forked from TensorLayer/tensorlayer3
1051 lines
25 KiB
Python
1051 lines
25 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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from __future__ import absolute_import, division, print_function
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from .tensorflow_nn import nchw_to_nhwc, nhwc_to_nchw
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import tensorflow as tf
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_dtypeDict = {
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'DType': tf.DType,
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'float16': tf.float16,
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'float32': tf.float32,
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'float64': tf.float64,
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'int8': tf.int8,
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'int16': tf.int16,
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'int32': tf.int32,
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'int64': tf.int64,
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'uint8': tf.uint8,
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'uint16': tf.uint16,
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'uint32': tf.uint32,
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'uint64': tf.uint64
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}
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DType = tf.DType
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float16 = tf.float16
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float32 = tf.float32
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float64 = tf.float64
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int8 = tf.int8
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int16 = tf.int16
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int32 = tf.int32
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int64 = tf.int64
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uint8 = tf.uint8
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uint16 = tf.uint16
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uint32 = tf.uint32
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uint64 = tf.uint64
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# isinstance input output
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# TensorLike = tf_ops._TensorLike
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def set_context(**kwargs):
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raise Exception("Using TenosrFlow backend,You don't need to set context")
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def get_tensor_shape(x):
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return x.get_shape().as_list()
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# initializers
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def zeros(shape, dtype=tf.float32):
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"""
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Creates a tensor with all elements set to zero.
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Parameters
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----------
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shape : A list of integers
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a tuple of integers, or a 1-D Tensor of type int32.
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dtype : tensor
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The DType of an element in the resulting Tensor
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Returns
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-------
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A Tensor with all elements set to zero.
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"""
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return tf.zeros(shape=shape, dtype=dtype)
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def ones(shape, dtype=tf.float32):
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"""
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Creates a tensor with all elements set to ones.
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Parameters
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----------
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shape : A list of integers
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a tuple of integers, or a 1-D Tensor of type int32.
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dtype : tensor
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The DType of an element in the resulting Tensor
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Returns
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-------
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A Tensor with all elements set to zero.
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"""
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return tf.ones(shape=shape, dtype=dtype)
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def constant(value, dtype=tf.float32, shape=None):
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"""
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Creates a constant tensor from a tensor-like object.
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Parameters
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----------
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value : list
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A constant value (or list) of output type dtype.
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dtype : tensor
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The type of the elements of the resulting tensor.
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shape : tuple
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Optional dimensions of resulting tensor.
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Returns
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-------
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A Constant Tensor.
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"""
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return tf.constant(value=value, dtype=dtype, shape=shape)
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def random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None):
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"""
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Outputs random values from a uniform distribution.
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Parameters
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----------
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shape : tuple
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A 1-D integer Tensor or Python array. The shape of the output tensor.
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minval : int
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The lower bound on the range of random values to generate (inclusive). Defaults to 0.
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maxval : int
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The upper bound on the range of random values to generate (exclusive). Defaults to 1 if dtype is floating point.
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dtype : tensor
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The type of the output: float16, float32, float64, int32, or int64.
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seed : int
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Used in combination with tf.random.set_seed to create a reproducible sequence of tensors across multiple calls.
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Returns
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-------
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A tensor of the specified shape filled with random uniform values.
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"""
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outputs = tf.random.uniform(shape=shape, minval=minval, maxval=maxval, dtype=dtype, seed=seed)
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return outputs
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def random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.dtypes.float32, seed=None):
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"""
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Outputs random values from a normal distribution.
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Parameters
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----------
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shape : tuple
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A 1-D integer Tensor or Python array. The shape of the output tensor.
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mean : float
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The mean of the normal distribution
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stddev : float
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The standard deviation of the normal distribution.
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dtype : tensor
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The type of the output.
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seed : A Python integer
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Used to create a random seed for the distribution
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Returns
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-------
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A tensor of the specified shape filled with random normal values.
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"""
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outputs = tf.random.normal(shape=shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
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return outputs
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def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None):
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"""
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Outputs random values from a truncated normal distribution.
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Parameters
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----------
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shape : tuple
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A 1-D integer Tensor or Python array. The shape of the output tensor.
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mean : float
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The mean of the normal distribution
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stddev : float
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The standard deviation of the normal distribution.
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dtype : tensor
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The type of the output.
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seed : A Python integer
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Used to create a random seed for the distribution
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Returns
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-------
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A tensor of the specified shape filled with random truncated normal values.
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"""
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outputs = tf.random.truncated_normal(shape=shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
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return outputs
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def he_normal(shape, dtype, seed=None):
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"""
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He normal initializer.
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Parameters
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----------
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seed : A Python integer.
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Used to seed the random generator.
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shape : tuple
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A 1-D integer Tensor or Python array. The shape of the output tensor.
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dtype : tensor
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The type of the output.
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Returns
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-------
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A tensor of the specified shape filled with he normal values.
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"""
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return tf.initializers.he_normal(seed)(shape=shape, dtype=dtype)
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def Variable(initial_value, name, trainable=True):
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"""
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Creates a new variable with value initial_value.
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Parameters
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----------
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initial_value : tensor
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A Tensor, or Python object convertible to a Tensor
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name : str
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Optional name for the variable. Defaults to 'Variable' and gets uniquified automatically.
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Returns
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-------
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Variable
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"""
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var = tf.Variable(initial_value=initial_value, name=name, trainable=trainable)
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return var
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class MatMul(object):
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def __init__(self):
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pass
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def __call__(self, a, b):
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return tf.matmul(a, b)
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def matmul(a, b):
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"""
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Multiplies matrix a by matrix b, producing a * b.
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Parameters
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----------
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a : tensor
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type float16, float32, float64, int32, complex64, complex128 and rank > 1.
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b : tensor
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with same type and rank as a.
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Returns
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-------
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A Tensor of the same type as a and b
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"""
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outputs = tf.matmul(a, b)
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return outputs
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def add(value, bias):
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"""
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Returns x + y element-wise.
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Parameters
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----------
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value : tensor.
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Must be one of the following types: bfloat16, half, float32, float64,
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uint8, int8, int16, int32, int64, complex64, complex128, string.
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bias : tensor
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Must have the same type as a
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Returns
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-------
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A Tensor. Has the same type as a.
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"""
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outputs = tf.add(value, bias)
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return outputs
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def dtypes(dt):
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"""
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Data dtypes.
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Parameters
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----------
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dt : string
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It could be 'uint8', 'uint16', 'uint32', 'uint64', 'int8', 'int16',
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'int32', 'int64', 'float16', 'float32', 'float64', 'DType'.
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Returns
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-------
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Data dtypes
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"""
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if dt not in _dtypeDict.keys():
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raise Exception("Unsupported dtype: {}".format(dt))
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return _dtypeDict[dt]
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class Maximum(object):
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def __init__(self):
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pass
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def __call__(self, x, y):
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return tf.maximum(x=x, y=y)
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class Minimum(object):
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def __init__(self):
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pass
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def __call__(self, x, y):
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return tf.minimum(x=x, y=y)
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def minimum(x, y):
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"""
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Returns the min of x and y (i.e. x < y ? x : y) element-wise.
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Parameters
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----------
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x : tensor.
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Must be one of the following types: bfloat16, half, float32, float64, int32, int64.
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y : A Tensor.
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Must have the same type as x.
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Returns
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-------
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A Tensor. Has the same type as x
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"""
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outputs = tf.minimum(x=x, y=y)
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return outputs
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class FlattenReshape(object):
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def __init__(self):
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pass
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def __call__(self, inputs):
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dim = 1
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for d in get_tensor_shape(inputs)[1:]:
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dim *= d
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return tf.reshape(inputs, [-1, dim])
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class Reshape(object):
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def __init__(self, shape):
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self.shape = shape
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def __call__(self, tensor):
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return tf.reshape(tensor, self.shape)
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def reshape(tensor, shape):
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"""
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Reshapes a tensor.
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Parameters
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----------
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tensor : tensor
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A Tensor.
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shape : tensor
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Defines the shape of the output tensor.
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Returns
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-------
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A Tensor. Has the same type as tensor
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"""
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return tf.reshape(tensor, shape)
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class Concat(object):
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def __init__(self, axis):
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super(Concat, self).__init__()
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self.axis = axis
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def __call__(self, values):
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return tf.concat(values=values, axis=self.axis)
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def concat(values, axis):
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"""
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Concatenates tensors along one dimension.
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Parameters
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----------
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values : list
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A list of Tensor objects or a single Tensor
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axis : int
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0-D int32 Tensor. Dimension along which to concatenate
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Returns
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-------
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A Tensor resulting from concatenation of the input tensors.
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"""
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return tf.concat(values, axis)
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def convert_to_tensor(value, dtype=None):
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"""
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Converts the given value to a Tensor.
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Parameters
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----------
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value : object
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An object whose type has a registered Tensor conversion function.
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dtype : optional
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Optional element type for the returned tensor. If missing, the type is inferred from the type of value.
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Returns
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-------
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A Tensor based on value.
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"""
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return tf.convert_to_tensor(value, dtype)
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def convert_to_numpy(value):
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return value.numpy()
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def sqrt(x):
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"""
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Computes square root of x element-wise.
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Parameters
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----------
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x : tensor
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Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
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Returns
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-------
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A Tensor. Has the same type as x.
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"""
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return tf.sqrt(x)
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class ReduceSum(object):
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def __init__(self, axis=None):
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self.axis = axis
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def __call__(self, input):
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return tf.reduce_sum(input, axis=self.axis)
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class ReduceMean(object):
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def __init__(self, axis):
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self.axis = axis
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def __call__(self, inputs):
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output = tf.reduce_mean(inputs, self.axis)
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return output
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def reduce_mean(input_tensor, axis=None):
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"""
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Computes the mean of elements across dimensions of a tensor.
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Parameters
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----------
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input_tensor : tensor
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The tensor to reduce. Should have numeric type.
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axis : list
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The dimensions to reduce. If None (the default), reduces all dimensions.
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Must be in the range [-rank(input_tensor), rank(input_tensor)).
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name : str
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A name for the operation (optional).
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Returns
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-------
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The reduced tensor.
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"""
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return tf.reduce_mean(input_tensor, axis=axis)
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class ReduceMax(object):
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def __init__(self, axis):
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self.axis = axis
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def __call__(self, inputs):
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output = tf.reduce_max(inputs, self.axis)
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return output
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def reduce_max(input_tensor, axis=None):
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"""
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Computes the maximum of elements across dimensions of a tensor.
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Parameters
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----------
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input_tensor : tensor
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The tensor to reduce. Should have real numeric type.
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axis : int
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The dimensions to reduce. If None (the default), reduces all dimensions.
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Must be in the range [-rank(input_tensor), rank(input_tensor)).
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name : str
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A name for the operation (optional).
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Returns
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-------
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The reduced tensor.
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"""
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return tf.reduce_max(input_tensor, axis=axis)
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def reduce_min(input_tensor, axis=None):
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"""
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Computes the minimum of elements across dimensions of a tensor.
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Parameters
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----------
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input_tensor : tensor
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The tensor to reduce. Should have real numeric type.
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axis : int
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The dimensions to reduce. If None (the default), reduces all dimensions.
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Must be in the range [-rank(input_tensor), rank(input_tensor)).
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name : str
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A name for the operation (optional).
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Returns
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-------
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The reduced tensor.
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"""
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return tf.reduce_min(input_tensor, axis=axis)
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class Pad(object):
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def __init__(self, paddings, mode="REFLECT", constant_values=0):
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if mode not in ['CONSTANT', 'REFLECT', 'SYMMETRIC']:
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raise Exception("Unsupported mode: {}".format(mode))
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self.paddings = paddings
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self.mode = mode
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self.constant_values = constant_values
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def __call__(self, x):
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outputs = tf.pad(x, self.paddings, mode=self.mode, constant_values=self.constant_values)
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return outputs
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def pad(tensor, paddings, mode='CONSTANT', constant_values=0):
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"""
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Pads a tensor.
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Parameters
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----------
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tensor : tensor
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A Tensor.
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paddings : tensor
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A Tensor of type int32.
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mode : str
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One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)
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constant_values : int
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In "CONSTANT" mode, the scalar pad value to use. Must be same type as tensor.
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Returns
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-------
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A Tensor. Has the same type as tensor.
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"""
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if mode not in ['CONSTANT', 'REFLECT', 'SYMMETRIC']:
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raise Exception("Unsupported mode: {}".format(mode))
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outputs = tf.pad(tensor, paddings, mode=mode, constant_values=constant_values)
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return outputs
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class Unstack(object):
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def __init__(self, axis, num=None):
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self.axis = axis
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self.num = num
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def __call__(self, values):
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return tf.unstack(values, num=self.num, axis=self.axis)
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class Stack(object):
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def __init__(self, axis=0):
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self.axis = axis
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def __call__(self, values):
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return tf.stack(values, axis=self.axis)
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def stack(values, axis=0):
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"""
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Stacks a list of rank-R tensors into one rank-(R+1) tensor.
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Parameters
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----------
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values : list
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A list of Tensor objects with the same shape and type.
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axis : int
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An int. The axis to stack along. Defaults to the first dimension.
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Negative values wrap around, so the valid range is [-(R+1), R+1).
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Returns
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-------
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A stacked Tensor with the same type as values.
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"""
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return tf.stack(values, axis=axis)
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class Meshgrid(object):
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def __init__(self, indexing='xy'):
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super(Meshgrid, self).__init__()
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self.index = indexing
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def __call__(self, inputs):
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return tf.meshgrid(inputs)
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def meshgrid(*args, **kwargs):
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"""
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Broadcasts parameters for evaluation on an N-D grid.
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Parameters
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----------
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x : tensor
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Tensors with rank 1.
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y : tensor
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Tensors with rank 1.
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Returns
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-------
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A list of N Tensors with rank N.
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"""
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return tf.meshgrid(*args, **kwargs)
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def range(start, limit=None, delta=1, dtype=None):
|
|
"""
|
|
Creates a sequence of numbers.
|
|
|
|
Parameters
|
|
----------
|
|
start : tensor
|
|
A 0-D Tensor (scalar). Acts as first entry in the range if limit is not None;
|
|
otherwise, acts as range limit and first entry defaults to 0.
|
|
limit : tensor
|
|
A 0-D Tensor (scalar). Upper limit of sequence, exclusive. If None,
|
|
defaults to the value of start while the first entry of the range defaults to 0.
|
|
delta : tensor
|
|
A 0-D Tensor (scalar). Number that increments start. Defaults to 1.
|
|
dtype : type
|
|
The type of the elements of the resulting tensor.
|
|
|
|
Returns
|
|
-------
|
|
An 1-D Tensor of type dtype.
|
|
"""
|
|
|
|
if limit is None:
|
|
outputs = tf.range(start, delta=delta, dtype=dtype)
|
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else:
|
|
outputs = tf.range(start, limit, delta=delta, dtype=dtype)
|
|
return outputs
|
|
|
|
|
|
class ExpandDims(object):
|
|
|
|
def __init__(self, axis):
|
|
self.axis = axis
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|
|
|
def __call__(self, input):
|
|
return tf.expand_dims(input, axis=self.axis)
|
|
|
|
|
|
def expand_dims(input, axis):
|
|
"""
|
|
Inserts a dimension of 1 into a tensor's shape.
|
|
|
|
Parameters
|
|
----------
|
|
input : tensor
|
|
A Tensor.
|
|
axis : int
|
|
0-D (scalar). Specifies the dimension index at which to expand the shape of input.
|
|
Must be in the range [-rank(input) - 1, rank(input)].
|
|
|
|
Returns
|
|
-------
|
|
A Tensor with the same data as input, but its shape has an additional dimension of size 1 added.
|
|
"""
|
|
|
|
return tf.expand_dims(input, axis)
|
|
|
|
|
|
class Tile(object):
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
def __call__(self, input, multiples):
|
|
return tf.tile(input, multiples)
|
|
|
|
|
|
def tile(input, multiples):
|
|
"""
|
|
Constructs a tensor by tiling a given tensor.
|
|
|
|
Parameters
|
|
----------
|
|
input : tensor
|
|
A Tensor. 1-D or higher.
|
|
multiples : tensor
|
|
Must be one of the following types: int32, int64. 1-D.
|
|
Length must be the same as the number of dimensions in input
|
|
|
|
Returns
|
|
-------
|
|
A Tensor. Has the same type as input.
|
|
"""
|
|
|
|
return tf.tile(input, multiples)
|
|
|
|
|
|
class Cast(object):
|
|
|
|
def __init__(self, dtype):
|
|
self.dtype = dtype
|
|
|
|
def __call__(self, x):
|
|
return tf.cast(x, dtype=self.dtype)
|
|
|
|
|
|
def cast(x, dtype):
|
|
"""
|
|
Casts a tensor to a new type.
|
|
|
|
Parameters
|
|
----------
|
|
x : tensor
|
|
A Tensor or SparseTensor or IndexedSlices of numeric type.
|
|
It could be uint8, uint16, uint32, uint64, int8, int16, int32, int64, float16, float32, float64.
|
|
dtype : dtpye
|
|
The destination type. The list of supported dtypes is the same as x
|
|
|
|
Returns
|
|
-------
|
|
A Tensor or SparseTensor or IndexedSlices with same shape as x and same type as dtype.
|
|
"""
|
|
|
|
return tf.cast(x, dtype=dtype)
|
|
|
|
|
|
class Transpose(object):
|
|
|
|
def __init__(self, perm, conjugate=False):
|
|
self.perm = perm
|
|
self.conjugate = conjugate
|
|
|
|
def __call__(self, a):
|
|
return tf.transpose(a, self.perm, self.conjugate)
|
|
|
|
|
|
def transpose(a, perm=None, conjugate=False):
|
|
"""
|
|
Transposes a.
|
|
|
|
Parameters
|
|
----------
|
|
a : tensor
|
|
A Tensor.
|
|
perm : list / int
|
|
A permutation of the dimensions of a.
|
|
conjugate : bool
|
|
Setting it to True is mathematically equivalent to tf.math.conj(tf.transpose(input)).
|
|
|
|
Returns
|
|
-------
|
|
A transposed Tensor.
|
|
"""
|
|
|
|
return tf.transpose(a, perm, conjugate)
|
|
|
|
|
|
def gather_nd(params, indices, batch_dims=0):
|
|
"""
|
|
Gather slices from params into a Tensor with shape specified by indices.
|
|
|
|
Parameters
|
|
----------
|
|
params : tensor
|
|
The tensor from which to gather values.
|
|
indices : tensor
|
|
Must be one of the following types: int32, int64. Index tensor.
|
|
batch_dims : int
|
|
An integer or a scalar 'Tensor'. The number of batch dimensions.
|
|
|
|
Returns
|
|
-------
|
|
A Tensor. Has the same type as params.
|
|
"""
|
|
|
|
return tf.gather_nd(params, indices, batch_dims)
|
|
|
|
|
|
def clip_by_value(t, clip_value_min, clip_value_max):
|
|
"""
|
|
Clips tensor values to a specified min and max.
|
|
|
|
Parameters
|
|
----------
|
|
t : tensor
|
|
A Tensor or IndexedSlices
|
|
clip_value_min : tensor
|
|
A 0-D (scalar) Tensor, or a Tensor with the same shape as t. The minimum value to clip by
|
|
clip_value_max : tensor
|
|
A 0-D (scalar) Tensor, or a Tensor with the same shape as t. The minimum value to clip by
|
|
|
|
Returns
|
|
-------
|
|
A clipped Tensor or IndexedSlices.
|
|
"""
|
|
|
|
return tf.clip_by_value(t, clip_value_min, clip_value_max)
|
|
|
|
|
|
def split(value, num_or_size_splits, axis=0, num=None):
|
|
"""
|
|
Splits a tensor into sub tensors.
|
|
|
|
Parameters
|
|
----------
|
|
value : tensor
|
|
The Tensor to split.
|
|
num_or_size_splits : list
|
|
Either an integer indicating the number of splits along split_dim or a 1-D integer Tensor or
|
|
Python list containing the sizes of each output tensor along split_dim.
|
|
axis : int
|
|
The dimension along which to split. Must be in the range [-rank(value), rank(value)). Defaults to 0.
|
|
num : int
|
|
used to specify the number of outputs when it cannot be inferred from the shape of size_splits.
|
|
|
|
Returns
|
|
-------
|
|
Tensor objects resulting from splitting value.
|
|
"""
|
|
|
|
return tf.split(value=value, num_or_size_splits=num_or_size_splits, axis=axis, num=num)
|
|
|
|
|
|
class Floor(object):
|
|
|
|
def __call__(self, x):
|
|
return tf.floor(x)
|
|
|
|
|
|
def floor(x):
|
|
return tf.floor(x)
|
|
|
|
|
|
def gather(params, indices):
|
|
return tf.gather(params, indices)
|
|
|
|
|
|
def linspace(start, stop, num):
|
|
return tf.linspace(start, stop, num)
|
|
|
|
|
|
def slice(inputs, starts, sizes):
|
|
return tf.slice(inputs, starts, sizes)
|
|
|
|
|
|
def add_n(inputs):
|
|
return tf.add_n(inputs)
|
|
|
|
|
|
class OneHot(object):
|
|
|
|
def __init__(self, depth, on_value, off_value, axis, dtype):
|
|
self.depth = depth
|
|
self.on_value = on_value
|
|
self.off_value = off_value
|
|
self.axis = axis
|
|
self.dtype = dtype
|
|
|
|
def __call__(self, inputs):
|
|
outputs = tf.one_hot(
|
|
inputs, self.depth, on_value=self.on_value, off_value=self.off_value, axis=self.axis, dtype=self.dtype
|
|
)
|
|
return outputs
|
|
|
|
|
|
class L2Normalize(object):
|
|
|
|
def __init__(self, axis=None, epsilon=1e-12):
|
|
self.axis = axis
|
|
self.epsilon = epsilon
|
|
|
|
def __call__(self, input, *args, **kwargs):
|
|
outputs = tf.math.l2_normalize(input, axis=self.axis, epsilon=self.epsilon)
|
|
return outputs
|
|
|
|
|
|
class EmbeddingLookup(object):
|
|
|
|
def __init__(self, max_norm=None):
|
|
self.max_norm = max_norm
|
|
|
|
def __call__(self, params, ids):
|
|
outputs = tf.nn.embedding_lookup(params=params, ids=ids, max_norm=self.max_norm)
|
|
return outputs
|
|
|
|
|
|
class NCELoss(object):
|
|
|
|
def __init__(self, num_true=1, sampled_values=None, remove_accidental_hits=False):
|
|
self.num_true = num_true
|
|
self.sampled_values = sampled_values
|
|
self.remove_accidental_hits = remove_accidental_hits
|
|
|
|
def __call__(self, weights, biases, labels, inputs, num_sampled, num_classes):
|
|
outputs = tf.nn.nce_loss(
|
|
weights=weights, biases=biases, inputs=inputs, labels=labels, num_sampled=num_sampled,
|
|
num_classes=num_classes
|
|
)
|
|
return outputs
|
|
|
|
|
|
class NotEqual(object):
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
def __call__(self, x, y):
|
|
return tf.not_equal(x, y)
|
|
|
|
|
|
class CountNonzero(object):
|
|
|
|
def __init__(self, keepdims=None, dtype=int64):
|
|
self.keepdims = keepdims
|
|
self.dtype = dtype
|
|
|
|
def __call__(self, input, axis=None):
|
|
return tf.math.count_nonzero(input, axis=axis, keepdims=self.keepdims, dtype=self.dtype)
|
|
|
|
|
|
class Resize:
|
|
|
|
def __init__(self, scale, method, antialias=False, data_format='channels_last', ksize=None):
|
|
self.method = method
|
|
self.antialias = antialias
|
|
self.scale = scale
|
|
self.data_format = data_format
|
|
|
|
def __call__(self, inputs):
|
|
if self.data_format == 'channels_first':
|
|
inputs = nchw_to_nhwc(inputs)
|
|
if len(get_tensor_shape(inputs)) == 4:
|
|
output_size = [int(inputs.shape[1] * self.scale[0]), int(inputs.shape[2] * self.scale[1])]
|
|
else:
|
|
raise ("The inputs shape must be 4-D Tensor.")
|
|
outputs = tf.image.resize(inputs, size=output_size, method=self.method, antialias=self.antialias)
|
|
if self.data_format == 'channels_first':
|
|
outputs = nhwc_to_nchw(outputs)
|
|
return outputs
|
|
|
|
|
|
def resize(inputs, output_size, method, antialias):
|
|
return tf.image.resize(inputs, size=output_size, method=method, antialias=antialias)
|
|
|
|
|
|
class ZeroPadding1D(object):
|
|
|
|
def __init__(self, padding):
|
|
self.zeropad = tf.keras.layers.ZeroPadding1D(padding=padding)
|
|
|
|
def __call__(self, inputs):
|
|
return self.zeropad(inputs)
|
|
|
|
|
|
class ZeroPadding2D(object):
|
|
|
|
def __init__(self, padding):
|
|
self.zeropad = tf.keras.layers.ZeroPadding2D(padding=padding)
|
|
|
|
def __call__(self, inputs):
|
|
return self.zeropad(inputs)
|
|
|
|
|
|
class ZeroPadding3D(object):
|
|
|
|
def __init__(self, padding):
|
|
self.zeropad = tf.keras.layers.ZeroPadding3D(padding=padding)
|
|
|
|
def __call__(self, inputs):
|
|
return self.zeropad(inputs)
|
|
|
|
|
|
class Sign(object):
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
def __call__(self, x):
|
|
return tf.sign(x)
|
|
|
|
|
|
class Ceil(object):
|
|
|
|
def __call__(self, x):
|
|
return tf.math.ceil(x)
|
|
|
|
|
|
def ceil(x):
|
|
return tf.math.ceil(x)
|
|
|
|
|
|
def multiply(x, y):
|
|
return tf.multiply(x, y)
|
|
|
|
|
|
def divide(x, y):
|
|
return tf.divide(x, y)
|
|
|
|
|
|
def identity(x):
|
|
return tf.identity(x)
|
|
|
|
|
|
class BatchToSpace(object):
|
|
|
|
def __init__(self, block_size, crops):
|
|
self.bolock_size = block_size
|
|
self.crops = crops
|
|
|
|
def __call__(self, input_x):
|
|
return tf.batch_to_space(input=input_x, block_shape=self.bolock_size, crops=self.crops)
|
|
|
|
|
|
class DepthToSpace(object):
|
|
|
|
def __init__(self, block_size, data_format='NHWC'):
|
|
self.block_size = block_size
|
|
self.data_format = data_format
|
|
|
|
def __call__(self, input):
|
|
return tf.nn.depth_to_space(input, block_size=self.block_size, data_format=self.data_format)
|