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Vengery / gligen /trainer.py
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import torch
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.util import instantiate_from_config
import numpy as np
import random
import time
from dataset.concat_dataset import ConCatDataset #, collate_fn
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import os
import shutil
import torchvision
import math
from torch.nn.parallel import DistributedDataParallel as DDP
from tqdm import tqdm
from distributed import get_rank, synchronize, get_world_size
from transformers import get_cosine_schedule_with_warmup, get_constant_schedule_with_warmup
from copy import deepcopy
try:
from apex import amp
except:
pass
# = = = = = = = = = = = = = = = = = = useful functions = = = = = = = = = = = = = = = = = #
class ImageCaptionSaver:
def __init__(self, base_path, nrow=8, normalize=True, scale_each=True, range=(-1,1) ):
self.base_path = base_path
self.nrow = nrow
self.normalize = normalize
self.scale_each = scale_each
self.range = range
def __call__(self, images, real, captions, seen):
save_path = os.path.join(self.base_path, str(seen).zfill(8)+'.png')
torchvision.utils.save_image( images, save_path, nrow=self.nrow, normalize=self.normalize, scale_each=self.scale_each, range=self.range )
save_path = os.path.join(self.base_path, str(seen).zfill(8)+'_real.png')
torchvision.utils.save_image( real, save_path, nrow=self.nrow)
assert images.shape[0] == len(captions)
save_path = os.path.join(self.base_path, 'captions.txt')
with open(save_path, "a") as f:
f.write( str(seen).zfill(8) + ':\n' )
for cap in captions:
f.write( cap + '\n' )
f.write( '\n' )
def read_official_ckpt(ckpt_path):
"Read offical pretrained ckpt and convert into my style"
state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"]
out = {}
out["model"] = {}
out["text_encoder"] = {}
out["autoencoder"] = {}
out["unexpected"] = {}
out["diffusion"] = {}
for k,v in state_dict.items():
if k.startswith('model.diffusion_model'):
out["model"][k.replace("model.diffusion_model.", "")] = v
elif k.startswith('cond_stage_model'):
out["text_encoder"][k.replace("cond_stage_model.", "")] = v
elif k.startswith('first_stage_model'):
out["autoencoder"][k.replace("first_stage_model.", "")] = v
elif k in ["model_ema.decay", "model_ema.num_updates"]:
out["unexpected"][k] = v
else:
out["diffusion"][k] = v
return out
def batch_to_device(batch, device):
for k in batch:
if isinstance(batch[k], torch.Tensor):
batch[k] = batch[k].to(device)
return batch
def sub_batch(batch, num=1):
# choose first num in given batch
num = num if num > 1 else 1
for k in batch:
batch[k] = batch[k][0:num]
return batch
def wrap_loader(loader):
while True:
for batch in loader: # TODO: it seems each time you have the same order for all epoch??
yield batch
def disable_grads(model):
for p in model.parameters():
p.requires_grad = False
def count_params(params):
total_trainable_params_count = 0
for p in params:
total_trainable_params_count += p.numel()
print("total_trainable_params_count is: ", total_trainable_params_count)
def update_ema(target_params, source_params, rate=0.99):
for targ, src in zip(target_params, source_params):
targ.detach().mul_(rate).add_(src, alpha=1 - rate)
def create_expt_folder_with_auto_resuming(OUTPUT_ROOT, name):
#curr_folder_name = os.getcwd().split("/")[-1]
name = os.path.join( OUTPUT_ROOT, name )
writer = None
checkpoint = None
if os.path.exists(name):
all_tags = os.listdir(name)
all_existing_tags = [ tag for tag in all_tags if tag.startswith('tag') ]
all_existing_tags.sort()
all_existing_tags = all_existing_tags[::-1]
for previous_tag in all_existing_tags:
potential_ckpt = os.path.join( name, previous_tag, 'checkpoint_latest.pth' )
if os.path.exists(potential_ckpt):
checkpoint = potential_ckpt
if get_rank() == 0:
print('ckpt found '+ potential_ckpt)
break
curr_tag = 'tag'+str(len(all_existing_tags)).zfill(2)
name = os.path.join( name, curr_tag ) # output/name/tagxx
else:
name = os.path.join( name, 'tag00' ) # output/name/tag00
if get_rank() == 0:
os.makedirs(name)
os.makedirs( os.path.join(name,'Log') )
writer = SummaryWriter( os.path.join(name,'Log') )
return name, writer, checkpoint
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = #
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = #
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = #
class Trainer:
def __init__(self, config):
self.config = config
self.device = torch.device("cuda")
self.l_simple_weight = 1
self.name, self.writer, checkpoint = create_expt_folder_with_auto_resuming(config.OUTPUT_ROOT, config.name)
if get_rank() == 0:
shutil.copyfile(config.yaml_file, os.path.join(self.name, "train_config_file.yaml") )
torch.save( vars(config), os.path.join(self.name, "config_dict.pth") )
# = = = = = = = = = = create model and diffusion = = = = = = = = = = #
self.model = instantiate_from_config(config.model).to(self.device)
self.autoencoder = instantiate_from_config(config.autoencoder).to(self.device)
self.text_encoder = instantiate_from_config(config.text_encoder).to(self.device)
self.diffusion = instantiate_from_config(config.diffusion).to(self.device)
state_dict = read_official_ckpt( os.path.join(config.DATA_ROOT, config.official_ckpt_name) )
missing_keys, unexpected_keys = self.model.load_state_dict( state_dict["model"], strict=False )
assert unexpected_keys == []
original_params_names = list( state_dict["model"].keys() )
self.autoencoder.load_state_dict( state_dict["autoencoder"] )
self.text_encoder.load_state_dict( state_dict["text_encoder"] )
self.diffusion.load_state_dict( state_dict["diffusion"] )
self.autoencoder.eval()
self.text_encoder.eval()
disable_grads(self.autoencoder)
disable_grads(self.text_encoder)
# = = load from ckpt: (usually second stage whole model finetune) = = #
if self.config.ckpt is not None:
first_stage_ckpt = torch.load(self.config.ckpt, map_location="cpu")
self.model.load_state_dict(first_stage_ckpt["model"])
# = = = = = = = = = = create opt = = = = = = = = = = #
print(" ")
print("IMPORTANT: following code decides which params trainable!")
print(" ")
if self.config.whole:
print("Entire model is trainable")
params = list(self.model.parameters())
else:
print("Only new added components will be updated")
params = []
trainable_names = []
for name, p in self.model.named_parameters():
if ("transformer_blocks" in name) and ("fuser" in name):
params.append(p)
trainable_names.append(name)
elif "position_net" in name:
params.append(p)
trainable_names.append(name)
else:
# all new added trainable params have to be haddled above
# otherwise it will trigger the following error
assert name in original_params_names, name
all_params_name = list( self.model.state_dict().keys() )
assert set(all_params_name) == set(trainable_names + original_params_names)
self.opt = torch.optim.AdamW(params, lr=config.base_learning_rate, weight_decay=config.weight_decay)
count_params(params)
self.master_params = list(self.model.parameters()) # note: you cannot assign above params as master_params since that is only trainable one
if config.enable_ema:
self.ema = deepcopy(self.model)
self.ema_params = list(self.ema.parameters())
self.ema.eval()
# = = = = = = = = = = create scheduler = = = = = = = = = = #
if config.scheduler_type == "cosine":
self.scheduler = get_cosine_schedule_with_warmup(self.opt, num_warmup_steps=config.warmup_steps, num_training_steps=config.total_iters)
elif config.scheduler_type == "constant":
self.scheduler = get_constant_schedule_with_warmup(self.opt, num_warmup_steps=config.warmup_steps)
else:
assert False
# = = = = = = = = = = create data = = = = = = = = = = #
train_dataset_repeats = config.train_dataset_repeats if 'train_dataset_repeats' in config else None
dataset_train = ConCatDataset(config.train_dataset_names, config.DATA_ROOT, config.which_embedder, train=True, repeats=train_dataset_repeats)
sampler = DistributedSampler(dataset_train) if config.distributed else None
loader_train = DataLoader( dataset_train, batch_size=config.batch_size,
shuffle=(sampler is None),
num_workers=config.workers,
pin_memory=True,
sampler=sampler)
self.dataset_train = dataset_train
self.loader_train = wrap_loader(loader_train)
if get_rank() == 0:
total_image = dataset_train.total_images()
print("Total training images: ", total_image)
# = = = = = = = = = = load from autoresuming ckpt = = = = = = = = = = #
self.starting_iter = 0
if checkpoint is not None:
checkpoint = torch.load(checkpoint, map_location="cpu")
self.model.load_state_dict(checkpoint["model"])
if config.enable_ema:
self.ema.load_state_dict(checkpoint["ema"])
self.opt.load_state_dict(checkpoint["opt"])
self.scheduler.load_state_dict(checkpoint["scheduler"])
self.starting_iter = checkpoint["iters"]
if self.starting_iter >= config.total_iters:
synchronize()
print("Training finished. Start exiting")
exit()
# = = = = = misc = = = = = #
if get_rank() == 0:
print("Actual total need see images is: ", config.total_iters*config.total_batch_size)
print("Equivalent training epoch is: ", (config.total_iters*config.total_batch_size) / len(dataset_train) )
self.image_caption_saver = ImageCaptionSaver(self.name)
# self.counter = Counter(config.total_batch_size, config.save_every_images)
if config.use_o2:
self.model, self.opt = amp.initialize(self.model, self.opt, opt_level="O2")
self.model.use_o2 = True
# = = = = = wrap into ddp = = = = = #
if config.distributed:
self.model = DDP( self.model, device_ids=[config.local_rank], output_device=config.local_rank, broadcast_buffers=False )
@torch.no_grad()
def get_input(self, batch):
z = self.autoencoder.encode( batch["image"] )
context = self.text_encoder.encode( batch["caption"] )
_t = torch.rand(z.shape[0]).to(z.device)
t = (torch.pow(_t, self.config.resample_step_gamma) * 1000).long()
t = torch.where(t!=1000, t, 999) # if 1000, then replace it with 999
return z, t, context
def run_one_step(self, batch):
x_start, t, context = self.get_input(batch)
noise = torch.randn_like(x_start)
x_noisy = self.diffusion.q_sample(x_start=x_start, t=t, noise=noise)
input = dict(x = x_noisy,
timesteps = t,
context = context,
boxes = batch['boxes'],
masks = batch['masks'],
text_masks = batch['text_masks'],
image_masks = batch['image_masks'],
text_embeddings = batch["text_embeddings"],
image_embeddings = batch["image_embeddings"] )
model_output = self.model(input)
loss = torch.nn.functional.mse_loss(model_output, noise) * self.l_simple_weight
self.loss_dict = {"loss": loss.item()}
return loss
def start_training(self):
if not self.config.use_o2:
# use pytorch mixed training which is similar to o1 but faster
scaler = torch.cuda.amp.GradScaler()
iterator = tqdm(range(self.starting_iter, self.config.total_iters), desc='Training progress', disable=get_rank() != 0 )
self.model.train()
for iter_idx in iterator: # note: iter_idx is not from 0 if resume training
self.iter_idx = iter_idx
self.opt.zero_grad()
batch = next(self.loader_train)
batch_to_device(batch, self.device)
if self.config.use_o2:
loss = self.run_one_step(batch)
with amp.scale_loss(loss, self.opt) as scaled_loss:
scaled_loss.backward()
self.opt.step()
else:
enabled = True if self.config.use_mixed else False
with torch.cuda.amp.autocast(enabled=enabled): # with torch.autocast(enabled=True):
loss = self.run_one_step(batch)
scaler.scale(loss).backward()
scaler.step(self.opt)
scaler.update()
self.scheduler.step()
if self.config.enable_ema:
update_ema(self.ema_params, self.master_params, self.config.ema_rate)
if (get_rank() == 0):
if (iter_idx % 10 == 0):
self.log_loss()
if (iter_idx == 0) or ( iter_idx % self.config.save_every_iters == 0 ) or (iter_idx == self.config.total_iters-1):
self.save_ckpt_and_result()
synchronize()
synchronize()
print("Training finished. Start exiting")
exit()
def log_loss(self):
for k, v in self.loss_dict.items():
self.writer.add_scalar( k, v, self.iter_idx+1 ) # we add 1 as the actual name
@torch.no_grad()
def save_ckpt_and_result(self):
model_wo_wrapper = self.model.module if self.config.distributed else self.model
iter_name = self.iter_idx + 1 # we add 1 as the actual name
if not self.config.disable_inference_in_training:
# Do a quick inference on one training batch
batch_here = self.config.batch_size
batch = sub_batch( next(self.loader_train), batch_here)
batch_to_device(batch, self.device)
real_images_with_box_drawing = [] # we save this durining trianing for better visualization
for i in range(batch_here):
temp_data = {"image": batch["image"][i], "boxes":batch["boxes"][i]}
im = self.dataset_train.datasets[0].vis_getitem_data(out=temp_data, return_tensor=True, print_caption=False)
real_images_with_box_drawing.append(im)
real_images_with_box_drawing = torch.stack(real_images_with_box_drawing)
uc = self.text_encoder.encode( batch_here*[""] )
context = self.text_encoder.encode( batch["caption"] )
ddim_sampler = PLMSSampler(self.diffusion, model_wo_wrapper)
shape = (batch_here, model_wo_wrapper.in_channels, model_wo_wrapper.image_size, model_wo_wrapper.image_size)
input = dict( x = None,
timesteps = None,
context = context,
boxes = batch['boxes'],
masks = batch['masks'],
text_masks = batch['text_masks'],
image_masks = batch['image_masks'],
text_embeddings = batch["text_embeddings"],
image_embeddings = batch["image_embeddings"] )
samples = ddim_sampler.sample(S=50, shape=shape, input=input, uc=uc, guidance_scale=5)
# old
# autoencoder_wo_wrapper = self.autoencoder # Note itself is without wrapper since we do not train that.
# autoencoder_wo_wrapper = autoencoder_wo_wrapper.cpu() # To save GPU
# samples = autoencoder_wo_wrapper.decode(samples.cpu())
# autoencoder_wo_wrapper = autoencoder_wo_wrapper.to(self.device)
# new
autoencoder_wo_wrapper = self.autoencoder # Note itself is without wrapper since we do not train that.
samples = autoencoder_wo_wrapper.decode(samples).cpu()
self.image_caption_saver(samples, real_images_with_box_drawing, batch["caption"], iter_name)
ckpt = dict(model = model_wo_wrapper.state_dict(),
opt = self.opt.state_dict(),
scheduler= self.scheduler.state_dict(),
iters = self.iter_idx+1 )
if self.config.enable_ema:
ckpt["ema"] = self.ema.state_dict()
torch.save( ckpt, os.path.join(self.name, "checkpoint_"+str(iter_name).zfill(8)+".pth") )
torch.save( ckpt, os.path.join(self.name, "checkpoint_latest.pth") )