Code clean

calibration.py

@@ -192,24 +192,17 @@ def eval(args):
     
     t = fluid.transpiler.InferenceTranspiler()
     t.transpile(test_program, fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace())
-    # for i in test_program.current_block().ops:
-    #     print i
-    # sys.exit(0)
+
     conv_op_index = [index for index, value in enumerate(test_program.global_block().ops) if value.type == 'conv2d']
-    pooling_op_index =  [index for index, value in enumerate(test_program.global_block().ops) if value.type == 'pool2d']
     weights_var_name = []
     conv_input_var_name = []
     conv_output_var_name = []
-    # weights_channel = {}
+
     for i in conv_op_index[1:]:
         weights_var_name.append(test_program.current_block().ops[i].input('Filter')[0])
         conv_input_var_name.append(test_program.current_block().ops[i].input('Input')[0])
         conv_output_var_name.append(test_program.current_block().ops[i].output('Output')[0])
     
-    for i in pooling_op_index:
-        conv_input_var_name.append(test_program.current_block().ops[i].input('X')[0])
-        conv_output_var_name.append(test_program.current_block().ops[i].output('Out')[0])
-    
     not_persistable_vars = (i for i in test_program.list_vars() if not i.persistable)
     back_program = test_program.clone()
     for i in not_persistable_vars:
@@ -287,6 +280,7 @@ def eval(args):
         break
     
     int8_prog = back_program.clone()
+    
     # for index, value in enumerate(conv_op_index[1:]):
     #     # print index,conv_input_var_name[index], ["{}_scale.input.test".format(conv_input_var_name[index])]
     #     int8_prog.current_block().ops[value].desc.set_input("Scale_in", ["{}_scale.input.test".format(conv_input_var_name[index])])
@@ -325,6 +319,7 @@ def eval(args):
     # for i in int8_prog.current_block().ops[quantize_pos[0] + 2:]:
     #     if i.type == 'conv2d' and i.input('Input')[0] == int8_prog.current_block().ops[quantize_pos[0] + 1].output('Out')[0]:
     #         i.desc.set_input("Input",  ["conv2_quantize_tmp"])
+   
     # dequantize_pos = get_dequantization_op_pos(int8_prog)
     # dequantize_tmp_var = int8_prog.current_block().create_var(
     #     name="dequantize_tmp_var",
@@ -332,7 +327,7 @@ def eval(args):
     #     persistable=True,
     #     #shape= (np.array(fluid.global_scope().find_var('pool2d_0.tmp_0').get_tensor())).shape
     # )
-
+    
     # op = int8_prog.current_block()._insert_op(
     #    index=dequantize_pos[0] + 1,
         
@@ -347,7 +342,7 @@ def eval(args):
     # int8_prog.current_block().ops[dequantize_pos[0] + 2].desc.set_input("X", ["dequantize_tmp_var"])
 
     #Step 3 Save the new model 
-    print int8_prog
+    # print int8_prog
     # for i in int8_prog.current_block().ops:
     #     print '********'
     #     print i
@@ -367,6 +362,7 @@ def eval(args):
     #     dot(int8_prog)
     # for i in int8_prog.current_block().ops:
     #     print i
+    print int8_prog
     for batch_id, data in enumerate(val_reader()):
         loss, acc1, acc5 = exe.run(int8_prog,
                             fetch_list=fetch_list,