def test_two_component_invalid_dependencies_fail(lightning_squeezenet1_1_obj):
comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
comp2 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
with pytest.raises(RuntimeError, match="Cannot create cycle"):
comp1.inputs["tag"] << comp1.outputs.predicted_tag
with pytest.raises(RuntimeError, match="Cannot create cycle"):
comp1.inputs.tag << comp1.outputs["predicted_tag"]
with pytest.raises(AttributeError):
comp1.inputs["tag"] >> comp2.inputs["label"]
with pytest.raises(AttributeError):
comp1.inputs.tag >> comp2.inputs.label
with pytest.raises(AttributeError):
comp1.inputs["tag"] >> comp2.outputs["label"]
with pytest.raises(AttributeError):
comp1.inputs.tag >> comp2.outputs.label
with pytest.raises(TypeError):
comp2.outputs["predicted_tag"] >> comp1.outputs["predicted_tag"]
with pytest.raises(TypeError):
comp2.outputs.predicted_tag >> comp1.outputs.predicted_tag
class Foo:
def __init__(self):
pass
foo = Foo()
with pytest.raises(TypeError):
comp1.inputs["tag"] >> foo
def test_component_initialization(lightning_squeezenet1_1_obj):
with pytest.raises(TypeError):
ClassificationInferenceComposable(wrongname=lightning_squeezenet1_1_obj)
comp = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
assert comp.uid == "callnum_1"
assert hasattr(comp.inputs, "img")
assert hasattr(comp.inputs, "tag")
assert hasattr(comp.outputs, "predicted_tag")
assert hasattr(comp.outputs, "cropped_img")
assert "img" in comp.inputs
assert "predicted_tag" in comp.outputs
def test_composition_recieve_wrong_arg_type(lightning_squeezenet1_1_obj):
# no endpoints or components
with pytest.raises(TypeError):
_ = Composition(hello="world")
# no endpoints multiple components
from tests.core.serve.models import ClassificationInferenceComposable
comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
comp2 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
with pytest.raises(ValueError):
_ = Composition(c1=comp1, c2=comp2)
def test_complex_spec_single_endpoint(tmp_path, lightning_squeezenet1_1_obj):
from tests.core.serve.models import ClassificationInferenceComposable
comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
comp2 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
comp3 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
comp1.outputs.predicted_tag >> comp3.inputs.tag # skipcq: PYL-W0104
comp2.outputs.cropped_img >> comp3.inputs.img # skipcq: PYL-W0104
comp1.outputs.predicted_tag >> comp2.inputs.tag # skipcq: PYL-W0104
ep = Endpoint(
route="/predict",
inputs={
"img_1": comp1.inputs.img,
"img_2": comp2.inputs.img,
"tag_1": comp1.inputs.tag,
},
outputs={"prediction": comp3.outputs.predicted_tag},
)
composit = Composition(comp1=comp1,
comp2=comp2,
comp3=comp3,
predict_compositon_ep=ep)
connections = [str(c) for c in composit.connections]
assert connections == [
"callnum_1.outputs.predicted_tag >> callnum_3.inputs.tag",
"callnum_1.outputs.predicted_tag >> callnum_2.inputs.tag",
"callnum_2.outputs.cropped_img >> callnum_3.inputs.img",
]
assert composit.component_uid_names == {
"callnum_1": "comp1",
"callnum_2": "comp2",
"callnum_3": "comp3",
}
actual_endpoints = {k: asdict(v) for k, v in composit.endpoints.items()}
assert actual_endpoints == {
"predict_compositon_ep": {
"inputs": {
"img_1": "callnum_1.inputs.img",
"img_2": "callnum_2.inputs.img",
"tag_1": "callnum_1.inputs.tag",
},
"outputs": {
"prediction": "callnum_3.outputs.predicted_tag",
},
"route": "/predict",
}
}
def test_model_compute_dependencies(lightning_squeezenet1_1_obj):
comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
comp2 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
comp1.inputs.tag << comp2.outputs.predicted_tag
res = [{
"source_component": "callnum_2",
"source_key": "predicted_tag",
"target_component": "callnum_1",
"target_key": "tag",
}]
assert list(map(lambda x: x._asdict(),
comp1._flashserve_meta_.connections)) == res
assert list(comp2._flashserve_meta_.connections) == []
def test_create_invalid_endpoint(lightning_squeezenet1_1_obj):
from flash.core.serve import Endpoint
comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
with pytest.raises(TypeError, match="route parameter must be type"):
_ = Endpoint(
route=b"/INVALID",
inputs={"inp": comp1.inputs.img},
outputs={"out": comp1.outputs.cropped_img},
)
with pytest.raises(ValueError, match="route must begin with"):
_ = Endpoint(
route="hello",
inputs={"inp": comp1.inputs.img},
outputs={"out": comp1.outputs.cropped_img},
)
with pytest.raises(TypeError, match="inputs k=inp, v=b'INVALID'"):
_ = Endpoint(
route="/hello",
inputs={"inp": b"INVALID"},
outputs={"out": comp1.outputs.cropped_img},
)
with pytest.raises(TypeError, match="k=out, v=b'INVALID'"):
_ = Endpoint(route="/hello", inputs={"inp": comp1.inputs.img}, outputs={"out": b"INVALID"})
def test_cycle_in_connection_fails(session_global_datadir,
lightning_squeezenet1_1_obj):
from tests.core.serve.models import ClassificationInferenceComposable
c1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
with pytest.raises(RuntimeError):
c1.outputs.cropped_img >> c1.inputs.img
def test_component_parameters(lightning_squeezenet1_1_obj):
comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
comp2 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
with pytest.raises(TypeError):
# Immutability test
comp1.inputs["newkey"] = comp2.inputs["tag"]
first_tag = comp1.outputs["predicted_tag"]
second_tag = comp2.inputs["tag"]
assert isinstance(first_tag.datatype, Label)
assert first_tag.connections == []
first_tag >> second_tag
assert str(first_tag.connections[0]) == ("callnum_1.outputs.predicted_tag >> callnum_2.inputs.tag")
assert second_tag.connections == []
assert first_tag.connections == comp1._flashserve_meta_.connections
def test_connection_invalid_raises(lightning_squeezenet1_1_obj):
comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
with pytest.raises(RuntimeError, match="Cannot compose a parameters of same components"):
comp1.outputs["predicted_tag"] >> comp1.outputs["predicted_tag"]
class FakeParam:
position = "outputs"
fake_param = FakeParam()
with pytest.raises(TypeError, match="Can only Compose another `Parameter`"):
comp1.outputs.predicted_tag >> fake_param
def test_composit_endpoint_data(lightning_squeezenet1_1_obj):
from tests.core.serve.models import ClassificationInferenceComposable
comp = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
composit = Composition(comp=comp)
assert composit.component_uid_names == {"callnum_1": "comp"}
assert composit.connections == []
actual_endpoints = {k: asdict(v) for k, v in composit.endpoints.items()}
assert actual_endpoints == {
"classify_ENDPOINT": {
"inputs": {
"img": "callnum_1.inputs.img",
"tag": "callnum_1.inputs.tag"
},
"outputs": {
"cropped_img": "callnum_1.outputs.cropped_img",
"predicted_tag": "callnum_1.outputs.predicted_tag",
},
"route": "/classify",
}
}
ep = Endpoint(
route="/predict",
inputs={
"label_1": comp.inputs.img,
"tag_1": comp.inputs.tag,
},
outputs={
"prediction": comp.outputs.predicted_tag,
"cropped": comp.outputs.cropped_img,
},
)
composit = Composition(comp=comp, predict_ep=ep)
actual_endpoints = {k: asdict(v) for k, v in composit.endpoints.items()}
assert actual_endpoints == {
"predict_ep": {
"inputs": {
"label_1": "callnum_1.inputs.img",
"tag_1": "callnum_1.inputs.tag"
},
"outputs": {
"cropped": "callnum_1.outputs.cropped_img",
"prediction": "callnum_1.outputs.predicted_tag",
},
"route": "/predict",
}
}
def test_model_compute_call_method(lightning_squeezenet1_1_obj):
comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
img = torch.arange(195075).reshape((1, 255, 255, 3))
tag = None
out_res, out_img = comp1(img, tag)
assert out_res.item() == 753
def test_endpoint_errors_on_wrong_key_name(lightning_squeezenet1_1_obj):
from tests.core.serve.models import ClassificationInferenceComposable
comp1 = ClassificationInferenceComposable(lightning_squeezenet1_1_obj)
# input key does not exist
with pytest.raises(AttributeError):
_ = Endpoint(
route="/predict",
inputs={
"label_1": comp1.inputs.img,
"tag_1": comp1.inputs.DOESNOTEXIST,
},
outputs={
"prediction": comp1.outputs.predicted_tag,
"cropped": comp1.outputs.cropped_img,
},
)
# output key does not exist
with pytest.raises(AttributeError):
_ = Endpoint(
route="/predict",
inputs={
"label_1": comp1.inputs.img,
"tag_1": comp1.inputs.tag,
},
outputs={
"prediction": comp1.outputs.predicted_tag,
"cropped": comp1.outputs.DOESNOTEXIST,
},
)
# output key does not exist
ep = Endpoint(
route="/predict",
inputs={
"label_1": comp1.inputs.img,
"tag_1": comp1.inputs.tag,
},
outputs={
"prediction": comp1.outputs.predicted_tag,
"cropped": "callnum_1.outputs.DOESNOTEXIST",
},
)
with pytest.raises(AttributeError):
_ = Composition(comp1=comp1, predict_ep=ep)
# input function does not exist
ep = Endpoint(
route="/predict",
inputs={
"label_1": comp1.inputs.img,
"tag_1": "DOESNOTEXIST.inputs.tag",
},
outputs={
"prediction": comp1.outputs.predicted_tag,
"cropped": comp1.outputs.cropped_img,
},
)
with pytest.raises(AttributeError):
_ = Composition(comp1=comp1, predict_ep=ep)
# output function does not exist
ep = Endpoint(
route="/predict",
inputs={
"label_1": comp1.inputs.img,
"tag_1": comp1.inputs.tag,
},
outputs={
"prediction": comp1.outputs.predicted_tag,
"cropped": "DOESNOTEXIST.outputs.cropped_img",
},
)
with pytest.raises(AttributeError):
_ = Composition(comp1=comp1, predict_ep=ep)
def test_start_server_from_composition(tmp_path, squeezenet_servable, session_global_datadir):
from tests.core.serve.models import ClassificationInferenceComposable
squeezenet_gm, _ = squeezenet_servable
comp1 = ClassificationInferenceComposable(squeezenet_gm)
comp2 = ClassificationInferenceComposable(squeezenet_gm)
comp3 = ClassificationInferenceComposable(squeezenet_gm)
comp1.outputs.predicted_tag >> comp3.inputs.tag # skipcq: PYL-W0104
comp2.outputs.cropped_img >> comp3.inputs.img # skipcq: PYL-W0104
comp1.outputs.predicted_tag >> comp2.inputs.tag # skipcq: PYL-W0104
ep1 = Endpoint(
route="/predict",
inputs={
"img_1": comp1.inputs.img,
"img_2": comp2.inputs.img,
"tag_1": comp1.inputs.tag,
},
outputs={"prediction": comp3.outputs.predicted_tag},
)
ep2 = Endpoint(
route="/other_predict",
inputs={
"img_1": comp1.inputs.img,
"img_2": comp2.inputs.img,
"tag_1": comp1.inputs.tag,
},
outputs={
"prediction_3": comp3.outputs.predicted_tag,
"prediction_2": comp2.outputs.cropped_img,
},
)
composit = Composition(
comp1=comp1,
comp2=comp2,
comp3=comp3,
predict_compositon_ep=ep1,
other_predict_ep=ep2,
TESTING=True,
DEBUG=True,
)
with (session_global_datadir / "cat.jpg").open("rb") as f:
cat_imgstr = base64.b64encode(f.read()).decode("UTF-8")
with (session_global_datadir / "fish.jpg").open("rb") as f:
fish_imgstr = base64.b64encode(f.read()).decode("UTF-8")
data = {
"session": "session_uuid",
"payload": {
"img_1": {
"data": cat_imgstr
},
"img_2": {
"data": fish_imgstr
},
"tag_1": {
"label": "stingray"
},
},
}
expected_response = {
"result": {
"prediction": "goldfish, Carassius auratus"
},
"session": "session_uuid",
}
app = composit.serve(host="0.0.0.0", port=8000)
with TestClient(app) as tc:
res = tc.post("http://127.0.0.1:8000/predict", json=data)
assert res.status_code == 200
assert res.json() == expected_response
