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- import numpy as np
- import tensorflow as tf
- def kernel_initializer(kernels):
- """ Wrapper for an initializer of convolution weights.
- :return: Callable initializer object.
- """
- assert len(kernels.shape) == 3
- kernels = kernels.astype(np.float32)
- def _initializer(shape, dtype=tf.float32, partition_info=None):
- """Initializer function which is called from tensorflow internally.
- :param shape: Runtime / Construction time shape of the tensor.
- :param dtype: Data type of the resulting tensor.
- :param partition_info: Placeholder for internal tf call.
- :return: 4D numpy array with weights [filter_height, filter_width, in_channels, out_channels].
- """
- if shape:
- # second last dimension is input, last dimension is output
- fan_in = float(shape[-2]) if len(shape) > 1 else float(shape[-1])
- fan_out = float(shape[-1])
- else:
- fan_in = 1.0
- fan_out = 1.0
- assert fan_out == 1 and fan_in == kernels.shape[-1]
- # define weight matrix (set dtype always to float32)
- # weights = np.expand_dims(kernels, axis=2)
- weights = np.expand_dims(kernels, axis=-1)
- return weights
- return _initializer
- def bilinear_filter(filter_size=(4, 4)):
- """
- Make a 2D bilinear kernel suitable for upsampling of the given (h, w) size.
- Also allows asymmetric kernels.
- :param filter_size: Tuple defining the filter size in width and height.
- :return: 2D numpy array containing bilinear weights.
- """
- assert isinstance(filter_size, (list, tuple)) and len(filter_size) == 2
- factor = [(size + 1) // 2 for size in filter_size]
- # define first center dimension
- if filter_size[0] % 2 == 1:
- center_x = factor[0] - 1
- else:
- center_x = factor[0] - 0.5
- # define second center dimension
- if filter_size[1] % 2 == 1:
- center_y = factor[1] - 1
- else:
- center_y = factor[1] - 0.5
- og = np.ogrid[:filter_size[0], :filter_size[1]]
- kernel = (1 - abs(og[0] - center_x) / float(factor[0])) * (1 - abs(og[1] - center_y) / float(factor[1]))
- return kernel
- def get_default_session_config(memory_fraction=0.9):
- """ Returns default session configuration
- :param memory_fraction: percentage of the memory which should be kept free (growing is allowed).
- :return: tensorflow session configuration object
- """
- conf = tf.ConfigProto()
- conf.gpu_options.per_process_gpu_memory_fraction = memory_fraction
- conf.gpu_options.allocator_type = 'BFC'
- conf.gpu_options.allow_growth = True
- conf.allow_soft_placement = True
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