def update_soda_ccm(self, x, size, L=None, step=None):
assert x.size(-1) == size
aug_x = x.clone()
x = augmentations.random_crop(x)
aug_x = augmentations.random_crop(aug_x)
x_pos = augmentations.random_overlay(aug_x)
self.ccm_optimizer.zero_grad()
# identical encoders
z_1 = self.ccm_head(x)
z_2 = self.ccm_head(x_pos)
# empirical cross-correlation matrix
c = self.bn(z_1).T @ self.bn(z_2)
# cross-correlation matrix
c.div_(z_1.size(0)) # DxD
on_diag = torch.diagonal(c).add_(-1).pow_(2).sum().mul(1 / 32)
off_diag = off_diagonal(c).pow_(2).sum().mul(1 / 32)
ccm_loss = 0.01 * (on_diag + 3.9e-3 * off_diag)
ccm_loss.backward()
self.ccm_optimizer.step()
if L is not None:
L.log('train/ccm_loss', ccm_loss, step)
def sample(self, n=None):
idxs = self._get_idxs(n)
obs = torch.as_tensor(self.obs[idxs]).cuda().float()
actions = torch.as_tensor(self.actions[idxs]).cuda()
rewards = torch.as_tensor(self.rewards[idxs]).cuda()
next_obs = torch.as_tensor(self.next_obs[idxs]).cuda().float()
not_dones = torch.as_tensor(self.not_dones[idxs]).cuda()
obs = augmentations.random_crop(obs)
next_obs = augmentations.random_crop(next_obs)
return obs, actions, rewards, next_obs, not_dones
def update_soda(self, replay_buffer, L=None, step=None):
x = replay_buffer.sample_soda(self.soda_batch_size)
assert x.size(-1) == 100
aug_x = x.clone()
x = augmentations.random_crop(x)
aug_x = augmentations.random_crop(aug_x)
# print(x.shape, aug_x.shape)
aug_x = augmentations.random_overlay(aug_x, self.args)
# print(x.shape, aug_x.shape)
soda_loss = self.compute_soda_loss(aug_x, x)
self.soda_optimizer.zero_grad()
soda_loss.backward()
self.soda_optimizer.step()
if L is not None:
L.log('train/aux_loss', soda_loss, step)
utils.soft_update_params(self.predictor, self.predictor_target,
self.soda_tau)
def update_soda_curl(self, x, size, L=None, step=None):
assert x.size(-1) == size
aug_x = x.clone()
x = augmentations.random_crop(x)
aug_x = augmentations.random_crop(aug_x)
x_pos = augmentations.random_overlay(aug_x)
z_a = self.curl_head.encoder(x)
with torch.no_grad():
z_pos = self.critic_target.encoder(x_pos)
logits = self.curl_head.compute_logits(z_a, z_pos)
labels = torch.arange(logits.shape[0]).long().cuda()
curl_loss = F.cross_entropy(logits, labels)
self.curl_optimizer.zero_grad()
curl_loss.backward()
self.curl_optimizer.step()
if L is not None:
L.log('train/aux_loss', curl_loss, step)
gt_path = img_path.replace("img", "annot")
image, gt = get_image_gt(img_path)
rand0 = np.random.rand()
if rand0 > 0.3:
image, gt = random_rotation(image, gt, normal=True)
rand1 = np.random.rand()
if rand1 > 0.3:
image, gt = elastic_deformation(image, gt, 5000, 100)
rand2 = np.random.rand()
if rand2 > 0.5:
rand = np.random.rand()
crop_size = rand + 1
image, gt = random_crop(image, gt, int(image.shape[0]/crop_size), int(image.shape[1]/crop_size))
if rand0 <= 0.3 and rand1 <= 0.3 and rand2 <= 0.5:
image, gt = elastic_deformation(image, gt, 5000, 80)
new_img_path = img_path.replace(".png", "_{num:04d}.png".format(num=n))
new_gt_path = gt_path.replace(".png", "_{num:04d}.png".format(num=n))
write_image(image, new_img_path)
write_image(gt, new_gt_path)
n += 1
# # elastic deformation
# train_paths = get_file_list(data_path)
# for i in tqdm(range(2000, 5000)):
# img_path = train_paths[i % len(train_paths)]