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train_2stages.py
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train_2stages.py
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import os
import shutil
import sys
import time
from functools import partial
import numpy as np
import torch
import tqdm
from peft import LoraConfig, get_peft_model
from model.LISA_Dahi import LISAForCausalLM
from model.llava import conversation as conversation_lib
from utils.dataset import HybridDataset, ValDataset, collate_fn,collate_fn3
from utils.utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
AverageMeter, ProgressMeter, Summary, dict_to_cuda,
intersectionAndUnionGPU)
from utils.cd_dataset import Contrastive_CD_Dataset
import cv2
import random
import my_utils
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from transformers import get_linear_schedule_with_warmup
import torch.nn as nn
args = my_utils.parse_args(sys.argv[1:])
args.exp_name = "NP_S1_cls_1_noCELoss_2"
args.const_seg_data="xbd"
args.version="./mbin/test/LLaVA-7B-Lightening-v1-1/"
args.constrative_dataset_dir="/localscratch/gna23/cd-datasets/"
args.dataset_dir="/localscratch/gna23/cd-datasets/"
args.use_scheduler = False
args.lr = 0.0001
args.epochs = 300
args.ce_loss_weight = 0.0
args.num_classes_per_sample = 5
# args.local_rank = "cpu"
# args.version = "mmaaz60/LLaVA-7B-Lightening-v1-1"
# args.vision_pretrained="./mbin/sam_vit_h_4b8939.pth"
# args.workers = 0
wandb = my_utils.wandb_init(args)
tokenizer = my_utils.get_tokenizer(args)
tokenizer.pad_token = tokenizer.unk_token
num_added_tokens = tokenizer.add_tokens("[SEG]")
args.seg_token_idx = tokenizer("[SEG]", add_special_tokens=False).input_ids[0]
if args.use_mm_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
model_args = my_utils.get_model_args(args)
torch_dtype = torch.float32
if args.precision == "bf16":
torch_dtype = torch.bfloat16
elif args.precision == "fp16":
torch_dtype = torch.half
model = LISAForCausalLM.from_pretrained(args.version, torch_dtype=torch_dtype, low_cpu_mem_usage=True, **model_args)
model.config.eos_token_id = tokenizer.eos_token_id
model.config.bos_token_id = tokenizer.bos_token_id
model.config.pad_token_id = tokenizer.pad_token_id
model.enable_input_require_grads()
model.gradient_checkpointing_enable()
model.get_model().initialize_vision_modules(model.get_model().config)
vision_tower = model.get_model().get_vision_tower()
vision_tower.to(dtype=torch_dtype, device=args.local_rank)
if args.constrative:
model.cross_attn.load_state_dict(torch.load('./mbin/cross_attn_dahi.pt'), strict=True)
model.cross_attn.to(dtype=torch_dtype, device=args.local_rank)
print("****** Loading Pretrained weights ******")
model.load_state_dict(torch.load("./runs/lisa-7b-xbd-14days/ckpt_model/pytorch_model.bin"),strict=False)
model.get_model().initialize_lisa_modules(model.get_model().config)
for p in vision_tower.parameters():
p.requires_grad = False
for p in model.get_model().mm_projector.parameters():
p.requires_grad = False
conversation_lib.default_conversation = conversation_lib.conv_templates[args.conv_type]
lora_r = args.lora_r
if lora_r > 0:
def find_linear_layers(model, lora_target_modules):
cls = torch.nn.Linear
lora_module_names = set()
for name, module in model.named_modules():
if (isinstance(module, cls) and all([x not in name for x in ["visual_model","vision_tower","mm_projector","text_hidden_fcs",]])
and any([x in name for x in lora_target_modules])):
lora_module_names.add(name)
return sorted(list(lora_module_names))
lora_alpha = args.lora_alpha
lora_dropout = args.lora_dropout
lora_target_modules = find_linear_layers(model, args.lora_target_modules.split(","))
lora_config = LoraConfig(
r=lora_r,
lora_alpha=lora_alpha,
target_modules=lora_target_modules,
lora_dropout=lora_dropout,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
model.resize_token_embeddings(len(tokenizer))
new_model = torch.load("./new_pipeline_model/NP_S1_cls_1_noCELoss/best.pth")
# from collections import OrderedDict
# corrected_model = OrderedDict()
# for ech_lay in new_model:
# corrected_model[ech_lay.replace("base_model.model.","")] = new_model[ech_lay]
model.load_state_dict(new_model,strict=True)
for n, p in model.named_parameters():
if any([x in n for x in ["lm_head", "embed_tokens", "mask_decoder", "text_hidden_fcs", "lora_"]]):
print("n: ", n, "p.shape: ", p.shape)
p.requires_grad = False
model.cross_attn.train()
for param in model.cross_attn.parameters():
param.requires_grad = True
train_dataset = HybridDataset(
args.constrative_dataset_dir,
tokenizer,
args.vision_tower,
samples_per_epoch=3000,
precision=args.precision,
image_size=args.image_size,
num_classes_per_sample=args.num_classes_per_sample,
exclude_val=False,
dataset=args.dataset,
sample_rate=[1],
sem_seg_data=args.sem_seg_data,
refer_seg_data=args.refer_seg_data,
vqa_data=args.vqa_data,
reason_seg_data=args.reason_seg_data,
explanatory=args.explanatory,
const_seg_data = args.const_seg_data
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=False,
collate_fn=partial(
collate_fn3,
tokenizer=tokenizer,
conv_type=args.conv_type,
use_mm_start_end=args.use_mm_start_end,
local_rank=args.local_rank,
),
)
val_dataset = HybridDataset(
args.constrative_dataset_dir,
tokenizer,
args.vision_tower,
samples_per_epoch=1200,
precision=args.precision,
image_size=args.image_size,
num_classes_per_sample=args.num_classes_per_sample,
exclude_val=True,
dataset=args.dataset,
sample_rate=[1],
sem_seg_data=args.sem_seg_data,
refer_seg_data=args.refer_seg_data,
vqa_data=args.vqa_data,
reason_seg_data=args.reason_seg_data,
explanatory=args.explanatory,
const_seg_data = args.const_seg_data
)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=1,
shuffle=True,
num_workers=args.workers,
pin_memory=False,
collate_fn=partial(
collate_fn3,
tokenizer=tokenizer,
conv_type=args.conv_type,
use_mm_start_end=args.use_mm_start_end,
local_rank=args.local_rank,
),
)
# optimizer
optimizer = optim.AdamW(
model.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2),
weight_decay=0.0
)
if args.use_scheduler:
# Learning rate scheduler setup
total_steps = args.epochs * args.steps_per_epoch
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=100,
num_training_steps=total_steps
)
# Mixed precision training
# scaler = torch.cuda.amp.GradScaler(enabled=(args.precision in ["fp16", "bf16"]))
# val_dict = {}
# for ech in range(len(train_dataset)):
# print(ech)
# name = train_dataset.__getitem__(ech)[0][0]
# val_dict[name] = int(val_dict.get(name,0)) + 1
# model.bfloat16()
# model.to(device=args.local_rank)
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device = torch.device("cuda:0")
# model = nn.DataParallel(model,device_ids=[0,1,2,3])
model = model.to(dtype=torch_dtype)
model = model.to(device)
clss = [
"no building","undamaged building", "building with minor damage",
"building with major damage", "completely destroyed building"
]
best_iou = 0
for epoch in range(args.epochs):
losses = AverageMeter("Loss", ":.4f")
ce_losses = AverageMeter("CeLoss", ":.4f")
mask_bce_losses = AverageMeter("MaskBCELoss", ":.4f")
mask_dice_losses = AverageMeter("MaskDICELoss", ":.4f")
mask_losses = AverageMeter("MaskLoss", ":.4f")
model.train()
# if epoch > 50:
# for n, p in model.named_parameters():
# if any([x in n for x in ["lm_head", "embed_tokens", "mask_decoder", "text_hidden_fcs", "lora_"]]):
# print("n: ", n, "p.shape: ", p.shape)
# p.requires_grad = True
#
# model.cross_attn.train()
# for param in model.cross_attn.parameters():
# param.requires_grad = True
#
# for param_group in optimizer.param_groups:
# param_group['lr'] = 0.0001
for train_idx,input_dict in enumerate(train_loader):
print(train_idx,end='\r')
optimizer.zero_grad()
input_dict = my_utils.typecasting_inputs(input_dict,args,device)
output_dict = model(**input_dict)
loss = output_dict["loss"]
loss.backward()
# Gradient clipping
# torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
if args.use_scheduler:
scheduler.step()
losses.update(loss.item(), input_dict["images"].size(0))
ce_losses.update(output_dict["ce_loss"].item(), input_dict["images"].size(0))
mask_bce_losses.update(output_dict["mask_bce_loss"].item(), input_dict["images"].size(0))
mask_dice_losses.update(output_dict["mask_dice_loss"].item(), input_dict["images"].size(0))
mask_losses.update(output_dict["mask_loss"].item(), input_dict["images"].size(0))
if train_idx % 100 ==0:
print("epoch : ",epoch," iter : ",train_idx," loss : ",losses.avg)
wandb.log({
"train/loss":losses.avg,
"train/ce_loss": ce_losses.avg,
"train/mask_bce_loss": mask_bce_losses.avg,
"train/mask_dice_loss": mask_dice_losses.avg,
"train/mask_loss": mask_losses.avg,
"train/lr": optimizer.param_groups[0]['lr']
})
losses.reset()
ce_losses.reset()
mask_bce_losses.reset()
mask_dice_losses.reset()
mask_losses.reset()
# break
print("Eval pipeline")
torch.cuda.empty_cache()
model.eval()
iou_dict = {}
for val_idx, input_dict in enumerate(val_loader):
print(val_idx, end='\r')
input_dict = my_utils.typecasting_inputs(input_dict, args, device)
input_dict['inference'] = True
save_name = input_dict['image_paths'][0][0].split('/')[-1]
with torch.no_grad():
output_dict = model(**input_dict)
pred_masks = output_dict["pred_masks"]
masks_list = output_dict["gt_masks"][0].int()
output_list = (pred_masks[0] > 0).int()
log_exp_img = []
image_logger = {}
for mask_i, output_i, prmpt in zip(masks_list, output_list, input_dict['sampled_classes_list'][0]):
pd = output_i.cpu().numpy().astype(np.uint8)
gt = mask_i.cpu().numpy().astype(np.uint8)
if len(np.unique(gt)) == 1 and len(np.unique(pd)) == 1:
iou_score = int(np.unique(gt)[0] == np.unique(pd)[0])
else:
intersection = np.logical_and(pd, gt)
union = np.logical_or(pd, gt)
iou_score = np.sum(intersection) / np.sum(union)
# print("iou_score : ",iou_score)
iou_score = round(iou_score, 2)
if np.isnan(iou_score):
# print("Caught")
iou_score = 0
iou_lists = iou_dict.get(prmpt, [])
iou_lists.append(iou_score)
iou_dict[prmpt] = iou_lists
pd = cv2.cvtColor(cv2.resize(pd, (224, 224)), cv2.COLOR_GRAY2RGB)
gt = cv2.cvtColor(cv2.resize(gt, (224, 224)), cv2.COLOR_GRAY2RGB)
sv_image = np.zeros([224, 448, 3], np.uint8)
sv_image[:224, :224] = pd
sv_image[:224, 224:] = gt
sv_image = sv_image*255.0
cv2.putText(sv_image, prmpt, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 1)
temp_name_i = str(clss.index(prmpt)) + "("+str(iou_score)+")_" + save_name
image_logger[str(clss.index(prmpt))] = wandb.Image(sv_image, caption=f"{temp_name_i}")
# log_exp_img.append(wandb.Image(sv_image, caption=f"{temp_name_i}"))
for ech_cls in ['0','1','2','3','4']:
if ech_cls in image_logger:
log_exp_img.append(image_logger[ech_cls])
else:
log_exp_img.append(wandb.Image(sv_image * 0, caption=f"{ech_cls}_fillers"))
wandb.log({"visualization": log_exp_img})
total_avg = []
wandb_dict = {}
for ech in iou_dict:
cur_avg = np.average(iou_dict[ech])
wandb_dict['val/'+ech]=cur_avg
total_avg.append(cur_avg)
cur_iou = np.average(total_avg)
wandb_dict['val/iou'] = np.average(total_avg)
wandb.log(wandb_dict)
ckpt_pth = os.path.join("./new_pipeline_model",args.exp_name)
if not os.path.exists(ckpt_pth):
os.makedirs(ckpt_pth)
print(f"Directory '{ckpt_pth}' created.")
if cur_iou>best_iou:
torch.save(model.state_dict(), os.path.join(ckpt_pth,'{}_{}.pth'.format(epoch,round(cur_iou,4))))
best_iou = cur_iou