From a81fc1e6e970bc27734f6495a0b86f3bdfa11159 Mon Sep 17 00:00:00 2001
From: Tobi-Alonso <tobi.alonso@gmail.com>
Date: Wed, 1 Jul 2020 09:43:59 +0100
Subject: [PATCH] [Test] Add test cases for QuantAvgPool2d to
test_convert_to_hls_pool_batch
---
.../test_convert_to_hls_pool_batch.py | 71 +++++++++++++++++--
1 file changed, 65 insertions(+), 6 deletions(-)
diff --git a/tests/fpgadataflow/test_convert_to_hls_pool_batch.py b/tests/fpgadataflow/test_convert_to_hls_pool_batch.py
index c9f78dcea..809147be2 100644
--- a/tests/fpgadataflow/test_convert_to_hls_pool_batch.py
+++ b/tests/fpgadataflow/test_convert_to_hls_pool_batch.py
@@ -77,27 +77,70 @@ def make_single_maxpool_modelwrapper(k, stride, pad, ifm_ch, ifm_dim, ofm_dim, i
return model
+def make_single_quantavpool_modelwrapper(k, stride, ifm_ch, ifm_dim, ofm_dim, idt, odt):
+ inp = helper.make_tensor_value_info(
+ "inp", TensorProto.FLOAT, [1, ifm_ch, ifm_dim, ifm_dim]
+ )
+ outp = helper.make_tensor_value_info(
+ "outp", TensorProto.FLOAT, [1, ifm_ch, ofm_dim, ofm_dim]
+ )
+
+ mp_node = helper.make_node(
+ "QuantAvgPool2d",
+ ["inp"],
+ ["outp"],
+ domain="finn",
+ stride=stride,
+ kernel=k,
+ ibits=idt.bitwidth(),
+ obits=odt.bitwidth(),
+ signed=1 if idt.signed() else 0,
+ data_layout="NCHW",
+ )
+ graph = helper.make_graph(
+ nodes=[mp_node], name="mp_graph", inputs=[inp], outputs=[outp]
+ )
+
+ model = helper.make_model(graph, producer_name="mp-model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", idt)
+ model.set_tensor_datatype("outp", odt)
+ model = model.transform(InferShapes())
+
+ return model
+
+
def prepare_inputs(input_tensor):
return {"inp": input_tensor}
+# Note: QuantAvgPool2d with idt = DataType.UINT4 and odt = DataType.UINT8
+# (And in general seems to be a problem when odt.bitwidth() > idt.bitwidth())
+# passes cppsim but fails rtlsim(Verilator). Cosim with same parameters in
+# Vivado_HLS passes.
+
# input datatype
@pytest.mark.parametrize("idt", [DataType.UINT4, DataType.INT4])
+# output datatype
+@pytest.mark.parametrize("odt", [DataType.UINT4, DataType.INT4])
# pool configuration: ( k,stride, pad, ifm_dim )
@pytest.mark.parametrize(
- "pool_config", [(3, 2, 0, 5), (3, 2, 1, 5), (2, 2, 0, 8), (5, 2, 2, 7)]
+ "pool_config", [(7, 7, 0, 7), (3, 2, 0, 5), (3, 2, 1, 5), (2, 2, 0, 8)]
)
# input channels
@pytest.mark.parametrize("ifm_ch", [1, 4, 20])
# number of out channel computed in parallel
-@pytest.mark.parametrize("pe", [1, 4, 20])
+@pytest.mark.parametrize("pe", [1, 4])
+# pool type
+@pytest.mark.parametrize("op_type", ["QuantAvgPool2d", "MaxPool"])
# execution mode
@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
-# pool type
-@pytest.mark.parametrize("op_type", ["MaxPool"])
@pytest.mark.slow
@pytest.mark.vivado
-def test_convert_to_hls_pool_batch(idt, pool_config, ifm_ch, pe, exec_mode, op_type):
+def test_convert_to_hls_pool_batch(
+ idt, odt, pool_config, ifm_ch, pe, op_type, exec_mode
+):
k, stride, pad, ifm_dim = pool_config
if ifm_ch % pe != 0:
@@ -113,9 +156,25 @@ def test_convert_to_hls_pool_batch(idt, pool_config, ifm_ch, pe, exec_mode, op_t
# prepare input data
input_dict = prepare_inputs(x)
if op_type == "MaxPool":
+ # if idt.signed():
+ # pytest.skip("""No support for signed input (see accu initialization
+ # in Pool_batch HLSLIB function). Skipping""")
+
+ if idt != odt:
+ pytest.skip("Skipping Maxpool with idt != odt")
+
model = make_single_maxpool_modelwrapper(
k, stride, pad, ifm_ch, ifm_dim, ofm_dim, idt
)
+ elif op_type == "QuantAvgPool2d":
+ if pad != 0:
+ pytest.skip("No padding support for QuantAvgPool2d. Skipping")
+
+ if idt.signed() != odt.signed():
+ pytest.skip("Skipping QuantAvgPool2d with idt.signed() != odt.signed()")
+ model = make_single_quantavpool_modelwrapper(
+ k, stride, ifm_ch, ifm_dim, ofm_dim, idt, odt
+ )
else:
assert False, "{} is not a supported op_type".format(op_type)
@@ -151,7 +210,7 @@ def test_convert_to_hls_pool_batch(idt, pool_config, ifm_ch, pe, exec_mode, op_t
# execute new_model
y_produced = oxe.execute_onnx(new_model, input_dict)["outp"]
assert (y_produced == y_expected).all()
- if stride != k:
+ if stride <= k:
if pad == 0 or ifm_ch == pe:
assert len(new_model.graph.node) == 4
else:
--
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