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# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_lockfile/linklockfile.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_lockfile/mkdirlockfile.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_lockfile/pidlockfile.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/std/chain.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/std/__init__.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/std/full.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/std/loss.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/std/elemwise.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/std/trainable.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/std/softmax.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_ext/__init__.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_ext/deepity_smat.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_io_/pydot.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# list of... | DeepBind-master | code/libs/deepity/deepity/_io_/__init__.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_io_/write_svg.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/deepity/deepity/_io_/load.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/demo_nnet.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
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# list of... | DeepBind-master | code/libs/smat/py/demo_minimize.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/run_tests.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/demo_convnet.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/smat/util.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/smat/__init__.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/smat/smat.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/smat/smat_dll.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/smat/tests/unittest.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/smat/tests/perftest.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/smat/tests/__init__.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/smat/py/smat/tests/testutil.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/simplify.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/gradmap.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/util.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/globals.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/predict.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/__init__.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/model.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/train.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/statistics.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/data.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/basic/__init__.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/basic/model.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/basic/report.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/basic/data.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/_ext/dropoutord.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
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# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/_ext/__init__.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/_ext/corr1ord.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/_ext/poolrgn.py |
# Copyright (c) 2015, Andrew Delong and Babak Alipanahi All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of... | DeepBind-master | code/libs/kangaroo/kangaroo/_ext/kangaroo_smat.py |
from setuptools import setup, find_packages
setup(
name = 'timesformer-pytorch',
packages = find_packages(),
version = '0.4.1',
license='MIT',
description = 'TimeSformer - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/TimeSformer-pytorch',
... | TimeSformer-pytorch-main | setup.py |
from timesformer_pytorch.timesformer_pytorch import TimeSformer
| TimeSformer-pytorch-main | timesformer_pytorch/__init__.py |
from math import log, pi
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
def rotate_every_two(x):
x = rearrange(x, '... (d j) -> ... d j', j = 2)
x1, x2 = x.unbind(dim = -1)
x = torch.stack((-x2, x1), dim = -1)
return rearrange(x, '... d j ... | TimeSformer-pytorch-main | timesformer_pytorch/rotary.py |
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from timesformer_pytorch.rotary import apply_rot_emb, AxialRotaryEmbedding, RotaryEmbedding
# helpers
def exists(val):
return val is not None
# classes
class PreNorm(nn.Module):
def __init__(self,... | TimeSformer-pytorch-main | timesformer_pytorch/timesformer_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'RQ-transformer',
packages = find_packages(exclude=[]),
version = '0.1.9',
license='MIT',
description = 'RQ Transformer - Autoregressive Transformer for Residual Quantized Codes',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url ... | RQ-Transformer-main | setup.py |
from rq_transformer import HierarchicalCausalTransformer
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
# constants
NUM_BATCHES = int(1e5)
BATCH_SIZE = 4
GRADIENT_ACCUMULATE_EVERY ... | RQ-Transformer-main | train.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops_exts import rearrange_with_anon_dims
from einops import rearrange, reduce, repeat
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def remainder_to_mult(num, mult):
... | RQ-Transformer-main | rq_transformer/rq_transformer.py |
from rq_transformer.rq_transformer import RQTransformer
from rq_transformer.hierarchical_causal_transformer import HierarchicalCausalTransformer
| RQ-Transformer-main | rq_transformer/__init__.py |
import math
import functools
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops_exts import rearrange_with_anon_dims
from einops import rearrange, reduce, repeat
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def re... | RQ-Transformer-main | rq_transformer/hierarchical_causal_transformer.py |
from all_normalization_transformer import TransformerLM
from all_normalization_transformer.autoregressive_wrapper import AutoregressiveWrapper
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, ... | all-normalization-transformer-master | train_enwik8.py |
from functools import partial
import torch
import random
from torch import nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
def default(value, default):
return value if value is not None else default
def log(t, eps=1e-9):
return torch.log(t + eps)
def top_p(logits, thres = 0.9):... | all-normalization-transformer-master | all_normalization_transformer/autoregressive_wrapper.py |
import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
# helpers
def cum_mean(t):
device = t.device
running_num = torch.arange(t.shape[-1], device=t.device) + 1
return t.cumsum(dim=-1) / running_num
def normalize(t, eps=1e-8):
t -= t.mean(dim=-1, keepdim=True)
... | all-normalization-transformer-master | all_normalization_transformer/all_normalization_transformer.py |
from all_normalization_transformer.all_normalization_transformer import TransformerLM
from all_normalization_transformer.autoregressive_wrapper import AutoregressiveWrapper
| all-normalization-transformer-master | all_normalization_transformer/__init__.py |
from setuptools import setup, find_packages
setup(
name = 'gsa-pytorch',
packages = find_packages(),
version = '0.2.2',
license='MIT',
description = 'Global Self-attention Network (GSA) - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/global-... | global-self-attention-network-main | setup.py |
from gsa_pytorch.gsa_pytorch import GSA
| global-self-attention-network-main | gsa_pytorch/__init__.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange
from inspect import isfunction
# helpers
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def exists(val):
return val is not None
def calc_reindexing_tensor(l,... | global-self-attention-network-main | gsa_pytorch/gsa_pytorch.py |
from setuptools import setup, find_packages
setup(
name="protein-bert-pytorch",
packages=find_packages(),
version="0.1.0",
license="MIT",
description="ProteinBERT - Pytorch",
author="Phil Wang",
author_email="lucidrains@gmail.com",
url="https://github.com/lucidrains/protein-bert-pytorch... | protein-bert-pytorch-main | setup.py |
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops.layers.torch import Rearrange, Reduce
from einops import rearrange, repeat
# helpers
def exists(val):
return val is not None
def max_neg_value(t):
return -torch.finfo(t.dtype).max
# helper classes
class Resid... | protein-bert-pytorch-main | protein_bert_pytorch/protein_bert_pytorch.py |
from protein_bert_pytorch.protein_bert_pytorch import ProteinBERT, PretrainingWrapper
| protein-bert-pytorch-main | protein_bert_pytorch/__init__.py |
from setuptools import setup, find_packages
exec(open('audiolm_pytorch/version.py').read())
setup(
name = 'audiolm-pytorch',
packages = find_packages(exclude=[]),
version = __version__,
license='MIT',
description = 'AudioLM - Language Modeling Approach to Audio Generation from Google Research - Pytorch',
a... | audiolm-pytorch-main | setup.py |
__version__ = '1.4.1'
| audiolm-pytorch-main | audiolm_pytorch/version.py |
import torch
import transformers
from transformers import T5Tokenizer, T5EncoderModel, T5Config
from beartype import beartype
from beartype.typing import Union, List
# less warning messages since only using encoder
transformers.logging.set_verbosity_error()
# helper functions
def exists(val):
return val is not... | audiolm-pytorch-main | audiolm_pytorch/t5.py |
from pathlib import Path
import torch
from torch import nn, einsum
from torchaudio.functional import resample
from einops import rearrange, repeat, pack, unpack
from audiolm_pytorch.utils import curtail_to_multiple
# suppress a few warnings
def noop(*args, **kwargs):
pass
import warnings
import logging
logg... | audiolm-pytorch-main | audiolm_pytorch/hubert_kmeans.py |
import torch
from packaging import version
if version.parse(torch.__version__) >= version.parse('2.0.0'):
from einops._torch_specific import allow_ops_in_compiled_graph
allow_ops_in_compiled_graph()
from audiolm_pytorch.audiolm_pytorch import AudioLM
from audiolm_pytorch.soundstream import SoundStream, AudioL... | audiolm-pytorch-main | audiolm_pytorch/__init__.py |
import functools
from itertools import cycle
from pathlib import Path
from functools import partial, wraps
from itertools import zip_longest
from typing import Optional
import torch
from torch import nn, einsum
from torch.autograd import grad as torch_grad
import torch.nn.functional as F
from torch.linalg import vect... | audiolm-pytorch-main | audiolm_pytorch/soundstream.py |
import torch
from torch import nn, einsum
import torch.nn.functional as F
from collections import namedtuple
from functools import wraps
from packaging import version
from einops import rearrange
# constants
Config = namedtuple('Config', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
# helpers
def exist... | audiolm-pytorch-main | audiolm_pytorch/attend.py |
from torch import nn
# functions
def round_down_nearest_multiple(num, divisor):
return num // divisor * divisor
def curtail_to_multiple(t, mult, from_left = False):
data_len = t.shape[-1]
rounded_seq_len = round_down_nearest_multiple(data_len, mult)
seq_slice = slice(None, rounded_seq_len) if not fro... | audiolm-pytorch-main | audiolm_pytorch/utils.py |
from pathlib import Path
import torch
from torch import nn
from einops import rearrange
import fairseq
from torchaudio.functional import resample
from audiolm_pytorch.utils import curtail_to_multiple
import logging
logging.root.setLevel(logging.ERROR)
def exists(val):
return val is not None
class FairseqVQWa... | audiolm-pytorch-main | audiolm_pytorch/vq_wav2vec.py |
from lion_pytorch import Lion
from torch.optim import AdamW, Adam
def separate_weight_decayable_params(params):
wd_params, no_wd_params = [], []
for param in params:
param_list = no_wd_params if param.ndim < 2 else wd_params
param_list.append(param)
return wd_params, no_wd_params
def get_o... | audiolm-pytorch-main | audiolm_pytorch/optimizer.py |
import math
from functools import partial, wraps
from beartype.typing import Optional, Union, List
from beartype import beartype
import torch
from torch import nn, einsum, Tensor
from torch.autograd import grad as torch_grad
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
import torchaudi... | audiolm-pytorch-main | audiolm_pytorch/audiolm_pytorch.py |
import re
from math import sqrt
import copy
from random import choice
from pathlib import Path
from shutil import rmtree
from collections import Counter
from beartype.typing import Union, List, Optional, Tuple
from typing_extensions import Annotated
from beartype import beartype
from beartype.door import is_bearable
... | audiolm-pytorch-main | audiolm_pytorch/trainer.py |
from functools import reduce
from einops import rearrange, pack, unpack
import torch
from torch import nn
from torchaudio.functional import resample
from vector_quantize_pytorch import ResidualVQ
from encodec import EncodecModel
from encodec.utils import _linear_overlap_add
# helper functions
def exists(val):
... | audiolm-pytorch-main | audiolm_pytorch/encodec.py |
from pathlib import Path
from functools import partial, wraps
from beartype import beartype
from beartype.typing import Tuple, Union, Optional
from beartype.door import is_bearable
import torchaudio
from torchaudio.functional import resample
import torch
import torch.nn.functional as F
from torch.nn.utils.rnn import... | audiolm-pytorch-main | audiolm_pytorch/data.py |
from setuptools import setup, find_packages
exec(open('imagen_pytorch/version.py').read())
setup(
name = 'imagen-pytorch',
packages = find_packages(exclude=[]),
include_package_data = True,
entry_points={
'console_scripts': [
'imagen_pytorch = imagen_pytorch.cli:main',
'imagen = imagen_pytorch.... | imagen-pytorch-main | setup.py |
import math
import copy
import operator
import functools
from typing import List
from tqdm.auto import tqdm
from functools import partial, wraps
from contextlib import contextmanager, nullcontext
from collections import namedtuple
from pathlib import Path
import torch
import torch.nn.functional as F
from torch import ... | imagen-pytorch-main | imagen_pytorch/imagen_video.py |
from math import sqrt
from random import random
from functools import partial
from contextlib import contextmanager, nullcontext
from typing import List, Union
from collections import namedtuple
from tqdm.auto import tqdm
import torch
import torch.nn.functional as F
from torch import nn, einsum
from torch.cuda.amp imp... | imagen-pytorch-main | imagen_pytorch/elucidated_imagen.py |
import json
from pydantic import BaseModel, validator
from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
from enum import Enum
from imagen_pytorch.imagen_pytorch import Imagen, Unet, Unet3D, NullUnet
from imagen_pytorch.trainer import ImagenTrainer
from imagen_pytorch.elucidated_imagen import Elucida... | imagen-pytorch-main | imagen_pytorch/configs.py |
__version__ = '1.25.6'
| imagen-pytorch-main | imagen_pytorch/version.py |
import torch
import transformers
from typing import List
from transformers import T5Tokenizer, T5EncoderModel, T5Config
from einops import rearrange
transformers.logging.set_verbosity_error()
def exists(val):
return val is not None
def default(val, d):
if exists(val):
return val
return d() if cal... | imagen-pytorch-main | imagen_pytorch/t5.py |
import torch
from packaging import version
if version.parse(torch.__version__) >= version.parse('2.0.0'):
from einops._torch_specific import allow_ops_in_compiled_graph
allow_ops_in_compiled_graph()
from imagen_pytorch.imagen_pytorch import Imagen, Unet
from imagen_pytorch.imagen_pytorch import NullUnet
from ... | imagen-pytorch-main | imagen_pytorch/__init__.py |
import click
import torch
from pathlib import Path
import pkgutil
from imagen_pytorch import load_imagen_from_checkpoint
from imagen_pytorch.version import __version__
from imagen_pytorch.data import Collator
from imagen_pytorch.utils import safeget
from imagen_pytorch import ImagenTrainer, ElucidatedImagenConfig, Ima... | imagen-pytorch-main | imagen_pytorch/cli.py |
import torch
from torch import nn
from functools import reduce
from pathlib import Path
from imagen_pytorch.configs import ImagenConfig, ElucidatedImagenConfig
from ema_pytorch import EMA
def exists(val):
return val is not None
def safeget(dictionary, keys, default = None):
return reduce(lambda d, key: d.get... | imagen-pytorch-main | imagen_pytorch/utils.py |
import os
import time
import copy
from pathlib import Path
from math import ceil
from contextlib import contextmanager, nullcontext
from functools import partial, wraps
from collections.abc import Iterable
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import random_split, Data... | imagen-pytorch-main | imagen_pytorch/trainer.py |
import math
import copy
from random import random
from beartype.typing import List, Union
from beartype import beartype
from tqdm.auto import tqdm
from functools import partial, wraps
from contextlib import contextmanager, nullcontext
from collections import namedtuple
from pathlib import Path
import torch
import torc... | imagen-pytorch-main | imagen_pytorch/imagen_pytorch.py |
from pathlib import Path
from functools import partial
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms as T, utils
import torch.nn.functional as F
from imagen_pytorch import t5
from torch.nn.utils.rnn import pad_sequence
from PIL import Image
from... | imagen-pytorch-main | imagen_pytorch/data.py |
from imagen_pytorch.test import test_trainer | imagen-pytorch-main | imagen_pytorch/test/__init__.py |
from imagen_pytorch.trainer import ImagenTrainer
from imagen_pytorch.configs import ImagenConfig
from imagen_pytorch.t5 import t5_encode_text
from torch.utils.data import Dataset
import torch
def test_trainer_instantiation():
unet1 = dict(
dim = 8,
dim_mults = (1, 1, 1, 1),
num_resnet_block... | imagen-pytorch-main | imagen_pytorch/test/test_trainer.py |
from setuptools import setup, find_packages
setup(
name = 'h-transformer-1d',
packages = find_packages(),
version = '0.1.8',
license='MIT',
description = 'H-Transformer 1D - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/lucidrains/h-transformer-1d',
... | h-transformer-1d-main | setup.py |
from h_transformer_1d import HTransformer1D
from h_transformer_1d.autoregressive_wrapper import AutoregressiveWrapper
import random
import tqdm
import gzip
import numpy as np
import torch
import torch.optim as optim
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
# constants
NUM... | h-transformer-1d-main | train.py |
import torch
from torch import nn
import torch.nn.functional as F
# helper function
def exists(val):
return val is not None
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, **kwargs)
model.train(was_tr... | h-transformer-1d-main | h_transformer_1d/autoregressive_wrapper.py |
import torch
import torch.nn as nn
from operator import itemgetter
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
# for routing arguments into the functions of the reversible layer
def route_args(router, args, depth):
routed_args = [(dict(), dic... | h-transformer-1d-main | h_transformer_1d/reversible.py |
from h_transformer_1d.h_transformer_1d import HTransformer1D
| h-transformer-1d-main | h_transformer_1d/__init__.py |
from math import log2, ceil
from functools import wraps
import torch
from torch import nn, einsum, diagonal
import torch.nn.functional as F
from h_transformer_1d.reversible import ReversibleSequence, SequentialSequence
from rotary_embedding_torch import apply_rotary_emb, RotaryEmbedding
from einops import rearrange, ... | h-transformer-1d-main | h_transformer_1d/h_transformer_1d.py |
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