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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fastonn | fastonn-master/fastonn/OpNetwork.py | from .OpTier import *
import torchvision
from .utils import *
from .osl import *
import matplotlib.pyplot as plt
class OpNetwork(nn.Module):
def __init__(self,in_channels,tier_sizes,kernel_sizes,operators,sampling_factors,OPLIB,pad=-1,optimize=False):
""" Constuctor function """
super().__init__()... | 2,451 | 37.920635 | 189 | py |
fastonn | fastonn-master/fastonn/osl.py | import torch
from torch.autograd import Function
########### NODAL FUNCTIONS ################
def mul(x,w):
return x[:,:,None,:,:].mul(w[None,:,:,:,None])
def cubic(x,w,K_CUB=100):
return K_CUB*mul(x.pow(3),w)
def sine(x,w,K_SIN=100):
return torch.sin(K_SIN*mul(x,w))
def expp(x,w): return (torch.exp(... | 3,649 | 26.037037 | 103 | py |
fastonn | fastonn-master/fastonn/trainer.py | import torch
import matplotlib.pyplot as plt
import numpy as np
from tqdm.notebook import tqdm
import random, string,time
from copy import deepcopy
from .utils import *
import h5py
# Helper class to train, validate and evaluate torch models
class Trainer:
# Constructor function
def __init__(self,model,train_dl... | 11,957 | 39.673469 | 242 | py |
fastonn | fastonn-master/fastonn/OpTier.py | from .osl import *
from .utils import *
from .OpBlock import *
class OpTier(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size,operators,OPLIB,padding=-1,sampling_factor=1,layer_idx=-1,optimize=True):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_... | 2,874 | 40.666667 | 132 | py |
fastonn | fastonn-master/fastonn/utils/cgd.py | import torch
from torch.optim.optimizer import Optimizer
def normGrad(x,thr=10):
maxx = torch.max(abs(x.data))
factor = 1 if maxx<thr else thr/maxx
x.data *= factor
return x
class CGD(Optimizer):
def __init__(self, params, lr=0.001,alpha=1.05,beta=0.7):
if lr < 0.0: raise ValueError("Inv... | 1,939 | 29.793651 | 90 | py |
fastonn | fastonn-master/fastonn/utils/utils.py | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 12 17:00:21 2019
@author: HM17901
"""
import torch
from torch.utils.data import Dataset,DataLoader,Subset
from torchvision import transforms
from torchvision.utils import make_grid
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
from sc... | 14,212 | 35.073604 | 160 | py |
fastonn | fastonn-master/fastonn/utils/adam.py | import math
import torch
from torch.optim.optimizer import Optimizer
class Adam(Optimizer):
"""Implements Adam algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
paramete... | 8,352 | 37.671296 | 116 | py |
direct | direct-main/setup.py | #!/usr/bin/env python
# coding=utf-8
"""The setup script."""
import ast
import pathlib
from setuptools import Extension, find_packages, setup # type: ignore
from setuptools.command.build_ext import build_ext
class _build_ext(build_ext):
def run(self):
import numpy as np
self.include_dirs.appen... | 2,842 | 27.717172 | 119 | py |
direct | direct-main/projects/predict_val.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import logging
import os
import pathlib
import sys
import torch
from direct.cli.utils import file_or_url
from direct.common.subsample import build_masking_function
from direct.environment import Args
from direct.inference import build_inference_tran... | 5,406 | 36.289655 | 115 | py |
direct | direct-main/projects/spie2022_radial_subsampling/compute_metrics.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import argparse
import glob
import json
import logging
import os
import pathlib
import h5py
from direct.data.transforms import *
from direct.functionals.challenges import *
logger = logging.getLogger(__name__)
def _get_filenames_from_lists(path_to_lst):
names... | 3,192 | 31.252525 | 112 | py |
direct | direct-main/projects/calgary_campinas/predict_test.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import logging
import os
import pathlib
import sys
import numpy as np
import torch
import direct.launch
from direct.common.subsample import CalgaryCampinasMaskFunc
from direct.data.mri_transforms import Compose
from direct.environment import Args
fr... | 5,165 | 32.764706 | 199 | py |
direct | direct-main/tests/test_checkpointer.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import datetime
import pathlib
import tempfile
import pytest
import torch
import torch.nn as nn
from direct.checkpointer import Checkpointer
def create_checkpointables(*keys):
checkpointables = dict()
checkpointables["model"] = nn.Linear(2, 2)
if "opt... | 2,272 | 30.136986 | 115 | py |
direct | direct-main/tests/tests_nn/test_kikinet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.kikinet.kikinet import KIKINet
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[3, 3, 32, ... | 1,255 | 21.428571 | 98 | py |
direct | direct-main/tests/tests_nn/test_unet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import DefaultConfig, FunctionConfig, LossConfig, TrainingConfig, ValidationConfig
from direct.data.transforms import fft2, ifft2
from direct.nn.unet.config import Unet2dConfi... | 2,981 | 36.746835 | 110 | py |
direct | direct-main/tests/tests_nn/test_rim.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.rim.rim import RIM
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[2, 3, 11, 12],
],
... | 2,619 | 22.185841 | 109 | py |
direct | direct-main/tests/tests_nn/test_varnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import DefaultConfig, FunctionConfig, LossConfig, TrainingConfig, ValidationConfig
from direct.data.transforms import fft2, ifft2
from direct.nn.varnet.varnet import EndToEndV... | 2,603 | 36.2 | 110 | py |
direct | direct-main/tests/tests_nn/test_didn.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.nn.didn.didn import DIDN
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[3, 2, 32, 32],
[3, 2, 16, 16],
],
)
@pytest.mark.param... | 907 | 16.803922 | 88 | py |
direct | direct-main/tests/tests_nn/test_resnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.nn.resnet.resnet import ResNet
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[3, 2, 32, 32],
[3, 2, 16, 16],
[3, 2, 15, 17... | 1,001 | 17.218182 | 84 | py |
direct | direct-main/tests/tests_nn/test_conjgradnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import DefaultConfig, FunctionConfig, LossConfig, TrainingConfig, ValidationConfig
from direct.data.transforms import fft2, ifft2
from direct.nn.conjgradnet.conjgrad import CG... | 3,032 | 36.444444 | 110 | py |
direct | direct-main/tests/tests_nn/test_recurrent.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.nn.recurrent.recurrent import Conv2dGRU, NormConv2dGRU
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[3, 2, 32, 32],
[3, 2, 16, 16... | 807 | 17.790698 | 66 | py |
direct | direct-main/tests/tests_nn/test_multidomainnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.multidomainnet.multidomain import MultiDomainUnet2d
from direct.nn.multidomainnet.multidomainnet import MultiDomainNet
def create_input(shape):
data = torch.rand(shape).flo... | 1,698 | 19.719512 | 86 | py |
direct | direct-main/tests/tests_nn/test_conjgradnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import complex_multiplication, conjugate, fft2, ifft2
from direct.nn.conjgradnet.conjgrad import CGUpdateType
from direct.nn.conjgradnet.conjgradnet import ConjGradNet, ConjGradNetInitType
from direct.nn.get_nn_m... | 2,228 | 28.72 | 116 | py |
direct | direct-main/tests/tests_nn/test_lpd.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.lpd.lpd import LPDNet
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[3, 3, 32, 32],
... | 1,646 | 21.561644 | 83 | py |
direct | direct-main/tests/tests_nn/test_kikinet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import pathlib
import tempfile
import numpy as np
import pytest
import torch
from direct.config.defaults import (
DefaultConfig,
FunctionConfig,
InferenceConfig,
LossConfig,
TrainingConfig,
ValidationConfig,
)
from direct.dat... | 2,563 | 30.654321 | 110 | py |
direct | direct-main/tests/tests_nn/test_mri_models.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import pathlib
import tempfile
import numpy as np
import pytest
import torch
from direct.config.defaults import (
CheckpointerConfig,
DefaultConfig,
FunctionConfig,
InferenceConfig,
LossConfig,
TrainingConfig,
ValidationC... | 6,457 | 33.351064 | 114 | py |
direct | direct-main/tests/tests_nn/test_iterdualnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.iterdualnet.iterdualnet import IterDualNet
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize("shape", [[3, 3, 32, 32], [... | 1,516 | 30.604167 | 106 | py |
direct | direct-main/tests/tests_nn/test_multidomainnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import (
DefaultConfig,
FunctionConfig,
InferenceConfig,
LossConfig,
TrainingConfig,
ValidationConfig,
)
from direct.data.transforms import fft2, ifft2... | 2,987 | 34.571429 | 116 | py |
direct | direct-main/tests/tests_nn/test_recurrentvarnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.recurrentvarnet.recurrentvarnet import RecurrentVarNet
from direct.nn.types import InitType
def create_input(shape):
data = torch.rand(shape).float()
return data
@pyt... | 2,094 | 23.647059 | 99 | py |
direct | direct-main/tests/tests_nn/test_recurrentvarnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import DefaultConfig, FunctionConfig, LossConfig, TrainingConfig, ValidationConfig
from direct.data.transforms import fft2, ifft2
from direct.nn.recurrentvarnet.recurrentvarne... | 3,034 | 33.101124 | 110 | py |
direct | direct-main/tests/tests_nn/test_unet_2d.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import numpy as np
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.unet.unet_2d import NormUnetModel2d, Unet2d
def create_input(shape):
data = np.random.randn(*shape).copy()
data = torch.from_numpy(data).float()
... | 1,214 | 18.596774 | 72 | py |
direct | direct-main/tests/tests_nn/test_rim_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import DefaultConfig, FunctionConfig, LossConfig, TrainingConfig, ValidationConfig
from direct.data.transforms import fft2, ifft2
from direct.nn.rim.config import RIMConfig
fr... | 2,627 | 34.513514 | 118 | py |
direct | direct-main/tests/tests_nn/test_varsplitnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.get_nn_model_config import ModelName
from direct.nn.types import ActivationType, InitType
from direct.nn.varsplitnet.varsplitnet import MRIVarSplitNet
def create_input(shape):
... | 2,689 | 29.224719 | 119 | py |
direct | direct-main/tests/tests_nn/test_xpdnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.xpdnet.xpdnet import XPDNet
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[3, 3, 32, 32]... | 1,896 | 22.134146 | 104 | py |
direct | direct-main/tests/tests_nn/test_cirim.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.cirim.cirim import CIRIM
def create_input(shape):
return torch.rand(shape).float()
@pytest.mark.parametrize(
"shape",
[
[3, 3, 16, 16],
[2, 5, 16,... | 1,357 | 21.633333 | 105 | py |
direct | direct-main/tests/tests_nn/test_jointicnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.jointicnet.jointicnet import JointICNet
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[3... | 1,030 | 19.62 | 79 | py |
direct | direct-main/tests/tests_nn/test_iterdualnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import (
DefaultConfig,
FunctionConfig,
InferenceConfig,
LossConfig,
TrainingConfig,
ValidationConfig,
)
from direct.data.transforms import fft2, ifft2... | 2,515 | 33.944444 | 110 | py |
direct | direct-main/tests/tests_nn/test_varsplitnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import DefaultConfig, FunctionConfig, LossConfig, TrainingConfig, ValidationConfig
from direct.data.transforms import fft2, ifft2
from direct.nn.varsplitnet.config import MRIV... | 2,995 | 35.536585 | 113 | py |
direct | direct-main/tests/tests_nn/test_xpdnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import DefaultConfig, FunctionConfig, LossConfig, TrainingConfig, ValidationConfig
from direct.data.transforms import fft2, ifft2
from direct.nn.xpdnet.xpdnet import XPDNet
fr... | 2,992 | 37.87013 | 110 | py |
direct | direct-main/tests/tests_nn/test_varnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
from direct.data.transforms import fft2, ifft2
from direct.nn.varnet.varnet import EndToEndVarNet
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[[4, 3, 32, 32],... | 941 | 20.906977 | 102 | py |
direct | direct-main/tests/tests_nn/test_lpd_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import DefaultConfig, FunctionConfig, LossConfig, TrainingConfig, ValidationConfig
from direct.data.transforms import fft2, ifft2
from direct.nn.lpd.lpd import LPDNet
from dir... | 2,333 | 36.645161 | 116 | py |
direct | direct-main/tests/tests_nn/test_cirim_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import pathlib
import tempfile
import numpy as np
import pytest
import torch
from direct.config.defaults import DefaultConfig, FunctionConfig, LossConfig, TrainingConfig, ValidationConfig
from direct.data.transforms import fft2, ifft2
from direct.nn... | 3,682 | 33.745283 | 116 | py |
direct | direct-main/tests/tests_nn/test_mwcnn.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
import torch.nn as nn
from direct.nn.mwcnn.mwcnn import MWCNN
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[3, 2, 32, 32],
[3, 2, 20, 34],
... | 917 | 16.653846 | 87 | py |
direct | direct-main/tests/tests_nn/test_jointicnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import functools
import numpy as np
import pytest
import torch
from direct.config.defaults import (
DefaultConfig,
FunctionConfig,
InferenceConfig,
LossConfig,
TrainingConfig,
ValidationConfig,
)
from direct.data.transforms import fft2, ifft2... | 2,774 | 34.126582 | 118 | py |
direct | direct-main/tests/tests_nn/test_conv.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
import torch
import torch.nn as nn
from direct.nn.conv.conv import Conv2d
def create_input(shape):
data = torch.rand(shape).float()
return data
@pytest.mark.parametrize(
"shape",
[
[3, 2, 32, 32],
[3, 2, 16, 16],
... | 932 | 16.942308 | 84 | py |
direct | direct-main/tests/tests_utils/test_imports.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import pytest
from direct.utils.imports import _module_available
@pytest.mark.parametrize(
["module", "is_available"],
[
("torch", True),
("numpy", True),
("non-existent", False),
],
)
def test_module_available(module, is_availab... | 378 | 18.947368 | 52 | py |
direct | direct-main/tests/tests_utils/test_utils.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Tests for the direct.utils module."""
import pathlib
import tempfile
import numpy as np
import pytest
import torch
from direct.utils import is_power_of_two, normalize_image, remove_keys, set_all_seeds
from direct.utils.asserts import assert_complex
from direct.ut... | 4,030 | 30.248062 | 109 | py |
direct | direct-main/tests/tests_functionals/test_gradloss.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import numpy as np
import pytest
import torch
from skimage.color import rgb2gray
from sklearn.datasets import load_sample_image
from direct.functionals import SobelGradL1Loss, SobelGradL2Loss
# Load two images and convert them to grayscale
flower = rgb2gray(load_sam... | 1,074 | 30.617647 | 78 | py |
direct | direct-main/tests/tests_functionals/test_nmse.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import numpy as np
import pytest
import torch
from skimage.color import rgb2gray
from skimage.metrics import normalized_root_mse as nrmse
from sklearn.datasets import load_sample_image
from direct.functionals.challenges import fastmri_nmse
from direct.functionals.nms... | 1,780 | 33.25 | 81 | py |
direct | direct-main/tests/tests_functionals/test_psnr.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import numpy as np
import pytest
import torch
from skimage.color import rgb2gray
from skimage.metrics import peak_signal_noise_ratio
from sklearn.datasets import load_sample_image
from direct.functionals.challenges import calgary_campinas_psnr, fastmri_psnr
from dire... | 3,963 | 34.392857 | 118 | py |
direct | direct-main/tests/tests_functionals/test_nmae.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import numpy as np
import pytest
import torch
from skimage.color import rgb2gray
from sklearn.datasets import load_sample_image
from direct.functionals.nmae import NMAELoss
# Load two images and convert them to grayscale
flower = rgb2gray(load_sample_image("flower.j... | 1,243 | 30.1 | 89 | py |
direct | direct-main/tests/tests_functionals/test_ssim.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import numpy as np
import pytest
import torch
from skimage.color import rgb2gray
from skimage.metrics import structural_similarity
from sklearn.datasets import load_sample_image
from direct.functionals.challenges import calgary_campinas_ssim, fastmri_ssim
from direct... | 4,650 | 34.234848 | 115 | py |
direct | direct-main/tests/tests_common/test_subsample.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Tests for the direct.common.subsample module."""
# Code and comments can be shared with code of FastMRI under the same MIT license:
# https://github.com/facebookresearch/fastMRI/
# The code has been adjusted to our needs.
import numpy as np
import pytest
import t... | 10,412 | 29.626471 | 98 | py |
direct | direct-main/tests/tests_data/test_datasets.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Tests for the direct.data.datasets module."""
import pathlib
import tempfile
import h5py
import ismrmrd
import numpy as np
import pytest
from direct.data.datasets import (
CalgaryCampinasDataset,
ConcatDataset,
FakeMRIBlobsDataset,
FastMRIDataset... | 8,459 | 29 | 114 | py |
direct | direct-main/tests/tests_data/test_samplers.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Tests for direct.data.samplers module."""
import random
import pytest
from torch.utils.data import ConcatDataset
from direct.data.samplers import (
BatchVolumeSampler,
ConcatDatasetBatchSampler,
DistributedSampler,
DistributedSequentialSampler,
)... | 3,496 | 31.682243 | 88 | py |
direct | direct-main/tests/tests_data/test_lr_scheduler.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Tests for the direct.data.lr_scheduler module."""
import numpy as np
import pytest
import torch
from direct.data.lr_scheduler import LRScheduler, WarmupCosineLR, WarmupMultiStepLR
def create_model():
return torch.nn.Linear(2, 3)
def create_optimizer(model... | 1,602 | 29.245283 | 106 | py |
direct | direct-main/tests/tests_data/test_algorithms.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Tests for the direct.algorithms module."""
import pytest
import torch
from direct.algorithms.optimization import MaximumEigenvaluePowerMethod
@pytest.mark.parametrize("size", [20, 30])
def test_power_method(size):
mat = torch.rand((size, size)) + torch.rand... | 662 | 23.555556 | 71 | py |
direct | direct-main/tests/tests_data/test_mri_transforms.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Tests for the direct.data.mri_transforms module."""
import functools
import numpy as np
import pytest
import torch
from direct.data.mri_transforms import (
ApplyMask,
ApplyZeroPadding,
Compose,
ComputeImage,
ComputeScalingFactor,
ComputeZ... | 17,244 | 29.960503 | 119 | py |
direct | direct-main/tests/tests_data/test_transforms.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Tests for the direct.data.transforms module."""
import numpy as np
import pytest
import torch
from direct.data import transforms
from direct.data.transforms import tensor_to_complex_numpy
def create_input(shape):
data = np.random.randn(*shape).copy()
da... | 15,078 | 27.6673 | 113 | py |
direct | direct-main/docs/conf.py | #!/usr/bin/env python
# coding=utf-8
#
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default... | 8,379 | 28.198606 | 106 | py |
direct | direct-main/direct/inference.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import logging
import sys
from functools import partial
from typing import Callable, DefaultDict, Dict, List, Optional, Union
import torch
from omegaconf import DictConfig
from direct.data.datasets import build_dataset_from_input
from direct.data.mri_transforms impo... | 7,259 | 33.736842 | 120 | py |
direct | direct-main/direct/engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Main engine of DIRECT.
Implements all the main training, testing and validation logic.
"""
import functools
import gc
import logging
import pathlib
import signal
import sys
import warnings
from abc import ABC, abstractmethod
from collections import namedtuple
fro... | 28,872 | 39.381818 | 120 | py |
direct | direct-main/direct/environment.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import argparse
import logging
import os
import pathlib
import sys
from collections import namedtuple
from typing import Callable, Dict, Optional, Tuple, Union
import torch
from omegaconf import DictConfig, ListConfig, OmegaConf
from torch.utils import collect_env
i... | 20,838 | 30.961656 | 155 | py |
direct | direct-main/direct/types.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from __future__ import annotations
import pathlib
from enum import Enum
from typing import NewType, Union
import torch
from omegaconf.omegaconf import DictConfig
from torch import nn as nn
from torch.cuda.amp import GradScaler
DictOrDictConfig = Union[dict, DictCon... | 4,280 | 31.679389 | 110 | py |
direct | direct-main/direct/launch.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Taken from Detectron 2, licensed under Apache 2.0.
# https://github.com/facebookresearch/detectron2/blob/903d28b63c02dffc81935a38a85ab5a16450a445/detectron2/engine/launch.py
# Changes:
# - Docstr... | 7,219 | 30.946903 | 122 | py |
direct | direct-main/direct/checkpointer.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""Checkpointer module.
Handles all logic related to checkpointing.
"""
import datetime
import logging
import pathlib
import re
import urllib.parse
import warnings
from pickle import UnpicklingError
from typing import Dict, Mapping, Optional, Union, get_args
import t... | 9,576 | 37.773279 | 117 | py |
direct | direct-main/direct/predict.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import argparse
import functools
import logging
import os
import torch
from direct.common.subsample import build_masking_function
from direct.inference import build_inference_transforms, setup_inference_save_to_h5
from direct.launch import launch
from direct.utils i... | 1,673 | 25.15625 | 101 | py |
direct | direct-main/direct/train.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import argparse
import functools
import logging
import os
import pathlib
import sys
import urllib.parse
from collections import defaultdict
from typing import Callable, Dict, List, Optional, Union
import numpy as np
import torch
from omegaconf import DictConfig
from ... | 12,278 | 36.665644 | 120 | py |
direct | direct-main/direct/nn/mri_models.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
"""MRI model engine of DIRECT."""
import gc
import pathlib
import time
from collections import defaultdict
from os import PathLike
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.cu... | 31,405 | 36.657074 | 120 | py |
direct | direct-main/direct/nn/get_nn_model_config.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from torch import nn
from direct.constants import COMPLEX_SIZE
from direct.nn.conv.conv import Conv2d
from direct.nn.didn.didn import DIDN
from direct.nn.resnet.resnet import ResNet
from direct.nn.types import ActivationType, ModelName
from direct.nn.unet.unet_2d imp... | 2,468 | 38.190476 | 115 | py |
direct | direct-main/direct/nn/mwcnn/mwcnn.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from collections import OrderedDict
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
class DWT(nn.Module):
"""2D Discrete Wavelet Transform as implemented in [1]_.
References
----------
..... | 14,117 | 31.306636 | 156 | py |
direct | direct-main/direct/nn/lpd/lpd_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class LPDNetEngine(MRIModelEngine):
"""LPDNet Engine."""
def __init__(
s... | 1,312 | 27.543478 | 87 | py |
direct | direct-main/direct/nn/lpd/lpd.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable
import torch
import torch.nn as nn
import direct.data.transforms as T
from direct.nn.conv.conv import Conv2d
from direct.nn.didn.didn import DIDN
from direct.nn.mwcnn.mwcnn import MWCNN
from direct.nn.unet.unet_2d import NormUnetModel2d, ... | 11,148 | 36.921769 | 209 | py |
direct | direct-main/direct/nn/varnet/varnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable
import torch
import torch.nn as nn
from direct.data.transforms import expand_operator, reduce_operator
from direct.nn.unet import UnetModel2d
class EndToEndVarNet(nn.Module):
"""End-to-End Variational Network based on [1]_.
Ref... | 6,222 | 33.381215 | 111 | py |
direct | direct-main/direct/nn/varnet/varnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
import direct.data.transforms as T
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class EndToEndVarNetEngine(MRIModelEngine):
"""End... | 1,480 | 28.62 | 90 | py |
direct | direct-main/direct/nn/xpdnet/xpdnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class XPDNetEngine(MRIModelEngine):
"""XPDNet Engine."""
def __init__(
s... | 1,327 | 26.666667 | 82 | py |
direct | direct-main/direct/nn/xpdnet/xpdnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable, Optional
import torch.nn as nn
from direct.nn.conv.conv import Conv2d
from direct.nn.crossdomain.crossdomain import CrossDomainNetwork
from direct.nn.crossdomain.multicoil import MultiCoil
from direct.nn.didn.didn import DIDN
from direct... | 5,027 | 37.090909 | 203 | py |
direct | direct-main/direct/nn/recurrentvarnet/recurrentvarnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
import direct.data.transforms as T
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class RecurrentVarNetEngine(MRIModelEngine):
"""Re... | 1,564 | 29.096154 | 90 | py |
direct | direct-main/direct/nn/recurrentvarnet/recurrentvarnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from direct.constants import COMPLEX_SIZE
from direct.data.transforms import complex_multiplication, conjugate, expand_operator,... | 16,200 | 36.941452 | 119 | py |
direct | direct-main/direct/nn/crossdomain/crossdomain.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable, Optional, Union
import torch
import torch.nn as nn
import direct.data.transforms as T
class CrossDomainNetwork(nn.Module):
"""This performs optimisation in both, k-space ("K") and image ("I") domains according to domain_sequence.""... | 7,736 | 35.495283 | 117 | py |
direct | direct-main/direct/nn/crossdomain/multicoil.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
import torch
import torch.nn as nn
class MultiCoil(nn.Module):
"""This makes the forward pass of multi-coil data of shape (N, N_coils, H, W, C) to a model.
If coil_to_batch is set to True, coil dimension is moved to the batch dimension. Otherwise, it passes... | 2,232 | 32.328358 | 118 | py |
direct | direct-main/direct/nn/varsplitnet/varsplitnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class MRIVarSplitNetEngine(MRIModelEngine):
"""MRIVarSplitNet Engine."""
def __i... | 1,969 | 27.970588 | 87 | py |
direct | direct-main/direct/nn/varsplitnet/varsplitnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable, Optional
import torch
from torch import nn
from direct.constants import COMPLEX_SIZE
from direct.data.transforms import expand_operator, reduce_operator
from direct.nn.get_nn_model_config import ModelName, _get_model_config
from direct.n... | 8,866 | 40.050926 | 119 | py |
direct | direct-main/direct/nn/cirim/cirim.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, List, Optional, Tuple, Union
import torch
from torch import nn
from direct.data.transforms import expand_operator, reduce_operator
from direct.nn.rim.rim import MRILogLikelihood
class ConvRNNStack(nn.Module):
"""
A stack o... | 17,951 | 32.121771 | 118 | py |
direct | direct-main/direct/nn/cirim/cirim_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable, Dict, Optional
import torch
from torch import nn
from torch.cuda.amp import autocast
from direct.config import BaseConfig
from direct.engine import DoIterationOutput
from direct.nn.mri_models import MRIModelEngine
from direct.utils impor... | 4,969 | 36.651515 | 117 | py |
direct | direct-main/direct/nn/jointicnet/jointicnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class JointICNetEngine(MRIModelEngine):
"""Joint-ICNet Engine."""
def __init__(
... | 1,277 | 26.191489 | 82 | py |
direct | direct-main/direct/nn/jointicnet/jointicnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable
import torch
import torch.nn as nn
import direct.data.transforms as T
from direct.nn.unet.unet_2d import NormUnetModel2d, UnetModel2d
class JointICNet(nn.Module):
"""Joint Deep Model-Based MR Image and Coil Sensitivity Reconstructio... | 8,699 | 38.545455 | 302 | py |
direct | direct-main/direct/nn/conv/conv.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import List
import torch
import torch.nn as nn
class Conv2d(nn.Module):
"""Implementation of a simple cascade of 2D convolutions.
If `batchnorm` is set to True, batch normalization layer is applied after each convolution.
"""
def __ini... | 2,044 | 26.266667 | 110 | py |
direct | direct-main/direct/nn/mobilenet/mobilenet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Taken and adapted from: https://raw.githubusercontent.com/pytorch/vision/master/torchvision/models/mobilenet.py
from typing import Any, Callable
from torch import nn
from direct.utils import str_to_class
__all__ = ["MobileNetV2"]
def _make_divisible(v, divisor... | 7,071 | 32.67619 | 113 | py |
direct | direct-main/direct/nn/iterdualnet/iterdualnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable
import torch
from torch import nn
import direct.data.transforms as T
from direct.constants import COMPLEX_SIZE
from direct.nn.unet.unet_2d import NormUnetModel2d, UnetModel2d
class IterDualNet(nn.Module):
r"""Iterative Dual Network ... | 8,313 | 39.955665 | 117 | py |
direct | direct-main/direct/nn/iterdualnet/iterdualnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class IterDualNetEngine(MRIModelEngine):
def __init__(
self,
cfg: Bas... | 1,794 | 27.492063 | 82 | py |
direct | direct-main/direct/nn/multidomainnet/multidomain.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiDomainConv2d(nn.Module):
def __init__(
self,
forward_operator: Callable,
backward_operator: Callable,
in_channels: int,
... | 11,794 | 30.96477 | 120 | py |
direct | direct-main/direct/nn/multidomainnet/multidomainnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.cuda.amp import autocast
import direct.data.transforms as T
from direct.config import BaseConfig
from direct.engine import DoIterationOutput
from direct.nn.mri_model... | 1,570 | 28.641509 | 82 | py |
direct | direct-main/direct/nn/multidomainnet/multidomainnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable
import torch
import torch.nn as nn
import direct.data.transforms as T
from direct.nn.multidomainnet.multidomain import MultiDomainUnet2d
class StandardizationLayer(nn.Module):
r"""Multi-channel data standardization method. Inspired ... | 5,599 | 33.355828 | 238 | py |
direct | direct-main/direct/nn/unet/unet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
import direct.data.transforms as T
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class Unet2dEngine(MRIModelEngine):
"""Unet2d Mode... | 1,369 | 26.4 | 82 | py |
direct | direct-main/direct/nn/unet/unet_2d.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
# Code borrowed / edited from: https://github.com/facebookresearch/fastMRI/blob/
import math
from typing import Callable, List, Optional, Tuple
import torch
from torch import nn
from torch.nn import functional as F
from direct.data import transforms as T
class Con... | 15,254 | 32.235294 | 322 | py |
direct | direct-main/direct/nn/conjgradnet/conjgradnet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class ConjGradNetEngine(MRIModelEngine):
def __init__(
self,
cfg: Bas... | 1,792 | 28.393443 | 82 | py |
direct | direct-main/direct/nn/conjgradnet/conjgrad.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable, List
import torch
from torch import nn
from direct.data.transforms import (
complex_division,
complex_dot_product,
complex_multiplication,
expand_operator,
reduce_operator,
)
from direct.types import DirectEnum
clas... | 10,400 | 31.101852 | 118 | py |
direct | direct-main/direct/nn/conjgradnet/conjgradnet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable, Optional, Tuple
import torch
from torch import nn
from direct.data.transforms import reduce_operator
from direct.nn.conjgradnet.conjgrad import CGUpdateType, ConjGrad
from direct.nn.get_nn_model_config import ModelName, _get_model_config... | 6,325 | 37.573171 | 120 | py |
direct | direct-main/direct/nn/kikinet/kikinet_engine.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Any, Callable, Dict, Optional, Tuple
import torch
from torch import nn
from direct.config import BaseConfig
from direct.nn.mri_models import MRIModelEngine
class KIKINetEngine(MRIModelEngine):
"""KIKINet Engine."""
def __init__(
... | 1,330 | 26.729167 | 82 | py |
direct | direct-main/direct/nn/kikinet/kikinet.py | # coding=utf-8
# Copyright (c) DIRECT Contributors
from typing import Callable, Optional
import torch
import torch.nn as nn
import direct.data.transforms as T
from direct.nn.conv.conv import Conv2d
from direct.nn.crossdomain.multicoil import MultiCoil
from direct.nn.didn.didn import DIDN
from direct.nn.mwcnn.mwcnn i... | 7,362 | 38.8 | 260 | py |
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