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2 Parent(s): c7776fb d9f0f39

Merge branch 'main' into chore-mv

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{examples → dev/notebooks/vqgan}/JAX_VQGAN_f16_16384_Reconstruction.ipynb RENAMED
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examples/vqgan-jax-encoding-howto.py DELETED
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- #!/usr/bin/env python
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- # coding: utf-8
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-
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- # VQGAN-JAX - Encoding HowTo
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-
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- import numpy as np
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-
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- # For data loading
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- import torch
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- import torchvision.transforms.functional as TF
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- from torch.utils.data import Dataset, DataLoader
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- from torchvision.datasets.folder import default_loader
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- from torchvision.transforms import InterpolationMode
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-
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- # For data saving
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- from pathlib import Path
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- import pandas as pd
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- from tqdm import tqdm
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-
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- import jax
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- from jax import pmap
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-
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- from vqgan_jax.modeling_flax_vqgan import VQModel
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-
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- ## Params and arguments
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-
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- image_list = '/sddata/dalle-mini/CC12M/10k.tsv' # List of paths containing images to encode
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- output_tsv = 'output.tsv' # Encoded results
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- batch_size = 64
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- num_workers = 4 # TPU v3-8s have 96 cores, so feel free to increase this number when necessary
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-
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- # Load model
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- model = VQModel.from_pretrained("flax-community/vqgan_f16_16384")
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-
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- ## Data Loading.
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-
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- # Simple torch Dataset to load images from paths.
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- # You can use your own pipeline instead.
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- class ImageDataset(Dataset):
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- def __init__(self, image_list_path: str, image_size: int, max_items=None):
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- """
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- :param image_list_path: Path to a file containing a list of all images. We assume absolute paths for now.
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- :param image_size: Image size. Source images will be resized and center-cropped.
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- :max_items: Limit dataset size for debugging
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- """
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- self.image_list = pd.read_csv(image_list_path, sep='\t', header=None)
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- if max_items is not None: self.image_list = self.image_list[:max_items]
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- self.image_size = image_size
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-
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- def __len__(self):
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- return len(self.image_list)
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-
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- def _get_raw_image(self, i):
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- image_path = Path(self.image_list.iloc[i][0])
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- return default_loader(image_path)
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-
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- def resize_image(self, image):
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- s = min(image.size)
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- r = self.image_size / s
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- s = (round(r * image.size[1]), round(r * image.size[0]))
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- image = TF.resize(image, s, interpolation=InterpolationMode.LANCZOS)
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- image = TF.center_crop(image, output_size = 2 * [self.image_size])
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- image = np.expand_dims(np.array(image), axis=0)
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- return image
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-
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- def __getitem__(self, i):
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- image = self._get_raw_image(i)
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- return self.resize_image(image)
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-
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- ## Encoding
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-
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- # Encoding function to be parallelized with `pmap`
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- # Note: images have to be square
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- def encode(model, batch):
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- _, indices = model.encode(batch)
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- return indices
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-
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- # Alternative: create a batch with num_tpus*batch_size and use `shard` to distribute.
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- def superbatch_generator(dataloader, num_tpus):
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- iter_loader = iter(dataloader)
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- for batch in iter_loader:
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- superbatch = [batch.squeeze(1)]
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- try:
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- for _ in range(num_tpus-1):
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- batch = next(iter_loader)
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- if batch is None:
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- break
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- # Skip incomplete last batch
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- if batch.shape[0] == dataloader.batch_size:
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- superbatch.append(batch.squeeze(1))
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- except StopIteration:
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- pass
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- superbatch = torch.stack(superbatch, axis=0)
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- yield superbatch
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-
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- def encode_dataset(dataset, batch_size=32):
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- dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
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- superbatches = superbatch_generator(dataloader, num_tpus=jax.device_count())
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-
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- num_tpus = jax.device_count()
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- dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
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- superbatches = superbatch_generator(dataloader, num_tpus=num_tpus)
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-
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- p_encoder = pmap(lambda batch: encode(model, batch))
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-
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- # We save each superbatch to avoid reallocation of buffers as we process them.
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- # We keep the file open to prevent excessive file seeks.
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- with open(output_tsv, "w") as file:
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- iterations = len(dataset) // (batch_size * num_tpus)
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- for n in tqdm(range(iterations)):
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- superbatch = next(superbatches)
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- encoded = p_encoder(superbatch.numpy())
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- encoded = encoded.reshape(-1, encoded.shape[-1])
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-
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- # Extract paths from the dataset, and save paths and encodings (as string) to disk
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- start_index = n * batch_size * num_tpus
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- end_index = (n+1) * batch_size * num_tpus
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- paths = dataset.image_list[start_index:end_index][0].values
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- encoded_as_string = list(map(lambda item: np.array2string(item, separator=',', max_line_width=50000, formatter={'int':lambda x: str(x)}), encoded))
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- batch_df = pd.DataFrame.from_dict({"image_file": paths, "encoding": encoded_as_string})
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- batch_df.to_csv(file, sep='\t', header=(n==0), index=None)
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-
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- dataset = ImageDataset(image_list, image_size=256)
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- encoded_dataset = encode_dataset(dataset, batch_size=batch_size)