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Commit 1a93bbda authored by Hugo LE BLEVEC's avatar Hugo LE BLEVEC
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src/finn/transformation/streamline/reorder.py
+ 11
− 3
View file @ 1a93bbda
...@@ -722,11 +722,13 @@ class MakeMaxPoolNHWC(Transformation): ...@@ -722,11 +722,13 @@ class MakeMaxPoolNHWC(Transformation):
graph_modified = True graph_modified = True
return (model, graph_modified) return (model, graph_modified)
class MakeScaleResizeNHWC(Transformation): class MakeScaleResizeNHWC(Transformation):
""" """
Converts the inputs and outputs for all scales Resize and Upsample nodes Converts the inputs and outputs for all scales Resize and Upsample nodes
from NCHW to NHWC. from NCHW to NHWC.
""" """
def apply(self, model): def apply(self, model):
graph = model.graph graph = model.graph
node_ind = 0 node_ind = 0
...@@ -738,12 +740,15 @@ class MakeScaleResizeNHWC(Transformation): ...@@ -738,12 +740,15 @@ class MakeScaleResizeNHWC(Transformation):
if n.op_type == "Upsample": if n.op_type == "Upsample":
scales_ind = 1 scales_ind = 1
else: else:
scales_ind = 2 scales_ind = 2
if producer is not None and producer.op_type == "Transpose": if producer is not None and producer.op_type == "Transpose":
perms = list(get_by_name(producer.attribute, "perm").ints) perms = list(get_by_name(producer.attribute, "perm").ints)
if perms == [0, 3, 1, 2]: if perms == [0, 3, 1, 2]:
old_value = model.get_initializer(n.input[scales_ind]) old_value = model.get_initializer(n.input[scales_ind])
new_value = np.array([old_value[idx] for idx in (0, 2, 3, 1)], dtype=np.dtype('float32')) new_value = np.array(
[old_value[idx] for idx in (0, 2, 3, 1)],
dtype=np.dtype("float32"),
)
model.set_initializer(n.input[scales_ind], new_value) model.set_initializer(n.input[scales_ind], new_value)
start_name = producer.input[0] start_name = producer.input[0]
mid_name = n.input[0] mid_name = n.input[0]
...@@ -762,7 +767,10 @@ class MakeScaleResizeNHWC(Transformation): ...@@ -762,7 +767,10 @@ class MakeScaleResizeNHWC(Transformation):
perms = list(get_by_name(consumer.attribute, "perm").ints) perms = list(get_by_name(consumer.attribute, "perm").ints)
if perms == [0, 2, 3, 1]: if perms == [0, 2, 3, 1]:
old_value = model.get_initializer(n.input[scales_ind]) old_value = model.get_initializer(n.input[scales_ind])
new_value = np.array([old_value[idx] for idx in (0, 2, 3, 1)], dtype=np.dtype('float32')) new_value = np.array(
[old_value[idx] for idx in (0, 2, 3, 1)],
dtype=np.dtype("float32"),
)
model.set_initializer(n.input[scales_ind], new_value) model.set_initializer(n.input[scales_ind], new_value)
start_name = n.input[0] start_name = n.input[0]
mid_name = consumer.input[0] mid_name = consumer.input[0]
......
tests/transformation/streamline/test_scale_resize_nhwc.py
+ 25
− 31
View file @ 1a93bbda
import pytest import pytest
import numpy as np import numpy as np
import onnx import onnx
import onnx.version_converter as vc
import onnx.helper as oh import onnx.helper as oh
from onnx import TensorProto from onnx import TensorProto
from qonnx.core.datatype import DataType from qonnx.core.datatype import DataType
...@@ -12,6 +12,7 @@ from qonnx.util.basic import gen_finn_dt_tensor ...@@ -12,6 +12,7 @@ from qonnx.util.basic import gen_finn_dt_tensor
import finn.core.onnx_exec as oxe import finn.core.onnx_exec as oxe
from finn.transformation.streamline.reorder import MakeScaleResizeNHWC from finn.transformation.streamline.reorder import MakeScaleResizeNHWC
def create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt): def create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt):
ofm_dim_h = ifm_dim[0] * scales[2] ofm_dim_h = ifm_dim[0] * scales[2]
ofm_dim_w = ifm_dim[1] * scales[3] ofm_dim_w = ifm_dim[1] * scales[3]
...@@ -19,14 +20,10 @@ def create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -19,14 +20,10 @@ def create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt):
"inp", TensorProto.FLOAT, [1, ifm_ch, ifm_dim[0], ifm_dim[1]] "inp", TensorProto.FLOAT, [1, ifm_ch, ifm_dim[0], ifm_dim[1]]
) )
param = oh.make_tensor_value_info( param = oh.make_tensor_value_info("scales", TensorProto.FLOAT, [4])
"scales", TensorProto.FLOAT, [4]
)
# Not actually used, only needed for compliance with the Resize node interface # Not actually used, only needed for compliance with the Resize node interface
roi = oh.make_tensor_value_info( roi = oh.make_tensor_value_info("roi", TensorProto.FLOAT, [4])
"roi", TensorProto.FLOAT, [4]
)
outp_up = oh.make_tensor_value_info( outp_up = oh.make_tensor_value_info(
"outp_up", TensorProto.FLOAT, [1, ifm_ch, ofm_dim_h, ofm_dim_w] "outp_up", TensorProto.FLOAT, [1, ifm_ch, ofm_dim_h, ofm_dim_w]
...@@ -39,7 +36,7 @@ def create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -39,7 +36,7 @@ def create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt):
"Resize", "Resize",
inputs=["inp", "roi", "scales"], inputs=["inp", "roi", "scales"],
outputs=["outp_up"], outputs=["outp_up"],
name = "Resize1", name="Resize1",
mode=mode, mode=mode,
) )
...@@ -68,6 +65,7 @@ def create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -68,6 +65,7 @@ def create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt):
return model return model
def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt): def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt):
ofm_dim_h = ifm_dim[0] * scales[2] ofm_dim_h = ifm_dim[0] * scales[2]
ofm_dim_w = ifm_dim[1] * scales[3] ofm_dim_w = ifm_dim[1] * scales[3]
...@@ -75,16 +73,12 @@ def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -75,16 +73,12 @@ def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt):
"inp", TensorProto.FLOAT, [1, ifm_dim[0], ifm_dim[1], ifm_ch] "inp", TensorProto.FLOAT, [1, ifm_dim[0], ifm_dim[1], ifm_ch]
) )
param = oh.make_tensor_value_info( param = oh.make_tensor_value_info("scales", TensorProto.FLOAT, [4])
"scales", TensorProto.FLOAT, [4]
)
# Not actually used, only needed for compliance with the Resize node interface # Not actually used, only needed for compliance with the Resize node interface
roi = oh.make_tensor_value_info( roi = oh.make_tensor_value_info("roi", TensorProto.FLOAT, [4])
"roi", TensorProto.FLOAT, [4]
)
outp= oh.make_tensor_value_info( outp = oh.make_tensor_value_info(
"outp", TensorProto.FLOAT, [1, ifm_ch, ofm_dim_h, ofm_dim_w] "outp", TensorProto.FLOAT, [1, ifm_ch, ofm_dim_h, ofm_dim_w]
) )
outp_tr = oh.make_tensor_value_info( outp_tr = oh.make_tensor_value_info(
...@@ -103,7 +97,7 @@ def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -103,7 +97,7 @@ def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt):
"Resize", "Resize",
inputs=["outp_tr", "roi", "scales"], inputs=["outp_tr", "roi", "scales"],
outputs=["outp"], outputs=["outp"],
name = "Resize1", name="Resize1",
mode=mode, mode=mode,
) )
...@@ -114,7 +108,7 @@ def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -114,7 +108,7 @@ def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt):
outputs=[outp], outputs=[outp],
value_info=[outp_tr, param, roi], value_info=[outp_tr, param, roi],
) )
model = oh.make_model(graph, producer_name="resize_model2") model = oh.make_model(graph, producer_name="resize_model2")
model = ModelWrapper(model) model = ModelWrapper(model)
model.set_tensor_datatype("inp", idt) model.set_tensor_datatype("inp", idt)
...@@ -124,6 +118,7 @@ def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -124,6 +118,7 @@ def create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt):
return model return model
def create_transpose_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt): def create_transpose_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt):
ofm_dim_h = ifm_dim[0] * scales[2] ofm_dim_h = ifm_dim[0] * scales[2]
ofm_dim_w = ifm_dim[1] * scales[3] ofm_dim_w = ifm_dim[1] * scales[3]
...@@ -131,14 +126,10 @@ def create_transpose_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -131,14 +126,10 @@ def create_transpose_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt):
"inp", TensorProto.FLOAT, [1, ifm_dim[0], ifm_dim[1], ifm_ch] "inp", TensorProto.FLOAT, [1, ifm_dim[0], ifm_dim[1], ifm_ch]
) )
param = oh.make_tensor_value_info( param = oh.make_tensor_value_info("scales", TensorProto.FLOAT, scales)
"scales", TensorProto.FLOAT, scales
)
# Not actually used, only needed for compliance with the Resize node interface # Not actually used, only needed for compliance with the Resize node interface
roi = oh.make_tensor_value_info( roi = oh.make_tensor_value_info("roi", TensorProto.FLOAT, [4])
"roi", TensorProto.FLOAT, [4]
)
outp_tr = oh.make_tensor_value_info( outp_tr = oh.make_tensor_value_info(
"outp_tr", TensorProto.FLOAT, [1, ifm_ch, ifm_dim[0], ifm_dim[1]] "outp_tr", TensorProto.FLOAT, [1, ifm_ch, ifm_dim[0], ifm_dim[1]]
...@@ -163,7 +154,7 @@ def create_transpose_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -163,7 +154,7 @@ def create_transpose_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt):
"Resize", "Resize",
inputs=["outp_tr", "roi", "scales"], inputs=["outp_tr", "roi", "scales"],
outputs=["outp_up"], outputs=["outp_up"],
name = "Resize1", name="Resize1",
mode=mode, mode=mode,
) )
...@@ -195,11 +186,13 @@ def create_transpose_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -195,11 +186,13 @@ def create_transpose_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt):
@pytest.mark.streamline @pytest.mark.streamline
# input dimension # input dimension
@pytest.mark.parametrize("ifm_dim", [[2**i, 2**i] for i in range(3,6)]) @pytest.mark.parametrize("ifm_dim", [[2**i, 2**i] for i in range(3, 6)])
# input channels # input channels
@pytest.mark.parametrize("ifm_ch", [3]) @pytest.mark.parametrize("ifm_ch", [3])
# scales # scales
@pytest.mark.parametrize("scales", [[1,1,i,j] for i in range(2,5) for j in range(2,5)]) @pytest.mark.parametrize(
"scales", [[1, 1, i, j] for i in range(2, 5) for j in range(2, 5)]
)
# mode # mode
@pytest.mark.parametrize("mode", ["nearest"]) @pytest.mark.parametrize("mode", ["nearest"])
# input datatype # input datatype
...@@ -208,9 +201,11 @@ def test_scale_resize_nhwc(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -208,9 +201,11 @@ def test_scale_resize_nhwc(ifm_dim, ifm_ch, scales, mode, idt):
# create models # create models
resize_model1 = create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt) resize_model1 = create_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt)
resize_model2 = create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt) resize_model2 = create_transpose_resize(ifm_dim, ifm_ch, scales, mode, idt)
resize_model3 = create_transpose_resize_transpose(ifm_dim, ifm_ch, scales, mode, idt) resize_model3 = create_transpose_resize_transpose(
ifm_dim, ifm_ch, scales, mode, idt
)
#set initializers # set initializers
resize_model1.set_initializer("scales", np.array(scales, dtype=np.float32)) resize_model1.set_initializer("scales", np.array(scales, dtype=np.float32))
resize_model2.set_initializer("scales", np.array(scales, dtype=np.float32)) resize_model2.set_initializer("scales", np.array(scales, dtype=np.float32))
resize_model3.set_initializer("scales", np.array(scales, dtype=np.float32)) resize_model3.set_initializer("scales", np.array(scales, dtype=np.float32))
...@@ -221,7 +216,6 @@ def test_scale_resize_nhwc(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -221,7 +216,6 @@ def test_scale_resize_nhwc(ifm_dim, ifm_ch, scales, mode, idt):
input_dict_nchw = {"inp": input_tensor_nchw} input_dict_nchw = {"inp": input_tensor_nchw}
input_dict_nhwc = {"inp": input_tensor_nhwc} input_dict_nhwc = {"inp": input_tensor_nhwc}
# execute first model # execute first model
output_dict1 = oxe.execute_onnx(resize_model1, input_dict_nchw) output_dict1 = oxe.execute_onnx(resize_model1, input_dict_nchw)
expected1 = output_dict1["outp"] expected1 = output_dict1["outp"]
...@@ -263,12 +257,12 @@ def test_scale_resize_nhwc(ifm_dim, ifm_ch, scales, mode, idt): ...@@ -263,12 +257,12 @@ def test_scale_resize_nhwc(ifm_dim, ifm_ch, scales, mode, idt):
# transform Resize into ResizeNHWC # transform Resize into ResizeNHWC
resize_model3 = resize_model3.transform(MakeScaleResizeNHWC()) resize_model3 = resize_model3.transform(MakeScaleResizeNHWC())
# execute transformed model # execute transformed model
output_node_name3 = resize_model3.graph.output[0].name output_node_name3 = resize_model3.graph.output[0].name
output_dict3 = oxe.execute_onnx( output_dict3 = oxe.execute_onnx(
resize_model3, input_dict_nhwc, return_full_exec_context=False resize_model3, input_dict_nhwc, return_full_exec_context=False
) )
output3 = output_dict3[output_node_name3] output3 = output_dict3[output_node_name3]
# compare outputs # compare outputs
assert (expected3 == output3).all() assert (expected3 == output3).all()
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