524 lines
20 KiB
Python
524 lines
20 KiB
Python
# --------------------------------------------------------
|
|
# mcan-vqa (Deep Modular Co-Attention Networks)
|
|
# Licensed under The MIT License [see LICENSE for details]
|
|
# Written by Yuhao Cui https://github.com/cuiyuhao1996
|
|
# --------------------------------------------------------
|
|
|
|
from core.data.dataset import VideoQA_Dataset
|
|
from core.model.net import Net1, Net2, Net3, Net4
|
|
from core.model.optim import get_optim, adjust_lr
|
|
from core.metrics import get_acc
|
|
from tqdm import tqdm
|
|
from core.data.utils import shuffle_list
|
|
|
|
import os, json, torch, datetime, pickle, copy, shutil, time, math
|
|
import numpy as np
|
|
import torch.nn as nn
|
|
import torch.utils.data as Data
|
|
from tensorboardX import SummaryWriter
|
|
from torch.autograd import Variable as var
|
|
|
|
class Execution:
|
|
def __init__(self, __C):
|
|
self.__C = __C
|
|
print('Loading training set ........')
|
|
__C_train = copy.deepcopy(self.__C)
|
|
setattr(__C_train, 'RUN_MODE', 'train')
|
|
self.dataset = VideoQA_Dataset(__C_train)
|
|
|
|
self.dataset_eval = None
|
|
if self.__C.EVAL_EVERY_EPOCH:
|
|
__C_eval = copy.deepcopy(self.__C)
|
|
setattr(__C_eval, 'RUN_MODE', 'val')
|
|
|
|
print('Loading validation set for per-epoch evaluation ........')
|
|
self.dataset_eval = VideoQA_Dataset(__C_eval)
|
|
self.dataset_eval.ans_list = self.dataset.ans_list
|
|
self.dataset_eval.ans_to_ix, self.dataset_eval.ix_to_ans = self.dataset.ans_to_ix, self.dataset.ix_to_ans
|
|
self.dataset_eval.token_to_ix, self.dataset_eval.pretrained_emb = self.dataset.token_to_ix, self.dataset.pretrained_emb
|
|
|
|
__C_test = copy.deepcopy(self.__C)
|
|
setattr(__C_test, 'RUN_MODE', 'test')
|
|
|
|
self.dataset_test = VideoQA_Dataset(__C_test)
|
|
self.dataset_test.ans_list = self.dataset.ans_list
|
|
self.dataset_test.ans_to_ix, self.dataset_test.ix_to_ans = self.dataset.ans_to_ix, self.dataset.ix_to_ans
|
|
self.dataset_test.token_to_ix, self.dataset_test.pretrained_emb = self.dataset.token_to_ix, self.dataset.pretrained_emb
|
|
|
|
self.writer = SummaryWriter(self.__C.TB_PATH)
|
|
|
|
def train(self, dataset, dataset_eval=None):
|
|
# Obtain needed information
|
|
data_size = dataset.data_size
|
|
token_size = dataset.token_size
|
|
ans_size = dataset.ans_size
|
|
pretrained_emb = dataset.pretrained_emb
|
|
net = self.construct_net(self.__C.MODEL_TYPE)
|
|
if os.path.isfile(self.__C.PRETRAINED_PATH) and self.__C.MODEL_TYPE == 11:
|
|
print('Loading pretrained DNC-weigths')
|
|
net.load_pretrained_weights()
|
|
net.cuda()
|
|
net.train()
|
|
|
|
# Define the multi-gpu training if needed
|
|
if self.__C.N_GPU > 1:
|
|
net = nn.DataParallel(net, device_ids=self.__C.DEVICES)
|
|
|
|
# Define the binary cross entropy loss
|
|
# loss_fn = torch.nn.BCELoss(size_average=False).cuda()
|
|
loss_fn = torch.nn.BCELoss(reduction='sum').cuda()
|
|
# Load checkpoint if resume training
|
|
if self.__C.RESUME:
|
|
print(' ========== Resume training')
|
|
|
|
if self.__C.CKPT_PATH is not None:
|
|
print('Warning: you are now using CKPT_PATH args, '
|
|
'CKPT_VERSION and CKPT_EPOCH will not work')
|
|
|
|
path = self.__C.CKPT_PATH
|
|
else:
|
|
path = self.__C.CKPTS_PATH + \
|
|
'ckpt_' + self.__C.CKPT_VERSION + \
|
|
'/epoch' + str(self.__C.CKPT_EPOCH) + '.pkl'
|
|
|
|
# Load the network parameters
|
|
print('Loading ckpt {}'.format(path))
|
|
ckpt = torch.load(path)
|
|
print('Finish!')
|
|
net.load_state_dict(ckpt['state_dict'])
|
|
|
|
# Load the optimizer paramters
|
|
optim = get_optim(self.__C, net, data_size, ckpt['optim'], lr_base=ckpt['lr_base'])
|
|
optim._step = int(data_size / self.__C.BATCH_SIZE * self.__C.CKPT_EPOCH)
|
|
optim.optimizer.load_state_dict(ckpt['optimizer'])
|
|
|
|
start_epoch = self.__C.CKPT_EPOCH
|
|
|
|
else:
|
|
if ('ckpt_' + self.__C.VERSION) in os.listdir(self.__C.CKPTS_PATH):
|
|
shutil.rmtree(self.__C.CKPTS_PATH + 'ckpt_' + self.__C.VERSION)
|
|
|
|
os.mkdir(self.__C.CKPTS_PATH + 'ckpt_' + self.__C.VERSION)
|
|
|
|
optim = get_optim(self.__C, net, data_size, self.__C.OPTIM)
|
|
start_epoch = 0
|
|
|
|
loss_sum = 0
|
|
named_params = list(net.named_parameters())
|
|
grad_norm = np.zeros(len(named_params))
|
|
|
|
# Define multi-thread dataloader
|
|
if self.__C.SHUFFLE_MODE in ['external']:
|
|
dataloader = Data.DataLoader(
|
|
dataset,
|
|
batch_size=self.__C.BATCH_SIZE,
|
|
shuffle=False,
|
|
num_workers=self.__C.NUM_WORKERS,
|
|
pin_memory=self.__C.PIN_MEM,
|
|
drop_last=True
|
|
)
|
|
else:
|
|
dataloader = Data.DataLoader(
|
|
dataset,
|
|
batch_size=self.__C.BATCH_SIZE,
|
|
shuffle=True,
|
|
num_workers=self.__C.NUM_WORKERS,
|
|
pin_memory=self.__C.PIN_MEM,
|
|
drop_last=True
|
|
)
|
|
|
|
# Training script
|
|
for epoch in range(start_epoch, self.__C.MAX_EPOCH):
|
|
|
|
# Save log information
|
|
logfile = open(
|
|
self.__C.LOG_PATH +
|
|
'log_run_' + self.__C.VERSION + '.txt',
|
|
'a+'
|
|
)
|
|
logfile.write(
|
|
'nowTime: ' +
|
|
datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') +
|
|
'\n'
|
|
)
|
|
logfile.close()
|
|
|
|
# Learning Rate Decay
|
|
if epoch in self.__C.LR_DECAY_LIST:
|
|
adjust_lr(optim, self.__C.LR_DECAY_R)
|
|
|
|
# Externally shuffle
|
|
if self.__C.SHUFFLE_MODE == 'external':
|
|
shuffle_list(dataset.ans_list)
|
|
|
|
time_start = time.time()
|
|
# Iteration
|
|
for step, (
|
|
ques_ix_iter,
|
|
frames_feat_iter,
|
|
clips_feat_iter,
|
|
ans_iter,
|
|
_,
|
|
_,
|
|
_,
|
|
_
|
|
) in enumerate(dataloader):
|
|
|
|
ques_ix_iter = ques_ix_iter.cuda()
|
|
frames_feat_iter = frames_feat_iter.cuda()
|
|
clips_feat_iter = clips_feat_iter.cuda()
|
|
ans_iter = ans_iter.cuda()
|
|
|
|
optim.zero_grad()
|
|
|
|
for accu_step in range(self.__C.GRAD_ACCU_STEPS):
|
|
|
|
sub_frames_feat_iter = \
|
|
frames_feat_iter[accu_step * self.__C.SUB_BATCH_SIZE:
|
|
(accu_step + 1) * self.__C.SUB_BATCH_SIZE]
|
|
sub_clips_feat_iter = \
|
|
clips_feat_iter[accu_step * self.__C.SUB_BATCH_SIZE:
|
|
(accu_step + 1) * self.__C.SUB_BATCH_SIZE]
|
|
sub_ques_ix_iter = \
|
|
ques_ix_iter[accu_step * self.__C.SUB_BATCH_SIZE:
|
|
(accu_step + 1) * self.__C.SUB_BATCH_SIZE]
|
|
sub_ans_iter = \
|
|
ans_iter[accu_step * self.__C.SUB_BATCH_SIZE:
|
|
(accu_step + 1) * self.__C.SUB_BATCH_SIZE]
|
|
|
|
pred = net(
|
|
sub_frames_feat_iter,
|
|
sub_clips_feat_iter,
|
|
sub_ques_ix_iter
|
|
)
|
|
|
|
loss = loss_fn(pred, sub_ans_iter)
|
|
|
|
# only mean-reduction needs be divided by grad_accu_steps
|
|
# removing this line wouldn't change our results because the speciality of Adam optimizer,
|
|
# but would be necessary if you use SGD optimizer.
|
|
# loss /= self.__C.GRAD_ACCU_STEPS
|
|
# start_backward = time.time()
|
|
loss.backward()
|
|
|
|
if self.__C.VERBOSE:
|
|
if dataset_eval is not None:
|
|
mode_str = self.__C.SPLIT['train'] + '->' + self.__C.SPLIT['val']
|
|
else:
|
|
mode_str = self.__C.SPLIT['train'] + '->' + self.__C.SPLIT['test']
|
|
|
|
# logging
|
|
|
|
self.writer.add_scalar(
|
|
'train/loss',
|
|
loss.cpu().data.numpy() / self.__C.SUB_BATCH_SIZE,
|
|
global_step=step + epoch * math.ceil(data_size / self.__C.BATCH_SIZE))
|
|
|
|
self.writer.add_scalar(
|
|
'train/lr',
|
|
optim._rate,
|
|
global_step=step + epoch * math.ceil(data_size / self.__C.BATCH_SIZE))
|
|
|
|
print("\r[exp_name %s][version %s][epoch %2d][step %4d/%4d][%s] loss: %.4f, lr: %.2e" % (
|
|
self.__C.EXP_NAME,
|
|
self.__C.VERSION,
|
|
epoch + 1,
|
|
step,
|
|
int(data_size / self.__C.BATCH_SIZE),
|
|
mode_str,
|
|
loss.cpu().data.numpy() / self.__C.SUB_BATCH_SIZE,
|
|
optim._rate,
|
|
), end=' ')
|
|
|
|
# Gradient norm clipping
|
|
if self.__C.GRAD_NORM_CLIP > 0:
|
|
nn.utils.clip_grad_norm_(
|
|
net.parameters(),
|
|
self.__C.GRAD_NORM_CLIP
|
|
)
|
|
|
|
# Save the gradient information
|
|
for name in range(len(named_params)):
|
|
norm_v = torch.norm(named_params[name][1].grad).cpu().data.numpy() \
|
|
if named_params[name][1].grad is not None else 0
|
|
grad_norm[name] += norm_v * self.__C.GRAD_ACCU_STEPS
|
|
|
|
optim.step()
|
|
|
|
time_end = time.time()
|
|
print('Finished in {}s'.format(int(time_end-time_start)))
|
|
|
|
epoch_finish = epoch + 1
|
|
|
|
# Save checkpoint
|
|
state = {
|
|
'state_dict': net.state_dict(),
|
|
'optimizer': optim.optimizer.state_dict(),
|
|
'lr_base': optim.lr_base,
|
|
'optim': optim.lr_base, }
|
|
|
|
torch.save(
|
|
state,
|
|
self.__C.CKPTS_PATH +
|
|
'ckpt_' + self.__C.VERSION +
|
|
'/epoch' + str(epoch_finish) +
|
|
'.pkl'
|
|
)
|
|
|
|
# Logging
|
|
logfile = open(
|
|
self.__C.LOG_PATH +
|
|
'log_run_' + self.__C.VERSION + '.txt',
|
|
'a+'
|
|
)
|
|
logfile.write(
|
|
'epoch = ' + str(epoch_finish) +
|
|
' loss = ' + str(loss_sum / data_size) +
|
|
'\n' +
|
|
'lr = ' + str(optim._rate) +
|
|
'\n\n'
|
|
)
|
|
logfile.close()
|
|
|
|
# Eval after every epoch
|
|
if dataset_eval is not None:
|
|
self.eval(
|
|
net,
|
|
dataset_eval,
|
|
self.writer,
|
|
epoch,
|
|
valid=True,
|
|
)
|
|
|
|
loss_sum = 0
|
|
grad_norm = np.zeros(len(named_params))
|
|
|
|
|
|
# Evaluation
|
|
def eval(self, net, dataset, writer, epoch, valid=False):
|
|
|
|
ans_ix_list = []
|
|
pred_list = []
|
|
q_type_list = []
|
|
q_bin_list = []
|
|
ans_rarity_list = []
|
|
|
|
ans_qtype_dict = {'what': [], 'who': [], 'how': [], 'when': [], 'where': []}
|
|
pred_qtype_dict = {'what': [], 'who': [], 'how': [], 'when': [], 'where': []}
|
|
|
|
|
|
ans_qlen_bin_dict = {'1-3': [], '4-8': [], '9-15': []}
|
|
pred_qlen_bin_dict = {'1-3': [], '4-8': [], '9-15': []}
|
|
|
|
ans_ans_rarity_dict = {'0-99': [], '100-299': [], '300-999': []}
|
|
pred_ans_rarity_dict = {'0-99': [], '100-299': [], '300-999': []}
|
|
|
|
data_size = dataset.data_size
|
|
|
|
net.eval()
|
|
|
|
if self.__C.N_GPU > 1:
|
|
net = nn.DataParallel(net, device_ids=self.__C.DEVICES)
|
|
|
|
dataloader = Data.DataLoader(
|
|
dataset,
|
|
batch_size=self.__C.EVAL_BATCH_SIZE,
|
|
shuffle=False,
|
|
num_workers=self.__C.NUM_WORKERS,
|
|
pin_memory=True
|
|
)
|
|
|
|
for step, (
|
|
ques_ix_iter,
|
|
frames_feat_iter,
|
|
clips_feat_iter,
|
|
_,
|
|
ans_iter,
|
|
q_type,
|
|
qlen_bin,
|
|
ans_rarity
|
|
) in enumerate(dataloader):
|
|
print("\rEvaluation: [step %4d/%4d]" % (
|
|
step,
|
|
int(data_size / self.__C.EVAL_BATCH_SIZE),
|
|
), end=' ')
|
|
ques_ix_iter = ques_ix_iter.cuda()
|
|
frames_feat_iter = frames_feat_iter.cuda()
|
|
clips_feat_iter = clips_feat_iter.cuda()
|
|
with torch.no_grad():
|
|
|
|
pred = net(
|
|
frames_feat_iter,
|
|
clips_feat_iter,
|
|
ques_ix_iter
|
|
)
|
|
|
|
pred_np = pred.cpu().data.numpy()
|
|
pred_argmax = np.argmax(pred_np, axis=1)
|
|
pred_list.extend(pred_argmax)
|
|
ans_ix_list.extend(ans_iter.tolist())
|
|
q_type_list.extend(q_type.tolist())
|
|
q_bin_list.extend(qlen_bin.tolist())
|
|
ans_rarity_list.extend(ans_rarity.tolist())
|
|
|
|
print('')
|
|
|
|
assert len(pred_list) == len(ans_ix_list) == len(q_type_list) == len(q_bin_list) == len(ans_rarity_list)
|
|
pred_list = [dataset.ix_to_ans[pred] for pred in pred_list]
|
|
ans_ix_list = [dataset.ix_to_ans[ans] for ans in ans_ix_list]
|
|
|
|
# Run validation script
|
|
scores_per_qtype = {
|
|
'what': {},
|
|
'who': {},
|
|
'how': {},
|
|
'when': {},
|
|
'where': {},
|
|
}
|
|
scores_per_qlen_bin = {
|
|
'1-3': {},
|
|
'4-8': {},
|
|
'9-15': {},
|
|
}
|
|
scores_ans_rarity_dict = {
|
|
'0-99': {},
|
|
'100-299': {},
|
|
'300-999': {}
|
|
}
|
|
|
|
if valid:
|
|
# create vqa object and vqaRes object
|
|
for pred, ans, q_type in zip(pred_list, ans_ix_list, q_type_list):
|
|
pred_qtype_dict[dataset.idx_to_qtypes[q_type]].append(pred)
|
|
ans_qtype_dict[dataset.idx_to_qtypes[q_type]].append(ans)
|
|
|
|
print('----------------- Computing scores -----------------')
|
|
acc = get_acc(ans_ix_list, pred_list)
|
|
print('----------------- Overall -----------------')
|
|
print('acc: {}'.format(acc))
|
|
writer.add_scalar('acc/overall', acc, global_step=epoch)
|
|
|
|
for q_type in scores_per_qtype:
|
|
print('----------------- Computing "{}" q-type scores -----------------'.format(q_type))
|
|
# acc, wups_0, wups_1 = get_scores(
|
|
# ans_ix_dict[q_type], pred_ix_dict[q_type])
|
|
acc = get_acc(ans_qtype_dict[q_type], pred_qtype_dict[q_type])
|
|
print('acc: {}'.format(acc))
|
|
writer.add_scalar(
|
|
'acc/{}'.format(q_type), acc, global_step=epoch)
|
|
else:
|
|
for pred, ans, q_type, qlen_bin, a_rarity in zip(
|
|
pred_list, ans_ix_list, q_type_list, q_bin_list, ans_rarity_list):
|
|
|
|
pred_qtype_dict[dataset.idx_to_qtypes[q_type]].append(pred)
|
|
ans_qtype_dict[dataset.idx_to_qtypes[q_type]].append(ans)
|
|
|
|
pred_qlen_bin_dict[dataset.idx_to_qlen_bins[qlen_bin]].append(pred)
|
|
ans_qlen_bin_dict[dataset.idx_to_qlen_bins[qlen_bin]].append(ans)
|
|
|
|
pred_ans_rarity_dict[dataset.idx_to_ans_rare[a_rarity]].append(pred)
|
|
ans_ans_rarity_dict[dataset.idx_to_ans_rare[a_rarity]].append(ans)
|
|
|
|
print('----------------- Computing overall scores -----------------')
|
|
acc = get_acc(ans_ix_list, pred_list)
|
|
|
|
print('----------------- Overall -----------------')
|
|
print('acc:{}'.format(acc))
|
|
|
|
|
|
print('----------------- Computing q-type scores -----------------')
|
|
for q_type in scores_per_qtype:
|
|
acc = get_acc(ans_qtype_dict[q_type], pred_qtype_dict[q_type])
|
|
print(' {} '.format(q_type))
|
|
print('acc:{}'.format(acc))
|
|
|
|
print('----------------- Computing qlen-bins scores -----------------')
|
|
for qlen_bin in scores_per_qlen_bin:
|
|
|
|
acc = get_acc(ans_qlen_bin_dict[qlen_bin], pred_qlen_bin_dict[qlen_bin])
|
|
print(' {} '.format(qlen_bin))
|
|
print('acc:{}'.format(acc))
|
|
|
|
print('----------------- Computing ans-rarity scores -----------------')
|
|
for a_rarity in scores_ans_rarity_dict:
|
|
acc = get_acc(ans_ans_rarity_dict[a_rarity], pred_ans_rarity_dict[a_rarity])
|
|
print(' {} '.format(a_rarity))
|
|
print('acc:{}'.format(acc))
|
|
net.train()
|
|
|
|
def construct_net(self, model_type):
|
|
if model_type == 1:
|
|
net = Net1(
|
|
self.__C,
|
|
self.dataset.pretrained_emb,
|
|
self.dataset.token_size,
|
|
self.dataset.ans_size
|
|
)
|
|
elif model_type == 2:
|
|
net = Net2(
|
|
self.__C,
|
|
self.dataset.pretrained_emb,
|
|
self.dataset.token_size,
|
|
self.dataset.ans_size
|
|
)
|
|
elif model_type == 3:
|
|
net = Net3(
|
|
self.__C,
|
|
self.dataset.pretrained_emb,
|
|
self.dataset.token_size,
|
|
self.dataset.ans_size
|
|
)
|
|
elif model_type == 4:
|
|
net = Net4(
|
|
self.__C,
|
|
self.dataset.pretrained_emb,
|
|
self.dataset.token_size,
|
|
self.dataset.ans_size
|
|
)
|
|
else:
|
|
raise ValueError('Net{} is not supported'.format(model_type))
|
|
return net
|
|
|
|
def run(self, run_mode, epoch=None):
|
|
self.set_seed(self.__C.SEED)
|
|
if run_mode == 'train':
|
|
self.empty_log(self.__C.VERSION)
|
|
self.train(self.dataset, self.dataset_eval)
|
|
|
|
elif run_mode == 'val':
|
|
self.eval(self.dataset, valid=True)
|
|
|
|
elif run_mode == 'test':
|
|
net = self.construct_net(self.__C.MODEL_TYPE)
|
|
assert epoch is not None
|
|
path = self.__C.CKPTS_PATH + \
|
|
'ckpt_' + self.__C.VERSION + \
|
|
'/epoch' + str(epoch) + '.pkl'
|
|
print('Loading ckpt {}'.format(path))
|
|
state_dict = torch.load(path)['state_dict']
|
|
net.load_state_dict(state_dict)
|
|
net.cuda()
|
|
self.eval(net, self.dataset_test, self.writer, 0)
|
|
|
|
else:
|
|
exit(-1)
|
|
|
|
def set_seed(self, seed):
|
|
"""Sets the seed for reproducibility.
|
|
Args:
|
|
seed (int): The seed used
|
|
"""
|
|
torch.manual_seed(seed)
|
|
torch.cuda.manual_seed(seed)
|
|
torch.backends.cudnn.deterministic = True
|
|
torch.backends.cudnn.benchmark = False
|
|
np.random.seed(seed)
|
|
print('\nSeed set to {}...\n'.format(seed))
|
|
|
|
def empty_log(self, version):
|
|
print('Initializing log file ........')
|
|
if (os.path.exists(self.__C.LOG_PATH + 'log_run_' + version + '.txt')):
|
|
os.remove(self.__C.LOG_PATH + 'log_run_' + version + '.txt')
|
|
print('Finished!')
|
|
print('')
|