[Linting] Run pre-commit on files

Xilinx · Aug 2, 2023 · a4c15df · a4c15df
1 parent 5942839
commit a4c15df
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Showing 4 changed files with 13 additions and 44 deletions.
diff --git a/src/finn/custom_op/fpgadataflow/fmpadding_pixel.py b/src/finn/custom_op/fpgadataflow/fmpadding_pixel.py
@@ -288,9 +288,7 @@ def pragmas(self):
         self.code_gen_dict["$PRAGMAS$"].append(
             "#pragma HLS INTERFACE axis port=out name=out_" + self.hls_sname()
         )
-        self.code_gen_dict["$PRAGMAS$"].append(
-            "#pragma HLS INTERFACE ap_ctrl_none port=return"
-        )
+        self.code_gen_dict["$PRAGMAS$"].append("#pragma HLS INTERFACE ap_ctrl_none port=return")
 
     def execute_node(self, context, graph):
         mode = self.get_nodeattr("exec_mode")

diff --git a/src/finn/transformation/fpgadataflow/infer_pixel_padding_deconv.py b/src/finn/transformation/fpgadataflow/infer_pixel_padding_deconv.py
@@ -28,10 +28,7 @@ def apply(self, model):
                 idt = model.get_tensor_datatype(deconv_input)
                 odt = model.get_tensor_datatype(deconv_output)
                 if not idt.is_integer():
-                    warnings.warn(
-                        "%s : Input is not int. Can't infer PixelPaddingDeconv."
-                        % n.name
-                    )
+                    warnings.warn("%s : Input is not int. Can't infer PixelPaddingDeconv." % n.name)
                     continue
                 # extract conv transpose parameters
                 k_h = get_by_name(n.attribute, "kernel_shape").ints[0]
@@ -86,13 +83,9 @@ def apply(self, model):
                 # Im2Col node belongs to a depthwise convolution
                 dw = False
                 if group == ifm_ch and ofm_ch == ifm_ch:
-                    W_sparse = np.zeros(
-                        (ifm_ch, ofm_ch, k_h, k_w)
-                    )  # (IFM, OFM, k_H, k_W)
+                    W_sparse = np.zeros((ifm_ch, ofm_ch, k_h, k_w))  # (IFM, OFM, k_H, k_W)
                     for ch in range(ofm_ch):
-                        W_sparse[ch][ch] = W_conv[ch][
-                            0
-                        ]  # W_conv = [IFM, OFM, k_H, k_W]
+                        W_sparse[ch][ch] = W_conv[ch][0]  # W_conv = [IFM, OFM, k_H, k_W]
                     W_conv = W_sparse.astype(np.float32)
                     # we need to store information of the
                     # sparsity of the weight matrix. For this
@@ -148,13 +141,7 @@ def apply(self, model):
                     padding = 0
 
                 # k_h=k_w==1: pointwise convolution, thus no im2col needed
-                if (
-                    k_h == 1
-                    and k_w == 1
-                    and padding == 0
-                    and stride_h == 1
-                    and stride_w == 1
-                ):
+                if k_h == 1 and k_w == 1 and padding == 0 and stride_h == 1 and stride_w == 1:
                     need_im2col = False
 
                 if need_im2col:
@@ -208,17 +195,13 @@ def apply(self, model):
                         stride=[1, 1],
                         kernel_size=[k_h, k_w],
                         pad_amount=conv_padding,
-                        input_shape="(1,{},{},{})".format(
-                            padded_odim_h, padded_odim_w, ifm_ch
-                        ),
+                        input_shape="(1,{},{},{})".format(padded_odim_h, padded_odim_w, ifm_ch),
                         depthwise=dw,
                         dilations=dilation,
                     )
 
                 # do matmul
-                matmul_node = helper.make_node(
-                    "MatMul", [matmul_input, weight_name], [matmul_out]
-                )
+                matmul_node = helper.make_node("MatMul", [matmul_input, weight_name], [matmul_out])
                 # NHWC -> NCHW
                 out_trans_node = helper.make_node(
                     "Transpose", [matmul_out], [deconv_output], perm=[0, 3, 1, 2]
@@ -237,8 +220,6 @@ def apply(self, model):
                 # remove old nodes
                 graph.node.remove(n)
 
-        model = model.transform(
-            InferConvInpGen(use_rtl_variant=self.use_convinpgen_rtl_variant)
-        )
+        model = model.transform(InferConvInpGen(use_rtl_variant=self.use_convinpgen_rtl_variant))
         model = model.transform(InferQuantizedMatrixVectorActivation())
         return (model, graph_modified)
diff --git a/tests/brevitas/test_brevitas_deconv.py b/tests/brevitas/test_brevitas_deconv.py
@@ -66,9 +66,7 @@ def test_brevitas_QTransposeConv(ifm_ch, ofm_ch, mh, mw, padding, stride, kw, bi
         bias=bias,
     )
     # outp = el(inp) # expects NCHW data format
-    export_qonnx(
-        b_deconv.cpu(), input_t=inp.cpu(), export_path=export_path, opset_version=11
-    )
+    export_qonnx(b_deconv.cpu(), input_t=inp.cpu(), export_path=export_path, opset_version=11)
     model = ModelWrapper(export_path)
     qonnx_cleanup(model)
     model = model.transform(ConvertQONNXtoFINN())

diff --git a/tests/fpgadataflow/test_fpgadataflow_deconv.py b/tests/fpgadataflow/test_fpgadataflow_deconv.py
@@ -75,9 +75,7 @@ def set_up_reference_model(idt, wdt, k, idim, ifm_ch, ofm_ch, stride, padding):
             idim_w,
         ],
     )
-    outp = helper.make_tensor_value_info(
-        "outp", TensorProto.FLOAT, [1, ofm_ch, odim_h, odim_w]
-    )
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, ofm_ch, odim_h, odim_w])
 
     W = helper.make_tensor_value_info("W", TensorProto.FLOAT, [ifm_ch, ofm_ch, k, k])
 
@@ -148,9 +146,7 @@ def test_fpgadataflow_deconv(idim, stride, ifm_ch, ofm_ch, simd, pe, k, padding)
     else:
         convinpgen_rtl = True
 
-    ref_model = set_up_reference_model(
-        idt, wdt, k, idim, ifm_ch, ofm_ch, stride, padding
-    )
+    ref_model = set_up_reference_model(idt, wdt, k, idim, ifm_ch, ofm_ch, stride, padding)
 
     odim_h = (idim_h - 1) * stride_h - 2 * padding + (k - 1) + 1
     odim_w = (idim_w - 1) * stride_w - 2 * padding + (k - 1) + 1
@@ -198,15 +194,11 @@ def test_fpgadataflow_deconv(idim, stride, ifm_ch, ofm_ch, simd, pe, k, padding)
     dataflow_model_filename = sdp_node.get_nodeattr("model")
     model = ModelWrapper(dataflow_model_filename)
     model.save("after_partition.onnx")
-    model = model.transform(
-        CreateStitchedIP(test_fpga_part, target_clk_ns, vitis=False)
-    )
+    model = model.transform(CreateStitchedIP(test_fpga_part, target_clk_ns, vitis=False))
     model = model.transform(PrepareRTLSim())
     model = model.transform(GiveReadableTensorNames())
     model = model.transform(SetExecMode("rtlsim"))
     model.save("stitched_ip.onnx")
-    y_produced = oxe.execute_onnx(model, input_dict_tr)["global_out"].transpose(
-        0, 3, 1, 2
-    )
+    y_produced = oxe.execute_onnx(model, input_dict_tr)["global_out"].transpose(0, 3, 1, 2)
     assert y_produced.shape == expected_oshape
     assert (y_produced == y_expected).all()