add ColoDiffusion codes: /ldm/module/, /ldm/data/, /scripts/test/

examples/images/diffusion/ldm/data/base.py

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+import math
+from abc import abstractmethod
+
+import torch
+from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
+import os
+import numpy as np
+import cv2
+
+class Txt2ImgIterableBaseDataset(IterableDataset):
+    '''
+    Define an interface to make the IterableDatasets for text2img data chainable
+    '''
+    def __init__(self, file_path: str, rank, world_size):
+        super().__init__()
+        self.file_path = file_path
+        self.folder_list = []
+        self.file_list = []
+        self.txt_list = []
+        self.info = self._get_file_info(file_path)
+        self.start = self.info['start']
+        self.end = self.info['end']
+        self.rank = rank
+
+        self.world_size = world_size
+        # self.per_worker = int(math.floor((self.end - self.start) / float(self.world_size)))
+        # self.iter_start = self.start + self.rank * self.per_worker
+        # self.iter_end = min(self.iter_start + self.per_worker, self.end)
+        # self.num_records = self.iter_end - self.iter_start
+        # self.valid_ids = [i for i in range(self.iter_end)]
+        self.num_records = self.end - self.start
+        self.valid_ids = [i for i in range(self.end)]
+
+        print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
+
+    def __len__(self):
+        # return self.iter_end - self.iter_start
+        return self.end - self.start
+
+    def __iter__(self):
+        sample_iterator = self._sample_generator(self.start, self.end)
+        # sample_iterator = self._sample_generator(self.iter_start, self.iter_end)
+        return sample_iterator
+
+    def _sample_generator(self, start, end):
+        for idx in range(start, end):
+            file_name = self.file_list[idx]
+            txt_name = self.txt_list[idx]
+            f_ = open(txt_name, 'r')
+            txt_ = f_.read()
+            f_.close()
+            image = cv2.imdecode(np.fromfile(file_name, dtype=np.uint8), 1)
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            image = torch.from_numpy(image) / 255
+            yield {"caption": txt_, "image":image}
+
+
+    def _get_file_info(self, file_path):
+        info = \
+        {
+            "start": 1,
+            "end": 0,
+        }
+        self.folder_list = [file_path + i for i in os.listdir(file_path) if '.' not in i]
+        for folder in self.folder_list:
+            files = [folder + '/' + i for i in os.listdir(folder) if 'jpg' in i]
+            txts = [k.replace('jpg', 'txt') for k in files]
+            self.file_list.extend(files)
+            self.txt_list.extend(txts)
+        info['end'] = len(self.file_list)
+        # with open(file_path, 'r') as fin:
+        #     for _ in enumerate(fin):
+        #         info['end'] += 1
+        # self.txt_list = [k.replace('jpg', 'txt') for k in self.file_list]
+        return info