def test_use_case(self):
with tempfile.TemporaryDirectory() as tempdir:
img_ = nib.Nifti1Image(np.random.randint(0, 2, size=(20, 20, 20)),
np.eye(4))
seg_ = nib.Nifti1Image(np.random.randint(0, 2, size=(20, 20, 20)),
np.eye(4))
img_name, seg_name = os.path.join(tempdir,
"img.nii.gz"), os.path.join(
tempdir, "seg.nii.gz")
nib.save(img_, img_name)
nib.save(seg_, seg_name)
img_list, seg_list = [img_name], [seg_name]
img_xform = _TestCompose([
EnsureChannelFirst(),
Spacing(pixdim=(1.5, 1.5, 3.0)),
RandAdjustContrast()
])
seg_xform = _TestCompose([
EnsureChannelFirst(),
Spacing(pixdim=(1.5, 1.5, 3.0), mode="nearest")
])
img_dataset = ImageDataset(
image_files=img_list,
seg_files=seg_list,
transform=img_xform,
seg_transform=seg_xform,
image_only=False,
transform_with_metadata=True,
)
self.assertTupleEqual(img_dataset[0][0].shape, (1, 14, 14, 7))
self.assertTupleEqual(img_dataset[0][1].shape, (1, 14, 14, 7))
def test_correct_results(self, gamma):
adjuster = RandAdjustContrast(prob=1.0, gamma=gamma)
result = adjuster(self.imt)
epsilon = 1e-7
img_min = self.imt.min()
img_range = self.imt.max() - img_min
expected = np.power(((self.imt - img_min) / float(img_range + epsilon)), adjuster.gamma_value) * \
img_range + img_min
np.testing.assert_allclose(expected, result, rtol=1e-05)
def test_correct_results(self, gamma):
adjuster = RandAdjustContrast(prob=1.0, gamma=gamma)
for p in TEST_NDARRAYS:
result = adjuster(p(self.imt))
epsilon = 1e-7
img_min = self.imt.min()
img_range = self.imt.max() - img_min
expected = (np.power(
((self.imt - img_min) / float(img_range + epsilon)),
adjuster.gamma_value) * img_range + img_min)
assert_allclose(expected, result, rtol=1e-05, type_test=False)
def test_tranform_randomized(self, input):
# Compose deterministic and randomized transforms
transforms = Compose([
Range("flip")(Flip()),
Rotate90(),
Range()(RandAdjustContrast(prob=0.0)),
Range("random flip")(RandFlip(prob=1.0)),
ToTensor(),
])
# Apply transforms
output = transforms(input)
# Decorate with NVTX Range
transforms1 = Range()(transforms)
transforms2 = Range("Transforms2")(transforms)
transforms3 = Range(name="Transforms3", methods="__call__")(transforms)
# Apply transforms with Range
output1 = transforms1(input)
output2 = transforms2(input)
output3 = transforms3(input)
# Check if the outputs are equal
self.assertIsInstance(output, torch.Tensor)
self.assertIsInstance(output1, torch.Tensor)
self.assertIsInstance(output2, torch.Tensor)
self.assertIsInstance(output3, torch.Tensor)
np.testing.assert_equal(output.numpy(), output1.numpy())
np.testing.assert_equal(output.numpy(), output2.numpy())
np.testing.assert_equal(output.numpy(), output3.numpy())
# Check if the first randomized is RandAdjustContrast
for tran in transforms.transforms:
if isinstance(tran, Randomizable):
self.assertIsInstance(tran, RandAdjustContrast)
break
RandGaussianNoise(prob=1.0)
]),
Compose([
LoadNifti(image_only=True),
AddChannel(),
RandGaussianNoise(prob=1.0)
]),
(0, 1),
(1, 128, 128, 128),
]
TEST_CASE_2 = [
Compose([
LoadNifti(image_only=True),
AddChannel(),
RandAdjustContrast(prob=1.0)
]),
Compose([
LoadNifti(image_only=True),
AddChannel(),
RandAdjustContrast(prob=1.0)
]),
(0, 1),
(1, 128, 128, 128),
]
class TestCompose(Compose):
def __call__(self, input_):
img, metadata = self.transforms[0](input_)
img = self.transforms[1](img)
import numpy as np
from parameterized import parameterized
from torch.utils.data import DataLoader
from monai.data import ArrayDataset
from monai.transforms import AddChannel, Compose, LoadNifti, RandAdjustContrast, RandGaussianNoise, Spacing
TEST_CASE_1 = [
Compose([LoadNifti(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]),
Compose([LoadNifti(image_only=True), AddChannel(), RandGaussianNoise(prob=1.0)]),
(0, 1),
(1, 128, 128, 128),
]
TEST_CASE_2 = [
Compose([LoadNifti(image_only=True), AddChannel(), RandAdjustContrast(prob=1.0)]),
Compose([LoadNifti(image_only=True), AddChannel(), RandAdjustContrast(prob=1.0)]),
(0, 1),
(1, 128, 128, 128),
]
class TestCompose(Compose):
def __call__(self, input_):
img, metadata = self.transforms[0](input_)
img = self.transforms[1](img)
img, _, _ = self.transforms[2](img, metadata["affine"])
return self.transforms[3](img), metadata
TEST_CASE_3 = [
TEST_CASE_ARRAY_1 = [np.random.randn(3, 10, 10)]
TEST_CASE_DICT_0 = [{"image": np.random.randn(3, 3)}]
TEST_CASE_DICT_1 = [{"image": np.random.randn(3, 10, 10)}]
TEST_CASE_TORCH_0 = [torch.randn(3, 3)]
TEST_CASE_TORCH_1 = [torch.randn(3, 10, 10)]
TEST_CASE_WRAPPER = [np.random.randn(3, 10, 10)]
TEST_CASE_RECURSIVE_0 = [
torch.randn(3, 3),
Compose([
ToNumpy(),
Flip(),
RandAdjustContrast(prob=0.0),
RandFlip(prob=1.0),
ToTensor()
]),
]
TEST_CASE_RECURSIVE_1 = [
torch.randn(3, 3),
Compose([
ToNumpy(),
Flip(),
Compose([RandAdjustContrast(prob=0.0),
RandFlip(prob=1.0)]),
ToTensor()
]),
]
TEST_CASE_RECURSIVE_2 = [
def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
# suppress printing if not master
if args.multiprocessing_distributed and args.gpu != 0:
def print_pass(*args):
pass
builtins.print = print_pass
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
if args.rank == 0:
configure(os.path.join('./exp', args.exp_name))
# create model
model_patch = moco.builder_v3.MoCo(Encoder, args.num_patch, args.moco_dim,
args.moco_k_patch, args.moco_m,
args.moco_t, args.mlp)
model_graph = moco.builder_graph.MoCo(GraphNet, args.gpu, args.moco_dim,
args.moco_k_graph, args.moco_m,
args.moco_t, args.mlp)
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model_patch.cuda(args.gpu)
model_graph.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size_patch = int(args.batch_size_patch / ngpus_per_node)
args.batch_size_graph = int(args.batch_size_graph / ngpus_per_node)
args.workers_patch = int(
(args.workers_patch + ngpus_per_node - 1) / ngpus_per_node)
args.workers_graph = int(
(args.workers_graph + ngpus_per_node - 1) / ngpus_per_node)
model_patch = torch.nn.parallel.DistributedDataParallel(
model_patch, device_ids=[args.gpu])
model_graph = torch.nn.parallel.DistributedDataParallel(
model_graph,
device_ids=[args.gpu],
find_unused_parameters=True)
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
# comment out the following line for debugging
raise NotImplementedError("Only DistributedDataParallel is supported.")
else:
# AllGather implementation (batch shuffle, queue update, etc.) in
# this code only supports DistributedDataParallel.
raise NotImplementedError("Only DistributedDataParallel is supported.")
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
optimizer_patch = torch.optim.SGD(model_patch.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer_graph = torch.optim.SGD(model_graph.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# save the initial model
if not args.resume:
save_checkpoint(
{
'epoch': 0,
'arch': args.arch,
'state_dict': model_patch.state_dict(),
'optimizer': optimizer_patch.state_dict(),
},
is_best=False,
filename=os.path.join(os.path.join('./exp', args.exp_name),
'checkpoint_patch_init.pth.tar'))
save_checkpoint(
{
'epoch': 0,
'arch': args.arch,
'state_dict': model_graph.state_dict(),
'optimizer': optimizer_graph.state_dict(),
},
is_best=False,
filename=os.path.join(os.path.join('./exp', args.exp_name),
'checkpoint_graph_init.pth.tar'))
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint_patch = torch.load(args.resume)
checkpoint_graph = torch.load(args.resume_graph)
else:
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint_patch = torch.load(args.resume, map_location=loc)
checkpoint_graph = torch.load(args.resume_graph,
map_location=loc)
args.start_epoch = checkpoint_patch['epoch']
model_patch.load_state_dict(checkpoint_patch['state_dict'])
model_graph.load_state_dict(checkpoint_graph['state_dict'])
optimizer_patch.load_state_dict(checkpoint_patch['optimizer'])
optimizer_graph.load_state_dict(checkpoint_graph['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint_patch['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
exit()
transform_re = Rand3DElastic(
mode='bilinear',
prob=1.0,
sigma_range=(8, 12),
magnitude_range=(0, 1024 + 240), #[-1024, 240] -> [0, 1024+240]
spatial_size=(32, 32, 32),
translate_range=(12, 12, 12),
rotate_range=(np.pi / 18, np.pi / 18, np.pi / 18),
scale_range=(0.1, 0.1, 0.1),
padding_mode='border',
device=torch.device('cuda:' + str(args.gpu)))
transform_rgn = RandGaussianNoise(prob=0.25, mean=0.0, std=50)
transform_rac = RandAdjustContrast(prob=0.25)
train_tratransforms = Compose([transform_rac, transform_rgn, transform_re])
train_dataset_patch = COPD_dataset_patch(
"train", args, moco.loader.TwoCropsTransform(train_tratransforms))
train_dataset_graph = COPD_dataset_graph(
"train", args, moco.loader.TwoCropsTransform(train_tratransforms))
if args.distributed:
train_sampler_patch = torch.utils.data.distributed.DistributedSampler(
train_dataset_patch)
train_sampler_graph = torch.utils.data.distributed.DistributedSampler(
train_dataset_graph)
else:
train_sampler = None
train_loader_patch = torch.utils.data.DataLoader(
train_dataset_patch,
batch_size=args.batch_size_patch,
shuffle=(train_sampler_patch is None),
num_workers=args.workers_patch,
pin_memory=True,
sampler=train_sampler_patch,
drop_last=True)
train_loader_graph = torch.utils.data.DataLoader(
train_dataset_graph,
batch_size=args.batch_size_graph,
shuffle=(train_sampler_graph is None),
num_workers=args.workers_graph,
pin_memory=True,
sampler=train_sampler_graph,
drop_last=True)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler_patch.set_epoch(epoch)
adjust_learning_rate(optimizer_patch, epoch, args)
# train for one epoch
train_patch(train_loader_patch, model_patch, criterion,
optimizer_patch, epoch, args)
# save model for every epoch
if not args.multiprocessing_distributed or (
args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0):
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model_patch.state_dict(),
'optimizer': optimizer_patch.state_dict(),
},
is_best=False,
filename=os.path.join(
os.path.join('./exp', args.exp_name),
'checkpoint_patch_{:04d}.pth.tar'.format(epoch)))
for sub_epoch in range(args.num_sub_epoch):
if args.distributed:
train_sampler_graph.set_epoch(args.num_sub_epoch * epoch +
sub_epoch)
adjust_learning_rate(optimizer_graph,
args.num_sub_epoch * epoch + sub_epoch, args)
train_graph(train_loader_graph, model_graph, model_patch,
criterion, optimizer_graph,
args.num_sub_epoch * epoch + sub_epoch, args)
if not args.multiprocessing_distributed or (
args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0):
save_checkpoint(
{
'epoch': args.num_sub_epoch * epoch + sub_epoch + 1,
'arch': args.arch,
'state_dict': model_graph.state_dict(),
'optimizer': optimizer_graph.state_dict(),
},
is_best=False,
filename=os.path.join(
os.path.join('./exp', args.exp_name),
'checkpoint_graph_{:04d}.pth.tar'.format(
args.num_sub_epoch * epoch + sub_epoch)))
# limitations under the License.
import unittest
import os
import shutil
import numpy as np
import tempfile
import nibabel as nib
from parameterized import parameterized
from monai.data import ArrayDataset
from monai.transforms import Compose, LoadNifti, AddChannel, RandAdjustContrast, Spacing
TEST_CASE_1 = [
Compose([LoadNifti(image_only=True),
AddChannel(),
RandAdjustContrast()]),
Compose([LoadNifti(image_only=True),
AddChannel(),
RandAdjustContrast()]),
(0, 1),
(1, 128, 128, 128),
]
class TestCompose(Compose):
def __call__(self, input_):
img, metadata = self.transforms[0](input_)
img = self.transforms[1](img)
img, _, _ = self.transforms[2](img, metadata["affine"])
return self.transforms[3](img), metadata
