def __init__(self, class_no=14): print('create object _unet') self.class_no = class_no self.kernel_size1 = 1 self.kernel_size2 = 3 self.log_ext = '_' self.seed_no=200 self.upsampling3d=upsampling()
def __init__(self, trainable, reuse, class_no=14): print('create object _unet') self.class_no = class_no self.kernel_size1 = 1 self.kernel_size2 = 3 self.log_ext = '_' self.seed = 200 self.upsampling3d = upsampling() self.layers = layers() self.trainable = trainable self.reuse = reuse
def __init__(self, class_no=14): print('create object _unet') self.class_no = class_no self.kernel_size1 = 1 self.kernel_size2 = 3 self.log_ext = '_' self.seed_no=200 self.upsampling3d=upsampling() self.maxpool = False self.norm_method = 'batch_normalization' self.layers=layers()
def __init__(self, class_no=14): print('create object _unet') self.class_no = class_no self.kernel_size1 = 1 self.kernel_size2 = 3 self.log_ext = '_' self.seed_no = 200 self.upsampling3d = upsampling() self.maxpool = False # use maxpool or strided conv for downsampling self.norm_method = 'batch_normalization' self.augmentation = augmentation(self.seed_no)
def __init__(self, trainable, file_name): print('create object _unet') self.upsampling3d = upsampling() self.layers = layers() self.trainable = trainable self.seed = 200 self.kernel_loader = loader(file_name) # self.kernel_loader.print_tensors_in_checkpoint_file(file_name, False, # True, False) [ self.conv_init1_ld1, self.bias_init1_ld1, self.beta_init1_ld1, self.gamma_init1_ld1, self.moving_mean_init1_ld1, self.moving_var1_ld1, self.conv_init2_ld1, self.bias_init2_ld1, self.beta_init2_ld1, self.gamma_init2_ld1, self.moving_mean_init2_ld1, self.moving_var2_ld1, self.conv_init1_ld2, self.bias_init1_ld2, self.beta_init1_ld2, self.gamma_init1_ld2, self.moving_mean_init1_ld2, self.moving_var1_ld2, self.conv_init2_ld2, self.bias_init2_ld2, self.beta_init2_ld2, self.gamma_init2_ld2, self.moving_mean_init2_ld2, self.moving_var2_ld2, self.conv_init1_ld3, self.bias_init1_ld3, self.beta_init1_ld3, self.gamma_init1_ld3, self.moving_mean_init1_ld3, self.moving_var1_ld3, self.conv_init2_ld3, self.bias_init2_ld3, self.beta_init2_ld3, self.gamma_init2_ld3, self.moving_mean_init2_ld3, self.moving_var2_ld3 ] = self.kernel_loader.return_tensor_value_list_by_name([ 'U_LD_DS1/U_LD_DS1U_conv1_conv3d/kernel', 'U_LD_DS1/U_LD_DS1U_conv1_conv3d/bias', 'U_LD_DS1/U_LD_DS1U_conv1_bn/beta', 'U_LD_DS1/U_LD_DS1U_conv1_bn/gamma', 'U_LD_DS1/U_LD_DS1U_conv1_bn/moving_mean', 'U_LD_DS1/U_LD_DS1U_conv1_bn/moving_variance', 'U_LD_DS1/U_LD_DS1U_conv2_conv3d/kernel', 'U_LD_DS1/U_LD_DS1U_conv2_conv3d/bias', 'U_LD_DS1/U_LD_DS1U_conv2_bn/beta', 'U_LD_DS1/U_LD_DS1U_conv2_bn/gamma', 'U_LD_DS1/U_LD_DS1U_conv2_bn/moving_mean', 'U_LD_DS1/U_LD_DS1U_conv2_bn/moving_variance', 'U_LD_DS2/U_LD_DS2U_conv1_conv3d/kernel', 'U_LD_DS2/U_LD_DS2U_conv1_conv3d/bias', 'U_LD_DS2/U_LD_DS2U_conv1_bn/beta', 'U_LD_DS2/U_LD_DS2U_conv1_bn/gamma', 'U_LD_DS2/U_LD_DS2U_conv1_bn/moving_mean', 'U_LD_DS2/U_LD_DS2U_conv1_bn/moving_variance', 'U_LD_DS2/U_LD_DS2U_conv2_conv3d/kernel', 'U_LD_DS2/U_LD_DS2U_conv2_conv3d/bias', 'U_LD_DS2/U_LD_DS2U_conv2_bn/beta', 'U_LD_DS2/U_LD_DS2U_conv2_bn/gamma', 'U_LD_DS2/U_LD_DS2U_conv2_bn/moving_mean', 'U_LD_DS2/U_LD_DS2U_conv2_bn/moving_variance', 'U_LD_US1/U_LD_US1U_conv1_conv3d/kernel', 'U_LD_US1/U_LD_US1U_conv1_conv3d/bias', 'U_LD_US1/U_LD_US1U_conv1_bn/beta', 'U_LD_US1/U_LD_US1U_conv1_bn/gamma', 'U_LD_US1/U_LD_US1U_conv1_bn/moving_mean', 'U_LD_US1/U_LD_US1U_conv1_bn/moving_variance', 'U_LD_US1/U_LD_US1U_conv2_conv3d/kernel', 'U_LD_US1/U_LD_US1U_conv2_conv3d/bias', 'U_LD_US1/U_LD_US1U_conv2_bn/beta', 'U_LD_US1/U_LD_US1U_conv2_bn/gamma', 'U_LD_US1/U_LD_US1U_conv2_bn/moving_mean', 'U_LD_US1/U_LD_US1U_conv2_bn/moving_variance', ])