From 6af98efea5681026d2d158fa138f1bb4e487afaa Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Tue, 26 Jul 2022 16:39:28 +0200
Subject: [PATCH] [Test] broaden scope of Downsampler tests
---
.../test_fpgadataflow_downsampler.py | 41 +++++++++++++------
1 file changed, 29 insertions(+), 12 deletions(-)
diff --git a/tests/fpgadataflow/test_fpgadataflow_downsampler.py b/tests/fpgadataflow/test_fpgadataflow_downsampler.py
index 8b5c5c77f..11e5cca28 100644
--- a/tests/fpgadataflow/test_fpgadataflow_downsampler.py
+++ b/tests/fpgadataflow/test_fpgadataflow_downsampler.py
@@ -50,17 +50,24 @@ from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
-def build_model(is_1d, in_dim, k, stride, dt_in, dt_w, pad_half=0):
+def build_model(is_1d, in_dim, k, stride, dt_in, dt_w, pad_half=0, flip_1d=False):
np.random.seed(0)
out_dim = compute_conv_output_dim(in_dim, k, stride, 2 * pad_half)
ifm = 8
ofm = 16
if is_1d:
- shape_in = [1, ifm, in_dim, 1]
- shape_out = [1, ofm, out_dim, 1]
- shape_k = [k, 1]
- shape_s = [stride, 1]
- shape_p = [pad_half, 0, pad_half, 0]
+ if flip_1d:
+ shape_in = [1, ifm, 1, in_dim]
+ shape_out = [1, ofm, 1, out_dim]
+ shape_k = [1, k]
+ shape_s = [1, stride]
+ shape_p = [0, pad_half, 0, pad_half]
+ else:
+ shape_in = [1, ifm, in_dim, 1]
+ shape_out = [1, ofm, out_dim, 1]
+ shape_k = [k, 1]
+ shape_s = [stride, 1]
+ shape_p = [pad_half, 0, pad_half, 0]
else:
shape_in = [1, ifm, in_dim, in_dim]
shape_out = [1, ofm, out_dim, out_dim]
@@ -101,19 +108,25 @@ def build_model(is_1d, in_dim, k, stride, dt_in, dt_w, pad_half=0):
return model
-@pytest.mark.parametrize("is_1d", [False])
-@pytest.mark.parametrize("exec_mode", ["cppsim"])
-def test_fpgadataflow_downsampler(is_1d, exec_mode):
+@pytest.mark.parametrize("is_1d", [True, False])
+@pytest.mark.parametrize("flip_1d", [True, False])
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+def test_fpgadataflow_downsampler(is_1d, flip_1d, exec_mode):
+ if flip_1d and not is_1d:
+ pytest.skip("flip_1d only applicable for is_1d")
in_dim = 32
k = 1
stride = 2
dt_in = DataType["UINT8"]
dt_w = DataType["INT2"]
- model = build_model(is_1d, in_dim, k, stride, dt_in, dt_w)
+ model = build_model(
+ is_1d, in_dim, k, stride, dt_in, dt_w, pad_half=0, flip_1d=flip_1d
+ )
inp = gen_finn_dt_tensor(dt_in, model.get_tensor_shape("in0"))
idict = {"in0": inp}
y_expected = execute_onnx(model, idict)["out0"]
model = model.transform(to_hls.InferConvInpGen())
+ assert len(model.get_nodes_by_op_type("DownSampler")) == 1
if exec_mode == "cppsim":
model = model.transform(SetExecMode("cppsim"))
model = model.transform(PrepareCppSim())
@@ -127,14 +140,18 @@ def test_fpgadataflow_downsampler(is_1d, exec_mode):
else:
raise Exception("Unknown exec_mode")
y_produced = execute_onnx(model, idict)["out0"]
- assert len(model.get_nodes_by_op_type("DownSampler")) == 1
assert (y_produced == y_expected).all()
-
if exec_mode == "rtlsim":
node = model.get_nodes_by_op_type("DownSampler")[0]
inst = getCustomOp(node)
cycles_rtlsim = inst.get_nodeattr("cycles_rtlsim")
exp_cycles_dict = model.analysis(exp_cycles_per_layer)
exp_cycles = exp_cycles_dict[node.name]
+ # small adjustment for 2D testcase due to how rtlsim works:
+ # output is finished before all pixels are read, since last
+ # row is dropped (rtlsim finishes based on # of expected
+ # pixels)
+ if not is_1d:
+ exp_cycles = exp_cycles - in_dim
assert np.isclose(exp_cycles, cycles_rtlsim, atol=10)
assert exp_cycles != 0
--
GitLab