Merge branch 'master' of https://github.com/fastNLP/fastNLP into to_merge

# Conflicts:
#	fastNLP/core/metrics.py
#	fastNLP/core/predictor.py

.travis.yml

@@ -0,0 +1,14 @@
+language: python
+python:
+  - "3.6"
+# command to install dependencies
+install:
+  - pip install --quiet -r requirements.txt
+  - pip install pytest pytest-cov
+  - pip install -U scikit-learn
+# command to run tests
+script:
+  - pytest --cov=./
+
+after_success:
+  - codecov

README.md

@@ -1,4 +1,6 @@
 # FastNLP
+[![Build Status](https://travis-ci.org/fastnlp/fastNLP.svg?branch=master)](https://travis-ci.org/fastnlp/fastNLP)
+[![codecov](https://codecov.io/gh/fastnlp/fastNLP/branch/master/graph/badge.svg)](https://codecov.io/gh/fastnlp/fastNLP)
 ```
 FastNLP
 ├── docs

fastNLP/core/metrics.py

@@ -1,5 +1,4 @@
 import warnings
-
 import numpy as np
 import torch
 

fastNLP/core/predictor.py

@@ -84,8 +84,8 @@ class Predictor(object):
         data_iterator = iter(Batchifier(SequentialSampler(data), self.batch_size, drop_last=False))
 
         for batch_x in self.make_batch(data_iterator, use_cuda=False):
-
-            prediction = self.data_forward(network, batch_x)
+            with torch.no_grad():
+                prediction = self.data_forward(network, batch_x)
 
             self.batch_output.append(prediction)
 

fastNLP/core/tester.py

@@ -50,15 +50,16 @@ class BaseTester(object):
         step = 0
 
         for batch_x, batch_y in self.make_batch(iterator, dev_data):
-
-            prediction = self.data_forward(network, batch_x)
-
-            eval_results = self.evaluate(prediction, batch_y)
+            with torch.no_grad():
+                prediction = self.data_forward(network, batch_x)
+                eval_results = self.evaluate(prediction, batch_y)
 
             if self.save_output:
                 self.batch_output.append(prediction)
             if self.save_loss:
                 self.eval_history.append(eval_results)
+            if step % n_print == 0:
+                print('[test step: {:>4}]'.format(step))
             step += 1
 
     def prepare_input(self, data_path):
@@ -157,7 +158,7 @@ class SeqLabelTester(BaseTester):
         results = torch.Tensor(prediction).view(-1,)
         # make sure "results" is in the same device as "truth"
         results = results.to(truth)
-        accuracy = torch.sum(results == truth.view((-1,))) / results.shape[0]
+        accuracy = torch.sum(results == truth.view((-1,))).to(torch.float) / results.shape[0]
         return [loss.data, accuracy.data]
 
     def metrics(self):
@@ -176,7 +177,6 @@ class SeqLabelTester(BaseTester):
     def make_batch(self, iterator, data):
         return Action.make_batch(iterator, use_cuda=self.use_cuda, output_length=True)
 
-
 class ClassificationTester(BaseTester):
     """Tester for classification."""
 

test/test_metrics.py

@@ -1,20 +1,24 @@
 import sys, os
+
 sys.path = [os.path.join(os.path.dirname(__file__), '..')] + sys.path
 
-from fastNLP.action import metrics
+from fastNLP.core import metrics
 from sklearn import metrics as skmetrics
 import unittest
 import numpy as np
 from numpy import random
 
+
 def generate_fake_label(low, high, size):
     return random.randint(low, high, size), random.randint(low, high, size)
 
+
 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]:
@@ -22,12 +26,12 @@ class TestMetrics(unittest.TestCase):
                     ans = skmetrics.accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight)
                     test = metrics.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)
             labels = list(range(y_true.shape[1])) if len(y_true.shape) >= 2 else None
-            ans = skmetrics.recall_score(y_true, y_pred,labels=labels, average=None)
+            ans = skmetrics.recall_score(y_true, y_pred, labels=labels, average=None)
             test = metrics.recall_score(y_true, y_pred, labels=labels, average=None)
             ans = list(ans)
             if not isinstance(test, list):
@@ -45,7 +49,7 @@ class TestMetrics(unittest.TestCase):
         for y_true, y_pred in self.fake_data:
             # print(y_true.shape)
             labels = list(range(y_true.shape[1])) if len(y_true.shape) >= 2 else None
-            ans = skmetrics.precision_score(y_true, y_pred,labels=labels, average=None)
+            ans = skmetrics.precision_score(y_true, y_pred, labels=labels, average=None)
             test = metrics.precision_score(y_true, y_pred, labels=labels, average=None)
             ans, test = list(ans), list(test)
             for a, b in zip(test, ans):
@@ -56,12 +60,12 @@ class TestMetrics(unittest.TestCase):
         ans = skmetrics.precision_score(y_true, y_pred)
         test = metrics.precision_score(y_true, y_pred)
         self.assertAlmostEqual(ans, test, delta=self.delta)
-    
+
     def test_precision_score(self):
         for y_true, y_pred in self.fake_data:
             # print(y_true.shape)
             labels = list(range(y_true.shape[1])) if len(y_true.shape) >= 2 else None
-            ans = skmetrics.precision_score(y_true, y_pred,labels=labels, average=None)
+            ans = skmetrics.precision_score(y_true, y_pred, labels=labels, average=None)
             test = metrics.precision_score(y_true, y_pred, labels=labels, average=None)
             ans, test = list(ans), list(test)
             for a, b in zip(test, ans):
@@ -77,7 +81,7 @@ class TestMetrics(unittest.TestCase):
         for y_true, y_pred in self.fake_data:
             # print(y_true.shape)
             labels = list(range(y_true.shape[1])) if len(y_true.shape) >= 2 else None
-            ans = skmetrics.f1_score(y_true, y_pred,labels=labels, average=None)
+            ans = skmetrics.f1_score(y_true, y_pred, labels=labels, average=None)
             test = metrics.f1_score(y_true, y_pred, labels=labels, average=None)
             ans, test = list(ans), list(test)
             for a, b in zip(test, ans):
@@ -89,5 +93,6 @@ class TestMetrics(unittest.TestCase):
         test = metrics.f1_score(y_true, y_pred)
         self.assertAlmostEqual(ans, test, delta=self.delta)
 
+
 if __name__ == '__main__':
-    unittest.main()
+    unittest.main()