A Classification Example in Forte Pipeline using CNN Classifier and Bert Classifier

examples/Classification_new/cnn.py

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+# Copyright 2020 The Forte Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""This file predict the ner tag for conll03 dataset."""
+
+import torch
+from texar.torch.modules.embedders import WordEmbedder
+from texar.torch.modules.classifiers.conv_classifiers import Conv1DClassifier
+from torch import nn
+from texar.torch.data import Batch
+
+def pad_each_bach(word, max_sen_len):
+    batch_size = word.shape[0]
+    curr_len = word.shape[1]
+    word_list = word.tolist()
+
+    # Line 0 in word_embedding_table is padding vec
+    for i in range(batch_size):
+        for j in range(max_sen_len-curr_len):
+            word_list[i].append(0)
+
+    return torch.LongTensor(word_list)
+
+
+class CNN_Classifier(nn.Module):
+    def __init__(self, in_channels, word_embedding_table):
+        super().__init__()
+        self.embedder = WordEmbedder(init_value=word_embedding_table)
+
+        self.classifier = \
+            Conv1DClassifier(in_channels=in_channels,
+                             in_features=word_embedding_table.size()[0])
+
+        self.max_sen_len = in_channels
+
+    def forward(self, batch: Batch):
+        word = batch["text_tag"]["data"]
+
+        word_pad = pad_each_bach(word, self.max_sen_len)
+
+        word_pad_embed = self.embedder(word_pad)
+
+        logits, pred = self.classifier(word_pad_embed)
+
+        return logits, pred
+

examples/Classification_new/main_predict_bert.py

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+# Copyright 2020 The Forte Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""This file predict the ner tag for conll03 dataset."""
+
+import yaml
+import torch
+from forte.pipeline import Pipeline
+from forte.predictor import Predictor
+from ft.onto.base_ontology import Sentence
+from forte.data.readers.imdb_reader import IMDBReader
+
+def pad_each_bach(word, max_sen_len):
+    batch_size = word.shape[0]
+    curr_len = word.shape[1]
+    word_list = word.tolist()
+
+    # Line 0 in word_embedding_table is padding vec
+    for i in range(batch_size):
+        for j in range(max_sen_len-curr_len):
+            word_list[i].append(0)
+
+    return torch.LongTensor(word_list)
+
+
+def predict_forward_fn(model, batch):
+    '''Use model and batch data to predict label.'''
+    word = batch["text_tag"]["data"]
+    logits, pred = model(pad_each_bach(word, 500))
+    pred = pred.numpy()
+    print(pred)
+    return {"label_tag": pred}
+
+
+config_predict = yaml.safe_load(open("config_predict.yml", "r"))
+saved_model = torch.load(config_predict['model_path'])
+train_state = torch.load(config_predict['train_state_path'])
+
+reader = IMDBReader()
+predictor = Predictor(batch_size=config_predict['batch_size'],
+                model=saved_model,
+                predict_forward_fn=predict_forward_fn,
+                feature_resource=train_state['feature_resource'])
+#evaluator = CoNLLNEREvaluator()
+
+pl = Pipeline()
+pl.set_reader(reader)
+pl.add(predictor)
+#pl.add(evaluator)
+pl.initialize()
+
+for pack in pl.process_dataset(config_predict['test_path']):
+    print("---- pack ----")
+    for instance in pack.get(Sentence):
+        sentence = instance.text
+        predicts = []
+        for entry in pack.get(Sentence, instance):
+            predicts.append(entry.speaker)
+        print('---- example -----')
+        print("sentence: ", sentence)
+        print("predict sentiment: ", predicts)
+    #print(evaluator.get_result())

examples/Classification_new/main_predict_cnn.py

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+# Copyright 2020 The Forte Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""This file predict the ner tag for conll03 dataset."""
+
+import yaml
+import torch
+import examples.Classification_new.cnn
+from forte.pipeline import Pipeline
+from forte.predictor import Predictor
+from ft.onto.base_ontology import Sentence
+from forte.data.readers.imdb_reader import IMDBReader
+
+
+def predict_forward_fn(model, batch):
+    '''Use model and batch data to predict label.'''
+    logits, pred = model(batch)
+    pred = pred.numpy()
+    print(pred)
+    return {"label_tag": pred}
+
+
+config_predict = yaml.safe_load(open("config_predict.yml", "r"))
+saved_model = torch.load(config_predict['model_path'])
+train_state = torch.load(config_predict['train_state_path'])
+
+reader = IMDBReader()
+predictor = Predictor(batch_size=config_predict['batch_size'],
+                model=saved_model,
+                predict_forward_fn=predict_forward_fn,
+                feature_resource=train_state['feature_resource'])
+#evaluator = CoNLLNEREvaluator()
+
+pl = Pipeline()
+pl.set_reader(reader)
+pl.add(predictor)
+#pl.add(evaluator)
+pl.initialize()
+
+for pack in pl.process_dataset(config_predict['test_path']):
+    print("---- pack ----")
+    for instance in pack.get(Sentence):
+        sentence = instance.text
+        predicts = []
+        for entry in pack.get(Sentence, instance):
+            predicts.append(entry.speaker)
+        print('---- example -----')
+        print("sentence: ", sentence)
+        print("predict sentiment: ", predicts)
+    #print(evaluator.get_result())