def _load_networks(cls, network_paths, model_dir=None, progress=True): nets = [] for out_channels, encoder_path, decoder_path in network_paths: nets.append( TaskonomyNetwork(out_channels=out_channels, load_encoder_path=encoder_path, load_decoder_path=decoder_path, model_dir=model_dir, progress=progress)) return nets
def _load_networks(cls, network_paths, model_dir=None, progress=True): nets = [] for ( out_channels, encoder_path, decoder_path, is_decoder_mlp, apply_tanh, ) in network_paths: nets.append( TaskonomyNetwork( out_channels=out_channels, load_encoder_path=encoder_path, load_decoder_path=decoder_path, model_dir=model_dir, is_decoder_mlp=is_decoder_mlp, apply_tanh=apply_tanh, progress=progress, )) return nets
)) nets = [] for ( out_channels, encoder_path, decoder_path, is_decoder_mlp, apply_tanh, ) in net_paths_to_load: nets.append( TaskonomyNetwork( out_channels=out_channels, load_encoder_path=encoder_path, load_decoder_path=decoder_path, model_dir=None, is_decoder_mlp=is_decoder_mlp, apply_tanh=apply_tanh, progress=True, )) ## For initial performance decoder_nets = nets[0].to(default_device) ## For trained network performance # model_path = 'trained_normals.pth' # decoder_nets = nets[0].to(default_device) # decoder_nets_checkpoint = torch.load(model_path) # decoder_nets.load_state_dict(decoder_nets_checkpoint) # decoder_nets.eval()
t0 = time.time() #vp = VisualPrior() #feature_tasks= ["normal"] #vr = VisualPriorRepresentation() #vr._load_unloaded_nets(feature_tasks) TASKONOMY_PRETRAINED_WEIGHT_FILES = [ "normal_decoder-8f18bfb30ee733039f05ed4a65b4db6f7cc1f8a4b9adb4806838e2bf88e020ec.pth", "normal_encoder-f5e2c7737e4948e3b2a822f584892c342eaabbe66661576ba50db7cdd40561c5.pth" ] #path = "pretrained_model_weights" path_de = 'https://github.com/alexsax/visual-prior/raw/networks/assets/pytorch/' + str( TASKONOMY_PRETRAINED_WEIGHT_FILES[0]) path_en = 'https://github.com/alexsax/visual-prior/raw/networks/assets/pytorch/' + str( TASKONOMY_PRETRAINED_WEIGHT_FILES[1]) model = TaskonomyNetwork(load_encoder_path=path_en, load_decoder_path=path_de) model.encoder.eval_only = False model.decoder.eval_only = False for param in model.parameters(): param.requires_grad = True model.train() lr = 3e-4 optimizer = optim.Adam(model.parameters(), lr=lr) from replay_buffer_depth import ReplayBufferDepth size = 256 memory = ReplayBufferDepth((size, size), (size, size, 3), (size, size, 3),