Merge branch 'master' into gomachine/tracing

examples/tiny-yolo-v3-coco/README.md

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+# Tiny YOLO v3
+
+## Table of Contents
+
+- [About](#about)
+- [Theory](#theory)
+- [Run example](#run-example)
+- [Benchmark](#benchmark)
+
+## About
+This is an example of Tiny YOLO v3 neural network.
+
+**Note: do not try to use common yolov3, because shortcut layer is not implemented here**
+
+Folder `model` contains yolov3-tiny.cfg on which file `yolov3_tiny.go` based.
+
+Folder `data` contains image file `dog_416x416.jpg` - this is scaled to 416x416 image for make it better understanding of how net works.
+
+## Theory
+You can read about this network [here](https://pjreddie.com/darknet/yolo/).
+
+Architecture of network:
+```
+0 Convolutional 16 3 × 3/1 416 × 416 × 3 416 × 416 × 16
+1 Maxpool    2 × 2/2 416 × 416 × 16 208 × 208 × 16
+2 Convolutional 32 3 × 3/1 208 × 208 × 16 208 × 208 × 32
+3 Maxpool    2 × 2/2 208 × 208 × 32 104 × 104 × 32
+4 Convolutional 64 3 × 3/1 104 × 104 × 32 104 × 104 × 64
+5 Maxpool    2 × 2/2 104 × 104 × 64 52 × 52 × 64
+6 Convolutional 128 3 × 3/1 52 × 52 × 64 52 × 52 × 128
+7 Maxpool    2 × 2/2 52 × 52 × 128 26 × 26 × 128
+8 Convolutional 256 3 × 3/1 26 × 26 × 128 26 × 26 × 256
+9 Maxpool    2 × 2/2 26 × 26 × 256 13 × 13 × 256
+10 Convolutional 512 3 × 3/1 13 × 13 × 256 13 × 13 × 512
+11 Maxpool    2 × 2/1 13 × 13 × 512 13 × 13 × 512
+12 Convolutional 1024 3 × 3/1 13 × 13 × 512 13 × 13 × 1024
+13 Convolutional 256 1 × 1/1 13 × 13 × 1024 13 × 13 × 256
+14 Convolutional 512 3 × 3/1 13 × 13 × 256 13 × 13 × 512
+15 Convolutional 255 1 × 1/1 13 × 13 × 512 13 × 13 × 255
+16 YOLO        
+17 Route 13       
+18 Convolutional 128 1 × 1/1 13 × 13 × 256 13 × 13 × 128
+19 Up‐sampling    2 × 2/1 13 × 13 × 128 26 × 26 × 128
+20 Route 19 8       
+21 Convolutional 256 3 × 3/1 13 × 13 × 384 13 × 13 × 256
+22 Convolutional 255 1 × 1/1 13 × 13 × 256 13 × 13 × 256
+23 YOLO 
+```
+
+You can see source code for each layer's implementation in corresponding files:
+
+Convolution - https://github.com/gorgonia/gorgonia/blob/master/nn.go#L237
+
+Maxpool - https://github.com/gorgonia/gorgonia/blob/master/nn.go#L332
+
+Up-sampling - https://github.com/gorgonia/gorgonia/blob/master/op_upsample.go
+
+Route - [route](route_layer.go)
+
+YOLO - [op_yolo](../../op_yolo.go)
+
+## Run example
+How to run:
+```go
+go run .
+```
+
+What you can expect to see:
+
+![std out](output.png)
+
+## Benchmark
+Benchmark for network's feedforward function provided [here](main_benchmark_test.go)

examples/tiny-yolo-v3-coco/conv_layer.go

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+package main
+
+import (
+	"fmt"
+
+	"github.com/pkg/errors"
+	"gorgonia.org/gorgonia"
+	"gorgonia.org/tensor"
+)
+
+type convLayer struct {
+	filters            int
+	padding            int
+	kernelSize         int
+	stride             int
+	activation         string
+	activationReLUCoef float64
+	batchNormalize     int
+	bias               bool
+	biases             []float32
+
+	layerIndex int
+
+	convNode *gorgonia.Node
+	biasNode *gorgonia.Node
+}
+
+func (l *convLayer) String() string {
+	return fmt.Sprintf(
+		"Convolution layer: Filters->%[1]d Padding->%[2]d Kernel->%[3]dx%[3]d Stride->%[4]d Activation->%[5]s Batch->%[6]d Bias->%[7]t",
+		l.filters, l.padding, l.kernelSize, l.stride, l.activation, l.batchNormalize, l.bias,
+	)
+}
+
+func (l *convLayer) Type() string {
+	return "convolutional"
+}
+
+func (l *convLayer) ToNode(g *gorgonia.ExprGraph, input ...*gorgonia.Node) (*gorgonia.Node, error) {
+
+	convOut, err := gorgonia.Conv2d(input[0], l.convNode, tensor.Shape{l.kernelSize, l.kernelSize}, []int{l.padding, l.padding}, []int{l.stride, l.stride}, []int{1, 1})
+	if err != nil {
+		return &gorgonia.Node{}, errors.Wrap(err, "Can't prepare convolution operation")
+	}
+
+	shp := convOut.Shape()
+	iters := shp.TotalSize() / len(l.biases)
+	dataF32 := []float32{}
+	for b := 0; b < len(l.biases); b++ {
+		for j := 0; j < iters; j++ {
+			dataF32 = append(dataF32, l.biases[b])
+		}
+	}
+	biasTensor := tensor.New(tensor.WithBacking(dataF32), tensor.WithShape(shp...))
+	l.biasNode = gorgonia.NewTensor(g, tensor.Float32, 4, gorgonia.WithShape(shp...), gorgonia.WithName(fmt.Sprintf("bias_%d", l.layerIndex)), gorgonia.WithValue(biasTensor))
+
+	biasOut, err := gorgonia.Add(convOut, l.biasNode)
+	if err != nil {
+		return &gorgonia.Node{}, errors.Wrap(err, "Can't prepare bias add operation")
+	}
+
+	if l.activation == "leaky" {
+		activationOut, err := gorgonia.LeakyRelu(biasOut, l.activationReLUCoef)
+		if err != nil {
+			return &gorgonia.Node{}, errors.Wrap(err, "Can't prepare activation operation")
+		}
+		return activationOut, nil
+	}
+	return convOut, nil
+}

examples/tiny-yolo-v3-coco/data/test_yolo_op/test.txt

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+16 0.01 0.01 0.1947 0.1971
+58 0.625 0.96 0.5865 0.1995

examples/tiny-yolo-v3-coco/data/yolov3-tiny.cfg

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+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=2
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=1
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+batch_normalize=1
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 8
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+[yolo]
+mask = 0,1,2
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1