def test_shape(self, input_data, expected_shape):
if input_data["model"] == "densenet2d":
model = densenet121(spatial_dims=2, in_channels=1, out_channels=3)
if input_data["model"] == "densenet3d":
model = DenseNet(spatial_dims=3,
in_channels=1,
out_channels=3,
init_features=2,
growth_rate=2,
block_config=(6, ))
if input_data["model"] == "senet2d":
model = se_resnet50(spatial_dims=2, in_channels=3, num_classes=4)
if input_data["model"] == "senet3d":
model = se_resnet50(spatial_dims=3, in_channels=3, num_classes=4)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()
cam = CAM(nn_module=model,
target_layers=input_data["target_layers"],
fc_layers=input_data["fc_layers"])
image = torch.rand(input_data["shape"], device=device)
result = cam(x=image, layer_idx=-1)
fea_shape = cam.feature_map_size(input_data["shape"], device=device)
self.assertTupleEqual(fea_shape, input_data["feature_shape"])
self.assertTupleEqual(result.shape, expected_shape)
def run_inference_test(root_dir, test_x, test_y,
device=torch.device("cuda:0")):
# define transforms for image and classification
val_transforms = Compose(
[LoadPNG(image_only=True),
AddChannel(),
ScaleIntensity(),
ToTensor()])
val_ds = MedNISTDataset(test_x, test_y, val_transforms)
val_loader = DataLoader(val_ds, batch_size=300, num_workers=10)
model = densenet121(
spatial_dims=2,
in_channels=1,
out_channels=len(np.unique(test_y)),
).to(device)
model_filename = os.path.join(root_dir, "best_metric_model.pth")
model.load_state_dict(torch.load(model_filename))
model.eval()
y_true = list()
y_pred = list()
with torch.no_grad():
for test_data in val_loader:
test_images, test_labels = test_data[0].to(
device), test_data[1].to(device)
pred = model(test_images).argmax(dim=1)
for i in range(len(pred)):
y_true.append(test_labels[i].item())
y_pred.append(pred[i].item())
tps = [
np.sum((np.asarray(y_true) == idx) & (np.asarray(y_pred) == idx))
for idx in np.unique(test_y)
]
return tps
def test_121_3d_shape_pretrain(self, input_param, input_shape,
expected_shape):
net = densenet121(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(torch.randn(input_shape))
self.assertEqual(result.shape, expected_shape)
def classify_image(image):
# make conversions to image and save temporarily
im = Image.open(image)
im = im.convert(mode='L')
im = im.resize((64, 64))
im.save('conversion.jpeg', 'JPEG')
# Define MONAI transforms, Dataset and Dataloader to process image
val_transforms = Compose([
LoadImage(image_only=True),
AddChannel(),
ScaleIntensity(),
ToTensor()
])
test_ds = MedNISTDataset(['conversion.jpeg'], [0], val_transforms)
test_loader = torch.utils.data.DataLoader(test_ds)
# Define Network
device = torch.device("cpu")
model = densenet121(spatial_dims=2, in_channels=1,
out_channels=num_class).to(device)
# Make prediction
model.load_state_dict(
torch.load(
"SmartEMR_Imaging/MONAI_DATA_DIRECTORY/best_metric_model_cpu.pth"))
model.eval()
y_true = list()
y_pred = list()
with torch.no_grad():
for test_data in test_loader:
test_images, test_labels = (
test_data[0].to(device),
test_data[1].to(device),
)
pred = model(test_images).argmax(dim=1)
for i in range(len(pred)):
y_true.append(test_labels[i].item())
y_pred.append(pred[i].item())
# clean up
os.remove('conversion.jpeg')
return class_tags[y_pred[0]]
def test_shape(self, input_data, expected_shape):
if input_data["model"] == "densenet2d":
model = densenet121(spatial_dims=2, in_channels=1, out_channels=3)
if input_data["model"] == "densenet3d":
model = DenseNet(
spatial_dims=3, in_channels=1, out_channels=3, init_features=2, growth_rate=2, block_config=(6,)
)
if input_data["model"] == "senet2d":
model = se_resnet50(spatial_dims=2, in_channels=3, num_classes=4)
if input_data["model"] == "senet3d":
model = se_resnet50(spatial_dims=3, in_channels=3, num_classes=4)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()
cam = GradCAM(nn_module=model, target_layers=input_data["target_layers"])
image = torch.rand(input_data["shape"], device=device)
result = cam(x=image, layer_idx=-1)
np.testing.assert_array_equal(cam.nn_module.class_idx.cpu(), model(image).max(1)[-1].cpu())
fea_shape = cam.feature_map_size(input_data["shape"], device=device)
self.assertTupleEqual(fea_shape, input_data["feature_shape"])
self.assertTupleEqual(result.shape, expected_shape)
# check result is same whether class_idx=None is used or not
result2 = cam(x=image, layer_idx=-1, class_idx=model(image).max(1)[-1].cpu())
np.testing.assert_array_almost_equal(result, result2)
def run_training_test(root_dir,
train_x,
train_y,
val_x,
val_y,
device="cuda:0",
num_workers=10):
monai.config.print_config()
# define transforms for image and classification
train_transforms = Compose([
LoadPNG(image_only=True),
AddChannel(),
ScaleIntensity(),
RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),
RandFlip(spatial_axis=0, prob=0.5),
RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),
ToTensor(),
])
train_transforms.set_random_state(1234)
val_transforms = Compose(
[LoadPNG(image_only=True),
AddChannel(),
ScaleIntensity(),
ToTensor()])
# create train, val data loaders
train_ds = MedNISTDataset(train_x, train_y, train_transforms)
train_loader = DataLoader(train_ds,
batch_size=300,
shuffle=True,
num_workers=num_workers)
val_ds = MedNISTDataset(val_x, val_y, val_transforms)
val_loader = DataLoader(val_ds, batch_size=300, num_workers=num_workers)
model = densenet121(spatial_dims=2,
in_channels=1,
out_channels=len(np.unique(train_y))).to(device)
loss_function = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), 1e-5)
epoch_num = 4
val_interval = 1
# start training validation
best_metric = -1
best_metric_epoch = -1
epoch_loss_values = list()
metric_values = list()
model_filename = os.path.join(root_dir, "best_metric_model.pth")
for epoch in range(epoch_num):
print("-" * 10)
print(f"Epoch {epoch + 1}/{epoch_num}")
model.train()
epoch_loss = 0
step = 0
for batch_data in train_loader:
step += 1
inputs, labels = batch_data[0].to(device), batch_data[1].to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_loss /= step
epoch_loss_values.append(epoch_loss)
print(f"epoch {epoch + 1} average loss:{epoch_loss:0.4f}")
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[0].to(
device), val_data[1].to(device)
y_pred = torch.cat([y_pred, model(val_images)], dim=0)
y = torch.cat([y, val_labels], dim=0)
auc_metric = compute_roc_auc(y_pred,
y,
to_onehot_y=True,
softmax=True)
metric_values.append(auc_metric)
acc_value = torch.eq(y_pred.argmax(dim=1), y)
acc_metric = acc_value.sum().item() / len(acc_value)
if auc_metric > best_metric:
best_metric = auc_metric
best_metric_epoch = epoch + 1
torch.save(model.state_dict(), model_filename)
print("saved new best metric model")
print(
f"current epoch {epoch +1} current AUC: {auc_metric:0.4f} "
f"current accuracy: {acc_metric:0.4f} best AUC: {best_metric:0.4f} at epoch {best_metric_epoch}"
)
print(
f"train completed, best_metric: {best_metric:0.4f} at epoch: {best_metric_epoch}"
)
return epoch_loss_values, best_metric, best_metric_epoch
import torchvision.transforms as transforms
from PIL import Image
import numpy as np
#MONAI
import monai
from monai.networks.nets import densenet121
from monai.transforms import \
Compose, LoadPNG, AddChannel, ScaleIntensity, ToTensor, RandRotate, RandFlip, RandZoom
from torch.utils.data import Dataset, DataLoader
import matplotlib.image as mpimg
# load model
num_class = 4
torch.cuda.empty_cache()
device = torch.device("cpu")
model = densenet121(spatial_dims=2, in_channels=1,
out_channels=num_class).to(device)
PATH = 'best_metric_model.pth'
model.load_state_dict(torch.load(PATH, map_location=torch.device('cpu')))
model.eval()
# convert image -> tensor
def transforms_image(image_bytes):
image_transforms = Compose([
# LoadPNG(image_only=True),
ScaleIntensity(),
ToTensor()
])
# 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 unittest
import torch
from parameterized import parameterized
from monai.metrics import compute_occlusion_sensitivity
from monai.networks.nets import DenseNet, densenet121
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_2d = densenet121(spatial_dims=2, in_channels=1, out_channels=3).to(device)
model_3d = DenseNet(
spatial_dims=3, in_channels=1, out_channels=3, init_features=2, growth_rate=2, block_config=(6,)
).to(device)
model_2d.eval()
model_3d.eval()
# 2D w/ bounding box
TEST_CASE_0 = [
{
"model": model_2d,
"image": torch.rand(1, 1, 48, 64).to(device),
"label": torch.tensor([[0]], dtype=torch.int64).to(device),
"b_box": [-1, -1, 2, 40, 1, 62],
},
(39, 62),
def test_121_3d_shape(self, input_param, input_data, expected_shape):
net = densenet121(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
input_image = transforms.Resize(255)(img)
input_new = transformations_new(input_image)
input_new = input_new.unsqueeze(0)
if uploaded_file is not None:
c6.image(input_image, channel='BGR')
else:
c6.button(
"""Awaiting X_ray image to be uploaded. Currently using sample X_Ray image (shown below)"""
)
c6.image(input_image)
import monai
from monai.networks.nets import densenet121
model = densenet121(
spatial_dims=2, in_channels=1, out_channels=2
) #.to(device)#spatial_dims=2, in_channels=1,out_channels=num_classes
model.eval()
output = model.load_state_dict(
torch.load("best_metric_model_400epochs.pth",
map_location=torch.device('cpu')))
output = model(input_new)
malignant_probability = nn.Softmax()(output.data[0])[1].cpu().numpy()
st.subheader('Predicted Malignant Probability')
st.write(np.around(malignant_probability * 100, 2), '%')
localisation_model = densenet121(spatial_dims=2, in_channels=1, out_channels=3)
localisation_model.eval()
if malignant_probability > 0.5:
