def test_make_input_chanlast(): # load the onnx model raw_m = get_data("finn.data", "onnx/mnist-conv/model.onnx") model = ModelWrapper(raw_m) iname = model.graph.input[0].name assert tuple(model.get_tensor_shape(iname)) == (1, 1, 28, 28) model = model.transform(MakeInputChannelsLast()) assert model.graph.node[0].op_type == "Transpose" assert tuple(model.get_tensor_shape(iname)) == (1, 28, 28, 1) assert model.get_tensor_layout(iname) == data_layout.NHWC
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 == "MultiThreshold" inp_layout = model.get_tensor_layout(inp_name) assert inp_layout is None inp_layout = DataLayout.NCHW model.set_tensor_layout(inp_name, inp_layout) assert model.get_tensor_layout(inp_name) == inp_layout inp_sparsity = model.get_tensor_sparsity(inp_name) assert inp_sparsity is None inp_sparsity = {"dw": {"kernel_shape": 3}} model.set_tensor_sparsity(inp_name, inp_sparsity) assert model.get_tensor_sparsity(inp_name) == inp_sparsity 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
def test_infer_data_layouts(): raw_m = get_data("finn.data", "onnx/mnist-conv/model.onnx") model = ModelWrapper(raw_m) model = model.transform(InferShapes()) model = model.transform(FoldConstants()) model = model.transform(GiveUniqueNodeNames()) model = model.transform(GiveReadableTensorNames()) model = model.transform(InferDataLayouts()) assert model.get_tensor_layout("global_in") == DataLayout.NCHW assert model.get_tensor_layout("Conv_0_out0") == DataLayout.NCHW assert model.get_tensor_layout("MaxPool_0_out0") == DataLayout.NCHW assert model.get_tensor_layout("Reshape_0_out0") == DataLayout.NC assert model.get_tensor_layout("MatMul_0_out0") == DataLayout.NC assert model.get_tensor_layout("global_out") == DataLayout.NC model = model.transform(LowerConvsToMatMul()) model = model.transform(GiveUniqueNodeNames()) model = model.transform(GiveReadableTensorNames()) model = model.transform(InferDataLayouts()) assert model.get_tensor_layout("global_in") == DataLayout.NCHW assert model.get_tensor_layout("Transpose_0_out0") == DataLayout.NHWC assert model.get_tensor_layout("Im2Col_0_out0") == DataLayout.NHWC assert model.get_tensor_layout("MatMul_0_out0") == DataLayout.NHWC assert model.get_tensor_layout("MaxPool_0_out0") == DataLayout.NCHW assert model.get_tensor_layout("Reshape_0_out0") == DataLayout.NC assert model.get_tensor_layout("MatMul_2_out0") == DataLayout.NC assert model.get_tensor_layout("global_out") == DataLayout.NC
def test_infer_data_layouts_cnv(): cnv = get_test_model_trained("CNV", 1, 1) bo.export_finn_onnx(cnv, (1, 3, 32, 32), export_onnx_path_cnv) model = ModelWrapper(export_onnx_path_cnv) model = model.transform(InferShapes()) model = model.transform(FoldConstants()) model = model.transform(GiveUniqueNodeNames()) model = model.transform(GiveReadableTensorNames()) model = model.transform(Streamline()) model = model.transform(InferDataLayouts()) assert model.get_tensor_layout("global_in") == DataLayout.NCHW assert model.get_tensor_layout("Conv_0_out0") == DataLayout.NCHW assert model.get_tensor_layout("MaxPool_0_out0") == DataLayout.NCHW assert model.get_tensor_layout("MultiThreshold_6_out0") == DataLayout.NCHW assert model.get_tensor_layout("Reshape_0_out0") == DataLayout.NC assert model.get_tensor_layout("MatMul_0_out0") == DataLayout.NC assert model.get_tensor_layout("global_out") == DataLayout.NC model = model.transform(LowerConvsToMatMul()) model = model.transform(MakeMaxPoolNHWC()) model = model.transform(GiveUniqueNodeNames()) model = model.transform(GiveReadableTensorNames()) model = model.transform(InferDataLayouts()) assert model.get_tensor_layout("global_in") == DataLayout.NCHW assert model.get_tensor_layout("Transpose_0_out0") == DataLayout.NHWC assert model.get_tensor_layout("Im2Col_0_out0") == DataLayout.NHWC # note: im2col output isn't really NHWC or any other common layout # since the concept of channels changes with lowering... but it is # conceptually close to NHWC since the innermost dim gets multiplied assert model.get_tensor_layout("MatMul_0_out0") == DataLayout.NHWC assert model.get_tensor_layout("Transpose_1_out0") == DataLayout.NCHW assert model.get_tensor_layout("Transpose_2_out0") == DataLayout.NHWC assert model.get_tensor_layout("MaxPoolNHWC_0_out0") == DataLayout.NHWC assert model.get_tensor_layout("Reshape_0_out0") == DataLayout.NC assert model.get_tensor_layout("global_out") == DataLayout.NC model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold()) model = model.transform(ConvertBipolarMatMulToXnorPopcount()) model = model.transform(Streamline()) model = model.transform(to_hls.InferBinaryStreamingFCLayer()) model = model.transform(to_hls.InferQuantizedStreamingFCLayer()) model = model.transform(to_hls.InferConvInpGen()) model = model.transform(to_hls.InferStreamingMaxPool()) model = model.transform(GiveUniqueNodeNames()) model = model.transform(GiveReadableTensorNames()) model = model.transform(InferDataLayouts()) assert model.get_tensor_layout("global_in") == DataLayout.NCHW assert model.get_tensor_layout("Transpose_0_out0") == DataLayout.NHWC # note: im2col output isn't really NHWC or any other common layout # since the concept of channels changes with lowering... but it is # conceptually close to NHWC since the innermost dim gets multiplied assert (model.get_tensor_layout("ConvolutionInputGenerator_0_out0") == DataLayout.NHWC) assert model.get_tensor_layout( "StreamingFCLayer_Batch_3_out0") == DataLayout.NHWC assert model.get_tensor_layout("Reshape_0_out0") == DataLayout.NC assert model.get_tensor_layout( "StreamingFCLayer_Batch_6_out0") == DataLayout.NC assert model.get_tensor_layout("global_out") == DataLayout.NC os.remove(export_onnx_path_cnv)