| import torch |
| import json |
| import copy |
| from torch.utils.data import Dataset |
| import os |
| from PIL import Image |
|
|
| def geneval_save_fn(image, metadata, root_path): |
| path = os.path.join(root_path, metadata['filename']) |
| if not os.path.exists(path): |
| os.makedirs(path, exist_ok=True) |
| |
| image_path = os.path.join(path, "samples", f"{metadata['seed']}.png") |
| if not os.path.exists(os.path.dirname(image_path)): |
| os.makedirs(os.path.dirname(image_path), exist_ok=True) |
| Image.fromarray(image).save(image_path) |
| |
| metadata_path = os.path.join(path, "metadata.jsonl") |
| with open(metadata_path, "w") as fp: |
| json.dump(metadata, fp) |
|
|
| class GenEvalDataset(Dataset): |
| def __init__(self, meta_json_path, num_samples_per_instance, latent_shape): |
| self.latent_shape = latent_shape |
| self.meta_json_path = meta_json_path |
| with open(meta_json_path) as fp: |
| self.metadatas = [json.loads(line) for line in fp] |
| self.num_instances = len(self.metadatas) |
| self.num_samples_per_instance = num_samples_per_instance |
| self.num_samples = self.num_instances * self.num_samples_per_instance |
|
|
| def __len__(self): |
| return self.num_samples |
|
|
| def __getitem__(self, idx): |
| instance_idx = idx // self.num_samples_per_instance |
| sample_idx = idx % self.num_samples_per_instance |
| metadata = copy.deepcopy(self.metadatas[instance_idx]) |
| generator = torch.Generator().manual_seed(sample_idx) |
| condition = metadata["prompt"] |
| latent = torch.randn(self.latent_shape, generator=generator, dtype=torch.float32) |
| filename = f"{idx}" |
| metadata["seed"] = sample_idx |
| metadata["filename"] = filename |
| metadata["save_fn"] = geneval_save_fn |
| return latent, condition, metadata |
