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from cnn import CNN,Model |
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from utils import loader_test |
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import torch |
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device = torch.device('cuda') |
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model = CNN().to(device) |
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model.load_state_dict(torch.load('net_params.pth')) |
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def test(): |
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model.eval() |
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correct = 0 |
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total = 0 |
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for i ,(input_ids, attention_mask, token_type_ids, labels) in enumerate(loader_test): |
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print(i) |
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input_ids = input_ids.to(device) |
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attention_mask = attention_mask.to(device) |
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token_type_ids = token_type_ids.to(device) |
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labels = labels.to(device) |
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with torch.no_grad(): |
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out = model(input_ids=input_ids,attention_mask=attention_mask,token_type_ids=token_type_ids) |
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out = out.argmax(dim=1) |
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correct += (out == labels).sum().item() |
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total += len(labels) |
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print('correct: ',correct,'total: ',total) |
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print('accuracy:',correct/total) |
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test() |
