def fit(epochs, model, loss_fn, opt, data, callbacks=None, metrics=None):
cb_handler = CallbackHandler(callbacks)
pbar = master_bar(range(epochs))
cb_handler.on_train_begin(epochs, pbar=pbar, metrics=metrics)
exception = False
try:
for epoch in pbar:
model.train()
cb_handler.on_epoch_begin()
for xb, yb in progress_bar(data.train_dl, parent=pbar):
xb, yb = cb_handler.on_batch_begin(xb, yb)
loss, _ = loss_batch(model, xb, yb, loss_fn, opt, cb_handler)
if cb_handler.on_batch_end(loss): break
if hasattr(data, 'valid_dl') and data.valid_dl is not None:
model.eval()
with torch.no_grad():
*val_metrics, nums = zip(*[
loss_batch(model,
xb,
yb,
loss_fn,
cb_handler=cb_handler,
metrics=metrics)
for xb, yb in progress_bar(data.valid_dl, parent=pbar)
])
val_metrics = [
np.sum(np.multiply(val, nums)) / np.sum(nums)
for val in val_metrics
]
else:
val_metrics = None
if cb_handler.on_epoch_end(val_metrics): break
except Exception as e:
exception = e
raise e
finally:
cb_handler.on_train_end(exception)
def validate(model,
dl,
loss_fn=None,
metrics=None,
cb_handler=None,
pbar=None):
model.eval()
with torch.no_grad():
return zip(*[
loss_batch(
model, xb, yb, loss_fn, cb_handler=cb_handler, metrics=metrics)
for xb, yb in progress_bar(dl, parent=pbar)
])
def fit(epochs, model, loss_fn, opt, data, callbacks=None, metrics=None):
cb_handler = CallbackHandler(callbacks)
pbar = master_bar(range(epochs))
cb_handler.on_train_begin(epochs, pbar=pbar, metrics=metrics)
exception = False
try:
for epoch in pbar:
model.train()
cb_handler.on_epoch_begin()
for xb, yb in progress_bar(data.train_dl, parent=pbar):
xb, yb = cb_handler.on_batch_begin(xb, yb)
loss, _ = loss_batch(model, xb, yb, loss_fn, opt, cb_handler)
if cb_handler.on_batch_end(loss): break
if hasattr(data, 'valid_dl') and data.valid_dl is not None:
*val_metrics, nums = validate(model,
data.valid_dl,
loss_fn=loss_fn,
cb_handler=cb_handler,
metrics=metrics,
pbar=pbar)
nums = np.array(nums, dtype=np.float32)
val_metrics = [
(torch.stack(val).cpu().numpy() * nums).sum() / nums.sum()
for val in val_metrics
]
else:
val_metrics = None
if cb_handler.on_epoch_end(val_metrics): break
except Exception as e:
exception = e
raise e
finally:
cb_handler.on_train_end(exception)