From 05f11518f765b3167566263eb062e9588366c922 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Thu, 14 Jul 2022 15:28:43 +0200
Subject: [PATCH] [Test] add 1D upsample testcase, currently fails
---
tests/fpgadataflow/test_fpgadataflow_upsampler.py | 11 ++++++++---
1 file changed, 8 insertions(+), 3 deletions(-)
diff --git a/tests/fpgadataflow/test_fpgadataflow_upsampler.py b/tests/fpgadataflow/test_fpgadataflow_upsampler.py
index d1ef0b890..534e1ce50 100644
--- a/tests/fpgadataflow/test_fpgadataflow_upsampler.py
+++ b/tests/fpgadataflow/test_fpgadataflow_upsampler.py
@@ -117,7 +117,7 @@ class PyTorchTestModel(nn.Module):
# param datatype
@pytest.mark.parametrize("dt", [DataType["INT8"]])
-# Width/height of square input feature map
+# spatial dim input feature map
@pytest.mark.parametrize("IFMDim", [3, 5])
# upscaling factor
@pytest.mark.parametrize("scale", [2, 3])
@@ -125,14 +125,19 @@ class PyTorchTestModel(nn.Module):
@pytest.mark.parametrize("NumChannels", [4])
# execution mode
@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+# whether to use 1D or 2D square testcases
+@pytest.mark.parametrize("is_1d", [False, True])
@pytest.mark.fpgadataflow
@pytest.mark.vivado
@pytest.mark.slow
-def test_fpgadataflow_upsampler(dt, IFMDim, scale, NumChannels, exec_mode):
+def test_fpgadataflow_upsampler(dt, IFMDim, scale, NumChannels, exec_mode, is_1d):
atol = 1e-3
# Create the test model and inputs for it
torch_model = PyTorchTestModel(upscale_factor=scale)
- input_shape = (1, NumChannels, IFMDim, IFMDim)
+ if is_1d:
+ input_shape = (1, NumChannels, IFMDim, 1)
+ else:
+ input_shape = (1, NumChannels, IFMDim, IFMDim)
test_in = torch.arange(0, np.prod(np.asarray(input_shape)))
# Limit the input to values valid for the given datatype
test_in %= dt.max() - dt.min() + 1
--
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