def test_convert_nn_spec_to_half_precision(self):
# simple network with quantization layer
input_features = [('data', datatypes.Array(3))]
output_features = [('out', datatypes.Array(3))]
builder = NeuralNetworkBuilder(input_features, output_features)
weights = np.random.uniform(-0.5, 0.5, (3, 3))
builder.add_inner_product(name='inner_product',
W=weights,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name='data',
output_name='out')
model = MLModel(builder.spec)
spec = convert_neural_network_spec_weights_to_fp16(model.get_spec())
self.assertIsNotNone(spec)
# simple network without quantization layer
input_features = [('data', datatypes.Array(3))]
output_features = [('out', datatypes.Array(3))]
builder = NeuralNetworkBuilder(input_features, output_features)
builder.add_lrn(name='lrn',
input_name='data',
output_name='out',
alpha=2,
beta=3,
local_size=1,
k=8)
model = MLModel(builder.spec)
spec = convert_neural_network_spec_weights_to_fp16(model.get_spec())
self.assertIsNotNone(spec)
def test_lrn_converter(self):
input_dim = (3,)
output_dim = (3,)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
builder.add_lrn(name='LRN', input_name='input', output_name='output', alpha=0.5, beta=2, k=1, local_size=2)
model_onnx = convert_coreml(builder.spec)
self.assertTrue(model_onnx is not None)
def test_lrn_converter(self):
input_dim = (3, )
output_dim = (3, )
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*output_dim))]
builder = NeuralNetworkBuilder(input, output)
builder.add_lrn(name='LRN',
input_name='input',
output_name='output',
alpha=0.5,
beta=2,
k=1,
local_size=2)
context = ConvertContext()
node = LRNLayerConverter.convert(context,
builder.spec.neuralNetwork.layers[0],
['input'], ['output'])
self.assertTrue(node is not None)
def verify_lrn(input_dim, size, bias, alpha, beta):
dtype = "float32"
axis=1
a_np = np.random.uniform(size=input_dim).astype(dtype)
b_np = topi.testing.lrn_python(a_np, size, axis, bias, alpha, beta)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*b_np.shape))]
builder = NeuralNetworkBuilder(input, output)
builder.add_lrn(name='LRN',
input_name='input',
output_name='output',
alpha=alpha,
beta=beta,
k=bias,
local_size=size)
model = cm.models.MLModel(builder.spec)
for target, ctx in ctx_list():
out = run_tvm_graph(model, target, ctx, a_np, 'input', b_np.shape, dtype)
tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def verify_lrn(input_dim, size, bias, alpha, beta):
dtype = "float32"
axis=1
a_np = np.random.uniform(size=input_dim).astype(dtype)
b_np = topi.testing.lrn_python(a_np, size, axis, bias, alpha, beta)
input = [('input', datatypes.Array(*input_dim))]
output = [('output', datatypes.Array(*b_np.shape))]
builder = NeuralNetworkBuilder(input, output)
builder.add_lrn(name='LRN',
input_name='input',
output_name='output',
alpha=alpha,
beta=beta,
k=bias,
local_size=size)
model = cm.models.MLModel(builder.spec)
for target, ctx in ctx_list():
out = run_tvm_graph(model, a_np, 'input', b_np.shape, dtype)
tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
def verify_lrn(input_dim, size, bias, alpha, beta):
dtype = "float32"
axis = 1
a_np = np.random.uniform(size=input_dim).astype(dtype)
b_np = tvm.topi.testing.lrn_python(a_np, size, axis, bias, alpha, beta)
input = [("input", datatypes.Array(*input_dim))]
output = [("output", datatypes.Array(*b_np.shape))]
builder = NeuralNetworkBuilder(input, output)
builder.add_lrn(
name="LRN",
input_name="input",
output_name="output",
alpha=alpha,
beta=beta,
k=bias,
local_size=size,
)
model = cm.models.MLModel(builder.spec)
for target, dev in tvm.testing.enabled_targets():
out = run_tvm_graph(model, target, dev, a_np, "input", b_np.shape, dtype)
tvm.testing.assert_allclose(out, b_np, rtol=1e-5)
