From cbcd5bac832786141e9a6b7f077a10cdaa17d236 Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Fri, 12 Jun 2020 09:52:03 +0100
Subject: [PATCH] [Test] Reset QuantAvgPool2d export and test to general
version
---
.../brevitas/test_brevitas_avg_pool_export.py | 106 +++++++-----------
1 file changed, 42 insertions(+), 64 deletions(-)
diff --git a/tests/brevitas/test_brevitas_avg_pool_export.py b/tests/brevitas/test_brevitas_avg_pool_export.py
index 11d4a8783..34b154c4e 100644
--- a/tests/brevitas/test_brevitas_avg_pool_export.py
+++ b/tests/brevitas/test_brevitas_avg_pool_export.py
@@ -10,6 +10,7 @@ from brevitas.core.quant import QuantType
from finn.core.modelwrapper import ModelWrapper
from finn.core.datatype import DataType
from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
from finn.util.basic import gen_finn_dt_tensor
import finn.core.onnx_exec as oxe
@@ -18,13 +19,13 @@ import pytest
export_onnx_path = "test_avg_pool.onnx"
-@pytest.mark.parametrize("kernel_size", [2]) # , 3])
-@pytest.mark.parametrize("stride", [1]) # , 2])
-@pytest.mark.parametrize("signed", [True]) # False, True])
-@pytest.mark.parametrize("bit_width", [4]) # 2, 4])
-@pytest.mark.parametrize("input_bit_width", [4]) # , 8, 32])
-@pytest.mark.parametrize("channels", [2]) # , 4])
-@pytest.mark.parametrize("idim", [7]) # , 8])
+@pytest.mark.parametrize("kernel_size", [2, 3])
+@pytest.mark.parametrize("stride", [1, 2])
+@pytest.mark.parametrize("signed", [False, True])
+@pytest.mark.parametrize("bit_width", [2, 4])
+@pytest.mark.parametrize("input_bit_width", [4, 8, 32])
+@pytest.mark.parametrize("channels", [2, 4])
+@pytest.mark.parametrize("idim", [7, 8])
def test_brevitas_avg_pool_export(
kernel_size, stride, signed, bit_width, input_bit_width, channels, idim
):
@@ -39,70 +40,47 @@ def test_brevitas_avg_pool_export(
max_overall_bit_width=bit_width,
quant_type=QuantType.INT,
)
- ## call forward pass manually once to cache scale factor and bitwidth
- # input_tensor = torch.from_numpy(np.zeros(ishape)).float()
- # scale = np.ones((1, channels, 1, 1))
- # output_scale = torch.from_numpy(scale).float()
- # input_quant_tensor = pack_quant_tensor(
- # tensor=input_tensor, scale=output_scale, bit_width=ibw_tensor
- # )
- # bo.export_finn_onnx(b_avgpool, ishape, export_onnx_path, input_t=input_quant_tensor)
- # model = ModelWrapper(export_onnx_path)
+ # call forward pass manually once to cache scale factor and bitwidth
+ input_tensor = torch.from_numpy(np.zeros(ishape)).float()
+ scale = np.ones((1, channels, 1, 1))
+ output_scale = torch.from_numpy(scale).float()
+ input_quant_tensor = pack_quant_tensor(
+ tensor=input_tensor, scale=output_scale, bit_width=ibw_tensor
+ )
+ bo.export_finn_onnx(b_avgpool, ishape, export_onnx_path, input_t=input_quant_tensor)
+ model = ModelWrapper(export_onnx_path)
+
# determine input FINN datatype
- # if signed is True:
- # prefix = "INT"
- # else:
- # prefix = "UINT"
- # dt_name = prefix + str(input_bit_width)
- # dtype = DataType[dt_name]
- # model = model.transform(InferShapes())
+ if signed is True:
+ prefix = "INT"
+ else:
+ prefix = "UINT"
+ dt_name = prefix + str(input_bit_width)
+ dtype = DataType[dt_name]
+ model = model.transform(InferShapes())
+ model.set_tensor_datatype(model.graph.input[0].name, dtype)
+ model = model.transform(InferDataTypes())
# execution with input tensor using integers and scale = 1
# calculate golden output
- # inp = gen_finn_dt_tensor(dtype, ishape)
- # input_tensor = torch.from_numpy(inp).float()
- # input_quant_tensor = pack_quant_tensor(
- # tensor=input_tensor, scale=output_scale, bit_width=ibw_tensor
- # )
- # b_avgpool.eval()
- # expected = b_avgpool.forward(input_quant_tensor).tensor.detach().numpy()
+ inp = gen_finn_dt_tensor(dtype, ishape)
+ input_tensor = torch.from_numpy(inp).float()
+ input_quant_tensor = pack_quant_tensor(
+ tensor=input_tensor, scale=output_scale, bit_width=ibw_tensor
+ )
+ b_avgpool.eval()
+ expected = b_avgpool.forward(input_quant_tensor).tensor.detach().numpy()
- ## finn execution
- # idict = {model.graph.input[0].name: inp}
- # odict = oxe.execute_onnx(model, idict, True)
- # produced = odict[model.graph.output[0].name]
- # assert (expected == produced).all()
+ # finn execution
+ idict = {model.graph.input[0].name: inp}
+ odict = oxe.execute_onnx(model, idict, True)
+ produced = odict[model.graph.output[0].name]
+ assert (expected == produced).all()
# execution with input tensor using float and scale != 1
- # scale = np.random.uniform(low=0, high=1, size=(1, channels, 1, 1)).astype(
- # np.float32
- # )
- inp = np.asarray(
- [
- [
- [
- [4.0, -2.0, 4.0, -1.0, -8.0, -5.0, 0.0],
- [7.0, -3.0, -5.0, 2.0, -4.0, 4.0, -2.0],
- [1.0, 7.0, -7.0, -7.0, 3.0, -7.0, -3.0],
- [-2.0, -6.0, -1.0, -5.0, -3.0, -2.0, -4.0],
- [0.0, -8.0, -1.0, 6.0, 1.0, 2.0, -1.0],
- [6.0, 2.0, -5.0, 6.0, 4.0, -7.0, -5.0],
- [1.0, -5.0, 4.0, -7.0, -6.0, -2.0, 2.0],
- ],
- [
- [-7.0, 0.0, -6.0, -6.0, 0.0, 3.0, 6.0],
- [0.0, -8.0, 6.0, 2.0, -4.0, 6.0, -3.0],
- [-1.0, 3.0, -1.0, 4.0, 1.0, 6.0, -7.0],
- [-3.0, -5.0, 5.0, -7.0, 3.0, 6.0, 6.0],
- [5.0, -8.0, 6.0, 1.0, 1.0, 3.0, -3.0],
- [6.0, 1.0, 3.0, -1.0, 6.0, 4.0, -8.0],
- [-1.0, -3.0, 6.0, -8.0, 0.0, 5.0, 2.0],
- ],
- ]
- ],
- dtype=np.float32,
+ scale = np.random.uniform(low=0, high=1, size=(1, channels, 1, 1)).astype(
+ np.float32
)
- scale = np.asarray([[[[0.37314373]], [[0.9042693]]]], dtype=np.float32)
inp_tensor = inp * scale
input_tensor = torch.from_numpy(inp_tensor).float()
input_scale = torch.from_numpy(scale).float()
@@ -122,4 +100,4 @@ def test_brevitas_avg_pool_export(
assert np.isclose(expected, produced).all()
- # os.remove(export_onnx_path)
+ os.remove(export_onnx_path)
--
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