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