<gh_stars>100-1000
# TODO: Add properties to solver
# TODO: snapshot_format not available in this version. update later.

__author__ = 'hugh'
bl_info = {
    "name": "Create Caffe solution",
    "category": "Object",
}

import os
import bpy
import string

tab = '    '
tab2 = tab + tab
tab3 = tab2 + tab


def getFillerString(filler, name):
    fillerString = tab3 + 'type: "%s"\n' % filler.type

    if filler.type == 'constant':
        fillerString += tab3 + 'value: %f\n' % (filler.value)
    elif filler.type == 'xavier' or filler.type == 'msra':
        fillerString += tab3 + 'variance_norm: %s\n' % (filler.variance_norm)
    elif filler.type == 'gaussian':
        fillerString += tab3 + 'mean: %f\n' % filler.mean
        fillerString += tab3 + 'std: %f\n' % filler.std
        if filler.is_sparse:
            fillerString += tab3 + 'sparse: %i\n' % (filler.sparse)
    elif filler.type == 'uniform':
        fillerString += tab3 + 'min: %f\n' % filler.min
        fillerString += tab3 + 'max: %f\n' % filler.max

    string = '''\
        %s {
%s
        }
''' % (name, fillerString)
    return string


def conv_template(node):
    if node.square_padding:
        padding_string = tab2 + 'pad: %i\n' % node.pad
    else:
        padding_string = tab2 + 'pad_h: %i\n' % node.pad_h
        padding_string += tab2 + 'pad_w: %i\n' % node.pad_w

    if node.square_kernel:
        kernel_string = tab2 + 'kernel_size: %i\n' % node.kernel_size
    else:
        kernel_string = tab2 + 'kernel_h: %i\n' % node.kernel_h
        kernel_string += tab2 + 'kernel_w: %i\n' % node.kernel_w

    if node.square_stride:
        stride_string = tab2 + 'stride: %i\n' % node.stride
    else:
        stride_string = tab2 + 'stride_h: %i\n' % node.stride_h
        stride_string += tab2 + 'stride_w: %i\n' % node.stride_w

    weight_filler_string = getFillerString(node.weight_filler, 'weight_filler')
    bias_filler_string = getFillerString(node.bias_filler, 'bias_filler')

    string = '''\
    convolution_param {
        num_output: %i
%s
%s
%s
%s
%s
    }
''' % (node.num_output, padding_string, kernel_string, stride_string, weight_filler_string,
       bias_filler_string)
    # loadable
    return string


def data_param_template(node, source, batch_size):
    string = '''\
    data_param {
        source: "%s"
        backend: %s
        batch_size: %i
        rand_skip: %i
    }
''' % (source, node.db_type, batch_size, node.rand_skip)
    return string


def image_data_param_template(node, source, batch_size):
    string = '''\
    image_data_param {
        source: "%s"
        batch_size: %i
        rand_skip: %i
        shuffle: %i
        new_height: %i
        new_width: %i
        is_color: %i
    }
''' % (source, batch_size, node.rand_skip, node.shuffle, node.new_height, node.new_width, node.is_color)
    return string


# TODO: Finish mean_value and random crop
def transform_param_template(node):
    mean_file_string = ''
    if node.use_mean_file:
        mean_file_string = tab2 + 'mean_file: "%s"\n' % node.mean_file

    string = '''\
    transform_param {
        scale: %f
        mirror: %i
%s
    }
''' % (node.scale, node.mirror, mean_file_string)

    return string


def hdf5_data_template(node, source, batch_size):
    string = '''\
    hdf5_data_param {
        source: "%s"
        batch_size: %i
        shuffle: %i
    }
''' % (source, batch_size, node.shuffle)

    return string


def pool_template(node):
    string = '''\
    pooling_param {
        pool: %s
        kernel_size: %i
        stride: %i
    }
''' % (node.mode, node.kernel_size, node.stride)
    # Loadable
    return string


def mvntemplate(node):
    string = '''\
        mvn_param  {
        normalize_variance: %s
        across_channels: %s
        eps: %f
        }
''' % (node.normalize_variance, node.across_channels, node.eps)
    # Loadable
    return string


def eltwisetemplate(node):
    if node.operation == 'PROD':
        coeffstring = 'coeff: %f' % node.coeff
    elif node.operation == 'SUM':
        coeffstring = 'stable_prod_grad: %i' % node.stable_prod_grad
    else:
        coeffstring = ''
    string = '''\
        eltwise_param  {
        operation: %s
        %s
        }
''' % (node.operation, coeffstring)
    return string


def FC_template(node):
    weight_filler_string = getFillerString(node.weight_filler, 'weight_filler')
    bias_filler_string = getFillerString(node.bias_filler, 'bias_filler')
    if node.specax:
        axstring = 'axis: %i' % node.axis
    else:
        axstring = ''
    string = '''\
    inner_product_param {
        num_output: %i
%s
%s
%s
    }
''' % (node.num_output, weight_filler_string, bias_filler_string, axstring)

    return string


def PReLU_template(node):
    filler_string = getFillerString(node.filler, 'filler')
    string = '''\
    prelu_param {
        channel_shared: %i
%s
    }
''' % (node.channel_shared, filler_string)
    return string


def Concattemplate(node):
    string = '''\
    concat_param {
        axis: %i
    }
    ''' % (node.axis)
    return string


def pythonLosstemplate(node):
    string = '''\
    python_param {
        module:"%s"
        layer:"%s"
    }
    ''' % (node.module,node.layer)
    return string


def argmaxtemplate(node):
    string = '''\
    argmax_param {
        out_max_val: %i
        top_k: %i
    }
''' % (node.OutMaxVal, node.TopK)
    return string


def hdf5outputtemplate(node):
    string = '''\
    hdf5_output_param {
        file_name: "%s"
    }
}
''' % (node.filename)
    return string


def logtemplate(node):
    string = '''\
    log_param {
        scale: %f
        shift: %f
        base: %f
    }
''' % (node.scale, node.shift, node.base)
    return string


def powertemplate(node):
    string = '''\
    power_param {
        power: %f
        scale: %f
        shift: %f
    }
''' % (node.power, node.scale, node.shift)
    return string


def exptemplate(node):
    string = '''\
    exp_param {
        base: %f
        scale: %f
        shift: %f
    }
''' % (node.base, node.scale, node.shift)
    return string


def reductiontemplate(node):
    string = '''\
    reduction_param {
        operation: %s
        axis: %i
        coeff: %f
    }
''' % (node.operation, node.axis, node.coeff)
    return string


def slicetemplate(node):
    slice_points_string = '\n'.join(map(lambda x: tab2 + 'slice_point: %i' % x.slice_point, node.slice_points))

    string = '''\
    slice_param {
        axis: %i
%s
    }
''' % (node.axis, slice_points_string)
    return string


def solver_template(node):
    net_path = node.config_path + '%s_train_test.prototxt' % node.solvername

    lr_string = ''
    if node.lr_policy == 'step':
        lr_string += 'gamma: %i\n' % node.gamma
        lr_string += 'stepsize: %i\n' % node.stepsize
    elif node.lr_policy == 'exp':
        lr_string += 'gamma: %i\n' % node.gamma
    elif node.lr_policy == 'inv':
        lr_string += 'gamma: %i\n' % node.gamma
        lr_string += 'power: %i\n' % node.power
    elif node.lr_policy == 'multistep':
        pass
    elif node.lr_policy == 'poly':
        lr_string += 'power: %i\n' % node.power
    elif node.lr_policy == 'sigmoid':
        lr_string += 'gamma: %i\n' % node.gamma
        lr_string += 'stepsize: %i\n' % node.stepsize

    random_seed_string = ''
    if node.use_random_seed:
        random_seed_string = 'random_seed: %i' % node.random_seed
    momentumstring = ''
    delta_string = ''
    if node.solver_type not in ['AdaGrad','RMSProp']:
        momentumstring = 'momentum: %f'%node.momentum
    if node.solver_type == 'AdaDelta':
        delta_string = 'delta: %f' % node.delta
    if node.solver_type == 'RMSProp':
        delta_string = 'rms_decay: %f' % node.RMSdecay
    if node.solver_type == 'Adam':
        delta_string = 'momentum2: %f'%node.momentum2
    if node.regularization_type != 'NONE':
        string = ''' \
regularization_type: "%s"
        ''' % node.regularization_type
    else:
        string = ''
    string += ''' \
net: "%s"
test_iter: %i
test_interval: %i
test_compute_loss: %i
test_initialization: %i
base_lr: %f
display: %i
average_loss: %i
max_iter: %i
iter_size: %i
lr_policy: "%s"
%s
%s
weight_decay: %f
snapshot: %i
snapshot_prefix: "%s"
snapshot_diff: %i
solver_mode: %s
%s
type: "%s"
%s
debug_info: %i
snapshot_after_train: %i
''' % (net_path, node.test_iter, node.test_interval, node.test_compute_loss, node.test_initialization, node.base_lr,
       node.display, node.average_loss, node.max_iter,
       node.iter_size, node.lr_policy, lr_string, momentumstring, node.weight_decay,
       node.snapshot, node.snapshot_prefix + node.solvername, node.snapshot_diff,
       node.solver_mode, random_seed_string, node.solver_type, delta_string, node.debug_info, node.snapshot_after_train)
    return "\n".join(filter(lambda x: x.strip(), string.splitlines())) + "\n"


def deploytemplate(batch, channels, xsize, ysize, datain):
    deploystring = '''\
name: 'Autogen'
input: "%s"
input_shape {
  dim: %i
  dim: %i
  dim: %i
  dim: %i
}
''' % (datain, batch, channels, xsize, ysize)
    return deploystring


class script(object):
    def __init__(self,caffepath, configpath, solvername, gpus, solver):
        self.caffepath = caffepath
        self.configpath = configpath
        self.solvername = solvername
        self.gpus = gpus
        self.solver = solver

    def make(self):
        if hasattr(self,'extra_paths'):
            pathadd = 'PYTHONPATH=$PYTHONPATH:'+':'.join(self.extra_paths)
        else:
            pathadd = ''
        gpustring = ''
        usedcount = 0

        extrastring = ''
        if self.solver == 'GPU' and self.gpus:
            extrastring = '--gpu=%s' % self.gpus[-1]

        solverstring = self.configpath + '%s_solver.prototxt' % self.solvername
        caffestring = self.caffepath + 'caffe'

        string = "#!/usr/bin/env sh \n%s '%s' train --solver='%s' %s" % (pathadd,caffestring, solverstring, extrastring)
        return string


def loss_weight_template(loss_weight):
    return tab + 'loss_weight: %f' % loss_weight


def param_template(param):
    string = tab + 'param {\n'

    if param.name.strip():
        string += tab2 + 'name: "%s"\n' % param.name

    string += tab2 + 'lr_mult: %f\n' % param.lr_mult
    string += tab2 + 'decay_mult: %f\n' % param.decay_mult
    #    string += tab2 + 'share_mode: %s\n' % param.share_mode
    string += tab + '}'
    return string


def get_params(node):
    params = []
    if node.extra_params:
        params.append(param_template(node.weight_params))
        params.append(param_template(node.bias_params))
    return params


def get_include_in(node):
    if node.include_in == "BOTH":
        return ''

    string = '''\
    include {
        phase: %s
    }
''' % node.include_in

    return string


def layer_template(node, tops, bottoms, special_params):
    tops_string = '\n'.join(map(lambda x: tab + 'top: "%s"' % x, tops))
    bottoms_string = '\n'.join(map(lambda x: tab + 'bottom: "%s"' % x, bottoms))
    params_string = '\n'.join(get_params(node))
    special_params_string = '\n'.join(special_params)
    include_in_string = get_include_in(node)

    string = '''\
layer {
    name: "%s"
    type: "%s"
%s
%s
%s
%s
%s
}
''' % (node.name, node.n_type, tops_string, bottoms_string, params_string, special_params_string, include_in_string)

    return "\n".join(filter(lambda x: x.strip(), string.splitlines())) + "\n"


def LRNtemplate(node):
    string = '''\
    lrn_param {
        local_size: %i
        alpha: %f
        beta: %f
        norm_region: %s
    }
    ''' % (node.size, node.alpha, node.beta, node.mode)
    return string


def Relutemplate(node):
    if node.negslope:
        string = '''\
        relu_param {
            negative_slope: %f
        }
        ''' % (node.negative_slope)
    else:
        string = ''
    return string


def dropouttemplate(node):
    string = '''\
    dropout_param {
        dropout_ratio: %f
    }
    ''' % (node.dropout_ratio)
    return string

def batchnormtemplate(node):
    string = '''\
    param {
        lr_mult: 0
      }
      param {
        lr_mult: 0
      }
      param {
        lr_mult: 0
      }
    '''
    return string

class Vertex():
    pass


def multiplemin(iterable, key):
    toreturn = []
    firstmin = min(iterable, key=key)
    toreturn.append(firstmin)
    smalleriterable = [i for i in iterable if i not in toreturn]
    if smalleriterable:
        nextmin = min(smalleriterable, key=key)
        while key(nextmin) == key(firstmin):
            #print (nextmin.string)
            toreturn.append(nextmin)
            smalleriterable = [i for i in iterable if i not in toreturn]
            if smalleriterable:
                nextmin = min(smalleriterable, key=key)
            else:
                return toreturn
    return toreturn


def reorder(graph):
    res_string = []
    res_dstring = []
    while len(graph) > 0:
        earlynodes = multiplemin(graph, lambda x: len(x.bottoms))
        for curr in earlynodes:
            if len(curr.bottoms) != 0:
                print('Cycle in graph?!')
            res_string.append(curr.string)
            res_dstring.append(curr.dstring)

            for item in graph:
                for top in curr.tops:
                    try:
                        item.bottoms.remove(top)
                    except:
                        pass
            graph.remove(curr)
    return res_string, res_dstring


def nodebefore(innode, socket=0):
    return innode.inputs[socket].links[0].from_socket.node


def isinplace(node):
    if node.bl_idname == 'ReluNodeType':  # or node.bl_idname == 'DropoutNodeType':
        return 1
    else:
        return 0


def findsocket(socketname, node):  # Given a node, find the position of a certain output socket
    for number, socket in enumerate(node.outputs):
        if socket.name == socketname:
            return number
    raise TypeError


def autotop(node, socket, orderpass=0):  # Assigns an arbitrary top name to a node
    if isinplace(node) and not orderpass:
        top = autobottom(node, 0, orderpass=0)
    else:
        top = node.name + str(socket)
    return top


def autobottom(node, socketnum, orderpass=0):  # Finds the bottom of a node socket

    if isinplace(nodebefore(node, socketnum)) and not orderpass:
        socketbelow = nodebefore(node, socketnum).inputs[0].links[0].from_socket.name
        socketbelowposition = findsocket(socketbelow, nodebefore(nodebefore(node, socketnum)))
        bottom = nodebefore(nodebefore(node, socketnum), 0).name + str(socketbelowposition)
    else:
        socketbelow = node.inputs[socketnum].links[0].from_socket.name
        socketbelowposition = findsocket(socketbelow, nodebefore(node, socketnum))
        bottom = nodebefore(node, socketnum).name + str(socketbelowposition)
    return bottom


def getbottomsandtops(node, orderpass=0):
    bottoms = []
    for socknum, input in enumerate(node.inputs):
        if input.is_linked:
            bottom = input.links[0].from_socket.output_name
            if bottom != '':
                bottoms.extend([bottom])
            else:
                bottoms.extend([autobottom(node, socknum, orderpass)])
    tops = [x.output_name if x.output_name != '' else autotop(node, socket, orderpass) for socket, x in
            enumerate(node.outputs)]
    return bottoms, tops


def SolveFunction(context, operatorself=None):
    graph = []
    extra_paths = []
    DataNode = False
    for space in bpy.context.area.spaces:
        if space.type == 'NODE_EDITOR':
            tree = space.edit_tree
    bpy.ops.node.select_all()
    if len(context.selected_nodes) == 0:
        bpy.ops.node.select_all()
    ########################################### Main loop
    for node in context.selected_nodes:
        nname = node.name
        string = ''
        bottoms, tops = getbottomsandtops(node)
        special_params = []
        deploy = True
        ###########################
        if node.bl_idname == 'DataNodeType':
            DataNode = True
            transform_param = transform_param_template(node)
            node.n_type = node.db_type
            Isize = [node.new_height, node.new_width, node.height, node.width]
            if node.db_type in ('LMDB', 'LEVELDB'):
                train_params = [data_param_template(node, node.train_path, node.train_batch_size)]
                test_params = [data_param_template(node, node.test_path, node.test_batch_size)]
                node.n_type = 'Data'
                train_params.append(transform_param)
                test_params.append(transform_param)
                y = node.height
                x = node.width
            elif node.db_type == 'ImageData':
                train_params = [image_data_param_template(node, node.train_data, node.train_batch_size)]
                test_params = [image_data_param_template(node, node.test_data, node.test_batch_size)]
                train_params.append(transform_param)
                test_params.append(transform_param)
                y = node.new_height
                x = node.new_width
            elif node.db_type == 'HDF5Data':
                train_params = [hdf5_data_template(node, node.train_data, node.train_batch_size)]
                test_params = [hdf5_data_template(node, node.test_data, node.test_batch_size)]
                y = node.height
                x = node.width
            else:
                y = node.height
                x = node.width
            origin = node.include_in
            node.include_in = "TRAIN"
            train_string = layer_template(node, tops, bottoms, train_params)
            node.include_in = "TEST"
            test_string = layer_template(node, tops, bottoms, test_params)
            node.include_in = origin
            if node.include_in == 'TRAIN':
                string = train_string
            elif node.include_in == 'TEST':
                string = test_string
            else:
                string = train_string + test_string
            if node.include_in != "TEST":
                dstring = deploytemplate(1,node.channels,y,x,tops[0])
        elif node.bl_idname == 'PoolNodeType':
            special_params.append(pool_template(node))
        elif node.bl_idname == 'EltwiseNodeType':
            special_params.append(eltwisetemplate(node))
        elif node.bl_idname == 'ExpNodeType':
            special_params.append(exptemplate(node))
        elif node.bl_idname == 'ConvNodeType':
            special_params.append(conv_template(node))
        elif node.bl_idname == 'DeConvNodeType':
            special_params.append(conv_template(node))
        elif node.bl_idname == 'FCNodeType':
            special_params.append(FC_template(node))
        elif node.bl_idname == 'LRNNodeType':
            special_params.append(LRNtemplate(node))
        # elif node.bl_idname == 'AcNodeType':
        #     node.type = node.mode
        elif node.bl_idname == 'ReluNodeType':
            special_params.append(Relutemplate(node))
        elif node.bl_idname == 'PReluNodeType':
            special_params.append(PReLU_template(node))
        elif node.bl_idname == 'DropoutNodeType':
            special_params.append(dropouttemplate(node))
        elif node.bl_idname == 'BatchNormNodeType':
            special_params.append(batchnormtemplate(node))
        elif node.bl_idname == 'SMLossNodeType':
            special_params.append(loss_weight_template(node.w))
            deploy = False
        elif node.bl_idname == 'SCELossNodeType':
            special_params.append(loss_weight_template(node.w))
            deploy = False
        elif node.bl_idname == 'EULossNodeType':
            special_params.append(loss_weight_template(node.w))
            deploy = False
        elif node.bl_idname == 'PythonLossNodeType':
            special_params.append(pythonLosstemplate(node))
            special_params.append(loss_weight_template(node.w))
            extra_paths.append(node.modulepath)
        elif node.bl_idname == 'AccuracyNodeType':
            deploy = False
        elif node.bl_idname == 'ConcatNodeType':
            special_params.append(Concattemplate(node))
        elif node.bl_idname == 'ArgMaxNodeType':
            special_params.append(argmaxtemplate(node))
        elif node.bl_idname == 'HDF5OutputNodeType':
            special_params.append(hdf5outputtemplate(node))
        elif node.bl_idname == 'LogNodeType':
            special_params.append(logtemplate(node))
        elif node.bl_idname == 'PowerNodeType':
            special_params.append(powertemplate(node))
        elif node.bl_idname == 'ReductionNodeType':
            special_params.append(reductiontemplate(node))
        elif node.bl_idname == 'SliceNodeType':
            special_params.append(slicetemplate(node))
        elif node.bl_idname == 'SolverNodeType':
            solverstring = solver_template(node)
            scriptmaker = script(node.caffe_exec, node.config_path, node.solvername, node.gpus,node.solver_mode)
            if not node.config_path and operatorself:
                operatorself.report({'ERROR'}, "Solver node config path not set")
                return {'FINISHED'}
            configpath = node.config_path
            solvername = node.solvername
        elif node.bl_idname == 'MVNNodeType':
            special_params.append(mvntemplate(node))
        elif string == 0:
            raise OSError
            pass
        if node.bl_idname != 'SolverNodeType':
            if node.bl_idname != 'DataNodeType':
                string = layer_template(node, tops, bottoms, special_params)
                if deploy:
                    dstring = string
                else:
                    dstring = ''
            ################################# Recalculate bottoms and tops for ordering
            bottoms, tops = getbottomsandtops(node, orderpass=1)  # when orderpass = 1, we ignore ReLu nodes
            #####################################
            v = Vertex()
            v.string = string
            v.dstring = dstring
            v.bottoms = bottoms
            v.tops = tops
            graph.append(v)
    if not DataNode:
        operatorself.report({'ERROR'}, "No Data Node in output. Output will be deploy only")
        Isize = [0,0,0,0]
    scriptmaker.extra_paths = extra_paths
    scriptstring = scriptmaker.make()
    strings, dstrings = reorder(graph)
    solution = ''.join(strings)
    dsolution = ''.join(dstrings)

    os.chdir(configpath)
    ttfile = open('%s_train_test.prototxt' % solvername, mode='w')
    ttfile.write(solution)
    ttfile.close()
    depfile = open('%s_deploy.prototxt' % solvername, mode='w')
    depfile.write(dsolution)
    depfile.close()
    solvefile = open('%s_solver.prototxt' % solvername, mode='w')
    solvefile.write(solverstring)
    solvefile.close()
    scriptfile = open('train_%s.sh' % solvername, mode='w')
    scriptfile.write(scriptstring)
    scriptfile.close()
    os.system("chmod 775 %s" % os.path.join(configpath,'train_%s.sh' % solvername))
    print ('Finished solving tree')
    protoandsolver = (solution + '\nlayer {\n' + 'type: "Solver"\n' + solverstring + '\n}\n').split('\n')
    return [[i + '\n' for i in protoandsolver], Isize], os.path.join(configpath,'train_%s.sh' % solvername)


class Solve(bpy.types.Operator):
    """Generate Caffe solver"""  # blender will use this as a tooltip for menu items and buttons.
    bl_idname = "nodes.make_solver"  # unique identifier for buttons and menu items to reference.
    bl_label = "Create Protoxt Only"  # display name in the interface.
    bl_options = {'REGISTER'}  # enable undo for the operator.

    def execute(self, context):  # execute() is called by blender when running the operator.
        SolveFunction(context, operatorself=self)
        return {'FINISHED'}  # this lets blender know the operator finished successfully.


def register():
    bpy.utils.register_class(Solve)


def unregister():
    bpy.utils.unregister_class(Solve)

# TODO: Add properties to solver
# TODO: snapshot_format not available in this version. update later.

__author__ = 'hugh'
bl_info = {
    "name": "Create Caffe solution",
    "category": "Object",
}

import os
import bpy
import string

tab = '    '
tab2 = tab + tab
tab3 = tab2 + tab


def getFillerString(filler, name):
    fillerString = tab3 + 'type: "%s"\n' % filler.type

    if filler.type == 'constant':
        fillerString += tab3 + 'value: %f\n' % (filler.value)
    elif filler.type == 'xavier' or filler.type == 'msra':
        fillerString += tab3 + 'variance_norm: %s\n' % (filler.variance_norm)
    elif filler.type == 'gaussian':
        fillerString += tab3 + 'mean: %f\n' % filler.mean
        fillerString += tab3 + 'std: %f\n' % filler.std
        if filler.is_sparse:
            fillerString += tab3 + 'sparse: %i\n' % (filler.sparse)
    elif filler.type == 'uniform':
        fillerString += tab3 + 'min: %f\n' % filler.min
        fillerString += tab3 + 'max: %f\n' % filler.max

    string = '''\
        %s {
%s
        }
''' % (name, fillerString)
    return string


def conv_template(node):
    if node.square_padding:
        padding_string = tab2 + 'pad: %i\n' % node.pad
    else:
        padding_string = tab2 + 'pad_h: %i\n' % node.pad_h
        padding_string += tab2 + 'pad_w: %i\n' % node.pad_w

    if node.square_kernel:
        kernel_string = tab2 + 'kernel_size: %i\n' % node.kernel_size
    else:
        kernel_string = tab2 + 'kernel_h: %i\n' % node.kernel_h
        kernel_string += tab2 + 'kernel_w: %i\n' % node.kernel_w

    if node.square_stride:
        stride_string = tab2 + 'stride: %i\n' % node.stride
    else:
        stride_string = tab2 + 'stride_h: %i\n' % node.stride_h
        stride_string += tab2 + 'stride_w: %i\n' % node.stride_w

    weight_filler_string = getFillerString(node.weight_filler, 'weight_filler')
    bias_filler_string = getFillerString(node.bias_filler, 'bias_filler')

    string = '''\
    convolution_param {
        num_output: %i
%s
%s
%s
%s
%s
    }
''' % (node.num_output, padding_string, kernel_string, stride_string, weight_filler_string,
       bias_filler_string)
    # loadable
    return string


def data_param_template(node, source, batch_size):
    string = '''\
    data_param {
        source: "%s"
        backend: %s
        batch_size: %i
        rand_skip: %i
    }
''' % (source, node.db_type, batch_size, node.rand_skip)
    return string


def image_data_param_template(node, source, batch_size):
    string = '''\
    image_data_param {
        source: "%s"
        batch_size: %i
        rand_skip: %i
        shuffle: %i
        new_height: %i
        new_width: %i
        is_color: %i
    }
''' % (source, batch_size, node.rand_skip, node.shuffle, node.new_height, node.new_width, node.is_color)
    return string


# TODO: Finish mean_value and random crop
def transform_param_template(node):
    mean_file_string = ''
    if node.use_mean_file:
        mean_file_string = tab2 + 'mean_file: "%s"\n' % node.mean_file

    string = '''\
    transform_param {
        scale: %f
        mirror: %i
%s
    }
''' % (node.scale, node.mirror, mean_file_string)

    return string


def hdf5_data_template(node, source, batch_size):
    string = '''\
    hdf5_data_param {
        source: "%s"
        batch_size: %i
        shuffle: %i
    }
''' % (source, batch_size, node.shuffle)

    return string


def pool_template(node):
    string = '''\
    pooling_param {
        pool: %s
        kernel_size: %i
        stride: %i
    }
''' % (node.mode, node.kernel_size, node.stride)
    # Loadable
    return string


def mvntemplate(node):
    string = '''\
        mvn_param  {
        normalize_variance: %s
        across_channels: %s
        eps: %f
        }
''' % (node.normalize_variance, node.across_channels, node.eps)
    # Loadable
    return string


def eltwisetemplate(node):
    if node.operation == 'PROD':
        coeffstring = 'coeff: %f' % node.coeff
    elif node.operation == 'SUM':
        coeffstring = 'stable_prod_grad: %i' % node.stable_prod_grad
    else:
        coeffstring = ''
    string = '''\
        eltwise_param  {
        operation: %s
        %s
        }
''' % (node.operation, coeffstring)
    return string


def FC_template(node):
    weight_filler_string = getFillerString(node.weight_filler, 'weight_filler')
    bias_filler_string = getFillerString(node.bias_filler, 'bias_filler')
    if node.specax:
        axstring = 'axis: %i' % node.axis
    else:
        axstring = ''
    string = '''\
    inner_product_param {
        num_output: %i
%s
%s
%s
    }
''' % (node.num_output, weight_filler_string, bias_filler_string, axstring)

    return string


def PReLU_template(node):
    filler_string = getFillerString(node.filler, 'filler')
    string = '''\
    prelu_param {
        channel_shared: %i
%s
    }
''' % (node.channel_shared, filler_string)
    return string


def Concattemplate(node):
    string = '''\
    concat_param {
        axis: %i
    }
    ''' % (node.axis)
    return string


def pythonLosstemplate(node):
    string = '''\
    python_param {
        module:"%s"
        layer:"%s"
    }
    ''' % (node.module,node.layer)
    return string


def argmaxtemplate(node):
    string = '''\
    argmax_param {
        out_max_val: %i
        top_k: %i
    }
''' % (node.OutMaxVal, node.TopK)
    return string


def hdf5outputtemplate(node):
    string = '''\
    hdf5_output_param {
        file_name: "%s"
    }
}
''' % (node.filename)
    return string


def logtemplate(node):
    string = '''\
    log_param {
        scale: %f
        shift: %f
        base: %f
    }
''' % (node.scale, node.shift, node.base)
    return string


def powertemplate(node):
    string = '''\
    power_param {
        power: %f
        scale: %f
        shift: %f
    }
''' % (node.power, node.scale, node.shift)
    return string


def exptemplate(node):
    string = '''\
    exp_param {
        base: %f
        scale: %f
        shift: %f
    }
''' % (node.base, node.scale, node.shift)
    return string


def reductiontemplate(node):
    string = '''\
    reduction_param {
        operation: %s
        axis: %i
        coeff: %f
    }
''' % (node.operation, node.axis, node.coeff)
    return string


def slicetemplate(node):
    slice_points_string = '\n'.join(map(lambda x: tab2 + 'slice_point: %i' % x.slice_point, node.slice_points))

    string = '''\
    slice_param {
        axis: %i
%s
    }
''' % (node.axis, slice_points_string)
    return string


def solver_template(node):
    net_path = node.config_path + '%s_train_test.prototxt' % node.solvername

    lr_string = ''
    if node.lr_policy == 'step':
        lr_string += 'gamma: %i\n' % node.gamma
        lr_string += 'stepsize: %i\n' % node.stepsize
    elif node.lr_policy == 'exp':
        lr_string += 'gamma: %i\n' % node.gamma
    elif node.lr_policy == 'inv':
        lr_string += 'gamma: %i\n' % node.gamma
        lr_string += 'power: %i\n' % node.power
    elif node.lr_policy == 'multistep':
        pass
    elif node.lr_policy == 'poly':
        lr_string += 'power: %i\n' % node.power
    elif node.lr_policy == 'sigmoid':
        lr_string += 'gamma: %i\n' % node.gamma
        lr_string += 'stepsize: %i\n' % node.stepsize

    random_seed_string = ''
    if node.use_random_seed:
        random_seed_string = 'random_seed: %i' % node.random_seed
    momentumstring = ''
    delta_string = ''
    if node.solver_type not in ['AdaGrad','RMSProp']:
        momentumstring = 'momentum: %f'%node.momentum
    if node.solver_type == 'AdaDelta':
        delta_string = 'delta: %f' % node.delta
    if node.solver_type == 'RMSProp':
        delta_string = 'rms_decay: %f' % node.RMSdecay
    if node.solver_type == 'Adam':
        delta_string = 'momentum2: %f'%node.momentum2
    if node.regularization_type != 'NONE':
        string = ''' \
regularization_type: "%s"
        ''' % node.regularization_type
    else:
        string = ''
    string += ''' \
net: "%s"
test_iter: %i
test_interval: %i
test_compute_loss: %i
test_initialization: %i
base_lr: %f
display: %i
average_loss: %i
max_iter: %i
iter_size: %i
lr_policy: "%s"
%s
%s
weight_decay: %f
snapshot: %i
snapshot_prefix: "%s"
snapshot_diff: %i
solver_mode: %s
%s
type: "%s"
%s
debug_info: %i
snapshot_after_train: %i
''' % (net_path, node.test_iter, node.test_interval, node.test_compute_loss, node.test_initialization, node.base_lr,
       node.display, node.average_loss, node.max_iter,
       node.iter_size, node.lr_policy, lr_string, momentumstring, node.weight_decay,
       node.snapshot, node.snapshot_prefix + node.solvername, node.snapshot_diff,
       node.solver_mode, random_seed_string, node.solver_type, delta_string, node.debug_info, node.snapshot_after_train)
    return "\n".join(filter(lambda x: x.strip(), string.splitlines())) + "\n"


def deploytemplate(batch, channels, xsize, ysize, datain):
    deploystring = '''\
name: 'Autogen'
input: "%s"
input_shape {
  dim: %i
  dim: %i
  dim: %i
  dim: %i
}
''' % (datain, batch, channels, xsize, ysize)
    return deploystring


class script(object):
    def __init__(self,caffepath, configpath, solvername, gpus, solver):
        self.caffepath = caffepath
        self.configpath = configpath
        self.solvername = solvername
        self.gpus = gpus
        self.solver = solver

    def make(self):
        if hasattr(self,'extra_paths'):
            pathadd = 'PYTHONPATH=$PYTHONPATH:'+':'.join(self.extra_paths)
        else:
            pathadd = ''
        gpustring = ''
        usedcount = 0

        extrastring = ''
        if self.solver == 'GPU' and self.gpus:
            extrastring = '--gpu=%s' % self.gpus[-1]

        solverstring = self.configpath + '%s_solver.prototxt' % self.solvername
        caffestring = self.caffepath + 'caffe'

        string = "#!/usr/bin/env sh \n%s '%s' train --solver='%s' %s" % (pathadd,caffestring, solverstring, extrastring)
        return string


def loss_weight_template(loss_weight):
    return tab + 'loss_weight: %f' % loss_weight


def param_template(param):
    string = tab + 'param {\n'

    if param.name.strip():
        string += tab2 + 'name: "%s"\n' % param.name

    string += tab2 + 'lr_mult: %f\n' % param.lr_mult
    string += tab2 + 'decay_mult: %f\n' % param.decay_mult
    #    string += tab2 + 'share_mode: %s\n' % param.share_mode
    string += tab + '}'
    return string


def get_params(node):
    params = []
    if node.extra_params:
        params.append(param_template(node.weight_params))
        params.append(param_template(node.bias_params))
    return params


def get_include_in(node):
    if node.include_in == "BOTH":
        return ''

    string = '''\
    include {
        phase: %s
    }
''' % node.include_in

    return string


def layer_template(node, tops, bottoms, special_params):
    tops_string = '\n'.join(map(lambda x: tab + 'top: "%s"' % x, tops))
    bottoms_string = '\n'.join(map(lambda x: tab + 'bottom: "%s"' % x, bottoms))
    params_string = '\n'.join(get_params(node))
    special_params_string = '\n'.join(special_params)
    include_in_string = get_include_in(node)

    string = '''\
layer {
    name: "%s"
    type: "%s"
%s
%s
%s
%s
%s
}
''' % (node.name, node.n_type, tops_string, bottoms_string, params_string, special_params_string, include_in_string)

    return "\n".join(filter(lambda x: x.strip(), string.splitlines())) + "\n"


def LRNtemplate(node):
    string = '''\
    lrn_param {
        local_size: %i
        alpha: %f
        beta: %f
        norm_region: %s
    }
    ''' % (node.size, node.alpha, node.beta, node.mode)
    return string


def Relutemplate(node):
    if node.negslope:
        string = '''\
        relu_param {
            negative_slope: %f
        }
        ''' % (node.negative_slope)
    else:
        string = ''
    return string


def dropouttemplate(node):
    string = '''\
    dropout_param {
        dropout_ratio: %f
    }
    ''' % (node.dropout_ratio)
    return string

def batchnormtemplate(node):
    string = '''\
    param {
        lr_mult: 0
      }
      param {
        lr_mult: 0
      }
      param {
        lr_mult: 0
      }
    '''
    return string

class Vertex():
    pass


def multiplemin(iterable, key):
    toreturn = []
    firstmin = min(iterable, key=key)
    toreturn.append(firstmin)
    smalleriterable = [i for i in iterable if i not in toreturn]
    if smalleriterable:
        nextmin = min(smalleriterable, key=key)
        while key(nextmin) == key(firstmin):
            #print (nextmin.string)
            toreturn.append(nextmin)
            smalleriterable = [i for i in iterable if i not in toreturn]
            if smalleriterable:
                nextmin = min(smalleriterable, key=key)
            else:
                return toreturn
    return toreturn


def reorder(graph):
    res_string = []
    res_dstring = []
    while len(graph) > 0:
        earlynodes = multiplemin(graph, lambda x: len(x.bottoms))
        for curr in earlynodes:
            if len(curr.bottoms) != 0:
                print('Cycle in graph?!')
            res_string.append(curr.string)
            res_dstring.append(curr.dstring)

            for item in graph:
                for top in curr.tops:
                    try:
                        item.bottoms.remove(top)
                    except:
                        pass
            graph.remove(curr)
    return res_string, res_dstring


def nodebefore(innode, socket=0):
    return innode.inputs[socket].links[0].from_socket.node


def isinplace(node):
    if node.bl_idname == 'ReluNodeType':  # or node.bl_idname == 'DropoutNodeType':
        return 1
    else:
        return 0


def findsocket(socketname, node):  # Given a node, find the position of a certain output socket
    for number, socket in enumerate(node.outputs):
        if socket.name == socketname:
            return number
    raise TypeError


def autotop(node, socket, orderpass=0):  # Assigns an arbitrary top name to a node
    if isinplace(node) and not orderpass:
        top = autobottom(node, 0, orderpass=0)
    else:
        top = node.name + str(socket)
    return top


def autobottom(node, socketnum, orderpass=0):  # Finds the bottom of a node socket

    if isinplace(nodebefore(node, socketnum)) and not orderpass:
        socketbelow = nodebefore(node, socketnum).inputs[0].links[0].from_socket.name
        socketbelowposition = findsocket(socketbelow, nodebefore(nodebefore(node, socketnum)))
        bottom = nodebefore(nodebefore(node, socketnum), 0).name + str(socketbelowposition)
    else:
        socketbelow = node.inputs[socketnum].links[0].from_socket.name
        socketbelowposition = findsocket(socketbelow, nodebefore(node, socketnum))
        bottom = nodebefore(node, socketnum).name + str(socketbelowposition)
    return bottom


def getbottomsandtops(node, orderpass=0):
    bottoms = []
    for socknum, input in enumerate(node.inputs):
        if input.is_linked:
            bottom = input.links[0].from_socket.output_name
            if bottom != '':
                bottoms.extend([bottom])
            else:
                bottoms.extend([autobottom(node, socknum, orderpass)])
    tops = [x.output_name if x.output_name != '' else autotop(node, socket, orderpass) for socket, x in
            enumerate(node.outputs)]
    return bottoms, tops


def SolveFunction(context, operatorself=None):
    graph = []
    extra_paths = []
    DataNode = False
    for space in bpy.context.area.spaces:
        if space.type == 'NODE_EDITOR':
            tree = space.edit_tree
    bpy.ops.node.select_all()
    if len(context.selected_nodes) == 0:
        bpy.ops.node.select_all()
    ########################################### Main loop
    for node in context.selected_nodes:
        nname = node.name
        string = ''
        bottoms, tops = getbottomsandtops(node)
        special_params = []
        deploy = True
        ###########################
        if node.bl_idname == 'DataNodeType':
            DataNode = True
            transform_param = transform_param_template(node)
            node.n_type = node.db_type
            Isize = [node.new_height, node.new_width, node.height, node.width]
            if node.db_type in ('LMDB', 'LEVELDB'):
                train_params = [data_param_template(node, node.train_path, node.train_batch_size)]
                test_params = [data_param_template(node, node.test_path, node.test_batch_size)]
                node.n_type = 'Data'
                train_params.append(transform_param)
                test_params.append(transform_param)
                y = node.height
                x = node.width
            elif node.db_type == 'ImageData':
                train_params = [image_data_param_template(node, node.train_data, node.train_batch_size)]
                test_params = [image_data_param_template(node, node.test_data, node.test_batch_size)]
                train_params.append(transform_param)
                test_params.append(transform_param)
                y = node.new_height
                x = node.new_width
            elif node.db_type == 'HDF5Data':
                train_params = [hdf5_data_template(node, node.train_data, node.train_batch_size)]
                test_params = [hdf5_data_template(node, node.test_data, node.test_batch_size)]
                y = node.height
                x = node.width
            else:
                y = node.height
                x = node.width
            origin = node.include_in
            node.include_in = "TRAIN"
            train_string = layer_template(node, tops, bottoms, train_params)
            node.include_in = "TEST"
            test_string = layer_template(node, tops, bottoms, test_params)
            node.include_in = origin
            if node.include_in == 'TRAIN':
                string = train_string
            elif node.include_in == 'TEST':
                string = test_string
            else:
                string = train_string + test_string
            if node.include_in != "TEST":
                dstring = deploytemplate(1,node.channels,y,x,tops[0])
        elif node.bl_idname == 'PoolNodeType':
            special_params.append(pool_template(node))
        elif node.bl_idname == 'EltwiseNodeType':
            special_params.append(eltwisetemplate(node))
        elif node.bl_idname == 'ExpNodeType':
            special_params.append(exptemplate(node))
        elif node.bl_idname == 'ConvNodeType':
            special_params.append(conv_template(node))
        elif node.bl_idname == 'DeConvNodeType':
            special_params.append(conv_template(node))
        elif node.bl_idname == 'FCNodeType':
            special_params.append(FC_template(node))
        elif node.bl_idname == 'LRNNodeType':
            special_params.append(LRNtemplate(node))
        # elif node.bl_idname == 'AcNodeType':
        #     node.type = node.mode
        elif node.bl_idname == 'ReluNodeType':
            special_params.append(Relutemplate(node))
        elif node.bl_idname == 'PReluNodeType':
            special_params.append(PReLU_template(node))
        elif node.bl_idname == 'DropoutNodeType':
            special_params.append(dropouttemplate(node))
        elif node.bl_idname == 'BatchNormNodeType':
            special_params.append(batchnormtemplate(node))
        elif node.bl_idname == 'SMLossNodeType':
            special_params.append(loss_weight_template(node.w))
            deploy = False
        elif node.bl_idname == 'SCELossNodeType':
            special_params.append(loss_weight_template(node.w))
            deploy = False
        elif node.bl_idname == 'EULossNodeType':
            special_params.append(loss_weight_template(node.w))
            deploy = False
        elif node.bl_idname == 'PythonLossNodeType':
            special_params.append(pythonLosstemplate(node))
            special_params.append(loss_weight_template(node.w))
            extra_paths.append(node.modulepath)
        elif node.bl_idname == 'AccuracyNodeType':
            deploy = False
        elif node.bl_idname == 'ConcatNodeType':
            special_params.append(Concattemplate(node))
        elif node.bl_idname == 'ArgMaxNodeType':
            special_params.append(argmaxtemplate(node))
        elif node.bl_idname == 'HDF5OutputNodeType':
            special_params.append(hdf5outputtemplate(node))
        elif node.bl_idname == 'LogNodeType':
            special_params.append(logtemplate(node))
        elif node.bl_idname == 'PowerNodeType':
            special_params.append(powertemplate(node))
        elif node.bl_idname == 'ReductionNodeType':
            special_params.append(reductiontemplate(node))
        elif node.bl_idname == 'SliceNodeType':
            special_params.append(slicetemplate(node))
        elif node.bl_idname == 'SolverNodeType':
            solverstring = solver_template(node)
            scriptmaker = script(node.caffe_exec, node.config_path, node.solvername, node.gpus,node.solver_mode)
            if not node.config_path and operatorself:
                operatorself.report({'ERROR'}, "Solver node config path not set")
                return {'FINISHED'}
            configpath = node.config_path
            solvername = node.solvername
        elif node.bl_idname == 'MVNNodeType':
            special_params.append(mvntemplate(node))
        elif string == 0:
            raise OSError
            pass
        if node.bl_idname != 'SolverNodeType':
            if node.bl_idname != 'DataNodeType':
                string = layer_template(node, tops, bottoms, special_params)
                if deploy:
                    dstring = string
                else:
                    dstring = ''
            ################################# Recalculate bottoms and tops for ordering
            bottoms, tops = getbottomsandtops(node, orderpass=1)  # when orderpass = 1, we ignore ReLu nodes
            #####################################
            v = Vertex()
            v.string = string
            v.dstring = dstring
            v.bottoms = bottoms
            v.tops = tops
            graph.append(v)
    if not DataNode:
        operatorself.report({'ERROR'}, "No Data Node in output. Output will be deploy only")
        Isize = [0,0,0,0]
    scriptmaker.extra_paths = extra_paths
    scriptstring = scriptmaker.make()
    strings, dstrings = reorder(graph)
    solution = ''.join(strings)
    dsolution = ''.join(dstrings)

    os.chdir(configpath)
    ttfile = open('%s_train_test.prototxt' % solvername, mode='w')
    ttfile.write(solution)
    ttfile.close()
    depfile = open('%s_deploy.prototxt' % solvername, mode='w')
    depfile.write(dsolution)
    depfile.close()
    solvefile = open('%s_solver.prototxt' % solvername, mode='w')
    solvefile.write(solverstring)
    solvefile.close()
    scriptfile = open('train_%s.sh' % solvername, mode='w')
    scriptfile.write(scriptstring)
    scriptfile.close()
    os.system("chmod 775 %s" % os.path.join(configpath,'train_%s.sh' % solvername))
    print ('Finished solving tree')
    protoandsolver = (solution + '\nlayer {\n' + 'type: "Solver"\n' + solverstring + '\n}\n').split('\n')
    return [[i + '\n' for i in protoandsolver], Isize], os.path.join(configpath,'train_%s.sh' % solvername)


class Solve(bpy.types.Operator):
    """Generate Caffe solver"""  # blender will use this as a tooltip for menu items and buttons.
    bl_idname = "nodes.make_solver"  # unique identifier for buttons and menu items to reference.
    bl_label = "Create Protoxt Only"  # display name in the interface.
    bl_options = {'REGISTER'}  # enable undo for the operator.

    def execute(self, context):  # execute() is called by blender when running the operator.
        SolveFunction(context, operatorself=self)
        return {'FINISHED'}  # this lets blender know the operator finished successfully.


def register():
    bpy.utils.register_class(Solve)


def unregister():
    bpy.utils.unregister_class(Solve)
# TODO: Add properties to solver # TODO: snapshot_format not available in this version. update later. \         %s { %s         } \     convolution_param {         num_output: %i %s %s %s %s %s     } # loadable \     data_param {         source: "%s"         backend: %s         batch_size: %i         rand_skip: %i     } \     image_data_param {         source: "%s"         batch_size: %i         rand_skip: %i         shuffle: %i         new_height: %i         new_width: %i         is_color: %i     } # TODO: Finish mean_value and random crop \     transform_param {         scale: %f         mirror: %i %s     } \     hdf5_data_param {         source: "%s"         batch_size: %i         shuffle: %i     } \     pooling_param {         pool: %s         kernel_size: %i         stride: %i     } # Loadable \         mvn_param  {         normalize_variance: %s         across_channels: %s         eps: %f         } # Loadable \         eltwise_param  {         operation: %s         %s         } \     inner_product_param {         num_output: %i %s %s %s     } \     prelu_param {         channel_shared: %i %s     } \     concat_param {         axis: %i     } \     python_param {         module:"%s"         layer:"%s"     } \     argmax_param {         out_max_val: %i         top_k: %i     } \     hdf5_output_param {         file_name: "%s"     } } \     log_param {         scale: %f         shift: %f         base: %f     } \     power_param {         power: %f         scale: %f         shift: %f     } \     exp_param {         base: %f         scale: %f         shift: %f     } \     reduction_param {         operation: %s         axis: %i         coeff: %f     } \     slice_param {         axis: %i %s     } \ regularization_type: "%s" \ net: "%s" test_iter: %i test_interval: %i test_compute_loss: %i test_initialization: %i base_lr: %f display: %i average_loss: %i max_iter: %i iter_size: %i lr_policy: "%s" %s %s weight_decay: %f snapshot: %i snapshot_prefix: "%s" snapshot_diff: %i solver_mode: %s %s type: "%s" %s debug_info: %i snapshot_after_train: %i \ name: 'Autogen' input: "%s" input_shape {   dim: %i   dim: %i   dim: %i   dim: %i } #    string += tab2 + 'share_mode: %s\n' % param.share_mode \     include {         phase: %s     } \ layer {     name: "%s"     type: "%s" %s %s %s %s %s } \     lrn_param {         local_size: %i         alpha: %f         beta: %f         norm_region: %s     } \         relu_param {             negative_slope: %f         } \     dropout_param {         dropout_ratio: %f     } \     param {         lr_mult: 0       }       param {         lr_mult: 0       }       param {         lr_mult: 0       } #print (nextmin.string) # or node.bl_idname == 'DropoutNodeType': # Given a node, find the position of a certain output socket # Assigns an arbitrary top name to a node # Finds the bottom of a node socket ########################################### Main loop ########################### # elif node.bl_idname == 'AcNodeType': #     node.type = node.mode ################################# Recalculate bottoms and tops for ordering # when orderpass = 1, we ignore ReLu nodes ##################################### Generate Caffe solver # blender will use this as a tooltip for menu items and buttons. # unique identifier for buttons and menu items to reference. # display name in the interface. # enable undo for the operator. # execute() is called by blender when running the operator. # this lets blender know the operator finished successfully.