104    else:105        raise NotImplementedError106    # Runner: building and running107    checkpoint = torch.load(args.ckpt_path, map_location="cpu")108    model_checkpoint = checkpoint['state_dict']  109    model.load_state_dict(get_dist_state_dict(model_checkpoint), strict=False)110    model.eval()111    logger.emph(f'Loaded ckpt path: {args.ckpt_path}')112    for name in cfg.data.valid_name:113        valid_loader = build_torch_valid_loader(cfg, name, mode='valid')114        with torch.no_grad():115            evaluate_benchmark(valid_loader, model, name)116if __name__ == "__main__":...

train.py

Source:train.py

1import argparse2import os3import torch4from copy import deepcopy5from apex.parallel import convert_syncbn_model6from zerovl.core import init_device7from zerovl.datasets import DATALOADER8from zerovl.models import PIPELINE9from zerovl.core import cfg, update_cfg10from zerovl.utils import build_from_cfg, ENV11from zerovl.tasks.linear_prob.linear_runner import LinearProbRunner12from zerovl.tasks.linear_prob.config import task_cfg_init_fn, update_clip_config13def parse_args():14    # Parse args with argparse tool15    parser = argparse.ArgumentParser(description='ZeroVL training')16    parser.add_argument('--cfg', type=str, required=True,17                        help='experiment configure file name')18    parser.add_argument("--local_rank", type=int, default=0)  # Compatibility with torch launch.py19    args, cfg_overrided = parser.parse_known_args()20    # Update config from yaml and argv for override21    update_cfg(task_cfg_init_fn, args.cfg, cfg_overrided, preprocess_fn=update_clip_config)22    # Record the global config and its snapshot (for easy experiment reproduction)23    ENV.cfg = cfg24    ENV.cfg_snapshot = deepcopy(cfg)25    ENV.local_rank = args.local_rank26def main():27    # Configuration: user config updating and global config generating28    parse_args()29    # Initialization: set device, generate global config and inform the user library30    init_device(cfg)31    # Build model32    model = build_from_cfg(cfg.model.name, cfg, PIPELINE).to(ENV.device)33    if cfg.model.syncbn:34        if cfg.dist.name == 'torch':35            model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)36        elif cfg.dist.name == 'apex':37            model = convert_syncbn_model(model)38        else:39            raise NotImplementedError40    41    # Context building: dataloader42    data_loaders = build_from_cfg(cfg.data.name, cfg, DATALOADER)43    # Runner: building and running44    runner = LinearProbRunner(cfg, data_loaders, model)45    runner.run()46if __name__ == '__main__':...

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