From 9bbe976170390664ef901e241aee3ef5e0d0475a Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Thu, 11 Jun 2020 17:06:05 +0100
Subject: [PATCH] [Test] Change test for quant avg pool to specific example
---
.../brevitas/test_brevitas_avg_pool_export.py | 113 +++++++++++-------
1 file changed, 70 insertions(+), 43 deletions(-)
diff --git a/tests/brevitas/test_brevitas_avg_pool_export.py b/tests/brevitas/test_brevitas_avg_pool_export.py
index 3e90e9830..11d4a8783 100644
--- a/tests/brevitas/test_brevitas_avg_pool_export.py
+++ b/tests/brevitas/test_brevitas_avg_pool_export.py
@@ -9,7 +9,6 @@ from brevitas.quant_tensor import pack_quant_tensor
from brevitas.core.quant import QuantType
from finn.core.modelwrapper import ModelWrapper
from finn.core.datatype import DataType
-from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.util.basic import gen_finn_dt_tensor
import finn.core.onnx_exec as oxe
@@ -19,13 +18,13 @@ 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", [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])
+@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])
def test_brevitas_avg_pool_export(
kernel_size, stride, signed, bit_width, input_bit_width, channels, idim
):
@@ -40,59 +39,87 @@ 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)
- # set FINN datatype
- if signed is True:
- prefix = "INT"
- else:
- prefix = "UINT"
- dt_name = prefix + str(input_bit_width)
- dtype = DataType[dt_name]
- model.set_tensor_datatype(model.graph.input[0].name, dtype)
- model = model.transform(InferShapes())
- model = model.transform(InferDataTypes())
+ ## 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())
# 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
+ # 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.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()
input_quant_tensor = pack_quant_tensor(
tensor=input_tensor, scale=input_scale, bit_width=ibw_tensor
)
+ # export again to set the scale values correctly
+ bo.export_finn_onnx(b_avgpool, ishape, export_onnx_path, input_t=input_quant_tensor)
+ model = ModelWrapper(export_onnx_path)
+ model = model.transform(InferShapes())
+ b_avgpool.eval()
expected = b_avgpool.forward(input_quant_tensor).tensor.detach().numpy()
# finn execution
idict = {model.graph.input[0].name: inp_tensor}
- model.set_initializer(model.graph.input[1].name, scale)
odict = oxe.execute_onnx(model, idict, True)
produced = odict[model.graph.output[0].name]
assert np.isclose(expected, produced).all()
- os.remove(export_onnx_path)
+ # os.remove(export_onnx_path)
--
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