def main():
# Configs
config = configs.config()
# Reading the selected data
DataCSVFrame = pd.read_csv("DataFrame.csv",
usecols=["Image_Index", "Finding_Labels"],
index_col=False)
labelsSet = set(DataCSVFrame["Finding_Labels"].values)
# Dictionary with the label as key and the index in the set as value
labelsDict = {}
# Dictionary that is the reverse of the one above, to change back from value to the corresponding label
labelDictClassify = {}
# Filling the dictionaries
for index, label in enumerate(labelsSet):
labelsDict[label] = index
labelDictClassify[index] = label
# Path where all the images are stored
imgPath = config.getImagePath()
# Creating the dataset
xrayDataset = DC.XRayDataset(DataCSVFrame, imgPath, labelsDict)
# # Getting the first image from the dataset
# imgs, labs = xrayDataset.__getitem__([8307])
# print(len(imgs))
# print(imgs)
# Get the device (cpu/gpu) to run the model on
device = DU.getDevice()
# Gets the ranges of training and test data
training, testing = DU.splitTrainTest(xrayDataset, config)
# Get the train and validation sets
trainSets, valSets = DU.trainValSets(training, config)
# Initialize the criterion, optimizer and model
criterion, optimizer, model = NM.modelInit(device)
# Get the batchsize
batchsize = config.getBatchSize()
# Train the model
trainedModel = NM.trainNetwork(device, xrayDataset, trainSets, valSets,
config, model, criterion, optimizer,
batchsize)
# Save the model to be used for testing
NM.save_model(trainedModel, config.getModelName())
def main():
# Configs
config = configs.config()
# Reading the selected data
DataCSVFrame = pd.read_csv("DataFrame.csv",
usecols=["Image_Index", "Finding_Labels"],
index_col=False)
# Get the labels
labelsSet = set(DataCSVFrame["Finding_Labels"].values)
# Dictionary with the label as key and the index in the set as value
labelsDict = {}
# Dictionary that is the reverse of the one above, to change back from value to the corresponding label
labelDictClassify = {}
# Filling the dictionaries
for index, label in enumerate(labelsSet):
labelsDict[label] = index
labelDictClassify[index] = label
# Path where all the images are stored
imgPath = config.getImagePath()
# Creating the dataset
xrayDataset = DC.XRayDataset(DataCSVFrame, imgPath, labelsDict)
#print(xrayDataset.xrayClassFrame)
# Get the device (cpu/gpu) to run the model on
device = DU.getDevice()
# Gets the ranges of training and test data
training, testing = DU.splitTrainTest(xrayDataset, config)
# Initialize the model
model = models.alexnet(pretrained=False, num_classes=4)
model.to(device)
if device == 'cuda':
model = torch.nn.DataParallel(model)
cudnn.benchmark = True
#print(model)
# Load the trained model
cwd = os.path.dirname(os.path.realpath(__file__))
model.load_state_dict(
torch.load("%s%s%s.pth" % (cwd, os.sep, config.getModelName())))
model.eval()
# Testing the model
wrongLabels, labelsCM, predsCM = NM.testing(xrayDataset, testing, model,
device, labelDictClassify)
# Confusion Matrix
CMPlot.plot_confusion_matrix(labelsCM,
predsCM,
list(labelsSet),
normalize=False,
title="Confusion Matrix")
# Show the matrix
plt.show()