Update to new version of framework

fastNLP/core/batch.py

@@ -69,6 +69,6 @@ class Batch(object):
                     else:
                         batch[name] = torch.stack(tensor_list, dim=0)
 
-            self.curidx += endidx
+            self.curidx = endidx
             return batch_x, batch_y
 

fastNLP/core/dataset.py

@@ -144,6 +144,15 @@ class DataSet(list):
             else:
                 self.convert(raw_data)
 
+    def load_raw(self, raw_data, vocabs):
+        """
+
+        :param raw_data:
+        :param vocabs:
+        :return:
+        """
+        self.convert_for_infer(raw_data, vocabs)
+
     def split(self, ratio, shuffle=True):
         """Train/dev splitting
 

fastNLP/core/metrics.py

@@ -38,14 +38,19 @@ class SeqLabelEvaluator(Evaluator):
     def __call__(self, predict, truth):
         """
 
-        :param predict: list of tensors, the network outputs from all batches.
+        :param predict: list of List, the network outputs from all batches.
         :param truth: list of dict, the ground truths from all batch_y.
         :return accuracy:
         """
         truth = [item["truth"] for item in truth]
-        truth = torch.cat(truth).view(-1, )
+        total_correct, total_count= 0., 0.
-        results = torch.Tensor(predict).view(-1, )
+        for x, y in zip(predict, truth):
-        accuracy = torch.sum(results.to(truth) == truth).to(torch.float) / results.shape[0]
+            mask = torch.Tensor(x).ge(1)
+            correct = torch.sum(torch.Tensor(x) * mask.float() == (y * mask.long()).float())
+            correct -= torch.sum(torch.Tensor(x).le(0))
+            total_correct += float(correct)
+            total_count += float(torch.sum(mask))
+        accuracy = total_correct / total_count
         return {"accuracy": float(accuracy)}
 
 

fastNLP/core/predictor.py

@@ -34,7 +34,7 @@ class Predictor(object):
         """Perform inference using the trained model.
 
         :param network: a PyTorch model (cpu)
-        :param data: list of list of strings, [num_examples, seq_len]
+        :param data: a DataSet object.
         :return: list of list of strings, [num_examples, tag_seq_length]
         """
         # transform strings into DataSet object

fastNLP/core/preprocess.py

@@ -18,6 +18,9 @@ def save_pickle(obj, pickle_path, file_name):
     :param pickle_path: str, the directory where the pickle file is to be saved
     :param file_name: str, the name of the pickle file. In general, it should be ended by "pkl".
     """
+    if not os.path.exists(pickle_path):
+        os.mkdir(pickle_path)
+        print("make dir {} before saving pickle file".format(pickle_path))
     with open(os.path.join(pickle_path, file_name), "wb") as f:
         _pickle.dump(obj, f)
     print("{} saved in {}".format(file_name, pickle_path))

fastNLP/fastnlp.py

@@ -4,6 +4,8 @@ from fastNLP.core.predictor import SeqLabelInfer, ClassificationInfer
 from fastNLP.core.preprocess import load_pickle
 from fastNLP.loader.config_loader import ConfigLoader, ConfigSection
 from fastNLP.loader.model_loader import ModelLoader
+from fastNLP.core.dataset import SeqLabelDataSet, TextClassifyDataSet
+
 
 """
 mapping from model name to [URL, file_name.class_name, model_pickle_name]
@@ -76,6 +78,8 @@ class FastNLP(object):
         self.model_dir = model_dir
         self.model = None
         self.infer_type = None  # "seq_label"/"text_class"
+        self.word_vocab = None
+        self.label_vocab = None
 
     def load(self, model_name, config_file="config", section_name="model"):
         """
@@ -100,10 +104,10 @@ class FastNLP(object):
         print("Restore model hyper-parameters {}".format(str(model_args.data)))
 
         # fetch dictionary size and number of labels from pickle files
-        word_vocab = load_pickle(self.model_dir, "word2id.pkl")
+        self.word_vocab = load_pickle(self.model_dir, "word2id.pkl")
-        model_args["vocab_size"] = len(word_vocab)
+        model_args["vocab_size"] = len(self.word_vocab)
-        label_vocab = load_pickle(self.model_dir, "class2id.pkl")
+        self.label_vocab = load_pickle(self.model_dir, "label2id.pkl")
-        model_args["num_classes"] = len(label_vocab)
+        model_args["num_classes"] = len(self.label_vocab)
 
         # Construct the model
         model = model_class(model_args)
@@ -130,8 +134,11 @@ class FastNLP(object):
         # tokenize: list of string ---> 2-D list of string
         infer_input = self.tokenize(raw_input, language="zh")
 
-        # 2-D list of string ---> 2-D list of tags
+        # create DataSet: 2-D list of strings ----> DataSet
-        results = infer.predict(self.model, infer_input)
+        infer_data = self._create_data_set(infer_input)
+
+        # DataSet ---> 2-D list of tags
+        results = infer.predict(self.model, infer_data)
 
         # 2-D list of tags ---> list of final answers
         outputs = self._make_output(results, infer_input)
@@ -154,6 +161,11 @@ class FastNLP(object):
         return module
 
     def _create_inference(self, model_dir):
+        """Specify which task to perform.
+
+        :param model_dir:
+        :return:
+        """
         if self.infer_type == "seq_label":
             return SeqLabelInfer(model_dir)
         elif self.infer_type == "text_class":
@@ -161,6 +173,24 @@ class FastNLP(object):
         else:
             raise ValueError("fail to create inference instance")
 
+    def _create_data_set(self, infer_input):
+        """Create a DataSet object given the raw inputs.
+
+        :param infer_input: 2-D lists of strings
+        :return data_set: a DataSet object
+        """
+        if self.infer_type == "seq_label":
+            data_set = SeqLabelDataSet()
+            data_set.load_raw(infer_input, {"word_vocab": self.word_vocab})
+            return data_set
+        elif self.infer_type == "text_class":
+            data_set = TextClassifyDataSet()
+            data_set.load_raw(infer_input, {"word_vocab": self.word_vocab})
+            return data_set
+        else:
+            raise RuntimeError("fail to make outputs with infer type {}".format(self.infer_type))
+
+
     def _load(self, model_dir, model_name):
         # To do
         return 0

fastNLP/saver/config_saver.py

@@ -18,7 +18,7 @@ class ConfigSaver(object):
         :return: The section.
         """
         sect = ConfigSection()
-        ConfigLoader(self.file_path).load_config(self.file_path, {sect_name: sect})
+        ConfigLoader().load_config(self.file_path, {sect_name: sect})
         return sect
 
     def _read_section(self):

test/core/test_batch.py

@@ -43,8 +43,10 @@ class TestCase1(unittest.TestCase):
 
         # use batch to iterate dataset
         data_iterator = Batch(data, 2, SeqSampler(), False)
+        total_data = 0
         for batch_x, batch_y in data_iterator:
-            self.assertEqual(len(batch_x), 2)
+            total_data += batch_x["text"].size(0)
+            self.assertTrue(batch_x["text"].size(0) == 2 or total_data == len(raw_texts))
             self.assertTrue(isinstance(batch_x, dict))
             self.assertTrue(isinstance(batch_x["text"], torch.LongTensor))
             self.assertTrue(isinstance(batch_y, dict))

test/core/test_metrics.py

@@ -1,20 +1,42 @@
-import sys, os
+import os
+import sys
+
 sys.path = [os.path.join(os.path.dirname(__file__), '..')] + sys.path
 
 from fastNLP.core import metrics
 # from sklearn import metrics as skmetrics
 import unittest
-import numpy as np
 from numpy import random
+from fastNLP.core.metrics import SeqLabelEvaluator
+import torch
+
 
 def generate_fake_label(low, high, size):
     return random.randint(low, high, size), random.randint(low, high, size)
 
+
+class TestEvaluator(unittest.TestCase):
+    def test_a(self):
+        evaluator = SeqLabelEvaluator()
+        pred = [[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]]
+        truth = [{"truth": torch.LongTensor([1, 2, 3, 3, 3])}, {"truth": torch.LongTensor([1, 2, 3, 3, 4])}]
+        ans = evaluator(pred, truth)
+        print(ans)
+
+    def test_b(self):
+        evaluator = SeqLabelEvaluator()
+        pred = [[1, 2, 3, 4, 5, 0, 0], [1, 2, 3, 4, 5, 0, 0]]
+        truth = [{"truth": torch.LongTensor([1, 2, 3, 3, 3, 0, 0])}, {"truth": torch.LongTensor([1, 2, 3, 3, 4, 0, 0])}]
+        ans = evaluator(pred, truth)
+        print(ans)
+
+
 class TestMetrics(unittest.TestCase):
     delta = 1e-5
     # test for binary, multiclass, multilabel
     data_types = [((1000,), 2), ((1000,), 10), ((1000, 10), 2)]
     fake_data = [generate_fake_label(0, high, shape) for shape, high in data_types]
+
     def test_accuracy_score(self):
         for y_true, y_pred in self.fake_data:
             for normalize in [True, False]:
@@ -22,7 +44,7 @@ class TestMetrics(unittest.TestCase):
                     test = metrics.accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight)
                     # ans = skmetrics.accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight)
                     # self.assertAlmostEqual(test, ans, delta=self.delta)
-    
+
     def test_recall_score(self):
         for y_true, y_pred in self.fake_data:
             # print(y_true.shape)
@@ -73,5 +95,6 @@ class TestMetrics(unittest.TestCase):
         # ans = skmetrics.f1_score(y_true, y_pred)
         # self.assertAlmostEqual(ans, test, delta=self.delta)
 
+
 if __name__ == '__main__':
     unittest.main()

test/core/test_predictor.py

@@ -2,9 +2,12 @@ import os
 import unittest
 
 from fastNLP.core.predictor import Predictor
+from fastNLP.core.dataset import TextClassifyDataSet, SeqLabelDataSet
 from fastNLP.core.preprocess import save_pickle
-from fastNLP.models.sequence_modeling import SeqLabeling
 from fastNLP.core.vocabulary import Vocabulary
+from fastNLP.loader.base_loader import BaseLoader
+from fastNLP.models.sequence_modeling import SeqLabeling
+from fastNLP.models.cnn_text_classification import CNNText
 
 
 class TestPredictor(unittest.TestCase):
@@ -28,23 +31,44 @@ class TestPredictor(unittest.TestCase):
         vocab = Vocabulary()
         vocab.word2idx = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, '!': 5, '@': 6, '#': 7, '$': 8, '?': 9}
         class_vocab = Vocabulary()
-        class_vocab.word2idx = {"0":0, "1":1, "2":2, "3":3, "4":4}
+        class_vocab.word2idx = {"0": 0, "1": 1, "2": 2, "3": 3, "4": 4}
 
         os.system("mkdir save")
-        save_pickle(class_vocab, "./save/", "class2id.pkl")
+        save_pickle(class_vocab, "./save/", "label2id.pkl")
         save_pickle(vocab, "./save/", "word2id.pkl")
 
-        model = SeqLabeling(model_args)
+        model = CNNText(model_args)
-        predictor = Predictor("./save/", task="seq_label")
+        import fastNLP.core.predictor as pre
+        predictor = Predictor("./save/", pre.text_classify_post_processor)
 
-        results = predictor.predict(network=model, data=infer_data)
+        # Load infer data
+        infer_data_set = TextClassifyDataSet(loader=BaseLoader())
+        infer_data_set.convert_for_infer(infer_data, vocabs={"word_vocab": vocab.word2idx})
+
+        results = predictor.predict(network=model, data=infer_data_set)
 
         self.assertTrue(isinstance(results, list))
         self.assertGreater(len(results), 0)
+        self.assertEqual(len(results), len(infer_data))
         for res in results:
+            self.assertTrue(isinstance(res, str))
+            self.assertTrue(res in class_vocab.word2idx)
+
+        del model, predictor, infer_data_set
+
+        model = SeqLabeling(model_args)
+        predictor = Predictor("./save/", pre.seq_label_post_processor)
+
+        infer_data_set = SeqLabelDataSet(loader=BaseLoader())
+        infer_data_set.convert_for_infer(infer_data, vocabs={"word_vocab": vocab.word2idx})
+
+        results = predictor.predict(network=model, data=infer_data_set)
+        self.assertTrue(isinstance(results, list))
+        self.assertEqual(len(results), len(infer_data))
+        for i in range(len(infer_data)):
+            res = results[i]
             self.assertTrue(isinstance(res, list))
-            self.assertEqual(len(res), 5)
+            self.assertEqual(len(res), len(infer_data[i]))
-            self.assertTrue(isinstance(res[0], str))
 
         os.system("rm -rf save")
         print("pickle path deleted")

test/core/test_tester.py

@@ -1,8 +1,9 @@
 import os
 import unittest
 
-from fastNLP.core.dataset import DataSet
+from fastNLP.core.dataset import SeqLabelDataSet
-from fastNLP.core.field import TextField
+from fastNLP.core.metrics import SeqLabelEvaluator
+from fastNLP.core.field import TextField, LabelField
 from fastNLP.core.instance import Instance
 from fastNLP.core.tester import SeqLabelTester
 from fastNLP.models.sequence_modeling import SeqLabeling
@@ -21,7 +22,7 @@ class TestTester(unittest.TestCase):
         }
         valid_args = {"save_output": True, "validate_in_training": True, "save_dev_input": True,
                       "save_loss": True, "batch_size": 2, "pickle_path": "./save/",
-                      "use_cuda": False, "print_every_step": 1}
+                      "use_cuda": False, "print_every_step": 1, "evaluator": SeqLabelEvaluator()}
 
         train_data = [
             [['a', 'b', 'c', 'd', 'e'], ['a', '@', 'c', 'd', 'e']],
@@ -34,16 +35,17 @@ class TestTester(unittest.TestCase):
         vocab = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, '!': 5, '@': 6, '#': 7, '$': 8, '?': 9}
         label_vocab = {'a': 0, '@': 1, 'c': 2, 'd': 3, 'e': 4}
 
-        data_set = DataSet()
+        data_set = SeqLabelDataSet()
         for example in train_data:
             text, label = example[0], example[1]
             x = TextField(text, False)
+            x_len = LabelField(len(text), is_target=False)
             y = TextField(label, is_target=True)
-            ins = Instance(word_seq=x, label_seq=y)
+            ins = Instance(word_seq=x, truth=y, word_seq_origin_len=x_len)
             data_set.append(ins)
 
         data_set.index_field("word_seq", vocab)
-        data_set.index_field("label_seq", label_vocab)
+        data_set.index_field("truth", label_vocab)
 
         model = SeqLabeling(model_args)
 

test/core/test_trainer.py

@@ -1,8 +1,9 @@
 import os
 import unittest
 
-from fastNLP.core.dataset import DataSet
+from fastNLP.core.dataset import SeqLabelDataSet
-from fastNLP.core.field import TextField
+from fastNLP.core.metrics import SeqLabelEvaluator
+from fastNLP.core.field import TextField, LabelField
 from fastNLP.core.instance import Instance
 from fastNLP.core.loss import Loss
 from fastNLP.core.optimizer import Optimizer
@@ -12,14 +13,15 @@ from fastNLP.models.sequence_modeling import SeqLabeling
 
 class TestTrainer(unittest.TestCase):
     def test_case_1(self):
-        args = {"epochs": 3, "batch_size": 2, "validate": True, "use_cuda": False, "pickle_path": "./save/",
+        args = {"epochs": 3, "batch_size": 2, "validate": False, "use_cuda": False, "pickle_path": "./save/",
                 "save_best_dev": True, "model_name": "default_model_name.pkl",
-                "loss": Loss(None),
+                "loss": Loss("cross_entropy"),
                 "optimizer": Optimizer("Adam", lr=0.001, weight_decay=0),
                 "vocab_size": 10,
                 "word_emb_dim": 100,
                 "rnn_hidden_units": 100,
-                "num_classes": 5
+                "num_classes": 5,
+                "evaluator": SeqLabelEvaluator()
                 }
         trainer = SeqLabelTrainer(**args)
 
@@ -34,16 +36,17 @@ class TestTrainer(unittest.TestCase):
         vocab = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, '!': 5, '@': 6, '#': 7, '$': 8, '?': 9}
         label_vocab = {'a': 0, '@': 1, 'c': 2, 'd': 3, 'e': 4}
 
-        data_set = DataSet()
+        data_set = SeqLabelDataSet()
         for example in train_data:
             text, label = example[0], example[1]
             x = TextField(text, False)
-            y = TextField(label, is_target=True)
+            x_len = LabelField(len(text), is_target=False)
-            ins = Instance(word_seq=x, label_seq=y)
+            y = TextField(label, is_target=False)
+            ins = Instance(word_seq=x, truth=y, word_seq_origin_len=x_len)
             data_set.append(ins)
 
         data_set.index_field("word_seq", vocab)
-        data_set.index_field("label_seq", label_vocab)
+        data_set.index_field("truth", label_vocab)
 
         model = SeqLabeling(args)
 

test/loader/config

@@ -9,10 +9,54 @@ input = [1,2,3]
 
 text = "this is text"
 
-doubles = 0.5
+doubles = 0.8
+
+tt = 0.5
+
+test = 105
+
+str = "this is a str"
+
+double = 0.5
+
 
 [t]
 x = "this is an test section"
 
+
+
 [test-case-2]
 double = 0.5
+
+doubles = 0.8
+
+tt = 0.5
+
+test = 105
+
+str = "this is a str"
+
+[another-test]
+doubles = 0.8
+
+tt = 0.5
+
+test = 105
+
+str = "this is a str"
+
+double = 0.5
+
+
+[one-another-test]
+doubles = 0.8
+
+tt = 0.5
+
+test = 105
+
+str = "this is a str"
+
+double = 0.5
+
+

test/loader/test_config_loader.py

@@ -31,7 +31,7 @@ class TestConfigLoader(unittest.TestCase):
             return dict
 
         test_arg = ConfigSection()
-        ConfigLoader("config").load_config(os.path.join("./test/loader", "config"), {"test": test_arg})
+        ConfigLoader().load_config(os.path.join("./test/loader", "config"), {"test": test_arg})
 
         section = read_section_from_config(os.path.join("./test/loader", "config"), "test")
 

test/loader/test_dataset_loader.py

@@ -1,3 +1,4 @@
+import os
 import unittest
 
 from fastNLP.loader.dataset_loader import POSDataSetLoader, LMDataSetLoader, TokenizeDataSetLoader, \
@@ -14,28 +15,28 @@ class TestDatasetLoader(unittest.TestCase):
 
     def test_case_TokenizeDatasetLoader(self):
         loader = TokenizeDataSetLoader()
-        data = loader.load("test/data_for_tests/", max_seq_len=32)
+        data = loader.load("./test/data_for_tests/cws_pku_utf_8", max_seq_len=32)
         print("pass TokenizeDataSetLoader test!")
 
     def test_case_POSDatasetLoader(self):
         loader = POSDataSetLoader()
-        data = loader.load()
+        data = loader.load("./test/data_for_tests/people.txt")
-        datas = loader.load_lines()
+        datas = loader.load_lines("./test/data_for_tests/people.txt")
         print("pass POSDataSetLoader test!")
 
     def test_case_LMDatasetLoader(self):
         loader = LMDataSetLoader()
-        data = loader.load()
+        data = loader.load("./test/data_for_tests/charlm.txt")
-        datas = loader.load_lines()
+        datas = loader.load_lines("./test/data_for_tests/charlm.txt")
         print("pass TokenizeDataSetLoader test!")
 
     def test_PeopleDailyCorpusLoader(self):
         loader = PeopleDailyCorpusLoader()
-        _, _ = loader.load()
+        _, _ = loader.load("./test/data_for_tests/people_daily_raw.txt")
 
     def test_ConllLoader(self):
-        loader = ConllLoader("./test/data_for_tests/conll_example.txt")
+        loader = ConllLoader()
-        _ = loader.load()
+        _ = loader.load("./test/data_for_tests/conll_example.txt")
 
 
 if __name__ == '__main__':

test/model/test_cws.py

@@ -13,10 +13,10 @@ from fastNLP.models.sequence_modeling import SeqLabeling
 from fastNLP.saver.model_saver import ModelSaver
 
 data_name = "pku_training.utf8"
-cws_data_path = "test/data_for_tests/cws_pku_utf_8"
+cws_data_path = "./test/data_for_tests/cws_pku_utf_8"
 pickle_path = "./save/"
-data_infer_path = "test/data_for_tests/people_infer.txt"
+data_infer_path = "./test/data_for_tests/people_infer.txt"
-config_path = "test/data_for_tests/config"
+config_path = "./test/data_for_tests/config"
 
 def infer():
     # Load infer configuration, the same as test

test/saver/test_config_saver.py

@@ -21,7 +21,7 @@ class TestConfigSaver(unittest.TestCase):
 
         standard_section = ConfigSection()
         t_section = ConfigSection()
-        ConfigLoader(config_file_path).load_config(config_file_path, {"test": standard_section, "t": t_section})
+        ConfigLoader().load_config(config_file_path, {"test": standard_section, "t": t_section})
 
         config_saver = ConfigSaver(config_file_path)
 
@@ -48,11 +48,11 @@ class TestConfigSaver(unittest.TestCase):
         one_another_test_section = ConfigSection()
         a_test_case_2_section = ConfigSection()
 
-        ConfigLoader(config_file_path).load_config(config_file_path, {"test": test_section,
+        ConfigLoader().load_config(config_file_path, {"test": test_section,
-                                                                      "another-test": another_test_section,
+                                                      "another-test": another_test_section,
-                                                                      "t": at_section,
+                                                      "t": at_section,
-                                                                      "one-another-test": one_another_test_section,
+                                                      "one-another-test": one_another_test_section,
-                                                                      "test-case-2": a_test_case_2_section})
+                                                      "test-case-2": a_test_case_2_section})
 
         assert test_section == standard_section
         assert at_section == t_section

test/test_fastNLP.py

@@ -54,7 +54,7 @@ def mock_cws():
     class2id = Vocabulary(need_default=False)
     label_list = ['B', 'M', 'E', 'S']
     class2id.update(label_list)
-    save_pickle(class2id, "./mock/", "class2id.pkl")
+    save_pickle(class2id, "./mock/", "label2id.pkl")
 
     model_args = {"vocab_size": len(word2id), "word_emb_dim": 50, "rnn_hidden_units": 50, "num_classes": len(class2id)}
     config_file = """
@@ -115,7 +115,7 @@ def mock_pos_tag():
     idx2label = Vocabulary(need_default=False)
     label_list = ['B-n', 'M-v', 'E-nv', 'S-adj', 'B-v', 'M-vn', 'S-adv']
     idx2label.update(label_list)
-    save_pickle(idx2label, "./mock/", "class2id.pkl")
+    save_pickle(idx2label, "./mock/", "label2id.pkl")
 
     model_args = {"vocab_size": len(vocab), "word_emb_dim": 50, "rnn_hidden_units": 50, "num_classes": len(idx2label)}
     config_file = """
@@ -163,7 +163,7 @@ def mock_text_classify():
     idx2label = Vocabulary(need_default=False)
     label_list = ['class_A', 'class_B', 'class_C', 'class_D', 'class_E', 'class_F']
     idx2label.update(label_list)
-    save_pickle(idx2label, "./mock/", "class2id.pkl")
+    save_pickle(idx2label, "./mock/", "label2id.pkl")
 
     model_args = {"vocab_size": len(vocab), "word_emb_dim": 50, "rnn_hidden_units": 50, "num_classes": len(idx2label)}
     config_file = """