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trainer.py
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trainer.py
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import math
import logging
from tqdm import tqdm
import numpy as np
from torchtext.data.metrics import bleu_score
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data.dataloader import DataLoader
from loss import Clip_loss
from utils import sample, save_loss_to_csv
logger = logging.getLogger(__name__)
class TrainerConfig:
# optimization parameters
max_epochs = 10
batch_size = 64
learning_rate = 3e-4
betas = (0.9, 0.95)
grad_norm_clip = 1.0
weight_decay = 0.1 # only applied on matmul weights
# learning rate decay params: linear warmup followed by cosine decay to 10% of original
lr_decay = False
warmup_tokens = 375e6 # these two numbers come from the GPT-3 paper, but may not be good defaults elsewhere
final_tokens = 260e9 # (at what point we reach 10% of original LR)
# checkpoint settings
ckpt_path = None
num_workers = 0 # for DataLoader
def __init__(self, **kwargs):
for k,v in kwargs.items():
setattr(self, k, v)
class Trainer:
def __init__(self, model, train_dataset, test_dataset, config, word_2_id, id_2_word):
self.model = model
self.train_dataset = train_dataset
self.test_dataset = test_dataset
self.config = config
self.word_2_id = word_2_id
self.id_2_word = id_2_word
# take over whatever gpus are on the system
self.device = 'cpu'
if torch.cuda.is_available():
self.device = torch.cuda.current_device()
self.model = torch.nn.DataParallel(self.model).to(self.device)
def generate(self, model, x, y, len_mark, label):
# print(x.shape, y.shape) # torch.Size([16, 224, 224]) torch.Size([16, 512, 1])
batch_size = x.shape[0]
ys = []
gens = []
for i in range(batch_size):
x_prime = x[i]
y_prime = y[i]
label_prime = label[i]
#print("decoder input shape:", x)
x_prime = x_prime.unsqueeze(0)
y_prime = y_prime.unsqueeze(0).unsqueeze(2)
y_idx = [item.item() for sublist in y_prime[0] for item in sublist]
gens.append(sample(model, x_prime, y_prime[:,0,:], label_prime, None, steps=30, train=True))
ys.append(y_idx)
y_texts = []
gen_texts = []
for i in range(batch_size):
gen_texts.append([self.id_2_word[k] if k != 2319 else '' for k in gens[i][1:-2]])
y_texts.append([self.id_2_word[k] if k != 2319 else '' for k in ys[i][1:-2]])
return gen_texts, y_texts
def save_checkpoint(self):
# DataParallel wrappers keep raw model object in .module attribute
raw_model = self.model.module if hasattr(self.model, "module") else self.model
logger.info("saving %s", self.config.ckpt_path)
torch.save(raw_model.state_dict(), self.config.ckpt_path)
def train(self):
model, config = self.model, self.config
raw_model = model.module if hasattr(self.model, "module") else model
optimizer = raw_model.configure_optimizers(config)
with torch.no_grad():
clip = Clip_loss()
def run_epoch(split):
is_train = split == 'train'
model.train(is_train)
data = self.train_dataset if is_train else self.test_dataset
loader = DataLoader(data, shuffle=True, pin_memory=True,
batch_size=config.batch_size,
num_workers=config.num_workers,
collate_fn = data.collate_fn)
losses = []
clip_losses = []
tgts = []
len_tgts = []
preds = []
pbar = tqdm(enumerate(loader), total=len(loader)) if is_train else enumerate(loader)
for it, (x, y, len_masks, labels) in pbar:
# print(x.shape, y.shape) # torch.Size([16, 224, 224]) torch.Size([16, 512])
# place data on the correct device
x = x.to(self.device)
y = y.to(self.device)
len_masks = len_masks.to(self.device)
labels = labels.to(self.device)
# forward the model
with torch.set_grad_enabled(is_train):
logits, loss, pred = model(x, y, len_masks, labels)
loss = loss.mean() # collapse all losses if they are scattered on multiple gpus
losses.append(loss.item())
tgts.append(y)
#len_tgts.append(leny)
preds.append(pred)
src_class, trg_class = self.generate(model, x, y, len_masks, labels)
clip_loss = 0.
batch_size = x.shape[0]
for i in range(batch_size):
src_text = ' '.join(src_class[i])
trg_text = ' '.join(trg_class[i])
src_class[i] = src_text
trg_class[i] = trg_text
# print(x.shape)
x_ = x.unsqueeze(1).repeat(1,3,1,1)
# print(x_.shape)
clip_loss = clip(x_, src_class, x_, trg_class)
clip_losses.append(clip_loss)
loss += clip_loss
if is_train:
# backprop and update the parameters
model.zero_grad()
loss.requires_grad_(True)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_norm_clip)
optimizer.step()
# decay the learning rate based on our progress
if config.lr_decay:
self.tokens += (y >= 0).sum() # number of tokens processed this step (i.e. label is not -100)
if self.tokens < config.warmup_tokens:
# linear warmup
lr_mult = float(self.tokens) / float(max(1, config.warmup_tokens))
else:
# cosine learning rate decay
progress = float(self.tokens - config.warmup_tokens) / float(max(1, config.final_tokens - config.warmup_tokens))
lr_mult = max(0.1, 0.5 * (1.0 + math.cos(math.pi * progress)))
lr = config.learning_rate * lr_mult
for param_group in optimizer.param_groups:
param_group['lr'] = lr
else:
lr = config.learning_rate
# report progress
pbar.set_description(f"epoch {epoch+1} iter {it}: train loss {loss.item():.5f}. clip loss {clip_loss.item():.5f}. lr {lr:e}")
if not is_train:
test_loss = float(np.mean(losses))
#logger.info("test loss: %f", test_loss)
#return test_loss
tgts = torch.vstack(tgts).cpu().numpy().tolist()
preds = torch.vstack(preds).cpu().numpy().tolist()
tgts_list = []
preds_list = []
for i in range(len(tgts)):
try:
eos_ind = tgts[i].index(2319)
except:
eos_ind = len(tgts[i])-1
tgts_list.append(tgts[i][:eos_ind])
tgts_list[-1] = [[self.id_2_word[x] for x in tgts_list[-1]]]
for i in range(len(preds)):
try:
eos_ind = preds[i].index(2319)
except:
eos_ind = len(preds[i])-1
preds_list.append(preds[i][:eos_ind])
preds_list[-1] = [str(self.id_2_word[x]) for x in preds_list[-1]]
assert(len(preds_list) == len(tgts_list))
#print(preds_list[10][:60])
#print(tgts_list[10][0][:60])
test_bleu = bleu_score(preds_list, tgts_list, max_n=2, weights=[0,1])
logger.info("test loss: %f \t bleu_score_2:%f", test_loss, test_bleu)
clip_losses_float = [float(value) for value in clip_losses]
save_loss_to_csv(epoch, losses, clip_losses_float, test_loss, test_bleu, '/content/drive/MyDrive/UNIST/2023_1/NLP/ChestXrayReportGen/cxr-report-generation/enc_dcd/loss_csv/test.csv')
return test_loss, test_bleu
best_loss = float('inf')
best_bleu = float('-inf')
self.tokens = 0 # counter used for learning rate decay
for epoch in range(config.max_epochs):
run_epoch('train')
if self.test_dataset is not None:
test_loss, test_bleu = run_epoch('test')
# supports early stopping based on the test loss, or just save always if no test set is provided
good_model = self.test_dataset is None or test_loss < 1.10*best_loss
if self.config.ckpt_path is not None and good_model:
if test_loss < best_loss:
best_loss = test_loss
best_bleu = test_bleu
self.save_checkpoint()