| def coinChange(denominations, multiplicities, amounts): | |
| if not amounts or len(amounts) == 0: | |
| return True | |
| if amounts[0] == 0: | |
| return coinChange(denominations, multiplicities, amounts[1:]) | |
| for j in range(len(amounts)): | |
| i = len(multiplicities) - 1 | |
| while i >= 0: |
| # https://bic-berkeley.github.io/psych-214-fall-2016/saving_images.html | |
| def array_to_nii(array_3d, scan, output_path): | |
| ''' | |
| Same affine and same header | |
| convert mask only contains 0 and 1, so convert data type to uint8 | |
| :param array_3d: | |
| :param scan: | |
| :param output_path: | |
| :return: |
(Result, number of patches predicted)
data/unknown/2018-03768
[('TCGA_Kidney', 17), ('TCGA_Lung', 9), ('TCGA_Breast', 8), ('TCGA_Glioma', 3), ('TCGA_Uterus', 0), ('TCGA_Thyroid', 0), ('TCGA_Colorectal', 0), ('TCGA_Bladder', 0)]
data/unknown/2017-17894
[('TCGA_Lung', 47), ('TCGA_Colorectal', 1), ('TCGA_Uterus', 0), ('TCGA_Thyroid', 0), ('TCGA_Kidney', 0), ('TCGA_Glioma', 0), ('TCGA_Breast', 0), ('TCGA_Bladder', 0)]
| import nibabel as nib | |
| def test_nifti_all_labels_dice_score(seg_path, seg_file, truth_path, truth_file): | |
| truth_uid = os.listdir(truth_path) | |
| print(truth_uid) | |
| dice_score = 0 | |
| for uid in truth_uid: | |
| seg_file_path = os.path.join(seg_path, uid, seg_file) | |
| truth_file_path = os.path.join(truth_path, uid, truth_file) | |
| seg_nib = nib.load(seg_file_path) |
| def load_async(arg): | |
| print('load_async') | |
| tick = time.time() | |
| filename, reader_location, level, reader_size, location, size = arg | |
| slide = openslide.open_slide(filename) | |
| region = slide.read_region(location=reader_location, level=level, size=reader_size) | |
| # Convert to numpy array | |
| cachedImage = np.array(region, dtype=np.uint8) |
| #!/bin/sh | |
| echo "# git secret hide!" | |
| exec git secret hide |
total: 64000 cny
config:
gtx 1080 ti * 4
server motherboard with dual cpu slot * 1
cpu * 2
ram 128gb * 1
hhd 10tb * 1
server case * 1
power supply * 1
| # source: https://github.com/shreyaspadhy/UNet-Zoo | |
| def test(train_accuracy=False, save_output=False): | |
| test_loss = 0 | |
| correct = 0 | |
| if train_accuracy: | |
| loader = train_loader | |
| else: | |
| loader = test_loader |
| class DICELossMultiClass(nn.Module): | |
| def __init__(self): | |
| super(DICELossMultiClass, self).__init__() | |
| def forward(self, output, mask): | |
| probs = output[:, 1, :, :] | |
| mask = torch.squeeze(mask, 1) |
| When installing latest docker-ec 18, I encountered libseccomp2 version problem | |
| https://www.ubuntuupdates.org/ppa/ubuntu_sdk_release?dist=xenial | |
| add this to update libseccomp2 | |
| then install docker-ce |