def define_F(opt, use_bn=False): gpu_ids = opt['gpu_ids'] device = torch.device('cuda' if gpu_ids else 'cpu') if 'unet' in opt and opt['unet'] == True: # Use UNet. netF = UNet(n_channels=3, n_classes=10) unet_model_path = opt['unet_model'] netF.load_state_dict(torch.load(unet_model_path)) if gpu_ids: netF = nn.DataParallel(netF) elif 'pnasnet' in opt and opt['pnasnet'] == True: netF = arch.PNasNetFeatureExtractor(use_input_norm=True, device=device) else: # pytorch pretrained VGG19-54, before ReLU. if use_bn: feature_layer = 49 else: feature_layer = 34 netF = arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn, use_input_norm=True, device=device) # netF = arch.ResNet101FeatureExtractor(use_input_norm=True, device=device) if gpu_ids: netF = nn.DataParallel(netF) netF.eval() # No need to train return netF
def define_F(opt, use_bn=False): gpu_ids = opt['gpu_ids'] tensor = torch.cuda.FloatTensor if gpu_ids else torch.FloatTensor # pytorch pretrained VGG19-54, before ReLU. if use_bn: feature_layer = 49 else: feature_layer = 34 netF = arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn, \ use_input_norm=True, tensor=tensor) if gpu_ids: netF = nn.DataParallel(netF).cuda() netF.eval() # No need to train return netF
def define_F(opt, use_bn=False): gpu_ids = opt['gpu_ids'] device = torch.device('cuda' if gpu_ids else 'cpu') # pytorch pretrained VGG19-54, before ReLU. if use_bn: feature_layer = 49 else: feature_layer = 34 netF = arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn, \ use_input_norm=True, device=device) if gpu_ids: netF = nn.DataParallel(netF) netF.eval() # No need to train return netF
def define_F(opt, use_bn=False): gpu_ids = opt["gpu_ids"] device = torch.device("cuda" if gpu_ids else "cpu") # pytorch pretrained VGG19-54, before ReLU. if use_bn: feature_layer = 49 else: feature_layer = 34 netF = arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn, use_input_norm=True, device=device) # netF = arch.ResNet101FeatureExtractor(use_input_norm=True, device=device) if gpu_ids: netF = nn.DataParallel(netF) netF.eval() # No need to train return netF
def define_F(opt, use_bn=False): gpu_ids = opt['gpu_ids'] device = torch.device('cuda' if gpu_ids else 'cpu') # pytorch pretrained VGG19-54, before ReLU. if use_bn: feature_layer = 49 else: feature_layer = 34 # ouyry if 'network_F' not in opt: mode = 'VGG19' else: mode = opt['network_F']['mode'] if mode == 'VGG19': netF = arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn, \ use_input_norm=True, device=device) elif mode == 'VGG16': feature_layer = 28 netF = arch.VGG16FeatureExtractor(feature_layer=feature_layer, use_bn=use_bn, \ use_input_norm=True, device=device) elif mode == 'VGG16-MINC': feature_layer = 28 model_path = opt['network_F']['path'] netF = arch.VGG16MINCFeatureExtractor(feature_layer=feature_layer, model_path=model_path,\ use_bn=use_bn, use_input_norm=True, device=device) elif mode == 'Sphere20a': feature_layer = 28 model_path = opt['network_F']['path'] if 'norm' in opt['network_F']: norm = opt['network_F']['norm'] else: norm = True netF = arch.Sphere20aFeatureExtractor(feature_layer=feature_layer, model_path=model_path,\ use_bn=use_bn, use_input_norm=norm, device=device) # netF = arch.ResNet101FeatureExtractor(use_input_norm=True, device=device) # if 'distributed' in opt: # assert torch.cuda.is_available() # netF = nn.parallel.DistributedDataParallel(netF) if gpu_ids: assert torch.cuda.is_available() netF = nn.DataParallel(netF) netF.eval() # No need to train return netF
def define_F(opt, use_bn=False,**kwargs): gpu_ids = opt['gpu_ids'] device = torch.device('cuda' if gpu_ids else 'cpu') # pytorch pretrained VGG19-54, before ReLU. if 'arch' in kwargs.keys()and 'vgg11' in kwargs['arch']: feature_layer = int(kwargs['arch'][len('vgg11_'):]) kwargs['arch'] = 'vgg11' else: if use_bn: feature_layer = 49 else: feature_layer = 34 netF = arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,use_input_norm=True, device=device,**kwargs) # netF = arch.ResNet101FeatureExtractor(use_input_norm=True, device=device) if gpu_ids: netF = nn.DataParallel(netF) netF.eval() # No need to train return netF