(opt)
| 59 | return tensors |
| 60 | |
| 61 | def create_model(opt): |
| 62 | print(opt.model) |
| 63 | if opt.model == 'vid2vid': |
| 64 | from .vid2vid_model_G import Vid2VidModelG |
| 65 | modelG = Vid2VidModelG() |
| 66 | if opt.isTrain: |
| 67 | from .vid2vid_model_D import Vid2VidModelD |
| 68 | modelD = Vid2VidModelD() |
| 69 | else: |
| 70 | raise ValueError("Model [%s] not recognized." % opt.model) |
| 71 | |
| 72 | if opt.isTrain: |
| 73 | from .flownet import FlowNet |
| 74 | flowNet = FlowNet() |
| 75 | |
| 76 | modelG.initialize(opt) |
| 77 | if opt.isTrain: |
| 78 | modelD.initialize(opt) |
| 79 | flowNet.initialize(opt) |
| 80 | if not opt.fp16: |
| 81 | modelG, modelD, flownet = wrap_model(opt, modelG, modelD, flowNet) |
| 82 | return [modelG, modelD, flowNet] |
| 83 | else: |
| 84 | return modelG |
| 85 | |
| 86 | def create_optimizer(opt, models): |
| 87 | modelG, modelD, flowNet = models |
no test coverage detected