Testing with defined pairs from files list!

Author: minar09

.gitignore

@@ -1,3 +1,4 @@
+.idea/
 results/
 runs/
 sample/

data/aligned_dataset.py

@@ -15,6 +15,22 @@ def initialize(self, opt):
         self.root = opt.dataroot
         self.diction = {}
 
+        self.fine_height = 256
+        self.fine_width = 192
+        self.radius = 5
+
+        # load data list from pairs file
+        human_names = []
+        cloth_names = []
+        with open(os.path.join(opt.dataroot, opt.datapairs), 'r') as f:
+            for line in f.readlines():
+                h_name, c_name = line.strip().split()
+                human_names.append(h_name)
+                cloth_names.append(c_name)
+        self.human_names = human_names
+        self.cloth_names = cloth_names
+        self.dataset_size = len(human_names)
+
         # input A (label maps)
         dir_A = '_A' if self.opt.label_nc == 0 else '_label'
         self.dir_A = os.path.join(opt.dataroot, opt.phase + dir_A)
@@ -99,7 +115,13 @@ def __getitem__(self, index):
         #    if '006581' in s:
         #        test = k
         #        break
-        A_path = self.A_paths[index]
+
+        # get names from the pairs file
+        c_name = self.cloth_names[index]
+        h_name = self.human_names[index]
+
+        # A_path = self.A_paths[index]
+        A_path = osp.join(self.dir_A, h_name.replace(".jpg", ".png"))
         A = Image.open(A_path).convert('L')
 
         params = get_params(self.opt, A.size)
@@ -114,7 +136,8 @@ def __getitem__(self, index):
         B_tensor = inst_tensor = feat_tensor = 0
         # input B (real images)
 
-        B_path = self.B_paths[index]
+        # B_path = self.B_paths[index]
+        B_path = osp.join(self.dir_B, h_name)
         name = B_path.split('/')[-1]
 
         B = Image.open(B_path).convert('RGB')
@@ -136,12 +159,14 @@ def __getitem__(self, index):
 
         ### input_C (color)
         # print(self.C_paths)
-        C_path = self.C_paths[test]
+        # C_path = self.C_paths[test]
+        C_path = osp.join(self.dir_C, c_name)
         C = Image.open(C_path).convert('RGB')
         C_tensor = transform_B(C)
 
         # Edge
-        E_path = self.E_paths[test]
+        # E_path = self.E_paths[test]
+        E_path = osp.join(self.dir_E, c_name)
         # print(E_path)
         E = Image.open(E_path).convert('L')
         E_tensor = transform_A(E)

models/base_model.py

@@ -1,9 +1,10 @@
-### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. 
-### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+# Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
+# Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
 import os
 import torch
 import sys
 
+
 class BaseModel(torch.nn.Module):
     def name(self):
         return 'BaseModel'
@@ -49,18 +50,18 @@ def save_network(self, network, network_label, epoch_label, gpu_ids):
         #     network.cuda()
 
     # helper loading function that can be used by subclasses
-    def load_network(self, network, network_label, epoch_label, save_dir=''):        
+    def load_network(self, network, network_label, epoch_label, save_dir=''):
         save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
-        print (save_filename)
+        print(save_filename)
         if not save_dir:
             save_dir = self.save_dir
-        save_path = os.path.join(save_dir, save_filename)        
+        save_path = os.path.join(save_dir, save_filename)
         if not os.path.isfile(save_path):
             print('%s not exists yet!' % save_path)
             if network_label == 'G':
                 raise('Generator must exist!')
         else:
-            #network.load_state_dict(torch.load(save_path))
+            # network.load_state_dict(torch.load(save_path))
 
             network.load_state_dict(torch.load(save_path))
             # except:

models/models.py

@@ -1,14 +1,15 @@
-### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. 
-### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
+# Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
+# Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
 import torch
 import ipdb
 
+
 def create_model(opt):
     if opt.model == 'pix2pixHD':
         from .pix2pixHD_model import Pix2PixHDModel, InferenceModel
         if opt.isTrain:
             model = Pix2PixHDModel()
-            #ipdb.set_trace()
+            # ipdb.set_trace()
         else:
             model = InferenceModel()