def load_tree_model(model_file, cuda_on=False,
soft_decision=True, stochastic=False,
breadth_first=False, fast=False,
):
"""Load a tree model. """
# load the model and set routers stochastic.
map_location = None
if not (cuda_on):
map_location = 'cpu'
tree_tmp = torch.load(model_file, map_location=map_location)
tree_struct, tree_modules = tree_tmp.tree_struct, tree_tmp.update_tree_modules()
for node in tree_modules:
node['router'].stochastic = stochastic
node['router'].soft_decision = soft_decision
node['router'].dropout_prob = 0.0
for node_meta in tree_struct:
if not ('extended' in node_meta.keys()):
node_meta['extended'] = False
model = models.Tree(
tree_struct, tree_modules,
split=False, cuda_on=cuda_on, soft_decision=soft_decision,
breadth_first=breadth_first,
)
if cuda_on:
model.cuda()
return model
def compute_error_general_ensemble(model_file_list,
data_loader,
cuda_on=False,
soft_decision=True,
stochastic=False,
breadth_first=False,
fast=False,
task="classification",
name=''):
"""Load an ensemble of models and compute the average prediction. """
# load the model and set routers stochastic.
model_list = []
map_location = None
if not (cuda_on):
map_location = 'cpu'
for model_file in model_file_list:
tree_tmp = torch.load(model_file, map_location=map_location)
tree_struct, tree_modules = tree_tmp.tree_struct, tree_tmp.update_tree_modules(
)
for node in tree_modules:
node['router'].stochastic = stochastic
node['router'].soft_decision = soft_decision
node['router'].dropout_prob = 0.0
for node_meta in tree_struct:
if not ('extended' in node_meta.keys()):
node_meta['extended'] = False
if task == "classification":
model = models.Tree(
tree_struct,
tree_modules,
split=False,
cuda_on=cuda_on,
soft_decision=soft_decision,
breadth_first=breadth_first,
)
if cuda_on:
model.cuda()
model_list.append(model)
# compute the error
for model in model_list:
model.eval()
test_loss = 0
correct = 0
for data, target in data_loader:
if cuda_on:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
# compute the average prediction over different models
output = 0.0
for model in model_list:
if fast:
output += model.fast_forward_BF(data)
else:
output += model.forward(data)
output /= len(model_list)
if task == "classification":
test_loss += F.nll_loss(output, target, size_average=False).data[0]
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
elif task == "regression":
# print(test_loss)
test_loss += F.mse_loss(output, target, size_average=False).data[0]
# Normalise the loss and print:
if task == "classification":
test_loss /= len(data_loader.dataset)
print(name + 'Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
test_loss, correct, len(data_loader.dataset), 100. * correct /
len(data_loader.dataset)))
elif task == "regression":
test_loss = test_loss / 7.0 / len(data_loader.dataset)
print('Average loss: {:.4f}'.format(test_loss))
def compute_error_general(model_file,
data_loader,
cuda_on=False,
soft_decision=True,
stochastic=False,
breadth_first=False,
fast=False,
task="classification",
name=''):
"""Load a model and perform stochastic inferenc
Args:
model_file (str): model parameters
data_dataloader (torch.utils.data.DataLoader): data loader
"""
# load the model and set routers stochastic.
map_location = None
if not (cuda_on):
map_location = 'cpu'
tree_tmp = torch.load(model_file, map_location=map_location)
tree_struct, tree_modules = \
tree_tmp.tree_struct, tree_tmp.update_tree_modules()
for node in tree_modules:
node['router'].stochastic = stochastic
node['router'].soft_decision = soft_decision
node['router'].dropout_prob = 0.0
for node_meta in tree_struct:
if not ('extended' in node_meta.keys()):
node_meta['extended'] = False
if task == "classification":
model = models.Tree(
tree_struct,
tree_modules,
split=False,
cuda_on=cuda_on,
soft_decision=soft_decision,
breadth_first=breadth_first,
)
if cuda_on:
model.cuda()
# compute the error
model.eval()
test_loss = 0
correct = 0
for data, target in data_loader:
if cuda_on:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
if fast:
output = model.fast_forward_BF(data)
else:
output = model.forward(data)
if task == "classification":
test_loss += F.nll_loss(output, target, size_average=False).data[0]
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
else:
raise NotImplementedError("The specified task is not supported")
# Normalise the loss and print:
if task == "classification":
test_loss /= len(data_loader.dataset)
print(name + 'Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
test_loss, correct, len(data_loader.dataset), 100. * correct /
len(data_loader.dataset)))
elif task == "regression":
test_loss = test_loss / 7.0 / len(data_loader.dataset)
print('Average loss: {:.4f}'.format(test_loss))