Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,087 Bytes
9b33fca |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 |
"""ImageNet classification config."""
from __future__ import annotations
from collections.abc import Sequence
from ml_collections import ConfigDict
from vis4d.config import class_config
from vis4d.config.typing import DataConfig
from vis4d.data.const import CommonKeys as K
from vis4d.data.data_pipe import DataPipe
from vis4d.data.datasets.imagenet import ImageNet
from vis4d.data.transforms.autoaugment import RandAug
from vis4d.data.transforms.base import RandomApply, compose
from vis4d.data.transforms.crop import (
CropImages,
GenCentralCropParameters,
GenRandomSizeCropParameters,
)
from vis4d.data.transforms.flip import FlipImages
from vis4d.data.transforms.mixup import (
GenMixupParameters,
MixupCategories,
MixupImages,
)
from vis4d.data.transforms.normalize import NormalizeImages
from vis4d.data.transforms.random_erasing import RandomErasing
from vis4d.data.transforms.resize import GenResizeParameters, ResizeImages
from vis4d.data.transforms.to_tensor import ToTensor
from vis4d.engine.connectors import data_key, pred_key
from vis4d.zoo.base import (
get_inference_dataloaders_cfg,
get_train_dataloader_cfg,
)
CONN_IMAGENET_CLS_EVAL = {
"prediction": pred_key("probs"),
"groundtruth": data_key("categories"),
}
def get_train_dataloader(
data_root: str,
split: str,
keys_to_load: Sequence[str],
image_size: tuple[int, int],
samples_per_gpu: int,
workers_per_gpu: int,
) -> ConfigDict:
"""Get the default train dataloader for ImageNet 1K dataset."""
# Train Dataset
train_dataset_cfg = class_config(
ImageNet,
data_root=data_root,
split=split,
num_classes=1000,
keys_to_load=keys_to_load,
)
flip_trans = class_config(
RandomApply,
transforms=[class_config(FlipImages)],
probability=0.5,
)
random_resized_crop_trans = [
class_config(GenRandomSizeCropParameters),
class_config(CropImages),
class_config(GenResizeParameters, shape=image_size, keep_ratio=False),
class_config(ResizeImages),
]
random_aug_trans = [
class_config(RandAug, magnitude=10, use_increasing=True),
class_config(RandomErasing),
]
normalize_trans = class_config(NormalizeImages)
train_preprocess_cfg = class_config(
compose,
transforms=[
flip_trans,
*random_resized_crop_trans,
*random_aug_trans,
normalize_trans,
],
)
mixup_trans = [
class_config(GenMixupParameters, alpha=0.2, out_shape=image_size),
class_config(MixupImages),
class_config(MixupCategories, num_classes=1000, label_smoothing=0.1),
]
train_batchprocess_cfg = class_config(
compose,
transforms=[
*mixup_trans,
class_config(ToTensor),
],
)
return get_train_dataloader_cfg(
datasets_cfg=class_config(
DataPipe,
datasets=train_dataset_cfg,
preprocess_fn=train_preprocess_cfg,
),
batchprocess_cfg=train_batchprocess_cfg,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=workers_per_gpu,
)
def get_test_dataloader(
data_root: str,
split: str,
keys_to_load: Sequence[str],
image_size: tuple[int, int],
samples_per_gpu: int,
workers_per_gpu: int,
crop_pct: float = 0.875,
) -> ConfigDict:
"""Get the default test dataloader for COCO detection."""
# Test Dataset
test_dataset_cfg = class_config(
ImageNet,
data_root=data_root,
split=split,
num_classes=1000,
keys_to_load=keys_to_load,
)
crop_size = tuple(int(size / crop_pct) for size in image_size)
resized_crop_trans = [
class_config(
GenResizeParameters,
shape=crop_size,
keep_ratio=True,
allow_overflow=True,
),
class_config(ResizeImages),
class_config(
GenCentralCropParameters, shape=image_size, keep_ratio=False
),
class_config(CropImages),
]
normalize_trans = class_config(NormalizeImages)
test_preprocess_cfg = class_config(
compose,
transforms=[
*resized_crop_trans,
normalize_trans,
],
)
mixup_trans = [
class_config(GenMixupParameters, alpha=0.2, out_shape=image_size),
class_config(MixupImages),
class_config(MixupCategories, num_classes=1000, label_smoothing=0.1),
]
test_batchprocess_cfg = class_config(
compose,
transforms=[
*mixup_trans,
class_config(ToTensor),
],
)
return get_inference_dataloaders_cfg(
datasets_cfg=class_config(
DataPipe,
datasets=test_dataset_cfg,
preprocess_fn=test_preprocess_cfg,
),
batchprocess_cfg=test_batchprocess_cfg,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=workers_per_gpu,
)
def get_imagenet_cls_cfg(
data_root: str = "data/imagenet",
train_split: str = "train",
train_keys_to_load: Sequence[str] = (
K.images,
K.categories,
),
test_split: str = "val",
test_keys_to_load: Sequence[str] = (
K.images,
K.categories,
),
image_size: tuple[int, int] = (224, 224),
samples_per_gpu: int = 256,
workers_per_gpu: int = 8,
) -> DataConfig:
"""Get the default config for COCO detection."""
data = DataConfig()
data.train_dataloader = get_train_dataloader(
data_root=data_root,
split=train_split,
keys_to_load=train_keys_to_load,
image_size=image_size,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=workers_per_gpu,
)
data.test_dataloader = get_test_dataloader(
data_root=data_root,
split=test_split,
keys_to_load=test_keys_to_load,
image_size=image_size,
samples_per_gpu=1,
workers_per_gpu=workers_per_gpu,
)
return data
|