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AMP: added Inter dimensions param. The model is not changed. Should be equal or more than AutoEncoder dimensions. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU.

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iperov 2021-06-26 10:55:33 +04:00
parent 5783191849
commit 3c6bbe22b9
1 changed files with 19 additions and 10 deletions
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29
models/Model_AMP/Model.py
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@ -36,6 +36,12 @@ class AMPModel(ModelBase):
default_models_opt_on_gpu = self.options['models_opt_on_gpu'] = self.load_or_def_option('models_opt_on_gpu', True)
default_ae_dims = self.options['ae_dims'] = self.load_or_def_option('ae_dims', 256)
inter_dims = self.load_or_def_option('inter_dims', None)
if inter_dims is None:
inter_dims = self.options['ae_dims']
default_inter_dims = self.options['inter_dims'] = inter_dims
default_e_dims = self.options['e_dims'] = self.load_or_def_option('e_dims', 64)
default_d_dims = self.options['d_dims'] = self.options.get('d_dims', None)
default_d_mask_dims = self.options['d_mask_dims'] = self.options.get('d_mask_dims', None)
@ -77,7 +83,9 @@ class AMPModel(ModelBase):
default_d_mask_dims = self.options['d_mask_dims'] = self.load_or_def_option('d_mask_dims', default_d_mask_dims)
if self.is_first_run():
self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dimensions", default_ae_dims, add_info="32-1024", help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dimensions", default_ae_dims, add_info="32-1024", help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 )
self.options['inter_dims'] = np.clip ( io.input_int("Inter dimensions", default_inter_dims, add_info="32-2048", help_message="Should be equal or more than AutoEncoder dimensions. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 2048 )
e_dims = np.clip ( io.input_int("Encoder dimensions", default_e_dims, add_info="16-256", help_message="More dims help to recognize more facial features and achieve sharper result, but require more VRAM. You can fine-tune model size to fit your GPU." ), 16, 256 )
self.options['e_dims'] = e_dims + e_dims % 2
@ -139,6 +147,7 @@ class AMPModel(ModelBase):
input_ch=3
ae_dims = self.ae_dims = self.options['ae_dims']
inter_dims = self.inter_dims = self.options['inter_dims']
e_dims = self.options['e_dims']
d_dims = self.options['d_dims']
d_mask_dims = self.options['d_mask_dims']
@ -203,13 +212,13 @@ class AMPModel(ModelBase):
def forward(self, inp):
x = inp
x = self.dense2(x)
x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, ae_dims)
x = nn.reshape_4D (x, lowest_dense_res, lowest_dense_res, inter_dims)
return x
class Decoder(nn.ModelBase):
def on_build(self ):
self.upscale0 = Upscale(ae_dims, d_dims*8, kernel_size=3)
self.upscale0 = Upscale(inter_dims, d_dims*8, kernel_size=3)
self.upscale1 = Upscale(d_dims*8, d_dims*8, kernel_size=3)
self.upscale2 = Upscale(d_dims*8, d_dims*4, kernel_size=3)
self.upscale3 = Upscale(d_dims*4, d_dims*2, kernel_size=3)
@ -219,7 +228,7 @@ class AMPModel(ModelBase):
self.res2 = ResidualBlock(d_dims*4, kernel_size=3)
self.res3 = ResidualBlock(d_dims*2, kernel_size=3)
self.upscalem0 = Upscale(ae_dims, d_mask_dims*8, kernel_size=3)
self.upscalem0 = Upscale(inter_dims, d_mask_dims*8, kernel_size=3)
self.upscalem1 = Upscale(d_mask_dims*8, d_mask_dims*8, kernel_size=3)
self.upscalem2 = Upscale(d_mask_dims*8, d_mask_dims*4, kernel_size=3)
self.upscalem3 = Upscale(d_mask_dims*4, d_mask_dims*2, kernel_size=3)
@ -402,9 +411,9 @@ class AMPModel(ModelBase):
gpu_src_code = gpu_src_inter_src_code * inter_rnd_binomial + gpu_src_inter_dst_code * (1-inter_rnd_binomial)
gpu_dst_code = gpu_dst_inter_dst_code
ae_dims_slice = tf.cast(ae_dims*self.morph_value_t[0], tf.int32)
gpu_src_dst_code = tf.concat( (tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, ae_dims_slice , lowest_dense_res, lowest_dense_res]),
tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,ae_dims-ae_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
inter_dims_slice = tf.cast(inter_dims*self.morph_value_t[0], tf.int32)
gpu_src_dst_code = tf.concat( (tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, inter_dims_slice , lowest_dense_res, lowest_dense_res]),
tf.slice(gpu_dst_inter_dst_code, [0,inter_dims_slice,0,0], [-1,ae_dims-inter_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder(gpu_src_code)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
@ -558,9 +567,9 @@ class AMPModel(ModelBase):
gpu_dst_inter_src_code = self.inter_src ( gpu_dst_code)
gpu_dst_inter_dst_code = self.inter_dst ( gpu_dst_code)
ae_dims_slice = tf.cast(ae_dims*self.morph_value_t[0], tf.int32)
gpu_src_dst_code = tf.concat( ( tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, ae_dims_slice , lowest_dense_res, lowest_dense_res]),
tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,ae_dims-ae_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
inter_dims_slice = tf.cast(inter_dims*self.morph_value_t[0], tf.int32)
gpu_src_dst_code = tf.concat( ( tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, inter_dims_slice , lowest_dense_res, lowest_dense_res]),
tf.slice(gpu_dst_inter_dst_code, [0,inter_dims_slice,0,0], [-1,ae_dims-inter_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code)