tensor_utils_5_channels.py

import os
from functools import reduce

import tensorflow as tf
import numpy as np
from cv2 import imwrite

def save_image(image, save_dir, name, mean=None):
    if mean:
        image = unprocess_image(image, mean)
    # misc.imsave(os.path.join(save_dir, name + ".png"), image)
    imwrite(os.path.join(save_dir, name + ".png"), image)

def get_variable(weights, name):
    init = tf.constant_initializer(weights, dtype=tf.float32)
    # init = tf.contrib.layers.xavier_initializer()
    var = tf.get_variable(name=name, initializer=init,  shape=weights.shape)
    return var


def weight_variable(shape, stddev=0.02, name=None):
    # print(shape)
    initial = tf.truncated_normal(shape, stddev=stddev)
    if name is None:
        return tf.Variable(initial)
    else:
        return tf.get_variable(name, initializer=initial)


def bias_variable(shape, name=None):
    initial = tf.constant(0.0, shape=shape)
    if name is None:
        return tf.Variable(initial)
    else:
        return tf.get_variable(name, initializer=initial)


def get_tensor_size(tensor):
    from operator import mul
    return reduce(mul, (d.value for d in tensor.get_shape()), 1)


def conv2d_basic(x, W, bias):
    conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
    return tf.nn.bias_add(conv, bias)


def conv2d_strided(x, W, b):
    conv = tf.nn.conv2d(x, W, strides=[1, 2, 2, 1], padding="SAME")
    return tf.nn.bias_add(conv, b)


def conv2d_transpose_strided(x, W, b, output_shape=None, stride = 2):
    if output_shape is None:
        output_shape = x.get_shape().as_list()
        output_shape[1] *= 2
        output_shape[2] *= 2
        output_shape[3] = W.get_shape().as_list()[2]
    conv = tf.nn.conv2d_transpose(x, W, output_shape, strides=[1, stride, stride, 1], padding="SAME")
    return tf.nn.bias_add(conv, b)


def leaky_relu(x, alpha=0.0, name=""):
    return tf.maximum(alpha * x, x, name)


def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")


def avg_pool_2x2(x):
    return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")


def local_response_norm(x):
    return tf.nn.lrn(x, depth_radius=5, bias=2, alpha=1e-4, beta=0.75)


def batch_norm(x, n_out, phase_train, scope='bn', decay=0.9, eps=1e-5):
    with tf.variable_scope(scope):
        beta = tf.get_variable(name='beta', shape=[n_out], initializer=tf.constant_initializer(0.0)
                               , trainable=True)
        gamma = tf.get_variable(name='gamma', shape=[n_out], initializer=tf.random_normal_initializer(1.0, 0.02),
                                trainable=True)
        batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')
        print(np.shape(beta), np.shape(gamma))
        print(np.shape(batch_mean), np.shape(batch_var))
        ema = tf.train.ExponentialMovingAverage(decay=decay)

        def mean_var_with_update():
            ema_apply_op = ema.apply([batch_mean, batch_var])
            with tf.control_dependencies([ema_apply_op]):
                return tf.identity(batch_mean), tf.identity(batch_var)

        mean, var = tf.cond(phase_train,
                            mean_var_with_update,
                            lambda: (ema.average(batch_mean), ema.average(batch_var)))
        print(np.shape(mean), np.shape(var))
        normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps)
        print(np.shape(normed))
    return normed


def process_image(image, mean_pixel):
    return image - mean_pixel


def unprocess_image(image, mean_pixel):
    return image + mean_pixel

def add_to_regularization_and_summary(var):
    if var is not None:
        tf.summary.histogram(var.op.name, var)
        tf.add_to_collection("reg_loss", tf.nn.l2_loss(var))


def add_activation_summary(var):
    if var is not None:
        tf.summary.histogram(var.op.name + "/activation", var)
        tf.summary.scalar(var.op.name + "/sparsity", tf.nn.zero_fraction(var))


def add_gradient_summary(grad, var):
    if grad is not None:
        tf.summary.histogram(var.op.name + "/gradient", grad)