add file: with_logist_dataset.py

data/image_folder.py

@@ -13,7 +13,7 @@ import os.path
 IMG_EXTENSIONS = [
     '.jpg', '.JPG', '.jpeg', '.JPEG',
     '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
-    '.tif', '.TIF', '.tiff', '.TIFF',
+    '.tif', '.TIF', '.tiff', '.TIFF', '.pth', 
 ]
 
 

data/with_logist_dataset.py

@@ -0,0 +1,86 @@
+import os.path
+from data.base_dataset import BaseDataset, get_transform
+from data.image_folder import make_dataset
+from PIL import Image
+import random
+import util.util as util
+from glob import glob
+import torch
+
+class UnalignedDataset(BaseDataset):
+    """
+    This dataset class can load unaligned/unpaired datasets.
+
+    It requires two directories to host training images from domain A '/path/to/data/trainA'
+    and from domain B '/path/to/data/trainB' respectively.
+    You can train the model with the dataset flag '--dataroot /path/to/data'.
+    Similarly, you need to prepare two directories:
+    '/path/to/data/testA' and '/path/to/data/testB' during test time.
+    """
+
+    def __init__(self, opt):
+        """Initialize this dataset class.
+
+        Parameters:
+            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
+        """
+        BaseDataset.__init__(self, opt)
+        self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')  # create a path '/path/to/data/trainA'
+        self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')  # create a path '/path/to/data/trainB'
+        self.dir_A_logi = '/home/openxs/kunyu/datasets/InfraredCity-Lite/Single/Monitor/trainA_dino'
+
+        if opt.phase == "test" and not os.path.exists(self.dir_A) \
+           and os.path.exists(os.path.join(opt.dataroot, "valA")):
+            self.dir_A = os.path.join(opt.dataroot, "valA")
+            self.dir_B = os.path.join(opt.dataroot, "valB")
+
+        self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size))   # load images from '/path/to/data/trainA'
+        self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size))    # load images from '/path/to/data/trainB'
+        self.A_logi_paths = sorted(make_dataset(self.dir_A_logi, opt.max_dataset_size))
+        self.A_size = len(self.A_paths)  # get the size of dataset A
+        self.B_size = len(self.B_paths)  # get the size of dataset B
+
+    def __getitem__(self, index):
+        """Return a data point and its metadata information.
+
+        Parameters:
+            index (int)      -- a random integer for data indexing
+
+        Returns a dictionary that contains A, B, A_paths and B_paths
+            A (tensor)       -- an image in the input domain
+            B (tensor)       -- its corresponding image in the target domain
+            A_paths (str)    -- image paths
+            B_paths (str)    -- image paths
+        """
+        A_path = self.A_paths[index % self.A_size]  # make sure index is within then range
+        A_logi_path = self.A_logi_paths[index % self.A_size]
+        if self.opt.serial_batches:   # make sure index is within then range
+            index_B = index % self.B_size
+        else:   # randomize the index for domain B to avoid fixed pairs.
+            index_B = random.randint(0, self.B_size - 1)
+        B_path = self.B_paths[index_B]
+        A_img = Image.open(A_path).convert('RGB')
+        B_img = Image.open(B_path).convert('RGB')
+        
+        # shape: [1, 150, 256, 256]
+        A_logi = torch.load(A_logi_path, map_location=f'cuda:{self.opt.gpu_id}')
+
+        # Apply image transformation
+        # For FastCUT mode, if in finetuning phase (learning rate is decaying),
+        # do not perform resize-crop data augmentation of CycleGAN.
+        #        print('current_epoch', self.current_epoch)
+        is_finetuning = self.opt.isTrain and self.current_epoch > self.opt.n_epochs
+        modified_opt = util.copyconf(self.opt, load_size=self.opt.crop_size if is_finetuning else self.opt.load_size)
+        transform = get_transform(modified_opt)
+        A = transform(A_img)
+        B = transform(B_img)
+
+        return {'A': A, 'B': B, 'A_paths': A_path, 'B_paths': B_path, 'A_logi': A_logi, 'A_logi_paths': A_logi_path}
+
+    def __len__(self):
+        """Return the total number of images in the dataset.
+
+        As we have two datasets with potentially different number of images,
+        we take a maximum of
+        """
+        return max(self.A_size, self.B_size)