import numpy as np
from sklearn.svm import LinearSVC
from sklearn.metrics import average_precision_score
from sklearn.model_selection import cross_val_score
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from virtex.config import Config
from virtex.factories import PretrainingModelFactory, DownstreamDatasetFactory
from virtex.models.downstream import FeatureExtractor
from virtex.utils.checkpointing import CheckpointManager
from virtex.utils.common import common_parser, common_setup
parser = common_parser(
description="Train SVMs for VOC2007 classification on a pretrained model.")
group = parser.add_argument_group("Downstream config arguments.")
group.add_argument("--down-config",
metavar="FILE",
help="Path to a downstream config file.")
group.add_argument(
"--down-config-override",
nargs="*",
default=[],
help="A list of key-value pairs to modify downstream config params.",
)
# fmt: off
parser.add_argument_group("Checkpointing")
group.add_argument("--layer",
choices=["layer1", "layer2", "layer3", "layer4", "avgpool"],
from detectron2.evaluation import (
LVISEvaluator,
PascalVOCDetectionEvaluator,
COCOEvaluator,
)
from detectron2.modeling.roi_heads import ROI_HEADS_REGISTRY, Res5ROIHeads
from virtex.config import Config
from virtex.factories import PretrainingModelFactory
from virtex.utils.checkpointing import CheckpointManager
from virtex.utils.common import common_parser
import virtex.utils.distributed as dist
# fmt: off
parser = common_parser(
description="Train object detectors from pretrained visual backbone."
)
parser.add_argument(
"--d2-config", required=True,
help="Path to a detectron2 config for downstream task finetuning."
)
parser.add_argument(
"--d2-config-override", nargs="*", default=[],
help="""Key-value pairs from Detectron2 config to override from file.
Some keys will be ignored because they are set from other args:
[DATALOADER.NUM_WORKERS, SOLVER.EVAL_PERIOD, SOLVER.CHECKPOINT_PERIOD,
TEST.EVAL_PERIOD, OUTPUT_DIR]""",
)
parser.add_argument_group("Checkpointing and Logging")
parser.add_argument(
# fmt: off
from virtex.config import Config
from virtex.factories import (
PretrainingDatasetFactory,
PretrainingModelFactory,
OptimizerFactory,
LRSchedulerFactory,
)
from virtex.utils.checkpointing import CheckpointManager
from virtex.utils.common import common_parser, common_setup, cycle
import virtex.utils.distributed as dist
from virtex.utils.timer import Timer
from virtex.data.transforms import IMAGENET_COLOR_MEAN, IMAGENET_COLOR_STD
from virtex.utils.metrics import compute_scts_reward, CiderEvaluator
parser = common_parser(
description="Train a VirTex model (CNN + Transformer) on COCO Captions.")
group = parser.add_argument_group("Checkpointing and Logging")
group.add_argument(
"--start-checkpoint",
required=True,
)
group.add_argument(
"--resume-from",
default=None,
help="Path to a checkpoint to resume training from (if provided).")
group.add_argument(
"--checkpoint-every",
type=int,
default=2000,
help="Serialize model to a checkpoint after every these many iterations.",
)
from virtex.config import Config
from virtex.factories import (
DownstreamDatasetFactory,
PretrainingModelFactory,
OptimizerFactory,
LRSchedulerFactory,
)
from virtex.utils.checkpointing import CheckpointManager
from virtex.utils.common import common_parser, common_setup, cycle
import virtex.utils.distributed as dist
from virtex.utils.metrics import TopkAccuracy
from virtex.utils.timer import Timer
# fmt: off
parser = common_parser(
description="""Do image classification with linear models and frozen
feature extractor, or fine-tune the feature extractor end-to-end.""")
group = parser.add_argument_group("Downstream config arguments.")
group.add_argument("--down-config",
metavar="FILE",
help="Path to a downstream config file.")
group.add_argument(
"--down-config-override",
nargs="*",
default=[],
help="A list of key-value pairs to modify downstream config params.",
)
parser.add_argument_group("Checkpointing and Logging")
parser.add_argument("--weight-init",
choices=["random", "imagenet", "torchvision", "virtex"],
from typing import Any, Dict, List
from loguru import logger
import torch
from torch.utils.data import DataLoader
from virtex.config import Config
from virtex.data import ImageDirectoryDataset
from virtex.factories import TokenizerFactory, PretrainingModelFactory
from virtex.utils.checkpointing import CheckpointManager
from virtex.utils.common import common_parser, common_setup
from virtex.utils.metrics import CocoCaptionsEvaluator
# fmt: off
parser = common_parser(
description="""Run image captioning inference on a pretrained model, and/or
evaluate pretrained model on COCO Captions val2017 split.""")
parser.add_argument(
"--data-root",
default=None,
help="""Path to a directory containing image files to generate captions for.
Default: COCO val2017 image directory as expected relative to project root."""
)
parser.add_argument(
"--checkpoint-path",
required=True,
help="Path to load checkpoint and run captioning evaluation.")
parser.add_argument("--output",
default=None,
help="Path to save predictions as a JSON file.")
parser.add_argument(
import os
from typing import Any, Dict, List
from loguru import logger
import torch
from torch.utils.data import DataLoader
# fmt: off
from virtex.config import Config
from virtex.data import CocoCaptionsEvalDataset
from virtex.factories import TokenizerFactory, PretrainingModelFactory
from virtex.utils.checkpointing import CheckpointManager
from virtex.utils.common import common_parser, common_setup
from virtex.utils.metrics import CocoCaptionsEvaluator
parser = common_parser(
description="Evaluate a pre-trained model based on captioning metrics.")
parser.add_argument(
"--checkpoint-path",
required=True,
help="Path to load checkpoint and run captioning evaluation.")
# fmt: on
def main(_A: argparse.Namespace):
if _A.num_gpus_per_machine == 0:
# Set device as CPU if num_gpus_per_machine = 0.
device = torch.device("cpu")
else:
# Get the current device (this will be zero here by default).
device = torch.cuda.current_device()
