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rm commented out code

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jh 2021-03-17 12:37:06 -07:00
commit c3a4e4159e
1 changed files with 2 additions and 43 deletions
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41
core/leras/ops/__init__.py
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@ -307,47 +307,6 @@ def dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03
nn.dssim = dssim nn.dssim = dssim
# def ms_ssim(img1, img2, resolution, kernel_size=11, k1=0.01, k2=0.03, max_value=1.0,
# power_factors=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333)):
#
# # restrict mssim factors to those greater/equal to kernel size
# power_factors = [power_factors[i] for i in range(len(power_factors)) if resolution//(2**i) >= kernel_size]
#
# # normalize power factors if reduced because of size
# if sum(power_factors) < 1.0:
# power_factors = [x/sum(power_factors) for x in power_factors]
#
# img_dtype = img1.dtype
# if img_dtype != img2.dtype:
# raise ValueError("img1.dtype != img2.dtype")
#
# if img_dtype != tf.float32:
# img1 = tf.cast(img1, tf.float32)
# img2 = tf.cast(img2, tf.float32)
#
# # Transpose images from NCHW to NHWC
# img1_t = tf.transpose(img1, [0, 2, 3, 1])
# img2_t = tf.transpose(img2, [0, 2, 3, 1])
#
# def assign_device(op):
# if op.type != 'ListDiff':
# return '/gpu:0'
# else:
# return '/cpu:0'
#
# with tf.device(assign_device):
# ms_ssim_val = tf.image.ssim_multiscale(img1_t, img2_t, max_val=max_value, power_factors=power_factors,
# filter_size=kernel_size, k1=k1, k2=k2)
# ms_ssim_loss = (1.0 - ms_ssim_val) / 2.0
#
# if img_dtype != tf.float32:
# ms_ssim_loss = tf.cast(ms_ssim_loss, img_dtype)
# return ms_ssim_loss
#
# nn.ms_ssim = ms_ssim
def space_to_depth(x, size): def space_to_depth(x, size):
if nn.data_format == "NHWC": if nn.data_format == "NHWC":
# match NCHW version in order to switch data_format without problems # match NCHW version in order to switch data_format without problems