def test_can_cast_dtype(self):
# Ensure that the data loader can only be used once
def load_data():
yield {"X": np.ones((1, 1), dtype=np.float32)}
cache = DataLoaderCache(load_data())
fp32_meta = TensorMetadata().add("X", dtype=np.float32, shape=(1, 1))
cache.set_input_metadata(fp32_meta)
feed_dict = cache[0]
assert feed_dict["X"].dtype == np.float32
fp64_meta = TensorMetadata().add("X", dtype=np.float64, shape=(1, 1))
cache.set_input_metadata(fp64_meta)
feed_dict = cache[0]
assert feed_dict["X"].dtype == np.float64
def test_will_not_give_up_on_first_cache_miss(self):
SHAPE = (32, 32)
DATA = [OrderedDict()]
DATA[0]["X"] = np.zeros(SHAPE, dtype=np.int64)
DATA[0]["Y"] = np.zeros(SHAPE, dtype=np.int64)
cache = DataLoaderCache(DATA)
cache.set_input_metadata(TensorMetadata().add("X", np.int64, shape=SHAPE).add("Y", np.int64, SHAPE))
# Populate the cache with bad X but good Y
cache.cache[0] = OrderedDict()
cache.cache[0]["X"] = np.ones((64, 64), dtype=np.int64)
cache.cache[0]["Y"] = np.ones(SHAPE, dtype=np.int64)
feed_dict = cache[0]
# Cache cannot reuse X, so it'll reload - we'll get all 0s from the data loader
assert np.all(feed_dict["X"] == 0)
# Cache can reuse Y, even though it's after X, so we'll get ones from the cache
assert np.all(feed_dict["Y"] == 1)