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Support for op_type "Resize" #35

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Support for op_type "Resize" #35

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3d0e126
add method stub
Jun 27, 2022
e750ee9
partially working prototype
Jun 28, 2022
a5a0492
raise error when using non supported resize method
jnnks Jun 29, 2022
82f842f
* take roi, scales, sizes from input!
Jul 5, 2022
577951e
impl feedback from review
Jul 31, 2022
0287f9f
Merge branch 'master' into resize_op
Jul 31, 2022
ee6c920
clean-up after merge mistakes
Jul 31, 2022
9374873
more cleanup
Jul 31, 2022
d9e7c27
replace get_shape
Jul 31, 2022
c5da17d
extend error message, fix return value
Jul 31, 2022
437e3dc
use Axon.Layers.resize instead of Axon.resize
Aug 10, 2022
fffbc46
Merge branch 'master' into resize_op
Sep 18, 2022
3fa675a
add test case test_resize_downsample_scales_nearest
Sep 19, 2022
25f612f
resize by scales proof of concept
Sep 19, 2022
7550e71
Merge branch 'master' into resize_op
Mar 25, 2023
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56 changes: 56 additions & 0 deletions lib/axon_onnx/deserialize.ex
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Original file line number Diff line number Diff line change
Expand Up @@ -2028,6 +2028,62 @@ defmodule AxonOnnx.Deserialize do
{updated_axon, params, used_params}
end

defp recur_nodes(
%Node{
op_type: "Resize",
input: [in_layer_name | rest_of_inputs],
attribute: attrs,
output: [out_layer_name]
},
{axon, params, used_params}
) do
# op_type spec: https://github.com/onnx/onnx/blob/main/docs/Operators.md#resize

inp = input!(in_layer_name, axon, params, used_params)

%{"mode" => mode} = options!(attrs)
method =
case mode do
"linear" -> :bilinear
"cubic" -> :bicubic
"nearest" -> :nearest
_mode -> raise ArgumentError, "only linear is implemented"
end

[roi, scales, sizes] =
case rest_of_inputs do
# ["", scales] -> input!(scales, axon, params, used_params)
["", scales] -> [nil, constant!(scales, axon, params, used_params), nil]
[roi, scales] -> [constant!(roi, axon, params, used_params), constant!(scales, axon, params, used_params), nil]

["", "", sizes] -> [nil, nil, constant!(sizes, axon, params, used_params)]
[roi, "", sizes] -> [constant!(roi, axon, params, used_params), nil, constant!(sizes, axon, params, used_params)]
_ -> raise ArgumentError, "only scales is implemented"
end

layer_inputs = inp
|> Axon.get_inputs()
|> Map.new(fn {name, shape} ->
{name, Nx.broadcast(0.0, shape)}
end)

output_shape =
Axon.get_output_shape(inp, layer_inputs)
|> Tuple.to_list()
|> Nx.tensor()
|> Nx.multiply(scales)
|> Nx.to_flat_list()
|> Enum.map(&Kernel.trunc/1)

output_shape = output_shape
|> Enum.drop(length(output_shape) - 2)
|> List.to_tuple()

layer = Axon.resize(inp, output_shape, method: method)
updated_axon = Map.put(axon, out_layer_name, layer)
{updated_axon, params, used_params}
end

defp recur_nodes(
%Node{
op_type: "Dropout",
Expand Down
47 changes: 24 additions & 23 deletions test/axon_onnx/deserialize_test.exs
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Expand Up @@ -683,29 +683,30 @@ defmodule DeserializeTest do
# check_onnx_test_case!("node", "test_reshape_zero_dim")
# end

# test "Resize" do
# check_onnx_test_case!("node", "test_resize_downsample_scales_cubic")
# check_onnx_test_case!("node", "test_resize_downsample_scales_cubic_A_n0p5_exclude_outside")
# check_onnx_test_case!("node", "test_resize_downsample_scales_cubic_align_corners")
# check_onnx_test_case!("node", "test_resize_downsample_scales_linear")
# check_onnx_test_case!("node", "test_resize_downsample_scales_linear_align_corners")
# check_onnx_test_case!("node", "test_resize_downsample_scales_nearest")
# check_onnx_test_case!("node", "test_resize_downsample_sizes_cubic")
# check_onnx_test_case!("node", "test_resize_downsample_sizes_linear_pytorch_half_pixel")
# check_onnx_test_case!("node", "test_resize_downsample_sizes_nearest")
# check_onnx_test_case!("node", "test_resize_downsample_sizes_nearest_tf_half_pixel_for_nn")
# check_onnx_test_case!("node", "test_resize_tf_crop_and_resize")
# check_onnx_test_case!("node", "test_resize_upsample_scales_cubic")
# check_onnx_test_case!("node", "test_resize_upsample_scales_cubic_A_n0p5_exclude_outside")
# check_onnx_test_case!("node", "test_resize_upsample_scales_cubic_align_corners")
# check_onnx_test_case!("node", "test_resize_upsample_scales_cubic_asymmetric")
# check_onnx_test_case!("node", "test_resize_upsample_scales_linear")
# check_onnx_test_case!("node", "test_resize_upsample_scales_linear_align_corners")
# check_onnx_test_case!("node", "test_resize_upsample_scales_nearest")
# check_onnx_test_case!("node", "test_resize_upsample_scales_nearest_ceil_half_pixel")
# check_onnx_test_case!("node", "test_resize_upsample_scales_nearest_floor_align_corners")
# check_onnx_test_case!("node", "test_resize_upsample_scales_nearest_round_prefer_ceil_asymmetric")
# end
@tag :resize
test "Resize" do
# check_onnx_test_case!("node", "test_resize_downsample_scales_cubic")
# check_onnx_test_case!("node", "test_resize_downsample_scales_cubic_A_n0p5_exclude_outside")
# check_onnx_test_case!("node", "test_resize_downsample_scales_cubic_align_corners")
# check_onnx_test_case!("node", "test_resize_downsample_scales_linear")
# check_onnx_test_case!("node", "test_resize_downsample_scales_linear_align_corners")
check_onnx_test_case!("..", "test_resize_downsample_scales_nearest")
# check_onnx_test_case!("node", "test_resize_downsample_sizes_cubic")
# check_onnx_test_case!("node", "test_resize_downsample_sizes_linear_pytorch_half_pixel")
# check_onnx_test_case!("node", "test_resize_downsample_sizes_nearest")
# check_onnx_test_case!("node", "test_resize_downsample_sizes_nearest_tf_half_pixel_for_nn")
# check_onnx_test_case!("node", "test_resize_tf_crop_and_resize")
# check_onnx_test_case!("node", "test_resize_upsample_scales_cubic")
# check_onnx_test_case!("node", "test_resize_upsample_scales_cubic_A_n0p5_exclude_outside")
# check_onnx_test_case!("node", "test_resize_upsample_scales_cubic_align_corners")
# check_onnx_test_case!("node", "test_resize_upsample_scales_cubic_asymmetric")
# check_onnx_test_case!("node", "test_resize_upsample_scales_linear")
# check_onnx_test_case!("node", "test_resize_upsample_scales_linear_align_corners")
# check_onnx_test_case!("node", "test_resize_upsample_scales_nearest")
# check_onnx_test_case!("node", "test_resize_upsample_scales_nearest_ceil_half_pixel")
# check_onnx_test_case!("node", "test_resize_upsample_scales_nearest_floor_align_corners")
# check_onnx_test_case!("node", "test_resize_upsample_scales_nearest_round_prefer_ceil_asymmetric")
end

# test "Round" do
# check_onnx_test_case!("node", "test_round")
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10 changes: 10 additions & 0 deletions test/test_resize_downsample_scales_nearest/decoded/input_0.txt
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@@ -0,0 +1,10 @@
#Nx.Tensor<
f32[1][1][2][4]
[
[
[
[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0]
]
]

4 changes: 4 additions & 0 deletions test/test_resize_downsample_scales_nearest/decoded/input_1.txt
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#Nx.Tensor<
f32[4]
[1.0, 1.0, 0.6000000238418579, 0.6000000238418579]
>
73 changes: 73 additions & 0 deletions test/test_resize_downsample_scales_nearest/decoded/model.txt
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@@ -0,0 +1,73 @@
ir_version: 7
producer_name: "backend-test"
graph {
node {
input: "X"
input: ""
input: "scales"
output: "Y"
op_type: "Resize"
attribute {
name: "mode"
s: "nearest"
type: STRING
}
}
name: "test_resize_downsample_scales_nearest"
initializer {
dims: 4
data_type: 1
float_data: 1
float_data: 1
float_data: 0.6
float_data: 0.6
name: "scales"
}
input {
name: "X"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 2
}
dim {
dim_value: 4
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 2
}
}
}
}
}
}
opset_import {
version: 13
}
10 changes: 10 additions & 0 deletions test/test_resize_downsample_scales_nearest/decoded/output_0.txt
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#Nx.Tensor<
f32[1][1][1][2]
[
[
[
[1.0, 3.0]
]
]
]
>
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