Skip all high mem test

PsychoinformaticsLab · Jan 18, 2021 · 02963d0 · 02963d0
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commit 02963d0
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Showing 4 changed files with 31 additions and 16 deletions.
diff --git a/pliers/tests/extractors/test_audio_extractors.py b/pliers/tests/extractors/test_audio_extractors.py
@@ -375,7 +375,8 @@ def test_percussion_extractor():
 @pytest.mark.parametrize('hop_size', [0.1, 1])
 @pytest.mark.parametrize('top_n', [5, 10])
 @pytest.mark.parametrize('target_sr', [22000, 14000])
-@pytest.mark.forked
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 def test_audioset_extractor(hop_size, top_n, target_sr):
     verify_dependencies(['tensorflow'])
 

diff --git a/pliers/tests/extractors/test_misc_extractors.py b/pliers/tests/extractors/test_misc_extractors.py
@@ -70,7 +70,8 @@ def dummy_list(array):
     assert r_lambda_df['custom_function'][0] == -4
 
 
-@pytest.mark.forked
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 def test_metric_er_as_stim():
     stim = ComplexTextStim(text = 'This is [MASK] test')
     ext_bert = BertLMExtractor(return_softmax=True)

diff --git a/pliers/tests/extractors/test_model_extractors.py b/pliers/tests/extractors/test_model_extractors.py
@@ -32,7 +32,8 @@
 SPEECH_URL = 'https://tfhub.dev/google/speech_embedding/1'
 
 
-@pytest.mark.forked
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 def test_tensorflow_keras_application_extractor():
     imgs = [join(IMAGE_DIR, f) for f in ['apple.jpg', 'obama.jpg']]
     imgs = [ImageStim(im, onset=4.2, duration=1) for im in imgs]
@@ -51,7 +52,8 @@ def test_tensorflow_keras_application_extractor():
     with pytest.raises(ValueError):
         TensorFlowKerasApplicationExtractor(architecture='foo')
 
-
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 @pytest.mark.forked
 def test_tfhub_image():
     stim = ImageStim(join(IMAGE_DIR, 'apple.jpg'))
@@ -61,19 +63,21 @@ def test_tfhub_image():
                for i in range(1000) ])
     assert np.argmax(np.array([df['feature_' + str(i)][0] \
                      for i in range(1000)])) == 948
-
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 
 @pytest.mark.forked
 def test_tfhub_image_reshape():
     stim = ImageStim(join(IMAGE_DIR, 'apple.jpg'))
     stim2 = ImageStim(join(IMAGE_DIR, 'obama.jpg'))
-    ext = TFHubImageExtractor(MNET_URL, 
+    ext = TFHubImageExtractor(MNET_URL,
                               reshape_input=(224,224,3),
                               features='feature_vector')
     df = merge_results(ext.transform([stim, stim2]),
                        extractor_names=False)
     assert df.shape[0] == 2
-    assert all([len(v) == 1280 for v in df['feature_vector']])
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')(v) == 1280 for v in df['feature_vector']])
 
 
 @pytest.mark.forked
@@ -82,7 +86,8 @@ def test_tfhub_text():
     ext = TFHubTextExtractor(SENTENC_URL, output_key=None)
     df = ext.transform(stim).to_df()
     assert all([f'feature_{i}' in df.columns for i in range(512)])
-    true = hub.KerasLayer(SENTENC_URL)([stim.text])[0,10].numpy()
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')sLayer(SENTENC_URL)([stim.text])[0,10].numpy()
     assert np.isclose(df['feature_10'][0], true)
 
 
@@ -96,7 +101,8 @@ def test_tfhub_text_one_feature():
     assert df.shape[0] == len(cstim.elements)
     true = hub.KerasLayer(GNEWS_URL)([cstim.elements[3].text])[0,2].numpy()
     assert np.isclose(df['embedding'][3][2], true)
-    with pytest.raises(ValueError) as err:
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')ses(ValueError) as err:
         TFHubTextExtractor(GNEWS_URL, output_key='key').transform(stim)
     assert 'not a dictionary' in str(err.value)
 
@@ -116,7 +122,8 @@ def test_tfhub_text_transformer_sentence():
                 ['pooled_output'][0,20].numpy()
     assert np.isclose(df['sent_encoding'][5][20], true)
     with pytest.raises(ValueError) as err:
-        TFHubTextExtractor(ELECTRA_URL,
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')tractor(ELECTRA_URL,
                            preprocessor_url_or_path=TOKENIZER_URL,
                            output_key='key').transform(stim)
     assert 'Check which keys' in str(err.value)
@@ -128,7 +135,8 @@ def test_tfhub_text_transformer_tokens():
     tkn_ext = TFHubTextExtractor(ELECTRA_URL,
                                  features='token_encodings',
                                  output_key='sequence_output',
-                                 preprocessor_url_or_path=TOKENIZER_URL)
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')              preprocessor_url_or_path=TOKENIZER_URL)
     tkn_df = merge_results(tkn_ext.transform(cstim.elements[:3]),
                            extractor_names=False)
     assert all([tkn_df['token_encodings'][i].shape == (128, 256) \

diff --git a/pliers/tests/extractors/test_text_extractors.py b/pliers/tests/extractors/test_text_extractors.py
@@ -273,7 +273,8 @@ def test_spacy_doc_extractor():
     assert result['is_sentenced'][3]
 
 
-@pytest.mark.forked
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 def test_bert_extractor():
     stim = ComplexTextStim(text='This is not a tokenized sentence.')
     stim_file = ComplexTextStim(join(TEXT_DIR, 'sentence_with_header.txt'))
@@ -320,7 +321,8 @@ def test_bert_extractor():
     del res, res_token, res_file, ext_base, ext_base_token
 
 
-@pytest.mark.forked
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 @pytest.mark.parametrize('model', ['bert-large-uncased',
                                    'distilbert-base-uncased',
                                    'roberta-base','camembert-base'])
@@ -342,7 +344,8 @@ def test_bert_other_models(model):
     del res, stim
 
 
-@pytest.mark.forked
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 def test_bert_sequence_extractor():
     stim = ComplexTextStim(text='This is not a tokenized sentence.')
     stim_file = ComplexTextStim(join(TEXT_DIR, 'sentence_with_header.txt'))
@@ -395,7 +398,8 @@ def test_bert_sequence_extractor():
     del ext_pooler, res_cls, res_max, res_pooler, res_sequence, res_file, stim
 
 
-@pytest.mark.forked
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 def test_bert_LM_extractor():
     stim = ComplexTextStim(text='This is not a tokenized sentence.')
     stim_masked = ComplexTextStim(text='This is MASK tokenized sentence.')
@@ -478,7 +482,8 @@ def test_bert_LM_extractor():
         res_threshold, res_default, res_return_mask
 
 
-@pytest.mark.forked
+@pytest.mark.skipif(environ.get('CI', False) == 'true',
+                    reason='high memory')
 def test_bert_sentiment_extractor():
     stim = ComplexTextStim(text='This is the best day of my life.')
     stim_file = ComplexTextStim(join(TEXT_DIR, 'sentence_with_header.txt'))