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fix: try inner function

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jh 2021-03-12 08:41:48 -08:00
commit f5edf30e84
2 changed files with 23 additions and 17 deletions
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38
core/leras/ops/__init__.py
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@ -308,15 +308,8 @@ 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, def ms_ssim(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)): power_factors=(0.0448, 0.2856, 0.3001, 0.2363, 0.1333)):
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)
# restrict mssim factors to those greater/equal to kernel size # 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] power_factors = [power_factors[i] for i in range(len(power_factors)) if resolution//(2**i) >= kernel_size]
@ -325,15 +318,28 @@ def ms_ssim(img1, img2, resolution, kernel_size=11, k1=0.01, k2=0.03, max_value=
if sum(power_factors) < 1.0: if sum(power_factors) < 1.0:
power_factors = [x/sum(power_factors) for x in power_factors] power_factors = [x/sum(power_factors) for x in power_factors]
# Transpose images from NCHW to NHWC def loss(img1, img2):
img1_t = tf.transpose(img1, [0, 2, 3, 1]) img_dtype = img1.dtype
img2_t = tf.transpose(img2, [0, 2, 3, 1]) if img_dtype != img2.dtype:
ms_ssim_val = tf.image.ssim_multiscale(img1_t, img2_t, max_val=max_value, power_factors=power_factors, raise ValueError("img1.dtype != img2.dtype")
filter_size=kernel_size, k1=k1, k2=k2)
loss = (1.0 - ms_ssim_val) / 2.0 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])
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
if img_dtype != tf.float32:
loss = tf.cast(loss, img_dtype)
return loss return loss
nn.ms_ssim = ms_ssim nn.ms_ssim = ms_ssim

2
models/Model_SAEHD/Model.py
View file

@ -426,7 +426,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_style_blur) gpu_psd_target_dst_style_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_style_blur)
if self.options['ms_ssim_loss']: if self.options['ms_ssim_loss']:
gpu_src_loss = tf.reduce_mean ( 10*nn.ms_ssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, resolution)) gpu_src_loss = tf.reduce_mean ( 10*nn.ms_ssim(resolution)(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt))
else: else:
if resolution < 256: if resolution < 256:
gpu_src_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1]) gpu_src_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])