Reorganization: move JAX VQGAN notebook to dev

And remove vqgan-jax-encoding-howto.py, which will be simply included
inline in the model card.

examples/vqgan-jax-encoding-howto.py

@@ -1,124 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# VQGAN-JAX - Encoding HowTo
-
-import numpy as np
-
-# For data loading
-import torch
-import torchvision.transforms.functional as TF
-from torch.utils.data import Dataset, DataLoader
-from torchvision.datasets.folder import default_loader
-from torchvision.transforms import InterpolationMode
-
-# For data saving
-from pathlib import Path
-import pandas as pd
-from tqdm import tqdm
-
-import jax
-from jax import pmap
-
-from vqgan_jax.modeling_flax_vqgan import VQModel
-
-## Params and arguments
-
-image_list = '/sddata/dalle-mini/CC12M/10k.tsv'     # List of paths containing images to encode
-output_tsv = 'output.tsv'                           # Encoded results
-batch_size = 64
-num_workers = 4    # TPU v3-8s have 96 cores, so feel free to increase this number when necessary
-
-# Load model
-model = VQModel.from_pretrained("flax-community/vqgan_f16_16384")
-
-## Data Loading.
-
-# Simple torch Dataset to load images from paths.
-# You can use your own pipeline instead.
-class ImageDataset(Dataset):
-    def __init__(self, image_list_path: str, image_size: int, max_items=None):
-        """
-        :param image_list_path: Path to a file containing a list of all images. We assume absolute paths for now.
-        :param image_size: Image size. Source images will be resized and center-cropped.
-        :max_items: Limit dataset size for debugging
-        """
-        self.image_list = pd.read_csv(image_list_path, sep='\t', header=None)
-        if max_items is not None: self.image_list = self.image_list[:max_items]
-        self.image_size = image_size
-        
-    def __len__(self):
-        return len(self.image_list)
-    
-    def _get_raw_image(self, i):
-        image_path = Path(self.image_list.iloc[i][0])
-        return default_loader(image_path)
-    
-    def resize_image(self, image):
-        s = min(image.size)
-        r = self.image_size / s
-        s = (round(r * image.size[1]), round(r * image.size[0]))
-        image = TF.resize(image, s, interpolation=InterpolationMode.LANCZOS)
-        image = TF.center_crop(image, output_size = 2 * [self.image_size])
-        image = np.expand_dims(np.array(image), axis=0)
-        return image
-    
-    def __getitem__(self, i):
-        image = self._get_raw_image(i)
-        return self.resize_image(image)
-
-## Encoding
-
-# Encoding function to be parallelized with `pmap`
-# Note: images have to be square
-def encode(model, batch):
-    _, indices = model.encode(batch)
-    return indices
-
-# Alternative: create a batch with num_tpus*batch_size and use `shard` to distribute.
-def superbatch_generator(dataloader, num_tpus):
-    iter_loader = iter(dataloader)
-    for batch in iter_loader:
-        superbatch = [batch.squeeze(1)]
-        try:
-            for _ in range(num_tpus-1):
-                batch = next(iter_loader)
-                if batch is None:
-                    break
-                # Skip incomplete last batch
-                if batch.shape[0] == dataloader.batch_size:
-                    superbatch.append(batch.squeeze(1))
-        except StopIteration:
-            pass
-        superbatch = torch.stack(superbatch, axis=0)
-        yield superbatch
-
-def encode_dataset(dataset, batch_size=32):
-    dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
-    superbatches = superbatch_generator(dataloader, num_tpus=jax.device_count())
-    
-    num_tpus = jax.device_count()
-    dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers)
-    superbatches = superbatch_generator(dataloader, num_tpus=num_tpus)
-    
-    p_encoder = pmap(lambda batch: encode(model, batch))
-
-    # We save each superbatch to avoid reallocation of buffers as we process them.
-    # We keep the file open to prevent excessive file seeks.
-    with open(output_tsv, "w") as file:
-        iterations = len(dataset) // (batch_size * num_tpus)
-        for n in tqdm(range(iterations)):
-            superbatch = next(superbatches)
-            encoded = p_encoder(superbatch.numpy())
-            encoded = encoded.reshape(-1, encoded.shape[-1])
-
-            # Extract paths from the dataset, and save paths and encodings (as string) to disk
-            start_index = n * batch_size * num_tpus
-            end_index = (n+1) * batch_size * num_tpus
-            paths = dataset.image_list[start_index:end_index][0].values
-            encoded_as_string = list(map(lambda item: np.array2string(item, separator=',', max_line_width=50000, formatter={'int':lambda x: str(x)}), encoded))
-            batch_df = pd.DataFrame.from_dict({"image_file": paths, "encoding": encoded_as_string})
-            batch_df.to_csv(file, sep='\t', header=(n==0), index=None)
-            
-dataset = ImageDataset(image_list, image_size=256)
-encoded_dataset = encode_dataset(dataset, batch_size=batch_size)