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Memory efficient patience #43

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Memory efficient patience #43

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7650481
Memory efficient patience
Jeanselme Feb 9, 2021
f72c2a7
Reinitialize patience for pretraining
Jeanselme Feb 9, 2021
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54 changes: 21 additions & 33 deletions dsm/utilities.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@ def get_optimizer(model, lr):
' is not implemented')

def pretrain_dsm(model, t_train, e_train, t_valid, e_valid,
n_iter=10000, lr=1e-2, thres=1e-4):
n_iter=10000, lr=1e-2):

premodel = DeepSurvivalMachinesTorch(1, 1,
dist=model.dist,
Expand All @@ -59,7 +59,7 @@ def pretrain_dsm(model, t_train, e_train, t_valid, e_valid,

optimizer = get_optimizer(model, lr)

oldcost = float('inf')
best_loss = float('inf')
patience = 0
costs = []
for _ in tqdm(range(n_iter)):
Expand All @@ -74,15 +74,18 @@ def pretrain_dsm(model, t_train, e_train, t_valid, e_valid,
valid_loss = 0
for r in range(model.risks):
valid_loss += unconditional_loss(premodel, t_valid, e_valid, str(r+1))
valid_loss = valid_loss.detach().cpu().numpy()
costs.append(valid_loss)
#print(valid_loss)
if np.abs(costs[-1] - oldcost) < thres:
valid_loss = valid_loss.item()

if best_loss < valid_loss:
patience += 1
if patience == 3:
if patience == 2:
break
oldcost = costs[-1]

else:
patience = 0
best_loss = valid_loss
best_params = deepcopy(premodel.state_dict())

premodel.load_state_dict(best_params)
return premodel

def _reshape_tensor_with_nans(data):
Expand Down Expand Up @@ -129,8 +132,7 @@ def train_dsm(model,
t_valid_,
e_valid_,
n_iter=10000,
lr=1e-2,
thres=1e-4)
lr=1e-2)

for r in range(model.risks):
model.shape[str(r+1)].data.fill_(float(premodel.shape[str(r+1)]))
Expand All @@ -140,7 +142,7 @@ def train_dsm(model,
optimizer = get_optimizer(model, lr)

patience = 0
oldcost = float('inf')
best_loss = float('inf')

nbatches = int(x_train.shape[0]/bs)+1

Expand All @@ -166,7 +168,6 @@ def train_dsm(model,
_reshape_tensor_with_nans(eb),
elbo=elbo,
risk=str(r+1))
#print ("Train Loss:", float(loss))
loss.backward()
optimizer.step()

Expand All @@ -179,30 +180,17 @@ def train_dsm(model,
elbo=False,
risk=str(r+1))

valid_loss = valid_loss.detach().cpu().numpy()
costs.append(float(valid_loss))
dics.append(deepcopy(model.state_dict()))

if costs[-1] >= oldcost:
valid_loss = valid_loss.item()
if valid_loss > best_loss:
if patience == 2:
minm = np.argmin(costs)
model.load_state_dict(dics[minm])

del dics
gc.collect()

return model, i
break
else:
patience += 1
else:
patience = 0

oldcost = costs[-1]

minm = np.argmin(costs)
model.load_state_dict(dics[minm])

del dics
gc.collect()
best_state = deepcopy(model.state_dict())
best_loss = valid_loss

model.load_state_dict(best_state)

return model, i