From 63f52f19db7078b23c7a2e0516c1cdaccaa4dc35 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Wed, 24 Nov 2021 11:27:16 +0100
Subject: [PATCH] [Test] enable 1D tests + restructure
convert_to_hls_pool_batch
---
.../test_convert_to_hls_pool_batch.py | 64 +++++++++++++------
1 file changed, 44 insertions(+), 20 deletions(-)
diff --git a/tests/fpgadataflow/test_convert_to_hls_pool_batch.py b/tests/fpgadataflow/test_convert_to_hls_pool_batch.py
index fa6848990..0dd9991b2 100644
--- a/tests/fpgadataflow/test_convert_to_hls_pool_batch.py
+++ b/tests/fpgadataflow/test_convert_to_hls_pool_batch.py
@@ -137,7 +137,7 @@ def prepare_inputs(input_tensor):
# number of out channel computed in parallel
@pytest.mark.parametrize("pe", [1, 2, 4])
# pool type
-@pytest.mark.parametrize("op_type", ["QuantAvgPool2d", "MaxPool"])
+@pytest.mark.parametrize("op_type", ["QuantAvgPool2d", "MaxPool", "MaxPool1D"])
# execution mode
@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
@pytest.mark.slow
@@ -156,7 +156,14 @@ def test_convert_to_hls_pool_batch(
np.random.seed(0)
ofm_dim = int(((ifm_dim + 2 * pad - k) / stride) + 1)
- x = gen_finn_dt_tensor(idt, (1, ifm_ch, ifm_dim, ifm_dim))
+ ishape = (1, ifm_ch, ifm_dim, ifm_dim)
+ use_1d = False
+ if op_type == "MaxPool1D":
+ use_1d = True
+ ishape = (1, ifm_ch, 1, ifm_dim)
+ op_type = "MaxPool"
+
+ x = gen_finn_dt_tensor(idt, ishape)
# prepare input data
input_dict = prepare_inputs(x)
if op_type == "MaxPool":
@@ -168,7 +175,7 @@ def test_convert_to_hls_pool_batch(
pytest.skip("Skipping Maxpool with idt != odt")
model = make_single_maxpool_modelwrapper(
- k, stride, pad, ifm_ch, ifm_dim, ofm_dim, idt
+ k, stride, pad, ifm_ch, ifm_dim, ofm_dim, idt, use_1d
)
elif op_type == "QuantAvgPool2d":
if pad != 0:
@@ -187,16 +194,40 @@ def test_convert_to_hls_pool_batch(
new_model = model.transform(to_hls.InferPool_Batch())
new_model = new_model.transform(GiveUniqueNodeNames())
- if ifm_ch != pe:
- new_model = new_model.transform(to_hls.InferConvInpGen())
- # Folding
- for n in new_model.graph.node:
- if n.op_type == "ConvolutionInputGenerator":
- inst = getCustomOp(n)
- inst.set_nodeattr("SIMD", pe)
- elif n.op_type == "Pool_Batch":
- inst = getCustomOp(n)
- inst.set_nodeattr("PE", pe)
+ new_model = new_model.transform(to_hls.InferConvInpGen())
+ # Folding
+ for n in new_model.graph.node:
+ if n.op_type.startswith("ConvolutionInputGenerator"):
+ inst = getCustomOp(n)
+ inst.set_nodeattr("SIMD", pe)
+ elif n.op_type == "Pool_Batch":
+ inst = getCustomOp(n)
+ inst.set_nodeattr("PE", pe)
+
+ if stride <= k:
+ if pad == 0:
+ assert len(new_model.graph.node) == 4
+ assert new_model.graph.node[0].op_type == "Transpose"
+ assert new_model.graph.node[1].op_type.startswith(
+ "ConvolutionInputGenerator"
+ )
+ assert new_model.graph.node[2].op_type == "Pool_Batch"
+ assert new_model.graph.node[3].op_type == "Transpose"
+ else:
+ assert len(new_model.graph.node) == 5
+ assert new_model.graph.node[0].op_type == "Transpose"
+ assert new_model.graph.node[1].op_type == "FMPadding_Batch"
+ assert new_model.graph.node[2].op_type.startswith(
+ "ConvolutionInputGenerator"
+ )
+ assert new_model.graph.node[3].op_type == "Pool_Batch"
+ assert new_model.graph.node[4].op_type == "Transpose"
+ else:
+ # not currently converted to HLS, node stays as-is
+ assert len(new_model.graph.node) == 1
+ assert new_model.graph.node[0].op_type in ["MaxPool", "QuantAvgPool2d"]
+ # no need to exec
+ return
if exec_mode == "cppsim":
new_model = new_model.transform(SetExecMode("cppsim"))
@@ -214,13 +245,6 @@ def test_convert_to_hls_pool_batch(
# execute new_model
y_produced = oxe.execute_onnx(new_model, input_dict)["outp"]
assert (y_produced == y_expected).all()
- if stride <= k:
- if pad == 0 or ifm_ch == pe:
- assert len(new_model.graph.node) == 4
- else:
- assert len(new_model.graph.node) == 5
- else:
- assert len(new_model.graph.node) == 1
if exec_mode == "rtlsim":
node = new_model.get_nodes_by_op_type("Pool_Batch")[0]
--
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