feat: more work done on the torch branch

.dockerignore

@@ -0,0 +1,5 @@
+tmp/
+testData/
+savedData/
+!savedData/.keep
+fyp

DockerfileServer

@@ -0,0 +1,60 @@
+FROM docker.io/nvidia/cuda:11.8.0-devel-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update
+RUN apt-get install -y wget sudo pkg-config libopencv-dev unzip python3-pip vim
+
+RUN pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0
+
+RUN mkdir /go
+ENV GOPATH=/go
+
+RUN wget https://go.dev/dl/go1.22.2.linux-amd64.tar.gz
+RUN tar -xvf go1.22.2.linux-amd64.tar.gz -C /usr/local
+ENV PATH=$PATH:/usr/local/go/bin
+
+RUN mkdir /app
+WORKDIR /app
+
+ADD  go.mod .
+ADD  go.sum .
+ADD  main.go .
+ADD  logic logic
+
+RUN go install || true
+
+WORKDIR /root
+
+RUN  wget https://git.andr3h3nriqu3s.com/andr3/gotch/raw/commit/22e75becf0432cda41a7c055a4d60ea435f76599/setup-libtorch.sh
+RUN chmod +x setup-libtorch.sh
+ENV CUDA_VER=11.8
+ENV GOTCH_VER=v0.9.2
+RUN bash setup-libtorch.sh
+ENV GOTCH_LIBTORCH="/usr/local/lib/libtorch"
+
+ENV REFRESH_SETUP=0
+
+ENV LIBRARY_PATH="$LIBRARY_PATH:$GOTCH_LIBTORCH/lib"
+ENV export CPATH="$CPATH:$GOTCH_LIBTORCH/lib:$GOTCH_LIBTORCH/include:$GOTCH_LIBTORCH/include/torch/csrc/api/include"
+ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$GOTCH_LIBTORCH/lib:/usr/lib64-nvidia:/usr/local/cuda-${CUDA_VERSION}/lib64"
+RUN wget https://git.andr3h3nriqu3s.com/andr3/gotch/raw/branch/master/setup-gotch.sh
+RUN chmod +x setup-gotch.sh
+RUN echo 'root ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+RUN bash setup-gotch.sh
+
+RUN ln -s /usr/local/lib/libtorch/include/torch/csrc /usr/local/lib/libtorch/include/torch/csrc/api/include/torch
+RUN mkdir -p /go/pkg/mod/git.andr3h3nriqu3s.com/andr3/gotch@v0.9.2/libtch/libtorch/include/torch/csrc/api
+RUN find /usr/local/lib/libtorch/include -maxdepth 1 -type d | tail -n +2 | grep -ve 'torch$' | xargs -I{} ln -s {} /go/pkg/mod/git.andr3h3nriqu3s.com/andr3/gotch@v0.9.2/libtch/libtorch/include
+RUN ln -s /usr/local/lib/libtorch/include/torch/csrc/api/include /go/pkg/mod/git.andr3h3nriqu3s.com/andr3/gotch@v0.9.2/libtch/libtorch/include/torch/csrc/api/include
+RUN find /usr/local/lib/libtorch/include/torch -maxdepth 1 -type f | xargs -I{} ln -s {} /go/pkg/mod/git.andr3h3nriqu3s.com/andr3/gotch@v0.9.2/libtch/libtorch/include/torch
+RUN ln -s /usr/local/lib/libtorch/lib/libcudnn.so.8 /usr/local/lib/libcudnn.so
+
+WORKDIR /app
+
+ENV CGO_CXXFLAGS="-I/usr/local/lib/libtorch/include/torch/csrc/api/include/ -I/usr/local/lib/libtorch/include"
+ENV CGO_CFLAGS="-I/usr/local/lib/libtorch/include/torch/csrc/api/include/ -I/usr/local/lib/libtorch/include"
+
+ADD . .
+RUN go build -x || true
+
+CMD ["bash", "-c", "go run ."]

go.mod

@@ -4,11 +4,10 @@ go 1.21
 
 require (
 	github.com/charmbracelet/log v0.3.1
-	github.com/galeone/tensorflow/tensorflow/go v0.0.0-20240119075110-6ad3cf65adfe
-	github.com/galeone/tfgo v0.0.0-20230715013254-16113111dc99
 	github.com/google/uuid v1.6.0
 	github.com/lib/pq v1.10.9
 	golang.org/x/crypto v0.19.0
+	git.andr3h3nriqu3s.com/andr3/gotch v0.9.2
 )
 
 require (

go.sum

@@ -1,3 +1,7 @@
+git.andr3h3nriqu3s.com/andr3/gotch v0.9.1 h1:1q34JKV8cX80n7LXbJswlXCiRtNbzcvJ/vbgb6an1tA=
+git.andr3h3nriqu3s.com/andr3/gotch v0.9.1/go.mod h1:FXusE3CHt8NLf5wynUGaHtIbToRuYifsZaC5EZH0pJY=
+git.andr3h3nriqu3s.com/andr3/gotch v0.9.2 h1:aZcsPgDVGVhrEFoer0upSkzPqJWNMxdUHRktP4s6MSc=
+git.andr3h3nriqu3s.com/andr3/gotch v0.9.2/go.mod h1:FXusE3CHt8NLf5wynUGaHtIbToRuYifsZaC5EZH0pJY=
 github.com/BurntSushi/toml v1.3.2 h1:o7IhLm0Msx3BaB+n3Ag7L8EVlByGnpq14C4YWiu/gL8=
 github.com/BurntSushi/toml v1.3.2/go.mod h1:CxXYINrC8qIiEnFrOxCa7Jy5BFHlXnUU2pbicEuybxQ=
 github.com/aymanbagabas/go-osc52/v2 v2.0.1 h1:HwpRHbFMcZLEVr42D4p7XBqjyuxQH5SMiErDT4WkJ2k=
@@ -13,12 +17,6 @@ github.com/charmbracelet/log v0.3.1/go.mod h1:OR4E1hutLsax3ZKpXbgUqPtTjQfrh1pG3z
 github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
 github.com/gabriel-vasile/mimetype v1.4.3 h1:in2uUcidCuFcDKtdcBxlR0rJ1+fsokWf+uqxgUFjbI0=
 github.com/gabriel-vasile/mimetype v1.4.3/go.mod h1:d8uq/6HKRL6CGdk+aubisF/M5GcPfT7nKyLpA0lbSSk=
-github.com/galeone/tensorflow/tensorflow/go v0.0.0-20221023090153-6b7fa0680c3e h1:9+2AEFZymTi25FIIcDwuzcOPH04z9+fV6XeLiGORPDI=
-github.com/galeone/tensorflow/tensorflow/go v0.0.0-20221023090153-6b7fa0680c3e/go.mod h1:TelZuq26kz2jysARBwOrTv16629hyUsHmIoj54QqyFo=
-github.com/galeone/tensorflow/tensorflow/go v0.0.0-20240119075110-6ad3cf65adfe h1:7yELf1NFEwECpXMGowkoftcInMlVtLTCdwWLmxKgzNM=
-github.com/galeone/tensorflow/tensorflow/go v0.0.0-20240119075110-6ad3cf65adfe/go.mod h1:TelZuq26kz2jysARBwOrTv16629hyUsHmIoj54QqyFo=
-github.com/galeone/tfgo v0.0.0-20230715013254-16113111dc99 h1:8Bt1P/zy1gb37L4n8CGgp1qmFwBV5729kxVfj0sqhJk=
-github.com/galeone/tfgo v0.0.0-20230715013254-16113111dc99/go.mod h1:3YgYBeIX42t83uP27Bd4bSMxTnQhSbxl0pYSkCDB1tc=
 github.com/go-logfmt/logfmt v0.6.0 h1:wGYYu3uicYdqXVgoYbvnkrPVXkuLM1p1ifugDMEdRi4=
 github.com/go-logfmt/logfmt v0.6.0/go.mod h1:WYhtIu8zTZfxdn5+rREduYbwxfcBr/Vr6KEVveWlfTs=
 github.com/go-playground/locales v0.14.1 h1:EWaQ/wswjilfKLTECiXz7Rh+3BjFhfDFKv/oXslEjJA=
@@ -74,7 +72,9 @@ github.com/rivo/uniseg v0.4.6 h1:Sovz9sDSwbOz9tgUy8JpT+KgCkPYJEN/oYzlJiYTNLg=
 github.com/rivo/uniseg v0.4.6/go.mod h1:FN3SvrM+Zdj16jyLfmOkMNblXMcoc8DfTHruCPUcx88=
 github.com/stretchr/objx v0.1.0/go.mod h1:HFkY916IF+rwdDfMAkV7OtwuqBVzrE8GR6GFx+wExME=
 github.com/stretchr/testify v1.3.0/go.mod h1:M5WIy9Dh21IEIfnGCwXGc5bZfKNJtfHm1UVUgZn+9EI=
+github.com/stretchr/testify v1.6.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
 github.com/stretchr/testify v1.7.0/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/h/Wwjteg=
+github.com/x448/float16 v0.8.4/go.mod h1:14CWIYCyZA/cWjXOioeEpHeN/83MdbZDRQHoFcYsOfg=
 golang.org/x/crypto v0.13.0 h1:mvySKfSWJ+UKUii46M40LOvyWfN0s2U+46/jDd0e6Ck=
 golang.org/x/crypto v0.13.0/go.mod h1:y6Z2r+Rw4iayiXXAIxJIDAJ1zMW4yaTpebo8fPOliYc=
 golang.org/x/crypto v0.18.0 h1:PGVlW0xEltQnzFZ55hkuX5+KLyrMYhHld1YHO4AKcdc=

lib

@@ -0,0 +1 @@
+/usr/local/lib

lib.go.back

@@ -0,0 +1,10 @@
+package libtch
+
+// #cgo LDFLAGS: -lstdc++ -ltorch -lc10 -ltorch_cpu -L${SRCDIR}/libtorch/lib
+// #cgo LDFLAGS: -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcudnn -lcaffe2_nvrtc -lnvrtc-builtins -lnvrtc -lnvToolsExt -lc10_cuda -ltorch_cuda
+// #cgo CFLAGS: -I${SRCDIR} -O3 -Wall -Wno-unused-variable -Wno-deprecated-declarations -Wno-c++11-narrowing -g -Wno-sign-compare -Wno-unused-function
+// #cgo CFLAGS: -D_GLIBCXX_USE_CXX11_ABI=0
+// #cgo CFLAGS: -I/usr/local/cuda/include
+// #cgo CXXFLAGS: -std=c++17 -I${SRCDIR} -g -O3
+// #cgo CXXFLAGS: -I${SRCDIR}/libtorch/lib -I${SRCDIR}/libtorch/include -I${SRCDIR}/libtorch/include/torch/csrc/api/include -I/opt/libtorch/include/torch/csrc/api/include
+import "C"

logic/db_types/classes.go

@@ -6,3 +6,19 @@ const (
 	DATA_POINT_MODE_TRAINING DATA_POINT_MODE = 1
 	DATA_POINT_MODE_TESTING                  = 2
 )
+
+type ModelClassStatus int
+
+const (
+	CLASS_STATUS_TO_TRAIN ModelClassStatus = iota + 1
+	CLASS_STATUS_TRAINING
+	CLASS_STATUS_TRAINED
+)
+
+type ModelClass struct {
+	Id         string `db:"mc.id"`
+	ModelId    string `db:"mc.model_id"`
+	Name       string `db:"mc.name"`
+	ClassOrder int    `db:"mc.class_order"`
+	Status     int    `db:"mc.status"`
+}

logic/db_types/definitions.go

@@ -0,0 +1,95 @@
+package dbtypes
+
+import (
+	"time"
+
+	"git.andr3h3nriqu3s.com/andr3/fyp/logic/db"
+)
+
+type DefinitionStatus int
+
+const (
+	DEFINITION_STATUS_CANCELD_TRAINING DefinitionStatus = -4
+	DEFINITION_STATUS_FAILED_TRAINING                   = -3
+	DEFINITION_STATUS_PRE_INIT                          = 1
+	DEFINITION_STATUS_INIT                              = 2
+	DEFINITION_STATUS_TRAINING                          = 3
+	DEFINITION_STATUS_PAUSED_TRAINING                   = 6
+	DEFINITION_STATUS_TRANIED                           = 4
+	DEFINITION_STATUS_READY                             = 5
+)
+
+type Definition struct {
+	Id             string    `db:"md.id"`
+	ModelId        string    `db:"md.model_id"`
+	Accuracy       float64   `db:"md.accuracy"`
+	TargetAccuracy int       `db:"md.target_accuracy"`
+	Epoch          int       `db:"md.epoch"`
+	Status         int       `db:"md.status"`
+	CreatedOn      time.Time `db:"md.created_on"`
+	EpochProgress  int       `db:"md.epoch_progress"`
+}
+
+type SortByAccuracyDefinitions []*Definition
+
+func (nf SortByAccuracyDefinitions) Len() int      { return len(nf) }
+func (nf SortByAccuracyDefinitions) Swap(i, j int) { nf[i], nf[j] = nf[j], nf[i] }
+func (nf SortByAccuracyDefinitions) Less(i, j int) bool {
+	return nf[i].Accuracy < nf[j].Accuracy
+}
+
+func GetDefinition(db db.Db, definition_id string) (definition Definition, err error) {
+	err = GetDBOnce(db, &definition, "model_definition as md where id=$1;", definition_id)
+	return
+}
+
+func MakeDefenition(db db.Db, model_id string, target_accuracy int) (definition Definition, err error) {
+	var NewDefinition = struct {
+		ModelId        string `db:"model_id"`
+		TargetAccuracy int    `db:"target_accuracy"`
+	}{ModelId: model_id, TargetAccuracy: target_accuracy}
+
+	id, err := InsertReturnId(db, &NewDefinition, "model_definition", "id")
+	if err != nil {
+		return
+	}
+	return GetDefinition(db, id)
+}
+
+func (d Definition) UpdateStatus(db db.Db, status DefinitionStatus) (err error) {
+	_, err = db.Exec("update model_definition set status=$1 where id=$2", status, d.Id)
+	return
+}
+
+func (d Definition) MakeLayer(db db.Db, layer_order int, layer_type LayerType, shape string) (layer Layer, err error) {
+	var NewLayer = struct {
+		DefinitionId string    `db:"def_id"`
+		LayerOrder   int       `db:"layer_order"`
+		LayerType    LayerType `db:"layer_type"`
+		Shape        string    `db:"shape"`
+	}{
+		DefinitionId: d.Id,
+		LayerOrder:   layer_order,
+		LayerType:    layer_type,
+		Shape:        shape,
+	}
+
+	id, err := InsertReturnId(db, &NewLayer, "model_definition_layer", "id")
+	if err != nil {
+		return
+	}
+
+	return GetLayer(db, id)
+}
+
+func (d Definition) GetLayers(db db.Db, filter string, args ...any) (layer []*Layer, err error) {
+	args = append(args, d.Id)
+	return GetDbMultitple[Layer](db, "model_definition_layer as mdl where mdl.def_id=$1 "+filter, args...)
+}
+
+func (d *Definition) UpdateAfterEpoch(db db.Db, accuracy float64) (err error) {
+	d.Accuracy = accuracy
+	d.Epoch += 1
+	_, err = db.Exec("update model_definition set epoch=$1, accuracy=$2 where id=$3", d.Epoch, d.Accuracy, d.Id)
+	return
+}

logic/db_types/layer.go

@@ -0,0 +1,50 @@
+package dbtypes
+
+import (
+	"encoding/json"
+
+	"git.andr3h3nriqu3s.com/andr3/fyp/logic/db"
+)
+
+type LayerType int
+
+const (
+	LAYER_INPUT        LayerType = 1
+	LAYER_DENSE                  = 2
+	LAYER_FLATTEN                = 3
+	LAYER_SIMPLE_BLOCK           = 4
+)
+
+type Layer struct {
+	Id           string    `db:"mdl.id"`
+	DefinitionId string    `db:"mdl.def_id"`
+	LayerOrder   string    `db:"mdl.layer_order"`
+	LayerType    LayerType `db:"mdl.layer_type"`
+	Shape        string    `db:"mdl.shape"`
+	ExpType      string    `db:"mdl.exp_type"`
+}
+
+func ShapeToString(args ...int) string {
+	text, err := json.Marshal(args)
+	if err != nil {
+		panic("Could not generate Shape")
+	}
+	return string(text)
+}
+
+func StringToShape(str string) (shape []int64) {
+	err := json.Unmarshal([]byte(str), &shape)
+	if err != nil {
+		panic("Could not parse Shape")
+	}
+	return
+}
+
+func (l Layer) GetShape() []int64 {
+	return StringToShape(l.Shape)
+}
+
+func GetLayer(db db.Db, layer_id string) (layer Layer, err error) {
+	err = GetDBOnce(db, &layer, "model_definition_layer as mdl where mdl.id=$1", layer_id)
+	return
+}

logic/db_types/types.go

@@ -1,9 +1,12 @@
 package dbtypes
 
 import (
-	"errors"
+	"fmt"
+	"os"
+	"path"
 
 	"git.andr3h3nriqu3s.com/andr3/fyp/logic/db"
+	"github.com/jackc/pgx/v5"
 )
 
 const (
@@ -24,36 +27,6 @@ const (
 	READY_RETRAIN_FAILED = -7
 )
 
-type ModelDefinitionStatus int
-
-type LayerType int
-
-const (
-	LAYER_INPUT        LayerType = 1
-	LAYER_DENSE                  = 2
-	LAYER_FLATTEN                = 3
-	LAYER_SIMPLE_BLOCK           = 4
-)
-
-const (
-	MODEL_DEFINITION_STATUS_CANCELD_TRAINING ModelDefinitionStatus = -4
-	MODEL_DEFINITION_STATUS_FAILED_TRAINING                        = -3
-	MODEL_DEFINITION_STATUS_PRE_INIT                               = 1
-	MODEL_DEFINITION_STATUS_INIT                                   = 2
-	MODEL_DEFINITION_STATUS_TRAINING                               = 3
-	MODEL_DEFINITION_STATUS_PAUSED_TRAINING                        = 6
-	MODEL_DEFINITION_STATUS_TRANIED                                = 4
-	MODEL_DEFINITION_STATUS_READY                                  = 5
-)
-
-type ModelClassStatus int
-
-const (
-	MODEL_CLASS_STATUS_TO_TRAIN ModelClassStatus = 1
-	MODEL_CLASS_STATUS_TRAINING                  = 2
-	MODEL_CLASS_STATUS_TRAINED                   = 3
-)
-
 type ModelHeadStatus int
 
 const (
@@ -78,8 +51,6 @@ type BaseModel struct {
 	CanTrain     int `db:"can_train"`
 }
 
-var ModelNotFoundError = errors.New("Model not found error")
-
 func GetBaseModel(db db.Db, id string) (base *BaseModel, err error) {
 	var model BaseModel
 	err = GetDBOnce(db, &model, "models where id=$1", id)
@@ -97,11 +68,104 @@ func (m BaseModel) CanEval() bool {
 	return true
 }
 
+func (m BaseModel) removeFailedDataPoints(c BasePack) (err error) {
+	rows, err := c.GetDb().Query("select mdp.id from model_data_point as mdp join model_classes as mc on mc.id=mdp.class_id where mc.model_id=$1 and mdp.status=-1;", m.Id)
+	if err != nil {
+		return
+	}
+	defer rows.Close()
+
+	base_path := path.Join("savedData", m.Id, "data")
+
+	for rows.Next() {
+		var dataPointId string
+		err = rows.Scan(&dataPointId)
+		if err != nil {
+			return
+		}
+
+		p := path.Join(base_path, dataPointId+"."+m.Format)
+
+		c.GetLogger().Warn("Removing image", "path", p)
+
+		err = os.RemoveAll(p)
+		if err != nil {
+			return
+		}
+	}
+
+	_, err = c.GetDb().Exec("delete from model_data_point as mdp using model_classes as mc where mdp.class_id = mc.id and mc.model_id=$1 and mdp.status=-1;", m.Id)
+	return
+}
+
+// DO NOT Pass un filtered data on filters
+func (m BaseModel) GetDefinitions(db db.Db, filters string, args ...any) ([]*Definition, error) {
+	n_args := []any{m.Id}
+	n_args = append(n_args, args...)
+	return GetDbMultitple[Definition](db, fmt.Sprintf("model_definition as md where md.model_id=$1 %s", filters), n_args...)
+}
+
+// DO NOT Pass un filtered data on filters
+func (m BaseModel) GetClasses(db db.Db, filters string, args ...any) ([]*ModelClass, error) {
+	n_args := []any{m.Id}
+	n_args = append(n_args, args...)
+	return GetDbMultitple[ModelClass](db, fmt.Sprintf("model_classes as mc where mc.model_id=$1 %s", filters), n_args...)
+}
+
+type DataPointIterator struct {
+	rows  pgx.Rows
+	Model BaseModel
+}
+
+type DataPoint struct {
+	Class int
+	Path  string
+}
+
+func (iter DataPointIterator) Close() {
+	iter.rows.Close()
+}
+
+func (m BaseModel) DataPoints(db db.Db, mode DATA_POINT_MODE) (data []DataPoint, err error) {
+	rows, err := db.Query(
+		"select mdp.id, mc.class_order, mdp.file_path from model_data_point as mdp inner "+
+			"join model_classes as mc on mc.id = mdp.class_id "+
+			"where mc.model_id = $1 and mdp.model_mode=$2;",
+		m.Id, mode)
+	if err != nil {
+		return
+	}
+	defer rows.Close()
+
+	data = []DataPoint{}
+
+	for rows.Next() {
+		var id string
+		var class_order int
+		var file_path string
+		if err = rows.Scan(&id, &class_order, &file_path); err != nil {
+			return
+		}
+		if file_path == "id://" {
+			data = append(data, DataPoint{
+				Path:  path.Join("./savedData", m.Id, "data", id+"."+m.Format),
+				Class: class_order,
+			})
+		} else {
+			panic("TODO remote file path")
+		}
+	}
+	return
+}
+
+const RGB string = "rgb"
+const GRAY string = "greyscale"
+
 func StringToImageMode(colorMode string) int {
 	switch colorMode {
-	case "greyscale":
+	case GRAY:
 		return 1
-	case "rgb":
+	case RGB:
 		return 3
 	default:
 		panic("unkown color mode")

logic/db_types/utils.go

@@ -14,11 +14,13 @@ import (
 	"github.com/charmbracelet/log"
 	"github.com/google/uuid"
 	"github.com/jackc/pgx/v5"
+	"github.com/jackc/pgx/v5/pgconn"
 
 	db "git.andr3h3nriqu3s.com/andr3/fyp/logic/db"
 )
 
 type BasePack interface {
+	db.Db
 	GetDb() db.Db
 	GetLogger() *log.Logger
 	GetHost() string
@@ -42,6 +44,18 @@ func (b BasePackStruct) GetLogger() *log.Logger {
 	return b.Logger
 }
 
+func (c BasePackStruct) Query(query string, args ...any) (pgx.Rows, error) {
+	return c.Db.Query(query, args...)
+}
+
+func (c BasePackStruct) Exec(query string, args ...any) (pgconn.CommandTag, error) {
+	return c.Db.Exec(query, args...)
+}
+
+func (c BasePackStruct) Begin() (pgx.Tx, error) {
+	return c.Db.Begin()
+}
+
 func CheckEmpty(f url.Values, path string) bool {
 	return !f.Has(path) || f.Get(path) == ""
 }

logic/models/classes/main.go

@@ -7,15 +7,15 @@ import (
 	. "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
 )
 
-type ModelClass struct {
+type ModelClassJSON struct {
 	Id      string `json:"id"`
 	ModelId string `json:"model_id" db:"model_id"`
 	Name    string `json:"name"`
 	Status  int    `json:"status"`
 }
 
-func ListClasses(c BasePack, model_id string) (cls []*ModelClass, err error) {
+func ListClassesJSON(c BasePack, model_id string) (cls []*ModelClassJSON, err error) {
-	return GetDbMultitple[ModelClass](c.GetDb(), "model_classes where model_id=$1", model_id)
+	return GetDbMultitple[ModelClassJSON](c.GetDb(), "model_classes where model_id=$1", model_id)
 }
 
 func ModelHasDataPoints(db db.Db, model_id string) (result bool, err error) {

logic/models/data.go

@@ -435,7 +435,7 @@ func handleDataUpload(handle *Handle) {
 		}
 
 		model, err := GetBaseModel(handle.Db, id)
-		if err == ModelNotFoundError {
+		if err == NotFoundError {
 			return c.SendJSONStatus(http.StatusNotFound, "Model not found")
 		} else if err != nil {
 			return c.Error500(err)
@@ -468,7 +468,7 @@ func handleDataUpload(handle *Handle) {
 	}
 	PostAuthJson(handle, "/models/data/class/new", User_Normal, func(c *Context, obj *CreateNewEmptyClass) *Error {
 		model, err := GetBaseModel(c.Db, obj.Id)
-		if err == ModelNotFoundError {
+		if err == NotFoundError {
 			return c.JsonBadRequest("Model not found")
 		} else if err != nil {
 			return c.E500M("Failed to get model information", err)
@@ -495,7 +495,7 @@ func handleDataUpload(handle *Handle) {
 			return c.E500M("Could not create class", err)
 		}
 
-		var modelClass model_classes.ModelClass
+		var modelClass model_classes.ModelClassJSON
 		err = GetDBOnce(c, &modelClass, "model_classes where id=$1;", id)
 		if err != nil {
 			return c.E500M("Failed to get class information but class was creted", err)
@@ -518,7 +518,7 @@ func handleDataUpload(handle *Handle) {
 		c.Logger.Info("model", "model", *model_id)
 
 		model, err := GetBaseModel(c.Db, *model_id)
-		if err == ModelNotFoundError {
+		if err == NotFoundError {
 			return c.JsonBadRequest("Could not find the model")
 		} else if err != nil {
 			return c.E500M("Error getting model information", err)
@@ -626,7 +626,7 @@ func handleDataUpload(handle *Handle) {
 		c.Logger.Info("Trying to expand model", "id", id)
 
 		model, err := GetBaseModel(handle.Db, id)
-		if err == ModelNotFoundError {
+		if err == NotFoundError {
 			return c.SendJSONStatus(http.StatusNotFound, "Model not found")
 		} else if err != nil {
 			return c.Error500(err)
@@ -670,7 +670,7 @@ func handleDataUpload(handle *Handle) {
 		}
 
 		model, err := GetBaseModel(handle.Db, dat.Id)
-		if err == ModelNotFoundError {
+		if err == NotFoundError {
 			return c.SendJSONStatus(http.StatusNotFound, "Model not found")
 		} else if err != nil {
 			return c.Error500(err)
@@ -704,7 +704,7 @@ func handleDataUpload(handle *Handle) {
 				return c.Error500(err)
 			}
 		} else {
-			_, err = handle.Db.Exec("delete from model_classes where model_id=$1 and status=$2;", model.Id, MODEL_CLASS_STATUS_TO_TRAIN)
+			_, err = handle.Db.Exec("delete from model_classes where model_id=$1 and status=$2;", model.Id, CLASS_STATUS_TO_TRAIN)
 			if err != nil {
 				return c.Error500(err)
 			}

logic/models/edit.go

@@ -24,7 +24,7 @@ func handleEdit(handle *Handle) {
 			return c.Error500(err)
 		}
 
-		cls, err := model_classes.ListClasses(c, model.Id)
+		cls, err := model_classes.ListClassesJSON(c, model.Id)
 		if err != nil {
 			return c.Error500(err)
 		}
@@ -35,9 +35,9 @@ func handleEdit(handle *Handle) {
 		}
 
 		type ReturnType struct {
-			Classes               []*model_classes.ModelClass `json:"classes"`
+			Classes               []*model_classes.ModelClassJSON `json:"classes"`
-			HasData               bool                        `json:"has_data"`
+			HasData               bool                            `json:"has_data"`
-			NumberOfInvalidImages int                         `json:"number_of_invalid_images"`
+			NumberOfInvalidImages int                             `json:"number_of_invalid_images"`
 		}
 
 		c.ShowMessage = false
@@ -109,7 +109,7 @@ func handleEdit(handle *Handle) {
 		layers := []layerdef{}
 
 		for _, def := range defs {
-			if def.Status == MODEL_DEFINITION_STATUS_TRAINING {
+			if def.Status == DEFINITION_STATUS_TRAINING {
 				rows, err := c.Db.Query("select id, layer_type, shape from model_definition_layer where def_id=$1 order by layer_order asc;", def.Id)
 				if err != nil {
 					return c.Error500(err)
@@ -166,7 +166,7 @@ func handleEdit(handle *Handle) {
 
 		for i, def := range defs {
 			var lay *[]layerdef = nil
-			if def.Status == MODEL_DEFINITION_STATUS_TRAINING && !setLayers {
+			if def.Status == DEFINITION_STATUS_TRAINING && !setLayers {
 				lay = &layers
 				setLayers = true
 			}

logic/models/train/reset.go

@@ -11,7 +11,7 @@ import (
 func handleRest(handle *Handle) {
 	DeleteAuthJson(handle, "/models/train/reset", User_Normal, func(c *Context, dat *JustId) *Error {
 		model, err := GetBaseModel(c.Db, dat.Id)
-		if err == ModelNotFoundError {
+		if err == NotFoundError {
 			return c.JsonBadRequest("Model not found")
 		} else if err != nil {
 			return c.E500M("Failed to get model", err)

logic/models/train/torch/modelloader/modelloader.go

@@ -0,0 +1,179 @@
+package imageloader
+
+import (
+	"git.andr3h3nriqu3s.com/andr3/fyp/logic/db"
+	types "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
+	"git.andr3h3nriqu3s.com/andr3/gotch"
+	torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
+	"git.andr3h3nriqu3s.com/andr3/gotch/vision"
+)
+
+type Dataset struct {
+	TrainImages     *torch.Tensor
+	TrainLabels     *torch.Tensor
+	TestImages      *torch.Tensor
+	TestLabels      *torch.Tensor
+	TrainImagesSize int
+	TestImagesSize  int
+	Device          gotch.Device
+}
+
+func LoadImagesAndLables(db db.Db, m *types.BaseModel, mode types.DATA_POINT_MODE, classStart int, classEnd int) (imgs, labels *torch.Tensor, count int, err error) {
+	train_points, err := m.DataPoints(db, types.DATA_POINT_MODE_TRAINING)
+	if err != nil {
+		return
+	}
+
+	size := int64(classEnd - classStart + 1)
+
+	pimgs := []*torch.Tensor{}
+	plabels := []*torch.Tensor{}
+
+	for _, point := range train_points {
+		var img, label *torch.Tensor
+		img, err = vision.Load(point.Path)
+		if err != nil {
+			return
+		}
+
+		pimgs = append(pimgs, img)
+
+		t_label := make([]int, size)
+		if point.Class <= classEnd && point.Class >= classStart {
+			t_label[point.Class-classStart] = 1
+		}
+
+		label, err = torch.OfSlice(t_label)
+		if err != nil {
+			return
+		}
+
+		plabels = append(plabels, label)
+	}
+
+	imgs, err = torch.Concat(pimgs, 0)
+	if err != nil {
+		return
+	}
+
+	labels, err = torch.Stack(plabels, 0)
+	if err != nil {
+		return
+	}
+
+	count = len(pimgs)
+
+	imgs, err = torch.Stack(pimgs, 0)
+    if err != nil {
+        return
+    }
+
+	imgs, err = imgs.ToDtype(gotch.Float, false, false, true)
+	if err != nil {
+		return
+	}
+
+	labels, err = labels.ToDtype(gotch.Float, false, false, true)
+	if err != nil {
+		return
+	}
+
+	return
+}
+
+func NewDataset(db db.Db, m *types.BaseModel, classStart int, classEnd int) (ds *Dataset, err error) {
+	trainImages, trainLabels, train_count, err := LoadImagesAndLables(db, m, types.DATA_POINT_MODE_TRAINING, classStart, classEnd)
+	if err != nil {
+		return
+	}
+
+	testImages, testLabels, test_count, err := LoadImagesAndLables(db, m, types.DATA_POINT_MODE_TESTING, classStart, classEnd)
+	if err != nil {
+		return
+	}
+
+	ds = &Dataset{
+		TrainImages:     trainImages,
+		TrainLabels:     trainLabels,
+		TestImages:      testImages,
+		TestLabels:      testLabels,
+		TrainImagesSize: train_count,
+		TestImagesSize:  test_count,
+		Device:          gotch.CPU,
+	}
+	return
+}
+
+func (ds *Dataset) To(device gotch.Device) (err error) {
+	ds.TrainImages, err = ds.TrainImages.ToDevice(device, ds.TrainImages.DType(), device.IsCuda(), true, true)
+	if err != nil {
+		return
+	}
+
+	ds.TrainLabels, err = ds.TrainLabels.ToDevice(device, ds.TrainLabels.DType(), device.IsCuda(), true, true)
+	if err != nil {
+		return
+	}
+
+	ds.TestImages, err = ds.TestImages.ToDevice(device, ds.TestImages.DType(), device.IsCuda(), true, true)
+	if err != nil {
+		return
+	}
+
+	ds.TestLabels, err = ds.TestLabels.ToDevice(device, ds.TestLabels.DType(), device.IsCuda(), true, true)
+	if err != nil {
+		return
+	}
+
+	ds.Device = device
+	return
+}
+
+func (ds *Dataset) TestIter(batchSize int64) *torch.Iter2 {
+	return torch.MustNewIter2(ds.TestImages, ds.TestLabels, batchSize)
+}
+
+func (ds *Dataset) TrainIter(batchSize int64) (iter *torch.Iter2, err error) {
+    
+    // Create a clone of the trainimages
+    size, err := ds.TrainImages.Size()
+    if err != nil {
+        return
+    }
+
+    train_images, err := torch.Zeros(size, gotch.Float, ds.Device)
+    if err != nil {
+        return
+    }
+
+    ds.TrainImages, err = ds.TrainImages.Clone(train_images, false)
+    if err != nil {
+        return
+    }
+
+
+
+    // Create a clone of the labels
+    size, err = ds.TrainLabels.Size()
+    if err != nil {
+        return
+    }
+
+    train_labels, err := torch.Zeros(size, gotch.Float, ds.Device)
+    if err != nil {
+        return
+    }
+
+    ds.TrainLabels, err = ds.TrainLabels.Clone(train_labels, false)
+    if err != nil {
+        return
+    }
+
+
+	iter, err = torch.NewIter2(train_images, train_labels, batchSize)
+	if err != nil {
+		return
+	}
+
+	return
+}

logic/models/train/torch/nn/linear.go

@@ -0,0 +1,174 @@
+package my_nn
+
+// linear is a fully-connected layer
+
+import (
+	"math"
+
+	"git.andr3h3nriqu3s.com/andr3/gotch/nn"
+	"git.andr3h3nriqu3s.com/andr3/gotch/ts"
+	"github.com/charmbracelet/log"
+)
+
+// LinearConfig is a configuration for a linear layer
+type LinearConfig struct {
+	WsInit nn.Init // iniital weights
+	BsInit nn.Init // optional initial bias
+	Bias   bool
+}
+
+// DefaultLinearConfig creates default LinearConfig with
+// weights initiated using KaimingUniform and Bias is set to true
+func DefaultLinearConfig() *LinearConfig {
+	negSlope := math.Sqrt(5)
+	return &LinearConfig{
+		// NOTE. KaimingUniform cause mem leak due to ts.Uniform()!!!
+		// Avoid using it now.
+		WsInit: nn.NewKaimingUniformInit(nn.WithKaimingNegativeSlope(negSlope)),
+		BsInit: nil,
+		Bias:   true,
+	}
+}
+
+// Linear is a linear fully-connected layer
+type Linear struct {
+	Ws *ts.Tensor
+    weight_name string
+	Bs *ts.Tensor
+    bias_name string
+}
+
+// NewLinear creates a new linear layer
+// y = x*wT + b
+// inDim - input dimension (x) [input features - columns]
+// outDim - output dimension (y) [output features - columns]
+// NOTE: w will have shape{outDim, inDim}; b will have shape{outDim}
+func NewLinear(vs *Path, inDim, outDim int64, c *LinearConfig) *Linear {
+    var bias_name string
+	var bs *ts.Tensor
+	var err error
+	if c.Bias {
+		switch {
+		case c.BsInit == nil:
+			shape := []int64{inDim, outDim}
+			fanIn, _, err := nn.CalculateFans(shape)
+            or_panic(err)
+			bound := 0.0
+			if fanIn > 0 {
+				bound = 1 / math.Sqrt(float64(fanIn))
+			}
+			bsInit := nn.NewUniformInit(-bound, bound)
+			bs, bias_name, err = vs.NewVarNamed("bias", []int64{outDim}, bsInit)
+			or_panic(err)
+
+            // Find better way to do this
+            bs, err = bs.T(true)
+            or_panic(err)
+            bs, err = bs.T(true)
+            or_panic(err)
+
+            bs, err = bs.SetRequiresGrad(true, true)
+            or_panic(err)
+
+            err = bs.RetainGrad(false)
+            or_panic(err)
+
+            vs.varstore.UpdateVarTensor(bias_name, bs, true)
+
+		case c.BsInit != nil:
+			bs, bias_name, err = vs.NewVarNamed("bias", []int64{outDim}, c.BsInit)
+			or_panic(err)
+		}
+	}
+
+	ws, weight_name, err := vs.NewVarNamed("weight", []int64{outDim, inDim}, c.WsInit)
+	or_panic(err)
+
+    ws, err = ws.T(true)
+    or_panic(err)
+
+    ws, err = ws.SetRequiresGrad(true, true)
+    or_panic(err)
+
+    err = ws.RetainGrad(false)
+    or_panic(err)
+
+
+    vs.varstore.UpdateVarTensor(weight_name, ws, true)
+
+
+	return &Linear{
+		Ws: ws,
+        weight_name: weight_name,
+		Bs: bs,
+        bias_name: bias_name,
+	}
+}
+
+func (l *Linear) Debug() {
+    log.Info("Ws", "ws", l.Ws.MustGrad(false).MustMax(false).Float64Values())
+    log.Info("Bs", "bs", l.Bs.MustGrad(false).MustMax(false).Float64Values())
+}
+
+func (l *Linear) ExtractFromVarstore(vs *VarStore) {
+    l.Ws = vs.GetTensorOfVar(l.weight_name)
+    l.Bs = vs.GetTensorOfVar(l.bias_name)
+}
+
+// Implement `Module` for `Linear` struct:
+// =======================================
+
+// Forward proceeds input node through linear layer.
+// NOTE:
+// - It assumes that node has dimensions of 2 (matrix).
+// To make it work for matrix multiplication, input node should
+// has same number of **column** as number of **column** in
+// `LinearLayer` `Ws` property as weights matrix will be
+// transposed before multiplied to input node. (They are all used `inDim`)
+// - Input node should have shape of `shape{batch size, input features}`.
+// (shape{batchSize, inDim}). The input features is `inDim` while the
+// output feature is `outDim` in `LinearConfig` struct.
+//
+// Example:
+//
+//	inDim := 3
+//	outDim := 2
+//	batchSize := 4
+//	weights: 2x3
+//	[ 1 1 1
+//		1 1 1 ]
+//
+//	input node: 3x4
+//	[ 1 1 1
+//	  1 1 1
+//	  1 1 1
+//		1 1 1 ]
+func (l *Linear) Forward(xs *ts.Tensor) (retVal *ts.Tensor) {
+	mul, err := xs.Matmul(l.Ws, false)
+    or_panic(err)
+	if l.Bs != nil {
+		mul, err = mul.Add(l.Bs, false)
+        or_panic(err)
+	}
+
+    out, err := mul.Relu(false)
+    or_panic(err)
+
+    return out
+}
+
+// ForwardT implements ModuleT interface for Linear layer.
+//
+// NOTE: train param will not be used.
+func (l *Linear) ForwardT(xs *ts.Tensor, train bool) (retVal *ts.Tensor) {
+	mul, err := xs.Matmul(l.Ws, true)
+    or_panic(err)
+
+
+	mul, err = mul.Add(l.Bs, true)
+    or_panic(err)
+
+    out, err := mul.Relu(true)
+    or_panic(err)
+    return out
+}

logic/models/train/torch/nn/optimizer.go

@@ -0,0 +1,603 @@
+package my_nn
+
+// Optimizers to be used for gradient-descent based training.
+
+import (
+	"fmt"
+	"math"
+
+	"github.com/charmbracelet/log"
+	"git.andr3h3nriqu3s.com/andr3/gotch/ts"
+)
+
+// Optimizer is a struct object to run gradient descent.
+type Optimizer struct {
+	varstore *VarStore
+	opt      *ts.COptimizer
+	// variablesInOptimizer uint8
+	variablesInOptimizer map[string]struct{}
+	config               OptimizerConfig //interface{}
+	stepCount            int
+	lr                   float64
+}
+
+func (o *Optimizer) Debug() {
+	for n, _ := range o.variablesInOptimizer {
+		v := o.varstore.GetVarOfName(n)
+        leaf, err := v.Tensor.IsLeaf(false)
+        or_panic(err)
+
+        retains, err := v.Tensor.RetainsGrad(false)
+        or_panic(err)
+
+		log.Info("[opt] var test", "n", n, "leaf", leaf, "retains", retains)
+	}
+}
+
+func (o *Optimizer) RefreshValues() (err error) {
+	opt, err := o.config.buildCOpt(o.lr)
+	if err != nil {
+		return
+	}
+
+	for name := range o.variablesInOptimizer {
+		v := o.varstore.GetVarOfName(name)
+		if v.Trainable {
+			if err = opt.AddParameter(v.Tensor, v.Group); err != nil {
+				err = fmt.Errorf("Optimizer defaultBuild - AddParameter failed: %w\n", err)
+				return
+			}
+		}
+	}
+
+    o.opt = opt
+    return
+}
+
+// OptimizerConfig defines Optimizer configurations. These configs can be used to build optimizer.
+type OptimizerConfig interface {
+	buildCOpt(lr float64) (*ts.COptimizer, error)
+
+	// Build builds an optimizer with the specified learning rate handling variables stored in `vs`.
+	//
+	// NOTE: Build is a 'default' method. It can be called by wrapping
+	// 'DefaultBuild' function
+	// E.g. AdamOptimizerConfig struct have a method to fullfil `Build` method of
+	// OptimizerConfig by wrapping `DefaultBuild` like
+	// (config AdamOptimizerConfig) Build(vs VarStore, lr float64) (retVal Optimizer, err error){
+	//		return defaultBuild(config, vs, lr)
+	// }
+	Build(vs *VarStore, lr float64) (*Optimizer, error)
+}
+
+// defaultBuild is `default` Build method for OptimizerConfig interface
+func defaultBuild(config OptimizerConfig, vs *VarStore, lr float64) (*Optimizer, error) {
+	opt, err := config.buildCOpt(lr)
+	if err != nil {
+		return nil, err
+	}
+
+	names := make(map[string]struct{})
+	for name, v := range vs.vars {
+		if v.Trainable {
+            log.Info("Adding parameter", "name", name, "g", v.Group)
+			if err = opt.AddParameter(v.Tensor, v.Group); err != nil {
+				err = fmt.Errorf("Optimizer defaultBuild - AddParameter failed: %w\n", err)
+				return nil, err
+			}
+		}
+		names[name] = struct{}{}
+	}
+
+	return &Optimizer{
+		varstore:             vs,
+		opt:                  opt,
+		variablesInOptimizer: names,
+		config:               config,
+		stepCount:            0,
+		lr:                   0,
+	}, nil
+}
+
+// SGD Optimizer:
+//===============
+
+// SGDConfig holds parameters for building the SGD (Stochastic Gradient Descent) optimizer.
+type SGDConfig struct {
+	Momentum  float64
+	Dampening float64
+	Wd        float64
+	Nesterov  bool
+}
+
+// DefaultSGDConfig creates SGDConfig with default values.
+func DefaultSGDConfig() *SGDConfig {
+	return &SGDConfig{
+		Momentum:  0.0,
+		Dampening: 0.0,
+		Wd:        0.0,
+		Nesterov:  false,
+	}
+}
+
+// NewSGD creates the configuration for a SGD optimizer with specified values
+func NewSGDConfig(momentum, dampening, wd float64, nesterov bool) *SGDConfig {
+	return &SGDConfig{
+		Momentum:  momentum,
+		Dampening: dampening,
+		Wd:        wd,
+		Nesterov:  nesterov,
+	}
+}
+
+// Implement OptimizerConfig interface for SGDConfig
+func (c *SGDConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
+	return ts.Sgd(lr, c.Momentum, c.Dampening, c.Wd, c.Nesterov)
+}
+
+func (c *SGDConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
+	return defaultBuild(c, vs, lr)
+}
+
+// Adam optimizer:
+// ===============
+
+type AdamConfig struct {
+	Beta1 float64
+	Beta2 float64
+	Wd    float64
+}
+
+// DefaultAdamConfig creates AdamConfig with default values
+func DefaultAdamConfig() *AdamConfig {
+	return &AdamConfig{
+		Beta1: 0.9,
+		Beta2: 0.999,
+		Wd:    0.0,
+	}
+}
+
+// NewAdamConfig creates AdamConfig with specified values
+func NewAdamConfig(beta1, beta2, wd float64) *AdamConfig {
+	return &AdamConfig{
+		Beta1: beta1,
+		Beta2: beta2,
+		Wd:    wd,
+	}
+}
+
+// Implement OptimizerConfig interface for AdamConfig
+func (c *AdamConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
+	return ts.Adam(lr, c.Beta1, c.Beta2, c.Wd)
+}
+
+func (c *AdamConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
+	return defaultBuild(c, vs, lr)
+}
+
+// AdamW optimizer:
+// ===============
+
+type AdamWConfig struct {
+	Beta1 float64
+	Beta2 float64
+	Wd    float64
+}
+
+// DefaultAdamWConfig creates AdamWConfig with default values
+func DefaultAdamWConfig() *AdamWConfig {
+	return &AdamWConfig{
+		Beta1: 0.9,
+		Beta2: 0.999,
+		Wd:    0.01,
+	}
+}
+
+// NewAdamWConfig creates AdamWConfig with specified values
+func NewAdamWConfig(beta1, beta2, wd float64) *AdamWConfig {
+	return &AdamWConfig{
+		Beta1: beta1,
+		Beta2: beta2,
+		Wd:    wd,
+	}
+}
+
+// Implement OptimizerConfig interface for AdamWConfig
+func (c *AdamWConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
+	return ts.AdamW(lr, c.Beta1, c.Beta2, c.Wd)
+}
+
+// Build builds AdamW optimizer
+func (c *AdamWConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
+	return defaultBuild(c, vs, lr)
+}
+
+// RMSProp optimizer:
+// ===============
+
+type RMSPropConfig struct {
+	Alpha    float64
+	Eps      float64
+	Wd       float64
+	Momentum float64
+	Centered bool
+}
+
+// DefaultAdamConfig creates AdamConfig with default values
+func DefaultRMSPropConfig() *RMSPropConfig {
+	return &RMSPropConfig{
+		Alpha:    0.99,
+		Eps:      1e-8,
+		Wd:       0.0,
+		Momentum: 0.0,
+		Centered: false,
+	}
+}
+
+// NewRMSPropConfig creates RMSPropConfig with specified values
+func NewRMSPropConfig(alpha, eps, wd, momentum float64, centered bool) *RMSPropConfig {
+	return &RMSPropConfig{
+		Alpha:    alpha,
+		Eps:      eps,
+		Wd:       wd,
+		Momentum: momentum,
+		Centered: centered,
+	}
+}
+
+// Implement OptimizerConfig interface for RMSPropConfig
+func (c *RMSPropConfig) buildCOpt(lr float64) (*ts.COptimizer, error) {
+	return ts.RmsProp(lr, c.Alpha, c.Eps, c.Wd, c.Momentum, c.Centered)
+}
+
+func (c *RMSPropConfig) Build(vs *VarStore, lr float64) (*Optimizer, error) {
+	return defaultBuild(c, vs, lr)
+}
+
+// Optimizer methods:
+// ==================
+
+func (opt *Optimizer) addMissingVariables() {
+	type param struct {
+		tensor *ts.Tensor
+		group  uint
+	}
+	trainables := make(map[string]param)
+	for name, v := range opt.varstore.vars {
+		if v.Trainable {
+			trainables[name] = param{tensor: v.Tensor, group: v.Group}
+		}
+	}
+	missingVariables := len(trainables) - len(opt.variablesInOptimizer)
+	if missingVariables > 0 {
+		log.Info("INFO: Optimizer.addMissingVariables()...")
+		for name, x := range trainables {
+			if _, ok := opt.variablesInOptimizer[name]; !ok {
+				opt.opt.AddParameter(x.tensor, x.group)
+				opt.variablesInOptimizer[name] = struct{}{}
+			}
+		}
+	}
+}
+
+// ZeroGrad zeroes the gradient for the tensors tracked by this optimizer.
+func (opt *Optimizer) ZeroGrad() error {
+	if err := opt.opt.ZeroGrad(); err != nil {
+		err = fmt.Errorf("Optimizer.ZeroGrad() failed: %w\n", err)
+		return err
+	}
+	return nil
+}
+
+// MustZeroGrad zeroes the gradient for the tensors tracked by this optimizer.
+func (opt *Optimizer) MustZeroGrad() {
+	err := opt.ZeroGrad()
+	if err != nil {
+		log.Fatal(err)
+	}
+}
+
+// Clips gradient value at some specified maximum value.
+func (opt *Optimizer) ClipGradValue(max float64) {
+	opt.varstore.Lock()
+	defer opt.varstore.Unlock()
+
+	for _, v := range opt.varstore.vars {
+		if v.Trainable {
+			// v.Tensor.MustGrad().Clamp_(ts.FloatScalar(-max), ts.FloatScalar(max))
+			gradTs := v.Tensor.MustGrad(false)
+			gradTs.Clamp_(ts.FloatScalar(-max), ts.FloatScalar(max))
+		}
+	}
+}
+
+// Step performs an optimization step, updating the tracked tensors based on their gradients.
+func (opt *Optimizer) Step() error {
+	err := opt.opt.Step()
+	if err != nil {
+		err = fmt.Errorf("Optimizer.Step() failed: %w\n", err)
+		return err
+	}
+	opt.stepCount += 1
+
+	return nil
+}
+
+// MustStep performs an optimization step, updating the tracked tensors based on their gradients.
+func (opt *Optimizer) MustStep() {
+	err := opt.Step()
+	if err != nil {
+		log.Fatal(err)
+	}
+}
+
+// ResetStepCount set step count to zero.
+func (opt *Optimizer) ResetStepCount() {
+	opt.stepCount = 0
+}
+
+// StepCount get current step count.
+func (opt *Optimizer) StepCount() int {
+	return opt.stepCount
+}
+
+// BackwardStep applies a backward step pass, update the gradients, and performs an optimization step.
+func (opt *Optimizer) BackwardStep(loss *ts.Tensor) error {
+	err := opt.opt.ZeroGrad()
+	if err != nil {
+		err = fmt.Errorf("Optimizer.BackwardStep() failed: %w\n", err)
+		return err
+	}
+
+	loss.MustBackward()
+	err = opt.opt.Step()
+	if err != nil {
+		err = fmt.Errorf("Optimizer.BackwardStep() failed: %w\n", err)
+		return err
+	}
+
+	return nil
+}
+
+// MustBackwardStep applies a backward step pass, update the gradients, and performs an optimization step.
+func (opt *Optimizer) MustBackwardStep(loss *ts.Tensor) {
+	err := opt.BackwardStep(loss)
+	if err != nil {
+		log.Fatal(err)
+	}
+}
+
+// BackwardStepClip applies a backward step pass, update the gradients, and performs an optimization step.
+//
+// The gradients are clipped based on `max` before being applied.
+func (opt *Optimizer) BackwardStepClip(loss *ts.Tensor, max float64) error {
+	err := opt.opt.ZeroGrad()
+	if err != nil {
+		err = fmt.Errorf("Optimizer.BackwardStepClip() failed: %w\n", err)
+		return err
+	}
+	loss.MustBackward()
+	opt.ClipGradValue(max)
+	err = opt.opt.Step()
+	if err != nil {
+		err = fmt.Errorf("Optimizer.BackwardStepClip() failed: %w\n", err)
+		return err
+	}
+	return nil
+}
+
+// MustBackwardStepClip applies a backward step pass, update the gradients, and performs an optimization step.
+//
+// The gradients are clipped based on `max` before being applied.
+func (opt *Optimizer) MustBackwardStepClip(loss *ts.Tensor, max float64) {
+	err := opt.BackwardStepClip(loss, max)
+	if err != nil {
+		log.Fatal(err)
+	}
+}
+
+type ClipOpts struct {
+	NormType         float64
+	ErrorIfNonFinite bool
+}
+
+type ClipOpt func(*ClipOpts)
+
+func defaultClipOpts() *ClipOpts {
+	return &ClipOpts{
+		NormType:         2.0,
+		ErrorIfNonFinite: false, // will switch to "true" in the future.
+	}
+}
+
+func WithNormType(v float64) ClipOpt {
+	return func(o *ClipOpts) {
+		o.NormType = v
+	}
+}
+
+func WithErrorIfNonFinite(v bool) ClipOpt {
+	return func(o *ClipOpts) {
+		o.ErrorIfNonFinite = v
+	}
+}
+
+// / Clips gradient L2 norm over all trainable parameters.
+//
+// The norm is computed over all gradients together, as if they were
+// concatenated into a single vector.
+//
+// / Args:
+// - max: max norm of the gradient
+// - o.NormType. Type of the used p-norm, can be "inf" for infinity norm. Default= 2.0
+// - o.ErrorIfNonFinite bool. If true, throw error if total norm of the gradients from paramters is "nan", "inf" or "-inf". Default=false
+// Returns: total norm of the parameters (viewed as a single vector)
+// ref. https://github.com/pytorch/pytorch/blob/cb4aeff7d8e4c70bb638cf159878c5204d0cc2da/torch/nn/utils/clip_grad.py#L59
+func (opt *Optimizer) ClipGradNorm(max float64, opts ...ClipOpt) error {
+	o := defaultClipOpts()
+	for _, option := range opts {
+		option(o)
+	}
+
+	opt.varstore.Lock()
+	defer opt.varstore.Unlock()
+	parameters := opt.varstore.TrainableVariables()
+	if len(parameters) == 0 {
+		// return ts.MustOfSlice([]float64{0.0}), nil
+		return nil
+	}
+
+	var (
+		norms     []*ts.Tensor
+		totalNorm *ts.Tensor
+	)
+
+	device := opt.varstore.device
+
+	// FIXME. What about mixed-precision?
+	dtype := parameters[0].DType()
+
+	if o.NormType == math.Inf(1) {
+		for _, v := range opt.varstore.vars {
+			n := v.Tensor.MustGrad(false).MustDetach(true).MustAbs(true).MustMax(true).MustTo(device, true)
+			norms = append(norms, n)
+		}
+		// total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
+		totalNorm = ts.MustStack(norms, 0).MustMax(true)
+	} else {
+		for _, v := range opt.varstore.vars {
+			// x := v.Tensor.MustGrad(false).MustNorm(true)
+
+			// NOTE. tensor.Norm() is going to be deprecated. So use linalg_norm
+			// Ref. https://pytorch.org/docs/stable/generated/torch.linalg.norm.html#torch.linalg.norm
+			x := v.Tensor.MustGrad(false).MustDetach(true).MustLinalgNorm(ts.FloatScalar(o.NormType), nil, false, dtype, true)
+			norms = append(norms, x)
+		}
+	}
+
+	// totalNorm = ts.MustStack(norms, 0).MustNorm(true).MustAddScalar(ts.FloatScalar(1e-6), true)
+	// total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
+	totalNorm = ts.MustStack(norms, 0).MustLinalgNorm(ts.FloatScalar(o.NormType), nil, false, dtype, true)
+	for _, x := range norms {
+		x.MustDrop()
+	}
+
+	totalNormVal := totalNorm.Float64Values(true)[0]
+	//  if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()):
+	if o.ErrorIfNonFinite && (math.IsNaN(totalNormVal) || math.IsInf(totalNormVal, 1)) {
+		err := fmt.Errorf("The total norm of order (%v) for gradients from 'parameters' is non-finite, so it cannot be clipped. To disable this error and scale the gradients by the non-finite norm anyway, set option.ErrorIfNonFinite= false", o.NormType)
+		return err
+	}
+
+	// clip_coef = max_norm / (total_norm + 1e-6)
+	// clipCoefTs := ts.TensorFrom([]float64{max}).MustDiv(totalNorm, true)
+	clipCoef := max / (totalNormVal + 1e-6)
+	// NOTE: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so
+	// avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization
+	// when the gradients do not reside in CPU memory.
+	// clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
+	if clipCoef > 1.0 {
+		clipCoef = 1.0
+	}
+	for _, v := range opt.varstore.vars {
+		if v.Trainable {
+			// p.grad.detach().mul_(clip_coef_clamped.to(p.grad.device))
+			// v.Tensor.MustGrad(false).MustDetach(true).MustMulScalar_(ts.FloatScalar(clipCoef))
+			v.Tensor.MustGrad(false).MustMulScalar_(ts.FloatScalar(clipCoef))
+		}
+	}
+
+	return nil
+}
+
+// BackwardStepClipNorm applies a backward step pass, update the gradients, and performs an optimization step.
+//
+// The gradients L2 norm is clipped based on `max`.
+func (opt *Optimizer) BackwardStepClipNorm(loss *ts.Tensor, max float64, opts ...ClipOpt) error {
+	err := opt.opt.ZeroGrad()
+	if err != nil {
+		err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
+		return err
+	}
+	err = loss.Backward()
+	if err != nil {
+		err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
+		return err
+	}
+
+	err = opt.ClipGradNorm(max, opts...)
+	if err != nil {
+		err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
+		return err
+	}
+
+	err = opt.Step()
+	if err != nil {
+		err := fmt.Errorf("Optimizer.BackwardStepClipNorm() failed: %w\n", err)
+		return err
+	}
+
+	return nil
+}
+
+// MustBackwardStepClipNorm applies a backward step pass, update the gradients, and performs an optimization step.
+//
+// The gradients L2 norm is clipped based on `max`.
+func (opt *Optimizer) MustBackwardStepClipNorm(loss *ts.Tensor, max float64, opts ...ClipOpt) {
+	err := opt.BackwardStepClipNorm(loss, max, opts...)
+	if err != nil {
+		log.Fatal(err)
+	}
+}
+
+// SetLR sets the optimizer learning rate.
+//
+// NOTE. it sets a SINGLE value of learning rate for all parameter groups.
+// Most of the time, there's one parameter group.
+func (opt *Optimizer) SetLR(lr float64) {
+	err := opt.opt.SetLearningRate(lr)
+	if err != nil {
+		log.Fatalf("Optimizer - SetLR  method call error: %v\n", err)
+	}
+}
+
+func (opt *Optimizer) GetLRs() []float64 {
+	lrs, err := opt.opt.GetLearningRates()
+	if err != nil {
+		log.Fatalf("Optimizer - GetLRs  method call error: %v\n", err)
+	}
+
+	return lrs
+}
+
+// SetLRs sets learning rates for ALL parameter groups respectively.
+func (opt *Optimizer) SetLRs(lrs []float64) {
+	err := opt.opt.SetLearningRates(lrs)
+	if err != nil {
+		log.Fatalf("Optimizer - SetLRs  method call error: %v\n", err)
+	}
+}
+
+// SetMomentum sets the optimizer momentum.
+func (opt *Optimizer) SetMomentum(m float64) {
+	err := opt.opt.SetMomentum(m)
+	if err != nil {
+		log.Fatalf("Optimizer - SetMomentum  method call error: %v\n", err)
+	}
+}
+
+func (opt *Optimizer) ParamGroupNum() int {
+	ngroup, err := opt.opt.ParamGroupNum()
+	if err != nil {
+		log.Fatalf("Optimizer - ParamGroupNum  method call error: %v\n", err)
+	}
+
+	return int(ngroup)
+}
+
+func (opt *Optimizer) AddParamGroup(tensors []*ts.Tensor) {
+	err := opt.opt.AddParamGroup(tensors)
+	if err != nil {
+		log.Fatalf("Optimizer - ParamGroupNum  method call error: %v\n", err)
+	}
+}

logic/models/train/torch/nn/utils.go

@@ -0,0 +1,18 @@
+package my_nn
+
+import (
+	torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
+)
+
+func or_panic(err error) {
+	if err != nil {
+		panic(err)
+	}
+}
+
+type MyLayer interface {
+    torch.ModuleT
+
+    ExtractFromVarstore(vs *VarStore)
+    Debug()
+}

logic/models/train/torch/torch.go

@@ -0,0 +1,120 @@
+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 {
+        log.Info("here")
+		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)
+}

logic/models/train/torch/utils.go

@@ -0,0 +1,152 @@
+package train
+
+import (
+	"unsafe"
+
+	my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
+
+	"github.com/charmbracelet/log"
+
+	"git.andr3h3nriqu3s.com/andr3/gotch/nn"
+	torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
+)
+
+func or_panic(err error) {
+	if err != nil {
+		log.Fatal(err)
+	}
+}
+
+type SimpleBlock struct {
+	C1, C2 *nn.Conv2D
+	BN1    *nn.BatchNorm
+}
+
+// BasicBlock returns a BasicBlockModule instance
+func NewSimpleBlock(_vs *my_nn.Path, inplanes int64) *SimpleBlock {
+    vs := (*nn.Path)(unsafe.Pointer(_vs))
+
+	conf1 := nn.DefaultConv2DConfig()
+	conf1.Stride = []int64{2, 2}
+
+	conf2 := nn.DefaultConv2DConfig()
+	conf2.Padding = []int64{2, 2}
+
+	b := &SimpleBlock{
+		C1:  nn.NewConv2D(vs, inplanes, 128, 3, conf1),
+		C2:  nn.NewConv2D(vs, 128, 128, 3, conf2),
+		BN1: nn.NewBatchNorm(vs, 2, 128, nn.DefaultBatchNormConfig()),
+	}
+	return b
+}
+
+// Forward method
+func (b *SimpleBlock) Forward(x *torch.Tensor) *torch.Tensor {
+	identity := x
+
+	out := b.C1.Forward(x)
+	out = out.MustRelu(false)
+
+	out = b.C2.Forward(out)
+	out = out.MustRelu(false)
+
+	shape, err := out.Size()
+	or_panic(err)
+
+	out, err = out.AdaptiveAvgPool2d(shape, false)
+	or_panic(err)
+
+	out = b.BN1.Forward(out)
+	out, err = out.LeakyRelu(false)
+	or_panic(err)
+
+	out = out.MustAdd(identity, false)
+	out = out.MustRelu(false)
+
+	return out
+}
+
+func (b *SimpleBlock) ForwardT(x *torch.Tensor, train bool) *torch.Tensor {
+	identity := x
+
+	out := b.C1.ForwardT(x, train)
+	out = out.MustRelu(false)
+
+	out = b.C2.ForwardT(out, train)
+	out = out.MustRelu(false)
+
+	shape, err := out.Size()
+	or_panic(err)
+
+	out, err = out.AdaptiveAvgPool2d(shape, false)
+	or_panic(err)
+
+	out = b.BN1.ForwardT(out, train)
+	out, err = out.LeakyRelu(false)
+	or_panic(err)
+
+	out = out.MustAdd(identity, false)
+	out = out.MustRelu(false)
+
+	return out
+}
+
+
+// BasicBlock returns a BasicBlockModule instance
+func NewLinear(vs *my_nn.Path, in, out int64) *my_nn.Linear {
+	config := my_nn.DefaultLinearConfig()
+	return my_nn.NewLinear(vs, in, out, config)
+}
+
+type Flatten struct{}
+
+// BasicBlock returns a BasicBlockModule instance
+func NewFlatten() *Flatten {
+	return &Flatten{}
+}
+
+// The flatten layer does not to move anything to the device
+func (b *Flatten) ExtractFromVarstore(vs *my_nn.VarStore) {}
+func (b *Flatten) Debug() {}
+
+// Forward method
+func (b *Flatten) Forward(x *torch.Tensor) *torch.Tensor {
+
+	out, err := x.Flatten(1, -1, false)
+	or_panic(err)
+
+	return out
+}
+
+func (b *Flatten) ForwardT(x *torch.Tensor, train bool) *torch.Tensor {
+
+	out, err := x.Flatten(1, -1, false)
+	or_panic(err)
+
+	return out
+}
+
+type Sigmoid struct{}
+
+func NewSigmoid() *Sigmoid {
+	return &Sigmoid{}
+}
+
+// The sigmoid layer does not need to move anything to another device
+func (b *Sigmoid) ExtractFromVarstore(vs *my_nn.VarStore) {}
+func (b *Sigmoid) Debug() {}
+
+func (b *Sigmoid) Forward(x *torch.Tensor) *torch.Tensor {
+	out, err := x.Sigmoid(false)
+	or_panic(err)
+
+	return out
+}
+
+func (b *Sigmoid) ForwardT(x *torch.Tensor, train bool) *torch.Tensor {
+    out, err := x.Sigmoid(false)
+    or_panic(err)
+
+    return out
+}
+

logic/stats/tasks.go

@@ -14,7 +14,7 @@ func handleTasksStats(handle *Handle) {
 	}
 	PostAuthJson(handle, "/stats/task/model/day", User_Normal, func(c *Context, dat *ModelTasksStatsRequest) *Error {
 		model, err := GetBaseModel(c, dat.ModelId)
-		if err == ModelNotFoundError {
+		if err == NotFoundError {
 			return c.JsonBadRequest("Model not found!")
 		} else if err != nil {
 			return c.E500M("Failed to get model", err)

logic/tasks/agreement.go

@@ -14,7 +14,7 @@ func handleRequests(x *Handle) {
 	PostAuthJson(x, "/task/agreement", User_Normal, func(c *Context, dat *AgreementRequest) *Error {
 		var task Task
 		err := GetDBOnce(c, &task, "tasks where id=$1", dat.Id)
-		if err == ModelNotFoundError {
+		if err == NotFoundError {
 			return c.JsonBadRequest("Model not found")
 		} else if err != nil {
 			return c.E500M("Failed to get task data", err)

logic/tasks/list.go

@@ -46,7 +46,7 @@ func handleList(handler *Handle) {
 
 		if requestData.ModelId != "" {
 			_, err := GetBaseModel(c.Db, requestData.ModelId)
-			if err == ModelNotFoundError {
+			if err == NotFoundError {
 				return c.SendJSONStatus(404, "Model not found!")
 			} else if err != nil {
 				return c.Error500(err)

logic/tasks/runner/runner.go

@@ -11,7 +11,8 @@ import (
 
 	"git.andr3h3nriqu3s.com/andr3/fyp/logic/db"
 	. "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
-	. "git.andr3h3nriqu3s.com/andr3/fyp/logic/models"
+
+	// . "git.andr3h3nriqu3s.com/andr3/fyp/logic/models"
 	. "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train"
 	. "git.andr3h3nriqu3s.com/andr3/fyp/logic/tasks/utils"
 	. "git.andr3h3nriqu3s.com/andr3/fyp/logic/users"
@@ -52,9 +53,10 @@ func runner(config Config, db db.Db, task_channel chan Task, index int, back_cha
 
 		if task.TaskType == int(TASK_TYPE_CLASSIFICATION) {
 			logger.Info("Classification Task")
-			if err = ClassifyTask(base, task); err != nil {
+			/*if err = ClassifyTask(base, task); err != nil {
 				logger.Error("Classification task failed", "error", err)
-			}
+			}*/
+			task.UpdateStatusLog(base, TASK_FAILED_RUNNING, "TODO move tasks to pytorch")
 
 			back_channel <- index
 			continue

logic/utils/handler.go

@@ -392,7 +392,7 @@ func (c *Context) GetModelFromId(id_path string) (*dbtypes.BaseModel, *Error) {
 	}
 
 	model, err := dbtypes.GetBaseModel(c.Db, id)
-	if err == dbtypes.ModelNotFoundError {
+	if err == dbtypes.NotFoundError {
 		return nil, c.SendJSONStatus(http.StatusNotFound, "Model not found")
 	} else if err != nil {
 		return nil, c.Error500(err)

main.go

@@ -23,7 +23,7 @@ const (
 	dbname   = "aistuff"
 )
 
-func main() {
+func main_() {
 
 	psqlInfo := fmt.Sprintf("host=%s port=%d user=%s "+
 		"password=%s dbname=%s sslmode=disable",

run.sh

@@ -0,0 +1,3 @@
+#!/bin/fish
+podman run --rm --network host --gpus all -ti -v (pwd):/app -e "TERM=xterm-256color" fyp-server bash
+

test.go

@@ -0,0 +1,120 @@
+package main
+
+import (
+	"git.andr3h3nriqu3s.com/andr3/gotch"
+
+	dbtypes "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
+	"git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch"
+
+	//my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
+
+	torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
+	"github.com/charmbracelet/log"
+)
+
+func main() {
+
+	log.Info("Hello world")
+
+	m := train.BuildModel([]*dbtypes.Layer{
+		&dbtypes.Layer{
+			LayerType: dbtypes.LAYER_INPUT,
+			Shape:     "[ 3, 28, 28 ]",
+		},
+		&dbtypes.Layer{
+			LayerType: dbtypes.LAYER_FLATTEN,
+		},
+		&dbtypes.Layer{
+			LayerType: dbtypes.LAYER_DENSE,
+			Shape:     "[ 27 ]",
+		},
+		&dbtypes.Layer{
+			LayerType: dbtypes.LAYER_DENSE,
+			Shape:     "[ 18 ]",
+		},
+		//		&dbtypes.Layer{
+		//			LayerType: dbtypes.LAYER_DENSE,
+		//			Shape:     "[ 9 ]",
+		//		},
+	}, 0, true)
+
+	//var err error
+
+	d := gotch.CudaIfAvailable()
+
+	log.Info("device", "d", d)
+
+	m.To(d)
+
+	var count = 0
+
+//	vars1 := m.Vs.Variables()
+//
+//    for k, v := range vars1 {
+//        ones := torch.MustOnes(v.MustSize(), gotch.Float, d)
+//        v := ones.MustSetRequiresGrad(true, false)
+//        v.MustDrop()
+//        ones.RetainGrad(false)
+//
+//        m.Vs.UpdateVarTensor(k, ones, true)
+//        m.Refresh()
+//    }
+//
+//	opt, err := my_nn.DefaultAdamConfig().Build(m.Vs, 0.001)
+//	if err != nil {
+//		return
+//	}
+
+    log.Info("start")
+
+	for count < 100 {
+
+		ones := torch.MustOnes([]int64{1, 3, 28, 28}, gotch.Float, d)
+		// ones = ones.MustSetRequiresGrad(true, true)
+		// ones.RetainGrad(false)
+
+		res := m.ForwardT(ones, true)
+		//res = res.MustSetRequiresGrad(true, true)
+		//res.RetainGrad(false)
+
+		outs := torch.MustZeros([]int64{1, 18}, gotch.Float, d)
+
+		loss, err := res.BinaryCrossEntropyWithLogits(outs, &torch.Tensor{}, &torch.Tensor{}, 2, false)
+		if err != nil {
+			log.Fatal(err)
+		}
+	//	loss = loss.MustSetRequiresGrad(true, true)
+
+		//opt.ZeroGrad()
+
+		log.Info("loss", "loss", loss.Float64Values())
+
+		loss.MustBackward()
+
+		//opt.Step()
+
+		// log.Info(mean.MustGrad(false).Float64Values())
+		//ones_grad = ones.MustGrad(true).MustMax(true).Float64Values()[0]
+	//	log.Info(res.MustGrad(true).MustMax(true).Float64Values())
+
+//		log.Info(ones_grad)
+
+		vars := m.Vs.Variables()
+
+		for k, v := range vars {
+			log.Info("[grad check]", "k", k, "grad", v.MustGrad(false).MustMax(true).Float64Values())
+		}
+
+		m.Debug()
+
+		outs.MustDrop()
+
+		count += 1
+
+		log.Fatal("grad zero")
+
+	}
+
+	log.Warn("out")
+
+}

webpage/src/routes/models/edit/+page.svelte

@@ -215,7 +215,7 @@
 					</div>
 				{:else if m.status == -3 || m.status == -4}
 					<BaseModelInfo model={m} />
-					<form on:submit={resetModel}>
+					<form on:submit|preventDefault={resetModel}>
 						Failed Prepare for training.<br />
 						<div class="spacer"></div>
 						<MessageSimple bind:this={resetMessages} />