1. Added one_direction_test_model that generates the outputs in only one direction

2. Changed the option naming from ntrain to max_dataset_size

1 files changed, 51 insertions, 0 deletions

diff --git a/models/one_direction_test_model.py b/models/one_direction_test_model.py
new file mode 100644
index 0000000..37e1bbb
--- /dev/null
+++ b/models/one_direction_test_model.py
@@ -0,0 +1,51 @@
+from torch.autograd import Variable
+from collections import OrderedDict
+import util.util as util
+from .base_model import BaseModel
+from . import networks
+
+
+class OneDirectionTestModel(BaseModel):
+    def name(self):
+        return 'OneDirectionTestModel'
+
+    def initialize(self, opt):
+        BaseModel.initialize(self, opt)
+
+        nb = opt.batchSize
+        size = opt.fineSize
+        self.input_A = self.Tensor(nb, opt.input_nc, size, size)
+
+        assert(not self.isTrain)
+        self.netG_A = networks.define_G(opt.input_nc, opt.output_nc,
+                                        opt.ngf, opt.which_model_netG,
+                                        opt.norm, opt.use_dropout,
+                                        self.gpu_ids)
+        which_epoch = opt.which_epoch
+        AtoB = self.opt.which_direction == 'AtoB'
+        which_network = 'G_A' if AtoB else 'G_B'
+        self.load_network(self.netG_A, which_network, which_epoch)
+
+        print('---------- Networks initialized -------------')
+        networks.print_network(self.netG_A)
+        print('-----------------------------------------------')
+
+    def set_input(self, input):
+        AtoB = self.opt.which_direction == 'AtoB'
+        input_A = input['A' if AtoB else 'B']
+        self.input_A.resize_(input_A.size()).copy_(input_A)
+        self.image_paths = input['A_paths' if AtoB else 'B_paths']
+
+    def test(self):
+        self.real_A = Variable(self.input_A)
+        self.fake_B = self.netG_A.forward(self.real_A)
+
+    #get image paths
+    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)])
+