增加cnn的测试

fastNLP/core/tester.py

@@ -27,11 +27,13 @@ Example::
     tester = Tester(dataset, model, metrics=AccuracyMetric())
     eval_results = tester.test()
 
-这里Metric的映射规律是和 :class:`fastNLP.Trainer` 中一致的，具体使用请参考 :doc:`trainer 模块<fastNLP.core.trainer>` 的1.3部分
-
+这里Metric的映射规律是和 :class:`fastNLP.Trainer` 中一致的，具体使用请参考 :doc:`trainer 模块<fastNLP.core.trainer>` 的1.3部分。
+Tester在验证进行之前会调用model.eval()提示当前进入了evaluation阶段，即会关闭nn.Dropout()等，在验证结束之后会调用model.train()恢复到训练状态。
 
 
 """
+import warnings
+
 import torch
 from torch import nn
 
@@ -72,6 +74,7 @@ class Tester(object):
 
         5. None. 为None则不对模型进行任何处理，如果传入的model为torch.nn.DataParallel该值必须为None。
 
+        如果模型是通过predict()进行预测的话，那么将不能使用多卡(DataParallel)进行验证，只会使用第一张卡上的模型。
     :param int verbose: 如果为0不输出任何信息; 如果为1，打印出验证结果。
     """
 
@@ -90,6 +93,13 @@ class Tester(object):
         self.batch_size = batch_size
         self.verbose = verbose
 
+        #  如果是DataParallel将没有办法使用predict方法
+        if isinstance(self._model, nn.DataParallel):
+            if hasattr(self._model.module, 'predict') and not hasattr(self._model, 'predict'):
+                warnings.warn("Cannot use DataParallel to test your model, because your model offer predict() function,"
+                              " while DataParallel has no predict() function.")
+                self._model = self._model.module
+
         # check predict
         if hasattr(self._model, 'predict'):
             self._predict_func = self._model.predict

fastNLP/core/trainer.py

@@ -462,7 +462,6 @@ class Trainer(object):
         self.best_dev_perf = None
         self.sampler = sampler if sampler is not None else RandomSampler()
         self.prefetch = prefetch
-        self.callback_manager = CallbackManager(env={"trainer": self}, callbacks=callbacks)
         self.n_steps = (len(self.train_data) // self.batch_size + int(
             len(self.train_data) % self.batch_size != 0)) * self.n_epochs
         
@@ -492,7 +491,6 @@ class Trainer(object):
         self.step = 0
         self.start_time = None  # start timestamp
         
-        print("callback_manager")
         self.callback_manager = CallbackManager(env={"trainer": self},
                                                 callbacks=callbacks)
     

fastNLP/models/cnn_text_classification.py

@@ -3,9 +3,9 @@
 
 import torch
 import torch.nn as nn
-import numpy as np
+from ..core.const import Const as C
 
-import fastNLP.modules.encoder as encoder
+from ..modules import encoder
 
 
 class CNNText(torch.nn.Module):
@@ -18,7 +18,7 @@ class CNNText(torch.nn.Module):
     :param int num_classes: 一共有多少类
     :param int,tuple(int) out_channels: 输出channel的数量。如果为list，则需要与kernel_sizes的数量保持一致
     :param int,tuple(int) kernel_sizes: 输出channel的kernel大小。
-    :param int padding:
+    :param int padding: 对句子前后的pad的大小, 用0填充。
     :param float dropout: Dropout的大小
     """
 
@@ -38,17 +38,7 @@ class CNNText(torch.nn.Module):
             kernel_sizes=kernel_sizes,
             padding=padding)
         self.dropout = nn.Dropout(dropout)
-        self.fc = encoder.Linear(sum(kernel_nums), num_classes)
-
-    def init_embed(self, embed):
-        """
-        加载预训练的模型
-        :param numpy.ndarray embed: vocab_size x embed_dim的embedding
-        :return:
-        """
-        assert isinstance(embed, np.ndarray)
-        assert embed.shape == self.embed.embed.weight.shape
-        self.embed.embed.weight.data = torch.from_numpy(embed)
+        self.fc = nn.Linear(sum(kernel_nums), num_classes)
 
     def forward(self, words, seq_len=None):
         """
@@ -61,7 +51,7 @@ class CNNText(torch.nn.Module):
         x = self.conv_pool(x)  # [N,L,C] -> [N,C]
         x = self.dropout(x)
         x = self.fc(x)  # [N,C] -> [N, N_class]
-        return {'pred': x}
+        return {C.OUTPUT: x}
 
     def predict(self, words, seq_len=None):
         """
@@ -71,5 +61,5 @@ class CNNText(torch.nn.Module):
         :return predict: dict of torch.LongTensor, [batch_size, ]
         """
         output = self(words, seq_len)
-        _, predict = output['pred'].max(dim=1)
-        return {'pred': predict}
+        _, predict = output[C.OUTPUT].max(dim=1)
+        return {C.OUTPUT: predict}

fastNLP/models/sequence_modeling.py

@@ -4,7 +4,8 @@ from .base_model import BaseModel
 from ..modules import decoder, encoder
 from ..modules.decoder.CRF import allowed_transitions
 from ..modules.utils import seq_mask
-
+from ..core.const import Const as C
+from torch import nn
 
 class SeqLabeling(BaseModel):
     """
@@ -24,7 +25,7 @@ class SeqLabeling(BaseModel):
 
         self.Embedding = encoder.embedding.Embedding(init_embed)
         self.Rnn = encoder.lstm.LSTM(self.Embedding.embedding_dim, hidden_size)
-        self.Linear = encoder.linear.Linear(hidden_size, num_classes)
+        self.Linear = nn.Linear(hidden_size, num_classes)
         self.Crf = decoder.CRF.ConditionalRandomField(num_classes)
         self.mask = None
 
@@ -46,7 +47,7 @@ class SeqLabeling(BaseModel):
         # [batch_size, max_len, hidden_size * direction]
         x = self.Linear(x)
         # [batch_size, max_len, num_classes]
-        return {"loss": self._internal_loss(x, target)}
+        return {C.LOSS: self._internal_loss(x, target)}
 
     def predict(self, words, seq_len):
         """
@@ -65,7 +66,7 @@ class SeqLabeling(BaseModel):
         x = self.Linear(x)
         # [batch_size, max_len, num_classes]
         pred = self._decode(x)
-        return {'pred': pred}
+        return {C.OUTPUT: pred}
 
     def _internal_loss(self, x, y):
         """

test/models/test_cnn.py

@@ -0,0 +1,22 @@
+
+import unittest
+
+from test.models.model_runner import *
+from fastNLP.models.cnn_text_classification import CNNText
+
+
+class TestCNNText(unittest.TestCase):
+    def test_case1(self):
+        # 测试能否正常运行CNN
+        init_emb = (VOCAB_SIZE, 30)
+        model = CNNText(init_emb,
+                        NUM_CLS,
+                        kernel_nums=(1, 3, 5),
+                        kernel_sizes=(2, 2, 2),
+                        padding=0,
+                        dropout=0.5)
+        RUNNER.run_model_with_task(TEXT_CLS, model)
+
+
+if __name__ == '__main__':
+    TestCNNText().test_case1()