python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
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# 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 __future__ import print_function
import pickle
import glob
import os
import pdb
import math
import numpy as np
import ... | deep-variance-reduction-main | reproduce/plot_finetuning.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 sys
import run
archs = ['default', 'resnet-small', 'densenet-40-36', 'resnet110']
try:
pindex = int(s... | deep-variance-reduction-main | reproduce/reproduce_ratio_plots.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 __future__ import print_function
import pickle
import glob
import os
import re
import numpy as np
import itertools
imp... | deep-variance-reduction-main | reproduce/plot_iterate_distance.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 sys
import run
methods = ["sgd", "recompute_svrg", "scsg"]
try:
pindex = int(sys.argv[1])
seed = i... | deep-variance-reduction-main | reproduce/reproduce_test_error_imagenet.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 sys
import run
methods = ["sgd", "recompute_svrg", "scsg"]
try:
pindex = int(sys.argv[1])
seed = i... | deep-variance-reduction-main | reproduce/reproduce_test_error_resnet110.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 __future__ import print_function
import pickle
import glob
import os
import pdb
import matplotlib as mpl
mpl.use('agg'... | deep-variance-reduction-main | reproduce/plot_test_error_with_bars.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 sys
import run
methods = ["sgd", "recompute_svrg", "scsg"]
try:
pindex = int(sys.argv[1])
seed = i... | deep-variance-reduction-main | reproduce/reproduce_test_error_lenet.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 __future__ import print_function
import pickle
import glob
import os
import re
import matplotlib.ticker as plticker
im... | deep-variance-reduction-main | reproduce/plot_variance_ratio.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 sys
import run
transform_locking = [True, False]
try:
pindex = int(sys.argv[1])
print(f"problem i... | deep-variance-reduction-main | reproduce/reproduce_locking_plot.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 __future__ import print_function
import pickle
import glob
import os
import pdb
import matplotlib as mpl
mpl.use('agg'... | deep-variance-reduction-main | reproduce/plot_test_error.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 __future__ import print_function
import pickle
import glob
import os
import re
import numpy as np
import itertools
imp... | deep-variance-reduction-main | reproduce/plot_transform_locking.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import sys
class Fab_HDD():
def __init__(self, config="BarraCuda"):
###############################
# Carbo... | ACT-main | hdd_model.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import sys
class Fab_Logic():
def __init__(self, process_node=14,
gpa="97",
c... | ACT-main | logic_model.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import sys
from dram_model import Fab_DRAM
from ssd_model import Fab_SSD
from logic_model import Fab_Logic
def main():
... | ACT-main | model.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import sys
class Fab_DRAM():
def __init__(self, config = "ddr4_10nm", fab_yield=0.875):
#########################... | ACT-main | dram_model.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import sys
class Fab_SSD():
def __init__(self, config="nand_10nm", fab_yield=0.875):
##############################... | ACT-main | ssd_model.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import sys
from dram_model import Fab_DRAM
from hdd_model import Fab_HDD
from ssd_model import Fab_SSD
from logic_model impo... | ACT-main | exps/dellr740/dellr740.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import sys
from dram_model import Fab_DRAM
from hdd_model import Fab_HDD
from ssd_model import Fab_SSD
from logic_model impo... | ACT-main | exps/fairphone3/fairphone3.py |
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffwave-master | setup.py |
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffwave-master | src/diffwave/params.py |
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffwave-master | src/diffwave/preprocess.py |
diffwave-master | src/diffwave/__init__.py | |
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffwave-master | src/diffwave/model.py |
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffwave-master | src/diffwave/dataset.py |
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffwave-master | src/diffwave/inference.py |
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffwave-master | src/diffwave/__main__.py |
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | diffwave-master | src/diffwave/learner.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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 time
import faiss
import numpy as np
from PIL import Image
from PIL import ImageFile
from scipy.sparse import csr_matrix... | deepcluster-main | clustering.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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 pickle
import numpy as np
import torch
from torch.utils.data.sampler import Sampler
import models
def load_... | deepcluster-main | util.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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
from collections import OrderedDict
import os
import pickle
import subprocess
import sys
import numpy as np
f... | deepcluster-main | eval_retrieval.py |
deepcluster-main | __init__.py | |
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import os
import math
import time
import glob
from collections im... | deepcluster-main | eval_voc_classif.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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 pickle
import time
import faiss
import numpy as np
from sklearn.metrics.cluster import normali... | deepcluster-main | main.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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 time
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cud... | deepcluster-main | eval_linear.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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
from scipy.ndimage.filters import gaussian_filter
import sys
import numpy as np
from PIL import Image... | deepcluster-main | visu/gradient_ascent.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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
from shutil import copyfile
import sys
import numpy as np
from PIL import Image
import torch
import t... | deepcluster-main | visu/activ-retrieval.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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 .vgg16 import *
from .alexnet import *
| deepcluster-main | models/__init__.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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 math
from random import random as rd
__all__ = [ 'VGG', 'vgg16']
class VGG(nn.Modul... | deepcluster-main | models/vgg16.py |
# Copyright (c) 2017-present, Facebook, Inc.
# 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 math
import numpy as np
import torch
import torch.nn as nn
__all__ = [ 'AlexNet', 'alexnet']
# (number of filters, ke... | deepcluster-main | models/alexnet.py |
import argparse
import os
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
import torchaudio
import hydra
from omegaconf import OmegaConf
from torch.distributions import Categorical
from tqdm.auto import tqdm
from src import utils
from src.dataloaders.audio import mu_law... | state-spaces-main | generate.py |
'''
Train an S4 model on sequential CIFAR10 / sequential MNIST with PyTorch for demonstration purposes.
This code borrows heavily from https://github.com/kuangliu/pytorch-cifar.
This file only depends on the standalone S4 layer
available in /models/s4/
* Train standard sequential CIFAR:
python -m example
* Train ... | state-spaces-main | example.py |
import copy
import os
import random
import time
from functools import partial, wraps
from typing import Callable, List, Optional
import hydra
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
import wandb
from hydra.utils import get_original_cwd
from omegaconf import DictConfig, Omeg... | state-spaces-main | train.py |
import argparse
import torch
from pathlib import Path
from train import SequenceLightningModule
parser = argparse.ArgumentParser()
parser.add_argument("ckpt_path", type=str)
args = parser.parse_args()
ckpt = torch.load(args.ckpt_path, map_location='cuda')
state_dict = ckpt['state_dict']
torch.save(state_dict, Pat... | state-spaces-main | checkpoints/convert_pl_to_pt.py |
from tqdm.auto import tqdm
import hydra
import torch
import numpy as np
from pathlib import Path
import pytorch_lightning as pl
import matplotlib.pyplot as plt
from torch.nn.modules import module
import torch.nn.functional as F
from torch.distributions import Categorical
from src import utils
from einops import rearran... | state-spaces-main | checkpoints/evaluate.py |
"""Convert a V3 model to V4. See checkpoints/README.md for usage."""
from tqdm.auto import tqdm
import hydra
import torch
import numpy as np
from pathlib import Path
import pytorch_lightning as pl
import matplotlib.pyplot as plt
from torch.nn.modules import module
import torch.nn.functional as F
from torch.distributio... | state-spaces-main | checkpoints/convert_v3_to_v4.py |
"""Standalone version of Structured State Space sequence model (S4)."""
from collections import defaultdict
from typing import Optional, Mapping, Tuple, Union
import logging
from functools import partial
import math
import numpy as np
from scipy import special as ss
import torch
import torch.nn as nn
import torch.nn.f... | state-spaces-main | models/s4/s4.py |
"""Minimal version of S4D with extra options and features stripped out, for pedagogical purposes."""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from src.models.nn import DropoutNd
class S4DKernel(nn.Module):
"""Generate convolution kernel f... | state-spaces-main | models/s4/s4d.py |
import numpy as np
from scipy import linalg
from scipy.stats import norm, entropy
from sklearn.cluster import KMeans
def fid(feat_data, feat_gen):
"""
Calculate Frechet Inception Distance
"""
# Means
mu_data = np.mean(feat_data, axis=0)
mu_gen = np.mean(feat_gen, axis=0)
# Covariances
... | state-spaces-main | models/sashimi/metrics.py |
"""
SaShiMi backbone.
Use this backbone in your own models. You'll also need to copy over the
standalone S4 layer, which can be found at `state-spaces/models/s4/`
It's Raw! Audio Generation with State-Space Models
Karan Goel, Albert Gu, Chris Donahue, Christopher Re.
"""
import sys
sys.path.append('../')
import torc... | state-spaces-main | models/sashimi/sashimi.py |
#!/usr/bin/env python
"""Train a CNN for Google speech commands."""
__author__ = 'Yuan Xu, Erdene-Ochir Tuguldur'
"""With modifications from Karan Goel."""
import argparse
import time
import torch
import torchvision
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torch.utils.data.sa... | state-spaces-main | models/sashimi/sc09_classifier/train_speech_commands.py |
"""Google speech commands dataset."""
__author__ = 'Yuan Xu'
"""With modifications by Karan Goel to support training an SC09 classifier."""
import os
import numpy as np
import librosa
from torch.utils.data import Dataset
__all__ = [ 'CLASSES', 'SpeechCommandsDataset', 'BackgroundNoiseDataset' ]
CLASSES = 'zero, on... | state-spaces-main | models/sashimi/sc09_classifier/speech_commands_dataset.py |
"""
Taken from https://github.com/tugstugi/pytorch-speech-commands and modified
by Karan Goel.
"""
import argparse
import os
import torch
import numpy as np
from functools import reduce
from natsort import natsorted
from scipy import linalg
from scipy.stats import norm, entropy
from sklearn.cluster import KMeans
fro... | state-spaces-main | models/sashimi/sc09_classifier/test_speech_commands.py |
"""Splits the google speech commands into train, validation and test sets.
"""
import os
import shutil
import argparse
def move_files(src_folder, to_folder, list_file):
with open(list_file) as f:
for line in f.readlines():
line = line.rstrip()
dirname = os.path.dirname(line)
... | state-spaces-main | models/sashimi/sc09_classifier/datasets/speech_commands/split_dataset.py |
# -*- coding: utf-8 -*-
"""Imported from https://github.com/prlz77/ResNeXt.pytorch/blob/master/models/model.py
and added support for the 1x32x32 mel spectrogram for the speech recognition.
Creates a ResNeXt Model as defined in:
Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2016).
Aggregated residual transform... | state-spaces-main | models/sashimi/sc09_classifier/models/resnext.py |
"""Transforms on raw wav samples."""
__author__ = 'Yuan Xu'
import random
import numpy as np
import librosa
import torch
from torch.utils.data import Dataset
def should_apply_transform(prob=0.5):
"""Transforms are only randomly applied with the given probability."""
return random.random() < prob
class Load... | state-spaces-main | models/sashimi/sc09_classifier/transforms/transforms_wav.py |
from .transforms_wav import *
from .transforms_stft import *
| state-spaces-main | models/sashimi/sc09_classifier/transforms/__init__.py |
"""Transforms on the short time fourier transforms of wav samples."""
__author__ = 'Erdene-Ochir Tuguldur'
import random
import numpy as np
import librosa
from torch.utils.data import Dataset
from .transforms_wav import should_apply_transform
class ToSTFT(object):
"""Applies on an audio the short time fourier... | state-spaces-main | models/sashimi/sc09_classifier/transforms/transforms_stft.py |
TEMPL = """
<style>
th, td {{
border: 2px solid black;
padding: 8px;
}}
th {{
width: 100px;
vertical-align: text-top;
font-weight: normal;
}}
.noborder {{
border: 0px solid black;
}}
.thlab {{
margin-bottom: 1em;
}}
.td {{
text-align: center;
vertical-align: middle;
}}
input[ty... | state-spaces-main | models/sashimi/mturk/template_speech.py |
import argparse
import numpy as np
import os
import shutil
from natsort import natsorted
digits = 'zero one two three four five six seven eight nine'.split()
def move_files(src_dir, src_files, target_dir, indices, discard=False):
os.makedirs(target_dir, exist_ok=True)
for i, digit in enumerate(digits):
... | state-spaces-main | models/sashimi/mturk/prepare_sc09.py |
TEMPL = """
<style>
th, td {{
border: 2px solid black;
padding: 8px;
}}
th {{
width: 100px;
vertical-align: text-top;
font-weight: normal;
}}
.noborder {{
border: 0px solid black;
}}
.thlab {{
margin-bottom: 1em;
}}
.td {{
text-align: center;
vertical-align: middle;
}}
input[ty... | state-spaces-main | models/sashimi/mturk/template_music.py |
import argparse
import random
import shutil
import uuid
from natsort import natsorted
from pathlib import Path
from types import SimpleNamespace
rd = random.Random()
rd.seed(0)
uuid.uuid4 = lambda: uuid.UUID(int=rd.getrandbits(128))
class Experiment:
def __init__(
self,
condition,
input_dir,
out... | state-spaces-main | models/sashimi/mturk/turk_create_batch.py |
import math
import torch
import torch.nn.functional as F
import pytest
from einops import rearrange
from src.ops.vandermonde import log_vandermonde, log_vandermonde_fast
@pytest.mark.parametrize('L', [3, 17, 489, 2**10, 1047, 2**11, 2**12])
@pytest.mark.parametrize('N', [4, 8, 16, 32, 64, 128, 256])
# @pytest.mark... | state-spaces-main | extensions/kernels/test_vandermonde.py |
import torch
from structured_kernels import vand_log_mult_sym_fwd, vand_log_mult_sym_bwd
def log_vandermonde_cuda(v, z, L):
""" Wrap the cuda method to deal with shapes """
v, z = torch.broadcast_tensors(v, z)
shape = v.shape
v = v.contiguous()
z = z.contiguous()
N = v.size(-1)
assert z.... | state-spaces-main | extensions/kernels/vandermonde.py |
from pathlib import Path
import torch
from einops import rearrange
from structured_kernels import cauchy_mult_sym_fwd, cauchy_mult_sym_bwd
# try:
# from cauchy_mult import cauchy_mult_sym_fwd, cauchy_mult_sym_bwd
# except ImportError:
# from torch.utils.cpp_extension import load
# current_dir = Path(__fil... | state-spaces-main | extensions/kernels/cauchy.py |
from setuptools import setup
import torch.cuda
from torch.utils.cpp_extension import CppExtension, CUDAExtension, BuildExtension
from torch.utils.cpp_extension import CUDA_HOME
ext_modules = []
if torch.cuda.is_available() and CUDA_HOME is not None:
extension = CUDAExtension(
'structured_kernels', [
... | state-spaces-main | extensions/kernels/setup.py |
import math
from functools import partial
import torch
from einops import rearrange
from .cauchy import cauchy_mult_torch, cauchy_mult_keops, cauchy_mult
from benchmark.utils import benchmark_all, benchmark_combined, benchmark_forward, benchmark_backward
def generate_data(batch_size, N, L, symmetric=True, device='... | state-spaces-main | extensions/kernels/benchmark_cauchy.py |
import importlib
import json
import argparse
import torch
from benchmark.utils import benchmark_forward
def generate_data(batch_size, N, L, symmetric=True, device='cuda'):
if not symmetric:
v = torch.randn(batch_size, N, dtype=torch.complex64, device=device, requires_grad=True)
w = torch.randn(b... | state-spaces-main | extensions/kernels/benchmark_cauchy_tune.py |
import os
import shutil
import subprocess
import sys
# import tempfile
# import importlib
import random
import string
import json
from functools import partial
from multiprocessing import Pipe, Pool, Process
from pathlib import Path
from tqdm import tqdm
import numpy as np
def read_file(filename):
""" return ... | state-spaces-main | extensions/kernels/tuner.py |
import os
from setuptools import setup
from pathlib import Path
import torch.cuda
from torch.utils.cpp_extension import CppExtension, CUDAExtension, BuildExtension
from torch.utils.cpp_extension import CUDA_HOME
extensions_dir = Path(os.getenv('TUNING_SOURCE_DIR')).absolute()
assert extensions_dir.exists()
source_fi... | state-spaces-main | extensions/kernels/tuning_setup.py |
import math
import json
import argparse
import itertools
from pathlib import Path
from tuner import KernelTuner
def forward_params_list(N):
blocksize_params = ('MAX_BLOCK_SIZE_VALUE', [64, 128, 256, 512, 1024])
thread_value_default = [2, 4, 8, 16, 32, 32, 32, 32, 32, 32]
thread_values_supported = [2, 4, ... | state-spaces-main | extensions/kernels/tune_cauchy.py |
import math
import torch
import pytest
from einops import rearrange
from cauchy import cauchy_mult_torch, cauchy_mult_keops, cauchy_mult
def generate_data(batch_size, N, L, symmetric=True, device='cuda'):
if not symmetric:
v = torch.randn(batch_size, N, dtype=torch.complex64, device=device, requires_gr... | state-spaces-main | extensions/kernels/test_cauchy.py |
"""Implementations of general metric functions."""
import math
import torch
import torch.nn.functional as F
from sklearn.metrics import f1_score, roc_auc_score
from functools import partial
def _student_t_map(mu, sigma, nu):
sigma = F.softplus(sigma)
nu = 2.0 + F.softplus(nu)
return mu.squeeze(axis=-1), s... | state-spaces-main | src/tasks/metrics.py |
"""Implements Task interface, which consists of encoder + decoder + loss/metrics."""
from typing import Optional, List, Tuple
import math
import functools
import collections
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from omegaconf import ListConfig
from src.models.... | state-spaces-main | src/tasks/tasks.py |
"""Decoders that interface between targets and model."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce
import src.models.nn.utils as U
import src.utils as utils
class Decoder(nn.Module):
"""Abstract class defining the interface for Decoders.
TODO: i... | state-spaces-main | src/tasks/decoders.py |
"""Encoders that interface between input data and model."""
import datetime
import math
from typing import ForwardRef
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
import src.models.nn.utils as U
import src.utils as utils
import src.utils.config
from src.models.sequen... | state-spaces-main | src/tasks/encoders.py |
"""Log parameter counts to WandB."""
from typing import Any
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from pytorch_lightning.utilities.parsing import AttributeDict
class ParamsLog(pl.Callback):
""" Log the number of parameters of the model """
def __init__(
... | state-spaces-main | src/callbacks/params.py |
"""PL callbacks for logging to WandB.
From https://github.com/HazyResearch/transformers/blob/master/src/callbacks/wandb_callbacks.py.
"""
import glob
import os
from typing import List
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
import torch
import wandb
from pytorch_lightning import Call... | state-spaces-main | src/callbacks/wandb.py |
"""PL callbacks for monitoring computation speed.
Adapted from https://github.com/HazyResearch/transformers/blob/master/src/callbacks/speed_monitor.py.
In turn adapted from https://pytorch-lightning.readthedocs.io/en/latest/_modules/pytorch_lightning/callbacks/gpu_stats_monitor.html#GPUStatsMonitor.
We only need the ... | state-spaces-main | src/callbacks/timer.py |
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from pytorch_lightning.utilities.parsing import AttributeDict
from omegaconf import OmegaConf
class TrackNorms(pl.Callback):
# TODO do callbacks happen before or after the method in the main LightningModule?
# @rank_zero_onl... | state-spaces-main | src/callbacks/norms.py |
"""Callbacks for progressive resizing of images, used in S4ND paper."""
import numpy as np
from pytorch_lightning.callbacks import Callback
import src.utils as utils
from src.utils import registry
class ProgressiveResizing(Callback):
def __init__(self, stage_params: list):
"""
stage_params is a... | state-spaces-main | src/callbacks/progressive_resizing.py |
"""Synthetic datasets."""
import numpy as np
import torch
import torchvision
from einops.layers.torch import Rearrange
from src.utils import permutations
from src.dataloaders.base import SequenceDataset
class Copying(SequenceDataset):
_name_ = "copying"
@property
def init_defaults(self):
return... | state-spaces-main | src/dataloaders/synthetic.py |
"""Long Range Arena datasets."""
import io
import logging
import os
import pickle
from pathlib import Path
import torch
from torch import nn
import torch.nn.functional as F
import torchtext
import torchvision
from einops.layers.torch import Rearrange, Reduce
from PIL import Image # Only used for Pathfinder
from data... | state-spaces-main | src/dataloaders/lra.py |
"""Miscellaneous vision datasets."""
import os
import torch
from torch import nn
from torch.nn import functional as F
import torchvision
from src.dataloaders.base import default_data_path, SequenceDataset
class CIFAR100(SequenceDataset):
_name_ = "cifar100"
d_output = 100
l_output = 0
@property
... | state-spaces-main | src/dataloaders/vision.py |
"""ET Dataset from Informer Paper.
Dataset: https://github.com/zhouhaoyi/ETDataset
Dataloader: https://github.com/zhouhaoyi/Informer2020
"""
from typing import List
import os
import numpy as np
import pandas as pd
from pandas.tseries import offsets
from pandas.tseries.frequencies import to_offset
import torch
from to... | state-spaces-main | src/dataloaders/et.py |
from . import audio, basic, et, lm, lra, synthetic, ts, vision
from .base import SequenceDataset
| state-spaces-main | src/dataloaders/__init__.py |
"""Time series datasets, especially for medical time series."""
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from src.dataloaders.base import default_data_path, SequenceDataset, deprecated
class BIDMC(SequenceDataset):
"""BIDMC datasets for Respiratory Rate / Heart R... | state-spaces-main | src/dataloaders/ts.py |
"""Implementation of basic benchmark datasets used in S4 experiments: MNIST, CIFAR10 and Speech Commands."""
import numpy as np
import torch
import torchvision
from einops.layers.torch import Rearrange
from src.utils import permutations
from src.dataloaders.base import default_data_path, ImageResolutionSequenceDatase... | state-spaces-main | src/dataloaders/basic.py |
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | state-spaces-main | src/dataloaders/lm.py |
"""Audio datasets and utilities."""
import os
from os import listdir
from os.path import join
import torch
import torchaudio
from torch import nn
from torch.nn import functional as F
from src.dataloaders.base import default_data_path, SequenceDataset, deprecated
def minmax_scale(tensor, range_min=0, range_max=1):
... | state-spaces-main | src/dataloaders/audio.py |
"""Core dataloader interface."""
import os
import pickle
from functools import partial
from pathlib import Path
import numpy as np
import torch
import torchaudio.functional as TF
import torchvision
from einops import rearrange
from einops.layers.torch import Rearrange
from src.utils import is_list, permutations
from ... | state-spaces-main | src/dataloaders/base.py |
"""Utilities for working with .ts files.
Taken from https://github.com/ChangWeiTan/TS-Extrinsic-Regression/blob/master/utils/data_loader.py.
Required to handle the @targetlabel tag which sktime.data_io.load_from_tsfile_to_dataframe does not support.
"""
import numpy as np
import pandas as pd
from tqdm import tqdm
na... | state-spaces-main | src/dataloaders/prepare/bidmc/data_loader.py |
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import sktime
from sktime.datasets import load_from_tsfile_to_dataframe
import data_loader as data
DATA_PATH = "data/"
def split_data(
X_train_orig, y_train_orig, X_test_orig, y_test_orig, shuffle=True, seed=0
... | state-spaces-main | src/dataloaders/prepare/bidmc/process_data.py |
"""Implementation of standard Copying dataset.
Originally used in Arjovsky's Unitary RNN, maybe earlier?
"""
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from src.utils import distributed
def np_copying_data(L, M, A, batch_shape=()):
seq = np.random.randin... | state-spaces-main | src/dataloaders/datasets/copying.py |
"""Speech Commands dataset.
Adapted from https://github.com/dwromero/ckconv/blob/dc84dceb490cab2f2ddf609c380083367af21890/datasets/speech_commands.py
which is
adapted from https://github.com/patrick-kidger/NeuralCDE/blob/758d3a7134e3a691013e5cc6b7f68f277e9e6b69/experiments/datasets/speech_commands.py
"""
import os
im... | state-spaces-main | src/dataloaders/datasets/sc.py |
"""RNN Vocal Generation Model.
Blizzard, Music, and Huckleberry Finn data feeders.
"""
import numpy as np
#import scikits.audiolab
import random
import time
import os
import glob
import torch
import sklearn
from scipy.io import wavfile
def normalize01(data):
"""To range [0., 1.]"""
data -= np.min(data)
... | state-spaces-main | src/dataloaders/datasets/music.py |
"""Implementation of Celeb-A dataset."""
from functools import partial
import torch
import os
import PIL
from typing import Any, Callable, List, Optional, Union, Tuple
from torchvision.datasets import VisionDataset
try:
import gdown
DOWNLOAD = True
except ImportError:
DOWNLOAD = False
import numpy as np
c... | state-spaces-main | src/dataloaders/datasets/celeba.py |
"""Implementation of standard Adding dataset.
Originally used in Arjovsky's Unitary RNN, maybe earlier?
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def torch_adding_data(L, batch_shape=()):
assert L >= 2
mid = L//2
idx0 = torch.randint(low=0, high=mid, size=batch_shape)
id... | state-spaces-main | src/dataloaders/datasets/adding.py |
"""Implementation of "Continuous Delay" dataset from How to Train Your HIPPO."""
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.dataloaders.utils.signal import whitesignal
class DelayTrainDataset(torch.utils.data.Dataset):
def __init__(self, samples, l_seq=1024, n_lag=1, l_lag=None, ... | state-spaces-main | src/dataloaders/datasets/delay.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.dataloaders.utils.signal import whitesignal
class ReconstructTrainDataset(torch.utils.data.Dataset):
def __init__(self, samples, l_seq=1024, l_mem=1024, dt=1e-3, freq=1.0, seed=0):
"""
"""
super().__init__()
... | state-spaces-main | src/dataloaders/datasets/reconstruct.py |
"""Data utilities for generating signals."""
import numpy as np
def whitesignal(period, dt, freq, rms=0.5, batch_shape=()):
"""
Produces output signal of length period / dt, band-limited to frequency freq
Output shape (*batch_shape, period/dt)
Adapted from the nengo library
"""
if freq is not... | state-spaces-main | src/dataloaders/utils/signal.py |
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