diff --git a/demo/lua2resnet18.lua b/demo/lua2resnet18.lua
new file mode 100644
index 0000000..aacc00a
--- /dev/null
+++ b/demo/lua2resnet18.lua
@@ -0,0 +1,67 @@
+loadfile("./util/lua2cc.lua")()
+loadfile("./util/ccfn.lua")()
+
+resnet18 = new_context({
+  name = "resnet18",
+  inputs = {
+    "in"
+  },
+  outputs = {
+    "out"
+  }
+})
+
+features   = new_module()
+classifier = new_module()
+
+make_basic_block = function(module, serial, stride)
+  module:append(fn.conv2d      , { stride = stride, padding = 1 })
+  module:append(fn.batch_norm2d)
+  module:append(fn.relu        , { name = "#NULL" })
+  module:append(fn.conv2d      , { stride = 1, padding = 1 })
+  module:append(fn.batch_norm2d, { output = "block"..serial.."_0i" })
+  if stride == 1 then
+    module:append(fn.add       , { alpha  = "block"..(serial - 1) })
+  else
+    module:append(fn.conv2d    , { input  = "block"..(serial - 1),
+    	output = "block"..serial.."_0t", stride = stride })
+    module:append(fn.batch_norm2d)
+    module:append(fn.add       , { alpha  = "block"..serial.."_0i" })
+  end
+  module:append(fn.relu        , { name   = "#NULL" })
+  module:append(fn.alias       , { output = "block"..serial.."_0" })
+  module:append(fn.conv2d      , { stride = 1, padding = 1 })
+  module:append(fn.batch_norm2d)
+  module:append(fn.relu        , { name   = "#NULL" })
+  module:append(fn.conv2d      , { stride = 1, padding = 1 })
+  module:append(fn.batch_norm2d, { output = "block"..serial.."_1" })
+  module:append(fn.add         , { alpha  = "block"..serial.."_0" })
+  module:append(fn.relu        , { name = "#NULL" })
+  module:append(fn.alias       , { output = "block"..serial })
+end
+
+-- block0
+features:append(fn.conv2d      , { input  = "#in", stride = 2, padding = 3 })
+features:append(fn.batch_norm2d)
+features:append(fn.relu        , { name   = "#NULL" })
+features:append(fn.max_pool2d  , { kernel = 3, stride = 2, output = "block0" })
+
+make_basic_block(features, 1, 1) -- block1
+make_basic_block(features, 2, 2) -- block2
+make_basic_block(features, 3, 2) -- block3
+make_basic_block(features, 4, 2) -- block4
+
+-- fc
+ --[[ Replace the kernel-stride fixed pooling to GAP ]]
+classifier:append(fn.avg_pool2d, { kernel = 7, stride = 7 })
+classifier:append(fn.reshape, { shape = { -1, 1, 1 } }) -- C * H * W
+classifier:append(fn.fully_connected)
+classifier:append(fn.softmax, { output = "#*out", name = "#NULL"})
+
+resnet18.block = "f" -- features
+features:impl_on(resnet18)
+
+resnet18.block = "c" -- classifier
+classifier:impl_on(resnet18)
+
+resnet18:generate()
diff --git a/demo/resnet18.c b/demo/resnet18.c
new file mode 100644
index 0000000..d70c1a6
--- /dev/null
+++ b/demo/resnet18.c
@@ -0,0 +1,190 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include "parg.h"
+#include "catcoon.h"
+
+#define INPUT_W 224
+#define INPUT_H 224
+
+char image_path[128];
+char parameters_path[128];
+
+void arg_parser(int argc, char* const argv[]);
+void resnet18(cc_tensor_t *in, cc_tensor_t **out);
+
+int main(int argc, const char *argv[])
+{
+	cc_int32 i, j;
+	cc_float32 v;
+	cc_tensor_t *input, *output;
+	arg_parser(argc, (char**)argv);
+	cc_tsrmgr_import(parameters_path);
+	input = cc_imread(image_path,
+		INPUT_H, INPUT_W, CC_RESIZE_LINEAR, CC_RGB, "input");
+	cc_image_norm(input, CC_IM_NORM_MINMAX_RGB);
+	cc_image_norm(input, CC_IM_NORM_ZSCORE_RGB);
+	resnet18(input, &output);
+	cc_tsrmgr_list();
+	v = 0.;
+	for (i = 0, j = 0; i < 1000; ++i) {
+		if (*((cc_float32*)output->data + i) > v) {
+			v = *((cc_float32*)output->data + i);
+			j = i;
+		}
+	}
+	printf("Result: [%d]\n", j);
+	cc_clear();
+	return 0;
+}
+
+void arg_parser(int argc, char* const argv[])
+{
+	struct parg_state ps;
+	int c;
+	parg_init(&ps);
+	while ((c = parg_getopt(&ps, argc, argv, "hp:v")) != -1) {
+		switch (c) {
+		case 1:
+			strcpy(image_path, ps.optarg);
+			break;
+		case 'h':
+			printf("Usage: [-h] -p parameters-path image-file\n");
+			printf("-h: Displays this message\n");
+			printf("-p: Choose parameters file path\n");
+			exit(EXIT_SUCCESS);
+			break;
+		case 'p':
+			strcpy(parameters_path, ps.optarg);
+			break;
+		case '?':
+			if (ps.optopt == 'p') {
+				printf("option -p requires an argument\n");
+			} else {
+				printf("unknown option -%c\n", ps.optopt);
+			}
+			exit(EXIT_FAILURE);
+			break;
+		default:
+			printf("error: unhandled option -%c\n", c);
+			exit(EXIT_FAILURE);
+			break;
+		}
+	}
+	if (!strlen(image_path) || !strlen(parameters_path)) {
+		printf("error: incomplete argument\n");
+		printf("run 'lenet -h' for help.\n");
+		exit(EXIT_FAILURE);
+	}
+}
+
+void resnet18(cc_tensor_t *in, cc_tensor_t **out)
+{
+	static cc_ssize _shape0[] = {-1, 1, 1, 0};
+	cc_tensor_t *c_0, *c_1, *c_2, *f_00, *f_01, *f_02, *f_03, *f_04, *f_05,
+	*f_06, *f_07, *f_08, *f_09, *f_10, *f_11, *f_12, *f_13, *f_14, *f_15,
+	*f_16, *f_17, *f_18, *f_19, *f_20, *f_21, *f_22, *f_23, *f_24, *f_25,
+	*f_26, *f_27, *f_28, *f_29, *f_30, *f_31, *f_32, *f_33, *f_34, *f_35,
+	*f_36, *f_37, *f_38, *f_39, *f_40, *f_41, *f_42, *f_43, *f_44, *f_45,
+	*f_46, *f_47, *f_48, *f_49, *f_50, *f_51, *f_52, *f_53, *f_block0,
+	*f_block1, *f_block1_0, *f_block1_0i, *f_block1_1, *f_block2,
+	*f_block2_0, *f_block2_0i, *f_block2_0t, *f_block2_1, *f_block3,
+	*f_block3_0, *f_block3_0i, *f_block3_0t, *f_block3_1, *f_block4,
+	*f_block4_0, *f_block4_0i, *f_block4_0t, *f_block4_1;
+	static const char *p_namels[] = {
+	"000.w", "000.b", "001.n", "002.w", "002.b", "003.n", "004.w", "004.b",
+	"005.n", "006.w", "006.b", "007.n", "008.w", "008.b", "009.n", "010.w",
+	"010.b", "011.n", "012.w", "012.b", "013.n", "014.w", "014.b", "015.n",
+	"016.w", "016.b", "017.n", "018.w", "018.b", "019.n", "020.w", "020.b",
+	"021.n", "022.w", "022.b", "023.n", "024.w", "024.b", "025.n", "026.w",
+	"026.b", "027.n", "028.w", "028.b", "029.n", "030.w", "030.b", "031.n",
+	"032.w", "032.b", "033.n", "034.w", "034.b", "035.n", "036.w", "036.b",
+	"037.n", "038.w", "038.b", "039.n", "040.w", "040.b"};
+	static cc_tensor_t *_p[62];
+	static int i;
+	for (; i < 62; ++i) {
+		_p[i] = cc_tsrmgr_get(p_namels[i]);
+	}
+	f_00 = cc_conv2d(in, _p[0], _p[1], 2, 3, 0, "resnet18/f_00");
+	f_01 = cc_batch_norm2d(f_00, _p[2], "resnet18/f_01");
+	f_02 = cc_relu(f_01, NULL);
+	f_block0 = cc_max_pool2d(f_02, 3, 2, 0, 0, "resnet18/f_block0");
+	f_03 = cc_conv2d(f_block0, _p[3], _p[4], 1, 1, 0, "resnet18/f_03");
+	f_04 = cc_batch_norm2d(f_03, _p[5], "resnet18/f_04");
+	f_05 = cc_relu(f_04, NULL);
+	f_06 = cc_conv2d(f_05, _p[6], _p[7], 1, 1, 0, "resnet18/f_06");
+	f_block1_0i = cc_batch_norm2d(f_06, _p[8], "resnet18/f_block1_0i");
+	f_07 = cc_elemwise(f_block1_0i, f_block0, '+', "resnet18/f_07");
+	f_08 = cc_relu(f_07, NULL);
+	f_block1_0 = f_08;
+	f_09 = cc_conv2d(f_block1_0, _p[9], _p[10], 1, 1, 0, "resnet18/f_09");
+	f_10 = cc_batch_norm2d(f_09, _p[11], "resnet18/f_10");
+	f_11 = cc_relu(f_10, NULL);
+	f_12 = cc_conv2d(f_11, _p[12], _p[13], 1, 1, 0, "resnet18/f_12");
+	f_block1_1 = cc_batch_norm2d(f_12, _p[14], "resnet18/f_block1_1");
+	f_13 = cc_elemwise(f_block1_1, f_block1_0, '+', "resnet18/f_13");
+	f_14 = cc_relu(f_13, NULL);
+	f_block1 = f_14;
+	f_15 = cc_conv2d(f_block1, _p[15], _p[16], 2, 1, 0, "resnet18/f_15");
+	f_16 = cc_batch_norm2d(f_15, _p[17], "resnet18/f_16");
+	f_17 = cc_relu(f_16, NULL);
+	f_18 = cc_conv2d(f_17, _p[18], _p[19], 1, 1, 0, "resnet18/f_18");
+	f_block2_0i = cc_batch_norm2d(f_18, _p[20], "resnet18/f_block2_0i");
+	f_block2_0t = cc_conv2d(f_block1, _p[21], _p[22], 2, 0, 0,
+	"resnet18/f_block2_0t");
+	f_19 = cc_batch_norm2d(f_block2_0t, _p[23], "resnet18/f_19");
+	f_20 = cc_elemwise(f_19, f_block2_0i, '+', "resnet18/f_20");
+	f_21 = cc_relu(f_20, NULL);
+	f_block2_0 = f_21;
+	f_22 = cc_conv2d(f_block2_0, _p[24], _p[25], 1, 1, 0, "resnet18/f_22");
+	f_23 = cc_batch_norm2d(f_22, _p[26], "resnet18/f_23");
+	f_24 = cc_relu(f_23, NULL);
+	f_25 = cc_conv2d(f_24, _p[27], _p[28], 1, 1, 0, "resnet18/f_25");
+	f_block2_1 = cc_batch_norm2d(f_25, _p[29], "resnet18/f_block2_1");
+	f_26 = cc_elemwise(f_block2_1, f_block2_0, '+', "resnet18/f_26");
+	f_27 = cc_relu(f_26, NULL);
+	f_block2 = f_27;
+	f_28 = cc_conv2d(f_block2, _p[30], _p[31], 2, 1, 0, "resnet18/f_28");
+	f_29 = cc_batch_norm2d(f_28, _p[32], "resnet18/f_29");
+	f_30 = cc_relu(f_29, NULL);
+	f_31 = cc_conv2d(f_30, _p[33], _p[34], 1, 1, 0, "resnet18/f_31");
+	f_block3_0i = cc_batch_norm2d(f_31, _p[35], "resnet18/f_block3_0i");
+	f_block3_0t = cc_conv2d(f_block2, _p[36], _p[37], 2, 0, 0,
+	"resnet18/f_block3_0t");
+	f_32 = cc_batch_norm2d(f_block3_0t, _p[38], "resnet18/f_32");
+	f_33 = cc_elemwise(f_32, f_block3_0i, '+', "resnet18/f_33");
+	f_34 = cc_relu(f_33, NULL);
+	f_block3_0 = f_34;
+	f_35 = cc_conv2d(f_block3_0, _p[39], _p[40], 1, 1, 0, "resnet18/f_35");
+	f_36 = cc_batch_norm2d(f_35, _p[41], "resnet18/f_36");
+	f_37 = cc_relu(f_36, NULL);
+	f_38 = cc_conv2d(f_37, _p[42], _p[43], 1, 1, 0, "resnet18/f_38");
+	f_block3_1 = cc_batch_norm2d(f_38, _p[44], "resnet18/f_block3_1");
+	f_39 = cc_elemwise(f_block3_1, f_block3_0, '+', "resnet18/f_39");
+	f_40 = cc_relu(f_39, NULL);
+	f_block3 = f_40;
+	f_41 = cc_conv2d(f_block3, _p[45], _p[46], 2, 1, 0, "resnet18/f_41");
+	f_42 = cc_batch_norm2d(f_41, _p[47], "resnet18/f_42");
+	f_43 = cc_relu(f_42, NULL);
+	f_44 = cc_conv2d(f_43, _p[48], _p[49], 1, 1, 0, "resnet18/f_44");
+	f_block4_0i = cc_batch_norm2d(f_44, _p[50], "resnet18/f_block4_0i");
+	f_block4_0t = cc_conv2d(f_block3, _p[51], _p[52], 2, 0, 0,
+	"resnet18/f_block4_0t");
+	f_45 = cc_batch_norm2d(f_block4_0t, _p[53], "resnet18/f_45");
+	f_46 = cc_elemwise(f_45, f_block4_0i, '+', "resnet18/f_46");
+	f_47 = cc_relu(f_46, NULL);
+	f_block4_0 = f_47;
+	f_48 = cc_conv2d(f_block4_0, _p[54], _p[55], 1, 1, 0, "resnet18/f_48");
+	f_49 = cc_batch_norm2d(f_48, _p[56], "resnet18/f_49");
+	f_50 = cc_relu(f_49, NULL);
+	f_51 = cc_conv2d(f_50, _p[57], _p[58], 1, 1, 0, "resnet18/f_51");
+	f_block4_1 = cc_batch_norm2d(f_51, _p[59], "resnet18/f_block4_1");
+	f_52 = cc_elemwise(f_block4_1, f_block4_0, '+', "resnet18/f_52");
+	f_53 = cc_relu(f_52, NULL);
+	f_block4 = f_53;
+	c_0 = cc_avg_pool2d(f_block4, 7, 7, 0, 0, "resnet18/c_0");
+	c_1 = cc_reshape(c_0, _shape0);
+	c_2 = cc_fully_connected(c_1, _p[60], _p[61], "resnet18/c_2");
+	*out = cc_softmax(c_2, NULL);
+}
diff --git a/makefile b/makefile
index 637cdfd..a1d6903 100644
--- a/makefile
+++ b/makefile
@@ -114,7 +114,7 @@ util_vec.o util_image.o global_fn_cfg.o
 
 CATCOON_A = libcatcoon.a
 
-APPS_DEMO = simple lenet vgg16
+APPS_DEMO = simple lenet vgg16 resnet18
 APPS_UTIL = lspkg mkpkg cclua
 
 APP_NAMES  = $(APPS_DEMO) $(APPS_UTIL)
diff --git a/util/ccfn.lua b/util/ccfn.lua
index 81e3370..eda25ca 100644
--- a/util/ccfn.lua
+++ b/util/ccfn.lua
@@ -1,4 +1,8 @@
 fn = {
+  alias = {
+    parameter = false,
+    template  = "$output = $input;"
+  },
   conv2d = {
     parameter = true,
     padding   = 0,
@@ -6,18 +10,32 @@ fn = {
     template  = "$output = cc_conv2d($input, "..
       "$pack.w, $pack.b, $stride, $padding, $offset, $name);"
   },
+  batch_norm2d = {
+    parameter = true,
+    template  = "$output = cc_batch_norm2d($input, $pack.n, $name);"
+  },
   fully_connected = {
     parameter = true,
     template  = "$output = cc_fully_connected($input, $pack.w, $pack.b, $name);"
   },
   add = {
     parameter = false,
-    layers    = { "alpha" }, 
-    template  = "$output = cc_add($input, $alpha, $name);"
+    layers    = { "alpha" },
+    template  = "$output = cc_elemwise($input, $alpha, '+', $name);"
+  },
+  avg_pool2d = {
+    parameter = false,
+    padding   = 0,
+    offset    = 0,
+    template  = "$output = cc_avg_pool2d($input, "..
+      "$kernel, $stride, $padding, $offset, $name);"
   },
   max_pool2d = {
     parameter = false,
-    template  = "$output = cc_max_pool2d($input, $kernel, $stride, $name);"
+    padding   = 0,
+    offset    = 0,
+    template  = "$output = cc_max_pool2d($input, "..
+      "$kernel, $stride, $padding, $offset, $name);"
   },
   relu = {
     parameter = false,
diff --git a/util/lua2cc.lua b/util/lua2cc.lua
index 204abf3..fc777e3 100644
--- a/util/lua2cc.lua
+++ b/util/lua2cc.lua
@@ -234,7 +234,7 @@ local context_dfl = {
     print_line("{")
     -- make shapes
     if #self.shape_list >= 1 then
-      local shape = "static int "
+      local shape = "static cc_ssize "
       for k, v in pairs(self.shape_list) do
         shape = shape..string.format(self.shapefmt.."[] = {", k - 1)
         for _, v in pairs(v) do