def _create_loss_function(self):
copy_to_gpu = is_on_gpu(self._losses)
self._losses.append(
torch.nn.modules.CrossEntropyLoss(
weight=self._weight, ignore_index=self._ignore_index
)
)
if copy_to_gpu:
self._losses.cuda()
return self
def _create_loss_function(self):
copy_to_gpu = is_on_gpu(self._losses)
# Instantiating CrossEntropyMultipleOutputSingleTargetLoss, which
# internally uses SmoothCrossEntropy loss to accommodate label smoothing,
# but defaults to vanilla cross-entropy if provided single-target labels.
self._losses.append(
SmoothCrossEntropy(weight=self._weight,
ignore_index=self._ignore_index))
if copy_to_gpu:
self._losses.cuda()
return self
def profile(
model,
batchsize_per_replica=32,
input_shape=(3, 224, 224),
use_nvprof=False,
input_key=None,
):
"""
Performs CPU or GPU profiling of the specified model on the specified input.
"""
# assertions:
if use_nvprof:
raise NotImplementedError
# FIXME (mannatsingh): in case of use_nvprof, exit() is called at the end
# and we do not return a profile.
assert is_on_gpu(model), "can only nvprof model that lives on GPU"
logging.info("CUDA profiling: Make sure you are running under nvprof!")
# input for model:
input = get_model_dummy_input(
model,
input_shape,
input_key,
batchsize=batchsize_per_replica,
non_blocking=False,
)
# perform profiling:
with torch.no_grad():
model(input) # warm up CUDA memory allocator and profiler
if use_nvprof: # nvprof profiling (TODO: Can we infer this?)
cudart().cudaProfilerStart()
model(input)
cudart().cudaProfilerStop()
exit() # exit gracefully
else: # regular profiling
with torch.autograd.profiler.profile(use_cuda=True) as profiler:
model(input)
return profiler
def profile(
model: nn.Module,
batchsize_per_replica: int = 32,
input_shape: Tuple[int] = (3, 224, 224),
use_nvprof: bool = False,
input_key: Optional[Union[str, List[str]]] = None,
):
"""
Performs CPU or GPU profiling of the specified model on the specified input.
"""
# assertions:
if use_nvprof:
raise ClassyProfilerError("Profiling not supported with nvprof")
# FIXME (mannatsingh): in case of use_nvprof, exit() is called at the end
# and we do not return a profile.
assert is_on_gpu(model), "can only nvprof model that lives on GPU"
logging.info("CUDA profiling: Make sure you are running under nvprof!")
# input for model:
input = get_model_dummy_input(
model,
input_shape,
input_key,
batchsize=batchsize_per_replica,
non_blocking=False,
)
# perform profiling in eval mode
with eval_model(model), torch.no_grad():
model(input) # warm up CUDA memory allocator and profiler
if use_nvprof: # nvprof profiling (TODO: Can we infer this?)
cudart().cudaProfilerStart()
model(input)
cudart().cudaProfilerStop()
exit() # exit gracefully
else: # regular profiling
with torch.autograd.profiler.profile(use_cuda=True) as profiler:
model(input)
return profiler
def _create_loss_function(self):
copy_to_gpu = is_on_gpu(self._losses)
self._losses.append(nn.BCEWithLogitsLoss(reduction=self._reduction))
if copy_to_gpu:
self._losses.cuda()
return self
