local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayer", "nerv.Layer")

function SoftmaxCELayer:__init(id, global_conf, layer_conf)
    nerv.Layer.__init(self, id, global_conf, layer_conf)
    self.compressed = layer_conf.compressed
    if self.compressed == nil then
        self.compressed = false
    end
    self:check_dim_len(2, -1) -- two inputs: nn output and label
end

function SoftmaxCELayer:bind_params()
    -- do nothing
end

function SoftmaxCELayer:init(batch_size, chunk_size)
    if not self.compressed and (self.dim_in[1] ~= self.dim_in[2]) then
        nerv.error("mismatching dimensions of previous network output and labels")
    end
    if chunk_size == nil then
        chunk_size = 1
    end
    self.total_ce = 0.0
    self.total_correct = 0
    self.total_frames = 0
    self.softmax = {}
    self.ce = {}
    for t = 1, chunk_size do
        self.softmax[t] = self.mat_type(batch_size, self.dim_in[1])
        self.ce[t] = self.softmax[t]:create()
    end
end

function SoftmaxCELayer:batch_resize(batch_size, chunk_size)
    if chunk_size == nil then
        chunk_size = 1
    end
    for t = 1, chunk_size do
        if self.softmax[t]:nrow() ~= batch_size then
            self.softmax[t] = self.mat_type(batch_size, self.dim_in[1])
            self.ce[t] = self.softmax:create()
        end
    end
end

function SoftmaxCELayer:update(bp_err, input, output, t)
    -- no params, therefore do nothing
end

function SoftmaxCELayer:propagate(input, output, t)
    if t == nil then
        t = 1
    end
    local softmax = self.softmax[t]
    local ce = self.ce[t]
    local classified = softmax:softmax(input[1])
    local label = input[2]
    ce:log_elem(softmax)
    if self.compressed then
        label = label:decompress(input[1]:ncol())
    end
    ce:mul_elem(ce, label)
    ce = ce:rowsum()
    ce:set_values_by_mask(self.gconf.mask[t], 0)
    if output[1] ~= nil then
        output[1]:copy_from(ce)
    end
    -- add total ce
    self.total_ce = self.total_ce - ce:colsum()[0][0]
    self.total_frames = self.total_frames + self.gconf.mask[t]:colsum()[0][0]
    -- TODO: add colsame for uncompressed label
    if self.compressed then
        classified:set_values_by_mask(self.gconf.mask[t], -1)
        self.total_correct = self.total_correct + classified:colsame(input[2])[0][0]
    end
end

function SoftmaxCELayer:back_propagate(bp_err, next_bp_err, input, output, t)
    -- softmax output - label
    if t == nil then
        t = 1
    end
    local label = input[2]
    if self.compressed then
        label = label:decompress(input[1]:ncol())
    end
    local nbe = next_bp_err[1]
    nbe:add(self.softmax[t], label, 1.0, -1.0)
    if bp_err[1] ~= nil then
        nbe:scale_rows_by_col(bp_err[1])
    end
end

function SoftmaxCELayer:get_params()
    return nerv.ParamRepo({}, self.loc_type)
end