python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
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import sys
from pathlib import Path
project_root = Path(__file__).absolute().parent.parent.parent
sys.path.insert(0, str(project_root))
import os
import time
import numpy as np
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import torchvision.models as models
from . import l... | butterfly-master | cnn/imagenet/training.py |
import os, sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import math
import unittest
import torch
from butterfly.permutation_multiply import permutation_mult_torch, permutation_mult
from butterfly.permutation_multiply import permutation_mult_single_factor_torch, permutation_mult... | butterfly-master | tests_old/test_permutation_multiply.py |
import os, sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import math
import unittest
import numpy as np
import torch
from butterfly import Butterfly
from butterfly.butterfly import ButterflyBmm
from butterfly.butterfly_multiply import butterfly_ortho_mult_tied
class Butterfl... | butterfly-master | tests_old/test_butterfly.py |
import os, sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import math
import unittest
import torch
from butterfly import Butterfly
from butterfly.utils import twiddle_normal_to_fast_format
from butterfly.butterfly_multiply import butterfly_mult_torch, butterfly_mult, butterfly_m... | butterfly-master | tests_old/test_butterfly_multiply.py |
import os, sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import math
import unittest
import numpy as np
import torch
from butterfly.permutation import Permutation, FixedPermutation, PermutationFactor
class PermutationTest(unittest.TestCase):
def test_permutation(self):
... | butterfly-master | tests_old/test_permutation.py |
import math
import numpy as np
import torch
from torch import nn
from torch.utils.dlpack import to_dlpack, from_dlpack
# Check if cupy is available
if torch.cuda.is_available():
use_cupy = True
try:
import cupy as cp
except:
use_cupy = False
# import warnings
# warnings.war... | butterfly-master | torch_butterfly/complex_utils.py |
import math
import numbers
import torch
from torch import nn
import torch.nn.functional as F
import torch_butterfly
from torch_butterfly.multiply import butterfly_multiply
from torch_butterfly.multiply import butterfly_multiply_torch
from torch_butterfly.complex_utils import real_dtype_to_complex, complex_reshape
fro... | butterfly-master | torch_butterfly/butterfly.py |
import copy
import torch
from torch import nn
from torch.nn import functional as F
from torch_butterfly import Butterfly
from torch_butterfly.permutation import FixedPermutation, bitreversal_permutation
def diagonal_butterfly(butterfly: Butterfly,
diagonal: torch.Tensor,
... | butterfly-master | torch_butterfly/combine.py |
import math
from typing import Tuple, Optional
import torch
from torch.nn import functional as F
@torch.jit.script
def butterfly_multiply_fw(twiddle: torch.Tensor, input: torch.Tensor, increasing_stride: bool,
output_size: Optional[int] = None) -> torch.Tensor:
return torch.ops.torch_bu... | butterfly-master | torch_butterfly/multiply.py |
import importlib
from pathlib import Path
import torch
__version__ = '0.0.0'
for library in ['_version', '_butterfly']:
torch.ops.load_library(importlib.machinery.PathFinder().find_spec(
# need str(Path) otherwise it can't find it
library, [str(Path(__file__).absolute().parent)]).origin)
def che... | butterfly-master | torch_butterfly/__init__.py |
import math
import torch
import torch.nn.functional as F
from torch_butterfly.multiply import butterfly_multiply
from benchmark_utils import benchmark, benchmark_fw_bw
batch_size = 2048
n = 512
log_n = int(math.log2(n))
assert n == 1 << log_n
input_size = n - 7
output_size = n - 5
input = torch.randn(batch_size, ... | butterfly-master | torch_butterfly/input_padding_benchmark.py |
import math
import torch
from torch.nn import functional as F
def butterfly_multiply_base4_torch(twiddle4, twiddle2, input, increasing_stride=True,
output_size=None):
batch_size, nstacks, input_size = input.shape
nblocks = twiddle4.shape[1]
log_n = twiddle4.shape[2] * 2... | butterfly-master | torch_butterfly/multiply_base4.py |
import math
import numpy as np
import torch
from torch import nn
from torch_butterfly.complex_utils import real_dtype_to_complex
class Diagonal(nn.Module):
def __init__(self, size=None, complex=False, diagonal_init=None):
"""Multiply by diagonal matrix
Parameter:
size: int
... | butterfly-master | torch_butterfly/diagonal.py |
import math
from typing import List, Tuple, Union
import numpy as np
import scipy.linalg
import torch
from torch import nn
from torch_butterfly import Butterfly
from torch_butterfly.complex_utils import index_last_dim, real2complex
def bitreversal_permutation(n, pytorch_format=False):
"""Return the bit reversa... | butterfly-master | torch_butterfly/permutation.py |
import math
import numbers
import torch
from torch import nn
import torch.nn.functional as F
from torch_butterfly import Butterfly
from torch_butterfly.multiply_base4 import butterfly_multiply_base4_torch
from torch_butterfly.multiply_base4 import twiddle_base2_to_base4
from torch_butterfly.complex_utils import real_... | butterfly-master | torch_butterfly/butterfly_base4.py |
from functools import partial
import numpy as np
import torch
def benchmark(fn, nrepeats=7):
res = []
for _ in range(nrepeats):
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
fn()
end.record()
torch.cuda.s... | butterfly-master | torch_butterfly/benchmark_utils.py |
import math
from functools import reduce
import torch
from torch import nn
from torch.nn import functional as F
import torch.fft
from torch_butterfly.butterfly import Butterfly, ButterflyUnitary
from torch_butterfly.permutation import FixedPermutation, bitreversal_permutation, invert
from torch_butterfly.permutation ... | butterfly-master | torch_butterfly/special.py |
"""My torch implementation of permutations and sinkhorn balancing ops.
A torch library of operations and sampling with permutations
and their approximation with doubly-stochastic matrices, through Sinkhorn balancing
"""
import numpy as np
from scipy.optimize import linear_sum_assignment
from scipy.stats import kenda... | butterfly-master | gumbel-sinkhorn/my_sinkhorn_ops.py |
"""Model class for sorting numbers."""
import torch.nn as nn
class Features(nn.Module):
def __init__(self, latent_dim, output_dim, dropout_prob):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
This Feature extractor class takes a... | butterfly-master | gumbel-sinkhorn/my_sorting_model.py |
import torch
import numpy
import torch.nn as nn
from torch.autograd import Variable
import matplotlib.pyplot as plt
import os
import argh
import my_sorting_model
import my_sinkhorn_ops
dir_path = os.path.dirname(os.path.realpath(__file__))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def ... | butterfly-master | gumbel-sinkhorn/my_sorting_train.py |
import torch
import numpy
import torch.nn as nn
import os
import argh
import my_sorting_model
import my_sinkhorn_ops
from my_sorting_train import make_random_batch
dir_path = os.path.dirname(os.path.realpath(__file__))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Test process
def test_mo... | butterfly-master | gumbel-sinkhorn/my_sinkhorn_eval.py |
import io
import glob
import os
from shutil import move
from os.path import join
from os import listdir, rmdir
target_folder = './val/'
test_folder = './test/'
os.mkdir(test_folder)
val_dict = {}
with open('./val/val_annotations.txt', 'r') as f:
for line in f.readlines():
split_line = line.split('\t')
... | butterfly-master | data/tiny-imagenet-200/val_format.py |
from pathlib import Path
project_root = Path(__file__).parent.absolute()
import os
import random
import math
from collections.abc import Sequence
from functools import partial
import torch
import pytorch_lightning as pl
from pytorch_lightning.callbacks import Callback
from munch import Munch
import ray
from ray im... | butterfly-master | convolution/ray_runner.py |
# Adapted from https://github.com/algrebe/python-tee, ported to Python 3
import os
import sys
from abc import ABCMeta, abstractmethod
class Tee(object):
"""
duplicates streams to a file.
credits : http://stackoverflow.com/q/616645
"""
def __init__(self, filename, mode="a", file_filters=None, strea... | butterfly-master | convolution/tee.py |
import torch
from torch import nn
from torch.nn import functional as F
class Task:
@staticmethod
def metrics(outs, y, len_batch=None):
return {}
@staticmethod
def metrics_epoch(outs, y, len_batch=None):
return {}
class BinaryClassification(Task):
@staticmethod
def loss(logit... | butterfly-master | convolution/tasks.py |
from pathlib import Path
import torch
import pytorch_lightning as pl
from pytorch_lightning.callbacks import Callback
def pl_train(cfg, pl_module_cls, **kwargs):
trainer_args = dict(
gpus=1,
max_epochs=1 if cfg.smoke_test else cfg.train.epochs,
checkpoint_callback=False, # Disable check... | butterfly-master | convolution/pl_runner.py |
import torch
def LeNetScheduler(optimizer, nepochs, **kwargs):
def sched(epoch):
if epoch < int(nepochs * 0.5):
return 1.0
elif epoch < int(nepochs * 0.75):
return 0.5
else:
return 0.1
return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=l... | butterfly-master | convolution/lr_schedulers.py |
import torch
from omegaconf.dictconfig import DictConfig
from munch import Munch
def remove_postfix(text, postfix):
if text.endswith(postfix):
return text[:-len(postfix)]
return text
# pytorch-lightning returns pytorch 0-dim tensor instead of python scalar
def to_scalar(x):
return x.item() if i... | butterfly-master | convolution/utils.py |
from pathlib import Path
PROJECT_ROOT = Path(__file__).parent.absolute()
import os
# Add to $PYTHONPATH so that ray workers can see
os.environ['PYTHONPATH'] = str(PROJECT_ROOT) + ":" + os.environ.get('PYTHONPATH', '')
import torch
import pytorch_lightning as pl
import hydra
from omegaconf import OmegaConf
import mod... | butterfly-master | convolution/train.py |
from .cifar import *
| butterfly-master | convolution/datamodules/__init__.py |
from pathlib import Path
current_dir = Path(__file__).parent.absolute()
import torch
from torch.utils.data import DataLoader, random_split
from torchvision import transforms, datasets
from pl_bolts.datamodules import CIFAR10DataModule
class CIFAR10(CIFAR10DataModule):
def __init__(self, data_dir=current_dir, e... | butterfly-master | convolution/datamodules/cifar.py |
import torch.nn as nn
import torch.nn.functional as F
from .lenet import LeNetPadded
from .kops import KOP2d
from .lops import LOP2d
class ButterfLeNet(LeNetPadded):
name = 'butterflenet'
def __init__(self, num_classes=10, pooling_mode='avg', butterfly=True, **kwargs):
nn.Module.__init__(self)
... | butterfly-master | convolution/models/butterflenet.py |
'''ResNet in PyTorch. Small variants for CIFAR
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
__all__ = ['ResNet8', 'ResNet14', 'Res... | butterfly-master | convolution/models/resnet_cifar.py |
'''Baseline CNN in PyTorch.'''
# Adapted from https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py
import torch.nn as nn
import torch.nn.functional as F
from .cnn5 import CNN5
from .kops import KOP2d
class CNN5Butterfly(CNN5):
name = 'cnn5butterfly'
def __init__(self, num_ch... | butterfly-master | convolution/models/cnn5_butterfly.py |
from .lenet import *
from .resnet import *
from .resnet_cifar import *
from .cnn5 import *
from .butterflenet import *
from .cnn5_butterfly import *
| butterfly-master | convolution/models/__init__.py |
'''Baseline CNN in PyTorch.'''
# Adapted from https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py
import torch.nn as nn
import torch.nn.functional as F
class CNN5(nn.Module):
name = 'cnn5'
def __init__(self, num_channels=32, num_classes=10):
super().__init__()
... | butterfly-master | convolution/models/cnn5.py |
'''LeNet in PyTorch.'''
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
name = 'lenet'
def __init__(self, num_classes=10):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
... | butterfly-master | convolution/models/lenet.py |
'''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['ResNet', 'ResNet18', 'ResNet... | butterfly-master | convolution/models/resnet.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.fft
import torch_butterfly
from torch_butterfly import Butterfly
from torch_butterfly.complex_utils import ComplexLinear
from torch_butterfly.complex_utils import Real2Complex, Complex2Real
from torch_butterfly.complex_utils i... | butterfly-master | convolution/models/lops.py |
import unittest
import torch
import torch.nn as nn
from kops import KOP2d
class KOP2dTest(unittest.TestCase):
def setUp(self):
self.rtol = 1e-4
self.atol = 1e-5
def test_fft_init(self):
batch_size = 10
in_ch, out_ch = 3, 6
for in_size in [(32, 32), (16, 16), (32, 16... | butterfly-master | convolution/models/test_kops.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_butterfly
from torch_butterfly import Butterfly
from torch_butterfly.complex_utils import Real2Complex, Complex2Real
from torch_butterfly.complex_utils import complex_matmul
from torch_butterfly.combine import TensorProduct
fr... | butterfly-master | convolution/models/kops.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
from configs import Config
########### running ###########
# torchrun --nproc_per_node=8 main.py <config>
def eval_yfcc15m_in1k_mocob16():
return Config(
output_dir="yfcc15m_in1k_mocob16",
eval=True,
resume="chec... | CiT-main | run_configs.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
import os
import inspect
from collections import OrderedDict
class Config:
dataset = "yfcc15m_tag"
root = "data/yfcc15m"
metadata = "data/yfcc15m/yfcc15m_w_tag.pkl"
# data adaptation
val_task = "imagenet"
max_sample ... | CiT-main | configs.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
import torch
from transformers import VisionTextDualEncoderModel
class CiTCLIPVisionTextDualEncoderModel(VisionTextDualEncoderModel):
'''a hf model wrapper to support forward with either or both image/text.
note that HF impl. uses a... | CiT-main | models_citclip.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# A script to run multinode training with submitit.
# ----------... | CiT-main | submitit_citclip.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
#... | CiT-main | engine.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
"""
pre-configed sweeps.
"""
import json
class alltask_5k_mr005:
batch_size = [1536], "bsz"
max_update = [5000], "s"
refilter = [100], "refilter"
prefilter = [0.45], ""
min_ratio = [0.05], "r"
sublist = [True], ""
... | CiT-main | sweeps.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import torch
import torch.nn as nn
import torch.nn.functional as ... | CiT-main | losses.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
#... | CiT-main | main.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
"""
pre-configed trainable weights.
"""
pre_projection_weights = ['logit_scale', 'visual_projection.weight', 'text_projection.weight']
# TODO: unify layer selection for all models.
pre_vision_trainable_weights = {
"moco": {
"hea... | CiT-main | weights.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
#... | CiT-main | util/misc.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import math
def adjust_step_learning_rate(optimizer, step, lr, ... | CiT-main | util/lr_sched.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
import torch
from transformers import (
PreTrainedModel,
PretrainedConfig,
AutoConfig,
AutoModel,
)
from transformers.modeling_outputs import BaseModelOutputWithPooling
import timm
assert timm.__version__ >= "0.4.12", "make ... | CiT-main | hfmodels/augreg.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
import torch
from transformers import (
PreTrainedModel,
PretrainedConfig,
AutoConfig,
AutoModel,
)
from transformers.modeling_outputs import BaseModelOutputWithPooling
class SwagConfig(PretrainedConfig):
model_type = "... | CiT-main | hfmodels/swag.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
from .moco import MoCoModel, MoCoConfig
from .augreg import AugRegModel, AugRegConfig
from .swag import SwagModel, SwagConfig | CiT-main | hfmodels/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
import torch
import sys
sys.path.append("moco-v3") # repo path to moco-v3
from transformers import (
PreTrainedModel,
PretrainedConfig,
AutoConfig,
AutoModel,
)
from torch import nn
from transformers.modeling_outputs import ... | CiT-main | hfmodels/moco.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import numpy as np
import pickle
import re
import time
import sq... | CiT-main | scripts/make_yfcc100m_dataset.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import numpy as np
import pickle
import re
from urllib.parse imp... | CiT-main | scripts/make_yfcc15m_dataset.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import json
import os
import pickle
import zipfile
import numpy ... | CiT-main | clipeval/datasets.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import torch
import json
import os
from sklearn import metrics
... | CiT-main | clipeval/eval_zeroshot.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import time
import yaml
import torch
import utils.logger
from utils import main_utils, eval_utils
import... | AVID-CMA-main | eval-action-recg.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import os
import random
import time
import warnings
import yaml
import torch
import torch.nn.parallel
im... | AVID-CMA-main | main-avid.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import time
import yaml
import torch
from utils import main_utils, eval_utils
import utils.logger
import... | AVID-CMA-main | eval-action-recg-linear.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import csv
import numpy as np
import glob
from datasets.video_db import VideoDataset
DATA_PATH = '/data/datasets/AS240/d... | AVID-CMA-main | datasets/audioset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from .audioset import AudioSet
from .kinetics import Kinetics
from .ucf import UCF
from .hmdb import HMDB | AVID-CMA-main | datasets/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import numpy as np
import random
import librosa
from utils.videotransforms import video_transforms, volume_t... | AVID-CMA-main | datasets/preprocessing.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import random
import torch
import numpy as np
import torch.utils.data as data
from utils.ioutils import av_wrappers
from ... | AVID-CMA-main | datasets/video_db.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
from datasets.video_db import VideoDataset
DATA_PATH = '/data/datasets/hmdb/videos'
ANNO_PATH = '/data/dataset... | AVID-CMA-main | datasets/hmdb.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from datasets.video_db import VideoDataset
DATA_PATH = '/data/datasets/UCF101/data'
ANNO_PATH = '/data/datasets/UCF101/u... | AVID-CMA-main | datasets/ucf.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import glob
import numpy as np
DATA_PATH = '/data/datasets/kinetics/'
from datasets.video_db import VideoDa... | AVID-CMA-main | datasets/kinetics.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
| AVID-CMA-main | utils/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import datetime
import sys
import torch
from torch import distributed as dist
class Logger(object):
def __init__(s... | AVID-CMA-main | utils/logger.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from torch import distributed as dist
def _gather_from_all(tensor):
"""
Gather tensors from all gp... | AVID-CMA-main | utils/distributed_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from collections import deque
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over t... | AVID-CMA-main | utils/metrics_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
class AliasMethod(object):
"""
From: https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-metho... | AVID-CMA-main | utils/alias_method.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import shutil
import torch
import numpy as np
import torch.distributed as dist
import datetime
from utils.logg... | AVID-CMA-main | utils/main_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from torch import nn
import torch.distributed as dist
import utils.logger
from utils import main_utils
impo... | AVID-CMA-main | utils/eval_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
| AVID-CMA-main | utils/ioutils/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import av
import numpy as np
from fractions import Fraction
av.logging.set_level(0)
def av_open(inpt):
return av.ope... | AVID-CMA-main | utils/ioutils/av_wrappers.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import random
import torch
from utils.videotransforms.utils import functional as F
class Normalize(object):
"""Norm... | AVID-CMA-main | utils/videotransforms/tensor_transforms.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy as np
from PIL import Image
import torch
from utils.videotransforms.utils import images as imageutils
cla... | AVID-CMA-main | utils/videotransforms/volume_transforms.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numbers
import numpy as np
import PIL
def crop_clip(clip, min_h, min_w, h, w):
if isinstance(clip[0], np.nd... | AVID-CMA-main | utils/videotransforms/functional.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy as np
import PIL
import torch
from utils.videotransforms.utils import images as imageutils
class ToStacke... | AVID-CMA-main | utils/videotransforms/stack_transforms.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numbers
import random
import numpy as np
import PIL
import torchvision
import warnings
import math
from utils.vid... | AVID-CMA-main | utils/videotransforms/video_transforms.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
def normalize(tensor, mean, std):
"""
Args:
tensor (Tensor): Tensor to normalize
Returns:
Te... | AVID-CMA-main | utils/videotransforms/utils/functional.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy as np
def convert_img(img):
"""Converts (H, W, C) numpy.ndarray to (C, W, H) format
"""
if len... | AVID-CMA-main | utils/videotransforms/utils/images.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from .video import *
from .audio import *
from .av_wrapper import *
| AVID-CMA-main | models/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.nn as nn
import numpy as np
class Basic2DBlock(nn.Module):
def __init__(self, in_planes, ... | AVID-CMA-main | models/network_blocks.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.nn as nn
__all__ = [
'av_wrapper'
]
class Head(nn.Module):
def __init__(self, input... | AVID-CMA-main | models/av_wrapper.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
from .network_blocks import Basic2DBlock
__all__ = [
'Conv2D'
]
class Conv2D(nn.Module):
... | AVID-CMA-main | models/audio.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
from models.network_blocks import BasicR2P1DBlock
class R2Plus1D(nn.Module):
"""
Adapted ... | AVID-CMA-main | models/video.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from .avid import *
from .avid_cma import * | AVID-CMA-main | criterions/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from torch import nn
from torch.nn import functional as F
import torch.distributed as dist
import pprint
fro... | AVID-CMA-main | criterions/avid.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from torch import nn
import torch.distributed as dist
from utils.distributed_utils import _gather_from_all
... | AVID-CMA-main | criterions/nce.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import multiprocessing as mp
mp.set_start_method('spawn', force=True)
import torch
from torch import nn
from torch.nn im... | AVID-CMA-main | criterions/avid_cma.py |
"""Init."""
| fm_data_tasks-main | fm_data_tasks/__init__.py |
"""Run inference."""
import argparse
import json
import logging
from pathlib import Path
import numpy as np
from manifest import Manifest
import fm_data_tasks.utils.data_utils as data_utils
import fm_data_tasks.utils.prompt_utils as prompt_utils
from fm_data_tasks.utils import constants
from fm_data_tasks.utils.utils... | fm_data_tasks-main | fm_data_tasks/run_inference.py |
"""Constants."""
import os
from pathlib import Path
DATASET_PATH = os.environ.get("DATASET_PATH", Path("data/datasets").resolve())
DATA2TASK = {
f"{DATASET_PATH}/entity_matching/structured/Amazon-Google": "entity_matching",
f"{DATASET_PATH}/entity_matching/structured/Beer": "entity_matching",
f"{DATASET_... | fm_data_tasks-main | fm_data_tasks/utils/constants.py |
"""Init."""
| fm_data_tasks-main | fm_data_tasks/utils/__init__.py |
"""Data utils."""
import logging
from functools import partial
from pathlib import Path
from typing import Dict, List
import pandas as pd
from fm_data_tasks.utils import constants
logger = logging.getLogger(__name__)
def sample_train_data(train: pd.DataFrame, n_rows: int):
"""
Sample train data.
Used ... | fm_data_tasks-main | fm_data_tasks/utils/data_utils.py |
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