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This commit is contained in:
Colombo 2020-01-26 12:56:21 +04:00
parent 76ca79216e
commit c485e1718a
4 changed files with 10 additions and 22 deletions
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12
core/leras/nn.py
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@ -182,23 +182,17 @@ class nn():
nn.conv2d_spatial_axes = [2,3] nn.conv2d_spatial_axes = [2,3]
@staticmethod @staticmethod
def get4Dshape ( w, h, c, data_format=None ): def get4Dshape ( w, h, c ):
""" """
returns 4D shape based on current data_format returns 4D shape based on current data_format
""" """
if data_format is None: if nn.data_format == "NHWC":
data_format = nn.data_format
if data_format == "NHWC":
return (None,h,w,c) return (None,h,w,c)
else: else:
return (None,c,h,w) return (None,c,h,w)
@staticmethod @staticmethod
def to_data_format( x, to_data_format, from_data_format=None): def to_data_format( x, to_data_format, from_data_format):
if from_data_format is None:
from_data_format = nn.data_format
if to_data_format == from_data_format: if to_data_format == from_data_format:
return x return x

2
core/leras/tensor_ops.py
View file

@ -35,7 +35,7 @@ def initialize_tensor_ops(nn):
gv = [*zip(grads,vars)] gv = [*zip(grads,vars)]
for g,v in gv: for g,v in gv:
if g is None: if g is None:
raise Exception("No gradient for variable {v.name}") raise Exception(f"No gradient for variable {v.name}")
return gv return gv
nn.tf_gradients = tf_gradients nn.tf_gradients = tf_gradients

7
models/Model_Quick96/Model.py
View file

@ -413,18 +413,15 @@ class QModel(ModelBase):
#override #override
def onGetPreview(self, samples): def onGetPreview(self, samples):
n_samples = min(4, self.get_batch_size() )
( (warped_src, target_src, target_srcm), ( (warped_src, target_src, target_srcm),
(warped_dst, target_dst, target_dstm) ) = \ (warped_dst, target_dst, target_dstm) ) = samples
[ [sample[0:n_samples] for sample in sample_list ]
for sample_list in samples ]
S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ] S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ] DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )] target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]
n_samples = min(4, self.get_batch_size() )
result = [] result = []
st = [] st = []
for i in range(n_samples): for i in range(n_samples):

11
models/Model_SAEHD/Model.py
View file

@ -450,9 +450,10 @@ class SAEHDModel(ModelBase):
for gpu_id in range(gpu_count): for gpu_id in range(gpu_count):
with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ): with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
with tf.device(f'/CPU:0'): with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first # slice on CPU, otherwise all batch data will be transfered to GPU first
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
gpu_warped_src = self.warped_src [batch_slice,:,:,:] gpu_warped_src = self.warped_src [batch_slice,:,:,:]
gpu_warped_dst = self.warped_dst [batch_slice,:,:,:] gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
gpu_target_src = self.target_src [batch_slice,:,:,:] gpu_target_src = self.target_src [batch_slice,:,:,:]
@ -646,7 +647,6 @@ class SAEHDModel(ModelBase):
model.init_weights() model.init_weights()
# initializing sample generators # initializing sample generators
if self.is_training: if self.is_training:
t = SampleProcessor.Types t = SampleProcessor.Types
if self.options['face_type'] == 'h': if self.options['face_type'] == 'h':
@ -710,12 +710,8 @@ class SAEHDModel(ModelBase):
#override #override
def onGetPreview(self, samples): def onGetPreview(self, samples):
n_samples = min(4, self.get_batch_size() )
( (warped_src, target_src, target_srcm), ( (warped_src, target_src, target_srcm),
(warped_dst, target_dst, target_dstm) ) = \ (warped_dst, target_dst, target_dstm) ) = samples
[ [sample[0:n_samples] for sample in sample_list ]
for sample_list in samples ]
if self.options['learn_mask']: if self.options['learn_mask']:
S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ] S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
@ -725,6 +721,7 @@ class SAEHDModel(ModelBase):
target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )] target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]
n_samples = min(4, self.get_batch_size() )
result = [] result = []
st = [] st = []
for i in range(n_samples): for i in range(n_samples):