def update(self, model_output, target, **kwargs):
"""
args:
model_output: tensor of shape (B, C) where each value is
either logit or class probability.
target: tensor of shape (B, C), which is one-hot /
multi-label encoded, or tensor of shape (B) /
(B, 1), integer encoded
"""
# Convert target to 0/1 encoding if isn't
target = maybe_convert_to_one_hot(target, model_output)
_, pred_classes = model_output.topk(max(self._topk),
dim=1,
largest=True,
sorted=True)
pred_mask_tensor = torch.zeros(target.size())
for i, k in enumerate(self._topk):
pred_mask_tensor.zero_()
self._curr_correct_predictions_k[i] += torch.sum(
# torch.min is used to simulate AND between binary
# tensors. If tensors are not binary, this will fail.
torch.min(
pred_mask_tensor.scatter_(1, pred_classes[:, :k], 1.0),
target.float(),
)).item()
self._curr_sample_count += model_output.shape[0]
def update(self, model_output, target, **kwargs):
"""
args:
model_output: tensor of shape (B, C) where each value is
either logit or class probability.
target: tensor of shape (B, C), which is one-hot /
multi-label encoded, or tensor of shape (B) /
(B, 1), integer encoded
"""
# Due to dummy samples, in some corner cases, the whole batch could
# be dummy samples, in that case we want to not update meters on that
# process
if model_output.shape[0] == 0:
return
# Convert target to 0/1 encoding if isn't
target = maybe_convert_to_one_hot(target, model_output)
_, pred = model_output.topk(max(self._topk),
dim=1,
largest=True,
sorted=True)
for i, k in enumerate(self._topk):
self._curr_correct_predictions_k[i] += (torch.gather(
target, dim=1,
index=pred[:, :k]).long().max(dim=1).values.sum().item())
self._curr_sample_count += model_output.shape[0]
def update(self, model_output, target, **kwargs):
"""
args:
model_output: tensor of shape (B, C) where each value is
either logit or class probability.
target: tensor of shape (B, C), which is one-hot /
multi-label encoded, or tensor of shape (B) /
(B, 1), integer encoded
"""
# Convert target to 0/1 encoding if isn't
target = maybe_convert_to_one_hot(target, model_output)
# If Pytorch AMP is being used, model outputs are probably fp16
# Since .topk() is not compatible with fp16, we promote the model outputs to full precision
_, pred = model_output.float().topk(
max(self._topk), dim=1, largest=True, sorted=True
)
for i, k in enumerate(self._topk):
self._curr_correct_predictions_k[i] += (
torch.gather(target, dim=1, index=pred[:, :k])
.max(dim=1)
.values.sum()
.item()
)
self._curr_sample_count += model_output.shape[0]
def update(self, model_output, target, **kwargs):
"""
args:
model_output: tensor of shape (B, C) where each value is
either logit or class probability.
target: tensor of shape (B, C), which is one-hot /
multi-label encoded, or tensor of shape (B) /
(B, 1), integer encoded
"""
# Convert target to 0/1 encoding if isn't
target = maybe_convert_to_one_hot(target, model_output)
_, pred = model_output.topk(max(self._topk), dim=1, largest=True, sorted=True)
for i, k in enumerate(self._topk):
self._curr_correct_predictions_k[i] += (
torch.gather(target, dim=1, index=pred[:, :k])
.max(dim=1)
.values.sum()
.item()
)
self._curr_sample_count += model_output.shape[0]
