mirror of https://github.com/hpcaitech/ColossalAI
[example] add cifar10 dadaset for diffusion (#1902)
* add cifar10 dadasets * Update README.md Co-authored-by: binmakeswell <binmakeswell@gmail.com>pull/1905/head
Fazzie-Maqianli
2 years ago
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GitHub
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ca6e75bc28
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351f0f64e6
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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.00085
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linear_end: 0.0120
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: image
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cond_stage_key: txt
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image_size: 64
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channels: 4
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.18215
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use_ema: False
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1.e-4 ]
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f_min: [ 1.e-10 ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 32 # unused
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from_pretrained: '/data/scratch/diffuser/stable-diffusion-v1-4/unet/diffusion_pytorch_model.bin'
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in_channels: 4
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out_channels: 4
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model_channels: 320
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: False
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 4
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from_pretrained: '/data/scratch/diffuser/stable-diffusion-v1-4/vae/diffusion_pytorch_model.bin'
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monitor: val/rec_loss
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ddconfig:
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double_z: true
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z_channels: 4
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult:
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- 1
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- 2
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- 4
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- 4
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num_res_blocks: 2
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attn_resolutions: []
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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params:
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use_fp16: True
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data:
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target: main.DataModuleFromConfig
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params:
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batch_size: 4
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num_workers: 4
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train:
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target: ldm.data.cifar10.hf_dataset
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params:
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name: cifar10
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 512
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interpolation: 3
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- target: torchvision.transforms.RandomCrop
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params:
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size: 512
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- target: torchvision.transforms.RandomHorizontalFlip
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lightning:
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trainer:
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accelerator: 'gpu'
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devices: 2
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log_gpu_memory: all
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max_epochs: 2
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precision: 16
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auto_select_gpus: False
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strategy:
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target: pytorch_lightning.strategies.ColossalAIStrategy
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params:
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use_chunk: False
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enable_distributed_storage: True,
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placement_policy: cuda
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force_outputs_fp32: False
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log_every_n_steps: 2
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logger: True
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default_root_dir: "/tmp/diff_log/"
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profiler: pytorch
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logger_config:
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wandb:
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target: pytorch_lightning.loggers.WandbLogger
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params:
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name: nowname
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save_dir: "/tmp/diff_log/"
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offline: opt.debug
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id: nowname
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@ -0,0 +1,184 @@
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from typing import Dict
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import numpy as np
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from omegaconf import DictConfig, ListConfig
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import torch
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from torch.utils.data import Dataset
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from pathlib import Path
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import json
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from PIL import Image
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from torchvision import transforms
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from einops import rearrange
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from ldm.util import instantiate_from_config
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from datasets import load_dataset
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def make_multi_folder_data(paths, caption_files=None, **kwargs):
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"""Make a concat dataset from multiple folders
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Don't suport captions yet
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If paths is a list, that's ok, if it's a Dict interpret it as:
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k=folder v=n_times to repeat that
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"""
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list_of_paths = []
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if isinstance(paths, (Dict, DictConfig)):
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assert caption_files is None, \
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"Caption files not yet supported for repeats"
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for folder_path, repeats in paths.items():
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list_of_paths.extend([folder_path]*repeats)
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paths = list_of_paths
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if caption_files is not None:
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datasets = [FolderData(p, caption_file=c, **kwargs) for (p, c) in zip(paths, caption_files)]
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else:
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datasets = [FolderData(p, **kwargs) for p in paths]
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return torch.utils.data.ConcatDataset(datasets)
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class FolderData(Dataset):
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def __init__(self,
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root_dir,
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caption_file=None,
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image_transforms=[],
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ext="jpg",
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default_caption="",
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postprocess=None,
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return_paths=False,
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) -> None:
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"""Create a dataset from a folder of images.
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If you pass in a root directory it will be searched for images
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ending in ext (ext can be a list)
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"""
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self.root_dir = Path(root_dir)
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self.default_caption = default_caption
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self.return_paths = return_paths
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if isinstance(postprocess, DictConfig):
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postprocess = instantiate_from_config(postprocess)
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self.postprocess = postprocess
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if caption_file is not None:
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with open(caption_file, "rt") as f:
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ext = Path(caption_file).suffix.lower()
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if ext == ".json":
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captions = json.load(f)
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elif ext == ".jsonl":
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lines = f.readlines()
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lines = [json.loads(x) for x in lines]
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captions = {x["file_name"]: x["text"].strip("\n") for x in lines}
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else:
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raise ValueError(f"Unrecognised format: {ext}")
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self.captions = captions
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else:
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self.captions = None
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if not isinstance(ext, (tuple, list, ListConfig)):
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ext = [ext]
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# Only used if there is no caption file
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self.paths = []
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for e in ext:
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self.paths.extend(list(self.root_dir.rglob(f"*.{e}")))
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if isinstance(image_transforms, ListConfig):
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image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
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image_transforms.extend([transforms.ToTensor(),
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transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
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image_transforms = transforms.Compose(image_transforms)
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self.tform = image_transforms
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def __len__(self):
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if self.captions is not None:
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return len(self.captions.keys())
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else:
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return len(self.paths)
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def __getitem__(self, index):
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data = {}
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if self.captions is not None:
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chosen = list(self.captions.keys())[index]
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caption = self.captions.get(chosen, None)
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if caption is None:
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caption = self.default_caption
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filename = self.root_dir/chosen
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else:
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filename = self.paths[index]
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if self.return_paths:
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data["path"] = str(filename)
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im = Image.open(filename)
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im = self.process_im(im)
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data["image"] = im
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if self.captions is not None:
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data["txt"] = caption
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else:
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data["txt"] = self.default_caption
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if self.postprocess is not None:
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data = self.postprocess(data)
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return data
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def process_im(self, im):
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im = im.convert("RGB")
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return self.tform(im)
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def hf_dataset(
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name,
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image_transforms=[],
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image_column="img",
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label_column="label",
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text_column="txt",
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split='train',
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image_key='image',
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caption_key='txt',
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):
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"""Make huggingface dataset with appropriate list of transforms applied
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"""
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ds = load_dataset(name, split=split)
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image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
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image_transforms.extend([transforms.ToTensor(),
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transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
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tform = transforms.Compose(image_transforms)
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assert image_column in ds.column_names, f"Didn't find column {image_column} in {ds.column_names}"
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assert label_column in ds.column_names, f"Didn't find column {label_column} in {ds.column_names}"
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def pre_process(examples):
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processed = {}
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processed[image_key] = [tform(im) for im in examples[image_column]]
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label_to_text_dict = {0: "airplane", 1: "automobile", 2: "bird", 3: "cat", 4: "deer", 5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck"}
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processed[caption_key] = [label_to_text_dict[label] for label in examples[label_column]]
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return processed
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ds.set_transform(pre_process)
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return ds
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class TextOnly(Dataset):
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def __init__(self, captions, output_size, image_key="image", caption_key="txt", n_gpus=1):
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"""Returns only captions with dummy images"""
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self.output_size = output_size
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self.image_key = image_key
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self.caption_key = caption_key
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if isinstance(captions, Path):
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self.captions = self._load_caption_file(captions)
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else:
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self.captions = captions
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if n_gpus > 1:
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# hack to make sure that all the captions appear on each gpu
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repeated = [n_gpus*[x] for x in self.captions]
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self.captions = []
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[self.captions.extend(x) for x in repeated]
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def __len__(self):
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return len(self.captions)
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def __getitem__(self, index):
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dummy_im = torch.zeros(3, self.output_size, self.output_size)
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dummy_im = rearrange(dummy_im * 2. - 1., 'c h w -> h w c')
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return {self.image_key: dummy_im, self.caption_key: self.captions[index]}
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def _load_caption_file(self, filename):
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with open(filename, 'rt') as f:
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captions = f.readlines()
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return [x.strip('\n') for x in captions]
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