def get_dataloader(batch_size):
train_data = KgForestDataset(
split='train-40479',
transform=Compose([
Lambda(lambda x: randomShiftScaleRotate(
x, u=0.75, shift_limit=6, scale_limit=6, rotate_limit=45)),
Lambda(lambda x: randomFlip(x)),
Lambda(lambda x: randomTranspose(x)),
Lambda(lambda x: toTensor(x)),
Normalize(mean=mean, std=std)
]),
height=256,
width=256)
train_data_loader = DataLoader(batch_size=batch_size,
dataset=train_data,
shuffle=True)
validation = KgForestDataset(
split='validation-3000',
transform=Compose([
# Lambda(lambda x: randomShiftScaleRotate(x, u=0.75, shift_limit=6, scale_limit=6, rotate_limit=45)),
Lambda(lambda x: randomFlip(x)),
Lambda(lambda x: randomTranspose(x)),
Lambda(lambda x: toTensor(x)),
Normalize(mean=mean, std=std)
]),
height=256,
width=256)
valid_dataloader = DataLoader(dataset=validation,
shuffle=False,
batch_size=batch_size)
return train_data_loader, valid_dataloader
best_score = score
threshold[i] = best_thresh
print(i, best_score, best_thresh)
return threshold
if __name__ == '__main__':
model1 = nn.DataParallel(densenet161(pretrained=True).cuda())
model1.load_state_dict(torch.load('../models/densenet161.pth'))
model1.cuda().eval()
validation = KgForestDataset(
split='validation-3000',
transform=Compose(
[
Lambda(lambda x: randomFlip(x)),
Lambda(lambda x: randomTranspose(x)),
Lambda(lambda x: toTensor(x)),
Normalize(mean=mean, std=std)
]
),
height=256,
width=256
)
valid_dataloader = DataLoader(dataset=validation, shuffle=False, batch_size=512)
threshold = np.zeros(17)
for i in range(10):
threshold += optimize_threshold([model1], [valid_dataloader])
print(threshold/10)
