1 files changed, 93 insertions, 0 deletions
diff --git a/nerv/tnn/layersT/softmax_ce_t.lua b/nerv/tnn/layersT/softmax_ce_t.lua
new file mode 100644
index 0000000..a9ce975
--- /dev/null
+++ b/nerv/tnn/layersT/softmax_ce_t.lua
@@ -0,0 +1,93 @@
+local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayerT", "nerv.LayerT")
+
+function SoftmaxCELayer:__init(id, global_conf, layer_conf)
+    self.id = id
+    self.gconf = global_conf
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    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: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_t = {}
+    self.ce_t = {}
+    for t = 1, chunk_size do
+        self.softmax_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
+        self.ce_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
+    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[t]:nrow() ~= batch_size then
+            self.softmax_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
+            self.ce_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
+        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[t]
+    local ce = self.ce_t[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()
+    if output[1] ~= nil then
+        output[1]:copy_fromd(ce)
+    end
+    -- add total ce
+    self.total_ce = self.total_ce - ce:colsum()[0][0]
+    self.total_frames = self.total_frames + softmax:nrow()
+    -- TODO: add colsame for uncompressed label
+    if self.compressed then
+        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[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({})
+end