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Commit 7886dec4 authored by Yaman Umuroglu's avatar Yaman Umuroglu
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[Tests] moving more tests to finn-base

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tests/transformation/test_conv_lowering.py deleted 100644 → 0
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# Copyright (c) 2020, Xilinx
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of FINN nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import pytest
import onnx.helper as oh
from onnx import TensorProto
import os
import pkg_resources as pk
import brevitas.onnx as bo
import numpy as np
from finn.core.modelwrapper import ModelWrapper
from finn.core.datatype import DataType
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.infer_shapes import InferShapes
from finn.util.test import get_test_model_trained
from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
import finn.core.onnx_exec as oxe
from finn.custom_op.im2col import compute_conv_output_dim
from finn.util.basic import gen_finn_dt_tensor
from finn.custom_op.registry import getCustomOp
export_onnx_path = "test_conv_lowering.onnx"
def test_conv_lowering_cnv_w1a1():
cnv = get_test_model_trained("CNV", 1, 1)
bo.export_finn_onnx(cnv, (1, 3, 32, 32), export_onnx_path)
model = ModelWrapper(export_onnx_path)
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
fn = pk.resource_filename("finn.qnn-data", "cifar10/cifar10-test-data-class3.npz")
input_tensor = np.load(fn)["arr_0"].astype(np.float32)
input_tensor = input_tensor / 255
assert input_tensor.shape == (1, 3, 32, 32)
# execute imported model to get expected answer
input_dict = {"0": input_tensor}
output_dict_e = oxe.execute_onnx(model, input_dict)
expected = output_dict_e[list(output_dict_e.keys())[0]]
# execute transformed model and compare
model = model.transform(LowerConvsToMatMul())
output_dict_p = oxe.execute_onnx(model, input_dict)
produced = output_dict_p[list(output_dict_p.keys())[0]]
assert np.isclose(produced, expected).all()
assert np.argmax(produced) == 3
os.remove(export_onnx_path)
# input datatype
@pytest.mark.parametrize("idt", [DataType.INT2, DataType.INT4])
# kernel size
@pytest.mark.parametrize("k", [2, 4])
# input dimension
@pytest.mark.parametrize("ifm_dim", [4, 6])
# input channels
@pytest.mark.parametrize("ifm_ch", [2, 3])
# stride
@pytest.mark.parametrize("stride", [1, 2])
# padding
@pytest.mark.parametrize("padding", [[0, 0, 0, 0], [1, 1, 1, 1]])
def test_depthwise_conv_lowering(idt, k, ifm_dim, ifm_ch, stride, padding):
wdt = idt
odt = DataType.INT32
ofm_ch = ifm_ch
ofm_dim = compute_conv_output_dim(ifm_dim, k, stride, pad=padding[0])
# set up onnx model
inp = oh.make_tensor_value_info(
"inp", TensorProto.FLOAT, [1, ifm_ch, ifm_dim, ifm_dim]
)
outp = oh.make_tensor_value_info(
"outp", TensorProto.FLOAT, [1, ofm_ch, ofm_dim, ofm_dim]
)
W = oh.make_tensor_value_info("W", TensorProto.FLOAT, [ofm_ch, 1, k, k])
dw_cnv = oh.make_node(
"Conv",
inputs=["inp", "W"],
outputs=["outp"],
kernel_shape=[k, k],
pads=padding,
strides=[stride, stride],
group=ifm_ch,
)
graph = oh.make_graph(
nodes=[dw_cnv],
name="dw_cnv_graph",
inputs=[inp],
outputs=[outp],
value_info=[W],
)
model = oh.make_model(graph, producer_name="dws_cnv-model")
model = ModelWrapper(model)
model.set_tensor_datatype("inp", idt)
model.set_tensor_datatype("outp", odt)
model.set_tensor_datatype("W", wdt)
w_tensor = gen_finn_dt_tensor(wdt, [ofm_ch, 1, k, k])
model.set_initializer("W", w_tensor)
model = model.transform(InferShapes())
input_tensor = gen_finn_dt_tensor(idt, [1, ifm_ch, ifm_dim, ifm_dim])
input_dict = {"inp": input_tensor}
output_dict = oxe.execute_onnx(model, input_dict)
expected = output_dict["outp"]
model = model.transform(LowerConvsToMatMul())
output_dict = oxe.execute_onnx(model, input_dict)
produced = output_dict["outp"]
assert (produced == expected).all()
# check if created nodes have attributes that indicate depthwise conv
assert model.get_tensor_sparsity("W") is not None
im2col_node = getCustomOp(model.graph.node[1])
assert im2col_node.get_nodeattr("depthwise") == 1
def test_conv_lowering_conv_1x1():
np.random.seed(0)
in_feature_dim = 7
in_chn = 3
kernel_size = 1
out_feature_dim = in_feature_dim
input_shape = [1, in_chn, in_feature_dim, in_feature_dim]
output_shape = [1, in_chn, out_feature_dim, out_feature_dim]
conv_param_shape = [in_chn, in_chn, kernel_size, kernel_size]
conv_config = {}
conv_config["dilations"] = [1, 1]
conv_config["group"] = 1
conv_config["kernel_shape"] = [kernel_size, kernel_size]
conv_config["pads"] = [0, 0, 0, 0]
conv_config["strides"] = [1, 1]
top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
value_info = [oh.make_tensor_value_info("p1", TensorProto.FLOAT, conv_param_shape)]
modelproto = oh.make_model(
oh.make_graph(
name="test",
inputs=[top_in],
outputs=[top_out],
value_info=value_info,
nodes=[oh.make_node("Conv", ["top_in", "p1"], ["top_out"], **conv_config)],
)
)
model = ModelWrapper(modelproto)
model = model.transform(InferShapes())
model.set_initializer("p1", np.random.rand(*conv_param_shape).astype(np.float32))
new_model = model.transform(LowerConvsToMatMul())
inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
assert oxe.compare_execution(model, new_model, inp_dict)
assert new_model.graph.node[0].op_type == "Transpose"
assert new_model.graph.node[1].op_type == "MatMul"
assert new_model.graph.node[2].op_type == "Transpose"
assert len(new_model.graph.node) == 3
tests/transformation/test_topk_insert.py deleted 100644 → 0
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import os
import onnx
from finn.util.test import get_test_model_trained
import brevitas.onnx as bo
import numpy as np
import onnx.numpy_helper as nph
import torch
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.fold_constants import FoldConstants
from finn.transformation.insert_topk import InsertTopK
import finn.core.onnx_exec as oxe
from pkgutil import get_data
import pytest
export_onnx_path = "test_topk_insert.onnx"
@pytest.mark.parametrize("k", [1, 2])
def test_topk_insert(k):
tfc = get_test_model_trained("TFC", 1, 1)
bo.export_finn_onnx(tfc, (1, 1, 28, 28), export_onnx_path)
model = ModelWrapper(export_onnx_path)
# do transformations (no topk)
model = model.transform(InferShapes())
model = model.transform(FoldConstants())
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferDataTypes())
# verification: generate random input, run through net, streamline,
# run again, check that output is top-k
raw_i = get_data("finn.base-data", "onnx/mnist-conv/test_data_set_0/input_0.pb")
input_tensor = onnx.load_tensor_from_string(raw_i)
input_brevitas = torch.from_numpy(nph.to_array(input_tensor)).float()
output_golden = tfc.forward(input_brevitas).detach().numpy()
output_golden_topk = np.flip(output_golden.flatten().argsort())[:k]
output_golden_topk = output_golden_topk.flatten()
input_dict = {"global_in": nph.to_array(input_tensor)}
# insert top-k
model = model.transform(InsertTopK(k))
model = model.transform(GiveUniqueNodeNames())
model = model.transform(GiveReadableTensorNames())
model = model.transform(InferShapes())
# verify output of top-k
output_dict_topk = oxe.execute_onnx(model, input_dict)
output_pysim_topk = output_dict_topk[list(output_dict_topk.keys())[0]]
output_pysim_topk = output_pysim_topk.astype(np.int).flatten()
assert np.array_equal(output_golden_topk, output_pysim_topk)
os.remove(export_onnx_path)
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