Upload src/dataloader.py with huggingface_hub

src/dataloader.py

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+from accelerate.logging import get_logger
+import torch
+import io
+logger = get_logger(__name__)
+from PIL import Image
+from .condition import Condition
+from diffusers.image_processor import VaeImageProcessor
+from datasets import load_dataset, concatenate_datasets
+def get_dataset(args):
+    dataset = []
+    assert isinstance(args.dataset_name,list),"dataset dir should be a list"
+    if args.dataset_name is not None:
+        for name in args.dataset_name:
+            # Downloading and loading a dataset from the hub.
+            dataset.append(load_dataset(name,cache_dir=args.cache_dir,split='train'))
+    dataset = concatenate_datasets(dataset)
+    return dataset
+
+def prepare_dataset(dataset, vae_scale_factor, accelerator, args):
+    image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * 2 ,do_resize=True,do_convert_rgb=True)
+
+    def preprocess_conditions(conditions):
+        conditioning_tensors = []
+        conditions_types = []
+        for cond in conditions:
+            conditioning_tensors.append(image_processor.preprocess(cond.condition,width=args.resolution,height=args.resolution).squeeze(0))
+            conditions_types.append(cond.condition_type)
+        return torch.stack(conditioning_tensors,dim=0),conditions_types
+    def preprocess(examples):
+        # images = [image_transforms(image) for image in images]
+        pixel_values =[]
+        condition_latents=[]
+        condition_types=[]
+        bboxes = []
+        for image,bbox,canny,depth in zip(examples[args.image_column],examples[args.bbox_column],examples[args.canny_column],examples[args.depth_column]):
+            image = image.convert("RGB") if not isinstance(image, str) else Image.open(image).convert("RGB")
+            width, height = image.size
+            # 检查宽度是否为偶数，以便可以均匀分割
+            if width % 2 != 0:
+                raise ValueError("Image width must be even to split into two equal parts.")
+            # 分割图像
+            left_image = image.crop((0, 0, width // 2, height))  # 左半部分
+            right_image = image.crop((width // 2, 0, width, height))  # 右半部分
+            # load mask image
+            image_width,image_height = image.size
+            bbox_pixel = [
+                bbox[0] * image_width,
+                bbox[1] * image_height,
+                bbox[2] * image_width,
+                bbox[3] * image_height
+            ]
+            left = bbox_pixel[0] - bbox_pixel[2] / 2
+            top = bbox_pixel[1] - bbox_pixel[3] / 2
+            right = bbox_pixel[0] + bbox_pixel[2] / 2
+            bottom = bbox_pixel[1] + bbox_pixel[3] / 2
+            masked_left_image = left_image.copy()
+            masked_left_image.paste((0, 0, 0), (int(left), int(top), int(right), int(bottom)))
+            bboxes.append([int(left*args.resolution/(width // 2)), int(top*args.resolution/height), int(right*args.resolution/(width // 2)), int(bottom*args.resolution/height)])
+            # 应用转换,将分割后的图像添加到列表中
+            pixel_values.append(image_processor.preprocess(left_image,width=args.resolution,height=args.resolution).squeeze(0))
+            conditions = []
+            for condition_type in args.condition_types:
+                if condition_type == "subject":
+                    conditions.append(Condition("subject", condition = right_image))
+                elif condition_type == "canny":
+                    conditions.append(Condition("canny", condition = Image.open(io.BytesIO(canny['bytes']))))
+                elif condition_type == "depth":
+                    conditions.append(Condition("depth", condition = Image.open(io.BytesIO(depth['bytes']))))
+                elif condition_type == "fill":
+                    conditions.append(Condition("fill", condition = masked_left_image))
+                else:
+                    raise ValueError("Only support for subject, canny, depth, fill")
+            cond_tensors, cond_types = preprocess_conditions(conditions)
+            condition_latents.append(cond_tensors)
+            condition_types.append(cond_types)
+        examples["pixel_values"] = pixel_values
+        examples["condition_latents"] = condition_latents
+        examples["condition_types"] = condition_types
+        examples["bbox"]=bboxes
+        return examples
+
+    with accelerator.main_process_first():
+        dataset = dataset.with_transform(preprocess)
+
+    return dataset
+
+def collate_fn(examples):
+    pixel_values = torch.stack([example["pixel_values"] for example in examples])
+    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
+    condition_latents = torch.stack([example["condition_latents"] for example in examples])
+    condition_latents = condition_latents.to(memory_format=torch.contiguous_format).float()
+    bboxes= [example["bbox"] for example in examples]
+    condition_types= [example["condition_types"] for example in examples]
+    descriptions = [example["description"]["description_0"] for example in examples]
+    items = [example["description"]["item"] for example in examples]
+    return {"pixel_values": pixel_values, "condition_latents": condition_latents,
+            "condition_types":condition_types,"descriptions": descriptions, "bboxes": bboxes,"items":items}
+