Merged develop

.golangci.yml

@@ -43,10 +43,17 @@ linters:
 linters-settings:
   gomnd:
     ignored-functions:
-      - 'strconv.ParseInt'
-      - 'strconv.ParseFloat'
-      - 'strconv.FormatInt'
-      - 'strconv.FormatFloat'
+      - "strconv.ParseInt"
+      - "strconv.ParseFloat"
+      - "strconv.FormatInt"
+      - "strconv.FormatFloat"
+  gocritic:
+    disabled-checks:
+      # In the world of AI tensor's are often denoted with a capital letter.
+      # We want to adopt the go style guide as much as possible but we also want
+      # to be able to easily show when a variable is a Tensor. So we chose to
+      # disable captLocal. Note that any other parameter should use a lower case letters.
+      - "captLocal"
 issues:
   max-issues-per-linter: 0
   max-same-issues: 0

errors.go

@@ -1,7 +1,32 @@
 package gonnx
 
-// InvalidShapeError is used when the shape of an input tensor does not match the expectation.
-const InvalidShapeError = "input shape does not match for %v: expected %v but got %v"
+import (
+	"errors"
+	"fmt"
 
-// SetOutputTensorsError is used when the output of an operation could not be set.
-const SetOutputTensorsError = "could not set output tensors, expected %v tensors but got %v"
+	"github.com/advancedclimatesystems/gonnx/onnx"
+)
+
+var errModel = errors.New("gonnx model error")
+
+type InvalidShapeError struct {
+	expected onnx.Shape
+	actual   []int
+}
+
+func (i InvalidShapeError) Error() string {
+	return fmt.Sprintf("invalid shape error expected: %v actual %v", i.expected, i.actual)
+}
+
+func ErrInvalidShape(expected onnx.Shape, actual []int) error {
+	return InvalidShapeError{
+		expected: expected,
+		actual:   actual,
+	}
+}
+
+// ErrModel is used for when an error ocured during setup of running onnx models.
+// The user can specify a formatted message using the standard formatting rules.
+func ErrModel(format string, a ...any) error {
+	return fmt.Errorf("%w: %s", errModel, fmt.Sprintf(format, a...))
+}

model.go

@@ -2,16 +2,16 @@ package gonnx
 
 import (
 	"archive/zip"
-	"fmt"
-	"io/ioutil"
+	"io"
+	"os"
 
 	"github.com/advancedclimatesystems/gonnx/onnx"
 	"github.com/advancedclimatesystems/gonnx/ops"
 	"google.golang.org/protobuf/proto"
 	"gorgonia.org/tensor"
 )
 
-// Tensors is a map with tensors
+// Tensors is a map with tensors.
 type Tensors map[string]tensor.Tensor
 
 // Model defines a model that can be used for inference.
@@ -23,7 +23,7 @@ type Model struct {
 
 // NewModelFromFile creates a new model from a path to a file.
 func NewModelFromFile(path string) (*Model, error) {
-	bytesModel, err := ioutil.ReadFile(path)
+	bytesModel, err := os.ReadFile(path)
 	if err != nil {
 		return nil, err
 	}
@@ -38,7 +38,7 @@ func NewModelFromZipFile(file *zip.File) (*Model, error) {
 		return nil, err
 	}
 
-	bytesModel, err := ioutil.ReadAll(fc)
+	bytesModel, err := io.ReadAll(fc)
 	if err != nil {
 		return nil, err
 	}
@@ -66,6 +66,7 @@ func NewModel(mp *onnx.ModelProto) (*Model, error) {
 	opsetImports := mp.GetOpsetImport()
 
 	var opsetID int64
+
 	for i := 0; i < len(opsetImports); i++ {
 		version := opsetImports[i].GetVersion()
 		if version > opsetID {
@@ -78,12 +79,11 @@ func NewModel(mp *onnx.ModelProto) (*Model, error) {
 		return nil, err
 	}
 
-	model := &Model{
+	return &Model{
 		mp:          mp,
 		parameters:  params,
 		GetOperator: GetOperator,
-	}
-	return model, nil
+	}, nil
 }
 
 // ModelProtoFromBytes creates an onnx.ModelProto based on a list of bytes.
@@ -92,6 +92,7 @@ func ModelProtoFromBytes(bytesModel []byte) (*onnx.ModelProto, error) {
 	if err := proto.Unmarshal(bytesModel, mp); err != nil {
 		return nil, err
 	}
+
 	return mp, nil
 }
 
@@ -108,16 +109,13 @@ func (m *Model) InputShapes() onnx.Shapes {
 // InputDimSize returns the size of the input dimension given an input tensor.
 func (m *Model) InputDimSize(input string, i int) (int, error) {
 	if !m.hasInput(input) {
-		return 0, fmt.Errorf("input %v does not exist", input)
+		return 0, ErrModel("input %v does not exist", input)
 	}
 
 	inputShape := m.mp.Graph.InputShapes()[input]
 
 	if i >= len(inputShape) {
-		err := fmt.Errorf(
-			"input %v only has %d dimensions, but index %d was required", input, len(inputShape), i,
-		)
-		return 0, err
+		return 0, ErrModel("input %v only has %d dimensions, but index %d was required", input, len(inputShape), i)
 	}
 
 	return int(inputShape[i].Size), nil
@@ -222,13 +220,13 @@ func (m *Model) validateShapes(inputTensors Tensors) error {
 
 		tensor, ok := inputTensors[name]
 		if !ok {
-			return fmt.Errorf("tensor: %v not found", name)
+			return ErrModel("tensor: %v not found", name)
 		}
 
 		shapeReceived := tensor.Shape()
 
 		if len(shapeReceived) != len(shapeExpected) {
-			return fmt.Errorf(InvalidShapeError, name, shapeExpected, shapeReceived)
+			return ErrInvalidShape(shapeExpected, shapeReceived)
 		}
 
 		for i, dim := range shapeExpected {
@@ -239,7 +237,7 @@ func (m *Model) validateShapes(inputTensors Tensors) error {
 			}
 
 			if dim.Size != int64(shapeReceived[i]) {
-				return fmt.Errorf(InvalidShapeError, name, shapeExpected, shapeReceived)
+				return ErrInvalidShape(shapeExpected, shapeReceived)
 			}
 		}
 	}
@@ -249,6 +247,7 @@ func (m *Model) validateShapes(inputTensors Tensors) error {
 
 func getInputTensorsForNode(names []string, tensors Tensors) ([]tensor.Tensor, error) {
 	var inputTensors []tensor.Tensor
+
 	for _, tensorName := range names {
 		// An empty name can happen in between optional inputs, like:
 		//   [<required_input>, <optional_input>, nil, <optional_input>]
@@ -259,7 +258,7 @@ func getInputTensorsForNode(names []string, tensors Tensors) ([]tensor.Tensor, e
 		} else if tensor, ok := tensors[tensorName]; ok {
 			inputTensors = append(inputTensors, tensor)
 		} else {
-			return nil, fmt.Errorf("no tensor yet for name %v", tensorName)
+			return nil, ErrModel("no tensor yet for name %v", tensorName)
 		}
 	}
 
@@ -270,7 +269,7 @@ func setOutputTensorsOfNode(
 	names []string, outputTensors []tensor.Tensor, tensors Tensors,
 ) error {
 	if len(names) != len(outputTensors) {
-		return fmt.Errorf(SetOutputTensorsError, len(names), len(outputTensors))
+		return ErrModel("could not set output tensor")
 	}
 
 	for i, tensor := range outputTensors {

model_test.go

@@ -1,8 +1,6 @@
 package gonnx
 
 import (
-	"errors"
-	"fmt"
 	"testing"
 
 	"github.com/advancedclimatesystems/gonnx/onnx"
@@ -39,9 +37,7 @@ func TestModel(t *testing.T) {
 				[][]float32{rangeFloat(16)},
 			),
 			nil,
-			errors.New(
-				"input shape does not match for data_input: expected [0 3] but got (2, 4, 2)",
-			),
+			ErrInvalidShape([]onnx.Dim{{IsDynamic: true, Name: "batch_size", Size: 0}, {IsDynamic: false, Name: "", Size: 3}}, []int{2, 4, 2}),
 		},
 		{
 			"./sample_models/onnx_models/mlp.onnx",
@@ -51,7 +47,7 @@ func TestModel(t *testing.T) {
 				[][]float32{rangeFloat(6)},
 			),
 			nil,
-			errors.New("tensor: data_input not found"),
+			ErrModel("tensor: %v not found", "data_input"),
 		},
 		{
 			"./sample_models/onnx_models/gru.onnx",
@@ -106,6 +102,7 @@ func TestModel(t *testing.T) {
 		outputs, err := model.Run(test.input)
 
 		assert.Equal(t, test.err, err)
+
 		if test.expected == nil {
 			assert.Nil(t, outputs)
 		} else {
@@ -128,6 +125,7 @@ func TestModelIOUtil(t *testing.T) {
 			{IsDynamic: false, Name: "", Size: 3},
 		},
 	}
+
 	assert.Equal(t, []string{"data_input"}, model.InputNames())
 	assert.Equal(t, expectedInputShapes, model.InputShapes())
 
@@ -137,6 +135,7 @@ func TestModelIOUtil(t *testing.T) {
 			{IsDynamic: false, Name: "", Size: 2},
 		},
 	}
+
 	assert.Equal(t, []string{"preds"}, model.OutputNames())
 	assert.Equal(t, expectedOutputShapes, model.OutputShapes())
 	assert.Equal(t, expectedOutputShapes["preds"], model.OutputShape("preds"))
@@ -165,11 +164,12 @@ func TestInputDimSizeInvalidInput(t *testing.T) {
 	assert.Nil(t, err)
 
 	_, err = model.InputDimSize("swagger", 0)
-	assert.Equal(t, fmt.Errorf("input swagger does not exist"), err)
+
+	assert.Equal(t, ErrModel("input %v does not exist", "swagger"), err)
 }
 
 // tensorsFixture creates Tensors with the given names shapes and backings. This is useful for
-// providing a model with inputs and checking it's outputs
+// providing a model with inputs and checking it's outputs.
 func tensorsFixture(names []string, shapes [][]int, backing [][]float32) Tensors {
 	res := make(Tensors, len(names))
 	for i, name := range names {
@@ -178,6 +178,7 @@ func tensorsFixture(names []string, shapes [][]int, backing [][]float32) Tensors
 			tensor.WithBacking(backing[i]),
 		)
 	}
+
 	return res
 }
 
@@ -186,6 +187,7 @@ func rangeFloat(size int) []float32 {
 	for i := 0; i < size; i++ {
 		res[i] = float32(i)
 	}
+
 	return res
 }
 
@@ -194,6 +196,7 @@ func rangeZeros(size int) []float32 {
 	for i := range res {
 		res[i] = 0.0
 	}
+
 	return res
 }