def get_encoder(model_path): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True return ImageOnnxEncoder(output_feature='mobilenetv20_features_relu1_fwd', model_path=model_path, metas=metas)
def test_metas_workspace_replica_peas(tmpdir, replica_id, pea_id): metas = get_default_metas() metas['workspace'] = str(tmpdir) metas['name'] = 'test' metas['replica_id'] = replica_id metas['pea_id'] = pea_id return metas
def get_encoder(): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True return BigTransferEncoder(model_path='pretrained', channel_axis=1, metas=metas)
def metas(tmpdir): os.environ['TEST_WORKSPACE'] = str(tmpdir) metas = get_default_metas() metas['workspace'] = os.environ['TEST_WORKSPACE'] metas['name'] = 'faiss_idx' yield metas del os.environ['TEST_WORKSPACE']
def test_metas(tmpdir, random_workspace_name): os.environ[random_workspace_name] = str(tmpdir) metas = get_default_metas() metas['workspace'] = os.environ[random_workspace_name] if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True yield metas del os.environ[random_workspace_name]
def test_metas(tmpdir, random_workspace_name): from jina.executors.metas import get_default_metas os.environ[random_workspace_name] = str(tmpdir) metas = get_default_metas() metas['workspace'] = os.environ[random_workspace_name] yield metas del os.environ[random_workspace_name]
def encoder(tmpdir): model_path = 'models/vision/classification/mobilenet/model/mobilenetv2-7.onnx' metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True metas['workspace'] = str(tmpdir) return ImageOnnxEncoder(output_feature='mobilenetv20_features_relu1_fwd', model_path=model_path, metas=metas)
def get_encoder(): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True path = tempfile.NamedTemporaryFile().name model = ExampleNet() torch.save(model, path) return CustomImageTorchEncoder(model_path=path, layer_name='conv1', metas=metas)
def get_encoder(): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True path = tempfile.NamedTemporaryFile().name model = TestNet().create_model() model.save(path) return CustomKerasImageEncoder(channel_axis=1, model_path=path, layer_name='dense', metas=metas)
def get_encoder(model_path_tmp_dir): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True metas['workspace'] = model_path_tmp_dir path = os.path.join(model_path_tmp_dir, 'model.pth') model = ExampleNet() torch.save(model, path) return CustomImageTorchEncoder(model_path=path, layer_name='conv1', metas=metas)
def get_encoder(model_path_tmp_dir): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True metas['workspace'] = model_path_tmp_dir path = os.path.join(model_path_tmp_dir, 'model.pth') model = TestNet().create_model() model.save(path) return CustomKerasImageEncoder(channel_axis=1, model_path=path, layer_name='dense', metas=metas)
def test_encoding_results(tmpdir): target_output_dim = 512 batch_size = 10 signal_length = 1024 test_data = np.random.randn(batch_size, signal_length).astype('f') metas = get_default_metas() metas['workspace'] = str(tmpdir) encoder = Wav2VecSpeechEncoder(model_path='/tmp/wav2vec_large.pt', input_sample_rate=16000, metas=metas) encoded_data = encoder.encode(test_data) assert encoded_data.shape[0] == batch_size assert encoded_data.shape[1] % target_output_dim == 0
def test_save_load_config(tmp_path): transforms = ['VerticalFlip', {'Resize': {'width': 200, 'height': 300}}] metas = get_default_metas() metas['workspace'] = str(tmp_path) orig_crafter = AC(transforms, metas=metas) orig_crafter.save_config() orig_trs = orig_crafter.transforms._to_dict() load_crafter1 = BaseExecutor.load_config('tests/config.yaml') load_crafter2 = BaseExecutor.load_config(orig_crafter.config_abspath) assert orig_trs == load_crafter1.transforms._to_dict() assert orig_trs == load_crafter2.transforms._to_dict()
def test_save_load_config(tmp_path): from jina.executors import BaseExecutor from jina.executors.metas import get_default_metas transforms = [{'RandomVerticalFlip': dict(p=1.0)}] metas = get_default_metas() metas['workspace'] = str(tmp_path) orig_crafter = ImageTorchTransformation(transforms, metas=metas) orig_crafter.save_config() orig_trs = orig_crafter.transforms_specification load_crafter1 = BaseExecutor.load_config( os.path.join(cur_dir, '../tests/config.yaml')) load_crafter2 = BaseExecutor.load_config(orig_crafter.config_abspath) assert orig_trs == load_crafter1.transforms_specification assert orig_trs == load_crafter2.transforms_specification
def get_encoder(*args, **kwargs): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True return VideoTorchEncoder(metas=metas)
def test_bad_metas_workspace(tmpdir): metas = get_default_metas() return metas
def test_metas_workspace_simple(tmpdir): metas = get_default_metas() metas['workspace'] = str(tmpdir) metas['name'] = 'test' return metas
def get_metas(): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True return metas
def test_metas(tmp_path): metas = get_default_metas() metas['workspace'] = str(tmp_path) if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True yield metas
from jina.executors import BaseExecutor from jina.executors.metas import get_default_metas def rm_files(tmp_files): for k in tmp_files: if os.path.exists(k): if os.path.isfile(k): os.remove(k) elif os.path.isdir(k): shutil.rmtree(k, ignore_errors=False, onerror=None) os.environ['TEST_WORKDIR'] = os.getcwd() metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True encoders = [ TransformerTorchEncoder( pretrained_model_name_or_path='bert-base-uncased', model_save_path='bert-base-uncased', metas=metas), TransformerTorchEncoder( pooling_strategy='mean', pretrained_model_name_or_path='bert-base-uncased', model_save_path='bert-base-uncased-mean', metas=metas), TransformerTorchEncoder( pooling_strategy='min',
def test_set_is_trained_meta(): metas = get_default_metas() metas['is_trained'] = True executor = BaseExecutor(metas=metas) assert executor.is_trained
def get_encoder(): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True return VideoPaddleEncoder(metas=metas)
def metas(tmpdir): metas = get_default_metas() metas['workspace'] = tmpdir metas['dump_path'] = os.path.join(tmpdir, 'dump') yield metas
def test_set_batch_size(): batch_size = 325 metas = get_default_metas() metas['batch_size'] = batch_size indexer = NumpyIndexer(index_filename=f'test.gz', metas=metas) assert indexer.batch_size == batch_size
def get_encoder(): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True return ImageKerasEncoder(channel_axis=1, metas=metas)
def test_metas(tmpdir, random_workspace_name): os.environ[random_workspace_name] = str(tmpdir) metas = get_default_metas() metas['workspace'] = os.environ[random_workspace_name] yield metas del os.environ[random_workspace_name]
def get_encoder(): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True return ImageTorchEncoder(metas=metas)
def metas(tmpdir): metas = get_default_metas() metas['workspace'] = str(tmpdir) yield metas
def metas(tmpdir): metas = get_default_metas() if 'JINA_TEST_GPU' in os.environ: metas['on_gpu'] = True metas['workspace'] = str(tmpdir) yield metas
def test_set_dummy_meta(): dummy = 325 metas = get_default_metas() metas['dummy'] = dummy executor = BaseExecutor(metas=metas) assert executor.dummy == dummy