| from torch.autograd import Variable |
| from collections import OrderedDict |
| import util.util as util |
| from .base_model import BaseModel |
| from . import networks |
|
|
|
|
| class TestModel(BaseModel): |
| def name(self): |
| return 'TestModel' |
|
|
| def initialize(self, opt): |
| assert(not opt.isTrain) |
| BaseModel.initialize(self, opt) |
| self.input_A = self.Tensor(opt.batchSize, opt.input_nc, opt.fineSize, opt.fineSize) |
|
|
| self.netG = networks.define_G(opt.input_nc, opt.output_nc, |
| opt.ngf, opt.which_model_netG, |
| opt.norm, not opt.no_dropout, |
| self.gpu_ids) |
| which_epoch = opt.which_epoch |
| self.load_network(self.netG, 'G', which_epoch) |
|
|
| print('---------- Networks initialized -------------') |
| networks.print_network(self.netG) |
| print('-----------------------------------------------') |
|
|
| def set_input(self, input): |
| |
| input_A = input['A'] |
| self.input_A.resize_(input_A.size()).copy_(input_A) |
| self.image_paths = input['A_paths'] |
|
|
| def test(self): |
| self.real_A = Variable(self.input_A) |
| self.fake_B = self.netG.forward(self.real_A) |
|
|
| |
| def get_image_paths(self): |
| return self.image_paths |
|
|
| def get_current_visuals(self): |
| real_A = util.tensor2im(self.real_A.data) |
| fake_B = util.tensor2im(self.fake_B.data) |
| return OrderedDict([('real_A', real_A), ('fake_B', fake_B)]) |
|
|
