fyp/logic/models/train/torch/torch.go

package train

import (
	types "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
	my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"

	"git.andr3h3nriqu3s.com/andr3/gotch"
	"github.com/charmbracelet/log"

	torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
)

type IForwardable interface {
	Forward(xs *torch.Tensor) *torch.Tensor
}

// Container for a model
type ContainerModel struct {
	Layers []my_nn.MyLayer
	Vs     *my_nn.VarStore
	path   *my_nn.Path
}

func (n *ContainerModel) ForwardT(x *torch.Tensor, train bool) *torch.Tensor {
	if len(n.Layers) == 0 {
		return x.MustShallowClone()
	}

	if len(n.Layers) == 1 {
		return n.Layers[0].ForwardT(x, train)
	}

	// forward sequentially
	outs := make([]*torch.Tensor, len(n.Layers))
	for i := 0; i < len(n.Layers); i++ {
		if i == 0 {
			outs[0] = n.Layers[i].ForwardT(x, train)
			//defer outs[0].MustDrop()
		} else if i == len(n.Layers)-1 {
            return n.Layers[i].ForwardT(outs[i-1], train)
		} else {
			outs[i] = n.Layers[i].ForwardT(outs[i-1], train)
		    //defer outs[i].MustDrop()
		}
	}
    panic("Do not reach here")
}

func (n *ContainerModel) To(device gotch.Device) {
	n.Vs.ToDevice(device)
	for _, layer := range n.Layers {
		layer.ExtractFromVarstore(n.Vs)
	}
}

func (n *ContainerModel) Refresh() {
	for _, layer := range n.Layers {
		layer.ExtractFromVarstore(n.Vs)
	}
}

func BuildModel(layers []*types.Layer, _lastLinearSize int64, addSigmoid bool) *ContainerModel {

	base_vs := my_nn.NewVarStore(gotch.CPU)
	vs := base_vs.Root()

	m_layers := []my_nn.MyLayer{}

	var lastLinearSize int64 = _lastLinearSize
	lastLinearConv := []int64{}

	for _, layer := range layers {
		if layer.LayerType == types.LAYER_INPUT {
			lastLinearConv = layer.GetShape()
			log.Info("Input: ", "In:", lastLinearConv)
		} else if layer.LayerType == types.LAYER_DENSE {
			shape := layer.GetShape()
			log.Info("New Dense: ", "In:", lastLinearSize, "out:", shape[0])
			m_layers = append(m_layers, NewLinear(vs, lastLinearSize, shape[0]))
			lastLinearSize = shape[0]
		} else if layer.LayerType == types.LAYER_FLATTEN {
			m_layers = append(m_layers, NewFlatten())
			lastLinearSize = 1
			for _, i := range lastLinearConv {
				lastLinearSize *= i
			}
			log.Info("Flatten: ", "In:", lastLinearConv, "out:", lastLinearSize)
		} else if layer.LayerType == types.LAYER_SIMPLE_BLOCK {
			panic("TODO")
			log.Info("New Block: ", "In:", lastLinearConv, "out:", []int64{lastLinearConv[1] / 2, lastLinearConv[2] / 2, 128})
			//m_layers = append(m_layers, NewSimpleBlock(vs, lastLinearConv[0]))
			lastLinearConv[0] = 128
			lastLinearConv[1] /= 2
			lastLinearConv[2] /= 2
		}
	}

	if addSigmoid {
		m_layers = append(m_layers, NewSigmoid())
	}

	b := &ContainerModel{
		Layers: m_layers,
		Vs:     base_vs,
		path:   vs,
	}
	return b
}

func (model *ContainerModel) Debug() {
    for _, v := range model.Layers {
        v.Debug()
    }
}

func SaveModel(model *ContainerModel, modelFn string) (err error) {
	model.Vs.ToDevice(gotch.CPU)
	return model.Vs.Save(modelFn)
}