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Colombo 2020-01-24 11:51:54 +04:00
parent be5df1e269
commit f7f4e44a98
2 changed files with 21 additions and 0 deletions
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core/leras/nn.py
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@ -42,6 +42,8 @@ class nn():
tf_random_binomial = None tf_random_binomial = None
tf_gaussian_blur = None tf_gaussian_blur = None
tf_style_loss = None tf_style_loss = None
tf_channel_histogram = None
tf_histogram = None
tf_dssim = None tf_dssim = None
# Layers # Layers

19
core/leras/tensor_ops.py
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@ -187,6 +187,25 @@ def initialize_tensor_ops(nn):
nn.tf_style_loss = tf_style_loss nn.tf_style_loss = tf_style_loss
def tf_channel_histogram (input, bins, data_range):
range_min, range_max = data_range
bin_range = (range_max-range_min) / (bins-1)
reduce_axes = [*range(input.shape.ndims)][1:]
x = input
x += bin_range/2
output = []
for i in range(bins-1, -1, -1):
y = x - (i*bin_range)
ones_mask = tf.sign( tf.nn.relu(y) )
x = x * (1.0 - ones_mask)
output.append ( tf.expand_dims(tf.reduce_sum (ones_mask, axis=reduce_axes ), -1) )
return tf.concat(output[::-1],-1)
nn.tf_channel_histogram = tf_channel_histogram
def tf_histogram(input, bins=256, data_range=(0,1.0)):
return tf.concat ( [tf.expand_dims( tf_channel_histogram( input[...,i], bins=bins, data_range=data_range ), -1 ) for i in range(input.shape[-1])], -1 )
nn.tf_histogram = tf_histogram
def tf_dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03): def tf_dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
ch = img2.shape[-1] ch = img2.shape[-1]