repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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P-DRO | P-DRO-main/pdro_compare_models.py | #!/usr/bin/env python3
import os.path
import numpy as np
import torch as th
from src.configuration import Experiment, ArgumentGroup
def load_model_and_adversaries(folder, exp_name, suffix=None, use_accs=False):
# Load the model losses
suffix = "" if suffix is None else f"_{suffix}"
losses_file = os.path.j... | 11,310 | 36.330033 | 79 | py |
P-DRO | P-DRO-main/non_param_dro.py | #!/usr/bin/env python3
"""
Main program
"""
import numpy as np
import torch as th
import scipy.optimize
import abc
MIN_REL_DIFFERENCE = 1e-5
def bisection_search(objective, min_val, max_val, xtol=1e-5, maxiter=100):
# Check boundary conditions
if objective(min_val) * objective(max_val) >= 0:
# In th... | 5,500 | 30.434286 | 79 | py |
P-DRO | P-DRO-main/pdro_main.py | #!/usr/bin/env python3
"""
Main program
"""
from src.models import build_model, ModelWithHead
from src.tasks import task_list, prepare_task
import traceback
import numpy as np
import torch as th
import torch.nn.functional as F
import os.path
import tqdm
import hashlib
import scipy.optimize
from typing import Optional, ... | 46,775 | 40.431355 | 98 | py |
P-DRO | P-DRO-main/training_lm.py | #!/usr/bin/env python3
"""
Trains a language model
"""
from src.models import build_model, ModelWithHead, architecture_list
from src.tasks import task_list, prepare_task
from argparse import ArgumentParser
import traceback
import numpy as np
import torch as th
import os.path
import tqdm
from torch.utils.data import Dat... | 12,297 | 37.192547 | 79 | py |
P-DRO | P-DRO-main/src/utils.py | #!/usr/bin/env python3
"""Utility functions"""
import os
import os.path
import shutil
import torch as th
from copy import deepcopy
from torch.nn import init
from torch.utils.data import (
DataLoader,
BatchSampler,
RandomSampler,
SequentialSampler,
)
import numpy as np
from src.data import ByTokensSampl... | 12,907 | 29.229508 | 77 | py |
P-DRO | P-DRO-main/src/products.py | """This holds functions for things like jvp/vjp and hvp"""
import torch as th
def _check_inputs_jvp(f, x, v):
# Default to list
if isinstance(x, th.Tensor):
x = [x]
if isinstance(v, th.Tensor):
v = [v]
if isinstance(f, th.Tensor):
f = [f]
# Validate the input
if len(v) ... | 3,402 | 28.850877 | 76 | py |
P-DRO | P-DRO-main/src/models/bert.py | from transformers import BertPreTrainedModel, BertModel, DistilBertModel
import torch as th
from torch import nn
class BERT(BertPreTrainedModel):
"""BERT that only returns one vector"""
def __init__(self, config):
super(BERT, self).__init__(config)
self.bert = BertModel(config)
self.... | 3,917 | 32.775862 | 79 | py |
P-DRO | P-DRO-main/src/models/lstm.py | import numpy as np
import torch as th
from torch import nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
INFINITY = 100
class LSTMLM(nn.Module):
"""LSTM language model
Args:
n_layers ([type]): [description]
embed_dim ([type]): [description]
hidden_dim ([typ... | 6,718 | 31.302885 | 79 | py |
P-DRO | P-DRO-main/src/models/pretrained_resnet.py | #!/usr/bin/env python3
"""Wrapper around pretrained resnets"""
from torch import nn
from torchvision import models
import os
def make_headless_resnet18(path):
_torch_home = None
if "TORCH_HOME" in os.environ:
_torch_home = os.environ["TORCH_HOME"]
os.environ["TORCH_HOME"] = os.path.abspath(path)
... | 1,025 | 27.5 | 52 | py |
P-DRO | P-DRO-main/src/models/bow.py | import numpy as np
import torch as th
from torch import nn
import torch.nn.functional as F
INFINITY = 100
class BoWClassifier(nn.Module):
def __init__(
self,
n_layers,
embed_dim,
hidden_dim,
n_classes,
dic,
dropout=0.0,
):
... | 6,784 | 30.55814 | 79 | py |
P-DRO | P-DRO-main/src/models/resnet.py | #!/usr/bin/env python3
"""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
Courtesy of https://github.com/kuangliu/pytorch-cifar
"""
from math import ceil
import to... | 6,444 | 27.144105 | 76 | py |
P-DRO | P-DRO-main/src/models/bilstm.py | import numpy as np
import torch as th
from torch import nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
INFINITY = 100
class BiLSTMEncoder(nn.Module):
"""Simple BiLSTM"""
def __init__(
self,
n_layers,
embed_dim,
hidd... | 4,370 | 30.905109 | 78 | py |
P-DRO | P-DRO-main/src/models/mlp.py | from torch import nn
class MLP(nn.Module):
"""Multilayer perceptron"""
def __init__(
self,
input_size,
output_size,
hidden_size=400,
hidden_layer_num=2,
hidden_dropout_prob=.5,
input_dropout_prob=.2,
):
# Configurations.
super().__in... | 1,234 | 29.875 | 72 | py |
P-DRO | P-DRO-main/src/models/utils.py | from torch import nn
class ModelWithHead(nn.Module):
"""A model with a classification head."""
def __init__(self, model, head=None, combination_fn=None):
super(ModelWithHead, self).__init__()
self.model = model
self.head = nn.Identity() if head is None else head
self.combinati... | 563 | 28.684211 | 62 | py |
P-DRO | P-DRO-main/src/models/architectures.py | from .mlp import MLP
from .smol_cnn import CNN
from .resnet import ResNetS
from .pretrained_resnet import make_headless_resnet18, make_headless_resnet50
from .bilstm import BiLSTMEncoder
from .lstm import LSTMLM, LSTMGenerative
from .bert import BERT, DistilBERT
from .gpt2 import GPT2, small_transformer
from .bow impor... | 8,167 | 24.766562 | 86 | py |
P-DRO | P-DRO-main/src/models/gpt2.py | import torch as th
from transformers import GPT2PreTrainedModel, GPT2Model, GPT2Config
from torch import nn
from typing import Optional
class GPT2(GPT2PreTrainedModel):
"""A rewriting of GPT2LMHeadModel handling a single sentence at a time"""
def __init__(self, config):
super(GPT2, self).__init__(con... | 7,139 | 33.162679 | 78 | py |
P-DRO | P-DRO-main/src/models/smol_cnn.py | from torch import nn
class CNN(nn.Module):
"""A smol convolutional net"""
def __init__(
self, input_shape,
output_size,
kernels=None,
hidden_dropout_prob=.5,
input_dropout_prob=.2,
pool_every=2,
):
# Configurations.
super().__init__()
... | 1,791 | 31.581818 | 77 | py |
P-DRO | P-DRO-main/src/models/layers/lstm_fixed.py | """A pure python LSTM implementation that supports double differentiation"""
import torch as th
from torch import nn
class MyLSTMCell(nn.LSTMCell):
def forward(self, x, hx=None):
self.check_forward_input(x)
if hx is None:
hx = x.new_zeros(x.size(0), self.hidden_size, requires_grad=Fal... | 10,058 | 38.602362 | 83 | py |
P-DRO | P-DRO-main/src/models/layers/lstm.py | """A pure python LSTM implementation that supports double differentiation"""
import torch as th
from torch import nn
class MyLSTMCell(nn.LSTMCell):
def forward(self, x, hx=None):
self.check_forward_input(x)
if hx is None:
hx = x.new_zeros(x.size(0), self.hidden_size, requires_grad=Fal... | 8,456 | 38.518692 | 78 | py |
P-DRO | P-DRO-main/src/models/layers/embeddings.py | """A pure python Embeddings implementation that supports double
differentiation"""
import torch as th
from torch import nn
class MyEmbedding(nn.Embedding):
def forward(self, input):
# Input has shape L x bsz
embeds = th.stack([self.weight[x] for x in input])
return embeds.masked_fill(inpu... | 1,235 | 31.526316 | 78 | py |
P-DRO | P-DRO-main/src/optim/sgd.py | import torch
from torch.optim import SGD
class SGDOverride(SGD):
"""A version of SGD where we can replace override the gradient"""
def step(self, grad_override=None, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that ree... | 1,662 | 32.26 | 78 | py |
P-DRO | P-DRO-main/src/optim/adam.py | import math
import torch
from torch.optim import Adam
class AdamOverride(Adam):
"""A version of Adam where we can replace override the gradient"""
def step(self, grad_override=None, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A ... | 3,125 | 38.075 | 79 | py |
P-DRO | P-DRO-main/src/optim/__init__.py | from .adam import AdamOverride
from .sgd import SGDOverride
from torch.optim import AdamW, RMSprop
from .lr_schedulers import get_lr_scheduler, lr_schedulers
optimizers = {}
def register_optimizer():
def register_optimizer_fn(fn):
name = fn.__name__
if name in optimizers:
raise Value... | 2,101 | 21.126316 | 77 | py |
P-DRO | P-DRO-main/src/optim/lr_schedulers.py | # from torch.optim.lr_scheduler import MyLambdaLR
lr_schedulers = {}
def register_lr_scheduler():
def register_lr_scheduler_fn(fn):
name = fn.__name__
if name in lr_schedulers:
raise ValueError(
f"Cannot register duplicate lr_scheduler ({name})"
)
i... | 2,001 | 28.441176 | 76 | py |
P-DRO | P-DRO-main/src/data/superglue.py | #!/usr/bin/env python3
"""Adapted from
https://github.com/huggingface/pytorch-pretrained-BERT
Classes to load SuperGLUE classes
"""
import os
import json
from transformers import InputExample, DataProcessor
from .multiple_choice import MCInputExample
superglue_processors = {}
class SuperGlueProcessor(DataProcesso... | 7,138 | 32.516432 | 77 | py |
P-DRO | P-DRO-main/src/data/text_dataset.py | #!/usr/bin/env python3
"""This code bracnhed off of the former pytorch-pretrained-bert repo
(now pytorch transformers: https://github.com/huggingface/transformers)"""
from collections import defaultdict
import torch as th
import os
from torch.utils.data import Dataset
from transformers import PreTrainedTokenizer, Input... | 21,485 | 32.106317 | 80 | py |
P-DRO | P-DRO-main/src/data/glue.py | #!/usr/bin/env python3
"""Adapted from
https://github.com/huggingface/pytorch-pretrained-BERT
Classes to load GLUE classes
"""
import os
from transformers import DataProcessor, glue_processors
from transformers.data.processors.glue import Sst2Processor
from .text_dataset import ClassificationExample
class NLIDiagn... | 4,237 | 32.634921 | 79 | py |
P-DRO | P-DRO-main/src/data/omniglot.py | """Alphabet-wise omniglot
Modified from https://raw.githubusercontent.com/pytorch/vision/master/
torchvision/datasets/omniglot.py
"""
from os.path import join
from torchvision.datasets import Omniglot
from torchvision.datasets.utils import list_dir, list_files
from .cached_dataset import InMemoryCachedDataset
class ... | 5,100 | 32.559211 | 79 | py |
P-DRO | P-DRO-main/src/data/dataset_utils.py | #!/usr/bin/env python3
import bisect
from torch.utils.data import ConcatDataset
from .minibatch import default_collate
class ConcatDatasetWithSource(ConcatDataset):
"""This acts like ConcatDataset but adds an attribute to each example
to track which dataset they came from
Arguments:
datasets (seq... | 1,390 | 33.775 | 79 | py |
P-DRO | P-DRO-main/src/data/language_modeling.py | import os.path
import torch as th
from sacremoses import MosesDetokenizer
from transformers import DataProcessor, InputFeatures, InputExample
from .text_dataset import (
encode_sentences,
TextDataset,
pad_sequences,
attention_masks,
InputFeaturesWithAttributes,
)
from .minibatch import TupleMiniBat... | 7,711 | 33.123894 | 84 | py |
P-DRO | P-DRO-main/src/data/text_tagging_dataset.py | import os.path
import torch as th
from sacremoses import MosesDetokenizer
from transformers import DataProcessor, InputFeatures, InputExample
from .text_dataset import (
encode_sentences,
TextDataset,
pad_sequences,
attention_masks,
)
from .minibatch import TupleMiniBatch
from .utils import as_tensor
... | 7,449 | 32.710407 | 79 | py |
P-DRO | P-DRO-main/src/data/cub.py | """
Code for reading the CUB dataset
"""
import os.path
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from .cached_dataset import InMemoryCachedDataset
class CUB(Dataset):
"""Adapted from https://github.com/cyvius96/prototypical-network-pytorch"""
def __init_... | 3,269 | 30.142857 | 79 | py |
P-DRO | P-DRO-main/src/data/waterbird.py | #!/usr/bin/bash
"""
Code for reading the Waterbird dataset
"""
import os.path
import tqdm
from torchvision import transforms
import torch as th
from typing import List, Tuple
from .image_dataset import ImageDataset, ImageFeatures
_WATERBIRD_METADATA = None
def _load_waterbird_metadata(path):
global _WATERBIRD_... | 5,764 | 30.502732 | 77 | py |
P-DRO | P-DRO-main/src/data/image_dataset.py | #!/usr/bin/bash
"""Code for image datasets"""
from PIL import Image
import torch as th
from torch.utils.data import Dataset
from typing import Optional, Union, List
from .minibatch import TupleMiniBatch
from .utils import as_tensor
from dataclasses import dataclass
@dataclass(frozen=False)
class ImageFeatures(obj... | 5,262 | 29.247126 | 78 | py |
P-DRO | P-DRO-main/src/data/utils.py | from torchvision import datasets, transforms
from torch.utils.data import Subset
import torch as th
import numpy as np
class Reservoir(object):
"""Reservoir sampling for the whole family"""
def __init__(self, capacity, rng=None):
self.capacity = capacity
self.container = []
self.count... | 2,424 | 24.260417 | 79 | py |
P-DRO | P-DRO-main/src/data/minibatch.py | #!/usr/bin/env python3
import torch as th
class BaseMiniBatch(object):
"""Base class for minibatch"""
@property
def inputs(self):
"""Input variables (typically word ids/pixel tensor)"""
raise NotImplementedError()
@property
def attributes(self):
"""Attributes (metadata th... | 5,153 | 27.955056 | 76 | py |
P-DRO | P-DRO-main/src/data/tcd.py | #!/usr/bin/env python3
"""Adapted from
https://github.com/huggingface/pytorch-pretrained-BERT
Classes to load TextClassificationDatasets
"""
import os
from .text_dataset import InputExample
from transformers.data.processors.utils import DataProcessor
class TCDProcessor(DataProcessor):
"""Processor for the MRP... | 6,492 | 27.353712 | 65 | py |
P-DRO | P-DRO-main/src/data/sampling.py | import numpy as np
import torch as th
from torch.utils.data import Sampler, RandomSampler
from itertools import islice
from .text_dataset import TextDataset
class MixtureSampler(Sampler):
"""This object samples from a mixture of datastreams
At each timestep it samples a data source according to weights
a... | 9,839 | 38.518072 | 82 | py |
P-DRO | P-DRO-main/src/data/mini_imagenet.py | """
Courtesy of https://github.com/cyvius96/prototypical-network-pytorch
"""
import os.path
from PIL import Image
import torch as th
from torch.utils.data import Dataset
from torchvision import transforms
from .cached_dataset import InMemoryCachedDataset
ROOT_PATH = './datasets/minimagenet'
class MiniImageNet(Datas... | 4,228 | 33.382114 | 79 | py |
P-DRO | P-DRO-main/src/data/hatespeech.py | #!/usr/bin/env python3
"""Adapted from
https://github.com/huggingface/pytorch-pretrained-BERT
Classes to load Hatespeech datasets
"""
import os
from transformers import DataProcessor
from .text_dataset import ClassificationExample
class HatespeechExample(ClassificationExample):
def __init__(self, guid, text_a, ... | 2,221 | 28.236842 | 79 | py |
P-DRO | P-DRO-main/src/data/celeba.py | """
Code for reading the CelebA dataset
"""
import os.path
import tqdm
from torchvision import transforms
import torch as th
from typing import List, Tuple
from .image_dataset import ImageDataset, ImageFeatures
_CELEBA_METADATA = None
def _load_celeba_metadata(path):
global _CELEBA_METADATA
_CELEBA_METADAT... | 5,949 | 30.151832 | 80 | py |
P-DRO | P-DRO-main/src/data/cached_dataset.py | import os
import os.path
import numpy as np
import torch as th
from PIL import Image
from torch.utils.data import Dataset
from .text_dataset import TextDataset
def identity(x):
return x
class InMemoryCachedDataset(Dataset):
def __init__(self, dataset, cache_file=None, transform=None):
self._datas... | 3,646 | 30.439655 | 80 | py |
P-DRO | P-DRO-main/src/tasks/superglue.py | import os.path
import torch as th
import torch.nn.functional as F
import numpy as np
from ..data import scoring, superglue
from ..data.cached_dataset import load_and_cache_examples
from ..data.minibatch import TupleMiniBatch
from ..utils import xavier_initialize
from .text_classification import TextClassificationTas... | 13,827 | 32.97543 | 79 | py |
P-DRO | P-DRO-main/src/tasks/permuted_mnist.py | #!/usr/bin/env python3
import numpy as np
import torch as th
from torch import nn
from torchvision import datasets, transforms
from torch.utils.data import random_split
from ..utils import _permutate_image_pixels
from .task import Task
class pMNIST(Task):
"""Permuted MNIST"""
def __init__(self, path, vali... | 2,278 | 28.986842 | 70 | py |
P-DRO | P-DRO-main/src/tasks/omniglot.py | #!/usr/bin/env python3
import os.path
from torchvision import transforms
from ..data.omniglot import OmniglotAlphabet, InMemoryOmniglotAlphabet
from .task import Task
class Omniglot(Task):
def __init__(self, path, alphabet, test_chars=2, in_memory=False):
super(Omniglot, self).__init__()
self.pa... | 2,381 | 31.189189 | 97 | py |
P-DRO | P-DRO-main/src/tasks/split_cifar.py | #!/usr/bin/env python3
import torch as th
from copy import deepcopy
from torchvision import datasets, transforms
from torch.utils.data import random_split
from ..utils import split_vision_dataset, split_vision_dataset_by_idx
from .task import Task
_CACHED_CIFAR100_TRAIN = None
_CACHED_CIFAR100_TEST = None
class ... | 8,557 | 39.947368 | 79 | py |
P-DRO | P-DRO-main/src/tasks/language_modeling.py | #!/usr/bin/env python3
import os.path
from .task import Task
from .text_classification import TextClassificationTask
from ..data.language_modeling import (
LanguageModelingDataset,
WikiTextProcessor,
)
import torch as th
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoad... | 12,966 | 34.623626 | 79 | py |
P-DRO | P-DRO-main/src/tasks/split_mnist.py | #!/usr/bin/env python3
import torch as th
from torchvision import datasets, transforms
from torch.utils.data import random_split
from ..utils import split_vision_dataset
from .task import Task
class SplitMNIST(Task):
"""Split MNIST into digits"""
def __init__(self, path, digits=None, valid_split=1000):
... | 2,361 | 31.356164 | 76 | py |
P-DRO | P-DRO-main/src/tasks/task.py | #!/usr/bin/env python3
from copy import deepcopy
import torch as th
from torch import nn
import torch.nn.functional as F
from typing import Union, Callable, Optional
from torch.utils.data import random_split, DataLoader, ConcatDataset
from torch.utils.data.dataloader import default_collate
from ..data.minibatch import ... | 11,672 | 31.88169 | 79 | py |
P-DRO | P-DRO-main/src/tasks/split_cub.py | #!/usr/bin/env python3
import os.path
import torch as th
from torch.utils.data import random_split
from ..data.cub import CUB, InMemoryCUB
from .task import Task
class SplitCUB(Task):
"""Split CIFAR into separate classes"""
def __init__(
self,
path,
classes=None,
split=None,... | 3,013 | 28.841584 | 79 | py |
P-DRO | P-DRO-main/src/tasks/text_classification.py | #!/usr/bin/env python3
import os.path
from copy import deepcopy
import numpy as np
import csv
import torch.nn.functional as F
from transformers import glue_processors
from ..data import tokenizers, scoring, tcd
from ..data.glue import MultiNLIProcessor # noqa
from ..data.cached_dataset import load_and_cache_examples
f... | 22,330 | 31.839706 | 79 | py |
P-DRO | P-DRO-main/src/tasks/split_miniimagenet.py | #!/usr/bin/env python3
import os.path
import torch as th
from torch.utils.data import random_split
from ..data.mini_imagenet import MiniImageNet, InMemoryMiniImagenet
from ..data.cached_dataset import InMemoryCachedDataset
from .task import Task
class SplitMiniImageNet(Task):
"""Split CIFAR into separate classe... | 3,520 | 31.906542 | 81 | py |
P-DRO | P-DRO-main/src/tasks/image_density_estimation.py | from .task import Task
import torch as th
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import numpy as np
def image_to_256(x):
# Rescale to [0,1]
xmin, xmax = x.min(), x.max()
x = x-xmin
if xmin < xmax:
x = x/(xmax-xmin)
# Now to 0-255
re... | 4,095 | 31.507937 | 79 | py |
SATRAM | SATRAM-main/examples/datasets/alanine_dipeptide_parallel_tempering.py | import os
import torch
import numpy as np
from tqdm import tqdm
import urllib.request as request
USER_AGENT = "pytorch/vision"
angles_file = "https://ftp.mi.fu-berlin.de/pub/cmb-data/alanine_dipeptide_parallel_tempering_dihedrals.npz"
energies_file = "https://ftp.mi.fu-berlin.de/pub/cmb-data/alanine_dipeptide_paralle... | 1,812 | 29.728814 | 108 | py |
SATRAM | SATRAM-main/examples/datasets/toy_problem.py | import math
import torch
import scipy.spatial
n_therm_states = 4
n_conf_states = 5
mu = torch.linspace(-1, 1, n_therm_states)
s = torch.Tensor([0, 0.5, 1, 2]) # np.linspace(0,num_therm_states-1,num_therm_states)
centers = torch.linspace(-1, 1, n_conf_states).reshape(-1, 1)
sigma2 = 0.05
T = int(1e4)
def get_ground... | 1,556 | 20.929577 | 86 | py |
SATRAM | SATRAM-main/tests/test_dataset.py | import pytest
import torch.utils.data
from satram.util.dataset import Dataset
from satram.util.data_handler import process_input
from examples.datasets.toy_problem import *
@pytest.mark.parametrize(
"device", ["cpu"],
)
def test_properties_set(device):
data, state_counts, transition_counts = process_input(ge... | 2,420 | 40.741379 | 111 | py |
SATRAM | SATRAM-main/tests/test_common.py | import torch
from satram.estimators._common import compute_v_R
def test_compute_v_R():
n_markov_states = 10
n_therm_states = 5
f = torch.zeros([n_therm_states, n_markov_states]) # exp will produce ones
log_v = torch.zeros_like(f)
transition_counts = torch.ones([n_therm_states, n_markov_states, n_... | 868 | 38.5 | 105 | py |
SATRAM | SATRAM-main/tests/test_data_handler.py | import pytest
import torch
from satram.util.data_handler import process_input
def random_input_data(n_therm_states, n_markov_states, traj_lengths, has_RE=False):
dtrajs = [torch.randint(low=0, high=n_markov_states, size=[l]) for l in traj_lengths]
# ensure we get the correct number of states by hard-coding a ... | 2,778 | 35.565789 | 111 | py |
SATRAM | SATRAM-main/tests/test_thermodynamic_estimator.py | """
Unit and regression test for the SATRAM package.
"""
import pytest
import torch
from satram import ThermodynamicEstimator
from examples.datasets import toy_problem
@pytest.mark.parametrize(
"solver_type",
["MBAR", "SAMBAR", "TRAM", "SATRAM"],
)
def test_fit(solver_type):
ttrajs, dtrajs, bias = toy_pro... | 4,030 | 27.588652 | 98 | py |
SATRAM | SATRAM-main/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup ------------------------------------------------------------... | 5,327 | 29.445714 | 129 | py |
SATRAM | SATRAM-main/satram/estimators/satram.py | import math
import torch.nn.functional as F
from ._common import *
def _compute_batch_delta_f(f, log_R, bias, ind_trajs):
return torch.logsumexp(F.log_softmax(log_R + f - bias[:, :, None], 1) + torch.log(ind_trajs[:, None, :]), 0)
def _compute_delta_f(delta_f, log_R, batch_size, lr, delta_f_max):
if len(del... | 1,915 | 33.214286 | 112 | py |
SATRAM | SATRAM-main/satram/estimators/mbar.py | from ._common import *
def _compute_batch_update_f(f, N_k_log, bias):
return torch.logsumexp(-bias.T - torch.logsumexp(N_k_log - bias + f, axis=1), 1)
def _update_f(f, N_k_log, dataloader, device):
f_new = []
for batch_idx, batch_data in enumerate(dataloader):
batch_data = batch_data.to(device)
... | 915 | 28.548387 | 114 | py |
SATRAM | SATRAM-main/satram/estimators/thermodynamic_estimator.py | import torch
from ._common import *
from .implementation_manager import *
from satram.util import *
class ThermodynamicEstimator():
"""Estimator of free energies.
Thermodynamic Estimator handles estimation of free energies. The specific
implementation is chosen by the user
Attributes
----------
... | 11,255 | 39.344086 | 120 | py |
SATRAM | SATRAM-main/satram/estimators/tram.py | from ._common import *
def _compute_batch_update_f(f, log_R, bias, ind_trajs, state_counts):
update = torch.logsumexp(-bias[:, :, None] - torch.logsumexp(log_R + f - bias[:, :, None], 1, keepdim=True) +
torch.log(ind_trajs[:, None, :]), 0)
update.T[torch.where(state_counts.sum(0) ... | 1,152 | 40.178571 | 113 | py |
SATRAM | SATRAM-main/satram/estimators/sambar.py | import math
import torch.nn.functional as F
from ._common import *
def _compute_delta_f(f, N_k_log, normalized_N_k, bias, batch_size):
return torch.exp(torch.logsumexp(F.log_softmax(N_k_log - bias + f, 1), axis=0)
- math.log(batch_size)) - normalized_N_k
def _update_f(f, N_k_log, normalized... | 1,117 | 30.942857 | 117 | py |
SATRAM | SATRAM-main/satram/estimators/_common.py | import torch
epsl = 1e-10
def compute_f_therm(f):
f_therm = -torch.logsumexp(-f, 1)
return f_therm - f_therm.min()
def compute_sample_weights_batch(f, log_R, bias, ind_trajs):
weights = -torch.logsumexp(f + log_R - bias[:, :, None], 1) + torch.log(ind_trajs)
# TODO: find cleaner solution. NaNs get ... | 2,314 | 35.171875 | 103 | py |
SATRAM | SATRAM-main/satram/util/dataset.py | import torch
import math
from torch.utils.data import RandomSampler, SequentialSampler
def _compute_max_batch_size(datarow):
# TODO: implement
return 8192
class Dataset:
def __init__(self, data, state_counts, transition_counts=None, device='cpu', batch_size=256, is_stochastic=False):
self._data... | 1,897 | 32.892857 | 118 | py |
SATRAM | SATRAM-main/satram/util/data_handler.py | import torch
from deeptime.markov.msm.tram import TRAMDataset
def _determine_n_states(dtrajs):
if isinstance(dtrajs, list):
return max([_determine_n_states(traj) for traj in dtrajs])
else:
return int(max(torch.max(d).item() for d in dtrajs) + 1)
def _determine_n_therm_states(dtrajs, ttrajs):... | 3,864 | 42.920455 | 94 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/utils_orig.py | import torch
import torch.distributed as dist
from model import DeepSpeech
#from model_split import DeepSpeech , NoiseClassifier
def reduce_tensor(tensor, world_size, reduce_op_max=False):
rt = tensor.clone()
dist.all_reduce(rt, op=dist.reduce_op.MAX if reduce_op_max is True else dist.reduce_op.SUM) # Defau... | 1,390 | 28.595745 | 113 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/model_split.py | import math
from collections import OrderedDict
import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import os
import numpy as np
supported_rnns = {
'lstm': nn.LSTM,
'rnn': nn.RNN,
'gru': nn.GRU
}
supported_rnns_inv = dict((v, k) for k, v in supported... | 14,742 | 32.506818 | 111 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/test.py | import argparse
import pdb
import numpy as np
import torch
from tqdm import tqdm
import pickle
from data.data_loader import SpectrogramDataset, AudioDataLoader
from decoder import GreedyDecoder
from opts import add_decoder_args, add_inference_args
from utils import load_model
parser = argparse.ArgumentParser(descripti... | 6,539 | 47.088235 | 157 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/trainTLNoisy.py | import argparse
import json
import os
import random
import time
import pdb
import numpy as np
import torch.distributed as dist
import torch.utils.data.distributed
from apex import amp
from apex.parallel import DistributedDataParallel
from warpctc_pytorch import CTCLoss
from data.data_loader_noisy import AudioDataLoade... | 18,896 | 51.34626 | 166 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/trainEnhanced.py | import argparse
import json
import os
import random
import time
import pdb
import numpy as np
import torch.distributed as dist
import torch.utils.data.distributed
from apex import amp
from apex.parallel import DistributedDataParallel
from warpctc_pytorch import CTCLoss
from data.data_loader import AudioDataLoader, Spe... | 18,696 | 51.08078 | 145 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/utils.py | import torch
import torch.distributed as dist
#from model import DeepSpeech
from model_split import DeepSpeech , NoiseClassifier
def reduce_tensor(tensor, world_size, reduce_op_max=False):
rt = tensor.clone()
dist.all_reduce(rt, op=dist.reduce_op.MAX if reduce_op_max is True else dist.reduce_op.SUM) # Defau... | 1,390 | 28.595745 | 113 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/model.py | import math
from collections import OrderedDict
import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
supported_rnns = {
'lstm': nn.LSTM,
'rnn': nn.RNN,
'gru': nn.GRU
}
supported_rnns_inv = dict((v, k) for k, v in supported_rnns.items())
cl... | 12,784 | 38.582043 | 120 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/test_noisy.py | import argparse
import pdb
import numpy as np
import torch
from tqdm import tqdm
import pickle
from data.data_loader_noisy import SpectrogramDataset, AudioDataLoader
from decoder import GreedyDecoder
from opts import add_decoder_args, add_inference_args
from utils import load_model, load_noise_model
parser = argparse.... | 11,141 | 51.805687 | 258 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/decoder.py | #!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright 2015-2016 Nervana Systems Inc.
# 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
#... | 8,119 | 40.010101 | 120 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/trainDiffAdvNoisy.py | import argparse
import json
import os
import random
import time
import pdb
import numpy as np
import torch.distributed as dist
import torch.utils.data.distributed
from apex import amp
from apex.parallel import DistributedDataParallel
from warpctc_pytorch import CTCLoss
from data.data_loader_noisy import AudioDataLoade... | 24,401 | 45.747126 | 165 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/test_enhanced.py | import argparse
import pdb
import re
import numpy as np
import torch
from tqdm import tqdm
import pickle
from data.data_loader import SpectrogramDataset, AudioDataLoader
from decoder import GreedyDecoder
from opts import add_decoder_args, add_inference_args
from utils_orig import load_model
parser = argparse.ArgumentP... | 8,099 | 49.310559 | 157 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/model_split_adversary.py | import math
from collections import OrderedDict
import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import os
import numpy as np
supported_rnns = {
'lstm': nn.LSTM,
'rnn': nn.RNN,
'gru': nn.GRU
}
supported_rnns_inv = dict((v, k) for k, v in supported... | 15,799 | 32.263158 | 111 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/trainMTLNoisy.py | import argparse
import json
import os
import random
import time
import pdb
import numpy as np
import torch.distributed as dist
import torch.utils.data.distributed
from apex import amp
from apex.parallel import DistributedDataParallel
from warpctc_pytorch import CTCLoss
from data.data_loader_noisy import AudioDataLoade... | 23,061 | 53.009368 | 145 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/data/data_loader.py | import os
import subprocess
from tempfile import NamedTemporaryFile
from torch.distributed import get_rank
from torch.distributed import get_world_size
from torch.utils.data.sampler import Sampler
import librosa
import numpy as np
import scipy.signal
import torch
from scipy.io.wavfile import read
import math
from tor... | 12,849 | 38.783282 | 120 | py |
Robust-E2E-ASR | Robust-E2E-ASR-main/Code/data/data_loader_noisy.py | import os
import subprocess
from tempfile import NamedTemporaryFile
import random
from torch.distributed import get_rank
from torch.distributed import get_world_size
from torch.utils.data.sampler import Sampler
import pdb
import librosa
import numpy as np
import scipy.signal
import torch
from scipy.io.wavfile import re... | 15,453 | 39.14026 | 120 | py |
OSFI-by-FineTuning | OSFI-by-FineTuning-main/main.py | import config
from dataset import Prep_CASIA_IJBC, open_set_folds, face_dataset
from model import fetch_encoder, head
from finetune import linear_probing, weight_imprinting, fine_tune
from utils import save_dir_far_curve, save_dir_far_excel
import os
import json
import pprint
import random
import argparse
import numpy... | 8,000 | 37.1 | 116 | py |
OSFI-by-FineTuning | OSFI-by-FineTuning-main/utils.py | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
def cosine(x,w):
# x, w shape: [B, d], where B = batch size, d = feature dim.
x_norm = F.normalize(x,dim=1)
w_norm = F.normalize(w,dim=1)
cos_sim = torch.mm(x_norm, w_norm.T).clamp(-1, 1... | 4,460 | 35.268293 | 103 | py |
OSFI-by-FineTuning | OSFI-by-FineTuning-main/dataset.py | import os
import pickle
import numpy as np
from PIL import Image, ImageFile
from torch.utils.data import Dataset
# to avoid ValueError: Decompressed Data Too Large
ImageFile.LOAD_TRUNCATED_IMAGES = True
class open_set_folds():
def __init__(self, image_directory, num_gallery, num_probe, known_ratio=0.5):
... | 4,430 | 39.651376 | 91 | py |
OSFI-by-FineTuning | OSFI-by-FineTuning-main/finetune.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms as transforms
def get_lr(optimizer):
return optimizer.param_groups[0]["lr"]
def linear_probing(args, galleryloader, encoder, classifier,verbose=True, target_acc=95):
CEloss = nn.Cros... | 3,422 | 39.75 | 104 | py |
OSFI-by-FineTuning | OSFI-by-FineTuning-main/model/head.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
class softmax_head(nn.Module):
def __init__(self, feat_dim, num_cls):
super(softmax_head, self).__init__()
self.feat_dim = feat_dim
self.num_cls = num_cls
self... | 5,204 | 33.019608 | 98 | py |
OSFI-by-FineTuning | OSFI-by-FineTuning-main/model/VGGNets_Adapt.py | # VGGNet Implementation: https://github.com/kuangliu/pytorch-cifar/blob/master/models/vgg.py
# Residual adapter implementation based on: https://github.com/srebuffi/residual_adapters
import torch.nn as nn
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
def co... | 2,636 | 35.625 | 112 | py |
OSFI-by-FineTuning | OSFI-by-FineTuning-main/model/ResNets_Adapt.py | """
@author: Jun Wang
@date: 20201019
@contact: jun21wangustc@gmail.com
"""
# based on:
# https://github.com/TreB1eN/InsightFace_Pytorch/blob/master/model.py
# Residual adapter implementation based on: https://github.com/srebuffi/residual_adapters
import torch.nn as nn
from collections import namedtuple
... | 5,395 | 38.101449 | 108 | py |
OSFI-by-FineTuning | OSFI-by-FineTuning-main/model/fetch_encoder.py | import torch
import torch.nn as nn
from model import ResNets_Adapt, VGGNets_Adapt
def fetch(device, config, encoder_type, finetune_layers, train_output=False):
adapt = True if finetune_layers == "PA" else False
if encoder_type == "VGG19":
encoder = VGGNets_Adapt.VGG("VGG19", adapt=adapt)
chkp... | 3,385 | 39.795181 | 103 | py |
PILF | PILF-main/SSR/code/c42conv_epixt.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# from einops import rearrange
import math
class Net(nn.Module):
def __init__(self, angRes, factor):
super(Net, self).__init__()
channels = 64
self.channels = channels
self.angRes = angRes
self.factor = fact... | 15,520 | 40.61126 | 165 | py |
PILF | PILF-main/SSR/code/pred2tiffrgb.py | import torch
import h5py
import scipy.io as scio
from tifffile import imwrite
import numpy as np
import os
import utils
# datasets = ['NTIRE_Val_Real', 'NTIRE_Val_Synth']
folder = '../TestResultsx4-full-pad-ep63'
# folder = '../TestResultsx2-full-pad8ifLytro-ep60'
datasets = ['EPFL', 'HCI_new', 'HCI_old', 'INRIA_Lytro'... | 6,146 | 41.6875 | 154 | py |
PILF | PILF-main/SSR/code/train_tb.py | import time
import argparse
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
from tqdm import tqdm
from utils import *
# from LFT import Net
from c42conv_epixt import Net
# from LFT_4D import Net
# from LFT_epif import Net
import os
from tensorboardX import SummaryWriter
#os.environ["CUDA_VIS... | 12,165 | 47.47012 | 168 | py |
PILF | PILF-main/SSR/code/test_full_x2_squre.py | import time
import argparse
import scipy.misc
import torch.backends.cudnn as cudnn
import os
import sys
from utils import *
#from model_HLFSR_re import Net
# from LFT import Net
from tqdm import tqdm
import scipy.io as sio
import time
from einops import rearrange
import torch
import torch.nn.functional as F
import h5... | 10,714 | 40.211538 | 178 | py |
PILF | PILF-main/SSR/code/utils.py | #from PIL import Image
import os
from torch.utils.data.dataset import Dataset
from torchvision.transforms import ToTensor
import random
import matplotlib.pyplot as plt
import torch
import numpy as np
import h5py
from torch.utils.data import DataLoader
from skimage import metrics
class TrainSetLoader(Dataset):
def ... | 15,345 | 34.441109 | 142 | py |
PILF | PILF-main/SSR/code/test_full_x4.py | import time
import argparse
import scipy.misc
import torch.backends.cudnn as cudnn
import os
import sys
from utils import *
#from model_HLFSR_re import Net
# from LFT import Net
from tqdm import tqdm
import scipy.io as sio
import time
from einops import rearrange
import torch
import torch.nn.functional as F
import h5... | 8,519 | 37.90411 | 178 | py |
PILF | PILF-main/SSR/code/model_bicubic.py | import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import scipy.io as scio
from math import sqrt
from numpy import clip
from torchvision.transforms import ToPILImage
import matplotlib.pyplot as plt
class Net(nn.Module):
def __init__(self, angRes, factor):
super(Net, self)... | 10,947 | 36.621993 | 155 | py |
CheXbert | CheXbert-master/src/run_bert.py | import os
import argparse
import time
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import utils
from models.bert_labeler import bert_labeler
from datasets.impressions_dataset import ImpressionsDataset
from constants import *
def collate_fn_labels(sample_list):
"""Custom collate functi... | 13,834 | 52.416988 | 134 | py |
CheXbert | CheXbert-master/src/utils.py | import copy
import torch
import torch.nn as nn
import pandas as pd
import numpy as np
import json
from models.bert_labeler import bert_labeler
from bert_tokenizer import tokenize
from sklearn.metrics import f1_score, confusion_matrix
from statsmodels.stats.inter_rater import cohens_kappa
from transformers import BertTo... | 15,021 | 38.427822 | 132 | py |
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