id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
1,859 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def compute_mppd(pl_module, batch):
infer = pl_mo... | null |
1,860 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def compute_mpfr(pl_module, batch):
infer = pl_mo... | null |
1,861 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def cost_matrix_cosine(x, y, eps=1e-5):
"""Compute... | null |
1,862 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def compute_imgcls(pl_module, batch):
infer = pl_... | null |
1,863 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def compute_vqa(pl_module, batch):
infer = pl_mod... | null |
1,864 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def compute_nlvr2(pl_module, batch):
infer1 = pl_... | null |
1,865 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def compute_irtr(pl_module, batch):
is_training_p... | null |
1,866 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def init_weights(module):
if isinstance(module, (... | null |
1,867 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def vqa_test_step(pl_module, batch, output):
id2a... | null |
1,868 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def arc_test_step(pl_module, batch, output):
retu... | null |
1,869 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def vqa_test_wrapup(outs, model_name):
rank = tor... | null |
1,870 | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from vilt.modules.dist_utils import all_gather
def arc_test_wrapup(outs, caplen, model_name):
ra... | null |
1,871 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | null |
1,872 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | null |
1,873 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | My custom 'small' ViT model. Depth=8, heads=8= mlp_ratio=3. |
1,874 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer. |
1,875 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights. |
1,876 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer. |
1,877 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer. |
1,878 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer. |
1,879 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights. |
1,880 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer. |
1,881 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer. |
1,882 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer. |
1,883 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer. |
1,884 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer. |
1,885 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer. |
1,886 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer. NOTE: converted weights not currently available, too large for github release hosting. |
1,887 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929). ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer. |
1,888 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer. |
1,889 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights. |
1,890 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights. |
1,891 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights. |
1,892 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights. |
1,893 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877). ImageNet-1k weights from https://github.com/facebookresearch/deit. |
1,894 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877). ImageNet-1k weights from https://github.com/facebookresearch/deit. |
1,895 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877). ImageNet-1k weights from https://github.com/facebookresearch/deit. |
1,896 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | DeiT base model @ 384x384 from paper (https://arxiv.org/abs/2012.12877). ImageNet-1k weights from https://github.com/facebookresearch/deit. |
1,897 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | DeiT-tiny distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877). ImageNet-1k weights from https://github.com/facebookresearch/deit. |
1,898 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | DeiT-small distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877). ImageNet-1k weights from https://github.com/facebookresearch/deit. |
1,899 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | DeiT-base distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877). ImageNet-1k weights from https://github.com/facebookresearch/deit. |
1,900 | import math
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import hashlib
import os
import urllib
import warnings
from functools import partial
from tqdm import tqdm
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.helper... | DeiT-base distilled model @ 384x384 from paper (https://arxiv.org/abs/2012.12877). ImageNet-1k weights from https://github.com/facebookresearch/deit. |
1,901 | import torch
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from transformers import (
DataCollatorForLanguageModeling,
DataCollatorForWholeWordMask,
BertTokenizer,
)
def get_pretrained_tokenizer(from_pretrained):
if torch.distributed.is_initialized():
... | null |
1,902 | from sacred import Experiment
def _loss_names(d):
def config():
exp_name = "vilt"
seed = 0
datasets = ["coco", "vg", "sbu", "gcc"]
loss_names = _loss_names({"itm": 1, "mlm": 1})
batch_size = 4096 # this is a desired batch size; pl trainer will accumulate gradients when per step batch is smaller.
... | null |
1,903 | from sacred import Experiment
def env_dandelin():
data_root = "/data2/dsets/dataset"
log_dir = "/data2/vilt/result"
num_gpus = 8
num_nodes = 1 | null |
1,904 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_mlm_itm():
exp_name = "mlm_itm"
datasets = ["coco", "vg", "sbu", "gcc"]
loss_names = _... | null |
1,905 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_mlm_itm_randaug():
exp_name = "mlm_itm_randaug"
datasets = ["coco", "vg", "sbu", "gcc"]
... | null |
1,906 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_mlm_itm_mpp():
exp_name = "mlm_itm_mpp"
datasets = ["coco", "vg", "sbu", "gcc"]
loss_n... | null |
1,907 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_finetune_nlvr2():
exp_name = "finetune_nlvr2"
datasets = ["nlvr2"]
loss_names = _loss_... | null |
1,908 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_finetune_nlvr2_randaug():
exp_name = "finetune_nlvr2_randaug"
datasets = ["nlvr2"]
tra... | null |
1,909 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_finetune_vqa():
exp_name = "finetune_vqa"
datasets = ["vqa"]
loss_names = _loss_names(... | null |
1,910 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_finetune_vqa_randaug():
exp_name = "finetune_vqa_randaug"
datasets = ["vqa"]
train_tra... | null |
1,911 | from sacred import Experiment
def _loss_names(d):
def task_finetune_irtr_coco():
exp_name = "finetune_irtr_coco"
datasets = ["coco"]
loss_names = _loss_names({"itm": 0.5, "irtr": 1})
batch_size = 256
max_epoch = 10
max_steps = None
warmup_steps = 0.1
get_recall_metric = True
draw_fa... | null |
1,912 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_finetune_irtr_coco_randaug():
exp_name = "finetune_irtr_coco_randaug"
datasets = ["coco"]
... | null |
1,913 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_finetune_irtr_f30k():
exp_name = "finetune_irtr_f30k"
datasets = ["f30k"]
loss_names =... | null |
1,914 | from sacred import Experiment
def _loss_names(d):
ret = {
"itm": 0,
"mlm": 0,
"mpp": 0,
"vqa": 0,
"nlvr2": 0,
"irtr": 0,
}
ret.update(d)
return ret
def task_finetune_irtr_f30k_randaug():
exp_name = "finetune_irtr_f30k_randaug"
datasets = ["f30k"]
... | null |
1,915 | from sacred import Experiment
def step25k():
max_epoch = 100
max_steps = 25000 | null |
1,916 | from sacred import Experiment
def step50k():
max_epoch = 100
max_steps = 50000 | null |
1,917 | from sacred import Experiment
def step100k():
max_epoch = 100
max_steps = 100000 | null |
1,918 | from sacred import Experiment
def step200k():
max_epoch = 200
max_steps = 200000 | null |
1,919 | from sacred import Experiment
def vit32_base():
vit = "vit_base_patch32_384"
patch_size = 32
hidden_size = 768
num_heads = 12
num_layers = 12 | null |
1,920 | import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def TranslateX(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
if random.random() > 0.5:
v = -v
v = v * img.size[0]
return img.transform(i... | null |
1,921 | import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def TranslateY(img, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
if random.random() > 0.5:
v = -v
v = v * img.size[1]
return img.transform(i... | null |
1,922 | import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def Invert(img, _):
return PIL.ImageOps.invert(img) | null |
1,923 | import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def Flip(img, _): # not from the paper
return PIL.ImageOps.mirror(img) | null |
1,924 | import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def CutoutAbs(img, v): # [0, 60] => percentage: [0, 0.2]
# assert 0 <= v <= 20
if v < 0:
return img
w, h = img.size
x0 = np.random.uniform(w)
y0 = np.random.uniform(... | null |
1,925 | import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def SamplePairing(imgs): # [0, 0.4]
def f(img1, v):
i = np.random.choice(len(imgs))
img2 = PIL.Image.fromarray(imgs[i])
return PIL.Image.blend(img1, img2, v)
r... | null |
1,926 | import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def Identity(img, v):
return img | null |
1,927 | import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def ShearX(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
if random.random() > 0.5:
v = -v
return img.transform(img.size, PIL.Image.AFFINE, (1, v, 0, 0, 1, 0))
def ShearY(im... | null |
1,928 | from .utils import (
inception_normalize,
MinMaxResize,
)
from torchvision import transforms
from .randaug import RandAugment
class MinMaxResize:
def __init__(self, shorter=800, longer=1333):
self.min = shorter
self.max = longer
def __call__(self, x):
w, h = x.size
scal... | null |
1,929 | from .utils import (
inception_normalize,
MinMaxResize,
)
from torchvision import transforms
from .randaug import RandAugment
class MinMaxResize:
def __init__(self, shorter=800, longer=1333):
self.min = shorter
self.max = longer
def __call__(self, x):
w, h = x.size
scal... | null |
1,930 | import json
import pandas as pd
import pyarrow as pa
import os
from tqdm import tqdm
from collections import defaultdict
def process(root, iden, row):
texts = [r["sentence"] for r in row]
labels = [r["label"] for r in row]
split = iden.split("-")[0]
if iden.startswith("train"):
directory = row[0... | null |
1,931 | import json
import pandas as pd
import pyarrow as pa
import random
import os
from tqdm import tqdm
from glob import glob
from collections import defaultdict
def path2rest(path, iid2captions, iid2split):
name = path.split("/")[-1]
with open(path, "rb") as fp:
binary = fp.read()
captions = iid2caption... | null |
1,932 | import json
import os
import pandas as pd
import pyarrow as pa
import random
from tqdm import tqdm
from glob import glob
from collections import defaultdict
def path2rest(path, iid2captions, iid2split):
name = path.split("/")[-1]
with open(path, "rb") as fp:
binary = fp.read()
captions = iid2caption... | null |
1,933 | import json
import pandas as pd
import pyarrow as pa
import random
import os
from tqdm import tqdm
from glob import glob
from collections import defaultdict
def path2rest(path, iid2captions):
name = path.split("/")[-1]
iid = int(name[:-4])
with open(path, "rb") as fp:
binary = fp.read()
cdicts =... | null |
1,934 | import json
import pandas as pd
import pyarrow as pa
import random
import os
from tqdm import tqdm
from glob import glob
from collections import defaultdict, Counter
from .glossary import normalize_word
def get_score(occurences):
if occurences == 0:
return 0.0
elif occurences == 1:
return 0.3
... | null |
1,935 | import json
import pandas as pd
import pyarrow as pa
import gc
import random
import os
from tqdm import tqdm
from glob import glob
def path2rest(path, iid2captions):
def make_arrow(root, dataset_root):
with open(f"{root}/annot.json", "r") as fp:
captions = json.load(fp)
iid2captions = dict()
for c... | null |
1,936 | import json
import pandas as pd
import pyarrow as pa
import gc
import random
import os
from tqdm import tqdm
from glob import glob
def path2rest(path, iid2captions):
def make_arrow(root, dataset_root):
for split in ["val", "train"]:
with open(f"{root}/{split}_annot.json", "r") as fp:
captions =... | null |
1,937 | import glob
import os
from setuptools import find_packages, setup
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
assert torch_ver >= [1, 3], "Requires PyTorch >= 1.3"
def get_extensions():
this_dir = os.path.d... | null |
1,938 | import os
import sys
import mock
import sphinx_rtd_theme
from recommonmark.parser import CommonMarkParser
import adet
def autodoc_skip_member(app, what, name, obj, skip, options):
# we hide something deliberately
if getattr(obj, "__HIDE_SPHINX_DOC__", False):
return True
# Hide some names that are d... | null |
1,939 | from torch import nn
from detectron2.layers import Conv2d
from .deform_conv import DFConv2d
from detectron2.layers.batch_norm import get_norm
class DFConv2d(nn.Module):
def __init__(
self,
in_channels,
out_channels,
with_modulated_dcn=True,
... | null |
1,940 | from detectron2.layers import batched_nms
The provided code snippet includes necessary dependencies for implementing the `ml_nms` function. Write a Python function `def ml_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores", label_field="labels")` to solve the following problem:
Performs non-max... | Performs non-maximum suppression on a boxlist, with scores specified in a boxlist field via score_field. Args: boxlist (detectron2.structures.Boxes): nms_thresh (float): max_proposals (int): if > 0, then only the top max_proposals are kept after non-maximum suppression score_field (str): |
1,941 | import random
import numpy as np
from fvcore.transforms import transform as T
from detectron2.data.transforms import RandomCrop, StandardAugInput
from detectron2.structures import BoxMode
def adjust_crop(x0, y0, crop_size, instances, eps=1e-3):
modified = False
x1 = x0 + crop_size[1]
y1 = y0 + crop_size[0]
... | Generate a CropTransform so that the cropping region contains the center of the given instance. Args: crop_size (tuple): h, w in pixels image_size (tuple): h, w instance (dict): an annotation dict of one instance, in Detectron2's dataset format. |
1,942 | import copy
import logging
import os.path as osp
import numpy as np
import torch
from fvcore.common.file_io import PathManager
from PIL import Image
from pycocotools import mask as maskUtils
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.data.dataset_map... | null |
1,943 | import os
from detectron2.data.datasets.register_coco import register_coco_instances
from detectron2.data.datasets.builtin_meta import _get_builtin_metadata
from .datasets.text import register_text_instances
_PREDEFINED_SPLITS_PIC = {
"pic_person_train": ("pic/image/train", "pic/annotations/train_person.json"),
... | null |
1,944 | import logging
import numpy as np
import torch
from detectron2.data import transforms as T
from detectron2.data.detection_utils import \
annotations_to_instances as d2_anno_to_inst
from detectron2.data.detection_utils import \
transform_instance_annotations as d2_transform_inst_anno
import math
def transform_be... | null |
1,945 | import logging
import numpy as np
import torch
from detectron2.data import transforms as T
from detectron2.data.detection_utils import \
annotations_to_instances as d2_anno_to_inst
from detectron2.data.detection_utils import \
transform_instance_annotations as d2_transform_inst_anno
import math
def annotations... | null |
1,946 | import logging
import numpy as np
import torch
from detectron2.data import transforms as T
from detectron2.data.detection_utils import \
annotations_to_instances as d2_anno_to_inst
from detectron2.data.detection_utils import \
transform_instance_annotations as d2_transform_inst_anno
import math
The provided co... | With option to don't use hflip Returns: list[Augmentation] |
1,947 | import torch
from torch.nn import functional as F
from detectron2.layers import cat
from detectron2.modeling.poolers import ROIPooler
class Blender(object):
def __init__(self, cfg):
# fmt: off
self.pooler_resolution = cfg.MODEL.BLENDMASK.BOTTOM_RESOLUTION
sampling_ratio = cfg.MODEL.B... | null |
1,948 | from typing import Dict
from torch import nn
from torch.nn import functional as F
from detectron2.utils.registry import Registry
from detectron2.layers import ShapeSpec
from adet.layers import conv_with_kaiming_uniform
BASIS_MODULE_REGISTRY = Registry("BASIS_MODULE")
BASIS_MODULE_REGISTRY.__doc__ = """
Registry for bas... | null |
1,949 | import torch.distributed as dist
from detectron2.utils.comm import get_world_size
from torch.nn import functional as F
from torch import nn
import torch
from detectron2.structures import ImageList
from adet.utils.comm import reduce_sum
from fvcore.nn import sigmoid_focal_loss_jit
def aligned_bilinear(tensor, factor):
... | null |
1,950 | import torch.distributed as dist
from detectron2.utils.comm import get_world_size
from torch.nn import functional as F
from torch import nn
import torch
from detectron2.structures import ImageList
from adet.utils.comm import reduce_sum
from fvcore.nn import sigmoid_focal_loss_jit
def compute_basis_stride(images, basis... | null |
1,951 | import torch.distributed as dist
from detectron2.utils.comm import get_world_size
from torch.nn import functional as F
from torch import nn
import torch
from detectron2.structures import ImageList
from adet.utils.comm import reduce_sum
from fvcore.nn import sigmoid_focal_loss_jit
class folder(nn.Module):
def __init... | null |
1,952 | import torch.distributed as dist
from detectron2.utils.comm import get_world_size
from torch.nn import functional as F
from torch import nn
import torch
from detectron2.structures import ImageList
from adet.utils.comm import reduce_sum
from fvcore.nn import sigmoid_focal_loss_jit
def process_gt_instances(gt_instances,... | null |
1,953 | import torch.distributed as dist
from detectron2.utils.comm import get_world_size
from torch.nn import functional as F
from torch import nn
import torch
from detectron2.structures import ImageList
from adet.utils.comm import reduce_sum
from fvcore.nn import sigmoid_focal_loss_jit
def reduce_sum(tensor):
world_size... | null |
1,954 | import torch.distributed as dist
from detectron2.utils.comm import get_world_size
from torch.nn import functional as F
from torch import nn
import torch
from detectron2.structures import ImageList
from adet.utils.comm import reduce_sum
from fvcore.nn import sigmoid_focal_loss_jit
def reduce_sum(tensor):
def compute_l... | null |
1,955 | import torch
from torch.nn import functional as F
from torch import nn
from detectron2.layers import cat
from detectron2.modeling.poolers import ROIPooler
from .utils import aligned_bilinear, compute_loss, compute_loss_softmax
from fvcore.nn import sigmoid_focal_loss_jit
from adet.utils.comm import reduce_sum
from dete... | null |
1,956 | import torch
from torch.nn import functional as F
from torch import nn
from detectron2.layers import cat
from detectron2.modeling.poolers import ROIPooler
from .utils import aligned_bilinear, compute_loss, compute_loss_softmax
from fvcore.nn import sigmoid_focal_loss_jit
from adet.utils.comm import reduce_sum
from dete... | null |
1,957 | import torch
from torch.nn import functional as F
from torch import nn
from detectron2.layers import cat
from detectron2.modeling.poolers import ROIPooler
from .utils import aligned_bilinear, compute_loss, compute_loss_softmax
from fvcore.nn import sigmoid_focal_loss_jit
from adet.utils.comm import reduce_sum
from dete... | null |
1,958 | import torch
from torch.nn import functional as F
from torch import nn
from detectron2.layers import cat
from detectron2.modeling.poolers import ROIPooler
from .utils import aligned_bilinear, compute_loss, compute_loss_softmax
from fvcore.nn import sigmoid_focal_loss_jit
from adet.utils.comm import reduce_sum
from dete... | null |
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