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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | # Copyright 2020 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, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import sys
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import monai
from monai.metrics import compute_roc_auc
from monai.transforms import AddChanneld, Compose, LoadNiftid, RandRotate90d, Resized, ScaleIntensityd, ToTensord
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
images = [
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI314-IOP-0889-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI249-Guys-1072-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI609-HH-2600-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI173-HH-1590-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI020-Guys-0700-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI342-Guys-0909-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI134-Guys-0780-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI577-HH-2661-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI066-Guys-0731-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI130-HH-1528-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI607-Guys-1097-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI175-HH-1570-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI385-HH-2078-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI344-Guys-0905-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI409-Guys-0960-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI584-Guys-1129-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI253-HH-1694-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI092-HH-1436-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI574-IOP-1156-T1.nii.gz"]),
os.sep.join(["workspace", "data", "medical", "ixi", "IXI-T1", "IXI585-Guys-1130-T1.nii.gz"]),
]
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0], dtype=np.int64)
train_files = [{"img": img, "label": label} for img, label in zip(images[:10], labels[:10])]
val_files = [{"img": img, "label": label} for img, label in zip(images[-10:], labels[-10:])]
# Define transforms for image
train_transforms = Compose(
[
LoadNiftid(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
RandRotate90d(keys=["img"], prob=0.8, spatial_axes=[0, 2]),
ToTensord(keys=["img"]),
]
)
val_transforms = Compose(
[
LoadNiftid(keys=["img"]),
AddChanneld(keys=["img"]),
ScaleIntensityd(keys=["img"]),
Resized(keys=["img"], spatial_size=(96, 96, 96)),
ToTensord(keys=["img"]),
]
)
# Define dataset, data loader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
check_loader = DataLoader(check_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
check_data = monai.utils.misc.first(check_loader)
print(check_data["img"].shape, check_data["label"])
# create a training data loader
train_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, pin_memory=torch.cuda.is_available())
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
# Create DenseNet121, CrossEntropyLoss and Adam optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = monai.networks.nets.densenet.densenet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
loss_function = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), 1e-5)
# start a typical PyTorch training
val_interval = 2
best_metric = -1
best_metric_epoch = -1
writer = SummaryWriter()
for epoch in range(5):
print("-" * 10)
print(f"epoch {epoch + 1}/{5}")
model.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data["img"].to(device), batch_data["label"].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_len = len(train_ds) // train_loader.batch_size
print(f"{step}/{epoch_len}, train_loss: {loss.item():.4f}")
writer.add_scalar("train_loss", loss.item(), epoch_len * epoch + step)
epoch_loss /= step
print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")
if (epoch + 1) % val_interval == 0:
model.eval()
with torch.no_grad():
y_pred = torch.tensor([], dtype=torch.float32, device=device)
y = torch.tensor([], dtype=torch.long, device=device)
for val_data in val_loader:
val_images, val_labels = val_data["img"].to(device), val_data["label"].to(device)
y_pred = torch.cat([y_pred, model(val_images)], dim=0)
y = torch.cat([y, val_labels], dim=0)
acc_value = torch.eq(y_pred.argmax(dim=1), y)
acc_metric = acc_value.sum().item() / len(acc_value)
auc_metric = compute_roc_auc(y_pred, y, to_onehot_y=True, softmax=True)
if acc_metric > best_metric:
best_metric = acc_metric
best_metric_epoch = epoch + 1
torch.save(model.state_dict(), "best_metric_model_classification3d_dict.pth")
print("saved new best metric model")
print(
"current epoch: {} current accuracy: {:.4f} current AUC: {:.4f} best accuracy: {:.4f} at epoch {}".format(
epoch + 1, acc_metric, auc_metric, best_metric, best_metric_epoch
)
)
writer.add_scalar("val_accuracy", acc_metric, epoch + 1)
print(f"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}")
writer.close()
if __name__ == "__main__":
main()
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