def predict(args):
device = torch.device(args.device)
cls_embed = torch.load(args.embedding_save_path)
model = textCNN(args.embedding_size, args.cls_num, args.l1_channels_num)
model.load_state_dict(torch.load(args.model_save_path))
model.eval()
model = model.to(device)
cls_embed = cls_embed.to(device)
cls_embed.eval()
dev_dataset = DataSet(args.predict_data, None, args.batch_size)
dev_dataset.reorderForEval()
towrite = open(args.predict_writeto, "w+")
towrite.write("idx,labels\n")
idx = 0
print("Begin Predict task...")
while (True):
(example, dump), p = dev_dataset.getPredictBatch(False)
example = cls_embed(example, device=device)
# print(example.size())
outs = model(example)
outs = (torch.argmax(outs, -1) + 1).squeeze().tolist()
for out in zip(example[1], outs):
towrite.write("{0},{1}\n".format(out[0], int(out[1])))
idx += 1
if (p): break
towrite.close()
print("Predict task Done!")
def predict(args):
device = torch.device(args.device)
cls_embed = torch.load(args.embedding_save_path)
model = textCNN(args.embedding_size, args.cls_num, args.l1_channels_num)
model.load_state_dict(torch.load(args.model_save_path))
model.eval()
model = model.to(device)
cls_embed = cls_embed.to(device)
cls_embed.eval()
dev_dataset = DataSet(args.predict_data, None, args.batch_size)
towrite = open(args.predict_writeto, "w+")
towrite.write("idx,labels\n")
idx = 0
print("Begin Predict task...")
while (True):
example, p = dev_dataset.getPredictBatch()
example = cls_embed(example, device=device)
# print(example.size())
out = model(example)
out = torch.argmax(out, -1).item() + 1
towrite.write("{0},{1}\n".format(idx, out))
idx += 1
if (p): break
towrite.close()
print("Predict task Done!")