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Running
on
Zero
Running
on
Zero
File size: 1,840 Bytes
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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)
# save image
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
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 |