这是indexloc提供的服务，不要输入任何密码
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Ability to run on CPU #6

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Feb 5, 2018
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Ability to run on CPU #6

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4c4dd09
CPU run: Added possibility to run entirely on CPU.
AlexGrig Jan 29, 2018
1d1cf15
CPU run: modified output.
AlexGrig Jan 29, 2018
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11 changes: 9 additions & 2 deletions infer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,12 @@
args = parser.parse_args()
print(args)

use_gpu = torch.cuda.is_available() # global flag
if use_gpu:
print('GPU is available.')
else:
print('GPU is not available.')

def main():
params = dict()
params['batch_size'] = 1
Expand Down Expand Up @@ -71,14 +77,15 @@ def main():
print('######################################################')
print('######################################################')
rencoder.eval()
rencoder = rencoder.cuda()
if use_gpu: rencoder = rencoder.cuda()

inv_userIdMap = {v: k for k, v in data_layer.userIdMap.items()}
inv_itemIdMap = {v: k for k, v in data_layer.itemIdMap.items()}

eval_data_layer.src_data = data_layer.data
with open(args.predictions_path, 'w') as outf:
for i, ((out, src), majorInd) in enumerate(eval_data_layer.iterate_one_epoch_eval(for_inf=True)):
inputs = Variable(src.cuda().to_dense())
inputs = Variable(src.cuda().to_dense() if use_gpu else src.to_dense())
targets_np = out.to_dense().numpy()[0, :]
outputs = rencoder(inputs).cpu().data.numpy()[0, :]
non_zeros = targets_np.nonzero()[0].tolist()
Expand Down
17 changes: 12 additions & 5 deletions run.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,13 +53,19 @@
args = parser.parse_args()
print(args)

use_gpu = torch.cuda.is_available() # global flag
if use_gpu:
print('GPU is available.')
else:
print('GPU is not available.')

def do_eval(encoder, evaluation_data_layer):
encoder.eval()
denom = 0.0
total_epoch_loss = 0.0
for i, (eval, src) in enumerate(evaluation_data_layer.iterate_one_epoch_eval()):
inputs = Variable(src.cuda().to_dense())
targets = Variable(eval.cuda().to_dense())
inputs = Variable(src.cuda().to_dense() if use_gpu else src.to_dense())
targets = Variable(eval.cuda().to_dense() if use_gpu else eval.to_dense())
outputs = encoder(inputs)
loss, num_ratings = model.MSEloss(outputs, targets)
total_epoch_loss += loss.data[0]
Expand Down Expand Up @@ -139,7 +145,8 @@ def main():
if len(gpu_ids)>1:
rencoder = nn.DataParallel(rencoder,
device_ids=gpu_ids)
rencoder = rencoder.cuda()

if use_gpu: rencoder = rencoder.cuda()

if args.optimizer == "adam":
optimizer = optim.Adam(rencoder.parameters(),
Expand Down Expand Up @@ -177,7 +184,7 @@ def main():
if args.optimizer == "momentum":
scheduler.step()
for i, mb in enumerate(data_layer.iterate_one_epoch()):
inputs = Variable(mb.cuda().to_dense())
inputs = Variable(mb.cuda().to_dense() if use_gpu else mb.to_dense())
optimizer.zero_grad()
outputs = rencoder(inputs)
loss, num_ratings = model.MSEloss(outputs, inputs)
Expand Down Expand Up @@ -232,4 +239,4 @@ def main():
torch.save(rencoder.state_dict(), model_checkpoint + ".last")

if __name__ == '__main__':
main()
main()