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fix blur_out_mask for tf 1.13, fix export model for dx12 live build.
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@ -390,18 +390,17 @@ class AMPModel(ModelBase):
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gpu_target_dstm_anti_blur = 1.0-gpu_target_dstm_blur
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if blur_out_mask:
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#gpu_target_src = gpu_target_src*gpu_target_srcm_blur + nn.gaussian_blur(gpu_target_src, resolution // 32)*gpu_target_srcm_anti_blur
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#gpu_target_dst = gpu_target_dst*gpu_target_dstm_blur + nn.gaussian_blur(gpu_target_dst, resolution // 32)*gpu_target_dstm_anti_blur
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bg_blur_div = 128
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sigma = resolution / 128
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gpu_target_src = gpu_target_src*gpu_target_srcm + \
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tf.math.divide_no_nan(nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, resolution / bg_blur_div),
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(1-nn.gaussian_blur(gpu_target_srcm, resolution / bg_blur_div) ) ) * gpu_target_srcm_anti
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gpu_target_dst = gpu_target_dst*gpu_target_dstm + \
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tf.math.divide_no_nan(nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, resolution / bg_blur_div),
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(1-nn.gaussian_blur(gpu_target_dstm, resolution / bg_blur_div)) ) * gpu_target_dstm_anti
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x = nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, sigma)
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y = 1-nn.gaussian_blur(gpu_target_srcm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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gpu_target_src = gpu_target_src*gpu_target_srcm + (x/y)*gpu_target_srcm_anti
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x = nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, sigma)
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y = 1-nn.gaussian_blur(gpu_target_dstm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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gpu_target_dst = gpu_target_dst*gpu_target_dstm + (x/y)*gpu_target_dstm_anti
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gpu_target_src_masked = gpu_target_src*gpu_target_srcm_blur
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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@ -628,7 +627,7 @@ class AMPModel(ModelBase):
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name='AMP',
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input_names=['in_face:0','morph_value:0'],
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output_names=['out_face_mask:0','out_celeb_face:0','out_celeb_face_mask:0'],
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opset=13,
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opset=9,
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output_path=output_path)
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#override
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@ -375,17 +375,18 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_target_dstm_anti = 1-gpu_target_dstm
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if blur_out_mask:
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#gpu_target_src = gpu_target_src*gpu_target_srcm_blur + nn.gaussian_blur(gpu_target_src, resolution // 32)*gpu_target_srcm_anti_blur
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#gpu_target_dst = gpu_target_dst*gpu_target_dstm_blur + nn.gaussian_blur(gpu_target_dst, resolution // 32)*gpu_target_dstm_anti_blur
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bg_blur_div = 128
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sigma = resolution / 128
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gpu_target_src = gpu_target_src*gpu_target_srcm + \
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tf.math.divide_no_nan(nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, resolution / bg_blur_div),
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(1-nn.gaussian_blur(gpu_target_srcm, resolution / bg_blur_div) ) ) * gpu_target_srcm_anti
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x = nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, sigma)
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y = 1-nn.gaussian_blur(gpu_target_srcm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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gpu_target_src = gpu_target_src*gpu_target_srcm + (x/y)*gpu_target_srcm_anti
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x = nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, sigma)
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y = 1-nn.gaussian_blur(gpu_target_dstm, sigma)
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y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
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gpu_target_dst = gpu_target_dst*gpu_target_dstm + (x/y)*gpu_target_dstm_anti
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gpu_target_dst = gpu_target_dst*gpu_target_dstm + \
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tf.math.divide_no_nan(nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, resolution / bg_blur_div),
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(1-nn.gaussian_blur(gpu_target_dstm, resolution / bg_blur_div)) ) * gpu_target_dstm_anti
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# process model tensors
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if 'df' in archi_type:
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@ -742,7 +743,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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name='SAEHD',
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input_names=['in_face:0'],
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output_names=['out_face_mask:0','out_celeb_face:0','out_celeb_face_mask:0'],
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opset=13,
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opset=9,
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output_path=output_path)
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#override
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