repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
flaxformer | flaxformer-main/flaxformer/t5x/configs/moe/__init__.py | 0 | 0 | 0 | py | |
flaxformer | flaxformer-main/flaxformer/t5x/configs/calm/gin_configs_test.py | # Copyright 2023 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 4,936 | 32.134228 | 79 | py |
flaxformer | flaxformer-main/flaxformer/t5x/configs/calm/__init__.py | 0 | 0 | 0 | py | |
flaxformer | flaxformer-main/flaxformer/t5x/configs/t5/gin_configs_test.py | # Copyright 2023 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 4,841 | 31.496644 | 79 | py |
flaxformer | flaxformer-main/flaxformer/t5x/configs/t5/__init__.py | 0 | 0 | 0 | py | |
flaxformer | flaxformer-main/flaxformer/t5x/configs/longt5/gin_configs_test.py | # Copyright 2023 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | 6,865 | 34.210256 | 79 | py |
flaxformer | flaxformer-main/flaxformer/t5x/configs/longt5/__init__.py | 0 | 0 | 0 | py | |
pytket | pytket-main/examples/spambench.py | ### Script for benchmarking different SPAM correction methods.
from collections import Counter
from random import seed, random, randrange
from time import perf_counter
from pytket.circuit import Node, Bit # type: ignore
from pytket.utils.spam import SpamCorrecter
from pytket.backends.backendresult import BackendResul... | 4,180 | 24.968944 | 113 | py |
pytket | pytket-main/examples/creating_backends_exercise.py | from pytket.circuit import OpType, Qubit, Bit, UnitID
from pytket.pauli import Pauli, QubitPauliString
from binarytree import Node
from typing import List, Optional, Iterator, Dict, Set, Tuple
from copy import copy
import numpy as np
class Gate:
"""Top-level class for Gates.
Handles the shared behaviour for ... | 21,732 | 41.282101 | 115 | py |
pytket | pytket-main/examples/oxfordQIS.py | ## EXAMPLE FILE FOR THE OXFORD QIS WORKSHOP FROM 22 FEB 2020
## THIS IS WRITTEN TO WORK WITH PYTKET v0.4.1 AND WILL NOT BE UPDATED IN FUTURE
from pytket.circuit import Circuit, PauliExpBox, Pauli
from pytket.predicates import CompilationUnit
from pytket.passes import DecomposeBoxes, PauliSimp, SequencePass
from pytket... | 6,560 | 36.278409 | 462 | py |
pytket | pytket-main/examples/python/conditional_gate_example.py | # # Conditional Execution
# Whilst any quantum process can be created by performing "pure" operations delaying all measurements to the end, this is not always practical and can greatly increase the resource requirements. It is much more convenient to alternate quantum gates and measurements, especially if we can use t... | 5,107 | 41.92437 | 575 | py |
pytket | pytket-main/examples/python/spam_example.py | # # Calibration and Correction of State Preparation and Measurement (SPAM)
# Quantum Computers available in the NISQ-era are limited by significant sources of device noise which cause errors in computation. One such noise source is errors in the preparation and measurement of quantum states, more commonly know as SPAM... | 8,107 | 48.139394 | 470 | py |
pytket | pytket-main/examples/python/circuit_generation_example.py | # # Circuit generation: tket example
# This notebook will provide a brief introduction to some of the more advanced methods of circuit generation available in `pytket`, including:
# * how to address wires and registers;
# * reading in circuits from QASM and Quipper ASCII files;
# * various types of 'boxes';
# * compos... | 11,156 | 30.877143 | 427 | py |
pytket | pytket-main/examples/python/creating_backends.py | # # How to create your own `Backend` using `pytket`
# In this tutorial, we will focus on:
# - the components of the abstract `Backend` class;
# - adaptations for statevector simulation versus measurement sampling.
# To run this example, you will only need the core `pytket` package.
#
# The `pytket` framework currentl... | 34,346 | 43.147815 | 655 | py |
pytket | pytket-main/examples/python/pytket-qujax-classification.py | from jax import numpy as jnp, random, vmap, value_and_grad, jit
from pytket import Circuit
from pytket.circuit.display import render_circuit_jupyter
from pytket.extensions.qujax import tk_to_qujax
import qujax
import matplotlib.pyplot as plt
# # Define the classification task
# We'll try and learn a _donut_ binary cla... | 5,758 | 37.393333 | 328 | py |
pytket | pytket-main/examples/python/ansatz_sequence_example.py | # # Ansatz Sequencing: tket example
# When performing variational algorithms like VQE, one common approach to generating circuit ansätze is to take an operator $U$ representing excitations and use this to act on a reference state $\lvert \phi_0 \rangle$. One such ansatz is the Unitary Coupled Cluster ansatz. Each exci... | 7,424 | 64.707965 | 782 | py |
pytket | pytket-main/examples/python/backends_example.py | # # Backends: tket example
# This example shows how to use `pytket` to execute quantum circuits on both simulators and real devices, and how to interpret the results. As tket is designed to be platform-agnostic, we have unified the interfaces of different providers as much as possible into the `Backend` class for maxi... | 16,076 | 62.545455 | 594 | py |
pytket | pytket-main/examples/python/measurement_reduction_example.py | # # Advanced Expectation Values and Measurement Reduction
# This notebook is an advanced follow-up to the "expectation_value_example" notebook, focussing on reducing the number of circuits required for measurement.
#
# When calculating the expectation value $\langle \psi \vert H \vert \psi \rangle$ of some operator $H... | 6,625 | 67.309278 | 839 | py |
pytket | pytket-main/examples/python/contextual_optimization.py | # # Contextual optimisation
# This notebook will illustrate the techniques of "contextual optimisation" available in TKET.
# See the user manaul for an introduction to the concept and methods. Here we will present an example showing how we can save some gates at the beginnning and end of a circuit, making no assumpti... | 2,911 | 40.6 | 298 | py |
pytket | pytket-main/examples/python/expectation_value_example.py | # # Expectation Values
# Given a circuit generating a quantum state $\lvert \psi \rangle$, it is very common to have an operator $H$ and ask for the expectation value $\langle \psi \vert H \vert \psi \rangle$. A notable example is in quantum computational chemistry, where $\lvert \psi \rangle$ encodes the wavefunction... | 13,903 | 53.3125 | 569 | py |
pytket | pytket-main/examples/python/comparing_simulators.py | # # Comparison of the simulators available through tket
# In this tutorial, we will focus on:
# - exploring the wide array of simulators available through the extension modules for `pytket`;
# - comparing their unique features and capabilities.
# This example assumes the reader is familiar with the basics of circuit ... | 11,216 | 38.083624 | 541 | py |
pytket | pytket-main/examples/python/circuit_analysis_example.py | # # Circuit analysis: tket example
# This notebook will introduce the basic methods of analysis and visualization of circuits available in `pytket`.
#
# It makes use of the modules `pytket_qiskit` and `pytket_cirq` for visualization; these need to be installed (with `pip`) in addition to `pytket`.
#
# We'll start by g... | 3,903 | 33.548673 | 368 | py |
pytket | pytket-main/examples/python/Forest_portability_example.py | # # Code Portability and Intro to Forest
# The quantum hardware landscape is incredibly competitive and rapidly changing. Many full-stack quantum software platforms lock users into them in order to use the associated devices and simulators. This notebook demonstrates how `pytket` can free up your existing high-level c... | 5,440 | 55.677083 | 511 | py |
pytket | pytket-main/examples/python/qiskit_integration.py | # # Integrating `pytket` into Qiskit software
# In this tutorial, we will focus on:
# - Using `pytket` for compilation or providing devices/simulators within Qiskit workflows;
# - Adapting Qiskit code to use `pytket` directly.
# This example assumes some familiarity with the Qiskit algorithms library. We have chosen ... | 5,151 | 61.072289 | 552 | py |
pytket | pytket-main/examples/python/symbolics_example.py | # # Symbolic compilation: tket example
# Motivation: in compilation, particularly of hybrid classical-quantum variational algorithms in which the structure of a circuit remains constant but the parameters of some gates change, it can be useful to compile using symbolic parameters and optimise the circuit without knowl... | 3,543 | 44.435897 | 367 | py |
pytket | pytket-main/examples/python/entanglement_swapping.py | # # Iterated Entanglement Swapping using tket
# In this tutorial, we will focus on:
# - designing circuits with mid-circuit measurement and conditional gates;
# - utilising noise models in supported simulators.
# This example assumes the reader is familiar with the Qubit Teleportation and Entanglement Swapping protoc... | 15,313 | 41.303867 | 596 | py |
pytket | pytket-main/examples/python/pytket-qujax_qaoa.py | # # Symbolic circuits with `qujax` and `pytket-qujax`
# In this notebook we will show how to manipulate symbolic circuits with the `pytket-qujax` extension. In particular, we will consider a QAOA and an Ising Hamiltonian.
from pytket import Circuit
from pytket.circuit.display import render_circuit_jupyter
from jax imp... | 6,609 | 47.962963 | 478 | py |
pytket | pytket-main/examples/python/compilation_example.py | # # Compilation passes: tket example
# There are numerous ways to optimize circuits in `pytket`. In this notebook we will introduce the basics of compilation passes and how to combine and apply them.
#
# We assume familiarity with the `pytket` `Circuit` class. The objective is to transform one `Circuit` into another, ... | 9,959 | 35.483516 | 489 | py |
pytket | pytket-main/examples/python/mapping_example.py | # # Respecting Device Constraints - Mapping physical circuits in TKET
# In this tutorial we will show how the problem of mapping from logical quantum circuits to physically permitted circuits is solved automatically in TKET. The basic examples require only the installation of pytket, ```pip install pytket```.
# Ther... | 24,081 | 46.687129 | 793 | py |
pytket | pytket-main/examples/python/pytket-qujax_heisenberg_vqe.py | from pytket import Circuit
from pytket.circuit.display import render_circuit_jupyter
from jax import numpy as jnp, random, vmap, grad, value_and_grad, jit
import matplotlib.pyplot as plt
import qujax
from pytket.extensions.qujax import tk_to_qujax
# # Let's start with a tket circuit
# We place barriers to stop tket a... | 7,440 | 42.261628 | 398 | py |
pytket | pytket-main/examples/python/ucc_vqe.py | # # VQE for Unitary Coupled Cluster using tket
# In this tutorial, we will focus on:
# - building parameterised ansätze for variational algorithms;
# - compilation tools for UCC-style ansätze.
# This example assumes the reader is familiar with the Variational Quantum Eigensolver and its application to electronic stru... | 23,593 | 44.724806 | 792 | py |
pytket | pytket-main/manual/conf.py | # -*- coding: utf-8 -*-
# Configuration file for the Sphinx documentation builder.
# See https://www.sphinx-doc.org/en/master/usage/configuration.html
copyright = "2020-2023 Quantinuum"
author = "Quantinuum"
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.autosummary",
"sphinx.ext.intersphinx",
"sph... | 1,109 | 22.617021 | 78 | py |
Resemblyzer | Resemblyzer-master/demo03_projection.py | from resemblyzer import preprocess_wav, VoiceEncoder
from demo_utils import *
from itertools import groupby
from pathlib import Path
from tqdm import tqdm
import numpy as np
# DEMO 03: we'll show one way to visualize these utterance embeddings. Since they are
# 256-dimensional, it is much simpler for us to get an ov... | 1,263 | 38.5 | 99 | py |
Resemblyzer | Resemblyzer-master/demo02_diarization.py | from resemblyzer import preprocess_wav, VoiceEncoder
from demo_utils import *
from pathlib import Path
# DEMO 02: we'll show how this similarity measure can be used to perform speaker diarization
# (telling who is speaking when in a recording).
## Get reference audios
# Load the interview audio from disk
# Source f... | 2,019 | 44.909091 | 92 | py |
Resemblyzer | Resemblyzer-master/setup.py | from setuptools import setup, find_packages
with open("README.md", "r") as f:
long_description = f.read()
with open("requirements_package.txt", "r") as f:
requirements = f.read().splitlines()
setup(
name="Resemblyzer",
version="0.1.3",
packages=find_packages(),
package_data={
"resembl... | 878 | 28.3 | 64 | py |
Resemblyzer | Resemblyzer-master/demo_utils.py | from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.animation import FuncAnimation
from resemblyzer import sampling_rate
from matplotlib import cm
from time import sleep, perf_counter as timer
from umap import UMAP
from sys import stderr
import matplotlib.pyplot as plt
import numpy as np
_default_c... | 6,793 | 32.633663 | 96 | py |
Resemblyzer | Resemblyzer-master/demo05_fake_speech_detection.py | from resemblyzer import preprocess_wav, VoiceEncoder
from demo_utils import *
from pathlib import Path
from tqdm import tqdm
import numpy as np
# DEMO 05: In this demo we'll show how we can achieve a modest form of fake speech detection with
# Resemblyzer. This method assumes you have some reference audio for the ta... | 2,863 | 42.393939 | 100 | py |
Resemblyzer | Resemblyzer-master/demo04_clustering.py | from sklearn.linear_model import LogisticRegression
from resemblyzer import preprocess_wav, VoiceEncoder
from demo_utils import *
from pathlib import Path
from tqdm import tqdm
import numpy as np
# DEMO 04: building from the previous demonstration, we'll show how natural properties of the
# voice can emerge through... | 2,928 | 46.241935 | 97 | py |
Resemblyzer | Resemblyzer-master/demo01_similarity.py | from resemblyzer import preprocess_wav, VoiceEncoder
from demo_utils import *
from itertools import groupby
from pathlib import Path
from tqdm import tqdm
import matplotlib.pyplot as plt
import numpy as np
# The demos are ordered so as to make the explanations in the comments consistent. If you only
# care about run... | 4,336 | 51.253012 | 103 | py |
Resemblyzer | Resemblyzer-master/resemblyzer/audio.py | from scipy.ndimage.morphology import binary_dilation
from resemblyzer.hparams import *
from pathlib import Path
from typing import Optional, Union
import numpy as np
import webrtcvad
import librosa
import struct
int16_max = (2 ** 15) - 1
def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray], source_sr: Optio... | 4,352 | 38.93578 | 97 | py |
Resemblyzer | Resemblyzer-master/resemblyzer/hparams.py |
## Mel-filterbank
mel_window_length = 25 # In milliseconds
mel_window_step = 10 # In milliseconds
mel_n_channels = 40
## Audio
sampling_rate = 16000
# Number of spectrogram frames in a partial utterance
partials_n_frames = 160 # 1600 ms
## Voice Activation Detection
# Window size of the VAD. Must be either... | 913 | 25.882353 | 88 | py |
Resemblyzer | Resemblyzer-master/resemblyzer/voice_encoder.py | from resemblyzer.hparams import *
from resemblyzer import audio
from pathlib import Path
from typing import Union, List
from torch import nn
from time import perf_counter as timer
import numpy as np
import torch
class VoiceEncoder(nn.Module):
def __init__(self, device: Union[str, torch.device]=None, verbose=True,... | 9,191 | 50.640449 | 114 | py |
Resemblyzer | Resemblyzer-master/resemblyzer/__init__.py | name = "resemblyzer"
from resemblyzer.audio import preprocess_wav, wav_to_mel_spectrogram, trim_long_silences, \
normalize_volume
from resemblyzer.hparams import sampling_rate
from resemblyzer.voice_encoder import VoiceEncoder
| 232 | 32.285714 | 91 | py |
rebias | rebias-master/main_biased_mnist.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Entry point of Biased-MNIST experiments.
This script provides full implementations including
- Various methods (ReBias, Vanilla, Biased, LearnedMixIn, RUBi)
- Target network: Stacked convolutional networks (kernel_size=7)
- Biased network: Stacked c... | 5,724 | 40.18705 | 95 | py |
rebias | rebias-master/logger.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import logging
class LoggerBase(object):
def __init__(self, **kwargs):
self.level = kwargs.get('level', logging.DEBUG)
self.logger = self.set_logger(**kwargs)
def set_logger(self, **kwargs):
return
def log(self, msg... | 2,318 | 27.62963 | 80 | py |
rebias | rebias-master/evaluator.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import torch
import numpy as np
def n_correct(pred, labels):
_, predicted = torch.max(pred.data, 1)
n_correct = (predicted == labels).sum().item()
return n_correct
class EvaluatorBase(object):
def __init__(self, device='cuda'):
... | 11,149 | 37.184932 | 122 | py |
rebias | rebias-master/make_clusters.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import argparse
import os
import time
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms
from torchvision.utils import save_image
import numpy as np
from PIL import Image
from sklearn.cluster import MiniBatchKMeans... | 5,502 | 34.503226 | 105 | py |
rebias | rebias-master/main_imagenet.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Entry point of 9-Class ImageNet experiments.
This script provides full implementations including
- Various methods (ReBias, Vanilla, Biased, LearnedMixIn, RUBi)
- Target network: ResNet-18
- Biased network: BagNet-18
- We do not provide Stylised... | 5,975 | 36.822785 | 89 | py |
rebias | rebias-master/trainer.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Unified implementation of the de-biasing minimax optimisation by various methods including,
- ReBias (ours, outer_criterion='RbfHSIC', inner criterion='MinusRbfHSIC')
- Vanilla and Biased baselines (f_lambda_outer=0, g_lambda_inner=0)
- Learned Mixin (outer_... | 20,817 | 42.280665 | 144 | py |
rebias | rebias-master/main_action.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Entry point of Kinetics experiments.
NOTE: We will not handle the issues from action recognition experiments.
This script provides full implementations including
- Various methods (ReBias, Vanilla, Biased, LearnedMixIn, RUBi)
- Target network: ResNet3D
... | 7,427 | 38.935484 | 92 | py |
rebias | rebias-master/optims/__init__.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Opitmizers for the training.
"""
from torch.optim import Adam
from torch.optim.lr_scheduler import StepLR, CosineAnnealingLR
from adamp import AdamP
__optim__ = ['Adam', 'AdamP']
__scheduler__ = ['StepLR', 'CosineAnnealingLR']
__all__ = ['Adam', 'AdamP',... | 977 | 24.736842 | 88 | py |
rebias | rebias-master/criterions/comparison_methods.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
De-biasing comparison methods.
Cadene, Remi, et al. "RUBi: Reducing Unimodal Biases for Visual Question Answering.",
Clark, Christopher, Mark Yatskar, and Luke Zettlemoyer. "Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biase... | 3,496 | 34.683673 | 157 | py |
rebias | rebias-master/criterions/sigma_utils.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
"""
import numpy as np
import torch
def _l2_dist(X):
X = X.view(len(X), -1)
XX = X @ X.t()
X_sqnorms = torch.diag(XX)
X_L2 = -2 * XX + X_sqnorms.unsqueeze(1) + X_sqnorms.unsqueeze(0)
return X_L2.clone().detach().cpu().numpy().reshape(-1)... | 1,810 | 26.029851 | 99 | py |
rebias | rebias-master/criterions/hsic.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Python Implementation of the finite sample estimator of Hilbert-Schmidt Independence Criterion (HSIC)
We provide both biased estimator and unbiased estimators (unbiased estimator is used in the paper)
"""
import torch
import torch.nn as nn
def to_numpy(x):... | 4,434 | 33.648438 | 172 | py |
rebias | rebias-master/criterions/dist.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Distance-based objective functions.
Re-implemented for the compatibility with other losses
"""
import torch.nn as nn
import torch.nn.functional as F
class MSELoss(nn.Module):
""" A simple mean squared error (MSE) implementation.
"""
def __init_... | 858 | 25.84375 | 66 | py |
rebias | rebias-master/criterions/__init__.py | """Criterions for de-biased representations.
This module contains three different types of criterions.
- HSIC: independence-based criterion used by ReBias (ours).
- Distance: L2 and L1 losses.
- Comparison methods: RUBi and LearnedMixin for comparisons.
"""
from criterions.hsic import RbfHSIC, MinusRbfHSIC
from criter... | 772 | 32.608696 | 80 | py |
rebias | rebias-master/models/rebias_models.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
ReBias model wrapper.
"""
import torch.nn as nn
class ReBiasModels(object):
"""A container for the target network and the intentionally biased network.
"""
def __init__(self, f_net, g_nets):
self.f_net = f_net
self.g_nets = g_ne... | 1,620 | 23.560606 | 79 | py |
rebias | rebias-master/models/imagenet_models.py | """ResNet and BagNet implementations.
original codes
- https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
- https://github.com/wielandbrendel/bag-of-local-features-models/blob/master/bagnets/pytorchnet.py
"""
import torch
import torch.nn as nn
import math
from torch.utils.model_zoo import load_u... | 15,233 | 38.466321 | 159 | py |
rebias | rebias-master/models/mnist_models.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Implementation for simple statcked convolutional networks.
"""
import torch
import torch.nn as nn
class SimpleConvNet(nn.Module):
def __init__(self, num_classes=None, kernel_size=7, feature_pos='post'):
super(SimpleConvNet, self).__init__()
... | 2,049 | 32.606557 | 86 | py |
rebias | rebias-master/models/__init__.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Target architectures and intentionally biased architectures for three benchmarks
- MNIST: deep stacked convolutional networks with different kernel size, i.e., 7 (target) and 1 (biased).
- ImageNet: ResNet-18 (target) and BagNet-18 (biased).
- Kinetics: spat... | 891 | 34.68 | 105 | py |
rebias | rebias-master/models/action_models/ResNet3D.py | import torch.nn as nn
from .weight_init_helper import init_weights
from .stem_helper import VideoModelStem
from .resnet_helper import ResStage
from .head_helper import ResNetBasicHead
# Number of blocks for different stages given the model depth.
_MODEL_STAGE_DEPTH = {18.1: (2, 2, 2, 2),
18: (2, ... | 8,939 | 33.921875 | 74 | py |
rebias | rebias-master/models/action_models/nonlocal_helper.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Non-local helper"""
import torch
import torch.nn as nn
class Nonlocal(nn.Module):
"""
Builds Non-local Neural Networks as a generic family of building
blocks for capturing long-range dependencies. Non-local... | 6,320 | 37.542683 | 80 | py |
rebias | rebias-master/models/action_models/head_helper.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""ResNe(X)t Head helper."""
import torch
import torch.nn as nn
class ResNetBasicHead(nn.Module):
"""
ResNe(X)t 3D head.
This layer performs a fully-connected projection during training, when the
input siz... | 5,073 | 36.308824 | 94 | py |
rebias | rebias-master/models/action_models/stem_helper.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""ResNe(X)t 3D stem helper."""
import torch.nn as nn
class VideoModelStem(nn.Module):
"""
Video 3D stem module. Provides stem operations of Conv, BN, ReLU, MaxPool
on input data tensor for one or multiple pat... | 5,867 | 33.116279 | 82 | py |
rebias | rebias-master/models/action_models/__init__.py | """Kinetics model implementations.
Original codes: https://github.com/facebookresearch/SlowFast
"""
| 100 | 24.25 | 60 | py |
rebias | rebias-master/models/action_models/weight_init_helper.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Utility function for weight initialization"""
import torch.nn as nn
from fvcore.nn.weight_init import c2_msra_fill
def init_weights(model, fc_init_std=0.01, zero_init_final_bn=True):
"""
Performs ResNet style w... | 1,444 | 33.404762 | 76 | py |
rebias | rebias-master/models/action_models/resnet_helper.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Video models."""
import torch.nn as nn
from .nonlocal_helper import Nonlocal
def get_trans_func(name):
"""
Retrieves the transformation module by name.
"""
trans_funcs = {
"bottleneck_transform... | 17,499 | 33.448819 | 83 | py |
rebias | rebias-master/datasets/colour_mnist.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Python implementation of Biased-MNIST.
"""
import os
import numpy as np
from PIL import Image
import torch
from torch.utils import data
from torchvision import transforms
from torchvision.datasets import MNIST
class BiasedMNIST(MNIST):
"""A base clas... | 7,875 | 40.235602 | 124 | py |
rebias | rebias-master/datasets/__init__.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Datasets used for the ``unbaised'' benchmarks
- Biased-MNIST: synthetic bias with background colours.
- 9-Class ImageNet: realistic bias where the unbiased performances are
computed by the proxy texture labels (by texture clustering).
- Kinetics-10: a su... | 1,110 | 41.730769 | 149 | py |
rebias | rebias-master/datasets/kinetics.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Dataset for the action recognition benchmarks.
We use the official implemenation of SlowFast by Facebook research.
https://github.com/facebookresearch/SlowFast
"""
import torch
from datasets.kinetics_tools.loader import construct_loader
def get_kinetics_d... | 831 | 32.28 | 67 | py |
rebias | rebias-master/datasets/imagenet.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
9-Class ImageNet wrapper. Many codes are borrowed from the official torchvision dataset.
https://github.com/pytorch/vision/blob/master/torchvision/datasets/imagenet.py
The following nine classes are selected to build the subset:
dog, cat, frog, turtle, ... | 6,565 | 38.317365 | 97 | py |
rebias | rebias-master/datasets/kinetics_tools/video_container.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import av
def get_video_container(path_to_vid):
"""
Given the path to the video, return the pyav video container.
Args:
path_to_vid (str): patth to the video.
Returns:
container (container):... | 409 | 23.117647 | 71 | py |
rebias | rebias-master/datasets/kinetics_tools/transform.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import numpy as np
import torch
def random_short_side_scale_jitter(images, min_size, max_size):
"""
Perform a spatial short scale jittering on the given images.
Args:
images (tensor): images... | 3,922 | 31.155738 | 79 | py |
rebias | rebias-master/datasets/kinetics_tools/decoder.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import numpy as np
import random
import torch
def temporal_sampling(frames, start_idx, end_idx, num_samples):
"""
Given the start and end frame index, sample num_samples frames between
the start and... | 8,893 | 36.527426 | 80 | py |
rebias | rebias-master/datasets/kinetics_tools/__init__.py | """Kinetics dataset implementations.
Original codes: https://github.com/facebookresearch/SlowFast
"""
| 102 | 24.75 | 60 | py |
rebias | rebias-master/datasets/kinetics_tools/kinetics.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import json
import random
import torch
import torch.utils.data
import datasets.kinetics_tools.decoder as decoder
import datasets.kinetics_tools.video_container as container
import datasets.kinetics_tools.transform... | 17,839 | 40.488372 | 118 | py |
rebias | rebias-master/datasets/kinetics_tools/loader.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Data loader."""
import torch
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data.sampler import RandomSampler
from datasets.kinetics_tools.kinetics import Kinetics
# Supported datasets.
_D... | 2,473 | 29.925 | 71 | py |
rebias | rebias-master/datasets/kinetics_tools/meters.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Meters."""
import datetime
import numpy as np
from collections import deque
import torch
import time
import slowfast.utils.logging as logging
import slowfast.utils.metrics as metrics
from fvcore.common.timer import Tim... | 13,430 | 31.442029 | 80 | py |
rebias | rebias-master/datasets/mimetics/download.py | import argparse
import glob
import json
import os
import shutil
import subprocess
import uuid
from collections import OrderedDict
from joblib import delayed
from joblib import Parallel
import pandas as pd
def create_video_folders(dataset, output_dir, tmp_dir):
"""Creates a directory for each label name in the da... | 8,387 | 36.28 | 79 | py |
TraBS | TraBS-main/setup.py | from setuptools import setup, find_packages
with open('README.md', encoding='utf-8') as f:
long_description = f.read()
with open('requirements.txt', encoding='utf-8') as f:
install_requires = f.read()
setup(
name='TraBS',
author="Gustav Müller-Franzes",
version=1,
description="Neural Networ... | 550 | 26.55 | 65 | py |
TraBS | TraBS-main/scripts/main_pretrain.py |
from pathlib import Path
from datetime import datetime
import torch
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
import numpy as np
import torchio as tio
from breaststudies.data import BreastDataModule, BreastDataModuleLR, BreastDataModule2D... | 5,913 | 39.506849 | 179 | py |
TraBS | TraBS-main/scripts/main_compute_segmentation_quality.py | import logging
from pathlib import Path
import numpy as np
import pandas as pd
import monai.metrics as mm
import torchio as tio
from breaststudies.utils import one_hot
from breaststudies.metrics import compute_surface_distances, compute_average_surface_distance
from breaststudies.data import BreastDatasetCreator,... | 4,308 | 36.469565 | 168 | py |
TraBS | TraBS-main/scripts/main_train.py |
from pathlib import Path
from datetime import datetime
import torch
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
import numpy as np
import torchio as tio
from breaststudies.data import BreastDataModule, BreastDataModuleLR, BreastDataModule2D,... | 6,986 | 42.12963 | 191 | py |
TraBS | TraBS-main/scripts/main_predict.py | from pathlib import Path
from datetime import datetime
from shutil import copyfile
import logging
import numpy as np
import torch
import torch.nn.functional as F
import SimpleITK as sitk
import torchio as tio
from breaststudies.data import BreastDatasetCreator
from breaststudies.models import UNet, nnUNet, SwinUN... | 6,468 | 41.559211 | 146 | py |
TraBS | TraBS-main/scripts/main_predict_kfold.py | from pathlib import Path
from shutil import copyfile
import logging
import sys
import numpy as np
import torch
import torchio as tio
import SimpleITK as sitk
from monai.metrics import compute_meandice
from breaststudies.augmentation.augmentations import Resample2, ZNormalization, ToOrientation, RandomDisableChan... | 8,639 | 44.235602 | 213 | py |
TraBS | TraBS-main/breaststudies/postprocessing/__init__.py | from .remove_fragments import close_holes, keep_connected, keep_inside | 70 | 70 | 70 | py |
TraBS | TraBS-main/breaststudies/postprocessing/remove_fragments.py |
import scipy.ndimage as ndimage
import skimage.measure as measure
import numpy as np
def _keep_connected_binary(binary_mask, voxel_vol, min_volume=None, keep_only_largest=1):
if keep_only_largest==0:
return np.zeros(binary_mask.shape, dtype=binary_mask.dtype)
mask_ind, num_features = measure.label(... | 2,684 | 40.953125 | 161 | py |
TraBS | TraBS-main/breaststudies/models/swin_unetr.py |
from breaststudies.models import BasicModel
import breaststudies.models.monai_mods as nets
class SwinUNETR(BasicModel):
def __init__(self,
in_ch,
out_ch,
roi_size,
spatial_dims = 3,
patch_sizes = ( (1,2,2), (1,2,2), 2, 2),
... | 1,286 | 25.265306 | 69 | py |
TraBS | TraBS-main/breaststudies/models/basic_unet.py |
from breaststudies.models import BasicModel
import monai.networks.nets as nets
class UNet(BasicModel):
def __init__(
self,
in_ch,
out_ch,
roi_size,
spatial_dims=3,
**kwargs
):
super().__init__(in_ch, out_ch, roi_size, **kwargs)
self.model =... | 550 | 19.407407 | 103 | py |
TraBS | TraBS-main/breaststudies/models/basic_model.py |
from pathlib import Path
import json
import torch
import torch.nn.functional as F
import pytorch_lightning as pl
from torchvision.utils import save_image
from pytorch_lightning.utilities.cloud_io import load as pl_load
from pytorch_lightning.utilities.migration import pl_legacy_patch
from pytorch_msssim import ssim
f... | 9,994 | 43.820628 | 169 | py |
TraBS | TraBS-main/breaststudies/models/nn_unet.py | from breaststudies.models import BasicModel
import monai.networks.nets as nets
class nnUNet(BasicModel):
def __init__(
self,
in_ch,
out_ch,
roi_size,
spatial_dims=3,
kernel_size=[[1,3,3], [1,3,3], 3, 3,3],
strides= [ 1, [1,2,2], [1,2,2],2,2]... | 1,961 | 34.672727 | 177 | py |
TraBS | TraBS-main/breaststudies/models/__init__.py | from .basic_model import BasicModel
from .basic_unet import UNet
from .nn_unet import nnUNet
from .swin_unetr import SwinUNETR | 126 | 30.75 | 35 | py |
TraBS | TraBS-main/breaststudies/models/monai_mods/swin_unetr.py | # Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... | 38,039 | 39.947255 | 473 | py |
TraBS | TraBS-main/breaststudies/models/monai_mods/__init__.py | from .swin_unetr import SwinUNETR, SwinTransformer | 50 | 50 | 50 | py |
TraBS | TraBS-main/breaststudies/models/monai_mods/blocks.py | from typing import Sequence, Type, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import LayerNorm
from monai.networks.layers import Conv, trunc_normal_
from monai.utils import ensure_tuple_rep, optional_import
from monai.utils.module import look_up_option
R... | 3,877 | 36.650485 | 111 | py |
TraBS | TraBS-main/breaststudies/metrics/deepmind_lookuptable.py | #####################
# https://github.com/deepmind/surface-distance
#######################
# Copyright 2018 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License a... | 22,852 | 55.42716 | 101 | py |
TraBS | TraBS-main/breaststudies/metrics/deepmind_distances.py |
#####################
# https://github.com/deepmind/surface-distance
#######################
# Copyright 2018 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License a... | 18,504 | 40.213808 | 111 | py |
TraBS | TraBS-main/breaststudies/metrics/__init__.py | from .deepmind_distances import compute_average_surface_distance, compute_surface_distances | 91 | 91 | 91 | py |
TraBS | TraBS-main/breaststudies/augmentation/augmentations.py | from typing import Iterable, Tuple, Union, List, Optional, Sequence, Dict
from numbers import Number
from pathlib import Path
import warnings
from tqdm import tqdm
import numpy as np
import nibabel as nib
import torch
import torchio as tio
from torchio import Subject, RandomAffine, IntensityTransform, CropOrPad, Re... | 31,025 | 40.983762 | 167 | py |
TraBS | TraBS-main/breaststudies/augmentation/helper_functions.py |
import numpy as np
from skimage.transform import resize
from skimage.transform import resize
from scipy.ndimage.interpolation import map_coordinates
from collections import OrderedDict
RESAMPLING_SEPARATE_Z_ANISO_THRESHOLD = 3
def uniform(low, high, size=None):
"""
wrapper for np.random.uniform to allo... | 14,633 | 39.65 | 155 | py |
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