delete predictor.py

fastNLP/core/predictor.py

@@ -1,79 +0,0 @@
-"""
-    ..todo::
-        检查这个类是否需要
-"""
-from collections import defaultdict
-
-import torch
-
-from . import DataSetIter
-from . import DataSet
-from . import SequentialSampler
-from .utils import _build_args, _move_dict_value_to_device, _get_model_device
-
-
-class Predictor(object):
-    """
-    一个根据训练模型预测输出的预测器（Predictor）
-
-    与测试器（Tester）不同的是，predictor不关心模型性能的评价指标，只做inference。
-    这是一个fastNLP调用的高级模型包装器。它与Trainer、Tester不共享任何操作。
-
-    :param torch.nn.Module network: 用来完成预测任务的模型
-    """
-
-    def __init__(self, network):
-        if not isinstance(network, torch.nn.Module):
-            raise ValueError(
-                "Only fastNLP.models.BaseModel or torch.nn,Module is allowed, not {}".format(type(network)))
-        self.network = network
-        self.batch_size = 1
-        self.batch_output = []
-
-    def predict(self, data: DataSet, seq_len_field_name=None):
-        """用已经训练好的模型进行inference.
-
-        :param fastNLP.DataSet data: 待预测的数据集
-        :param str seq_len_field_name: 表示序列长度信息的field名字
-        :return: dict dict里面的内容为模型预测的结果
-        """
-        if not isinstance(data, DataSet):
-            raise ValueError("Only Dataset class is allowed, not {}.".format(type(data)))
-        if seq_len_field_name is not None and seq_len_field_name not in data.field_arrays:
-            raise ValueError("Field name {} not found in DataSet {}.".format(seq_len_field_name, data))
-
-        prev_training = self.network.training
-        self.network.eval()
-        network_device = _get_model_device(self.network)
-        batch_output = defaultdict(list)
-        data_iterator = DataSetIter(data, batch_size=self.batch_size, sampler=SequentialSampler(), as_numpy=False)
-
-        if hasattr(self.network, "predict"):
-            predict_func = self.network.predict
-        else:
-            predict_func = self.network.forward
-
-        with torch.no_grad():
-            for batch_x, _ in data_iterator:
-                _move_dict_value_to_device(batch_x, _, device=network_device)
-                refined_batch_x = _build_args(predict_func, **batch_x)
-                prediction = predict_func(**refined_batch_x)
-
-                if seq_len_field_name is not None:
-                    seq_lens = batch_x[seq_len_field_name].tolist()
-
-                for key, value in prediction.items():
-                    value = value.cpu().numpy()
-                    if len(value.shape) == 1 or (len(value.shape) == 2 and value.shape[1] == 1):
-                        batch_output[key].extend(value.tolist())
-                    else:
-                        if seq_len_field_name is not None:
-                            tmp_batch = []
-                            for idx, seq_len in enumerate(seq_lens):
-                                tmp_batch.append(value[idx, :seq_len])
-                            batch_output[key].extend(tmp_batch)
-                        else:
-                            batch_output[key].append(value)
-
-        self.network.train(prev_training)
-        return batch_output

test/core/test_predictor.py

@@ -1,48 +0,0 @@
-import unittest
-from collections import defaultdict
-
-import numpy as np
-import torch
-
-from fastNLP.core.dataset import DataSet
-from fastNLP.core.instance import Instance
-from fastNLP.core.predictor import Predictor
-
-
-def prepare_fake_dataset():
-    mean = np.array([-3, -3])
-    cov = np.array([[1, 0], [0, 1]])
-    class_A = np.random.multivariate_normal(mean, cov, size=(1000,))
-
-    mean = np.array([3, 3])
-    cov = np.array([[1, 0], [0, 1]])
-    class_B = np.random.multivariate_normal(mean, cov, size=(1000,))
-
-    data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] +
-                       [Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B])
-    return data_set
-
-
-class LinearModel(torch.nn.Module):
-    def __init__(self):
-        super(LinearModel, self).__init__()
-        self.linear = torch.nn.Linear(2, 1)
-
-    def forward(self, x):
-        return {"predict": self.linear(x)}
-
-
-class TestPredictor(unittest.TestCase):
-    def test_simple(self):
-        model = LinearModel()
-        predictor = Predictor(model)
-        data = prepare_fake_dataset()
-        data.set_input("x")
-        ans = predictor.predict(data)
-        self.assertTrue(isinstance(ans, defaultdict))
-        self.assertTrue("predict" in ans)
-        self.assertTrue(isinstance(ans["predict"], list))
-
-    def test_sequence(self):
-        # test sequence input/output
-        pass