core/leras/ops/__init__.py

import numpy as np
from core.leras import nn
tf = nn.tf
from tensorflow.python.ops import array_ops, random_ops, math_ops, sparse_ops, gradients
from tensorflow.python.framework import sparse_tensor

def tf_get_value(tensor):
    return nn.tf_sess.run (tensor)
nn.tf_get_value = tf_get_value


def batch_set_value(tuples):
    if len(tuples) != 0:
        with nn.tf.device('/CPU:0'):
            assign_ops = []
            feed_dict = {}

            for x, value in tuples:
                if isinstance(value, nn.tf.Operation) or \
                    isinstance(value, nn.tf.Variable):
                    assign_ops.append(value)
                else:
                    value = np.asarray(value, dtype=x.dtype.as_numpy_dtype)
                    assign_placeholder = nn.tf.placeholder( x.dtype.base_dtype, shape=[None]*value.ndim )
                    assign_op = nn.tf.assign (x, assign_placeholder )
                    assign_ops.append(assign_op)
                    feed_dict[assign_placeholder] = value

            nn.tf_sess.run(assign_ops, feed_dict=feed_dict)
nn.batch_set_value = batch_set_value

def init_weights(weights):
    ops = []

    ca_tuples_w = []
    ca_tuples = []
    for w in weights:
        initializer = w.initializer
        for input in initializer.inputs:
            if "_cai_" in input.name:
                ca_tuples_w.append (w)
                ca_tuples.append ( (w.shape.as_list(), w.dtype.as_numpy_dtype) )
                break
        else:
            ops.append (initializer)

    if len(ops) != 0:
        nn.tf_sess.run (ops)

    if len(ca_tuples) != 0:
        nn.batch_set_value( [*zip(ca_tuples_w, nn.initializers.ca.generate_batch (ca_tuples))] )
nn.init_weights = init_weights

def tf_gradients ( loss, vars ):
    grads = gradients.gradients(loss, vars, colocate_gradients_with_ops=True )
    gv = [*zip(grads,vars)]
    for g,v in gv:
        if g is None:
            raise Exception(f"Variable {v.name} is declared as trainable, but no tensors flow through it.")
    return gv
nn.gradients = tf_gradients

def average_gv_list(grad_var_list, tf_device_string=None):
    if len(grad_var_list) == 1:
        return grad_var_list[0]

    e = tf.device(tf_device_string) if tf_device_string is not None else None
    if e is not None: e.__enter__()
    result = []
    for i, (gv) in enumerate(grad_var_list):
        for j,(g,v) in enumerate(gv):
            g = tf.expand_dims(g, 0)
            if i == 0:
                result += [ [[g], v]  ]
            else:
                result[j][0] += [g]

    for i,(gs,v) in enumerate(result):
        result[i] = ( tf.reduce_mean( tf.concat (gs, 0), 0 ), v )
    if e is not None: e.__exit__(None,None,None)
    return result
nn.average_gv_list = average_gv_list

def average_tensor_list(tensors_list, tf_device_string=None):
    if len(tensors_list) == 1:
        return tensors_list[0]

    e = tf.device(tf_device_string) if tf_device_string is not None else None
    if e is not None: e.__enter__()
    result = tf.reduce_mean(tf.concat ([tf.expand_dims(t, 0) for t in tensors_list], 0), 0)
    if e is not None: e.__exit__(None,None,None)
    return result
nn.average_tensor_list = average_tensor_list

def concat (tensors_list, axis):
    """
    Better version.
    """
    if len(tensors_list) == 1:
        return tensors_list[0]
    return tf.concat(tensors_list, axis)
nn.concat = concat

def gelu(x):
    cdf = 0.5 * (1.0 + tf.nn.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
    return x * cdf
nn.gelu = gelu

def upsample2d(x, size=2):
    if nn.data_format == "NCHW":
        x = tf.transpose(x, (0,2,3,1))
        x = tf.image.resize_nearest_neighbor(x, (x.shape[1]*size, x.shape[2]*size) )
        x = tf.transpose(x, (0,3,1,2))
        
        
        # b,c,h,w = x.shape.as_list()
        # x = tf.reshape (x, (-1,c,h,1,w,1) )
        # x = tf.tile(x, (1,1,1,size,1,size) )
        # x = tf.reshape (x, (-1,c,h*size,w*size) )
        return x
    else:
        return tf.image.resize_nearest_neighbor(x, (x.shape[1]*size, x.shape[2]*size) )
nn.upsample2d = upsample2d

def resize2d_bilinear(x, size=2):
    h = x.shape[nn.conv2d_spatial_axes[0]].value
    w = x.shape[nn.conv2d_spatial_axes[1]].value

    if nn.data_format == "NCHW":
        x = tf.transpose(x, (0,2,3,1))

    if size > 0:
        new_size = (h*size,w*size)
    else:
        new_size = (h//-size,w//-size)

    x = tf.image.resize(x, new_size, method=tf.image.ResizeMethod.BILINEAR)

    if nn.data_format == "NCHW":
        x = tf.transpose(x, (0,3,1,2))

    return x
nn.resize2d_bilinear = resize2d_bilinear

def resize2d_nearest(x, size=2):
    if size in [-1,0,1]:
        return x


    if size > 0:
        raise Exception("")
    else:
        if nn.data_format == "NCHW":
            x = x[:,:,::-size,::-size]
        else:
            x = x[:,::-size,::-size,:]
    return x

    h = x.shape[nn.conv2d_spatial_axes[0]].value
    w = x.shape[nn.conv2d_spatial_axes[1]].value

    if nn.data_format == "NCHW":
        x = tf.transpose(x, (0,2,3,1))

    if size > 0:
        new_size = (h*size,w*size)
    else:
        new_size = (h//-size,w//-size)

    x = tf.image.resize(x, new_size, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)

    if nn.data_format == "NCHW":
        x = tf.transpose(x, (0,3,1,2))

    return x
nn.resize2d_nearest = resize2d_nearest

def flatten(x):
    if nn.data_format == "NHWC":
        # match NCHW version in order to switch data_format without problems
        x = tf.transpose(x, (0,3,1,2) )
    return tf.reshape (x, (-1, np.prod(x.shape[1:])) )

nn.flatten = flatten

def max_pool(x, kernel_size=2, strides=2):
    if nn.data_format == "NHWC":
        return tf.nn.max_pool(x, [1,kernel_size,kernel_size,1], [1,strides,strides,1], 'SAME', data_format=nn.data_format)
    else:
        return tf.nn.max_pool(x, [1,1,kernel_size,kernel_size], [1,1,strides,strides], 'SAME', data_format=nn.data_format)

nn.max_pool = max_pool

def reshape_4D(x, w,h,c):
    if nn.data_format == "NHWC":
        # match NCHW version in order to switch data_format without problems
        x = tf.reshape (x, (-1,c,h,w))
        x = tf.transpose(x, (0,2,3,1) )
        return x
    else:
        return tf.reshape (x, (-1,c,h,w))
nn.reshape_4D = reshape_4D

def random_binomial(shape, p=0.0, dtype=None, seed=None):
    if dtype is None:
        dtype=tf.float32

    if seed is None:
        seed = np.random.randint(10e6)
    return array_ops.where(
        random_ops.random_uniform(shape, dtype=tf.float16, seed=seed) < p,
             array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
nn.random_binomial = random_binomial

def gaussian_blur(input, radius=2.0):
    def gaussian(x, mu, sigma):
        return np.exp(-(float(x) - float(mu)) ** 2 / (2 * sigma ** 2))

    def make_kernel(sigma):
        kernel_size = max(3, int(2 * 2 * sigma))
        if kernel_size % 2 == 0:
            kernel_size += 1
        mean = np.floor(0.5 * kernel_size)
        kernel_1d = np.array([gaussian(x, mean, sigma) for x in range(kernel_size)])
        np_kernel = np.outer(kernel_1d, kernel_1d).astype(np.float32)
        kernel = np_kernel / np.sum(np_kernel)
        return kernel, kernel_size

    gauss_kernel, kernel_size = make_kernel(radius)
    padding = kernel_size//2
    if padding != 0:
        if nn.data_format == "NHWC":
            padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
        else:
            padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
    else:
        padding = None
    gauss_kernel = gauss_kernel[:,:,None,None]

    x = input
    k = tf.tile (gauss_kernel, (1,1,x.shape[nn.conv2d_ch_axis],1) )
    x = tf.pad(x, padding )
    x = tf.nn.depthwise_conv2d(x, k, strides=[1,1,1,1], padding='VALID', data_format=nn.data_format)
    return x
nn.gaussian_blur = gaussian_blur

def style_loss(target, style, gaussian_blur_radius=0.0, loss_weight=1.0, step_size=1):
    def sd(content, style, loss_weight):
        content_nc = content.shape[ nn.conv2d_ch_axis ]
        style_nc = style.shape[nn.conv2d_ch_axis]
        if content_nc != style_nc:
            raise Exception("style_loss() content_nc != style_nc")
        c_mean, c_var = tf.nn.moments(content, axes=nn.conv2d_spatial_axes, keep_dims=True)
        s_mean, s_var = tf.nn.moments(style, axes=nn.conv2d_spatial_axes, keep_dims=True)
        c_std, s_std = tf.sqrt(c_var + 1e-5), tf.sqrt(s_var + 1e-5)
        mean_loss = tf.reduce_sum(tf.square(c_mean-s_mean), axis=[1,2,3])
        std_loss  = tf.reduce_sum(tf.square(c_std-s_std), axis=[1,2,3])
        return (mean_loss + std_loss) * ( loss_weight / content_nc.value )

    if gaussian_blur_radius > 0.0:
        target = gaussian_blur(target, gaussian_blur_radius)
        style = gaussian_blur(style, gaussian_blur_radius)

    return sd( target, style, loss_weight=loss_weight )

nn.style_loss = style_loss

def dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
    if img1.dtype != img2.dtype:
        raise ValueError("img1.dtype != img2.dtype")

    not_float32 = img1.dtype != tf.float32

    if not_float32:
        img_dtype = img1.dtype
        img1 = tf.cast(img1, tf.float32)
        img2 = tf.cast(img2, tf.float32)

    filter_size = max(1, filter_size)

    kernel = np.arange(0, filter_size, dtype=np.float32)
    kernel -= (filter_size - 1 ) / 2.0
    kernel = kernel**2
    kernel *= ( -0.5 / (filter_sigma**2) )
    kernel = np.reshape (kernel, (1,-1)) + np.reshape(kernel, (-1,1) )
    kernel = tf.constant ( np.reshape (kernel, (1,-1)), dtype=tf.float32 )
    kernel = tf.nn.softmax(kernel)
    kernel = tf.reshape (kernel, (filter_size, filter_size, 1, 1))
    kernel = tf.tile (kernel, (1,1, img1.shape[ nn.conv2d_ch_axis ] ,1))

    def reducer(x):
        return tf.nn.depthwise_conv2d(x, kernel, strides=[1,1,1,1], padding='VALID', data_format=nn.data_format)

    c1 = (k1 * max_val) ** 2
    c2 = (k2 * max_val) ** 2

    mean0 = reducer(img1)
    mean1 = reducer(img2)
    num0 = mean0 * mean1 * 2.0
    den0 = tf.square(mean0) + tf.square(mean1)
    luminance = (num0 + c1) / (den0 + c1)

    num1 = reducer(img1 * img2) * 2.0
    den1 = reducer(tf.square(img1) + tf.square(img2))
    c2 *= 1.0 #compensation factor
    cs = (num1 - num0 + c2) / (den1 - den0 + c2)

    ssim_val = tf.reduce_mean(luminance * cs, axis=nn.conv2d_spatial_axes )
    dssim = (1.0 - ssim_val ) / 2.0

    if not_float32:
        dssim = tf.cast(dssim, img_dtype)
    return dssim

nn.dssim = dssim

def space_to_depth(x, size):
    if nn.data_format == "NHWC":
        # match NCHW version in order to switch data_format without problems
        b,h,w,c = x.shape.as_list()
        oh, ow = h // size, w // size
        x = tf.reshape(x, (-1, size, oh, size, ow, c))
        x = tf.transpose(x, (0, 2, 4, 1, 3, 5))
        x = tf.reshape(x, (-1, oh, ow, size* size* c ))
        return x
    else:
        return tf.space_to_depth(x, size, data_format=nn.data_format)
nn.space_to_depth = space_to_depth

def depth_to_space(x, size):
    if nn.data_format == "NHWC":
        # match NCHW version in order to switch data_format without problems

        b,h,w,c = x.shape.as_list()
        oh, ow = h * size, w * size
        oc = c // (size * size)

        x = tf.reshape(x, (-1, h, w, size, size, oc, ) )
        x = tf.transpose(x, (0, 1, 3, 2, 4, 5))
        x = tf.reshape(x, (-1, oh, ow, oc, ))
        return x
    else:
        cfg = nn.getCurrentDeviceConfig()
        if not cfg.cpu_only:
            return tf.depth_to_space(x, size, data_format=nn.data_format)
        b,c,h,w = x.shape.as_list()
        oh, ow = h * size, w * size
        oc = c // (size * size)

        x = tf.reshape(x, (-1, size, size, oc, h, w, ) )
        x = tf.transpose(x, (0, 3, 4, 1, 5, 2))
        x = tf.reshape(x, (-1, oc, oh, ow))
        return x
nn.depth_to_space = depth_to_space

def rgb_to_lab(srgb):
    srgb_pixels = tf.reshape(srgb, [-1, 3])
    linear_mask = tf.cast(srgb_pixels <= 0.04045, dtype=tf.float32)
    exponential_mask = tf.cast(srgb_pixels > 0.04045, dtype=tf.float32)
    rgb_pixels = (srgb_pixels / 12.92 * linear_mask) + (((srgb_pixels + 0.055) / 1.055) ** 2.4) * exponential_mask
    rgb_to_xyz = tf.constant([
        #    X        Y          Z
        [0.412453, 0.212671, 0.019334], # R
        [0.357580, 0.715160, 0.119193], # G
        [0.180423, 0.072169, 0.950227], # B
    ])
    xyz_pixels = tf.matmul(rgb_pixels, rgb_to_xyz)

    xyz_normalized_pixels = tf.multiply(xyz_pixels, [1/0.950456, 1.0, 1/1.088754])

    epsilon = 6/29
    linear_mask = tf.cast(xyz_normalized_pixels <= (epsilon**3), dtype=tf.float32)
    exponential_mask = tf.cast(xyz_normalized_pixels > (epsilon**3), dtype=tf.float32)
    fxfyfz_pixels = (xyz_normalized_pixels / (3 * epsilon**2) + 4/29) * linear_mask + (xyz_normalized_pixels ** (1/3)) * exponential_mask

    fxfyfz_to_lab = tf.constant([
        #  l       a       b
        [  0.0,  500.0,    0.0], # fx
        [116.0, -500.0,  200.0], # fy
        [  0.0,    0.0, -200.0], # fz
    ])
    lab_pixels = tf.matmul(fxfyfz_pixels, fxfyfz_to_lab) + tf.constant([-16.0, 0.0, 0.0])
    return tf.reshape(lab_pixels, tf.shape(srgb))
nn.rgb_to_lab = rgb_to_lab

def total_variation_mse(images):
    """
    Same as generic total_variation, but MSE diff instead of MAE
    """
    pixel_dif1 = images[:, 1:, :, :] - images[:, :-1, :, :]
    pixel_dif2 = images[:, :, 1:, :] - images[:, :, :-1, :]
    
    tot_var = ( tf.reduce_sum(tf.square(pixel_dif1), axis=[1,2,3]) +
                tf.reduce_sum(tf.square(pixel_dif2), axis=[1,2,3]) )
    return tot_var
nn.total_variation_mse = total_variation_mse


def pixel_norm(x, axes):
    return x * tf.rsqrt(tf.reduce_mean(tf.square(x), axis=axes, keepdims=True) + 1e-06)
nn.pixel_norm = pixel_norm
        
"""
def tf_suppress_lower_mean(t, eps=0.00001):
    if t.shape.ndims != 1:
        raise ValueError("tf_suppress_lower_mean: t rank must be 1")
    t_mean_eps = tf.reduce_mean(t) - eps
    q = tf.clip_by_value(t, t_mean_eps, tf.reduce_max(t) )
    q = tf.clip_by_value(q-t_mean_eps, 0, eps)
    q = q * (t/eps)
    return q
"""



def _get_pixel_value(img, x, y):
    shape = tf.shape(x)
    batch_size = shape[0]
    height = shape[1]
    width = shape[2]

    batch_idx = tf.range(0, batch_size)
    batch_idx = tf.reshape(batch_idx, (batch_size, 1, 1))
    b = tf.tile(batch_idx, (1, height, width))

    indices = tf.stack([b, y, x], 3)
    
    return tf.gather_nd(img, indices)
    
def bilinear_sampler(img, x, y):
    H = tf.shape(img)[1]
    W = tf.shape(img)[2]
    H_MAX = tf.cast(H - 1, tf.int32)
    W_MAX = tf.cast(W - 1, tf.int32)

    # grab 4 nearest corner points for each (x_i, y_i)
    x0 = tf.cast(tf.floor(x), tf.int32)
    x1 = x0 + 1
    y0 = tf.cast(tf.floor(y), tf.int32)
    y1 = y0 + 1

    # clip to range [0, H-1/W-1] to not violate img boundaries
    x0 = tf.clip_by_value(x0, 0, W_MAX)
    x1 = tf.clip_by_value(x1, 0, W_MAX)
    y0 = tf.clip_by_value(y0, 0, H_MAX)
    y1 = tf.clip_by_value(y1, 0, H_MAX)

    # get pixel value at corner coords
    Ia = _get_pixel_value(img, x0, y0)
    Ib = _get_pixel_value(img, x0, y1)
    Ic = _get_pixel_value(img, x1, y0)
    Id = _get_pixel_value(img, x1, y1)

    # recast as float for delta calculation
    x0 = tf.cast(x0, tf.float32)
    x1 = tf.cast(x1, tf.float32)
    y0 = tf.cast(y0, tf.float32)
    y1 = tf.cast(y1, tf.float32)

    # calculate deltas
    wa = (x1-x) * (y1-y)
    wb = (x1-x) * (y-y0)
    wc = (x-x0) * (y1-y)
    wd = (x-x0) * (y-y0)

    # add dimension for addition
    wa = tf.expand_dims(wa, axis=3)
    wb = tf.expand_dims(wb, axis=3)
    wc = tf.expand_dims(wc, axis=3)
    wd = tf.expand_dims(wd, axis=3)

    # compute output
    out = tf.add_n([wa*Ia, wb*Ib, wc*Ic, wd*Id])

    return out
    
nn.bilinear_sampler = bilinear_sampler