mirror of
https://github.com/lucidrains/DALLE2-pytorch.git
synced 2025-12-19 09:44:19 +01:00
make sure optimizer and scaler is reloaded on resume for training diffusion prior script, move argparse to click
This commit is contained in:
Phil Wang
@@ -1,7 +1,7 @@
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import os
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from pathlib import Path
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import click
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import math
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import time
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import argparse
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import numpy as np
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import torch
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@@ -22,6 +22,17 @@ REPORT_METRICS_EVERY = 100 # for cosine similarity and other metric reporting du
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tracker = WandbTracker()
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# helpers functions
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class Timer:
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def __init__(self):
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self.reset()
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def reset(self):
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self.last_time = time.time()
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def elapsed(self):
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return time.time() - self.last_time
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# functions
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def eval_model(model,device,image_reader,text_reader,start,end,batch_size,loss_type,phase="Validation"):
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@@ -155,7 +166,7 @@ def train(image_embed_dim,
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# Load pre-trained model from DPRIOR_PATH
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if RESUME:
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diffusion_prior = load_diffusion_model(DPRIOR_PATH, device)
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diffusion_prior, loaded_obj = load_diffusion_model(DPRIOR_PATH, device)
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# TODO, optimizer and scaler needs to be loaded as well
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@@ -171,10 +182,15 @@ def train(image_embed_dim,
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amp = amp,
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).to(device)
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# load optimizer and scaler
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if RESUME:
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trainer.optimizer.load_state_dict(loaded_obj['optimizer'])
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trainer.scaler.load_state_dict(loaded_obj['scaler'])
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# Create save_path if it doesn't exist
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if not os.path.exists(save_path):
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os.makedirs(save_path)
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Path(save_path).mkdir(exist_ok = True, parents = True)
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# Get image and text embeddings from the servers
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@@ -185,8 +201,8 @@ def train(image_embed_dim,
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### Training code ###
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timer = Timer()
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epochs = num_epochs
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t = time.time()
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train_set_size = int(train_percent*num_data_points)
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val_set_size = int(val_percent*num_data_points)
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@@ -202,15 +218,15 @@ def train(image_embed_dim,
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emb_images_tensor = torch.tensor(emb_images[0]).to(device)
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emb_text_tensor = torch.tensor(emb_text[0]).to(device)
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loss = trainer(text_embed = emb_text_tensor,image_embed = emb_images_tensor)
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loss = trainer(text_embed = emb_text_tensor, image_embed = emb_images_tensor)
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# Samples per second
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samples_per_sec = batch_size*step/(time.time()-t)
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samples_per_sec = batch_size * step / timer.elapsed()
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# Save checkpoint every save_interval minutes
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if(int(time.time()-t) >= 60*save_interval):
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t = time.time()
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if(int(timer.elapsed()) >= 60 * save_interval):
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timer.reset()
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save_diffusion_model(
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save_path,
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@@ -253,67 +269,89 @@ def train(image_embed_dim,
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end = num_data_points
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eval_model(diffusion_prior,device,image_reader,text_reader,start,end,batch_size,dp_loss_type,phase="Test")
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def main():
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parser = argparse.ArgumentParser()
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# Logging
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parser.add_argument("--wandb-entity", type=str, default="laion")
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parser.add_argument("--wandb-project", type=str, default="diffusion-prior")
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parser.add_argument("--wandb-dataset", type=str, default="LAION-5B")
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parser.add_argument("--wandb-arch", type=str, default="DiffusionPrior")
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# URLs for embeddings
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parser.add_argument("--image-embed-url", type=str, default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/")
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parser.add_argument("--text-embed-url", type=str, default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/")
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# Hyperparameters
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parser.add_argument("--learning-rate", type=float, default=1.1e-4)
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parser.add_argument("--weight-decay", type=float, default=6.02e-2)
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parser.add_argument("--dropout", type=float, default=5e-2)
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parser.add_argument("--max-grad-norm", type=float, default=0.5)
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parser.add_argument("--batch-size", type=int, default=10**4)
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parser.add_argument("--num-epochs", type=int, default=5)
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# Image embed dimension
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parser.add_argument("--image-embed-dim", type=int, default=768)
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# Train-test split
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parser.add_argument("--train-percent", type=float, default=0.7)
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parser.add_argument("--val-percent", type=float, default=0.2)
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parser.add_argument("--test-percent", type=float, default=0.1)
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# LAION training(pre-computed embeddings)
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# DiffusionPriorNetwork(dpn) parameters
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parser.add_argument("--dpn-depth", type=int, default=6)
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parser.add_argument("--dpn-dim-head", type=int, default=64)
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parser.add_argument("--dpn-heads", type=int, default=8)
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# DiffusionPrior(dp) parameters
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parser.add_argument("--dp-condition-on-text-encodings", type=bool, default=False)
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parser.add_argument("--dp-timesteps", type=int, default=100)
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parser.add_argument("--dp-normformer", type=bool, default=False)
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parser.add_argument("--dp-cond-drop-prob", type=float, default=0.1)
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parser.add_argument("--dp-loss-type", type=str, default="l2")
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parser.add_argument("--clip", type=str, default=None)
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parser.add_argument("--amp", type=bool, default=False)
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# Model checkpointing interval(minutes)
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parser.add_argument("--save-interval", type=int, default=30)
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parser.add_argument("--save-path", type=str, default="./diffusion_prior_checkpoints")
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# Saved model path
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parser.add_argument("--pretrained-model-path", type=str, default=None)
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args = parser.parse_args()
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config = ({"learning_rate": args.learning_rate,
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"architecture": args.wandb_arch,
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"dataset": args.wandb_dataset,
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"weight_decay":args.weight_decay,
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"max_gradient_clipping_norm":args.max_grad_norm,
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"batch_size":args.batch_size,
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"epochs": args.num_epochs,
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"diffusion_prior_network":{"depth":args.dpn_depth,
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"dim_head":args.dpn_dim_head,
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"heads":args.dpn_heads,
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"normformer":args.dp_normformer},
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"diffusion_prior":{"condition_on_text_encodings": args.dp_condition_on_text_encodings,
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"timesteps": args.dp_timesteps,
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"cond_drop_prob":args.dp_cond_drop_prob,
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"loss_type":args.dp_loss_type,
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"clip":args.clip}
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})
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@click.command()
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@click.option("--wandb-entity", default="laion")
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@click.option("--wandb-project", default="diffusion-prior")
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@click.option("--wandb-dataset", default="LAION-5B")
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@click.option("--wandb-arch", default="DiffusionPrior")
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@click.option("--image-embed-url", default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/img_emb/")
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@click.option("--text-embed-url", default="https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion2B-en/text_emb/")
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@click.option("--learning-rate", default=1.1e-4)
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@click.option("--weight-decay", default=6.02e-2)
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@click.option("--dropout", default=5e-2)
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@click.option("--max-grad-norm", default=0.5)
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@click.option("--batch-size", default=10**4)
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@click.option("--num-epochs", default=5)
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@click.option("--image-embed-dim", default=768)
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@click.option("--train-percent", default=0.7)
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@click.option("--val-percent", default=0.2)
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@click.option("--test-percent", default=0.1)
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@click.option("--dpn-depth", default=6)
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@click.option("--dpn-dim-head", default=64)
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@click.option("--dpn-heads", default=8)
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@click.option("--dp-condition-on-text-encodings", default=False)
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@click.option("--dp-timesteps", default=100)
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@click.option("--dp-normformer", default=False)
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@click.option("--dp-cond-drop-prob", default=0.1)
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@click.option("--dp-loss-type", default="l2")
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@click.option("--clip", default=None)
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@click.option("--amp", default=False)
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@click.option("--save-interval", default=30)
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@click.option("--save-path", default="./diffusion_prior_checkpoints")
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@click.option("--pretrained-model-path", default=None)
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def main(
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wandb_entity,
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wandb_project,
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wandb_dataset,
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wandb_arch,
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image_embed_url,
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text_embed_url,
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learning_rate,
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weight_decay,
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dropout,
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max_grad_norm,
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batch_size,
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num_epochs,
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image_embed_dim,
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train_percent,
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val_percent,
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test_percent,
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dpn_depth,
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dpn_dim_head,
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dpn_heads,
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dp_condition_on_text_encodings,
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dp_timesteps,
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dp_normformer,
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dp_cond_drop_prob,
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dp_loss_type,
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clip,
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amp,
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save_interval,
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save_path,
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pretrained_model_path
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):
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config = {
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"learning_rate": learning_rate,
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"architecture": wandb_arch,
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"dataset": wandb_dataset,
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"weight_decay": weight_decay,
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"max_gradient_clipping_norm": max_grad_norm,
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"batch_size": batch_size,
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"epochs": num_epochs,
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"diffusion_prior_network": {
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"depth": dpn_depth,
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"dim_head": dpn_dim_head,
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"heads": dpn_heads,
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"normformer": dp_normformer
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},
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"diffusion_prior": {
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"condition_on_text_encodings": dp_condition_on_text_encodings,
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"timesteps": dp_timesteps,
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"cond_drop_prob": dp_cond_drop_prob,
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"loss_type": dp_loss_type,
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"clip": clip
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}
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}
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RESUME = False
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@@ -325,9 +363,10 @@ def main():
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RESUME = True
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else:
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tracker.init(
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entity=args.wandb_entity,
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project=args.wandb_project,
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config=config)
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entity = wandb_entity,
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project = wandb_project,
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config = config
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)
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# Obtain the utilized device.
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@@ -337,36 +376,36 @@ def main():
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torch.cuda.set_device(device)
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# Training loop
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train(args.image_embed_dim,
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args.image_embed_url,
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args.text_embed_url,
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args.batch_size,
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args.train_percent,
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args.val_percent,
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args.test_percent,
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args.num_epochs,
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args.dp_loss_type,
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args.clip,
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args.dp_condition_on_text_encodings,
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args.dp_timesteps,
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args.dp_normformer,
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args.dp_cond_drop_prob,
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args.dpn_depth,
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args.dpn_dim_head,
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args.dpn_heads,
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args.save_interval,
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args.save_path,
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train(image_embed_dim,
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image_embed_url,
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text_embed_url,
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batch_size,
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train_percent,
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val_percent,
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test_percent,
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num_epochs,
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dp_loss_type,
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clip,
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dp_condition_on_text_encodings,
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dp_timesteps,
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dp_normformer,
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dp_cond_drop_prob,
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dpn_depth,
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dpn_dim_head,
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dpn_heads,
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save_interval,
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save_path,
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device,
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RESUME,
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DPRIOR_PATH,
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config,
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args.wandb_entity,
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args.wandb_project,
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args.learning_rate,
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args.max_grad_norm,
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args.weight_decay,
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args.dropout,
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args.amp)
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wandb_entity,
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wandb_project,
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learning_rate,
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max_grad_norm,
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weight_decay,
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dropout,
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amp)
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if __name__ == "__main__":
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main()
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