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iperov
parent
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commit
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3 changed files with 0 additions and 575 deletions
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from functools import partial
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import cv2
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import numpy as np
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from facelib import FaceType
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from interact import interact as io
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from mathlib import get_power_of_two
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from models import ModelBase
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from nnlib import nnlib
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from samplelib import *
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class AVATARModel(ModelBase):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs,
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ask_sort_by_yaw=False,
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ask_random_flip=False,
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ask_src_scale_mod=False)
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#override
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def onInitializeOptions(self, is_first_run, ask_override):
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default_face_type = 'f'
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if is_first_run:
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self.options['resolution'] = io.input_int("Resolution ( 128,256 ?:help skip:128) : ", 128, [128,256], help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
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else:
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self.options['resolution'] = self.options.get('resolution', 128)
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#override
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def onInitialize(self, batch_size=-1, **in_options):
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exec(nnlib.code_import_all, locals(), globals())
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self.set_vram_batch_requirements({4:4})
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resolution = self.options['resolution']
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bgr_shape = (resolution, resolution, 3)
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mask_shape = (resolution, resolution, 1)
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bgrm_shape = (resolution, resolution, 4)
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ngf = 64
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ndf = 64
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lambda_A = 100
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lambda_B = 100
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use_batch_norm = True #created_batch_size > 1
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self.enc = modelify(AVATARModel.DFEncFlow ())( Input(bgrm_shape) )
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dec_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.enc.outputs ]
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self.decA = modelify(AVATARModel.DFDecFlow (bgr_shape[2])) (dec_Inputs)
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self.decB = modelify(AVATARModel.DFDecFlow (bgr_shape[2])) (dec_Inputs)
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#self.GA = modelify(AVATARModel.ResNet (bgr_shape[2], use_batch_norm, n_blocks=6, ngf=ngf, use_dropout=True))( Input(bgrm_shape) )
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#self.GB = modelify(AVATARModel.ResNet (bgr_shape[2], use_batch_norm, n_blocks=6, ngf=ngf, use_dropout=True))( Input(bgrm_shape) )
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#self.GA = modelify(UNet (bgr_shape[2], use_batch_norm, num_downs=get_power_of_two(resolution)-1, ngf=ngf, use_dropout=True))(Input(bgr_shape))
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#self.GB = modelify(UNet (bgr_shape[2], use_batch_norm, num_downs=get_power_of_two(resolution)-1, ngf=ngf, use_dropout=True))(Input(bgr_shape))
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def GA(x):
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return self.decA(self.enc(x))
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self.GA = GA
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def GB(x):
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return self.decB(self.enc(x))
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self.GB = GB
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#self.DA = modelify(AVATARModel.PatchDiscriminator(ndf=ndf) ) ( Input(bgrm_shape) )
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#self.DB = modelify(AVATARModel.PatchDiscriminator(ndf=ndf) ) ( Input(bgrm_shape) )
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self.DA = modelify(AVATARModel.NLayerDiscriminator(ndf=ndf) ) ( Input(bgrm_shape) )
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self.DB = modelify(AVATARModel.NLayerDiscriminator(ndf=ndf) ) ( Input(bgrm_shape) )
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if not self.is_first_run():
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weights_to_load = [
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# (self.GA, 'GA.h5'),
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# (self.GB, 'GB.h5'),
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(self.enc, 'enc.h5'),
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(self.decA, 'decA.h5'),
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(self.decB, 'decB.h5'),
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(self.DA, 'DA.h5'),
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(self.DB, 'DB.h5'),
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]
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self.load_weights_safe(weights_to_load)
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real_A0 = Input(bgr_shape)
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real_A0m = Input(mask_shape)
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real_A0m_sigm = (real_A0m + 1) / 2
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real_B0 = Input(bgr_shape)
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real_B0m = Input(mask_shape)
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real_B0m_sigm = (real_B0m + 1) / 2
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real_A0_B0m = K.concatenate([real_A0, real_B0m])
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real_B0_A0m = K.concatenate([real_B0, real_A0m])
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DA_ones = K.ones_like ( K.shape(self.DA.outputs[0]) )
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DA_zeros = K.zeros_like ( K.shape(self.DA.outputs[0] ))
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DB_ones = K.ones_like ( K.shape(self.DB.outputs[0] ))
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DB_zeros = K.zeros_like ( K.shape(self.DB.outputs[0] ))
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def DLoss(labels,logits):
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return K.mean(K.binary_crossentropy(labels,logits))
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def CycleLOSS(t1,t2):
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return dssim(kernel_size=int(resolution/11.6),max_value=2.0)(t1+1,t2+1 )
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#return K.mean(K.abs(t1 - t2))
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fake_B0 = self.GA(real_A0_B0m)
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fake_A0 = self.GB(real_B0_A0m)
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real_A0_d = self.DA( K.concatenate([ real_A0 * real_A0m_sigm , real_A0m]) )
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real_A0_d_ones = K.ones_like(real_A0_d)
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fake_A0_d = self.DA( K.concatenate([fake_A0, real_A0m]) )
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fake_A0_d_ones = K.ones_like(fake_A0_d)
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fake_A0_d_zeros = K.zeros_like(fake_A0_d)
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real_B0_d = self.DB( K.concatenate([real_B0 * real_B0m_sigm, real_B0m]) )
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real_B0_d_ones = K.ones_like(real_B0_d)
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fake_B0_d = self.DB( K.concatenate([fake_B0 , real_B0m]) )
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fake_B0_d_ones = K.ones_like(fake_B0_d)
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fake_B0_d_zeros = K.zeros_like(fake_B0_d)
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rec_A0 = self.GB ( K.concatenate([fake_B0, real_A0m]) )
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rec_B0 = self.GA ( K.concatenate([fake_A0, real_B0m]) )
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loss_GA = DLoss(fake_B0_d_ones, fake_B0_d ) + \
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lambda_A * (CycleLOSS(rec_B0*real_B0m_sigm, real_B0*real_B0m_sigm) )
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weights_GA = self.enc.trainable_weights + self.decA.trainable_weights
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loss_GB = DLoss(fake_A0_d_ones, fake_A0_d ) + \
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lambda_B * (CycleLOSS(rec_A0*real_A0m_sigm, real_A0*real_A0m_sigm) )
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weights_GB = self.enc.trainable_weights + self.decB.trainable_weights
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def opt():
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return Adam(lr=2e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=2)#, clipnorm=1)
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self.GA_train = K.function ([real_A0, real_A0m, real_B0, real_B0m],[loss_GA],
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opt().get_updates(loss_GA, weights_GA) )
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self.GB_train = K.function ([real_A0, real_A0m, real_B0, real_B0m],[loss_GB],
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opt().get_updates(loss_GB, weights_GB) )
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###########
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loss_D_A = ( DLoss(real_A0_d_ones, real_A0_d ) + \
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DLoss(fake_A0_d_zeros, fake_A0_d ) ) * 0.5
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self.DA_train = K.function ([real_A0, real_A0m, real_B0, real_B0m],[loss_D_A],
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opt().get_updates(loss_D_A, self.DA.trainable_weights) )
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############
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loss_D_B = ( DLoss(real_B0_d_ones, real_B0_d ) + \
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DLoss(fake_B0_d_zeros, fake_B0_d ) ) * 0.5
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self.DB_train = K.function ([real_A0, real_A0m, real_B0, real_B0m],[loss_D_B],
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opt().get_updates(loss_D_B, self.DB.trainable_weights) )
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############
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self.G_view = K.function([real_A0, real_A0m, real_B0, real_B0m],[fake_A0, rec_A0, fake_B0, rec_B0 ])
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if self.is_training_mode:
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f = SampleProcessor.TypeFlags
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face_type = f.FACE_TYPE_FULL
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output_sample_types=[ [f.SOURCE | face_type | f.MODE_BGR, resolution],
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[f.SOURCE | face_type | f.MODE_M | f.FACE_MASK_FULL, resolution],
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]
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self.set_training_data_generators ([
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SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, normalize_tanh = True),
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output_sample_types=output_sample_types ),
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SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, normalize_tanh = True),
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output_sample_types=output_sample_types )
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])
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else:
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self.G_convert = K.function([real_A0, real_B0m],[fake_B0])
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#override
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def onSave(self):
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self.save_weights_safe( [
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# [self.GA, 'GA.h5'],
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# [self.GB, 'GB.h5'],
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[self.enc, 'enc.h5'],
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[self.decA, 'decA.h5'],
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[self.decB, 'decB.h5'],
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[self.DA, 'DA.h5'],
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[self.DB, 'DB.h5'] ])
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#override
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def onTrainOneIter(self, generators_samples, generators_list):
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src, srcm = generators_samples[0]
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dst, dstm = generators_samples[1]
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feed = [src, srcm, dst, dstm]
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loss_GA, = self.GA_train ( feed )
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loss_GB, = self.GB_train ( feed )
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loss_DA, = self.DA_train( feed )
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loss_DB, = self.DB_train( feed )
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return ( ('GA', loss_GA), ('GB', loss_GB), ('DA', loss_DA), ('DB', loss_DB) )
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#override
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def onGetPreview(self, sample):
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test_A0 = sample[0][0][0:4]
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test_A0m = sample[0][1][0:4]
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test_B0 = sample[1][0][0:4]
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test_B0m = sample[1][1][0:4]
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G_view_result = self.G_view([test_A0, test_A0m, test_B0, test_B0m])
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fake_A0, rec_A0, fake_B0, rec_B0 = [ x[0] / 2 + 0.5 for x in G_view_result]
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test_A0, test_A0m, test_B0, test_B0m = [ x[0] / 2 + 0.5 for x in [test_A0, test_A0m, test_B0, test_B0m] ]
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r = np.concatenate ((np.concatenate ( (test_A0, fake_B0, rec_A0), axis=1),
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np.concatenate ( (test_B0, fake_A0, rec_B0), axis=1)
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), axis=0)
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return [ ('AVATAR', r ) ]
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def predictor_func (self, avaperator_face, target_face_mask):
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feed = [ avaperator_face[np.newaxis,...]*2-1, target_face_mask[np.newaxis,...]*2-1 ]
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x = self.G_convert (feed)[0]
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return np.clip ( x[0] / 2 + 0.5 , 0, 1)
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# #override
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# def get_converter(self, **in_options):
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# from models import ConverterImage
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# return ConverterImage(self.predictor_func,
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# predictor_input_size=self.options['resolution'],
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# **in_options)
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#override
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def get_converter(self):
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base_erode_mask_modifier = 30
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base_blur_mask_modifier = 0
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default_erode_mask_modifier = 0
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default_blur_mask_modifier = 0
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face_type = FaceType.FULL
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from converters import ConverterAvatar
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return ConverterAvatar(self.predictor_func,
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predictor_input_size=self.options['resolution'])
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@staticmethod
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def ResNet(output_nc, use_batch_norm, ngf=64, n_blocks=6, use_dropout=False):
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exec (nnlib.import_all(), locals(), globals())
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if not use_batch_norm:
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use_bias = True
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def XNormalization(x):
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return InstanceNormalization (axis=-1)(x)
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else:
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use_bias = False
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def XNormalization(x):
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return BatchNormalization (axis=-1)(x)
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XConv2D = partial(Conv2D, padding='same', use_bias=use_bias)
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XConv2DTranspose = partial(Conv2DTranspose, padding='same', use_bias=use_bias)
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def func(input):
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def ResnetBlock(dim, use_dropout=False):
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def func(input):
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x = input
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x = XConv2D(dim, 3, strides=1)(x)
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x = XNormalization(x)
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x = ReLU()(x)
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#if use_dropout:
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# x = Dropout(0.5)(x)
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x = XConv2D(dim, 3, strides=1)(x)
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x = XNormalization(x)
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return Add()([x,input])
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return func
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x = input
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x = XConv2D(ngf, 7, strides=1, use_bias=True)(x)
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x = ReLU()(XNormalization(XConv2D(ngf*2, 3, strides=2)(x)))
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x = ReLU()(XNormalization(XConv2D(ngf*4, 3, strides=2)(x)))
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for i in range(n_blocks):
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x = ResnetBlock(ngf*4, use_dropout=use_dropout)(x)
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x = ReLU()(XNormalization(XConv2DTranspose(ngf*2, 3, strides=2)(x)))
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x = ReLU()(XNormalization(XConv2DTranspose(ngf , 3, strides=2)(x)))
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x = XConv2D(output_nc, 7, strides=1, activation='tanh', use_bias=True)(x)
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return x
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return func
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@staticmethod
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def UNet(output_nc, use_batch_norm, ngf=64, use_dropout=False):
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exec (nnlib.import_all(), locals(), globals())
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if not use_batch_norm:
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use_bias = True
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def XNormalizationL():
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return InstanceNormalization (axis=-1)
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else:
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use_bias = False
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def XNormalizationL():
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return BatchNormalization (axis=-1)
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def XNormalization(x):
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return XNormalizationL()(x)
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XConv2D = partial(Conv2D, padding='same', use_bias=use_bias)
|
|
||||||
XConv2DTranspose = partial(Conv2DTranspose, padding='same', use_bias=use_bias)
|
|
||||||
|
|
||||||
def func(input):
|
|
||||||
|
|
||||||
b,h,w,c = K.int_shape(input)
|
|
||||||
|
|
||||||
n_downs = get_power_of_two(w) - 4
|
|
||||||
|
|
||||||
Norm = XNormalizationL()
|
|
||||||
Norm2 = XNormalizationL()
|
|
||||||
Norm4 = XNormalizationL()
|
|
||||||
Norm8 = XNormalizationL()
|
|
||||||
|
|
||||||
x = input
|
|
||||||
|
|
||||||
x = e1 = XConv2D( ngf, 4, strides=2, use_bias=True ) (x)
|
|
||||||
|
|
||||||
x = e2 = Norm2( XConv2D( ngf*2, 4, strides=2 )( LeakyReLU(0.2)(x) ) )
|
|
||||||
x = e3 = Norm4( XConv2D( ngf*4, 4, strides=2 )( LeakyReLU(0.2)(x) ) )
|
|
||||||
|
|
||||||
l = []
|
|
||||||
for i in range(n_downs):
|
|
||||||
x = Norm8( XConv2D( ngf*8, 4, strides=2 )( LeakyReLU(0.2)(x) ) )
|
|
||||||
l += [x]
|
|
||||||
|
|
||||||
x = XConv2D( ngf*8, 4, strides=2, use_bias=True )( LeakyReLU(0.2)(x) )
|
|
||||||
|
|
||||||
for i in range(n_downs):
|
|
||||||
x = Norm8( XConv2DTranspose( ngf*8, 4, strides=2 )( ReLU()(x) ) )
|
|
||||||
if i <= n_downs-2:
|
|
||||||
x = Dropout(0.5)(x)
|
|
||||||
x = Concatenate(axis=-1)([x, l[-i-1] ])
|
|
||||||
|
|
||||||
x = Norm4( XConv2DTranspose( ngf*4, 4, strides=2 )( ReLU()(x) ) )
|
|
||||||
x = Concatenate(axis=-1)([x, e3])
|
|
||||||
|
|
||||||
x = Norm2( XConv2DTranspose( ngf*2, 4, strides=2 )( ReLU()(x) ) )
|
|
||||||
x = Concatenate(axis=-1)([x, e2])
|
|
||||||
|
|
||||||
x = Norm( XConv2DTranspose( ngf, 4, strides=2 )( ReLU()(x) ) )
|
|
||||||
x = Concatenate(axis=-1)([x, e1])
|
|
||||||
|
|
||||||
x = XConv2DTranspose(output_nc, 4, strides=2, activation='tanh', use_bias=True)( ReLU()(x) )
|
|
||||||
|
|
||||||
return x
|
|
||||||
return func
|
|
||||||
nnlib.UNet = UNet
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def UNetTemporalPredictor(output_nc, use_batch_norm, ngf=64, use_dropout=False):
|
|
||||||
exec (nnlib.import_all(), locals(), globals())
|
|
||||||
def func(inputs):
|
|
||||||
past_2_image_tensor, past_1_image_tensor = inputs
|
|
||||||
|
|
||||||
x = Concatenate(axis=-1)([ past_2_image_tensor, past_1_image_tensor ])
|
|
||||||
x = UNet(3, use_batch_norm, ngf=ngf, use_dropout=use_dropout) (x)
|
|
||||||
|
|
||||||
return x
|
|
||||||
|
|
||||||
return func
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def PatchDiscriminator(ndf=64):
|
|
||||||
exec (nnlib.import_all(), locals(), globals())
|
|
||||||
|
|
||||||
#use_bias = True
|
|
||||||
#def XNormalization(x):
|
|
||||||
# return InstanceNormalization (axis=-1)(x)
|
|
||||||
use_bias = False
|
|
||||||
def XNormalization(x):
|
|
||||||
return BatchNormalization (axis=-1)(x)
|
|
||||||
|
|
||||||
XConv2D = partial(Conv2D, use_bias=use_bias)
|
|
||||||
|
|
||||||
def func(input):
|
|
||||||
b,h,w,c = K.int_shape(input)
|
|
||||||
|
|
||||||
x = input
|
|
||||||
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
x = XConv2D( ndf, 4, strides=2, padding='valid', use_bias=True)(x)
|
|
||||||
x = LeakyReLU(0.2)(x)
|
|
||||||
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
x = XConv2D( ndf*2, 4, strides=2, padding='valid')(x)
|
|
||||||
x = XNormalization(x)
|
|
||||||
x = LeakyReLU(0.2)(x)
|
|
||||||
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
x = XConv2D( ndf*4, 4, strides=2, padding='valid')(x)
|
|
||||||
x = XNormalization(x)
|
|
||||||
x = LeakyReLU(0.2)(x)
|
|
||||||
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
x = XConv2D( ndf*8, 4, strides=2, padding='valid')(x)
|
|
||||||
x = XNormalization(x)
|
|
||||||
x = LeakyReLU(0.2)(x)
|
|
||||||
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
x = XConv2D( ndf*8, 4, strides=2, padding='valid')(x)
|
|
||||||
x = XNormalization(x)
|
|
||||||
x = LeakyReLU(0.2)(x)
|
|
||||||
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
return XConv2D( 1, 4, strides=1, padding='valid', use_bias=True, activation='sigmoid')(x)#
|
|
||||||
return func
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def NLayerDiscriminator(ndf=64, n_layers=3):
|
|
||||||
exec (nnlib.import_all(), locals(), globals())
|
|
||||||
|
|
||||||
#use_bias = True
|
|
||||||
#def XNormalization(x):
|
|
||||||
# return InstanceNormalization (axis=-1)(x)
|
|
||||||
use_bias = False
|
|
||||||
def XNormalization(x):
|
|
||||||
return BatchNormalization (axis=-1)(x)
|
|
||||||
|
|
||||||
XConv2D = partial(Conv2D, use_bias=use_bias)
|
|
||||||
|
|
||||||
def func(input):
|
|
||||||
b,h,w,c = K.int_shape(input)
|
|
||||||
|
|
||||||
x = input
|
|
||||||
|
|
||||||
f = ndf
|
|
||||||
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
x = XConv2D( f, 4, strides=2, padding='valid', use_bias=True)(x)
|
|
||||||
f = min( ndf*8, f*2 )
|
|
||||||
x = LeakyReLU(0.2)(x)
|
|
||||||
|
|
||||||
for i in range(n_layers):
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
x = XConv2D( f, 4, strides=2, padding='valid')(x)
|
|
||||||
f = min( ndf*8, f*2 )
|
|
||||||
x = XNormalization(x)
|
|
||||||
x = Dropout(0.5)(x)
|
|
||||||
x = LeakyReLU(0.2)(x)
|
|
||||||
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
x = XConv2D( f, 4, strides=1, padding='valid')(x)
|
|
||||||
x = XNormalization(x)
|
|
||||||
x = LeakyReLU(0.2)(x)
|
|
||||||
|
|
||||||
x = ZeroPadding2D((1,1))(x)
|
|
||||||
return XConv2D( 1, 4, strides=1, padding='valid', use_bias=True, activation='sigmoid')(x)#
|
|
||||||
return func
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def DFEncFlow(padding='zero', **kwargs):
|
|
||||||
exec (nnlib.import_all(), locals(), globals())
|
|
||||||
|
|
||||||
use_bias = False
|
|
||||||
def XNormalization(x):
|
|
||||||
return BatchNormalization (axis=-1)(x)
|
|
||||||
XConv2D = partial(Conv2D, padding=padding, use_bias=use_bias)
|
|
||||||
|
|
||||||
def Act(lrelu_alpha=0.1):
|
|
||||||
return LeakyReLU(alpha=lrelu_alpha)
|
|
||||||
|
|
||||||
def downscale (dim, **kwargs):
|
|
||||||
def func(x):
|
|
||||||
return Act() ( XNormalization(XConv2D(dim, kernel_size=5, strides=2)(x)) )
|
|
||||||
return func
|
|
||||||
|
|
||||||
def upscale (dim, **kwargs):
|
|
||||||
def func(x):
|
|
||||||
return SubpixelUpscaler()(Act()( XNormalization(XConv2D(dim * 4, kernel_size=3, strides=1)(x))))
|
|
||||||
return func
|
|
||||||
|
|
||||||
upscale = partial(upscale)
|
|
||||||
downscale = partial(downscale)
|
|
||||||
|
|
||||||
|
|
||||||
def func(input):
|
|
||||||
b,h,w,c = K.int_shape(input)
|
|
||||||
lowest_dense_res = w // 16
|
|
||||||
|
|
||||||
x = input
|
|
||||||
|
|
||||||
dims = 64
|
|
||||||
x = downscale(dims)(x)
|
|
||||||
x = downscale(dims*2)(x)
|
|
||||||
x = downscale(dims*4)(x)
|
|
||||||
x = downscale(dims*8)(x)
|
|
||||||
|
|
||||||
x = Dense(256)(Flatten()(x))
|
|
||||||
x = Dense(lowest_dense_res * lowest_dense_res * 256)(x)
|
|
||||||
x = Reshape((lowest_dense_res, lowest_dense_res, 256))(x)
|
|
||||||
x = upscale(256)(x)
|
|
||||||
|
|
||||||
return x
|
|
||||||
return func
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def DFDecFlow(output_nc, padding='zero', **kwargs):
|
|
||||||
exec (nnlib.import_all(), locals(), globals())
|
|
||||||
|
|
||||||
use_bias = False
|
|
||||||
def XNormalization(x):
|
|
||||||
return BatchNormalization (axis=-1)(x)
|
|
||||||
XConv2D = partial(Conv2D, padding=padding, use_bias=use_bias)
|
|
||||||
|
|
||||||
def Act(lrelu_alpha=0.1):
|
|
||||||
return LeakyReLU(alpha=lrelu_alpha)
|
|
||||||
|
|
||||||
def downscale (dim, **kwargs):
|
|
||||||
def func(x):
|
|
||||||
return Act() ( XNormalization(XConv2D(dim, kernel_size=5, strides=2)(x)) )
|
|
||||||
return func
|
|
||||||
|
|
||||||
def upscale (dim, **kwargs):
|
|
||||||
def func(x):
|
|
||||||
return SubpixelUpscaler()(Act()( XNormalization(XConv2D(dim * 4, kernel_size=3, strides=1)(x))))
|
|
||||||
return func
|
|
||||||
|
|
||||||
def to_bgr (output_nc, **kwargs):
|
|
||||||
def func(x):
|
|
||||||
return XConv2D(output_nc, kernel_size=5, use_bias=True, activation='tanh')(x)
|
|
||||||
return func
|
|
||||||
|
|
||||||
upscale = partial(upscale)
|
|
||||||
downscale = partial(downscale)
|
|
||||||
to_bgr = partial(to_bgr)
|
|
||||||
|
|
||||||
dims = 64
|
|
||||||
|
|
||||||
def func(input):
|
|
||||||
x = input[0]
|
|
||||||
|
|
||||||
x1 = upscale(dims*8)( x )
|
|
||||||
x2 = upscale(dims*4)( x1 )
|
|
||||||
x3 = upscale(dims*2)( x2 )
|
|
||||||
|
|
||||||
return to_bgr(output_nc) ( x3 )
|
|
||||||
return func
|
|
||||||
|
|
||||||
Model = AVATARModel
|
|
||||||
|
|
@ -1 +0,0 @@
|
||||||
from .Model import Model
|
|
||||||
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Add table
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Reference in a new issue