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Remove manual torch.no_grad and train eval mode in LightningModule #1100

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Remove manual torch.no_grad and train eval mode in LightningModule #1100

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3dbbb44
remove manual no_grad and train mode
aniketmaurya May 16, 2022
13bb3e7
remove manual no_grad and train mode
aniketmaurya May 16, 2022
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13 changes: 4 additions & 9 deletions icevision/models/mmdet/lightning/model_adapter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,12 +52,10 @@ def training_step(self, batch, batch_idx):
def validation_step(self, batch, batch_idx):
data, records = batch

self.model.eval()
with torch.no_grad():
outputs = self.model.train_step(data=data, optimizer=None)
raw_preds = self.model.forward_test(
imgs=[data["img"]], img_metas=[data["img_metas"]]
)
outputs = self.model.train_step(data=data, optimizer=None)
raw_preds = self.model.forward_test(
imgs=[data["img"]], img_metas=[data["img_metas"]]
)

preds = self.convert_raw_predictions(
batch=data, raw_preds=raw_preds, records=records
Expand All @@ -67,8 +65,5 @@ def validation_step(self, batch, batch_idx):
for k, v in outputs["log_vars"].items():
self.log(f"valid/{k}", v)

# TODO: is train and eval model automatically set by lighnting?
self.model.train()

def validation_epoch_end(self, outs):
self.finalize_metrics()
17 changes: 8 additions & 9 deletions icevision/models/ross/efficientdet/lightning/model_adapter.py
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Expand Up @@ -41,15 +41,14 @@ def training_step(self, batch, batch_idx):
def validation_step(self, batch, batch_idx):
(xb, yb), records = batch

with torch.no_grad():
raw_preds = self(xb, yb)
preds = efficientdet.convert_raw_predictions(
batch=(xb, yb),
raw_preds=raw_preds["detections"],
records=records,
detection_threshold=0.0,
)
loss = efficientdet.loss_fn(raw_preds, yb)
raw_preds = self(xb, yb)
preds = efficientdet.convert_raw_predictions(
batch=(xb, yb),
raw_preds=raw_preds["detections"],
records=records,
detection_threshold=0.0,
)
loss = efficientdet.loss_fn(raw_preds, yb)

self.accumulate_metrics(preds)

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21 changes: 10 additions & 11 deletions icevision/models/torchvision/lightning_model_adapter.py
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Expand Up @@ -36,17 +36,16 @@ def training_step(self, batch, batch_idx):

def validation_step(self, batch, batch_idx):
(xb, yb), records = batch
with torch.no_grad():
self.train()
train_preds = self(xb, yb)
loss = loss_fn(train_preds, yb)

self.eval()
raw_preds = self(xb)
preds = self.convert_raw_predictions(
batch=batch, raw_preds=raw_preds, records=records
)
self.accumulate_metrics(preds=preds)
self.train()
train_preds = self(xb, yb)
loss = loss_fn(train_preds, yb)
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I wasn't able to understand why do we calculate training prediction here?

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Maybe the name is misleading , these are in fact validation preds. These are used to calculate validation loss and metrics


self.eval()
raw_preds = self(xb)
preds = self.convert_raw_predictions(
batch=batch, raw_preds=raw_preds, records=records
)
self.accumulate_metrics(preds=preds)

self.log("val_loss", loss)

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19 changes: 9 additions & 10 deletions icevision/models/ultralytics/yolov5/lightning/model_adapter.py
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Expand Up @@ -42,16 +42,15 @@ def training_step(self, batch, batch_idx):
def validation_step(self, batch, batch_idx):
(xb, yb), records = batch

with torch.no_grad():
inference_out, training_out = self(xb)
preds = yolov5.convert_raw_predictions(
batch=xb,
raw_preds=inference_out,
records=records,
detection_threshold=0.001,
nms_iou_threshold=0.6,
)
loss = self.compute_loss(training_out, yb)[0]
inference_out, training_out = self(xb)
preds = yolov5.convert_raw_predictions(
batch=xb,
raw_preds=inference_out,
records=records,
detection_threshold=0.001,
nms_iou_threshold=0.6,
)
loss = self.compute_loss(training_out, yb)[0]

self.accumulate_metrics(preds)

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