From 3ab4c48d22107489bdb33ee39a847e02e7ad3646 Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Mon, 6 Jul 2020 14:47:57 +0100
Subject: [PATCH] [Test] Update test for MergeONNXModels to see if quantization
annotations are correctly preserved
---
.../transformation/test_merge_onnx_models.py | 58 ++++++++++++++-----
1 file changed, 44 insertions(+), 14 deletions(-)
diff --git a/tests/transformation/test_merge_onnx_models.py b/tests/transformation/test_merge_onnx_models.py
index 4a92fab9e..c5156704f 100644
--- a/tests/transformation/test_merge_onnx_models.py
+++ b/tests/transformation/test_merge_onnx_models.py
@@ -29,25 +29,34 @@
from pkgutil import get_data
import numpy as np
+import onnx
+import onnx.numpy_helper as np_helper
from onnx import TensorProto, helper
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.transformation.infer_data_layouts import InferDataLayouts
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.merge_onnx_models import MergeONNXModels
import finn.core.onnx_exec as oxe
def test_merge_onnx_models():
- # load first model
+ # load pre model
raw_m = get_data("finn", "data/onnx/mnist-conv/model.onnx")
model1 = ModelWrapper(raw_m)
+ # the input for model1 comes from a uint8 vector so we set the finn datatype
+ # of the input tensor to DataType.UINT8 to verify that the datatypes are correctly
+ # preserved in the transformed model
+ model1.set_tensor_datatype(model1.graph.input[0].name, DataType.UINT8)
model1 = model1.transform(InferShapes())
model1 = model1.transform(GiveUniqueNodeNames())
model1 = model1.transform(GiveReadableTensorNames())
- # set up second model that should be inserted before the first model
- shape = [1, 1, 28, 28]
+ # set up post model
+ shape = [1, 10]
inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, shape)
a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, [])
a1 = helper.make_tensor_value_info("a1", TensorProto.FLOAT, [])
@@ -67,31 +76,52 @@ def test_merge_onnx_models():
model2 = helper.make_model(graph, producer_name="model2")
model2 = ModelWrapper(model2)
# initialize model2
- a0_value = np.random.uniform(low=0.1, high=0.99, size=(1)).astype(np.float32)
+ a0_value = np.random.uniform(low=0, high=1, size=(1)).astype(np.float32)
model2.set_initializer("a0", a0_value)
- a1_value = 1.0 / a0_value
+ a1_value = np.random.uniform(low=0.1, high=1, size=(1)).astype(np.float32)
model2.set_initializer("a1", a1_value)
+ # set a dummy sparsity annotation to check if it gets correctly transferred
+ # to the merged model
+ sparsity = {"dw": {"kernel_shape": 0}}
+ model2.set_tensor_sparsity("a1", sparsity)
model2 = model2.transform(InferShapes())
+ model2 = model2.transform(InferDataTypes())
+ model2 = model2.transform(InferDataLayouts())
model2 = model2.transform(GiveUniqueNodeNames())
model2 = model2.transform(GiveReadableTensorNames())
- # simulate the models before the merging and pass the output of model2 to model1
- inp_values = np.random.uniform(low=-1, high=1, size=tuple(shape)).astype(np.float32)
- idict = {model2.graph.input[0].name: inp_values}
- odict = oxe.execute_onnx(model2, idict)
- temp = odict[model2.graph.output[0].name]
-
- idict = {model1.graph.input[0].name: temp}
+ # simulate the models before the merging and pass the output of model1 to model2
+ # load one of the test vectors
+ raw_i = get_data("finn", "data/onnx/mnist-conv/test_data_set_0/input_0.pb")
+ inp_values = onnx.load_tensor_from_string(raw_i)
+ inp_values = np_helper.to_array(inp_values)
+ idict = {model1.graph.input[0].name: inp_values}
odict = oxe.execute_onnx(model1, idict)
- outp = odict[model1.graph.output[0].name]
+ temp = odict[model1.graph.output[0].name]
+
+ idict = {model2.graph.input[0].name: temp}
+ odict = oxe.execute_onnx(model2, idict)
+ outp = odict[model2.graph.output[0].name]
# merge models
- model_transformed = model1.transform(MergeONNXModels(model2))
+ model_transformed = model2.transform(MergeONNXModels(model1))
idict = {model_transformed.graph.input[0].name: inp_values}
odict = oxe.execute_onnx(model_transformed, idict)
outp_transformed = odict[model_transformed.graph.output[0].name]
+ model_transformed.save("test.onnx")
assert (outp == outp_transformed).all()
assert len(model_transformed.graph.node) == len(model1.graph.node) + len(
model2.graph.node
)
+ # to test if the value is preserved we set the sparsity annotation of input[1]
+ # of the division block to a dummy value, we can now look for the division block
+ # and check if the sparsity annotation is still the same
+ for n in model_transformed.graph.node:
+ if n.op_type == "Div":
+ tensor_name = n.input[1]
+ set_sparsity = model_transformed.get_tensor_sparsity(tensor_name)
+ assert sparsity == set_sparsity
+
+ # check if finn datatype of graph.input[0] is still set to UINT8
+ assert model_transformed.get_tensor_datatype("global_in") == DataType.UINT8
--
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