def test_state_dict_url(self, framework, should_be_available):
multi_layer_encoder = enc.alexnet_multi_layer_encoder(pretrained=False)
if should_be_available:
assert isinstance(multi_layer_encoder.state_dict_url(framework), str)
else:
with pytest.raises(RuntimeError):
multi_layer_encoder.state_dict_url(framework)
def test_alexnet_multi_layer_encoder_smoke(subtests):
multi_layer_encoder = enc.alexnet_multi_layer_encoder(pretrained=False)
assert isinstance(multi_layer_encoder,
enc.alexnet.AlexNetMultiLayerEncoder)
with subtests.test("repr"):
assert isinstance(multi_layer_encoder,
enc.alexnet.AlexNetMultiLayerEncoder)
def test_alexnet_multi_layer_encoder_weights():
weights = "weights"
with pytest.warns(UserWarning):
multi_layer_encoder = enc.alexnet_multi_layer_encoder(
pretrained=False, weights=weights, internal_preprocessing=False)
assert multi_layer_encoder.framework == weights
def test_alexnet_multi_layer_encoder_state_dict_url(subtests, frameworks):
def should_be_available(framework):
return framework == "torch"
multi_layer_encoder = enc.alexnet_multi_layer_encoder(pretrained=False)
for framework in frameworks:
with subtests.test(framework=framework):
if should_be_available(framework):
assert isinstance(
multi_layer_encoder.state_dict_url(framework), str)
else:
with pytest.raises(RuntimeError):
multi_layer_encoder.state_dict_url(framework)
def test_AlexNetMultiLayerEncoder(self):
asset = self.load_asset(path.join("enc", "alexnet"))
multi_layer_encoder = enc.alexnet_multi_layer_encoder(
weights="torch", preprocessing=False, allow_inplace=False)
layers = tuple(multi_layer_encoder.children_names())
with torch.no_grad():
encs = multi_layer_encoder(asset.input.image, layers)
actual = dict(
zip(
layers,
[
pystiche.TensorKey(x, precision=asset.params.precision)
for x in encs
],
))
desired = asset.output.enc_keys
self.assertDictEqual(actual, desired)
def test_alexnet_multi_layer_encoder(enc_asset_loader):
asset = enc_asset_loader("alexnet")
multi_layer_encoder = enc.alexnet_multi_layer_encoder(pretrained=True,
weights="torch",
preprocessing=False,
allow_inplace=False)
layers = tuple(multi_layer_encoder.children_names())
with torch.no_grad():
encs = multi_layer_encoder(asset.input.image, layers)
actual = dict(
zip(
layers,
[
pystiche.TensorKey(x, precision=asset.params.precision)
for x in encs
],
))
desired = asset.output.enc_keys
assert actual == desired
def test_main(self, enc_asset_loader):
asset = enc_asset_loader("alexnet")
multi_layer_encoder = enc.alexnet_multi_layer_encoder(
pretrained=True, weights="torch", preprocessing=False, allow_inplace=False
)
layers = tuple(multi_layer_encoder.children_names())
with torch.no_grad():
encs = multi_layer_encoder(asset.input.image, layers)
actual = dict(
zip(
layers,
[pystiche.TensorKey(x, precision=asset.params.precision) for x in encs],
)
)
desired = asset.output.enc_keys
assert actual == desired
@pytest.mark.parametrize(
("framework", "should_be_available"),
[
pytest.param(framework, should_be_available, id=framework)
for framework, should_be_available in [
("torch", True),
("caffe", False),
]
],
)
def test_state_dict_url(self, framework, should_be_available):
multi_layer_encoder = enc.alexnet_multi_layer_encoder(pretrained=False)
if should_be_available:
assert isinstance(multi_layer_encoder.state_dict_url(framework), str)
else:
with pytest.raises(RuntimeError):
multi_layer_encoder.state_dict_url(framework)
def test_load_state_dict_smoke(self):
model = models.alexnet(pretrained=False)
state_dict = model.state_dict()
multi_layer_encoder = enc.alexnet_multi_layer_encoder()
multi_layer_encoder.load_state_dict(state_dict)
def test_repr_smoke(self):
multi_layer_encoder = enc.alexnet_multi_layer_encoder(pretrained=False)
assert isinstance(repr(multi_layer_encoder), str)
def test_main_smoke(self):
multi_layer_encoder = enc.alexnet_multi_layer_encoder(pretrained=False)
assert isinstance(multi_layer_encoder, enc.alexnet.AlexNetMultiLayerEncoder)
def test_alexnet_multi_layer_encoder_smoke(self):
multi_layer_encoder = enc.alexnet_multi_layer_encoder()
self.assertIsInstance(multi_layer_encoder,
enc.alexnet.MultiLayerAlexNetEncoder)