File size: 3,593 Bytes
c99d198 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | """Functionality common to pretraining and evaluation."""
from typing import Dict
from ml_collections import ConfigDict
import torch
from xirl import factory
from xirl.models import SelfSupervisedModel
DataLoadersDict = Dict[str, torch.utils.data.DataLoader]
ModelType = SelfSupervisedModel
# def get_pretraining_dataloaders(
# config,
# debug = False,
# ):
# """Construct a train/valid pair of pretraining dataloaders.
# Args:
# config: ConfigDict object with config parameters.
# debug: When set to True, the following happens: 1. Data augmentation is
# disabled regardless of config values. 2. Sequential sampling of videos is
# turned on. 3. The number of dataloader workers is set to 0.
# Returns:
# A dict of train/valid pretraining dataloaders.
# """
# def _loader(split):
# dataset = factory.dataset_from_config(config, False, split, debug)
# batch_sampler = factory.video_sampler_from_config(
# config, dataset.dir_tree, downstream=False, sequential=debug
# )
# return torch.utils.data.DataLoader(
# dataset,
# collate_fn=dataset.collate_fn,
# batch_sampler=batch_sampler,
# num_workers=4 if torch.cuda.is_available() and not debug else 0,
# pin_memory=torch.cuda.is_available() and not debug,
# )
# return {
# "train": _loader("train"),
# "valid": _loader("valid"),
# }
def get_downstream_dataloaders(
config,
debug = False,
):
"""Construct a train/valid pair of downstream dataloaders.
Args:
config: ConfigDict object with config parameters.
debug: When set to True, the following happens: 1. Data augmentation is
disabled regardless of config values. 2. Sequential sampling of videos is
turned on. 3. The number of dataloader workers is set to 0.
Returns:
A dict of train/valid downstream dataloaders
"""
def _loader(split):
datasets = factory.dataset_from_config(config, True, split, debug)
loaders = {}
for action_class, dataset in datasets.items():
batch_sampler = factory.video_sampler_from_config(
config, dataset.dir_tree, downstream=True, sequential=debug
)
loaders[action_class] = torch.utils.data.DataLoader(
dataset,
collate_fn=dataset.collate_fn,
batch_sampler=batch_sampler,
num_workers=4 if torch.cuda.is_available() and not debug else 0,
pin_memory=torch.cuda.is_available() and not debug,
)
return loaders
return {
"train": _loader("train"),
"valid": _loader("valid"),
}
# def get_factories(
# config,
# device,
# debug = False,
# ):
# """Feed config to factories and return objects."""
# pretrain_loaders = get_pretraining_dataloaders(config, debug)
# downstream_loaders = get_downstream_dataloaders(config, debug)
# model = factory.model_from_config(config)
# optimizer = factory.optim_from_config(config, model)
# trainer = factory.trainer_from_config(config, model, optimizer, device)
# eval_manager = factory.evaluator_from_config(config)
# return (
# model,
# optimizer,
# pretrain_loaders,
# downstream_loaders,
# trainer,
# eval_manager,
# )
def get_model(config):
"""Construct a model from a config."""
return factory.model_from_config(config) |