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Fix test resources and List type handling in test_dl_tensorflow #728

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76 changes: 39 additions & 37 deletions python/src/pywy/tests/test_dl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,53 +26,55 @@
from pywy.basic.model.optimizer import GradientDescent
from pywy.basic.model.option import Option
from pywy.basic.model.models import DLModel
from importlib import resources
from pywy.tests import resources as resources_folder


# TODO: fix this test by giving it proper test resources & fixing some type issues with lists.
@pytest.mark.skip(reason="no way of currently testing this, since we are missing implementations for proper test resources & types in types.py")
def test_dl_tensorflow():
l1 = Linear(4, 64, True)
s1 = Sigmoid()
l2 = Linear(64, 3, True)
with resources.path(resources_folder, "sample_data.md") as resource_path, \
resources.path(resources_folder, "wordcount_out_python.txt") as output_path:
l1 = Linear(4, 64, True)
s1 = Sigmoid()
l2 = Linear(64, 3, True)

s1.with_ops(l1.with_ops(Input(Input.Type.FEATURES)))
l2.with_ops(s1)
s1.with_ops(l1.with_ops(Input(Input.Type.FEATURES)))
l2.with_ops(s1)

model = DLModel(l2)
model = DLModel(l2)

criterion = CrossEntropyLoss(3)
criterion.with_ops(
Input(Input.Type.PREDICTED),
Input(Input.Type.LABEL, Op.DType.INT32)
)
acc = Mean(0)
acc.with_ops(
Cast(Op.DType.FLOAT32).with_ops(
Eq().with_ops(
ArgMax(1).with_ops(
Input(Input.Type.PREDICTED)
),
Input(Input.Type.LABEL, Op.DType.INT32)
criterion = CrossEntropyLoss(3)
criterion.with_ops(
Input(Input.Type.PREDICTED),
Input(Input.Type.LABEL, Op.DType.INT32)
)
acc = Mean(0)
acc.with_ops(
Cast(Op.DType.FLOAT32).with_ops(
Eq().with_ops(
ArgMax(1).with_ops(
Input(Input.Type.PREDICTED)
),
Input(Input.Type.LABEL, Op.DType.INT32)
)
)
)
)

optimizer = GradientDescent(0.02)
option = Option(criterion, optimizer, 6, 100)
optimizer = GradientDescent(0.02)
option = Option(criterion, optimizer, 6, 100)

floats: List[List[int]] = [[5.1, 3.5, 1.4, 0.2]]
floats: List[List[float]] = [[5.1, 3.5, 1.4, 0.2]]

ints: List[List[int]] = [[0, 0, 1, 1, 2, 2]]
ints: List[List[int]] = [[0, 0, 1, 1, 2, 2]]

ctx = WayangContext() \
.register({JavaPlugin, SparkPlugin, TensorflowPlugin})
trainXSource = ctx.textfile("file:///var/www/html/README.md").map(lambda x: floats, str, List[List[float]])
trainYSource = ctx.textfile("file:///var/www/html/README.md").map(lambda x: floats, str, List[List[float]])
testXSource = ctx.textfile("file:///var/www/html/README.md").map(lambda x: floats, str, List[List[float]])
ctx = WayangContext() \
.register({JavaPlugin, SparkPlugin, TensorflowPlugin})
trainXSource = ctx.textfile(f"file://{resource_path}").map(lambda x: floats, str, List[List[float]])
trainYSource = ctx.textfile(f"file://{resource_path}").map(lambda x: floats, str, List[List[float]])
testXSource = ctx.textfile(f"file://{resource_path}").map(lambda x: floats, str, List[List[float]])

data_quanta = trainXSource.dlTraining(model, option, trainYSource, List[List[float]], List[List[float]]) \
.predict(testXSource, List[List[float]], List[List[float]]) \
.map(lambda x: "Test", List[List[float]], str) \
.store_textfile("file:///var/www/html/data/wordcount-out-python.txt", List[float])

assert data_quanta is not None
data_quanta = trainXSource.dlTraining(model, option, trainYSource, List[List[float]], List[List[float]]) \
.predict(testXSource, List[List[float]], List[List[float]]) \
.map(lambda x: "Test", List[List[float]], str) \
.store_textfile(f"file://{output_path}", str)
assert data_quanta is not None
4 changes: 2 additions & 2 deletions python/src/pywy/types.py
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Original file line number Diff line number Diff line change
Expand Up @@ -194,9 +194,9 @@ def typecheck(input_type: Type[ConstrainedOperatorType]):
origin = get_origin(input_type)
args = get_args(input_type)

if isinstance(input_type, List) and args:
if origin is list and args:
typecheck(args[0])
elif isinstance(input_type, Tuple):
elif origin is tuple:
if all(arg in allowed_types for arg in args):
return
else:
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