The ostec from chromatices

[#1] Stylegan2-ada Network transplantation dimension issue

Lines 31 to 67 in 3d34014

 class Generator: 

 def __init__(self, model, batch_size, custom_input=None, clipping_threshold=2, tiled_dlatent=True, model_res=1024, randomize_noise=False): 

 self.batch_size = batch_size 

 self.tiled_dlatent=tiled_dlatent 

 self.model_scale = int(2*(math.log(model_res,2)-1)) # For example, 1024 -> 18 

 if tiled_dlatent: 

 self.initial_dlatents = np.zeros((self.batch_size, 512)) 

 model.components.synthesis.run(np.zeros((self.batch_size, self.model_scale, 512)), 

 randomize_noise=randomize_noise, minibatch_size=self.batch_size, 

 custom_inputs=[partial(create_variable_for_generator, batch_size=batch_size, tiled_dlatent=True), 

 partial(create_stub, batch_size=batch_size)], 

 structure='fixed') 

 else: 

 self.initial_dlatents = np.zeros((self.batch_size, self.model_scale, 512)) 

 if custom_input is not None: 

 model.components.synthesis.run(self.initial_dlatents, 

 randomize_noise=randomize_noise, minibatch_size=self.batch_size, 

 custom_inputs=[partial(custom_input.eval(), batch_size=batch_size), partial(create_stub, batch_size=batch_size)], 

 structure='fixed') 

 else: 

 model.components.synthesis.run(self.initial_dlatents, 

 randomize_noise=randomize_noise, minibatch_size=self.batch_size, 

 custom_inputs=[partial(create_variable_for_generator, batch_size=batch_size, tiled_dlatent=False, model_scale=self.model_scale), 

 partial(create_stub, batch_size=batch_size)], 

 structure='fixed') 

 self.dlatent_avg_def = model.get_var('dlatent_avg') 

 self.reset_dlatent_avg() 

 self.sess = tf.get_default_session() 

 self.graph = tf.get_default_graph() 

 self.dlatent_variable = next(v for v in tf.global_variables() if 'learnable_dlatents' in v.name) 

 self._assign_dlatent_ph = tf.placeholder(tf.float32, name="assign_dlatent_ph") 

 self._assign_dlantent = tf.assign(self.dlatent_variable, self._assign_dlatent_ph) 

 self.set_dlatents(self.initial_dlatents)

When the model was loaded, there is occured issue of inconsistency of lhs dimension and rhs dimension.

OSTeC/core/generator_model.py

Lines 103 to 122 in 3d34014

 def set_dlatents(self, dlatents): 

 if self.tiled_dlatent: 

 if (dlatents.shape != (self.batch_size, 512)) and (dlatents.shape[1] != 512): 

 dlatents = np.mean(dlatents, axis=1) 

 if (dlatents.shape != (self.batch_size, 512)): 

 dlatents = np.vstack([dlatents, np.zeros((self.batch_size-dlatents.shape[0], 512))]) 

 assert (dlatents.shape == (self.batch_size, 512)) 

 else: 

 if (dlatents.shape[1] > self.model_scale): 

 dlatents = dlatents[:,:self.model_scale,:] 

 if (isinstance(dlatents.shape[0], int)): 

 if (dlatents.shape != (self.batch_size, self.model_scale, 512)): 

 dlatents = np.vstack([dlatents, np.zeros((self.batch_size-dlatents.shape[0], self.model_scale, 512))]) 

 assert (dlatents.shape == (self.batch_size, self.model_scale, 512)) 

 self.sess.run([self._assign_dlantent], {self._assign_dlatent_ph: dlatents}) 

 return 

 else: 

 self._assign_dlantent = tf.assign(self.dlatent_variable, dlatents) 

 return 

 self.sess.run([self._assign_dlantent], {self._assign_dlatent_ph: dlatents})

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[#1] Stylegan2-ada Network transplantation dimension issue

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	class Generator:
	def __init__(self, model, batch_size, custom_input=None, clipping_threshold=2, tiled_dlatent=True, model_res=1024, randomize_noise=False):
	self.batch_size = batch_size
	self.tiled_dlatent=tiled_dlatent
	self.model_scale = int(2*(math.log(model_res,2)-1)) # For example, 1024 -> 18

	if tiled_dlatent:
	self.initial_dlatents = np.zeros((self.batch_size, 512))
	model.components.synthesis.run(np.zeros((self.batch_size, self.model_scale, 512)),
	randomize_noise=randomize_noise, minibatch_size=self.batch_size,
	custom_inputs=[partial(create_variable_for_generator, batch_size=batch_size, tiled_dlatent=True),
	partial(create_stub, batch_size=batch_size)],
	structure='fixed')
	else:
	self.initial_dlatents = np.zeros((self.batch_size, self.model_scale, 512))
	if custom_input is not None:
	model.components.synthesis.run(self.initial_dlatents,
	randomize_noise=randomize_noise, minibatch_size=self.batch_size,
	custom_inputs=[partial(custom_input.eval(), batch_size=batch_size), partial(create_stub, batch_size=batch_size)],
	structure='fixed')
	else:
	model.components.synthesis.run(self.initial_dlatents,
	randomize_noise=randomize_noise, minibatch_size=self.batch_size,
	custom_inputs=[partial(create_variable_for_generator, batch_size=batch_size, tiled_dlatent=False, model_scale=self.model_scale),
	partial(create_stub, batch_size=batch_size)],
	structure='fixed')

	self.dlatent_avg_def = model.get_var('dlatent_avg')
	self.reset_dlatent_avg()
	self.sess = tf.get_default_session()
	self.graph = tf.get_default_graph()

	self.dlatent_variable = next(v for v in tf.global_variables() if 'learnable_dlatents' in v.name)
	self._assign_dlatent_ph = tf.placeholder(tf.float32, name="assign_dlatent_ph")
	self._assign_dlantent = tf.assign(self.dlatent_variable, self._assign_dlatent_ph)
	self.set_dlatents(self.initial_dlatents)

	def set_dlatents(self, dlatents):
	if self.tiled_dlatent:
	if (dlatents.shape != (self.batch_size, 512)) and (dlatents.shape[1] != 512):
	dlatents = np.mean(dlatents, axis=1)
	if (dlatents.shape != (self.batch_size, 512)):
	dlatents = np.vstack([dlatents, np.zeros((self.batch_size-dlatents.shape[0], 512))])
	assert (dlatents.shape == (self.batch_size, 512))
	else:
	if (dlatents.shape[1] > self.model_scale):
	dlatents = dlatents[:,:self.model_scale,:]
	if (isinstance(dlatents.shape[0], int)):
	if (dlatents.shape != (self.batch_size, self.model_scale, 512)):
	dlatents = np.vstack([dlatents, np.zeros((self.batch_size-dlatents.shape[0], self.model_scale, 512))])
	assert (dlatents.shape == (self.batch_size, self.model_scale, 512))
	self.sess.run([self._assign_dlantent], {self._assign_dlatent_ph: dlatents})
	return
	else:
	self._assign_dlantent = tf.assign(self.dlatent_variable, dlatents)
	return
	self.sess.run([self._assign_dlantent], {self._assign_dlatent_ph: dlatents})