def test_server_integration(csv_filename):
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
# Resnet encoder
input_features = [
image_feature(folder=image_dest_folder,
preprocessing={
'in_memory': True,
'height': 8,
'width': 8,
'num_channels': 3
},
fc_size=16,
num_filters=8),
text_feature(encoder='embed', min_len=1),
numerical_feature(normalization='zscore')
]
output_features = [category_feature(vocab_size=2), numerical_feature()]
rel_path = generate_data(input_features, output_features, csv_filename)
model, output_dir = train_model(input_features,
output_features,
data_csv=rel_path)
app = server(model)
client = TestClient(app)
response = client.get('/')
assert response.status_code == 200
response = client.post('/predict')
assert response.json() == ALL_FEATURES_PRESENT_ERROR
data_df = read_csv(rel_path)
first_entry = data_df.T.to_dict()[0]
data, files = convert_to_form(first_entry)
server_response = client.post('/predict', data=data, files=files)
server_response = server_response.json()
server_response_keys = sorted(list(server_response.keys()))
assert server_response_keys == sorted(output_keys_for(output_features))
model_output, _ = model.predict(dataset=[first_entry], data_format=dict)
model_output = model_output.to_dict('records')[0]
assert model_output == server_response
shutil.rmtree(output_dir, ignore_errors=True)
shutil.rmtree(image_dest_folder)
def test_server_integration(csv_filename):
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), 'generated_images')
# Resnet encoder
input_features = [
image_feature(
folder=image_dest_folder,
encoder='resnet',
preprocessing={
'in_memory': True,
'height': 8,
'width': 8,
'num_channels': 3
},
fc_size=16,
num_filters=8
),
text_feature(encoder='embed', min_len=1),
numerical_feature(normalization='zscore')
]
output_features = [
category_feature(vocab_size=2, reduce_input='sum'),
numerical_feature()
]
rel_path = generate_data(input_features, output_features, csv_filename)
model = train_model(input_features, output_features, data_csv=rel_path)
app = server(model)
client = TestClient(app)
response = client.post('/predict')
assert response.json() == ALL_FEATURES_PRESENT_ERROR
data_df = read_csv(rel_path)
data, files = convert_to_form(data_df.T.to_dict()[0])
response = client.post('/predict', data=data, files=files)
response_keys = sorted(list(response.json().keys()))
assert response_keys == sorted(output_keys_for(output_features))
shutil.rmtree(model.exp_dir_name, ignore_errors=True)
shutil.rmtree(image_dest_folder)
def test_server_integration_with_audio(single_record, csv_filename):
# Audio Inputs
audio_dest_folder = os.path.join(os.getcwd(), "generated_audio")
# Resnet encoder
input_features = [
audio_feature(
folder=audio_dest_folder,
),
text_feature(encoder="embed", min_len=1),
numerical_feature(normalization="zscore"),
]
output_features = [category_feature(vocab_size=4), numerical_feature()]
rel_path = generate_data(input_features, output_features, csv_filename)
model, output_dir = train_model(input_features, output_features, data_csv=rel_path)
app = server(model)
client = TestClient(app)
response = client.get("/")
assert response.status_code == 200
response = client.post("/predict")
# expect the HTTP 400 error code for this situation
assert response.status_code == 400
assert response.json() == ALL_FEATURES_PRESENT_ERROR
data_df = read_csv(rel_path)
if single_record:
# Single record prediction
first_entry = data_df.T.to_dict()[0]
data, files = convert_to_form(first_entry)
server_response = client.post("/predict", data=data, files=files)
assert server_response.status_code == 200
server_response = server_response.json()
server_response_keys = sorted(list(server_response.keys()))
assert server_response_keys == sorted(output_keys_for(output_features))
model_output, _ = model.predict(dataset=[first_entry], data_format=dict)
model_output = model_output.to_dict("records")[0]
assert model_output == server_response
else:
# Batch prediction
assert len(data_df) > 1
files = convert_to_batch_form(data_df)
server_response = client.post("/batch_predict", files=files)
assert server_response.status_code == 200
server_response = server_response.json()
server_response_keys = sorted(server_response["columns"])
assert server_response_keys == sorted(output_keys_for(output_features))
assert len(data_df) == len(server_response["data"])
model_output, _ = model.predict(dataset=data_df)
model_output = model_output.to_dict("split")
assert model_output == server_response
# Cleanup
shutil.rmtree(output_dir, ignore_errors=True)
shutil.rmtree(audio_dest_folder, ignore_errors=True)
def test_server_integration_with_images(csv_filename):
# Image Inputs
image_dest_folder = os.path.join(os.getcwd(), "generated_images")
# Resnet encoder
input_features = [
image_feature(
folder=image_dest_folder,
preprocessing={"in_memory": True, "height": 8, "width": 8, "num_channels": 3},
fc_size=16,
num_filters=8,
),
text_feature(encoder="embed", min_len=1),
numerical_feature(normalization="zscore"),
]
output_features = [category_feature(vocab_size=4), numerical_feature()]
np.random.seed(123) # reproducible synthetic data
rel_path = generate_data(input_features, output_features, csv_filename)
model, output_dir = train_model(input_features, output_features, data_csv=rel_path)
app = server(model)
client = TestClient(app)
response = client.get("/")
assert response.status_code == 200
response = client.post("/predict")
# expect the HTTP 400 error code for this situation
assert response.status_code == 400
assert response.json() == ALL_FEATURES_PRESENT_ERROR
data_df = read_csv(rel_path)
# One-off prediction
first_entry = data_df.T.to_dict()[0]
data, files = convert_to_form(first_entry)
server_response = client.post("/predict", data=data, files=files)
assert server_response.status_code == 200
server_response = server_response.json()
server_response_keys = sorted(list(server_response.keys()))
assert server_response_keys == sorted(output_keys_for(output_features))
model_output, _ = model.predict(dataset=[first_entry], data_format=dict)
model_output = model_output.to_dict("records")[0]
assert model_output == server_response
# Batch prediction
assert len(data_df) > 1
files = convert_to_batch_form(data_df)
server_response = client.post("/batch_predict", files=files)
assert server_response.status_code == 200
server_response = server_response.json()
server_response_keys = sorted(server_response["columns"])
assert server_response_keys == sorted(output_keys_for(output_features))
assert len(data_df) == len(server_response["data"])
model_output, _ = model.predict(dataset=data_df)
model_output = model_output.to_dict("split")
assert model_output == server_response
# Cleanup
shutil.rmtree(output_dir, ignore_errors=True)
shutil.rmtree(image_dest_folder, ignore_errors=True)