diff --git a/tests/fpgadataflow/test_convert_to_hls_conv_layer.py b/tests/fpgadataflow/test_convert_to_hls_conv_layer.py
index ee65326ec57fb7fa7fa0490a8980dbabb8efc13c..22c356a5869b25fcc7ae3ef0164ed61b53ef232c 100644
--- a/tests/fpgadataflow/test_convert_to_hls_conv_layer.py
+++ b/tests/fpgadataflow/test_convert_to_hls_conv_layer.py
@@ -5,10 +5,15 @@ import pytest
from finn.core.datatype import DataType
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.infer_datatypes import InferDataTypes
-from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
-from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames
from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.util.basic import gen_finn_dt_tensor
@@ -17,47 +22,40 @@ import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.custom_op.im2col import compute_conv_output_dim
+# conv_config kernel_size,stride, pad
-@pytest.mark.parametrize("padding", [True, False])
-@pytest.mark.parametrize("kernel_size", [3, 5])
+
+@pytest.mark.parametrize(
+ "conv_config", [(1, 2, 0), (1, 3, 0), (3, 2, 1), (3, 1, 0), (3, 1, 1), (5, 2, 1)]
+)
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
@pytest.mark.slow
@pytest.mark.vivado
-def test_convert_to_hls_conv_layer(padding, kernel_size):
-
- assert (
- kernel_size % 2 != 0
- ), """test_convert_to_hls_conv_layer test only
- supports odd kernel_size"""
-
+def test_convert_to_hls_conv_layer(conv_config, exec_mode):
+ kernel_size, stride, pad = conv_config
np.random.seed(0)
- padding = True
idt = DataType.UINT4
in_feature_dim = 7
- in_chn = 3
+ in_chn = 16
+ out_chn = 20
- stages = 1 # just one convolution
-
- out_feature_dim = (
- in_feature_dim if padding else in_feature_dim - (kernel_size // 2 * 2) * stages
- )
+ out_feature_dim = compute_conv_output_dim(in_feature_dim, kernel_size, stride, pad)
input_shape = [1, in_chn, in_feature_dim, in_feature_dim]
- output_shape = [1, in_chn, out_feature_dim, out_feature_dim]
+ output_shape = [1, out_chn, out_feature_dim, out_feature_dim]
- conv_param_shape = [in_chn, in_chn, kernel_size, kernel_size]
+ conv_param_shape = [out_chn, in_chn, kernel_size, kernel_size]
+ conv_weight_dt = DataType.UINT4
conv_config = {}
conv_config["dilations"] = [1, 1]
conv_config["group"] = 1
conv_config["kernel_shape"] = [kernel_size, kernel_size]
- if padding:
- pad = kernel_size // 2
- conv_config["pads"] = [pad, pad, pad, pad]
- else:
- conv_config["pads"] = [0, 0, 0, 0]
- conv_config["strides"] = [1, 1]
+ conv_config["pads"] = [pad, pad, pad, pad]
+ conv_config["strides"] = [stride, stride]
top_in = helper.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
top_out = helper.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
@@ -80,27 +78,35 @@ def test_convert_to_hls_conv_layer(padding, kernel_size):
model = ModelWrapper(modelproto)
model.set_tensor_datatype("top_in", idt)
model.set_tensor_datatype("top_out", idt)
- model.set_tensor_datatype("p1", DataType.UINT4)
+ model.set_tensor_datatype("p1", conv_weight_dt)
+ model.set_initializer("p1", gen_finn_dt_tensor(conv_weight_dt, conv_param_shape))
model = model.transform(InferShapes())
- model.set_initializer(
- "p1", np.round(np.random.rand(*conv_param_shape).astype(np.float32) * 16)
- )
-
- model.set_tensor_datatype(model.graph.input[0].name, idt)
- model = model.transform(InferShapes())
- model = model.transform(InferDataLayouts())
- model = model.transform(GiveUniqueNodeNames())
- model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataTypes())
new_model = model.transform(LowerConvsToMatMul())
new_model = new_model.transform(to_hls.InferConvInpGen())
- new_model = new_model.transform(PrepareCppSim())
- new_model = new_model.transform(CompileCppSim())
- new_model = new_model.transform(SetExecMode("cppsim"))
+ new_model = new_model.transform(GiveUniqueNodeNames())
+ new_model = new_model.transform(InferShapes())
+ new_model = new_model.transform(InferDataTypes())
+
+ if exec_mode == "cppsim":
+ new_model = new_model.transform(PrepareCppSim())
+ new_model = new_model.transform(CompileCppSim())
+ new_model = new_model.transform(SetExecMode("cppsim"))
+ elif exec_mode == "rtlsim":
+ new_model = new_model.transform(SetExecMode("rtlsim"))
+ new_model = new_model.transform(GiveUniqueNodeNames())
+ new_model = new_model.transform(PrepareIP("xc7z020clg400-1", 5))
+ new_model = new_model.transform(HLSSynthIP())
+ new_model = new_model.transform(ReplaceVerilogRelPaths())
+ new_model = new_model.transform(PrepareRTLSim())
+ else:
+ raise Exception("Unknown exec_mode")
x = gen_finn_dt_tensor(idt, input_shape)
inp_dict = {model.graph.input[0].name: x}
assert oxe.compare_execution(model, new_model, inp_dict)
+ if kernel_size == 1 and stride > 1 and pad == 0:
+ assert new_model.graph.node[1].op_type == "DownSampler"