def test_video_activations(self):
if not self.have_fixtures:
return
# Use /tmp for test fixtures
from _pytest.monkeypatch import MonkeyPatch
monkeypatch = MonkeyPatch()
TEST_TEMPDIR = os.path.join(
testconf.TEST_TEMPDIR_ROOT,
'test_bdd100k_mobilenet')
testconf.use_tempdir(monkeypatch, TEST_TEMPDIR)
from au.fixtures import nnmodel
from au.fixtures.tf import mobilenet
with testutils.LocalSpark.sess() as spark:
TestVideoFrameTable.setup(spark=spark)
class TestTable(nnmodel.ActivationsTable):
TABLE_NAME = 'bdd100k_mobilenet_activations_test'
NNMODEL_CLS = mobilenet.Mobilenet
MODEL_PARAMS = mobilenet.Mobilenet.Small()
IMAGE_TABLE_CLS = TestVideoFrameTable
TestTable.MODEL_PARAMS.INFERENCE_BATCH_SIZE = 10
TestTable.setup(spark=spark)
def test_mnist_igraph(monkeypatch):
testconf.use_tempdir(monkeypatch, TEST_TEMPDIR)
# TODO: model fixture
params = mnist.MNIST.Params()
params.TRAIN_EPOCHS = 1
params.LIMIT = 10
model = mnist.MNIST.load_or_train(params)
igraph = model.get_inference_graph()
assert igraph != nnmodel.TFInferenceGraphFactory()
# params = mnist.MNIST.Params()
# params.LIMIT = 100 # num images
mnist.MNISTDataset.setup(params=params)
rows = list(mnist.MNISTDataset.iter_all_rows())
filler = nnmodel.FillActivationsTFDataset(model=model)
out_rows = list(filler(rows))
assert len(out_rows) == len(rows)
for row in out_rows:
acts = row.attrs['activations']
act = acts[0]
assert act.model_name == igraph.model_name
tensor_to_value = act.tensor_to_value
for tensor_name in model.igraph.output_names:
assert tensor_name in tensor_to_value
# Check that we have a non-empty array
assert tensor_to_value[tensor_name].shape
def test_mnist_dataset(monkeypatch):
testconf.use_tempdir(monkeypatch, TEST_TEMPDIR)
params = mnist.MNIST.Params()
params.LIMIT = 100
mnist.MNISTDataset.setup(params=params)
rows = mnist.MNISTDataset.get_rows_by_uris(
('mnist_train_0', 'mnist_test_0', 'not_in_mnist'))
assert len(rows) == 2
rows = sorted(rows)
assert rows[0].uri == 'mnist_test_0'
assert rows[1].uri == 'mnist_train_0'
expected_bytes = open(testconf.MNIST_TEST_IMG_PATH, 'rb').read()
assert rows[0].image_bytes == expected_bytes
mnist.MNISTDataset.save_datasets_as_png(params=params)
TEST_PATH = os.path.join(
TEST_TEMPDIR, 'data/MNIST/test/MNIST-test-label_7-mnist_test_0.png')
assert os.path.exists(TEST_PATH)
import imageio
expected = imageio.imread(testconf.MNIST_TEST_IMG_PATH)
import numpy as np
image = imageio.imread(TEST_PATH)
np.testing.assert_array_equal(image, expected)
def setUpClass(cls):
cls.have_fixtures = False
try:
mscoco.Fixtures.create_test_fixtures()
cls.have_fixtures = True
except Exception as e:
print "Failed to create test fixtures: %s" % (e, )
from _pytest.monkeypatch import MonkeyPatch
monkeypatch = MonkeyPatch()
TEST_TEMPDIR = os.path.join(testconf.TEST_TEMPDIR_ROOT, 'test_mscoco')
testconf.use_tempdir(monkeypatch, TEST_TEMPDIR)
def test_mobilenet_activation_tables(monkeypatch):
testconf.use_tempdir(monkeypatch, TEST_TEMPDIR)
dataset.ImageTable.setup()
with testutils.LocalSpark.sess() as spark:
for params_cls in mobilenet.Mobilenet.ALL_PARAMS_CLSS:
params = params_cls()
class TestTable(nnmodel.ActivationsTable):
TABLE_NAME = 'Mobilenet_test_' + params_cls.__name__
NNMODEL_CLS = mobilenet.Mobilenet
MODEL_PARAMS = params
IMAGE_TABLE_CLS = dataset.ImageTable
TestTable.setup(spark=spark)
def _create_fixture(monkeypatch):
TEST_TEMPDIR = os.path.join(testconf.TEST_TEMPDIR_ROOT, 'sobel_test')
testconf.use_tempdir(monkeypatch, TEST_TEMPDIR)
dataset.ImageTable.setup()
model = Sobel.load_or_train()
filler = nnmodel.FillActivationsTFDataset(model=model)
rows = list(dataset.ImageTable.iter_all_rows())
assert rows
class Fixture(object):
pass
fixture = Fixture()
fixture.rows = rows
fixture.filler = filler
fixture.model = model
return fixture
def test_mobilenet_inference_graph(monkeypatch):
testconf.use_tempdir(monkeypatch, TEST_TEMPDIR)
dataset.ImageTable.setup()
params = mobilenet.Mobilenet.Small()
model = mobilenet.Mobilenet.load_or_train(params)
igraph = model.get_inference_graph()
rows = dataset.ImageTable.iter_all_rows()
filler = nnmodel.FillActivationsTFDataset(model=model)
out_rows = list(filler(rows))
# Test for smoke: logits for each image should be different ;)
all_preds = set()
for row in out_rows:
acts = row.attrs['activations']
tensor_to_value = acts[0].tensor_to_value
for tensor_name in model.igraph.output_names:
assert tensor_name in tensor_to_value
# Check that we have a non-empty array
assert tensor_to_value[tensor_name].shape
# If weights fail to load, the net will predict uniformly for
# everything. Make sure that doesn't happen!
if tensor_name == 'MobilenetV2/Predictions/Reshape_1:0':
preds = tuple(tensor_to_value[tensor_name])
assert preds not in all_preds
all_preds.add(preds)
# The Small model consistently gets this one right
assert '202228408_eccfe4790e.jpg' in ' '.join(
row.uri for row in out_rows)
if '202228408_eccfe4790e.jpg' in row.uri:
from slim.datasets import imagenet
label_map = imagenet.create_readable_names_for_imagenet_labels(
)
predicted = label_map[np.array(preds).argmax()]
assert predicted == 'soccer ball'