def _forward(self, batch: Dict[str, Tensor]) -> Dict[str, Any]:
# Move the sample list to device if it isn't as of now.
prepared_batch = to_device(batch, self.device)
self.profile("Batch prepare time")
# Arguments should be a dict at this point
with torch.cuda.amp.autocast(enabled=self.training_config.fp16):
model_output = self.model(prepared_batch)
report = Report(prepared_batch, model_output)
self.profile("Forward time")
return report
def test_meter_update_from_report(self):
meter = Meter()
prepared_batch = SampleList(
{"targets": torch.tensor([1, 2, 3, 4]), "dataset_type": "val"}
)
for idx in range(5):
model_output = {
"scores": torch.tensor([0, 1, 2, 3]),
"losses": {"loss": float(idx)},
}
report = Report(prepared_batch, model_output)
meter.update_from_report(report)
self.assertEqual(meter.loss.global_avg, 2.0)
self.assertEqual(meter.loss.avg, 2.0)
def test_argmax_prediction_processor(self):
processor = ArgMaxPredictionProcessor(config={})
batch = SampleList(
{"id": torch.tensor([1, 2, 3, 4, 5], dtype=torch.long)})
model_output = {"scores": torch.rand(5, 4)}
report = Report(batch, model_output)
predictions = processor(report)
expected_answers = [1, 1, 2, 1, 3]
expected = []
for idx, answer in enumerate(expected_answers):
expected.append({"id": idx + 1, "answer": answer})
self.assertEqual(predictions, expected)
def training_step(self, batch: SampleList, batch_idx: int, *args, **kwargs):
"""Member function of PL modules. Used only when PL enabled.
To be implemented by child class. Takes in a ``SampleList``,
batch_idx and returns back a dict.
Args:
sample_list (SampleList): SampleList returned by the DataLoader for
current iteration
Returns:
Dict: Dict containing loss.
"""
output = self._forward_lightning_step(batch, batch_idx)
report = Report(batch, output)
self.train_meter.update_from_report(report)
return output
def prediction_loop(self, dataset_type: str) -> None:
reporter = self.dataset_loader.get_test_reporter(dataset_type)
with torch.no_grad():
self.model.eval()
logger.info(f"Starting {dataset_type} inference predictions")
while reporter.next_dataset():
dataloader = reporter.get_dataloader()
for batch in tqdm.tqdm(dataloader):
prepared_batch = reporter.prepare_batch(batch)
model_output = self.model(prepared_batch)
report = Report(prepared_batch, model_output)
reporter.add_to_report(report, self.model)
logger.info("Finished predicting")
self.model.train()
def predict(self, dataset_type):
reporter = self.dataset_loader.get_test_reporter(dataset_type)
with torch.no_grad():
self.model.eval()
message = f"Starting {dataset_type} inference predictions"
self.writer.write(message)
while reporter.next_dataset():
dataloader = reporter.get_dataloader()
for batch in tqdm(dataloader):
prepared_batch = reporter.prepare_batch(batch)
model_output = self.model(prepared_batch)
report = Report(prepared_batch, model_output)
reporter.add_to_report(report, self.model)
self.writer.write("Finished predicting")
self.model.train()
def validation_step(self, batch: SampleList, batch_idx: int, *args,
**kwargs):
"""Member function of PL modules. Used only when PL enabled.
To be implemented by child class. Takes in a ``SampleList``,
batch_idx and returns back a dict.
Args:
sample_list (SampleList): SampleList returned by the DataLoader for
current iteration
Returns:
Dict
"""
output = self._forward_lightning_step(batch, batch_idx)
report = Report(batch, output).detach()
self.val_meter.update_from_report(report, should_update_loss=False)
report.metrics = self.metrics(report, report)
self.log_dict(report.metrics)
return output
def prediction_loop(self, dataset_type: str) -> None:
reporter = self.dataset_loader.get_test_reporter(dataset_type)
with torch.no_grad():
self.model.eval()
logger.info(f"Starting {dataset_type} inference predictions")
while reporter.next_dataset():
dataloader = reporter.get_dataloader()
for batch in tqdm.tqdm(dataloader):
prepared_batch = reporter.prepare_batch(batch)
prepared_batch = to_device(prepared_batch, torch.device("cuda"))
with torch.cuda.amp.autocast(enabled=self.training_config.fp16):
model_output = self.model(prepared_batch)
report = Report(prepared_batch, model_output)
reporter.add_to_report(report, self.model)
logger.info("Finished predicting")
self.model.train()
def forward(self, image_path: str, text: dict, image_format: str = "path"):
text_output = self.processor["text_processor"](text)
if image_format == "path":
img = np.array(Image.open(image_path))
elif image_format == "url":
img = np.array(
Image.open(requests.get(image_path, stream=True).raw))
img = torch.as_tensor(img)
if self.model_items["config"].image_feature_encodings.type == "frcnn":
max_detect = self.model_items[
"config"].image_feature_encodings.params.max_detections
image_preprocessed, sizes, scales_yx = self.processor[
"image_processor"](img)
image_output = self.feature_extractor(
image_preprocessed,
sizes=sizes,
scales_yx=scales_yx,
padding=None,
max_detections=max_detect,
return_tensors="pt",
)
image_output = image_output[0]
else:
image_preprocessed = self.processor["image_processor"](img)
image_output = self.feature_extractor(image_preprocessed)
sample = Sample(text_output)
sample.image_feature_0 = image_output
sample_list = SampleList([sample])
sample_list = sample_list.to(get_current_device())
self.model = self.model.to(get_current_device())
output = self.model(sample_list)
sample_list.id = [sample_list.input_ids[0][0]]
report = Report(sample_list, output)
answers = self.processor["output_processor"](report)
answer = self.processor["answer_processor"].idx2word(
answers[0]["answer"])
return answer
def prediction_loop(self, dataset_type: str) -> None:
reporter = self.dataset_loader.get_test_reporter(dataset_type)
skipped_batches = 0
loaded_batches = 0
with torch.no_grad():
self.model.eval()
logger.info(f"Starting {dataset_type} inference predictions")
while reporter.next_dataset():
dataloader = reporter.get_dataloader()
if self._can_use_tqdm(dataloader):
dataloader = tqdm.tqdm(dataloader)
for batch in dataloader:
# Do not timeout quickly on first batch, as workers might start at
# very different times.
with CompleteInTimeOrDie(600 if loaded_batches else 3600 *
24):
prepared_batch = reporter.prepare_batch(batch)
prepared_batch = to_device(prepared_batch, self.device)
loaded_batches += 1
if not validate_batch_sizes(
prepared_batch.get_batch_size()):
logger.info(
"Skip batch due to unequal batch sizes.")
skipped_batches += 1
continue
with torch.cuda.amp.autocast(
enabled=self.training_config.fp16):
model_output = self.model(prepared_batch)
report = Report(prepared_batch, model_output)
reporter.add_to_report(report, self.model)
report.detach()
reporter.postprocess_dataset_report()
logger.info(f"Finished predicting. Loaded {loaded_batches}")
logger.info(f" -- skipped {skipped_batches} batches.")
self.model.train()
def evaluation_loop(
self,
dataset_type: str,
use_tqdm: bool = False,
single_batch: bool = False) -> Tuple[Dict[str, Any], Type[Meter]]:
meter = Meter()
reporter = self.dataset_loader.get_test_reporter(dataset_type)
use_cpu = self.config.evaluation.get("use_cpu", False)
loaded_batches = 0
skipped_batches = 0
with torch.no_grad():
self.model.eval()
disable_tqdm = not use_tqdm or not is_master()
while reporter.next_dataset(flush_report=False):
dataloader = reporter.get_dataloader()
combined_report = None
if self._can_use_tqdm(dataloader):
dataloader = tqdm.tqdm(dataloader, disable=disable_tqdm)
for batch in dataloader:
# Do not timeout quickly on first batch, as workers might start at
# very different times.
with CompleteInTimeOrDie(600 if loaded_batches else 3600 *
24):
loaded_batches += 1
prepared_batch = reporter.prepare_batch(batch)
prepared_batch = to_device(prepared_batch, self.device)
if not validate_batch_sizes(
prepared_batch.get_batch_size()):
logger.info(
"Skip batch due to uneven batch sizes.")
skipped_batches += 1
continue
model_output = self.model(prepared_batch)
report = Report(prepared_batch, model_output)
report = report.detach()
meter.update_from_report(report)
moved_report = report
# Move to CPU for metrics calculation later if needed
# Explicitly use `non_blocking=False` as this can cause
# race conditions in next accumulate
if use_cpu:
moved_report = report.copy().to("cpu",
non_blocking=False)
# accumulate necessary params for metric calculation
if combined_report is None:
# make a copy of report since `reporter.add_to_report` will
# change some of the report keys later
combined_report = moved_report.copy()
else:
combined_report.accumulate_tensor_fields_and_loss(
moved_report, self.metrics.required_params)
combined_report.batch_size += moved_report.batch_size
# Each node generates a separate copy of predict JSON from the
# report, which will be used to evaluate dataset-level metrics
# (such as mAP in object detection or CIDEr in image captioning)
# Since `reporter.add_to_report` changes report keys,
# (e.g scores) do this after
# `combined_report.accumulate_tensor_fields_and_loss`
if "__prediction_report__" in self.metrics.required_params:
# Still need to use original report here on GPU/TPU since
# it will be gathered
reporter.add_to_report(
report,
self.model,
execute_on_master_only=False)
if single_batch is True:
break
logger.info(f"Finished training. Loaded {loaded_batches}")
logger.info(f" -- skipped {skipped_batches} batches.")
reporter.postprocess_dataset_report()
assert (combined_report is not None
), "Please check if your validation set is empty!"
# add prediction_report is used for set-level metrics
combined_report.prediction_report = reporter.report
combined_report.metrics = self.metrics(combined_report,
combined_report)
# Since update_meter will reduce the metrics over GPUs, we need to
# move them back to GPU but we will only move metrics and losses
# which are needed by update_meter to avoid OOM
# Furthermore, do it in a non_blocking way to avoid any issues
# in device to host or host to device transfer
if use_cpu:
combined_report = combined_report.to(
self.device,
fields=["metrics", "losses"],
non_blocking=False)
meter.update_from_report(combined_report,
should_update_loss=False)
# enable train mode again
self.model.train()
return combined_report, meter
def on_train_batch_end(
self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx
):
output = outputs[0][0]["extra"]
report = Report(output["input_batch"], output)
self.lightning_losses.append(report["losses"]["loss"].item())
def evaluation_loop(
self,
dataset_type: str,
use_tqdm: bool = False,
single_batch: bool = False) -> Tuple[Dict[str, Any], Type[Meter]]:
meter = Meter()
reporter = self.dataset_loader.get_test_reporter(dataset_type)
with torch.no_grad():
self.model.eval()
disable_tqdm = not use_tqdm or not is_master()
while reporter.next_dataset(flush_report=False):
dataloader = reporter.get_dataloader()
combined_report = None
if self._can_use_tqdm(dataloader):
dataloader = tqdm.tqdm(dataloader, disable=disable_tqdm)
for batch in dataloader:
prepared_batch = reporter.prepare_batch(batch)
prepared_batch = to_device(prepared_batch, self.device)
model_output = self.model(prepared_batch)
report = Report(prepared_batch, model_output)
meter.update_from_report(report)
# accumulate necessary params for metric calculation
if combined_report is None:
# make a copy of report since `reporter.add_to_report` will
# change some of the report keys later
combined_report = Report(report)
else:
combined_report.accumulate_tensor_fields_and_loss(
report, self.metrics.required_params)
combined_report.batch_size += report.batch_size
# Each node generates a separate copy of predict JSON from the
# report, which will be used to evaluate dataset-level metrics
# (such as mAP in object detection or CIDEr in image captioning)
# Since `reporter.add_to_report` changes report keys (e.g. scores),
# do this after `combined_report.accumulate_tensor_fields_and_loss`
if "__prediction_report__" in self.metrics.required_params:
reporter.add_to_report(report,
self.model,
execute_on_master_only=False)
if single_batch is True:
break
reporter.postprocess_dataset_report()
assert (combined_report is not None
), "Please check if your validation set is empty!"
# add prediction_report is used for set-level metrics
combined_report.prediction_report = reporter.report
combined_report.metrics = self.metrics(combined_report,
combined_report)
meter.update_from_report(combined_report,
should_update_loss=False)
# enable train mode again
self.model.train()
return combined_report, meter