def test_infer_shapes():
# load the onnx model
raw_m = get_data("finn.data", "onnx/mnist-conv/model.onnx")
model = ModelWrapper(raw_m)
graph = model.graph
# multi-thresholding node to be inserted between the first Relu and MaxPool node
# get Relu node to use data
Relu_node = graph.node[3]
assert Relu_node.op_type == "Relu", "The wrong model was chosen for the check"
# create thresholds tensor as constant
mt_thresh0 = helper.make_tensor_value_info("mt_thresh0", TensorProto.FLOAT,
[8, 7])
# random numbers for the thresholds
# thresholds for one channel have to be sorted to guarantee the correct behavior
mt_thresh0_values = np.empty([8, 7], dtype=np.float32)
for i in range(len(mt_thresh0_values)):
mt_thresh0_values[i] = np.sort(np.random.random_sample(7) * 10)
model.set_initializer(mt_thresh0.name, mt_thresh0_values)
# add multi-thresholding node and change Relu node
mt_node = helper.make_node(
"MultiThreshold",
["mt_v0", "mt_thresh0"],
[Relu_node.output[0]],
domain="finn.custom_op.general",
)
Relu_node.output[0] = "mt_v0"
# explicitly remove any present shape from ReLU and MultiThreshold outputs
util.remove_by_name(model.graph.value_info, Relu_node.output[0])
util.remove_by_name(model.graph.value_info, mt_node.output[0])
graph.node.insert(4, mt_node)
# first check routine
# check if at least one shape is not specified
assert not (
model.check_all_tensor_shapes_specified()
), "All tensors are already specified before the shape inference execution"
# perform shape inference on mixed model
model = model.transform(InferShapes())
# second check routine
# now all shapes should be specified and mt_node output shape is (1,8,28,28)
assert (model.check_all_tensor_shapes_specified()
), "There are still tensors that are not specified"
assert (model.get_tensor_shape(mt_node.output[0])) == ([
1, 8, 28, 28
]), "output of multi-thresholding node has wrong shape"
def test_modelwrapper():
lfc = get_test_model_trained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
assert model.check_all_tensor_shapes_specified() is False
inp_name = model.graph.input[0].name
inp_shape = model.get_tensor_shape(inp_name)
assert inp_shape == [1, 1, 28, 28]
# find first matmul node
l0_mat_tensor_name = ""
l0_inp_tensor_name = ""
for node in model.graph.node:
if node.op_type == "MatMul":
l0_inp_tensor_name = node.input[0]
l0_mat_tensor_name = node.input[1]
break
assert l0_mat_tensor_name != ""
l0_weights = model.get_initializer(l0_mat_tensor_name)
assert l0_weights.shape == (784, 1024)
l0_weights_hist = Counter(l0_weights.flatten())
assert (l0_weights_hist[1.0] + l0_weights_hist[-1.0]) == 784 * 1024
l0_weights_rand = np.random.randn(784, 1024)
model.set_initializer(l0_mat_tensor_name, l0_weights_rand)
assert (model.get_initializer(l0_mat_tensor_name) == l0_weights_rand).all()
assert l0_inp_tensor_name != ""
inp_cons = model.find_consumer(l0_inp_tensor_name)
assert inp_cons.op_type == "MatMul"
out_prod = model.find_producer(l0_inp_tensor_name)
assert out_prod.op_type == "Sign"
os.remove(export_onnx_path)
def test_modelwrapper():
lfc = get_test_model_trained("LFC", 1, 1)
bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
assert model.check_all_tensor_shapes_specified() is False
inp_shape = model.get_tensor_shape("0")
assert inp_shape == [1, 1, 28, 28]
l0_mat_tensor_name = "33"
l0_weights = model.get_initializer(l0_mat_tensor_name)
assert l0_weights.shape == (784, 1024)
l0_weights_hist = Counter(l0_weights.flatten())
assert l0_weights_hist[1.0] == 401311 and l0_weights_hist[-1.0] == 401505
l0_weights_rand = np.random.randn(784, 1024)
model.set_initializer(l0_mat_tensor_name, l0_weights_rand)
assert (model.get_initializer(l0_mat_tensor_name) == l0_weights_rand).all()
l0_inp_tensor_name = "32"
inp_cons = model.find_consumer(l0_inp_tensor_name)
assert inp_cons.op_type == "MatMul"
out_prod = model.find_producer(l0_inp_tensor_name)
assert out_prod.op_type == "Sign"
os.remove(export_onnx_path)
def test_modelwrapper():
raw_m = get_data("finn.data", "onnx/mnist-conv/model.onnx")
model = ModelWrapper(raw_m)
assert model.check_all_tensor_shapes_specified() is True
inp_name = model.graph.input[0].name
inp_shape = model.get_tensor_shape(inp_name)
assert inp_shape == [1, 1, 28, 28]
conv_nodes = model.get_nodes_by_op_type("Conv")
matmul_nodes = model.get_nodes_by_op_type("MatMul")
assert len(conv_nodes) == 2
assert len(matmul_nodes) == 1
first_conv = conv_nodes[0]
first_conv_iname = first_conv.input[0]
first_conv_wname = first_conv.input[1]
first_conv_oname = first_conv.output[0]
assert first_conv_iname != "" and (first_conv_iname is not None)
assert first_conv_wname != "" and (first_conv_wname is not None)
assert first_conv_oname != "" and (first_conv_oname is not None)
first_conv_weights = model.get_initializer(first_conv_wname)
assert first_conv_weights.shape == (8, 1, 5, 5)
first_conv_weights_rand = np.random.randn(8, 1, 5, 5)
model.set_initializer(first_conv_wname, first_conv_weights_rand)
assert (model.get_initializer(first_conv_wname) == first_conv_weights_rand
).all()
inp_cons = model.find_consumer(first_conv_iname)
assert inp_cons == first_conv
out_prod = model.find_producer(first_conv_oname)
assert out_prod == first_conv
inp_layout = model.get_tensor_layout(first_conv_iname)
assert inp_layout is None
inp_layout = DataLayout.NCHW
model.set_tensor_layout(first_conv_iname, inp_layout)
assert model.get_tensor_layout(first_conv_iname) == inp_layout
inp_sparsity = model.get_tensor_sparsity(first_conv_iname)
assert inp_sparsity is None
inp_sparsity = {"dw": {"kernel_shape": [3, 3]}}
model.set_tensor_sparsity(first_conv_iname, inp_sparsity)
assert model.get_tensor_sparsity(first_conv_iname) == inp_sparsity
