'accuracy: ' + str(acc)[0:7])
predict_for_mAP = np.array(predict_for_mAP)
label_for_mAP = np.array(label_for_mAP)
MAP = mAP(predict_for_mAP, label_for_mAP, 'Lsm')
acc = accuracy(predict_for_mAP, label_for_mAP, 'Lsm')
print("mAP: " + str(MAP) + ' ' + 'accuracy: ' + str(acc))
if acc > max_test_acc:
print('Saving')
max_test_acc = acc
torch.save(
{
'model': model.state_dict(),
'max_acc': max_test_acc,
'epoch': epoch,
'step': 0,
'opt': optimizer.state_dict()
}, opt.model_path + '/' + opt.model_name + '_' +
str(epoch) + '_' + str(max_test_acc)[0:6])
model.train()
test = False
predict_for_mAP = []
label_for_mAP = []
if opt.test:
exit()
class Trainer:
def __init__(self, logger, checkpoint, device_ids, config):
self.config = config
self.logger = logger
self.device_ids = device_ids
self.dataset, n_classes = get_dataset(config['dataset'],
config['dataset_params'])
if self.config['with_labels']:
self.config['generator_params']['n_classes'] = n_classes
self.config['discriminator_params']['n_classes'] = n_classes
self.config['n_classes'] = n_classes
else:
self.config['generator_params']['n_classes'] = None
self.config['discriminator_params']['n_classes'] = None
self.restore(checkpoint)
print("Generator...")
print(self.generator)
print("Discriminator...")
print(self.discriminator)
def restore(self, checkpoint):
self.epoch = 0
self.generator = DCGenerator(**self.config['generator_params'])
self.generator = DataParallelWithCallback(self.generator,
device_ids=self.device_ids)
self.optimizer_generator = torch.optim.Adam(
params=self.generator.parameters(),
lr=self.config['lr_generator'],
betas=(self.config['b1_generator'], self.config['b2_generator']),
weight_decay=0,
eps=1e-8)
self.discriminator = DCDiscriminator(
**self.config['discriminator_params'])
self.discriminator = DataParallelWithCallback(
self.discriminator, device_ids=self.device_ids)
self.optimizer_discriminator = torch.optim.Adam(
params=self.discriminator.parameters(),
lr=self.config['lr_discriminator'],
betas=(self.config['b1_discriminator'],
self.config['b2_discriminator']),
weight_decay=0,
eps=1e-8)
if checkpoint is not None:
data = torch.load(checkpoint)
for key, value in data:
if key == 'epoch':
self.epoch = value
else:
self.__dict__[key].load_state_dict(value)
lr_lambda = lambda epoch: 1 - epoch / self.config['num_epochs']
self.scheduler_generator = torch.optim.lr_scheduler.LambdaLR(
self.optimizer_generator, lr_lambda, last_epoch=self.epoch - 1)
self.scheduler_discriminator = torch.optim.lr_scheduler.LambdaLR(
self.optimizer_discriminator, lr_lambda, last_epoch=self.epoch - 1)
def save(self):
state_dict = {
'epoch': self.epoch,
'generator': self.generator.state_dict(),
'optimizer_generator': self.optimizer_generator.state_dict(),
'discriminator': self.discriminator.state_dict(),
'optimizer_discriminator':
self.optimizer_discriminator.state_dict()
}
torch.save(state_dict, os.path.join(self.logger.log_dir, 'cpk.pth'))
def train(self):
loader = DataLoader(self.dataset,
batch_size=self.config['discriminator_bs'],
shuffle=False,
drop_last=True,
num_workers=self.config['num_workers'])
noise = torch.zeros((max(self.config['generator_bs'],
self.config['discriminator_bs']),
self.config['generator_params']['dim_z'])).cuda()
if self.config['with_labels']:
labels_fake = torch.zeros(
max(self.config['generator_bs'],
self.config['discriminator_bs'])).type(
torch.LongTensor).cuda()
else:
labels_fake = None
y_fake = None
# Keep track of current iteration for update generator
current_iter = 0
loss_dict = defaultdict(lambda: 0.0)
for self.epoch in tqdm(range(self.epoch, self.config['num_epochs'])):
for data in tqdm(loader):
self.generator.train()
current_iter += 1
images, labels_real = data
y_real = None if not self.config['with_labels'] else labels_real
self.optimizer_generator.zero_grad()
self.optimizer_discriminator.zero_grad()
z = noise.normal_()[:self.config['discriminator_bs']]
if self.config['with_labels']:
y_fake = labels_fake.random_(
self.config['n_classes'])[:self.
config['discriminator_bs']]
with torch.no_grad():
images_fake = self.generator(z, y_fake)
logits_real = self.discriminator(images, y_real)
logits_fake = self.discriminator(images_fake, y_fake)
loss_fake = torch.relu(1 + logits_fake).mean()
loss_real = torch.relu(1 - logits_real).mean()
loss_dict['loss_fake'] += loss_fake.detach().cpu().numpy()
loss_dict['loss_real'] += loss_real.detach().cpu().numpy()
(loss_fake + loss_real).backward()
self.optimizer_discriminator.step()
if current_iter % self.config['num_discriminator_updates'] == 0:
self.optimizer_discriminator.zero_grad()
self.optimizer_generator.zero_grad()
z = noise.normal_()[:self.config['generator_bs']]
if self.config['with_labels']:
y_fake = labels_fake.random_(
self.config['n_classes'])[:self.
config['generator_bs']]
images_fake = self.generator(z, y_fake)
logits_fake = self.discriminator(images_fake, y_fake)
adversarial_loss = -logits_fake.mean()
loss_dict['adversarial_loss'] += adversarial_loss.detach(
).cpu().numpy()
adversarial_loss.backward()
self.optimizer_generator.step()
save_dict = {
key: value / current_iter
for key, value in loss_dict.items()
}
save_dict['lr'] = self.optimizer_generator.param_groups[0]['lr']
loss_dict = defaultdict(lambda: 0.0)
current_iter = 0
with torch.no_grad():
noise = noise.normal_()
if self.config['with_labels']:
labels_fake = labels_fake.random_(self.config['n_classes'])
images = self.generator(noise, labels_fake)
self.logger.save_images(self.epoch, images)
# if self.epoch % self.config['eval_frequency'] == 0 or self.epoch == self.config['num_epochs'] - 1:
# self.generator.eval()
#
# if self.config['samples_evaluation'] != 0:
# generated = []
# with torch.no_grad():
# for i in range(self.config['samples_evaluation'] // noise.shape[0] + 1):
# noise = noise.normal_()
# if self.config['with_labels']:
# labels_fake = labels_fake.random_(self.config['n_classes'])
#
# generated.append((127.5 * self.generator(noise, labels_fake) + 127.5).cpu().numpy())
#
# generated = np.concatenate(generated)[:self.config['samples_evaluation']]
# self.logger.save_evaluation_images(self.epoch, generated)
self.logger.log(self.epoch, save_dict)
self.scheduler_generator.step()
self.scheduler_discriminator.step()
self.save()
MAP = mAP(np.array(predict_for_mAP), np.array(label_for_mAP), 'Lsm')
acc = accuracy(np.array(predict_for_mAP), np.array(label_for_mAP), 'Lsm')
print(" Loss: " + str(TEST_LOSS.avg)[0:5] + ' ' + 'accuracy: ' + str(acc)[0:7])
predict_for_mAP = np.array(predict_for_mAP)
label_for_mAP = np.array(label_for_mAP)
MAP = mAP(predict_for_mAP, label_for_mAP, 'Lsm')
acc = accuracy(predict_for_mAP, label_for_mAP, 'Lsm')
print("mAP: " + str(MAP) + ' ' + 'accuracy: ' + str(acc))
if acc > max_test_acc:
print('Saving')
max_test_acc = acc
torch.save({'model': model.state_dict(), 'max_acc': max_test_acc, 'epoch': epoch, 'step': 0,
'opt': optimizer.state_dict()},
opt.model_path + '/' + opt.model_name + '_' + str(epoch) + '_' + str(max_test_acc)[0:6])
model.train()
test = False
predict_for_mAP = []
label_for_mAP = []
if opt.test:
exit()
if epoch % opt.saveInter == 0:
print('Saving')
torch.save({'model': model.state_dict(), 'max_acc': max_test_acc, 'epoch': epoch, 'step': 0, 'opt': optimizer.state_dict()}, opt.model_path + '/' + opt.model_name + '_' + str(epoch))
class Model:
def __init__(self,
hidden_dim,
lr,
hard_or_full_trip,
margin,
num_workers,
batch_size,
restore_iter,
total_iter,
save_name,
train_pid_num,
frame_num,
model_name,
train_source,
test_source,
img_size=64):
self.save_name = save_name
self.train_pid_num = train_pid_num
self.train_source = train_source
self.test_source = test_source
self.hidden_dim = hidden_dim
self.lr = lr
self.hard_or_full_trip = hard_or_full_trip
self.margin = margin
self.frame_num = frame_num
self.num_workers = num_workers
self.batch_size = batch_size
self.model_name = model_name
self.P, self.M = batch_size
self.restore_iter = restore_iter
self.total_iter = total_iter
self.img_size = img_size
self.encoder = SetNet(self.hidden_dim).float()
self.encoder = DataParallelWithCallback(self.encoder)
self.triplet_loss = TripletLoss(self.P * self.M, self.hard_or_full_trip, self.margin).float()
self.triplet_loss = DataParallelWithCallback(self.triplet_loss)
self.encoder.cuda()
self.triplet_loss.cuda()
self.optimizer = optim.Adam([
{'params': self.encoder.parameters()},
], lr=self.lr)
self.hard_loss_metric = []
self.full_loss_metric = []
self.full_loss_num = []
self.dist_list = []
self.mean_dist = 0.01
self.sample_type = 'all'
def collate_fn(self, batch):
batch_size = len(batch)
feature_num = len(batch[0][0])
seqs = [batch[i][0] for i in range(batch_size)]
frame_sets = [batch[i][1] for i in range(batch_size)]
view = [batch[i][2] for i in range(batch_size)]
seq_type = [batch[i][3] for i in range(batch_size)]
label = [batch[i][4] for i in range(batch_size)]
batch = [seqs, view, seq_type, label, None]
def select_frame(index):
sample = seqs[index]
frame_set = frame_sets[index]
if self.sample_type == 'random':
frame_id_list = random.choices(frame_set, k=self.frame_num)
_ = [feature.loc[frame_id_list].values for feature in sample]
else:
_ = [feature.values for feature in sample]
return _
seqs = list(map(select_frame, range(len(seqs))))
if self.sample_type == 'random':
seqs = [np.asarray([seqs[i][j] for i in range(batch_size)]) for j in range(feature_num)]
else:
gpu_num = min(torch.cuda.device_count(), batch_size)
batch_per_gpu = math.ceil(batch_size / gpu_num)
batch_frames = [[
len(frame_sets[i])
for i in range(batch_per_gpu * _, batch_per_gpu * (_ + 1))
if i < batch_size
] for _ in range(gpu_num)]
if len(batch_frames[-1]) != batch_per_gpu:
for _ in range(batch_per_gpu - len(batch_frames[-1])):
batch_frames[-1].append(0)
max_sum_frame = np.max([np.sum(batch_frames[_]) for _ in range(gpu_num)])
seqs = [[
np.concatenate([
seqs[i][j]
for i in range(batch_per_gpu * _, batch_per_gpu * (_ + 1))
if i < batch_size
], 0) for _ in range(gpu_num)]
for j in range(feature_num)]
seqs = [np.asarray([
np.pad(seqs[j][_],
((0, max_sum_frame - seqs[j][_].shape[0]), (0, 0), (0, 0)),
'constant',
constant_values=0)
for _ in range(gpu_num)])
for j in range(feature_num)]
batch[4] = np.asarray(batch_frames)
batch[0] = seqs
return batch
def fit(self):
if self.restore_iter != 0:
self.load(self.restore_iter)
self.encoder.train()
self.sample_type = 'random'
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.lr
triplet_sampler = TripletSampler(self.train_source, self.batch_size)
train_loader = tordata.DataLoader(
dataset=self.train_source,
batch_sampler=triplet_sampler,
collate_fn=self.collate_fn,
num_workers=self.num_workers)
train_label_set = list(self.train_source.label_set)
train_label_set.sort()
_time1 = datetime.now()
for seq, view, seq_type, label, batch_frame in train_loader:
self.restore_iter += 1
self.optimizer.zero_grad()
for i in range(len(seq)):
seq[i] = self.np2var(seq[i]).float()
if batch_frame is not None:
batch_frame = self.np2var(batch_frame).int()
feature, label_prob = self.encoder(*seq, batch_frame)
target_label = [train_label_set.index(l) for l in label]
target_label = self.np2var(np.array(target_label)).long()
triplet_feature = feature.permute(1, 0, 2).contiguous()
triplet_label = target_label.unsqueeze(0).repeat(triplet_feature.size(0), 1)
(full_loss_metric, hard_loss_metric, mean_dist, full_loss_num
) = self.triplet_loss(triplet_feature, triplet_label)
if self.hard_or_full_trip == 'hard':
loss = hard_loss_metric.mean()
elif self.hard_or_full_trip == 'full':
loss = full_loss_metric.mean()
self.hard_loss_metric.append(hard_loss_metric.mean().data.cpu().numpy())
self.full_loss_metric.append(full_loss_metric.mean().data.cpu().numpy())
self.full_loss_num.append(full_loss_num.mean().data.cpu().numpy())
self.dist_list.append(mean_dist.mean().data.cpu().numpy())
if loss > 1e-9:
loss.backward()
self.optimizer.step()
if self.restore_iter % 1000 == 0:
print(datetime.now() - _time1)
_time1 = datetime.now()
if self.restore_iter % 100 == 0:
self.save()
print('iter {}:'.format(self.restore_iter), end='')
print(', hard_loss_metric={0:.8f}'.format(np.mean(self.hard_loss_metric)), end='')
print(', full_loss_metric={0:.8f}'.format(np.mean(self.full_loss_metric)), end='')
print(', full_loss_num={0:.8f}'.format(np.mean(self.full_loss_num)), end='')
self.mean_dist = np.mean(self.dist_list)
print(', mean_dist={0:.8f}'.format(self.mean_dist), end='')
print(', lr=%f' % self.optimizer.param_groups[0]['lr'], end='')
print(', hard or full=%r' % self.hard_or_full_trip)
sys.stdout.flush()
self.hard_loss_metric = []
self.full_loss_metric = []
self.full_loss_num = []
self.dist_list = []
# Visualization using t-SNE
# if self.restore_iter % 500 == 0:
# pca = TSNE(2)
# pca_feature = pca.fit_transform(feature.view(feature.size(0), -1).data.cpu().numpy())
# for i in range(self.P):
# plt.scatter(pca_feature[self.M * i:self.M * (i + 1), 0],
# pca_feature[self.M * i:self.M * (i + 1), 1], label=label[self.M * i])
#
# plt.show()
if self.restore_iter == self.total_iter:
break
def ts2var(self, x):
return autograd.Variable(x).cuda()
def np2var(self, x):
return self.ts2var(torch.from_numpy(x))
def transform(self, flag, batch_size=1):
self.encoder.eval()
source = self.test_source if flag == 'test' else self.train_source
self.sample_type = 'all'
data_loader = tordata.DataLoader(
dataset=source,
batch_size=batch_size,
sampler=tordata.sampler.SequentialSampler(source),
collate_fn=self.collate_fn,
num_workers=self.num_workers)
feature_list = list()
view_list = list()
seq_type_list = list()
label_list = list()
for i, x in enumerate(data_loader):
seq, view, seq_type, label, batch_frame = x
for j in range(len(seq)):
seq[j] = self.np2var(seq[j]).float()
if batch_frame is not None:
batch_frame = self.np2var(batch_frame).int()
# print(batch_frame, np.sum(batch_frame))
feature, _ = self.encoder(*seq, batch_frame)
n, num_bin, _ = feature.size()
feature_list.append(feature.view(n, -1).data.cpu().numpy())
view_list += view
seq_type_list += seq_type
label_list += label
return np.concatenate(feature_list, 0), view_list, seq_type_list, label_list
def save(self):
os.makedirs(osp.join('checkpoint', self.model_name), exist_ok=True)
torch.save(self.encoder.state_dict(),
osp.join('checkpoint', self.model_name,
'{}-{:0>5}-encoder.ptm'.format(
self.save_name, self.restore_iter)))
torch.save(self.optimizer.state_dict(),
osp.join('checkpoint', self.model_name,
'{}-{:0>5}-optimizer.ptm'.format(
self.save_name, self.restore_iter)))
# restore_iter: iteration index of the checkpoint to load
def load(self, restore_iter):
self.encoder.load_state_dict(torch.load(osp.join(
'checkpoint', self.model_name,
'{}-{:0>5}-encoder.ptm'.format(self.save_name, restore_iter))))
self.optimizer.load_state_dict(torch.load(osp.join(
'checkpoint', self.model_name,
'{}-{:0>5}-optimizer.ptm'.format(self.save_name, restore_iter))))
class Trainer:
def __init__(self, logger, checkpoint, device_ids, config):
self.BtoA = config['cycle_loss_weight'] != 0
self.config = config
self.logger = logger
self.device_ids = device_ids
self.restore(checkpoint)
print("Generator...")
print(self.generatorB)
print("Discriminator...")
print(self.discriminatorB)
transform = list()
transform.append(T.Resize(config['load_size']))
transform.append(T.RandomCrop(config['crop_size']))
transform.append(T.ToTensor())
transform.append(T.Normalize(mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5)))
transform = T.Compose(transform)
self.dataset = ABDataset(config['root_dir'],
partition='train',
transform=transform)
def restore(self, checkpoint):
self.epoch = 0
self.generatorB = Generator(**self.config['generator_params']).cuda()
self.generatorB = DataParallelWithCallback(self.generatorB,
device_ids=self.device_ids)
self.optimizer_generatorB = torch.optim.Adam(
self.generatorB.parameters(),
lr=self.config['lr_generator'],
betas=(0.5, 0.999))
self.discriminatorB = Discriminator(
**self.config['discriminator_params']).cuda()
self.discriminatorB = DataParallelWithCallback(
self.discriminatorB, device_ids=self.device_ids)
self.optimizer_discriminatorB = torch.optim.Adam(
self.discriminatorB.parameters(),
lr=self.config['lr_discriminator'],
betas=(0.5, 0.999))
if self.BtoA:
self.generatorA = Generator(
**self.config['generator_params']).cuda()
self.generatorA = DataParallelWithCallback(
self.generatorA, device_ids=self.device_ids)
self.optimizer_generatorA = torch.optim.Adam(
self.generatorA.parameters(),
lr=self.config['lr_generator'],
betas=(0.5, 0.999))
self.discriminatorA = Discriminator(
**self.config['discriminator_params']).cuda()
self.discriminatorA = DataParallelWithCallback(
self.discriminatorA, device_ids=self.device_ids)
self.optimizer_discriminatorA = torch.optim.Adam(
self.discriminatorA.parameters(),
lr=self.config['lr_discriminator'],
betas=(0.5, 0.999))
if checkpoint is not None:
data = torch.load(checkpoint)
for key, value in data.items():
if key == 'epoch':
self.epoch = value
else:
self.__dict__[key].load_state_dict(value)
lr_lambda = lambda epoch: min(
1, 2 - 2 * epoch / self.config['num_epochs'])
self.scheduler_generatorB = torch.optim.lr_scheduler.LambdaLR(
self.optimizer_generatorB, lr_lambda, last_epoch=self.epoch - 1)
self.scheduler_discriminatorB = torch.optim.lr_scheduler.LambdaLR(
self.optimizer_discriminatorB,
lr_lambda,
last_epoch=self.epoch - 1)
if self.BtoA:
self.scheduler_generatorA = torch.optim.lr_scheduler.LambdaLR(
self.optimizer_generatorA,
lr_lambda,
last_epoch=self.epoch - 1)
self.scheduler_discriminatorA = torch.optim.lr_scheduler.LambdaLR(
self.optimizer_discriminatorA,
lr_lambda,
last_epoch=self.epoch - 1)
def save(self):
state_dict = {
'epoch': self.epoch,
'generatorB': self.generatorB.state_dict(),
'optimizer_generatorB': self.optimizer_generatorB.state_dict(),
'discriminatorB': self.discriminatorB.state_dict(),
'optimizer_discriminatorB':
self.optimizer_discriminatorB.state_dict()
}
if self.BtoA:
state_dict.update({
'generatorA':
self.generatorA.state_dict(),
'optimizer_generatorA':
self.optimizer_generatorA.state_dict(),
'discriminatorA':
self.discriminatorA.state_dict(),
'optimizer_discriminatorA':
self.optimizer_discriminatorA.state_dict()
})
torch.save(state_dict, os.path.join(self.logger.log_dir, 'cpk.pth'))
def train(self):
np.random.seed(0)
loader = DataLoader(self.dataset,
batch_size=self.config['bs'],
shuffle=False,
drop_last=True,
num_workers=4)
images_fixed = None
for self.epoch in tqdm(range(self.epoch, self.config['num_epochs'])):
loss_dict = defaultdict(lambda: 0.0)
iteration_count = 1
for inp in tqdm(loader):
images_A = inp['A'].cuda()
images_B = inp['B'].cuda()
if images_fixed is None:
images_fixed = {'A': images_A, 'B': images_B}
transform_fixed = Transform(
images_A.shape[0], **self.config['transform_params'])
if self.config['identity_loss_weight'] != 0:
images_trg = self.generatorB(images_B, source=False)
identity_loss = l1(images_trg, images_B)
identity_loss = self.config[
'identity_loss_weight'] * identity_loss
identity_loss.backward()
loss_dict['identity_loss_B'] += identity_loss.detach().cpu(
).numpy()
if self.config['identity_loss_weight'] != 0 and self.BtoA:
images_trg = self.generatorA(images_A, source=False)
identity_loss = l1(images_trg, images_A)
identity_loss = self.config[
'identity_loss_weight'] * identity_loss
identity_loss.backward()
loss_dict['identity_loss_A'] += identity_loss.detach().cpu(
).numpy()
generator_loss = 0
images_generatedB = self.generatorB(images_A, source=True)
logits = self.discriminatorB(images_generatedB)
adversarial_loss = gan_loss_generator(
logits, self.config['gan_loss_type'])
adversarial_loss = self.config[
'adversarial_loss_weight'] * adversarial_loss
generator_loss += adversarial_loss
loss_dict['adversarial_loss_B'] += adversarial_loss.detach(
).cpu().numpy()
if self.BtoA:
images_generatedA = self.generatorA(images_B, source=True)
logits = self.discriminatorA(images_generatedA)
adversarial_loss = gan_loss_generator(
logits, self.config['gan_loss_type'])
adversarial_loss = self.config[
'adversarial_loss_weight'] * adversarial_loss
generator_loss += adversarial_loss
loss_dict['adversarial_loss_A'] += adversarial_loss.detach(
).cpu().numpy()
if self.config['equivariance_loss_weight_generator'] != 0:
transform = Transform(images_generatedB.shape[0],
**self.config['transform_params'])
images_A_transformed = transform.transform_frame(images_A)
loss = corr(
self.generatorB(images_A_transformed, source=True),
transform.transform_frame(images_generatedB))
loss = self.config[
'equivariance_loss_weight_generator'] * loss
generator_loss += loss
loss_dict['equivariance_generator_B'] += loss.detach().cpu(
).numpy()
if self.config[
'equivariance_loss_weight_generator'] != 0 and self.BtoA:
transform = Transform(images_generatedA.shape[0],
**self.config['transform_params'])
images_B_transformed = transform.transform_frame(images_B)
loss = corr(
self.generatorB(images_B_transformed, source=True),
transform.transform_frame(images_generatedA))
loss = self.config[
'equivariance_loss_weight_generator'] * loss
generator_loss += loss
loss_dict['equivariance_generator_A'] += loss.detach().cpu(
).numpy()
if self.BtoA and self.config[
'cycle_loss_weight'] != 0 and self.BtoA:
images_cycled = self.generatorA(images_generatedB,
source=True)
cycle_loss = torch.abs(images_cycled - images_A).mean()
cycle_loss = self.config['cycle_loss_weight'] * cycle_loss
generator_loss += cycle_loss
loss_dict['cycle_loss_B'] += cycle_loss.detach().cpu(
).numpy()
images_cycled = self.generatorB(images_generatedA,
source=True)
cycle_loss = torch.abs(images_cycled - images_B).mean()
cycle_loss = self.config['cycle_loss_weight'] * cycle_loss
generator_loss += cycle_loss
loss_dict['cycle_loss_A'] += cycle_loss.detach().cpu(
).numpy()
generator_loss.backward()
self.optimizer_generatorB.step()
self.optimizer_generatorB.zero_grad()
self.optimizer_discriminatorB.zero_grad()
if self.BtoA:
self.optimizer_generatorA.step()
self.optimizer_generatorA.zero_grad()
self.optimizer_discriminatorA.zero_grad()
logits_fake = self.discriminatorB(images_generatedB.detach())
logits_real = self.discriminatorB(images_B)
discriminator_loss = gan_loss_discriminator(
logits_real, logits_fake, self.config['gan_loss_type'])
loss_dict['discriminator_loss_B'] += discriminator_loss.detach(
).cpu().numpy()
if self.config['equivariance_loss_weight_discriminator'] != 0:
images_join = torch.cat(
[images_generatedB.detach(), images_B])
logits_join = torch.cat([logits_fake, logits_real])
transform = Transform(images_join.shape[0],
**self.config['transform_params'])
images_transformed = transform.transform_frame(images_join)
loss = corr(self.discriminatorB(images_transformed),
transform.transform_frame(logits_join))
loss = self.config[
'equivariance_loss_weight_discriminator'] * loss
discriminator_loss += loss
loss_dict['equivariance_discriminator_B'] += loss.detach(
).cpu().numpy()
discriminator_loss.backward()
self.optimizer_discriminatorB.step()
self.optimizer_discriminatorB.zero_grad()
self.optimizer_generatorB.zero_grad()
if self.BtoA:
logits_fake = self.discriminatorA(
images_generatedA.detach())
logits_real = self.discriminatorA(images_A)
discriminator_loss = gan_loss_discriminator(
logits_real, logits_fake, self.config['gan_loss_type'])
loss_dict[
'discriminator_loss_B'] += discriminator_loss.detach(
).cpu().numpy()
if self.config[
'equivariance_loss_weight_discriminator'] != 0:
images_join = torch.cat(
[images_generatedA.detach(), images_A])
logits_join = torch.cat([logits_fake, logits_real])
transform = Transform(
images_join.shape[0],
**self.config['transform_params'])
images_transformed = transform.transform_frame(
images_join)
loss = corr(self.discriminatorA(images_transformed),
transform.transform_frame(logits_join))
loss = self.config[
'equivariance_loss_weight_discriminator'] * loss
discriminator_loss += loss
loss_dict[
'equivariance_discriminator_B'] += loss.detach(
).cpu().numpy()
discriminator_loss.backward()
self.optimizer_discriminatorA.step()
self.optimizer_discriminatorA.zero_grad()
self.optimizer_generatorA.zero_grad()
iteration_count += 1
with torch.no_grad():
if not self.BtoA:
self.generatorB.eval()
transformed = transform_fixed.transform_frame(
images_fixed['A'])
self.logger.save_images(
self.epoch, images_fixed['A'],
self.generatorB(images_fixed['A'], source=True),
transformed, self.generatorB(transformed, source=True))
self.generatorB.train()
else:
self.generatorA.eval()
self.generatorB.eval()
images_generatedB = self.generatorB(images_fixed['A'],
source=True)
images_generatedA = self.generatorA(images_fixed['B'],
source=True)
transformed = transform_fixed.transform_frame(
images_fixed['A'])
self.logger.save_images(
self.epoch, images_fixed['A'], images_generatedB,
transformed, self.generatorB(transformed, source=True),
self.generatorA(images_generatedB, source=True),
images_fixed['B'], images_generatedA,
self.generatorB(images_generatedA, source=True))
self.generatorA.train()
self.generatorB.train()
self.scheduler_generatorB.step()
self.scheduler_discriminatorB.step()
if self.BtoA:
self.scheduler_generatorA.step()
self.scheduler_discriminatorA.step()
save_dict = {
key: value / iteration_count
for key, value in loss_dict.items()
}
save_dict['lr'] = self.optimizer_generatorB.param_groups[0]['lr']
self.logger.log(self.epoch, save_dict)
self.save()
def main():
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser()
parser.add_argument('--LR',
type=list,
default=[1e-4, 1e-4],
help='learning rate') # start from 1e-4
parser.add_argument('--EPOCH', type=int, default=30, help='epoch')
parser.add_argument('--slice_num',
type=int,
default=6,
help='how many slices to cut')
parser.add_argument('--batch_size',
type=int,
default=40,
help='batch_size')
parser.add_argument('--frame_num',
type=int,
default=5,
help='how many frames in a slice')
parser.add_argument('--model_path',
type=str,
default='/Disk1/poli/models/DeepRNN/Kinetics_res18',
help='model_path')
parser.add_argument('--model_name',
type=str,
default='checkpoint',
help='model name')
parser.add_argument('--video_path',
type=str,
default='/home/poli/kinetics_scaled',
help='video path')
parser.add_argument('--class_num', type=int, default=400, help='class num')
parser.add_argument('--device_id',
type=list,
default=[0, 1, 2, 3],
help='learning rate')
parser.add_argument('--resume', action='store_true', help='whether resume')
parser.add_argument('--dropout',
type=list,
default=[0.2, 0.5],
help='dropout')
parser.add_argument('--weight_decay',
type=float,
default=1e-4,
help='weight decay')
parser.add_argument('--saveInter',
type=int,
default=1,
help='how many epoch to save once')
parser.add_argument('--TD_rate',
type=float,
default=0.0,
help='propabaility of detachout')
parser.add_argument('--img_size', type=int, default=224, help='image size')
parser.add_argument('--syn_bn', action='store_true', help='use syn_bn')
parser.add_argument('--logName',
type=str,
default='logs_res18',
help='log dir name')
parser.add_argument('--train', action='store_true', help='train the model')
parser.add_argument('--test', action='store_true', help='test the model')
parser.add_argument(
'--overlap_rate',
type=float,
default=0.25,
help='the overlap rate of the overlap coherence training scheme')
parser.add_argument('--lambdaa',
type=float,
default=0.0,
help='weight of the overlap coherence loss')
opt = parser.parse_args()
print(opt)
torch.cuda.set_device(opt.device_id[0])
# ######################## Module #################################
print('Building model')
model = actionModel(opt.class_num,
batch_norm=True,
dropout=opt.dropout,
TD_rate=opt.TD_rate,
image_size=opt.img_size,
syn_bn=opt.syn_bn,
test_scheme=3)
print(model)
if opt.syn_bn:
model = DataParallelWithCallback(model,
device_ids=opt.device_id).cuda()
else:
model = torch.nn.DataParallel(model, device_ids=opt.device_id).cuda()
print("Channels: " + str(model.module.channels))
# ########################Optimizer#########################
optimizer = torch.optim.SGD([{
'params': model.module.RNN.parameters(),
'lr': opt.LR[0]
}, {
'params': model.module.ShortCut.parameters(),
'lr': opt.LR[0]
}, {
'params': model.module.classifier.parameters(),
'lr': opt.LR[1]
}],
lr=opt.LR[1],
weight_decay=opt.weight_decay,
momentum=0.9)
# ###################### Loss Function ####################################
loss_classification_func = nn.NLLLoss(reduce=True)
def loss_overlap_coherence_func(pre, cur):
loss = nn.MSELoss()
return loss(cur, pre.detach())
# ###################### Resume ##########################################
resume_epoch = 0
resume_step = 0
max_test_acc = 0
if opt.resume or opt.test:
print("loading model")
checkpoint = torch.load(opt.model_path + '/' + opt.model_name,
map_location={
'cuda:0': 'cuda:' + str(opt.device_id[0]),
'cuda:1': 'cuda:' + str(opt.device_id[0]),
'cuda:2': 'cuda:' + str(opt.device_id[0]),
'cuda:3': 'cuda:' + str(opt.device_id[0]),
'cuda:4': 'cuda:' + str(opt.device_id[0]),
'cuda:5': 'cuda:' + str(opt.device_id[0]),
'cuda:6': 'cuda:' + str(opt.device_id[0]),
'cuda:7': 'cuda:' + str(opt.device_id[0])
})
model.load_state_dict(checkpoint['model'], strict=True)
try:
optimizer.load_state_dict(checkpoint['opt'], strict=True)
except:
pass
for group_id, param_group in enumerate(optimizer.param_groups):
if group_id == 0:
param_group['lr'] = opt.LR[0]
elif group_id == 1:
param_group['lr'] = opt.LR[0]
elif group_id == 2:
param_group['lr'] = opt.LR[1]
resume_epoch = checkpoint['epoch']
if 'step' in checkpoint:
resume_step = checkpoint['step'] + 1
if 'max_acc' in checkpoint:
max_test_acc = checkpoint['max_acc']
print('Finish Loading')
del checkpoint
# ###########################################################################
# training and testing
model.train()
predict_for_mAP = []
label_for_mAP = []
print("START")
KineticsLoader = torch.utils.data.DataLoader(
Kinetic_train_dataset.Kinetics(video_path=opt.video_path +
'/train_frames',
frame_num=opt.frame_num,
batch_size=opt.batch_size,
img_size=opt.img_size,
slice_num=opt.slice_num,
overlap_rate=opt.overlap_rate),
batch_size=1,
shuffle=True,
num_workers=8)
Loader_test = torch.utils.data.DataLoader(Kinetics_test_dataset.Kinetics(
video_path=opt.video_path + '/val_frames',
img_size=224,
space=5,
split_num=8,
lenn=60,
num_class=opt.class_num),
batch_size=64,
shuffle=True,
num_workers=4)
tensorboard_writer = SummaryWriter(
opt.logName,
purge_step=resume_epoch * len(KineticsLoader) * opt.slice_num +
(resume_step + resume_step) * opt.slice_num)
test = opt.test
for epoch in range(resume_epoch, opt.EPOCH):
predict_for_mAP = []
label_for_mAP = []
for step, (x, _, overlap_frame_num,
action) in enumerate(KineticsLoader): # gives batch data
if opt.train:
if step + resume_step >= len(KineticsLoader):
break
x = x[0]
action = action[0]
overlap_frame_num = overlap_frame_num[0]
c = [
Variable(
torch.from_numpy(
np.zeros(
(x.shape[1], model.module.channels[layer + 1],
model.module.input_size[layer],
model.module.input_size[layer]
)))).cuda().float()
for layer in range(model.module.RNN_layer)
]
for slice in range(x.shape[0]):
b_x = Variable(x[slice]).cuda()
b_action = Variable(action[slice]).cuda()
out, out_beforeMerge, c = model(b_x.float(),
c) # rnn output
for batch in range(len(out)):
predict_for_mAP.append(out[batch].data.cpu().numpy())
label_for_mAP.append(
b_action[batch][-1].data.cpu().numpy())
# ###################### overlap coherence loss #######################################################################################
loss_coherence = torch.zeros(1).cuda()
# claculate the coherence loss with the previous clip and current clip
if slice != 0:
for b in range(out.size()[0]):
loss_coherence += loss_overlap_coherence_func(
old_overlap[b],
torch.exp(out_beforeMerge[
b, :overlap_frame_num[slice, b, 0].int()]))
loss_coherence = loss_coherence / out.size()[0]
# record the previous clips output
old_overlap = []
for b in range(out.size()[0]):
old_overlap.append(
torch.exp(
out_beforeMerge[b,
-overlap_frame_num[slice, b,
0].int():]))
#######################################################################################################################################
loss_classification = loss_classification_func(
out, b_action[:, -1].long())
loss = loss_classification + opt.lambdaa * loss_coherence
tensorboard_writer.add_scalar(
'train/loss', loss,
epoch * len(KineticsLoader) * opt.slice_num +
(step + resume_step) * opt.slice_num + slice)
loss.backward(retain_graph=False)
predict_for_mAP = predict_for_mAP
label_for_mAP = label_for_mAP
mAPs = mAP(predict_for_mAP, label_for_mAP, 'Lsm')
acc = accuracy(predict_for_mAP, label_for_mAP, 'Lsm')
tensorboard_writer.add_scalar(
'train/mAP', mAPs,
epoch * len(KineticsLoader) * opt.slice_num +
(step + resume_step) * opt.slice_num + slice)
tensorboard_writer.add_scalar(
'train/acc', acc,
epoch * len(KineticsLoader) * opt.slice_num +
(step + resume_step) * opt.slice_num + slice)
print("Epoch: " + str(epoch) + " step: " +
str(step + resume_step) + " Loss: " +
str(loss.data.cpu().numpy()) + " Loss_coherence: " +
str(loss_coherence.data.cpu().numpy()) + " mAP: " +
str(mAPs)[0:7] + " acc: " + str(acc)[0:7])
for p in model.module.parameters():
p.grad.data.clamp_(min=-5, max=5)
if step % 2 == 1:
optimizer.step()
optimizer.zero_grad()
predict_for_mAP = []
label_for_mAP = []
# ################################### test ###############################
if (step + resume_step) % 700 == 699:
test = True
if test:
print('Start Test')
TEST_LOSS = AverageMeter()
with torch.no_grad():
model.eval()
predict_for_mAP = []
label_for_mAP = []
print("TESTING")
for step_test, (x, _, _, action) in tqdm(
enumerate(Loader_test)): # gives batch data
b_x = Variable(x).cuda()
b_action = Variable(action).cuda()
c = [
Variable(
torch.from_numpy(
np.zeros((len(b_x),
model.module.channels[layer + 1],
model.module.input_size[layer],
model.module.input_size[layer]
)))).cuda().float()
for layer in range(model.module.RNN_layer)
]
out, _, _ = model(b_x.float(), c) # rnn output
loss = loss_classification_func(
out, b_action[:, -1].long())
TEST_LOSS.update(val=loss.data.cpu().numpy())
for batch in range(len(out)):
predict_for_mAP.append(
out[batch].data.cpu().numpy())
label_for_mAP.append(
b_action[batch][-1].data.cpu().numpy())
if step_test % 50 == 0:
MAP = mAP(np.array(predict_for_mAP),
np.array(label_for_mAP), 'Lsm')
acc = accuracy(np.array(predict_for_mAP),
np.array(label_for_mAP), 'Lsm')
print(" Loss: " + str(TEST_LOSS.avg)[0:5] + ' ' +
'accuracy: ' + str(acc)[0:7])
predict_for_mAP = np.array(predict_for_mAP)
label_for_mAP = np.array(label_for_mAP)
MAP = mAP(predict_for_mAP, label_for_mAP, 'Lsm')
acc = accuracy(predict_for_mAP, label_for_mAP, 'Lsm')
print("mAP: " + str(MAP) + ' ' + 'accuracy: ' + str(acc))
if acc > max_test_acc:
print('Saving')
max_test_acc = acc
torch.save(
{
'model': model.state_dict(),
'max_acc': max_test_acc,
'epoch': epoch,
'step': 0,
'opt': optimizer.state_dict()
}, opt.model_path + '/' + opt.model_name + '_' +
str(epoch) + '_' + str(max_test_acc)[0:6])
model.train()
test = False
predict_for_mAP = []
label_for_mAP = []
if opt.test:
exit()
if epoch % opt.saveInter == 0:
print('Saving')
torch.save(
{
'model': model.state_dict(),
'max_acc': max_test_acc,
'epoch': epoch,
'step': 0,
'opt': optimizer.state_dict()
}, opt.model_path + '/' + opt.model_name + '_' + str(epoch))
resume_step = 0
print('Building model')
model = actionModel(args.class_num, batch_norm=True, dropout=[0, 0, 0])
model = DataParallelWithCallback(model, device_ids=args.device_id).cuda()
print("loading model")
checkpoint = torch.load(args.model_path + '/' + args.model_name, map_location={'cuda:1': 'cuda:' + str(args.device_id[0]),
'cuda:2': 'cuda:' + str(args.device_id[0]),
'cuda:3': 'cuda:' + str(args.device_id[0]),
'cuda:4': 'cuda:' + str(args.device_id[0]),
'cuda:5': 'cuda:' + str(args.device_id[0]),
'cuda:6': 'cuda:' + str(args.device_id[0]),
'cuda:7': 'cuda:' + str(args.device_id[0]),
'cuda:0': 'cuda:' + str(args.device_id[0])})
pre_train = checkpoint['model']
model_dict = model.state_dict()
for para in pre_train:
if para in model_dict:
model_dict[para] = pre_train[para]
model.load_state_dict(model_dict)
print('Finish Loading')
del checkpoint, pre_train, model_dict
print("Model: " + str(args.model_name))
predict_for_mAP = []
label_for_mAP = []
print("START")
UCF101Loader_test = torch.utils.data.DataLoader(
