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* 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
5 changed files with 326 additions and 5 deletions
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|
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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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|
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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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|
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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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