diff --git a/python/paddle/fluid/contrib/int8_inference/__init__.py b/python/paddle/fluid/contrib/int8_inference/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..eca2dce114b069bf9b455d77ce670d73b5047fd2
--- /dev/null
+++ b/python/paddle/fluid/contrib/int8_inference/__init__.py
@@ -0,0 +1,13 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/python/paddle/fluid/contrib/int8_inference/utility.py b/python/paddle/fluid/contrib/int8_inference/utility.py
new file mode 100644
index 0000000000000000000000000000000000000000..197fc5f2d261798dc17daa37c1b6d258936a8a39
--- /dev/null
+++ b/python/paddle/fluid/contrib/int8_inference/utility.py
@@ -0,0 +1,708 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import paddle.fluid.core as core
+import numpy as np
+import math
+import os
+import paddle.fluid as fluid
+
+
+class Calibrator(object):
+    '''
+    The calibrator class transforms the program and updates the calculated scale into it.
+    This is INT8 v1 calibration tool, mainly for the support of ResNet-50 and MobileNet.
+    '''
+    # TODO(guomingz): Below op list will be updated once more INT8 op kernels are supported.
+    non_conv_int8_op_type = ("pool2d")
+    supported_int8_op_type = ("conv2d", "pool2d")
+    const_sign_op_type = ('pool2d', 'reshape', 'concat', 'transpose')
+    u8_max = 255
+    s8_max = 127
+
+    def __init__(self, *args, **kwargs):
+        self.program = kwargs['program']
+        self.iterations = kwargs['iterations']
+        self.pretrained_model = kwargs['pretrained_model']
+        self.debug = kwargs['debug']
+        self.algo = kwargs['algo']
+
+        self._conv_input_var_name = []
+        self._conv_output_var_name = []
+        self._pool2d_output_var_name = []
+        self._weights_var_name = []
+        self._residual_input_var_name = []
+        self._int8_output_var_op_index_dict = {}
+        self._conv_op_index = [
+            index for index, value in enumerate(self.program.global_block().ops)
+            if value.type == 'conv2d'
+        ]
+
+        self._var_max_value_map = {}
+        self._var_max_range = {}
+        self._weights_scaling_factor = {}
+        self._u8_output_var = []
+        self._s8_output_var = []
+        self._persistable_vars = []
+
+    def generate_sampling_program(self):
+        self.__init_analysis()
+        self.__generate_output_program()
+
+    def generate_quantized_data(self, sampling_data):
+        self.__sampling(sampling_data)
+        self.__save_scale()
+        self.__update_program()
+        self.__update_output_program_attr()
+        self.__display_debug()
+
+    def __display_debug(self):
+        if self.debug:
+            self.__dot(self._output_program)
+            print(self._output_program)
+
+    def __get_max_range_by_var_name(self, program, var_name):
+        """
+        Check the specified variable was generated from Relu layer or not.
+        If the variable was the output of one of the pool2d/reshape/concat
+        /transpose, we keep trace the ancestor of this variable;
+        If the variable was the output the conv op, we check it's has_relu
+        attr;
+        Otherwise, we return the Calibrator.s8 as default value.
+        Returns:
+            Return Calibrator.u8_max if the variable was generated by Relu,
+            otherwise it will returns Calibrator.s8
+        """
+        search_end_index = -1
+        input_index_name = {}
+        output_index_name = {}
+        ops_type = []
+
+        for index, op in enumerate(program.current_block().ops):
+            ops_type.append(op.type)
+
+            input_index_name[index] = op.input_arg_names
+
+            output_index_name[index] = op.output_arg_names
+            if var_name in op.output_arg_names:
+                search_end_index = index
+
+        # analysis
+        while search_end_index >= 0:
+            if ops_type[search_end_index] == "relu":
+                return Calibrator.u8_max
+
+            input_name = input_index_name[search_end_index][0]
+
+            for i in output_index_name.keys():
+                if input_name in output_index_name[i]:
+                    search_end_index = i
+                    break
+
+            if ops_type[
+                    search_end_index] not in Calibrator.const_sign_op_type and ops_type[
+                        search_end_index] != 'conv2d':
+                return Calibrator.s8_max
+
+            if ops_type[search_end_index] != 'conv2d':
+                continue
+
+            if program.current_block().ops[search_end_index].has_attr(
+                    'fuse_relu') and program.current_block().ops[
+                        search_end_index].attr('fuse_relu'):
+                return Calibrator.u8_max
+            else:
+                return Calibrator.s8_max
+
+        return Calibrator.s8_max
+
+    def __check_op_type_with_specified_var_as_input(self,
+                                                    program,
+                                                    var_name,
+                                                    start_index=0):
+        '''
+        Check whether all the type of ops that use the specified variable as the
+        input.If one of those op is not int8-enabled, return False.
+        '''
+        op_type_list = [
+            op.type for op in program.current_block().ops[start_index:]
+            if var_name in op.input_arg_names
+        ]
+        for i in op_type_list:
+            if not i in Calibrator.supported_int8_op_type:
+                return False
+        return True
+
+    def __check_var_source_dt(self, var_name):
+        '''
+        Check whether the specified variable is the output of int8 conv op or not.
+        If true, return the original op index.
+        If false, return -1
+        '''
+        return self._int8_output_var_op_index_dict[
+            var_name] if var_name in self._int8_output_var_op_index_dict else -1
+
+    def __update_int8_output_var_op_index_dict(self, index, var_name=None):
+        '''
+        Update the int8_output_variable/op_index dictionary
+        '''
+        for k, v in self._int8_output_var_op_index_dict.items():
+            if v >= index:
+                self._int8_output_var_op_index_dict[k] = v + 1
+        if var_name:
+            self._int8_output_var_op_index_dict[var_name] = index
+
+    def __update_program(self):
+        '''
+        Update the program with the quantize/dequantize op insertion.
+        '''
+        quantize_index, dequantize_index = self.__get_quantize_dequantize_combination(
+            self._output_program)
+        inserted_op_length = 0
+        calc_max_func = self.__get_optimal_scaling_factor if self.algo == "KL" else np.max
+        insert_op_collection = sorted(quantize_index + dequantize_index)
+
+        for index in insert_op_collection:
+            if index in quantize_index:
+                quantize_tmp = self._output_program.current_block().create_var(
+                    name="quantize_{}_tmp".format(index),
+                    dtype=core.VarDesc.VarType.UINT8)
+                original_out_name = self._output_program.current_block().ops[
+                    index + inserted_op_length - 1].output_names[0]
+                original_out = self._output_program.current_block().ops[
+                    index + inserted_op_length - 1].output(original_out_name)[0]
+
+                op = self._output_program.current_block()._insert_op(
+                    index=index + inserted_op_length,
+                    type="quantize",
+                    inputs={"Input": original_out},
+                    outputs={"Output": quantize_tmp}, )
+
+                op._set_attr("data_format", "MKLDNNLAYOUT")
+                op._set_attr("use_mkldnn", 1)
+                op._set_attr(
+                    "Scale", self._var_max_range[original_out] /
+                    calc_max_func(self._var_max_value_map[original_out]))
+
+                if self.__get_max_range_by_var_name(
+                        self._output_program,
+                        original_out) == Calibrator.s8_max:
+                    op._set_attr("is_negative_input", 1)
+
+                self.__update_int8_output_var_op_index_dict(
+                    index + inserted_op_length, "quantize_{}_tmp".format(index))
+
+                inserted_op_length += 1
+                for op in self._output_program.current_block().ops[
+                        index + inserted_op_length:]:
+                    for j in op.input_names:
+                        if op.input(j) and op.input(
+                                j
+                        )[0] == original_out and op.type in Calibrator.supported_int8_op_type:
+                            op.desc.set_input(j,
+                                              ["{}".format(quantize_tmp.name)])
+            else:
+                start_index = index + inserted_op_length
+                dequantize_tmp_var = self._output_program.current_block(
+                ).create_var(
+                    name="dequantize_{}_tmp".format(index + 1),
+                    dtype="float32", )
+                original_out_var = None
+
+                for original_input in self._output_program.current_block().ops[
+                        start_index].input_arg_names:
+                    index_res = self.__get_op_index_by_output_var(
+                        self._output_program, original_input)
+                    if index_res != -1:
+                        original_out_var = original_input
+                        break
+
+                if original_out_var:
+                    op = self._output_program.current_block()._insert_op(
+                        index=start_index,
+                        type="dequantize",
+                        inputs={"Input": original_out_var},
+                        outputs={"Output": dequantize_tmp_var})
+                    op._set_attr("data_format", "MKLDNNLAYOUT")
+                    op._set_attr("use_mkldnn", 1)
+                    op._set_attr("Scale", self._var_max_range[original_out_var]
+                                 / calc_max_func(self._var_max_value_map[
+                                     original_out_var]))
+
+                    for op_index in range(
+                            start_index + 1,
+                            len(self._output_program.current_block().ops)):
+                        if self._output_program.current_block(
+                        ).ops[op_index].type == "conv2d" and self._output_program.current_block(
+                        ).ops[op_index].attr("force_fp32_output"):
+                            continue
+                        else:
+                            for j in self._output_program.current_block().ops[
+                                    op_index].input_names:
+                                if len(self._output_program.current_block().ops[
+                                        op_index].input(j)
+                                       ) and self._output_program.current_block(
+                                       ).ops[op_index].input(j)[
+                                           0] == original_out_var:
+                                    self._output_program.current_block(
+                                    ).ops[op_index].desc.set_input(
+                                        j,
+                                        ["{}".format(dequantize_tmp_var.name)])
+
+                    inserted_op_length += 1
+
+                    op._set_attr("data_format", "MKLDNNLAYOUT")
+                    op._set_attr("use_mkldnn", 1)
+
+    def __update_output_program_attr(self):
+        for i in self._output_program.list_vars():
+            if i.name in self._persistable_vars:
+                i.persistable = False
+                os.system("rm -rf {}/{}".format(self.pretrained_model, i.name))
+
+        for i in self._u8_output_var:
+            self._output_program.current_block().var(i).desc.set_dtype(
+                core.VarDesc.VarType.UINT8)
+
+        for i in self._s8_output_var:
+            self._output_program.current_block().var(i).desc.set_dtype(
+                core.VarDesc.VarType.INT8)
+
+    @property
+    def sampling_program(self):
+        return self._output_program
+
+    @property
+    def sampling_vars(self):
+        return self._weights_var_name + self._conv_input_var_name + self._conv_output_var_name + self._residual_input_var_name + self._pool2d_output_var_name
+
+    def _is_close(self, a, b, rel_tol=1e-09, abs_tol=0.0):
+        return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
+
+    def __generate_output_program(self):
+        for i in self.program.list_vars():
+            if not i.persistable and i.name in self.sampling_vars:
+                i.persistable = True
+                self._persistable_vars.append(i.name)
+
+        self._output_program = self.program.clone()
+
+    def __save_scale(self):
+        '''
+        Update the convolution scale information.
+        '''
+        func = self.__get_optimal_scaling_factor if self.algo == 'KL' else np.max
+        for i in self._conv_op_index[1:]:
+            weights_var_name = self.program.current_block().ops[i].input(
+                'Filter')[0]
+            input_var_name = self.program.current_block().ops[i].input('Input')[
+                0]
+            output_var_name = self.program.current_block().ops[i].output(
+                'Output')[0]
+            self._output_program.current_block().ops[i]._set_attr(
+                "Scale_weights", self._weights_scaling_factor[weights_var_name])
+
+            self._output_program.current_block().ops[i]._set_attr(
+                "Scale_in", self._var_max_range[input_var_name] /
+                func(self._var_max_value_map[input_var_name]))
+            self._output_program.current_block().ops[i]._set_attr(
+                "Scale_out", self._var_max_range[output_var_name] /
+                func(self._var_max_value_map[output_var_name]))
+            if self._output_program.current_block().ops[i].desc.input(
+                    "ResidualData"):
+                residual_var_name = self._output_program.current_block().ops[
+                    i].desc.input("ResidualData")[0]
+                self._output_program.current_block().ops[i]._set_attr(
+                    "Scale_in_eltwise", self._var_max_range[residual_var_name] /
+                    func(self._var_max_value_map[residual_var_name]))
+
+    def __sampling(self, sampling_data):
+        '''
+        Sampling the variables data range.
+        '''
+        for i in self.program.list_vars():
+            if i.name not in self.sampling_vars:
+                continue
+
+            if i.name in self._weights_var_name:
+                scaling_factor_per_channel = []
+                data = sampling_data[i.name][0]
+                for j in range(data.shape[0]):
+                    var_value = float(np.max(np.abs(data[j])))
+                    if not self._is_close(var_value, 0.0):
+                        scaling_factor_per_channel.append(Calibrator.s8_max /
+                                                          var_value)
+                    else:
+                        scaling_factor_per_channel.append(0.0)
+                self._weights_scaling_factor[
+                    i.name] = scaling_factor_per_channel
+            else:
+                if i.name in self._conv_output_var_name:
+                    op_pos = self.__get_op_index_by_output_var(self.program,
+                                                               i.name)
+                    cur_op = self.program.current_block().ops[op_pos]
+
+                    if cur_op.has_attr('fuse_relu') and cur_op.attr(
+                            'fuse_relu'):
+                        max_range = Calibrator.u8_max
+                        self._u8_output_var.append(i.name)
+                    else:
+                        max_range = Calibrator.s8_max
+                        self._s8_output_var.append(i.name)
+                else:
+                    max_range = self.__get_max_range_by_var_name(self.program,
+                                                                 i.name)
+                max_value = [[np.abs(np_data)]
+                             for np_data in sampling_data[i.name]]
+
+                self._var_max_range[i.name] = max_range
+                self._var_max_value_map[i.name] = max_value
+
+    def __check_force_fp32_attr_by_output_var(self, program, var_name):
+        for op in program.current_block().ops:
+            if op.type == "conv2d" and var_name in op.output_arg_names:
+                return op.attr("force_fp32_output")
+        return False
+
+    def __get_op_index_by_output_var(self, program, var_name, start_index=0):
+        '''
+        Check whether the specified input variable is the output of the
+        conv/pool2d op's output or not.
+
+        Returns:
+            The index if the variable is the output of any conv/pool2d op's
+            output.
+            -1 when the variable is not the output of any conv/pool2d op's 
+            output.
+        '''
+        for index, op in enumerate(program.current_block().ops[start_index:]):
+            if var_name in op.output_arg_names and op.type in Calibrator.supported_int8_op_type:
+                return index
+        return -1
+
+    def __get_op_index_by_input_var(self, program, var_name, start_index=0):
+        '''
+        Get the op index by specified input variable.
+        Returns:
+            The op index if the variable is the input of this op or -1 if the 
+            variable is not the input of any op. 
+        '''
+        for index, op in enumerate(program.current_block().ops[start_index:]):
+            if var_name in op.input_arg_names:
+                return index
+
+        return -1
+
+    def __get_quantize_dequantize_combination(self, program):
+        """
+        Get the quantize/dequantize op index for further inserting.
+        Args:
+            The program desc.
+        Returns:
+            Two lists contains the quantize op and dequantize op index information.
+        """
+        quantize_op_index = []
+        dequantize_op_index = []
+        minimal_conv_count = 2  # there must be two conv ops if not enable the first conv int8.
+        if len(self._conv_op_index) < minimal_conv_count:
+            return [], []
+
+        for index, value in enumerate(self._conv_op_index):
+            if index == 0:
+                quantize_op_index.append(self._conv_op_index[index + 1])
+            elif index == len(self._conv_op_index) - 1:
+                output_var = program.current_block().ops[value].output(
+                    "Output")[0]
+                if self.__check_op_type_with_specified_var_as_input(
+                        program, output_var, index):
+                    dequantize_op_index.append(self._conv_op_index[index] + 2)
+                else:
+                    program.current_block().ops[value]._set_attr(
+                        "force_fp32_output", True)
+
+            elif self._conv_op_index[index] + 1 < self._conv_op_index[index +
+                                                                      1]:
+
+                program.current_block().ops[self._conv_op_index[
+                    index]]._set_attr("force_fp32_output", True)
+
+                for op_index in range(self._conv_op_index[index + 1],
+                                      self._conv_op_index[index], -1):
+                    op_type = program.current_block().ops[op_index].type
+                    op_has_int8_input = False
+                    input_var_name = None
+                    input_length = len(program.current_block().ops[op_index]
+                                       .input_arg_names)
+
+                    for var_name in program.current_block().ops[
+                            op_index].input_arg_names:
+                        if self.__check_var_source_dt(var_name) != -1:
+                            op_has_int8_input = True
+                            input_var_name = var_name
+                            break
+
+                    if op_has_int8_input:
+                        if op_type == "conv2d":
+                            if program.current_block().ops[op_index +
+                                                           1].type == "conv2d":
+                                continue
+                            elif program.current_block(
+                            ).ops[op_index +
+                                  1].type in Calibrator.non_conv_int8_op_type:
+                                dequantize_op_index.append(op_index + 2)
+                                break
+                            else:
+                                program.current_block().ops[op_index]._set_attr(
+                                    "force_fp32_output", True)
+                                continue
+                        elif not self.__check_force_fp32_attr_by_output_var(
+                                program, input_var_name
+                        ) and op_index not in dequantize_op_index:
+                            share_input_flag = True
+                            for input_attr_name in program.current_block().ops[
+                                    op_index].input_names:
+                                input_var_name = program.current_block().ops[
+                                    op_index].input(input_attr_name)[0]
+                                cousin_op_index = self.__get_op_index_by_input_var(
+                                    program, input_var_name)
+                                if cousin_op_index != -1 and cousin_op_index in dequantize_op_index:
+                                    share_input_flag = False
+                                    break
+                            if share_input_flag:
+                                dequantize_op_index.append(op_index)
+
+                    elif input_length:
+                        output_is_to_int8_op = False
+                        share_input_flag = True
+                        for var_name in program.current_block().ops[
+                                op_index].input_arg_names:
+                            if not self.__check_op_type_with_specified_var_as_input(
+                                    program, var_name):
+                                share_input_flag = False
+                                break
+
+                        for var_name in program.current_block().ops[
+                                op_index].output_arg_names:
+                            if self.__get_op_index_by_output_var(
+                                    program, var_name, op_index) != -1:
+                                output_is_to_int8_op = True
+                                break
+
+                        if share_input_flag or output_is_to_int8_op:
+                            quantize_op_index.append(op_index)
+
+        return quantize_op_index, dequantize_op_index
+
+    def __init_analysis(self):
+        '''
+        Collect the variable names for sampling.
+        '''
+        start_index = 1  #analysis the conv op detail from second conv op.
+
+        for i in self._conv_op_index[start_index:]:
+            self._weights_var_name.append(self.program.current_block().ops[i]
+                                          .input('Filter')[0])
+            self._conv_input_var_name.append(self.program.current_block().ops[i]
+                                             .input('Input')[0])
+            self._conv_output_var_name.append(self.program.current_block().ops[
+                i].output('Output')[0])
+            self._int8_output_var_op_index_dict[self.program.current_block()
+                                                .ops[i].output('Output')[0]] = i
+            if self.program.current_block().ops[i].desc.input("ResidualData"):
+                self._residual_input_var_name.append(self.program.current_block(
+                ).ops[i].desc.input("ResidualData")[0])
+
+            if self.program.current_block().ops[i + 1].type == "pool2d":
+                self._pool2d_output_var_name.append(self.program.current_block(
+                ).ops[i + 1].output('Out')[0])
+
+    def __expand_quantized_bins(self, quantized_bins, reference_bins):
+        expanded_quantized_bins = [0] * len(reference_bins)
+        num_merged_bins = len(reference_bins) / len(quantized_bins)
+        j_start = 0
+        j_end = num_merged_bins
+        for idx in xrange(len(quantized_bins)):
+            zero_count = reference_bins[j_start:j_end].count(0)
+            num_merged_bins = j_end - j_start
+            if zero_count == num_merged_bins:
+                avg_bin_ele = 0
+            else:
+                avg_bin_ele = quantized_bins[idx] / (
+                    num_merged_bins - zero_count + 0.0)
+            for idx1 in xrange(j_start, j_end):
+                expanded_quantized_bins[idx1] = (0 if reference_bins[idx1] == 0
+                                                 else avg_bin_ele)
+            j_start += num_merged_bins
+            j_end += num_merged_bins
+            if (idx + 1) == len(quantized_bins) - 1:
+                j_end = len(reference_bins)
+        return expanded_quantized_bins
+
+    def __safe_entropy(self, reference_distr_P, P_sum, candidate_distr_Q,
+                       Q_sum):
+        '''
+        Calculate the entropy.
+        '''
+        assert len(reference_distr_P) == len(candidate_distr_Q)
+        tmp_sum1 = 0
+        tmp_sum2 = 0
+        for idx in range(len(reference_distr_P)):
+            p_idx = reference_distr_P[idx]
+            q_idx = candidate_distr_Q[idx]
+            if p_idx == 0:
+                tmp_sum1 += 0
+                tmp_sum2 += 0
+            else:
+                if q_idx == 0:
+                    print("Fatal error!, idx = " + str(idx) +
+                          " qindex = 0! p_idx = " + str(p_idx))
+                tmp_sum1 += p_idx * (math.log(Q_sum * p_idx))
+                tmp_sum2 += p_idx * (math.log(P_sum * q_idx))
+        return (tmp_sum1 - tmp_sum2) / P_sum
+
+    # Reference: http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf
+    def __get_optimal_scaling_factor(self,
+                                     activation_blob,
+                                     num_quantized_bins=255):
+        '''
+        Using the KL-divergenc method to get the more precise scaling factor.
+        '''
+        max_val = np.max(activation_blob)
+        min_val = np.min(activation_blob)
+        if min_val >= 0:
+            hist, hist_edeges = np.histogram(
+                activation_blob, bins=2048, range=(min_val, max_val))
+            ending_iter = 2047
+            starting_iter = int(ending_iter * 0.7)
+        else:
+            th = max(abs(max_val), abs(min_val))
+            hist, hist_edeges = np.histogram(
+                activation_blob, bins=2048, range=(-th, th))
+            starting_iter = 0
+            ending_iter = 2047
+            if abs(max_val) > abs(min_val):
+                while starting_iter < ending_iter:
+                    if hist[starting_iter] == 0:
+                        starting_iter += 1
+                        continue
+                    else:
+                        break
+                starting_iter += int((ending_iter - starting_iter) * 0.6)
+            else:
+                while ending_iter > 0:
+                    if hist[ending_iter] == 0:
+                        ending_iter -= 1
+                        continue
+                    else:
+                        break
+                starting_iter = int(0.6 * ending_iter)
+        bin_width = hist_edeges[1] - hist_edeges[0]
+        P_sum = len(activation_blob)
+        min_kl_divergence = 0
+        min_kl_index = 0
+        kl_inited = False
+        for i in range(starting_iter, ending_iter + 1):
+            reference_distr_P = hist[0:i].tolist()
+            outliers_count = sum(hist[i:2048])
+            if reference_distr_P[i - 1] == 0:
+                continue
+            reference_distr_P[i - 1] += outliers_count
+            reference_distr_bins = reference_distr_P[:]
+            candidate_distr_Q = hist[0:i].tolist()
+            num_merged_bins = i / num_quantized_bins
+            candidate_distr_Q_quantized = [0] * num_quantized_bins
+            j_start = 0
+            j_end = num_merged_bins
+            for idx in xrange(num_quantized_bins):
+                candidate_distr_Q_quantized[idx] = sum(candidate_distr_Q[
+                    j_start:j_end])
+                j_start += num_merged_bins
+                j_end += num_merged_bins
+                if (idx + 1) == num_quantized_bins - 1:
+                    j_end = i
+            candidate_distr_Q = self.__expand_quantized_bins(
+                candidate_distr_Q_quantized, reference_distr_bins)
+            Q_sum = sum(candidate_distr_Q)
+            kl_divergence = self.__safe_entropy(reference_distr_P, P_sum,
+                                                candidate_distr_Q, Q_sum)
+            if not kl_inited:
+                min_kl_divergence = kl_divergence
+                min_kl_index = i
+                kl_inited = True
+            elif kl_divergence < min_kl_divergence:
+                min_kl_divergence = kl_divergence
+                min_kl_index = i
+            else:
+                pass
+        if min_kl_index == 0:
+            while starting_iter > 0:
+                if hist[starting_iter] == 0:
+                    starting_iter -= 1
+                    continue
+                else:
+                    break
+            min_kl_index = starting_iter
+        return (min_kl_index + 0.5) * bin_width
+
+    @staticmethod
+    def __dot(program, output_name="model.dot"):
+        '''
+        Generate the graphiz dot file for debugging.
+        '''
+        dot_graph = ""
+        dot_nodes = []
+        dot_edges = []
+        dot_graph += "digraph pm {\n"
+        for block in program.blocks:
+            ops = list(block.ops)
+            for index, op in enumerate(ops):
+                op_type = op.type
+                op_name = op_type + "_" + op.output_arg_names[0].replace(
+                    ".", "_") + "___" + str(index)
+                for name in op.input_arg_names:
+                    name = name.replace(".", "_")
+                    dot_edge = name + " -> " + op_name
+                    if dot_edge not in dot_edges:
+                        dot_edges.append(dot_edge)
+                    dot_node = name + " [shape=oval, style=filled, fillcolor=yellow]"
+                    if dot_node not in dot_nodes:
+                        dot_nodes.append(dot_node)
+
+                for name in op.output_arg_names:
+                    name = name.replace(".", "_")
+                    dot_edge = op_name + " -> " + name
+                    if dot_edge not in dot_edges:
+                        dot_edges.append(dot_edge)
+                if op_type in Calibrator.supported_int8_op_type:
+                    if op_type == "conv2d" and op.has_attr(
+                            'force_fp32_output') and op.attr(
+                                "force_fp32_output"):
+                        dot_node = op_name + " [shape=box, style=filled, color=deeppink]"
+                    else:
+                        dot_node = op_name + " [shape=box, style=filled, color=greenyellow]"
+                elif op_type in ["quantize", "dequantize"]:
+                    dot_node = op_name + " [shape=box, style=filled, color=gold]"
+                else:
+                    dot_node = op_name + " [shape=box, style=filled, fillcolor=red]"
+
+                if dot_node not in dot_nodes:
+                    dot_nodes.append(dot_node)
+
+        for dot_edge in dot_edges:
+            dot_graph += dot_edge + "\n"
+        for dot_node in dot_nodes:
+            dot_graph += dot_node + "\n"
+        dot_graph += "}"
+
+        with open(output_name, 'w') as f:
+            f.write(dot_graph)
diff --git a/python/paddle/fluid/contrib/tests/CMakeLists.txt b/python/paddle/fluid/contrib/tests/CMakeLists.txt
index 79bec8c4ad34d682895250bc29b1fddb3a569bd4..81aee1233d1db756686d1a934b94672dc5c770fe 100644
--- a/python/paddle/fluid/contrib/tests/CMakeLists.txt
+++ b/python/paddle/fluid/contrib/tests/CMakeLists.txt
@@ -1,6 +1,10 @@
 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
+if(APPLE OR WIN32 OR NOT WITH_MKL)
+    list(REMOVE_ITEM TEST_OPS test_calibration)
+endif()
+
 foreach(src ${TEST_OPS})
     py_test(${src} SRCS ${src}.py)
 endforeach()
diff --git a/python/paddle/fluid/contrib/tests/test_calibration.py b/python/paddle/fluid/contrib/tests/test_calibration.py
new file mode 100644
index 0000000000000000000000000000000000000000..17e4eb8b831268bed00736db2e9706aece9fdd74
--- /dev/null
+++ b/python/paddle/fluid/contrib/tests/test_calibration.py
@@ -0,0 +1,230 @@
+#   copyright (c) 2018 paddlepaddle authors. all rights reserved.
+#
+# licensed under the apache license, version 2.0 (the "license");
+# you may not use this file except in compliance with the license.
+# you may obtain a copy of the license at
+#
+#     http://www.apache.org/licenses/license-2.0
+#
+# unless required by applicable law or agreed to in writing, software
+# distributed under the license is distributed on an "as is" basis,
+# without warranties or conditions of any kind, either express or implied.
+# see the license for the specific language governing permissions and
+# limitations under the license.
+import unittest
+import os
+import numpy as np
+import time
+import sys
+import random
+import paddle
+import paddle.fluid as fluid
+import argparse
+import functools
+import contextlib
+import paddle.fluid.profiler as profiler
+from PIL import Image, ImageEnhance
+import math
+sys.path.append('..')
+import int8_inference.utility as ut
+
+random.seed(0)
+np.random.seed(0)
+
+DATA_DIM = 224
+
+THREAD = 1
+BUF_SIZE = 102400
+
+DATA_DIR = 'data/ILSVRC2012'
+
+img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
+img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
+
+
+# TODO(guomingz): Remove duplicated code from line 45 ~ line 114
+def resize_short(img, target_size):
+    percent = float(target_size) / min(img.size[0], img.size[1])
+    resized_width = int(round(img.size[0] * percent))
+    resized_height = int(round(img.size[1] * percent))
+    img = img.resize((resized_width, resized_height), Image.LANCZOS)
+    return img
+
+
+def crop_image(img, target_size, center):
+    width, height = img.size
+    size = target_size
+    if center == True:
+        w_start = (width - size) / 2
+        h_start = (height - size) / 2
+    else:
+        w_start = np.random.randint(0, width - size + 1)
+        h_start = np.random.randint(0, height - size + 1)
+    w_end = w_start + size
+    h_end = h_start + size
+    img = img.crop((w_start, h_start, w_end, h_end))
+    return img
+
+
+def process_image(sample, mode, color_jitter, rotate):
+    img_path = sample[0]
+
+    img = Image.open(img_path)
+
+    img = resize_short(img, target_size=256)
+    img = crop_image(img, target_size=DATA_DIM, center=True)
+
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+
+    img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255
+    img -= img_mean
+    img /= img_std
+
+    return img, sample[1]
+
+
+def _reader_creator(file_list,
+                    mode,
+                    shuffle=False,
+                    color_jitter=False,
+                    rotate=False,
+                    data_dir=DATA_DIR):
+    def reader():
+        with open(file_list) as flist:
+            full_lines = [line.strip() for line in flist]
+            if shuffle:
+                np.random.shuffle(full_lines)
+
+            lines = full_lines
+
+            for line in lines:
+                img_path, label = line.split()
+                img_path = os.path.join(data_dir, img_path)
+                if not os.path.exists(img_path):
+                    continue
+                yield img_path, int(label)
+
+    mapper = functools.partial(
+        process_image, mode=mode, color_jitter=color_jitter, rotate=rotate)
+
+    return paddle.reader.xmap_readers(mapper, reader, THREAD, BUF_SIZE)
+
+
+def val(data_dir=DATA_DIR):
+    file_list = os.path.join(data_dir, 'val_list.txt')
+    return _reader_creator(file_list, 'val', shuffle=False, data_dir=data_dir)
+
+
+class TestCalibration(unittest.TestCase):
+    def setUp(self):
+        # TODO(guomingz): Put the download process in the cmake.
+        # Download and unzip test data set
+        imagenet_dl_url = 'http://paddle-inference-dist.bj.bcebos.com/int8/calibration_test_data.tar.gz'
+        zip_file_name = imagenet_dl_url.split('/')[-1]
+        cmd = 'rm -rf data {}  && mkdir data && wget {} && tar xvf {} -C data'.format(
+            zip_file_name, imagenet_dl_url, zip_file_name)
+        os.system(cmd)
+        # resnet50 fp32 data
+        resnet50_fp32_model_url = 'http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model.tar.gz'
+        resnet50_zip_name = resnet50_fp32_model_url.split('/')[-1]
+        resnet50_unzip_folder_name = 'resnet50_fp32'
+        cmd = 'rm -rf {} {} && mkdir {} && wget {} && tar xvf {} -C {}'.format(
+            resnet50_unzip_folder_name, resnet50_zip_name,
+            resnet50_unzip_folder_name, resnet50_fp32_model_url,
+            resnet50_zip_name, resnet50_unzip_folder_name)
+        os.system(cmd)
+
+        self.iterations = 100
+        self.skip_batch_num = 5
+
+    def run_program(self, model_path, generate_int8=False, algo='direct'):
+        image_shape = [3, 224, 224]
+        os.environ['FLAGS_use_mkldnn'] = 'True'
+
+        fluid.memory_optimize(fluid.default_main_program())
+
+        exe = fluid.Executor(fluid.CPUPlace())
+
+        [infer_program, feed_dict,
+         fetch_targets] = fluid.io.load_inference_model(model_path, exe)
+
+        t = fluid.transpiler.InferenceTranspiler()
+        t.transpile(infer_program, fluid.CPUPlace())
+
+        val_reader = paddle.batch(val(), batch_size=1)
+
+        if generate_int8:
+            int8_model = os.path.join(os.getcwd(), "calibration_out")
+
+            if os.path.exists(int8_model):
+                os.system("rm -rf " + int8_model)
+                os.system("mkdir " + int8_model)
+
+            print("Start calibration ...")
+
+            calibrator = ut.Calibrator(
+                program=infer_program,
+                pretrained_model=model_path,
+                iterations=100,
+                debug=False,
+                algo=algo)
+
+            sampling_data = {}
+
+            calibrator.generate_sampling_program()
+        test_info = []
+        cnt = 0
+        for batch_id, data in enumerate(val_reader()):
+            image = np.array(
+                [x[0].reshape(image_shape) for x in data]).astype("float32")
+            label = np.array([x[1] for x in data]).astype("int64")
+            label = label.reshape([-1, 1])
+            running_program = calibrator.sampling_program.clone(
+            ) if generate_int8 else infer_program.clone()
+            for op in running_program.current_block().ops:
+                if op.has_attr("use_mkldnn"):
+                    op._set_attr("use_mkldnn", True)
+
+            _, acc1, _ = exe.run(
+                running_program,
+                feed={feed_dict[0]: image,
+                      feed_dict[1]: label},
+                fetch_list=fetch_targets)
+            if generate_int8:
+                for i in calibrator.sampling_program.list_vars():
+                    if i.name in calibrator.sampling_vars:
+                        np_data = np.array(fluid.global_scope().find_var(i.name)
+                                           .get_tensor())
+                        if i.name not in sampling_data:
+                            sampling_data[i.name] = []
+                        sampling_data[i.name].append(np_data)
+
+            test_info.append(np.mean(acc1) * len(data))
+            cnt += len(data)
+
+            if batch_id != self.iterations - 1:
+                continue
+
+            break
+
+        if generate_int8:
+            calibrator.generate_quantized_data(sampling_data)
+            fluid.io.save_inference_model(int8_model, feed_dict, fetch_targets,
+                                          exe, calibrator.sampling_program)
+            print(
+                "Calibration is done and the corresponding files were generated at {}".
+                format(os.path.abspath("calibration_out")))
+        else:
+            return np.sum(test_info) / cnt
+
+    def test_calibration_for_resnet50(self):
+        fp32_acc1 = self.run_program("resnet50_fp32/model")
+        self.run_program("resnet50_fp32/model", True)
+        int8_acc1 = self.run_program("calibration_out")
+        delta_value = np.abs(fp32_acc1 - int8_acc1)
+        self.assertLess(delta_value, 0.01)
+
+
+if __name__ == '__main__':
+    unittest.main()