python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
# This file contains Att2in2, AdaAtt, AdaAttMO, UpDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning
#... | connect-caption-and-trace-main | captioning/models/AttModel_both_backup_2020_11_07.py |
"""
BertCapModel is using huggingface transformer bert model as seq2seq model.
The result is not as goog as original transformer.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import... | connect-caption-and-trace-main | captioning/models/BertCapModel.py |
# This file contains Att2in2, AdaAtt, AdaAttMO, UpDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning
#... | connect-caption-and-trace-main | captioning/models/AttModel_for_coco_caption_baseline.py |
# This file contains Att2in2, AdaAtt, AdaAttMO, UpDown model
# AdaAtt is from Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
# https://arxiv.org/abs/1612.01887
# AdaAttMO is a modified version with maxout lstm
# Att2in is from Self-critical Sequence Training for Image Captioning
#... | connect-caption-and-trace-main | captioning/models/AttModel_for_coco_caption_task.py |
import torch
from . import losses
from ..utils.rewards import init_scorer, get_self_critical_reward
class LossWrapper(torch.nn.Module):
def __init__(self, model, opt):
super(LossWrapper, self).__init__()
self.opt = opt
self.model = model
if opt.label_smoothing > 0:
self.... | connect-caption-and-trace-main | captioning/modules/loss_wrapper_caption_generation.py |
import torch
import torch.nn.functional as F
from . import losses
from ..utils.rewards import init_scorer, get_self_critical_reward
class LossWrapper(torch.nn.Module):
def __init__(self, model, opt):
super(LossWrapper, self).__init__()
self.opt = opt
self.model = model
if opt.label_... | connect-caption-and-trace-main | captioning/modules/loss_wrapper_show_control_tell.py |
import torch
import torch.nn.functional as F
from . import losses
from ..utils.rewards import init_scorer, get_self_critical_reward
from ..utils.local_optimal_transport import local_OT
class LossWrapper(torch.nn.Module):
def __init__(self, model, opt):
super(LossWrapper, self).__init__()
self.opt ... | connect-caption-and-trace-main | captioning/modules/loss_wrapper_trace_generation.py |
import torch
import torch.nn.functional as F
from . import losses
from ..utils.rewards import init_scorer, get_self_critical_reward
import numpy as np
import random
class LossWrapper(torch.nn.Module):
def __init__(self, model, opt):
super(LossWrapper, self).__init__()
self.opt = opt
self.mo... | connect-caption-and-trace-main | captioning/modules/loss_wrapper_joint.py |
import torch
import torch.nn.functional as F
from . import losses
from ..utils.rewards import init_scorer, get_self_critical_reward
class LossWrapper(torch.nn.Module):
def __init__(self, model, opt):
super(LossWrapper, self).__init__()
self.opt = opt
self.model = model
if opt.label_... | connect-caption-and-trace-main | captioning/modules/loss_wrapper_for_coco_caption.py |
import torch
import torch.nn as nn
from ..utils.rewards import get_scores, get_self_cider_scores
class RewardCriterion(nn.Module):
def __init__(self):
super(RewardCriterion, self).__init__()
def forward(self, input, seq, reward):
input = input.gather(2, seq.unsqueeze(2)).squeeze(2)
... | connect-caption-and-trace-main | captioning/modules/losses.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import h5py
import lmdb
import os
import numpy as np
import numpy.random as npr
import random
import torch
import torch.utils.data as data
import multiprocessing
import six
class HybridLoader:
... | connect-caption-and-trace-main | captioning/data/pth_loader.py |
connect-caption-and-trace-main | captioning/data/__init__.py | |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import h5py
import os
import numpy as np
import random
import torch
import skimage
import skimage.io
import scipy.misc
from torchvision import transforms as trn
preprocess = trn.Compose([
#... | connect-caption-and-trace-main | captioning/data/dataloaderraw.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import h5py
import lmdb
import os
import numpy as np
import numpy.random as npr
import random
import torch
import torch.utils.data as data
import multiprocessing
import six
class HybridLoader:
... | connect-caption-and-trace-main | captioning/data/dataloader.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import h5py
import lmdb
import os
import numpy as np
import numpy.random as npr
import random
import torch
import torch.utils.data as data
import multiprocessing
import six
class HybridLoader:
... | connect-caption-and-trace-main | captioning/data/dataloader_show_control_tell.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import base64
import numpy as np
import csv
import sys
import zlib
import time
import mmap
import argparse
parser = argparse.ArgumentParser()
# output_dir
parser.add_argument('--downloaded_feats', d... | connect-caption-and-trace-main | scripts/make_bu_data.py |
"""
Preprocess a raw json dataset into features files for use in data_loader.py
Input: json file that has the form
[{ file_path: 'path/img.jpg', captions: ['a caption', ...] }, ...]
example element in this list would look like
{'captions': [u'A man with a red helmet on a small moped on a dirt road. ', u'Man riding a m... | connect-caption-and-trace-main | scripts/prepro_feats.py |
# coding: utf-8
"""
Create a reference json file used for evaluation with `coco-caption` repo.
Used when reference json is not provided, (e.g., flickr30k, or you have your own split of train/val/test)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
impo... | connect-caption-and-trace-main | scripts/prepro_reference_json.py |
"""
Precompute ngram counts of captions, to accelerate cider computation during training time.
"""
import os
import json
import argparse
from six.moves import cPickle
import captioning.utils.misc as utils
from collections import defaultdict
import sys
sys.path.append("cider")
from pyciderevalcap.ciderD.ciderD_scorer ... | connect-caption-and-trace-main | scripts/prepro_ngrams.py |
import argparse
import h5py
import os
import numpy as np
import json
from tqdm import tqdm
def main(params):
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
N = len(imgs)
if params['fc_input_dir'] is not None:
print('processing fc')
with h5py.File(params['fc_o... | connect-caption-and-trace-main | scripts/dump_to_h5df.py |
"""
Preprocess a raw json dataset into hdf5/json files for use in data_loader.py
Input: json file that has the form
[{ file_path: 'path/img.jpg', captions: ['a caption', ...] }, ...]
example element in this list would look like
{'captions': [u'A man with a red helmet on a small moped on a dirt road. ', u'Man riding a ... | connect-caption-and-trace-main | scripts/prepro_labels.py |
import torch
import scipy.optimize
import numpy as np
m = 10
pred = torch.rand([10, m, 4])
label = torch.rand([10, m, 4])
def local_OT(D):
p = D.shape[1]; m = D.shape[2]
# construct the cx, ax=b
x = torch.rand([10,m*m])
A = torch.zeros([m+m,m*m])
b = torch.ones([m+m])
for i in range(p):
... | connect-caption-and-trace-main | scripts/my_local_optimal_transport.py |
# copy from https://github.com/Lyken17/Efficient-PyTorch/tools
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import os, sys
import os.path as osp
from PIL import Image
import six
import string
import lmdb
import pickle
imp... | connect-caption-and-trace-main | scripts/dump_to_lmdb.py |
import numpy as np
import os
import h5py
import numpy as np
import jsonlines
import re
import json
# The first directory should lead to your feature files extracted by detectrons, and the box_only and feats_only are the new folders for saving bounding boxes and features (which will be used during training).
i = 0
for... | connect-caption-and-trace-main | scripts/prepare_feats_boxes_from_npz.py |
"""
Preprocess a raw json dataset into hdf5/json files for use in data_loader.lua
Input: json file that has the form
[{ file_path: 'path/img.jpg', captions: ['a caption', ...] }, ...]
example element in this list would look like
{'captions': [u'A man with a red helmet on a small moped on a dirt road. ', u'Man riding a... | connect-caption-and-trace-main | scripts/build_bpe_subword_nmt.py |
"""
Preprocess PubTator snapshot for use with Snorkel v6.2
1) Download snapshot
2) Split snapshot into blocks
3) Parse blocks and load into database
"""
import os
import sys
import glob
import shutil
import argparse
from time import time
def split_pubtator_corpus(file_path, split_size=500000):
"""
Split Pub... | snorkel-biocorpus-master | parse_pubtator.py |
import re
import sys
from itertools import product
from sqlalchemy.sql import select
from collections import defaultdict
from snorkel.udf import UDF, UDFRunner
from snorkel.models import TemporarySpan, Sentence, Document, SequenceTag, Candidate
class SequenceTagCandidateExtractor(UDFRunner):
"""UDFRunner for Sequ... | snorkel-biocorpus-master | custom_cand_generator.py |
class Tagger(object):
pass
| snorkel-biocorpus-master | pubtator/tags.py |
import re
import sys
import codecs
import lxml.etree as et
from collections import namedtuple
from snorkel.models import Document, SequenceTag
Tag = namedtuple('Tag', 'document_id abs_char_start abs_char_end concept_type concept_uid source')
class MetadataProcessor(object):
"""
Load external information
... | snorkel-biocorpus-master | pubtator/metadata.py |
from .parsers import *
from .metadata import *
| snorkel-biocorpus-master | pubtator/__init__.py |
import re
import codecs
from snorkel.parser import DocPreprocessor
from snorkel.models import Document, split_stable_id
from snorkel.parser import Parser, ParserConnection, Spacy, Sentence
class PubTatorParser(Parser):
"""
Parser wrapper for PubTator annotations. Annotations require some
data munging to ma... | snorkel-biocorpus-master | pubtator/parsers.py |
import codecs
import spacy
from collections import defaultdict
from snorkel.models import construct_stable_id
from spacy.tokens import Doc
from snorkel.parser import DocPreprocessor
from snorkel.models import Document, split_stable_id
from snorkel.parser import Parser, ParserConnection, Spacy, Sentence
class LineCorpu... | snorkel-biocorpus-master | pubtator/doc_parsers.py |
"""An extensible library for opening URLs using a variety of protocols
The simplest way to use this module is to call the urlopen function,
which accepts a string containing a URL or a Request object (described
below). It opens the URL and returns the results as file-like
object; the returned object has some extra me... | snorkel-biocorpus-master | pubtator/api/urllib2.py |
import urllib2
import time
import sys
import getopt
inputfile = ''
bioconcept = ''
format = ''
try:
options, remainder = getopt.getopt(sys.argv[1:], 'i:b:f:', ['inputfile=','bioconcept=','format='])
except getopt.GetoptError, err:
print "\npython RESTful.client.get.py -i [inputfile] -b [bioconcept] -f [format]\n"
... | snorkel-biocorpus-master | pubtator/api/RESTful.client.get.py |
import urllib2
import time
import sys
import getopt
inputfile = ''
trigger = ''
taxonomy = ''
email = ''
PubTator_username = ''
url_Submit = ''
try:
options, remainder = getopt.getopt(sys.argv[1:], 'i:t:x:e:', ['inputfile=','trigger=','taxonomy=','email='])
except getopt.GetoptError, err:
print "\npython RESTful.cl... | snorkel-biocorpus-master | pubtator/api/RESTful.client.post.py |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
------------------------------------------------
Learn Common Phrases
------------------------------------------------
Train PMI-based phrase models. Currently this only assumes bigrams,
but it can be extended easily.
"""
import sys
import logging
import argparse
from... | snorkel-biocorpus-master | embeddings/train_pmi_phrases.py |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
------------------------------------------------
Create Word Embeddings
------------------------------------------------
Use Gensim's Word2Vec implementation to create
word (or short phrase) embeddings
'''
import re
import sys
import string
import logging
import argp... | snorkel-biocorpus-master | embeddings/train_emb.py |
"""
Generate external database key/value pairs for select fields that
we want to define joins or elastic search attributes over
(e.g., publication year, journal, mesh keywords). The rest of the
content we commit as a the raw XML tree.
"""
import glob
import lxml.etree as et
filelist = glob.glob("{}/*.xml".format())
... | snorkel-biocorpus-master | etl/pubmed/extract/extract_metadata.py |
'''
Dumps PubMed standoff abstracts to a common text file format for bulk loading
'''
import os
import glob
import codecs
import argparse
import lxml.etree as et
from pubtator.parsers import PubTatorDocPreprocessor
def parse_standoff_format(filename, outputdir, source_prefix="gold_cdr"):
"""
FORMAT:
... | snorkel-biocorpus-master | etl/pubmed/extract/extract_annotations.py |
'''
Dumps PubMed abstracts to a common text file format for bulk preprocessing
FORMAT:
~~_PMID_XXXXXX_~~
TEXT
..
'''
import os
import glob
import codecs
import argparse
import lxml.etree as et
from pubtator.parsers import PubTatorDocPreprocessor
def parse_xml_format(filename, outputdir):
"""
NLM XML Format
... | snorkel-biocorpus-master | etl/pubmed/extract/extract_text.py |
"""
Requires
1) Convert standoff format into 1 sentence per line.
2) Apply rule-based sentence boundary detection/tokenization fixes
The main goal of this step is to ensure high-quality SBD
which often breaks in the presence of complex chemical names
"""
import re
import os
import glob
import codecs
import argpars... | snorkel-biocorpus-master | etl/pubmed/extract/tokenization_fixes.py |
"""
"""
import re
import os
import sys
import glob
import codecs
import argparse
article_rgx = re.compile("~~_PMID_([0-9]+)_~~")
def load_line_corpus(filename, sentences=False, encoding="utf-8"):
corpus = {}
with codecs.open(filename, "rU", 'utf-8') as fp:
doc = []
for line in fp:
... | snorkel-biocorpus-master | etl/pubmed/extract/export_line_corpus.py |
"""
Transform PubMed text generated by extract_pubmed.py into tokenized
standoff format. This leverages 2 external software tools that fix
tokenization errors when using CoreNLP and spaCy are
used on biomedical text, primarily:
1) Errors tokenizing chemical names
2) Correctly identifying sentence boundaries in the ... | snorkel-biocorpus-master | etl/pubmed/extract/tokenize.py |
"""
Load PubTator snapshot for use with Snorkel v6.2
This loads tags into memory, so it works best when the input PubTator
file is split into smaller blocks.
"""
import os
import glob
import codecs
import argparse
def dump2delimited(tags, outfile, write_mode, sep=u"\t", encoding="utf-8"):
with codecs.open(outfil... | snorkel-biocorpus-master | OLD/extract_pubtator_tags.py |
model-patching-master | augmentation/__init__.py | |
import tensorflow as tf
import wandb
import yaml
import subprocess
from augmentation.utilities.visualize import gallery
from augmentation.utilities.wandb import *
from augmentation.utilities.checkpoint import load_tf_optimizer_state
def rewrite_config_for_resumption(config):
config.prev_wandb_entity = config.wand... | model-patching-master | augmentation/methods/robust/utils.py |
import argparse
import os
import yaml
import subprocess
import glob
import functools
from augmentation.augment.utils import create_multiple_train_eval_augmentation_pipelines
from augmentation.augment.static import create_multiple_train_eval_static_augmentation_pipelines
from augmentation.datasets.utils import *
from au... | model-patching-master | augmentation/methods/robust/train.py |
import tensorflow as tf
import numpy as np
from tensorflow_examples.models.pix2pix.pix2pix import upsample, downsample, InstanceNormalization
def unet_generator(output_channels, input_shape=(256, 256, 3), norm_type='batchnorm', output_init=0.02,
residual_output=False):
"""Modified u-net generat... | model-patching-master | augmentation/methods/cyclegan/models.py |
import datetime
import tensorflow as tf
import random
import wandb
from tensorflow_examples.models.pix2pix import pix2pix
from augmentation.dataflows.utils import create_paired_direct_dataflow, \
create_paired_parallel_dataflow_via_numpy
from augmentation.methods.cyclegan.models import mnist_unet_generator, mnist... | model-patching-master | augmentation/methods/cyclegan/utils.py |
import argparse
import os
import functools
import time
import subprocess
from augmentation.utilities.config import *
from augmentation.utilities.metrics import *
from augmentation.datasets.utils import get_processed_dataset_info, apply_modifier_to_dataset_payload, load_dataset
from augmentation.dataflows.utils import d... | model-patching-master | augmentation/methods/cyclegan/train.py |
model-patching-master | augmentation/autoaugment/__init__.py | |
# Copyright 2018 The TensorFlow Authors 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 applicab... | model-patching-master | augmentation/autoaugment/augmentation_transforms.py |
# Copyright 2018 The TensorFlow Authors 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 applicab... | model-patching-master | augmentation/autoaugment/policies.py |
from augmentation.dataflows.utils import create_parallel_dataflow_via_numpy, create_direct_dataflow
import augmentation.augment.static
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
import matplotlib.pyplot as plt
from multiprocessing import cpu_count
from types import SimpleNamespace
imp... | model-patching-master | augmentation/datasets/utils.py |
from types import SimpleNamespace
import tensorflow as tf
import augmentation.datasets.utils
CELEBA_BASE_VARIANTS = ['5_o_Clock_Shadow',
'Arched_Eyebrows',
'Attractive',
'Bags_Under_Eyes',
'Bald',
'B... | model-patching-master | augmentation/datasets/custom/celeba_128.py |
import tensorflow as tf
import os
import augmentation.datasets.utils
# Basic feature construction, taken from the tutorial on TFRecords
def _bytestring_feature(list_of_bytestrings):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=list_of_bytestrings))
def _int_feature(list_of_ints):
return tf.tr... | model-patching-master | augmentation/datasets/custom/tfrecords.py |
from types import SimpleNamespace
import tensorflow as tf
import augmentation.datasets.utils
WATERBIRDS_CLASSES = ['landbird', 'waterbird']
WATERBIRDS_DOMAINS = ['land', 'water']
# Group Sizes
# ------------------------------------
# [y, z] = [[0, 0], [0, 1], [1, 0], [1, 1]]
#
# Training Set (split = 0)
# [3498, 18... | model-patching-master | augmentation/datasets/custom/waterbirds.py |
from types import SimpleNamespace
import augmentation.datasets.utils
from augmentation.datasets.custom.mnist import MNIST_CORRUPTED_VARIANTS
import tensorflow as tf
# TODO multihead should be specified as an option to the dataset instead of a separate one
def load_mnist_correlation_yz_multihead(dataset_name, dataset... | model-patching-master | augmentation/datasets/custom/mnist_correlation.py |
from types import SimpleNamespace
import augmentation.datasets.utils
MNIST_CORRUPTED_VARIANTS = ['identity',
'shot_noise',
'impulse_noise',
'glass_blur',
'motion_blur',
'shear',
... | model-patching-master | augmentation/datasets/custom/mnist.py |
import numpy as np
import imgaug.augmenters as iaa
from imgaug.augmenters import *
from augmentation.methods.cyclegan.models import *
from augmentation.autoaugment import augmentation_transforms
from augmentation.autoaugment.augmentation_transforms import MEANS, STDS
from augmentation.autoaugment.policies import good_p... | model-patching-master | augmentation/augment/utils.py |
from augmentation.augment.utils import WandbModelPseudoLabelingPipeline, BinaryMNISTWandbModelPseudoLabelingPipeline, \
PretrainedMNISTCycleGANAugmentationPipeline, ResizeImage
from augmentation.utilities.wandb import load_pretrained_keras_model_from_wandb, particular_checkpoint_step_extractor, \
load_wandb_run... | model-patching-master | augmentation/augment/static.py |
import tensorflow.keras as keras
from classification_models.tfkeras import Classifiers
def simple_model(input_shape, n_classes):
inputs = keras.layers.Input(shape=input_shape, name='digits')
x = keras.layers.Flatten()(inputs)
x = keras.layers.Dense(64, activation='relu', name='dense_1')(x)
x = keras.l... | model-patching-master | augmentation/models/models.py |
# The code in this file is adapted from
# https://keras.io/examples/cifar10_resnet/
import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras.layers import AveragePooling2D, Input, Flatten
from tensorflow.keras.layers import Dense, Conv2D, BatchNormalization, Activation
from tensorflow.keras.model... | model-patching-master | augmentation/models/resnet.py |
import tensorflow as tf
import tensorflow.keras as keras
import wandb
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from tensorflow.python.keras import backend as K
from augmentation.methods.robust.utils import irm_penalty_explicit
class ConfusionMatrix(keras.metrics.Metric):
def __ini... | model-patching-master | augmentation/utilities/metrics.py |
import os
import yaml
from types import SimpleNamespace
def load_yaml_config(path: str, prefix_keys=False) -> SimpleNamespace:
"""
Load a yaml configuration file from the specified path, apply preprocessing operations to it and return
the configuration in a SimpleNamespace.
:param path: Path to the c... | model-patching-master | augmentation/utilities/config.py |
import pickle
import gzip
def compile_keras_models(models, optimizers):
# Compile the models: this is necessary in order to save model architecture, weights and optimizer to disk
# It doesn't matter what loss we use here since we're not going to be calling model.fit: TODO check!
for model, optimizer in zi... | model-patching-master | augmentation/utilities/checkpoint.py |
import wandb
import json
import time
import numpy as np
from collections import namedtuple
from augmentation.methods.cyclegan.models import mnist_unet_generator, unet_generator
from augmentation.models.models import create_keras_classification_model
WandbRun = namedtuple('WandbRun', 'path id name history files cfg url... | model-patching-master | augmentation/utilities/wandb.py |
import numpy as np
def gallery(array, ncols=None):
# https://stackoverflow.com/questions/42040747/more-idiomatic-way-to-display-images-in-a-grid-with-numpy
nindex, height, width, intensity = array.shape
if ncols is None:
ncols = int(np.floor(np.sqrt(nindex)))
while nindex % ncols != 0: ncols +... | model-patching-master | augmentation/utilities/visualize.py |
import tensorflow as tf
import numpy as np
import wandb
from types import SimpleNamespace
def set_global_seeds(seed):
"""
Set all the random seeds.
"""
tf.random.set_seed(seed)
np.random.seed(seed)
def basic_setup(seed, logical_gpu_memory_limits=(4096, 10240)):
"""
Function for setting u... | model-patching-master | augmentation/utilities/utils.py |
import augmentation.datasets.utils
from augmentation.augment.utils import WandbModelPseudoLabelingPipeline, BinaryMNISTWandbModelPseudoLabelingPipeline
def configure_pseudolabeler(pseudolabel: bool, pseudolabeler_builder, pseudolabeler_builder_args):
"""Pass in a class that can build a pseudolabeler (implementing... | model-patching-master | augmentation/utilities/labelers.py |
import tensorflow as tf
import tensorflow.keras as keras
def decay_weights(model, weight_decay_rate):
"""Calculates the loss for l2 weight decay and returns it."""
# @tf.function
def _decay_weights(weights, weight_decay_rate):
reg_loss = 0.
for var in weights:
reg_loss = reg_l... | model-patching-master | augmentation/utilities/losses.py |
from typing import List
import tensorflow as tf
import tensorflow.keras as keras
from augmentation.utilities.metrics import reset_metrics, update_metrics
def evaluate_model(model: keras.Model,
generator,
metrics: List[keras.metrics.Metric],
aggregate=None,
... | model-patching-master | augmentation/utilities/eval.py |
import tensorflow as tf
import tensorflow.keras as keras
import numpy as np
class LinearDecay(keras.optimizers.schedules.LearningRateSchedule):
# https://github.com/LynnHo/CycleGAN-Tensorflow-2/blob/master/module.py
# if `step` < `step_decay`: use fixed learning rate
# else: linearly decay the learning ra... | model-patching-master | augmentation/utilities/optim.py |
import tensorflow as tf
import dataflow as D
import time
import numpy as np
import datetime
from multiprocessing import cpu_count
from augmentation.augment.utils import compose_augmentations
def benchmark(dataflow, num_epochs=2, sleep=0.):
start_time = time.perf_counter()
for epoch_num in range(num_epochs):
... | model-patching-master | augmentation/dataflows/utils.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
This is the main script used for training Classy Vision jobs.
This can be used for training on your local machine,... | cv_bias_amplification-main | my-project-release/my-project/classy_train.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from pathlib import Path
from classy_vision.generic.registry_utils import import_all_modules
FILE_ROOT = Path(__file... | cv_bias_amplification-main | my-project-release/my-project/losses/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn.functional as F
from classy_vision.losses import ClassyLoss, register_loss
@register_loss("one_hot_bi... | cv_bias_amplification-main | my-project-release/my-project/losses/one_hot_binary_ce_loss.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import contextlib
import copy
import enum
import json
import logging
import math
import multiprocessing as mp
import ti... | cv_bias_amplification-main | my-project-release/my-project/tasks/biasamp_classification_task.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import traceback
from pathlib import Path
from classy_vision.generic.registry_utils import import_all_modules
from cl... | cv_bias_amplification-main | my-project-release/my-project/tasks/__init__.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.
from torchvision.datasets import FashionMNIST
import torch.utils.data
import torch
from torchvision import datasets, transforms
import clas... | cv_bias_amplification-main | my-project-release/my-project/datasets/cifar100_random_sample.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.
from PIL import Image
import numpy as np
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import json
from classy_visio... | cv_bias_amplification-main | my-project-release/my-project/datasets/inversion_transforms.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Callable, Dict, Optional, Union
from classy_vision.dataset import ClassyDataset, register_dat... | cv_bias_amplification-main | my-project-release/my-project/datasets/cifar100.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from pathlib import Path
from classy_vision.generic.registry_utils import import_all_modules
FILE_ROOT = Path(__file... | cv_bias_amplification-main | my-project-release/my-project/datasets/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Callable, Dict, Optional, Union
from classy_vision.dataset import ClassyDataset, register_dat... | cv_bias_amplification-main | my-project-release/my-project/datasets/fashionmnist.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Callable, Dict, Optional, Union
from classy_vision.dataset import ClassyDataset, register_dat... | cv_bias_amplification-main | my-project-release/my-project/datasets/cifar10_overlay.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from pathlib import Path
from classy_vision.generic.registry_utils import import_all_modules
FILE_ROOT = Path(__file... | cv_bias_amplification-main | my-project-release/my-project/models/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Option... | cv_bias_amplification-main | my-project-release/my-project/models/custom_resnet.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.
from copy import error
import torch
import torch.utils.data
import ast
import itertools
import json
import numpy as np
import pandas as pd... | cv_bias_amplification-main | my-project-release/my-project/configs/fashionmnist/scripts/training_measurements.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.
# Run from within the /scripts folder.
import json
import numpy as np
import pandas as pd
import classy_vision.generic.util as util
import... | cv_bias_amplification-main | my-project-release/my-project/configs/fashionmnist/scripts/generate_experiment_configs.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.
from copy import error
import torch
import torch.utils.data
import ast
import itertools
import json
import numpy as np
import pandas as pd... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar100_width/scripts/training_measurements.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 numpy as np
import classy_vision.generic.util as util
import random
import pandas as pd
import os
CONFIG_PATH = os.pat... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar100_width/scripts/generate_experiment_configs.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.
from copy import error
import torch
import torch.utils.data
import ast
import itertools
import json
import numpy as np
import pandas as pd... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar100/scripts/training_measurements_checkpoints.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 numpy as np
import classy_vision.generic.util as util
import random
import pandas as pd
import os
CONFIG_PATH = os.pat... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar100/scripts/generate_experiment_configs.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.
from copy import error
import torch
import torch.utils.data
import ast
import itertools
import json
import numpy as np
import pandas as pd... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar10_overlay/scripts/training_measurements.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 numpy as np
import classy_vision.generic.util as util
import random
import pandas as pd
import os
CONFIG_PATH = os.path... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar10_overlay/scripts/generate_experiment_configs.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.
from copy import error
import torch
import torch.utils.data
import ast
import itertools
import json
import numpy as np
import pandas as pd... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar100_trainingsize/scripts/training_measurements.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 numpy as np
import classy_vision.generic.util as util
import random
import pandas as pd
import os
CONFIG_PATH = os.pat... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar100_trainingsize/scripts/generate_experiment_configs.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.
from copy import error
import torch
import torch.utils.data
import ast
import itertools
import json
import numpy as np
import pandas as pd... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar100_regularization/scripts/training_measurements.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 numpy as np
import classy_vision.generic.util as util
import random
import pandas as pd
import os
CONFIG_PATH = os.pat... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar100_regularization/scripts/generate_experiment_configs.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.
from copy import error
import torch
import torch.utils.data
import ast
import itertools
import json
import numpy as np
import pandas as pd... | cv_bias_amplification-main | my-project-release/my-project/configs/cifar100_swapped/scripts/training_measurements_checkpoints.py |
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