def _create_discriminator(self): return NetworksFactory.get_by_name('global_local', input_nc=3 + self._D_cond_nc // 2, norm_type=self._opt.norm_type, ndf=64, n_layers=4, use_sigmoid=False)
def _init_create_networks(self): # features network self._net = NetworksFactory.get_by_name('prob_map_net3', conv_dim=16) self._net.init_weights() self._net = self._move_net_to_gpu(self._net) if len(self._gpu_ids) > 0: summary(self._net, (3, self._opt.net_image_size, self._opt.net_image_size))
def _init_create_networks(self): # features network self._net = NetworksFactory.get_by_name('uv_prob_net2', num_nc=32) self._net.init_weights() self._net = self._move_net_to_gpu(self._net) if len(self._gpu_ids) > 0: summary(self._net, (3, self._opt.net_image_size, self._opt.net_image_size))
def _init_create_networks(self): # features network self._net_features = NetworksFactory.get_by_name('vgg_features', freeze=True) self._net_features = self._move_net_to_gpu(self._net_features) # bb network if self._opt.lambda_bb > 0: self._net_bb = NetworksFactory.get_by_name('bb_net_values', freeze=False) self._net_bb = self._move_net_to_gpu(self._net_bb, output_gpu=self._gpu_bb) # prob network if self._opt.lambda_prob > 0: self._net_prob = NetworksFactory.get_by_name('prob_net', freeze=False) self._net_prob = self._move_net_to_gpu(self._net_prob)
def _init_create_networks(self): self._G = NetworksFactory.get_by_name( 'Ours_DeblurOnly', eventbins_between_frames=self.eventbins_between_frames, if_RGB=self._opt.channel, inter_num=self.inter_num) if len(self._opt.load_G) > 0: self._load_network(self._G, self._opt.load_G) else: raise ValueError("Weights file not found.") self._G.cuda()
def _init_create_networks(self): self._G = NetworksFactory.get_by_name( 'DeblurOnly', eventbins_between_frames=self.eventbins_between_frames, if_RGB=self._opt.channel, inter_num=self.inter_num) self._load_network(self._G, self._opt.load_G) self._G.cuda() n_parameters = sum(p.numel() for p in self._G.parameters() if p.requires_grad) print('number of parameters of G:', n_parameters)
def _create_generator(self): net = NetworksFactory.get_by_name(self._opt.gen_name, bg_dim=4, src_dim=3+self._G_cond_nc, tsf_dim=3+self._G_cond_nc, repeat_num=self._opt.repeat_num) if self._opt.load_path: self._load_params(net, self._opt.load_path) elif self._opt.load_epoch > 0: self._load_network(net, 'G', self._opt.load_epoch) else: raise ValueError('load_path {} is empty and load_epoch {} is 0'.format( self._opt.load_path, self._opt.load_epoch)) net.eval() return net
def _create_generator(self): return NetworksFactory.get_by_name( 'img_encoder', input_chann=4, output_dim=33) # 3-rot, 45-PCA, 3-translation
def _create_discriminator(self): return NetworksFactory.get_by_name('discriminator_wasserstein_gan', c_dim=self._opt.cond_nc)
def _create_bgnet(self): net = NetworksFactory.get_by_name('deepfillv2', c_dim=4) self._load_params(net, self._opt.bg_model, need_module=False) net.eval() return net
def _create_network(self, net_name): return NetworksFactory.get_by_name(net_name)
def _create_discriminator(self): return NetworksFactory.get_by_name('discriminator_patch_gan', input_nc=3 + self._D_cond_nc, norm_type=self._opt.norm_type, ndf=64, n_layers=4, use_sigmoid=False, sn=self._opt.spectral_norm)
def _create_generator(self): return NetworksFactory.get_by_name(self._opt.gen_name, bg_dim=4, src_dim=3+self._G_cond_nc, tsf_dim=3+self._G_cond_nc, repeat_num=self._opt.repeat_num)
def create_image_encoder_and_grasp_predictor(self): return NetworksFactory.get_by_name( 'img_encoder_and_grasp_predictor') # The output is 6 or 7 as we consider 6 or 7 grasp taxonomy classes for the Barrett hand
def _create_fcnet(self): return NetworksFactory.get_by_name( 'MLP_refine', input_dim=51 + 21, output_dim=51 ) # 51 for 3 wrist rotation, 45 PCA for joint rotation, 3 translation
def create_grasp_generator(self): return NetworksFactory.get_by_name( 'grasp_generator', input_dim=3 + 3 + 7) # 3 for rotation 3 for tranlsation and 7 for hand joints
def _init_create_networks(self): # features network # self._net = NetworksFactory.get_by_name('small_net', freeze=False) self._net = NetworksFactory.get_by_name('vgg_finetune') self._net = self._move_net_to_gpu(self._net)
def _create_generator(self): # _opt.cond_nc = 17 by default return NetworksFactory.get_by_name(self._opt.generator_name, self._opt)
def _create_discriminator(self): return NetworksFactory.get_by_name('mano_discriminator', input_size=51 + 21 + 4)
def create_discriminator(self): return NetworksFactory.get_by_name('discriminator', input_dim=3 + 3 + 1) # 3 for rotation 3 for tranlsation and 1 for finger spread