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Allow 1-D convolutions. (#86) 

* Fix check.

* Fix comment.
This commit is contained in:
Gheorghe-Teodor Bercea 2020-02-14 10:54:08 -05:00 committed by GitHub
parent b521719587
commit 17d84901b7
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 58 additions and 47 deletions
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6
src/dialect/onnx/onnx_ops.cpp
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@ -730,9 +730,9 @@ void ONNXConvNoBiasOp::inferShapes() {
auto dataShape = dataTy.getShape();
auto weightShape = weightTy.getShape();
// Lowest ranked input supported is of shape (N x C x H x W).
if (dataShape.size() < 4)
emitError("Data input shape must be at least (NxCxHxW).");
// Lowest supported convolution is a one dimensional convolution.
if (dataShape.size() < 3)
emitError("Data input shape must be at least (NxCxD1).");
// Check that shape of weight and data have same length.
if (dataShape.size() != weightShape.size())

99
test/mlir/onnx/onnx_shape_inference.mlir
View File

@ -143,27 +143,38 @@ func @test_matmul_10(%arg0 : tensor<?x42x32xf32>, %arg1 : tensor<32xf32>) -> ten
/// Test shape inference for ConvNoBias operation and all its attributes.
//===----------------------------------------------------------------------===//
/// Default and required attributes for 1-D convolution.
func @test_conv_no_bias_0(%arg0 : tensor<1x2x32xf32>, %arg1 : tensor<5x2x6xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_conv_no_bias_0
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>) -> tensor<1x5x27xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27xf32>
}
/// Default and required attributes.
func @test_conv_no_bias_1(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_1
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
// CHECK-LABEL: test_conv_no_bias_1
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
}
/// kernel_shape attribute.
func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_2
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
// CHECK-LABEL: test_conv_no_bias_2
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
}
/// pads attribute.
/// Use pads to make output size equal to input size by adding K - 1 to the result.
@ -171,53 +182,53 @@ func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7x
func @test_conv_no_bias_3(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_3
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
// CHECK-LABEL: test_conv_no_bias_3
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
}
/// auto_pad set to SAME_UPPER and SAME_LOWER.
func @test_conv_no_bias_4(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_4
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
// CHECK-LABEL: test_conv_no_bias_4
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
}
func @test_conv_no_bias_5(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_5
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
// CHECK-LABEL: test_conv_no_bias_5
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
}
/// auto_pad set to VALID.
func @test_conv_no_bias_6(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_6
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
// CHECK-LABEL: test_conv_no_bias_6
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
}
/// With strides attribute.
func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_7
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
// CHECK-LABEL: test_conv_no_bias_7
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
}
/// auto_pad set to SAME_UPPER with strides attribute.
/// The auto_pad will pas as if stride is equal to 1.
@ -225,41 +236,41 @@ func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7x
func @test_conv_no_bias_8(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_8
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
// CHECK-LABEL: test_conv_no_bias_8
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
}
/// dilations attribute.
func @test_conv_no_bias_9(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_9
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x20x42xf32>
// CHECK-LABEL: test_conv_no_bias_9
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x20x42xf32>
}
/// dilations attribute with stride.
func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_10
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x10x21xf32>
// CHECK-LABEL: test_conv_no_bias_10
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x10x21xf32>
}
/// dilations attribute with auto_pad set to SAME_UPPER.
func @test_conv_no_bias_11(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_11
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
// CHECK-LABEL: test_conv_no_bias_11
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
}