def test_nvtx_transfroms_dict(self, input):
# with prob == 0.0
transforms = Compose([
RandMarkD("Mark: Transforms (p=0) Start!"),
RandRangePushD("Range: RandFlipD"),
RandFlipD(keys="image", prob=0.0),
RandRangePopD(),
RangePushD("Range: ToTensorD"),
ToTensorD(keys=("image")),
RangePopD(),
MarkD("Mark: Transforms (p=0) End!"),
])
output = transforms(input)
self.assertIsInstance(output["image"], torch.Tensor)
np.testing.assert_array_equal(input["image"], output["image"])
# with prob == 1.0
transforms = Compose([
RandMarkD("Mark: Transforms (p=1) Start!"),
RandRangePushD("Range: RandFlipD"),
RandFlipD(keys="image", prob=1.0),
RandRangePopD(),
RangePushD("Range: ToTensorD"),
ToTensorD(keys=("image")),
RangePopD(),
MarkD("Mark: Transforms (p=1) End!"),
])
output = transforms(input)
self.assertIsInstance(output["image"], torch.Tensor)
np.testing.assert_array_equal(input["image"],
Flip()(output["image"].numpy()))
def test_tranform_dict(self, input):
transforms = Compose([
Range("random flip dict")(FlipD(keys="image")),
Range()(ToTensorD("image"))
])
# Apply transforms
output = transforms(input)["image"]
# 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)["image"]
output2 = transforms2(input)["image"]
output3 = transforms3(input)["image"]
# Check the outputs
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())
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
set_determinism(12345)
device = torch.device("cuda:0")
# load real data
mednist_url = "https://www.dropbox.com/s/5wwskxctvcxiuea/MedNIST.tar.gz?dl=1"
md5_value = "0bc7306e7427e00ad1c5526a6677552d"
extract_dir = "data"
tar_save_path = os.path.join(extract_dir, "MedNIST.tar.gz")
download_and_extract(mednist_url, tar_save_path, extract_dir, md5_value)
hand_dir = os.path.join(extract_dir, "MedNIST", "Hand")
real_data = [{
"hand": os.path.join(hand_dir, filename)
} for filename in os.listdir(hand_dir)]
# define real data transforms
train_transforms = Compose([
LoadPNGD(keys=["hand"]),
AddChannelD(keys=["hand"]),
ScaleIntensityD(keys=["hand"]),
RandRotateD(keys=["hand"], range_x=15, prob=0.5, keep_size=True),
RandFlipD(keys=["hand"], spatial_axis=0, prob=0.5),
RandZoomD(keys=["hand"], min_zoom=0.9, max_zoom=1.1, prob=0.5),
ToTensorD(keys=["hand"]),
])
# create dataset and dataloader
real_dataset = CacheDataset(real_data, train_transforms)
batch_size = 300
real_dataloader = DataLoader(real_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=10)
# define function to process batchdata for input into discriminator
def prepare_batch(batchdata):
"""
Process Dataloader batchdata dict object and return image tensors for D Inferer
"""
return batchdata["hand"]
# define networks
disc_net = Discriminator(in_shape=(1, 64, 64),
channels=(8, 16, 32, 64, 1),
strides=(2, 2, 2, 2, 1),
num_res_units=1,
kernel_size=5).to(device)
latent_size = 64
gen_net = Generator(latent_shape=latent_size,
start_shape=(latent_size, 8, 8),
channels=[32, 16, 8, 1],
strides=[2, 2, 2, 1])
# initialize both networks
disc_net.apply(normal_init)
gen_net.apply(normal_init)
# input images are scaled to [0,1] so enforce the same of generated outputs
gen_net.conv.add_module("activation", torch.nn.Sigmoid())
gen_net = gen_net.to(device)
# create optimizers and loss functions
learning_rate = 2e-4
betas = (0.5, 0.999)
disc_opt = torch.optim.Adam(disc_net.parameters(),
learning_rate,
betas=betas)
gen_opt = torch.optim.Adam(gen_net.parameters(),
learning_rate,
betas=betas)
disc_loss_criterion = torch.nn.BCELoss()
gen_loss_criterion = torch.nn.BCELoss()
real_label = 1
fake_label = 0
def discriminator_loss(gen_images, real_images):
"""
The discriminator loss is calculated by comparing D
prediction for real and generated images.
"""
real = real_images.new_full((real_images.shape[0], 1), real_label)
gen = gen_images.new_full((gen_images.shape[0], 1), fake_label)
realloss = disc_loss_criterion(disc_net(real_images), real)
genloss = disc_loss_criterion(disc_net(gen_images.detach()), gen)
return (genloss + realloss) / 2
def generator_loss(gen_images):
"""
The generator loss is calculated by determining how realistic
the discriminator classifies the generated images.
"""
output = disc_net(gen_images)
cats = output.new_full(output.shape, real_label)
return gen_loss_criterion(output, cats)
# initialize current run dir
run_dir = "model_out"
print("Saving model output to: %s " % run_dir)
# create workflow handlers
handlers = [
StatsHandler(
name="batch_training_loss",
output_transform=lambda x: {
Keys.GLOSS: x[Keys.GLOSS],
Keys.DLOSS: x[Keys.DLOSS]
},
),
CheckpointSaver(
save_dir=run_dir,
save_dict={
"g_net": gen_net,
"d_net": disc_net
},
save_interval=10,
save_final=True,
epoch_level=True,
),
]
# define key metric
key_train_metric = None
# create adversarial trainer
disc_train_steps = 5
num_epochs = 50
trainer = GanTrainer(
device,
num_epochs,
real_dataloader,
gen_net,
gen_opt,
generator_loss,
disc_net,
disc_opt,
discriminator_loss,
d_prepare_batch=prepare_batch,
d_train_steps=disc_train_steps,
latent_shape=latent_size,
key_train_metric=key_train_metric,
train_handlers=handlers,
)
# run GAN training
trainer.run()
# Training completed, save a few random generated images.
print("Saving trained generator sample output.")
test_img_count = 10
test_latents = make_latent(test_img_count, latent_size).to(device)
fakes = gen_net(test_latents)
for i, image in enumerate(fakes):
filename = "gen-fake-final-%d.png" % (i)
save_path = os.path.join(run_dir, filename)
img_array = image[0].cpu().data.numpy()
png_writer.write_png(img_array, save_path, scale=255)
train_datadict = [{"im": fname} for fname in all_filenames[:num_train]]
test_datadict = [{"im": fname} for fname in all_filenames[-num_test:]]
# test_datadict = [{"im": fname} for fname in all_testNames[:len( all_testNames ) ] ]
print(f"total number of images: {num_ims}")
print(f"number of images for training: {len(train_datadict)}")
print(f"number of images for testing: {len(test_datadict)}")
batch_size = 16
num_workers = 0
transforms = Compose([
LoadImageD(keys=["im"]),
AddChannelD(keys=["im"]),
ScaleIntensityD(keys=["im"]),
ToTensorD(keys=["im"]),
])
train_ds = CacheDataset(train_datadict, transforms, num_workers=num_workers)
train_loader = DataLoader(train_ds,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
test_ds = CacheDataset(test_datadict, transforms, num_workers=num_workers)
test_loader = DataLoader(test_ds,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
# For our loss we'll want to use a combination of a reconstruction loss (here, BCE) and KLD. By increasing the importance of the KLD loss with beta, we encourage the network to disentangle the latent generative factors.
def main(cfg):
# -------------------------------------------------------------------------
# Configs
# -------------------------------------------------------------------------
# Create log/model dir
log_dir = create_log_dir(cfg)
# Set the logger
logging.basicConfig(
format="%(asctime)s %(levelname)2s %(message)s",
level=logging.INFO,
datefmt="%Y-%m-%d %H:%M:%S",
)
log_name = os.path.join(log_dir, "logs.txt")
logger = logging.getLogger()
fh = logging.FileHandler(log_name)
fh.setLevel(logging.INFO)
logger.addHandler(fh)
# Set TensorBoard summary writer
writer = SummaryWriter(log_dir)
# Save configs
logging.info(json.dumps(cfg))
with open(os.path.join(log_dir, "config.json"), "w") as fp:
json.dump(cfg, fp, indent=4)
# Set device cuda/cpu
device = set_device(cfg)
# Set cudnn benchmark/deterministic
if cfg["benchmark"]:
torch.backends.cudnn.benchmark = True
else:
set_determinism(seed=0)
# -------------------------------------------------------------------------
# Transforms and Datasets
# -------------------------------------------------------------------------
# Pre-processing
preprocess_cpu_train = None
preprocess_gpu_train = None
preprocess_cpu_valid = None
preprocess_gpu_valid = None
if cfg["backend"] == "cucim":
preprocess_cpu_train = Compose([ToTensorD(keys="label")])
preprocess_gpu_train = Compose([
Range()(ToCupy()),
Range("ColorJitter")(RandCuCIM(name="color_jitter",
brightness=64.0 / 255.0,
contrast=0.75,
saturation=0.25,
hue=0.04)),
Range("RandomFlip")(RandCuCIM(name="image_flip",
apply_prob=cfg["prob"],
spatial_axis=-1)),
Range("RandomRotate90")(RandCuCIM(name="rand_image_rotate_90",
prob=cfg["prob"],
max_k=3,
spatial_axis=(-2, -1))),
Range()(CastToType(dtype=np.float32)),
Range("RandomZoom")(RandCuCIM(name="rand_zoom",
min_zoom=0.9,
max_zoom=1.1)),
Range("ScaleIntensity")(CuCIM(name="scale_intensity_range",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0)),
Range()(ToTensor(device=device)),
])
preprocess_cpu_valid = Compose([ToTensorD(keys="label")])
preprocess_gpu_valid = Compose([
Range("ValidToCupyAndCast")(ToCupy(dtype=np.float32)),
Range("ValidScaleIntensity")(CuCIM(name="scale_intensity_range",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0)),
Range("ValidToTensor")(ToTensor(device=device)),
])
elif cfg["backend"] == "numpy":
preprocess_cpu_train = Compose([
Range()(ToTensorD(keys=("image", "label"))),
Range("ColorJitter")(TorchVisionD(
keys="image",
name="ColorJitter",
brightness=64.0 / 255.0,
contrast=0.75,
saturation=0.25,
hue=0.04,
)),
Range()(ToNumpyD(keys="image")),
Range("RandomFlip")(RandFlipD(keys="image",
prob=cfg["prob"],
spatial_axis=-1)),
Range("RandomRotate90")(RandRotate90D(keys="image",
prob=cfg["prob"])),
Range()(CastToTypeD(keys="image", dtype=np.float32)),
Range("RandomZoom")(RandZoomD(keys="image",
prob=cfg["prob"],
min_zoom=0.9,
max_zoom=1.1)),
Range("ScaleIntensity")(ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0)),
Range()(ToTensorD(keys="image")),
])
preprocess_cpu_valid = Compose([
Range("ValidCastType")(CastToTypeD(keys="image",
dtype=np.float32)),
Range("ValidScaleIntensity")(ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0)),
Range("ValidToTensor")(ToTensorD(keys=("image", "label"))),
])
else:
raise ValueError(
f"Backend should be either numpy or cucim! ['{cfg['backend']}' is provided.]"
)
# Post-processing
postprocess = Compose([
Activations(sigmoid=True),
AsDiscrete(threshold=0.5),
])
# Create MONAI dataset
train_json_info_list = load_decathlon_datalist(
data_list_file_path=cfg["dataset_json"],
data_list_key="training",
base_dir=cfg["data_root"],
)
valid_json_info_list = load_decathlon_datalist(
data_list_file_path=cfg["dataset_json"],
data_list_key="validation",
base_dir=cfg["data_root"],
)
train_dataset = PatchWSIDataset(
data=train_json_info_list,
region_size=cfg["region_size"],
grid_shape=cfg["grid_shape"],
patch_size=cfg["patch_size"],
transform=preprocess_cpu_train,
image_reader_name="openslide" if cfg["use_openslide"] else "cuCIM",
)
valid_dataset = PatchWSIDataset(
data=valid_json_info_list,
region_size=cfg["region_size"],
grid_shape=cfg["grid_shape"],
patch_size=cfg["patch_size"],
transform=preprocess_cpu_valid,
image_reader_name="openslide" if cfg["use_openslide"] else "cuCIM",
)
# DataLoaders
train_dataloader = DataLoader(train_dataset,
num_workers=cfg["num_workers"],
batch_size=cfg["batch_size"],
pin_memory=cfg["pin"])
valid_dataloader = DataLoader(valid_dataset,
num_workers=cfg["num_workers"],
batch_size=cfg["batch_size"],
pin_memory=cfg["pin"])
# Get sample batch and some info
first_sample = first(train_dataloader)
if first_sample is None:
raise ValueError("First sample is None!")
for d in ["image", "label"]:
logging.info(f"[{d}] \n"
f" {d} shape: {first_sample[d].shape}\n"
f" {d} type: {type(first_sample[d])}\n"
f" {d} dtype: {first_sample[d].dtype}")
logging.info(f"Batch size: {cfg['batch_size']}")
logging.info(f"[Training] number of batches: {len(train_dataloader)}")
logging.info(f"[Validation] number of batches: {len(valid_dataloader)}")
# -------------------------------------------------------------------------
# Deep Learning Model and Configurations
# -------------------------------------------------------------------------
# Initialize model
model = TorchVisionFCModel("resnet18",
n_classes=1,
use_conv=True,
pretrained=cfg["pretrain"])
model = model.to(device)
# Loss function
loss_func = torch.nn.BCEWithLogitsLoss()
loss_func = loss_func.to(device)
# Optimizer
if cfg["novograd"] is True:
optimizer = Novograd(model.parameters(), lr=cfg["lr"])
else:
optimizer = SGD(model.parameters(), lr=cfg["lr"], momentum=0.9)
# AMP scaler
cfg["amp"] = cfg["amp"] and monai.utils.get_torch_version_tuple() >= (1, 6)
if cfg["amp"] is True:
scaler = GradScaler()
else:
scaler = None
# Learning rate scheduler
if cfg["cos"] is True:
scheduler = lr_scheduler.CosineAnnealingLR(optimizer,
T_max=cfg["n_epochs"])
else:
scheduler = None
# -------------------------------------------------------------------------
# Training/Evaluating
# -------------------------------------------------------------------------
train_counter = {"n_epochs": cfg["n_epochs"], "epoch": 1, "step": 1}
total_valid_time, total_train_time = 0.0, 0.0
t_start = time.perf_counter()
metric_summary = {"loss": np.Inf, "accuracy": 0, "best_epoch": 1}
# Training/Validation Loop
for _ in range(cfg["n_epochs"]):
t_epoch = time.perf_counter()
logging.info(
f"[Training] learning rate: {optimizer.param_groups[0]['lr']}")
# Training
with Range("Training Epoch"):
train_counter = training(
train_counter,
model,
loss_func,
optimizer,
scaler,
cfg["amp"],
train_dataloader,
preprocess_gpu_train,
postprocess,
device,
writer,
cfg["print_step"],
)
if scheduler is not None:
scheduler.step()
if cfg["save"]:
torch.save(
model.state_dict(),
os.path.join(log_dir,
f"model_epoch_{train_counter['epoch']}.pt"))
t_train = time.perf_counter()
train_time = t_train - t_epoch
total_train_time += train_time
# Validation
if cfg["validate"]:
with Range("Validation"):
valid_loss, valid_acc = validation(
model,
loss_func,
cfg["amp"],
valid_dataloader,
preprocess_gpu_valid,
postprocess,
device,
cfg["print_step"],
)
t_valid = time.perf_counter()
valid_time = t_valid - t_train
total_valid_time += valid_time
if valid_loss < metric_summary["loss"]:
metric_summary["loss"] = min(valid_loss,
metric_summary["loss"])
metric_summary["accuracy"] = max(valid_acc,
metric_summary["accuracy"])
metric_summary["best_epoch"] = train_counter["epoch"]
writer.add_scalar("valid/loss", valid_loss, train_counter["epoch"])
writer.add_scalar("valid/accuracy", valid_acc,
train_counter["epoch"])
logging.info(
f"[Epoch: {train_counter['epoch']}/{cfg['n_epochs']}] loss: {valid_loss:.3f}, accuracy: {valid_acc:.2f}, "
f"time: {t_valid - t_epoch:.1f}s (train: {train_time:.1f}s, valid: {valid_time:.1f}s)"
)
else:
logging.info(
f"[Epoch: {train_counter['epoch']}/{cfg['n_epochs']}] Train time: {train_time:.1f}s"
)
writer.flush()
t_end = time.perf_counter()
# Save final metrics
metric_summary["train_time_per_epoch"] = total_train_time / cfg["n_epochs"]
metric_summary["total_time"] = t_end - t_start
writer.add_hparams(hparam_dict=cfg,
metric_dict=metric_summary,
run_name=log_dir)
writer.close()
logging.info(f"Metric Summary: {metric_summary}")
# Save the best and final model
if cfg["validate"] is True:
copyfile(
os.path.join(log_dir,
f"model_epoch_{metric_summary['best_epoch']}.pth"),
os.path.join(log_dir, "model_best.pth"),
)
copyfile(
os.path.join(log_dir, f"model_epoch_{cfg['n_epochs']}.pth"),
os.path.join(log_dir, "model_final.pth"),
)
# Final prints
logging.info(
f"[Completed] {train_counter['epoch']} epochs -- time: {t_end - t_start:.1f}s "
f"(training: {total_train_time:.1f}s, validation: {total_valid_time:.1f}s)",
)
logging.info(f"Logs and model was saved at: {log_dir}")
def train(cfg):
log_dir = create_log_dir(cfg)
device = set_device(cfg)
# --------------------------------------------------------------------------
# Data Loading and Preprocessing
# --------------------------------------------------------------------------
# __________________________________________________________________________
# Build MONAI preprocessing
train_preprocess = Compose([
ToTensorD(keys="image"),
TorchVisionD(keys="image",
name="ColorJitter",
brightness=64.0 / 255.0,
contrast=0.75,
saturation=0.25,
hue=0.04),
ToNumpyD(keys="image"),
RandFlipD(keys="image", prob=0.5),
RandRotate90D(keys="image", prob=0.5),
CastToTypeD(keys="image", dtype=np.float32),
RandZoomD(keys="image", prob=0.5, min_zoom=0.9, max_zoom=1.1),
ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0),
ToTensorD(keys=("image", "label")),
])
valid_preprocess = Compose([
CastToTypeD(keys="image", dtype=np.float32),
ScaleIntensityRangeD(keys="image",
a_min=0.0,
a_max=255.0,
b_min=-1.0,
b_max=1.0),
ToTensorD(keys=("image", "label")),
])
# __________________________________________________________________________
# Create MONAI dataset
train_json_info_list = load_decathlon_datalist(
data_list_file_path=cfg["dataset_json"],
data_list_key="training",
base_dir=cfg["data_root"],
)
valid_json_info_list = load_decathlon_datalist(
data_list_file_path=cfg["dataset_json"],
data_list_key="validation",
base_dir=cfg["data_root"],
)
train_dataset = PatchWSIDataset(
train_json_info_list,
cfg["region_size"],
cfg["grid_shape"],
cfg["patch_size"],
train_preprocess,
image_reader_name="openslide" if cfg["use_openslide"] else "cuCIM",
)
valid_dataset = PatchWSIDataset(
valid_json_info_list,
cfg["region_size"],
cfg["grid_shape"],
cfg["patch_size"],
valid_preprocess,
image_reader_name="openslide" if cfg["use_openslide"] else "cuCIM",
)
# __________________________________________________________________________
# DataLoaders
train_dataloader = DataLoader(train_dataset,
num_workers=cfg["num_workers"],
batch_size=cfg["batch_size"],
pin_memory=True)
valid_dataloader = DataLoader(valid_dataset,
num_workers=cfg["num_workers"],
batch_size=cfg["batch_size"],
pin_memory=True)
# __________________________________________________________________________
# Get sample batch and some info
first_sample = first(train_dataloader)
if first_sample is None:
raise ValueError("Fist sample is None!")
print("image: ")
print(" shape", first_sample["image"].shape)
print(" type: ", type(first_sample["image"]))
print(" dtype: ", first_sample["image"].dtype)
print("labels: ")
print(" shape", first_sample["label"].shape)
print(" type: ", type(first_sample["label"]))
print(" dtype: ", first_sample["label"].dtype)
print(f"batch size: {cfg['batch_size']}")
print(f"train number of batches: {len(train_dataloader)}")
print(f"valid number of batches: {len(valid_dataloader)}")
# --------------------------------------------------------------------------
# Deep Learning Classification Model
# --------------------------------------------------------------------------
# __________________________________________________________________________
# initialize model
model = TorchVisionFCModel("resnet18",
num_classes=1,
use_conv=True,
pretrained=cfg["pretrain"])
model = model.to(device)
# loss function
loss_func = torch.nn.BCEWithLogitsLoss()
loss_func = loss_func.to(device)
# optimizer
if cfg["novograd"]:
optimizer = Novograd(model.parameters(), cfg["lr"])
else:
optimizer = SGD(model.parameters(), lr=cfg["lr"], momentum=0.9)
# AMP scaler
if cfg["amp"]:
cfg["amp"] = True if monai.utils.get_torch_version_tuple() >= (
1, 6) else False
else:
cfg["amp"] = False
scheduler = lr_scheduler.CosineAnnealingLR(optimizer,
T_max=cfg["n_epochs"])
# --------------------------------------------
# Ignite Trainer/Evaluator
# --------------------------------------------
# Evaluator
val_handlers = [
CheckpointSaver(save_dir=log_dir,
save_dict={"net": model},
save_key_metric=True),
StatsHandler(output_transform=lambda x: None),
TensorBoardStatsHandler(log_dir=log_dir,
output_transform=lambda x: None),
]
val_postprocessing = Compose([
ActivationsD(keys="pred", sigmoid=True),
AsDiscreteD(keys="pred", threshold=0.5)
])
evaluator = SupervisedEvaluator(
device=device,
val_data_loader=valid_dataloader,
network=model,
postprocessing=val_postprocessing,
key_val_metric={
"val_acc":
Accuracy(output_transform=from_engine(["pred", "label"]))
},
val_handlers=val_handlers,
amp=cfg["amp"],
)
# Trainer
train_handlers = [
LrScheduleHandler(lr_scheduler=scheduler, print_lr=True),
CheckpointSaver(save_dir=cfg["logdir"],
save_dict={
"net": model,
"opt": optimizer
},
save_interval=1,
epoch_level=True),
StatsHandler(tag_name="train_loss",
output_transform=from_engine(["loss"], first=True)),
ValidationHandler(validator=evaluator, interval=1, epoch_level=True),
TensorBoardStatsHandler(log_dir=cfg["logdir"],
tag_name="train_loss",
output_transform=from_engine(["loss"],
first=True)),
]
train_postprocessing = Compose([
ActivationsD(keys="pred", sigmoid=True),
AsDiscreteD(keys="pred", threshold=0.5)
])
trainer = SupervisedTrainer(
device=device,
max_epochs=cfg["n_epochs"],
train_data_loader=train_dataloader,
network=model,
optimizer=optimizer,
loss_function=loss_func,
postprocessing=train_postprocessing,
key_train_metric={
"train_acc":
Accuracy(output_transform=from_engine(["pred", "label"]))
},
train_handlers=train_handlers,
amp=cfg["amp"],
)
trainer.run()
