def on_batch_start(self, state: RunnerState):
if not self.is_needed:
return
if self.alpha > 0:
self.lam = np.random.beta(self.alpha, self.alpha)
else:
self.lam = 1
classes_in_batch = torch.unique(state.input[self.input_key]).cpu().numpy()
batch_idx = np.arange(state.input[self.fields[0]].shape[0])
# now make permutations per each class
for idx, image_class in enumerate(classes_in_batch):
# images with this class
class_mask = (state.input[self.input_key].cpu().numpy() == image_class).squeeze()
class_idx = batch_idx[class_mask]
index = np.random.permutation(class_idx)
index = torch.tensor(index, dtype=torch.long)
class_idx = torch.tensor(class_idx, dtype=torch.long)
index.to(state.device)
class_idx.to(state.device)
for f in self.fields:
state.input[f][class_idx] = self.lam * state.input[f][class_idx] + (1 - self.lam) * state.input[f][
index]
def on_batch_start(self, state: RunnerState):
if not self.is_needed:
return
if self.alpha > 0:
self.lam = np.random.beta(self.alpha, self.alpha)
else:
self.lam = 1
self.index = torch.randperm(state.input[self.fields[0]].shape[0])
self.index.to(state.device)
for f in self.fields:
state.input[f] = self.lam * state.input[f] + \
(1 - self.lam) * state.input[f][self.index]
def on_batch_start(self, state: RunnerState):
if not self.is_needed:
return
if self.alpha > 0:
self.lam = np.random.beta(self.alpha, self.alpha)
else:
self.lam = 1
self.index = torch.randperm(state.input[self.fields[0]].shape[0])
self.index.to(state.device)
bbx1, bby1, bbx2, bby2 = rand_bbox(
state.input[self.input_key].size(), self.lam)
for f in self.fields:
state.input[f][:, :, bbx1:bbx2,
bby1:bby2] = state.input[f][self.index, :, bbx1:bbx2, bby1:bby2]