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train.py
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train.py
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#
import argparse
import os
import pickle
import pprint
import numpy as np
import torch
import tqdm
from torch.nn.modules.loss import CrossEntropyLoss
from torch.utils.data.dataloader import DataLoader
import torch.nn.functional as F
from model.dfsp import DFSP
from parameters import parser, YML_PATH
from loss import loss_calu
# from test import *
import test as test
from dataset import CompositionDataset
from utils import *
def train_model(model, optimizer, config, train_dataset, val_dataset, test_dataset):
train_dataloader = DataLoader(
train_dataset,
batch_size=config.train_batch_size,
shuffle=True
)
model.train()
best_loss = 1e5
best_metric = 0
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.5)
attr2idx = train_dataset.attr2idx
obj2idx = train_dataset.obj2idx
train_pairs = torch.tensor([(attr2idx[attr], obj2idx[obj])
for attr, obj in train_dataset.train_pairs]).cuda()
train_losses = []
for i in range(config.epoch_start, config.epochs):
progress_bar = tqdm.tqdm(
total=len(train_dataloader), desc="epoch % 3d" % (i + 1)
)
epoch_train_losses = []
for bid, batch in enumerate(train_dataloader):
batch_img = batch[0].cuda()
predict = model(batch_img, train_pairs)
loss = loss_calu(predict, batch, config)
# normalize loss to account for batch accumulation
loss = loss / config.gradient_accumulation_steps
# backward pass
loss.backward()
# weights update
if ((bid + 1) % config.gradient_accumulation_steps == 0) or (bid + 1 == len(train_dataloader)):
optimizer.step()
optimizer.zero_grad()
epoch_train_losses.append(loss.item())
progress_bar.set_postfix({"train loss": np.mean(epoch_train_losses[-50:])})
progress_bar.update()
scheduler.step()
progress_bar.close()
progress_bar.write(f"epoch {i +1} train loss {np.mean(epoch_train_losses)}")
train_losses.append(np.mean(epoch_train_losses))
if (i + 1) % config.save_every_n == 0:
torch.save(model.state_dict(), os.path.join(config.save_path, f"{config.fusion}_epoch_{i}.pt"))
print("Evaluating val dataset:")
loss_avg, val_result = evaluate(model, val_dataset)
print("Loss average on val dataset: {}".format(loss_avg))
if config.best_model_metric == "best_loss":
if loss_avg.cpu().float() < best_loss:
best_loss = loss_avg.cpu().float()
print("Evaluating test dataset:")
evaluate(model, test_dataset)
torch.save(model.state_dict(), os.path.join(
config.save_path, f"{config.fusion}_best.pt"
))
else:
if val_result[config.best_model_metric] > best_metric:
best_metric = val_result[config.best_model_metric]
print("Evaluating test dataset:")
evaluate(model, test_dataset)
torch.save(model.state_dict(), os.path.join(
config.save_path, f"{config.fusion}_best.pt"
))
if i + 1 == config.epochs:
print("Evaluating test dataset on Closed World")
model.load_state_dict(torch.load(os.path.join(
config.save_path, f"{config.fusion}_best.pt"
)))
evaluate(model, test_dataset)
if config.save_model:
torch.save(model.state_dict(), os.path.join(config.save_path, f'final_model_{config.fusion}.pt'))
def evaluate(model, dataset):
model.eval()
evaluator = test.Evaluator(dataset, model=None)
all_logits, all_attr_gt, all_obj_gt, all_pair_gt, loss_avg = test.predict_logits(
model, dataset, config)
test_stats = test.test(
dataset,
evaluator,
all_logits,
all_attr_gt,
all_obj_gt,
all_pair_gt,
config
)
result = ""
key_set = ["best_seen", "best_unseen", "AUC", "best_hm", "attr_acc", "obj_acc"]
for key in test_stats:
if key in key_set:
result = result + key + " " + str(round(test_stats[key], 4)) + "| "
print(result)
model.train()
return loss_avg, test_stats
if __name__ == "__main__":
config = parser.parse_args()
load_args(YML_PATH[config.dataset], config)
print(config)
# set the seed value
set_seed(config.seed)
dataset_path = config.dataset_path
train_dataset = CompositionDataset(dataset_path,
phase='train',
split='compositional-split-natural')
val_dataset = CompositionDataset(dataset_path,
phase='val',
split='compositional-split-natural')
test_dataset = CompositionDataset(dataset_path,
phase='test',
split='compositional-split-natural')
allattrs = train_dataset.attrs
allobj = train_dataset.objs
classes = [cla.replace(".", " ").lower() for cla in allobj]
attributes = [attr.replace(".", " ").lower() for attr in allattrs]
offset = len(attributes)
model = DFSP(config, attributes=attributes, classes=classes, offset=offset).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=config.lr, weight_decay=config.weight_decay)
# if config.load_model is not False:
# model.load_state_dict(torch.load(config.load_model))
os.makedirs(config.save_path, exist_ok=True)
train_model(model, optimizer, config, train_dataset, val_dataset, test_dataset)
with open(os.path.join(config.save_path, "config.pkl"), "wb") as fp:
pickle.dump(config, fp)
print("done!")