Fix the shape issue on quantize op

calibration.py

@@ -176,7 +176,7 @@ def eval(args):
         acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
 
     test_program = fluid.default_main_program().clone(for_test=True)
-
+            
     if with_memory_optimization:
         fluid.memory_optimize(fluid.default_main_program())
 
@@ -197,7 +197,6 @@ def eval(args):
     # 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']
-    print (conv_op_index)
     weights_var_name = []
     conv_input_var_name = []
     conv_output_var_name = []
@@ -211,9 +210,8 @@ def eval(args):
         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:
         i.persistable= True
     
@@ -288,30 +286,31 @@ def eval(args):
                                    feed=feeder.feed(data))
         break
     
-    int8_prog = test_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])])
-        int8_prog.current_block().ops[value].desc.set_input("Scale_out", ["{}_scale.output.test".format(conv_output_var_name[index])])
-        int8_prog.current_block().ops[value].desc.set_input("Scale_weights", ["{}_scale.weights.test".format(weights_var_name[index])])
-        if int8_prog.current_block().ops[value].desc.input("ResidualData"):
-            name = int8_prog.current_block().ops[value].desc.input("ResidualData")[0]
-            int8_prog.current_block().ops[value].desc.set_input("Scale_in_eltwise", ["{}_scale.output.test".format(name)])
+    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])])
+    #     int8_prog.current_block().ops[value].desc.set_input("Scale_out", ["{}_scale.output.test".format(conv_output_var_name[index])])
+    #     int8_prog.current_block().ops[value].desc.set_input("Scale_weights", ["{}_scale.weights.test".format(weights_var_name[index])])
+    #     if int8_prog.current_block().ops[value].desc.input("ResidualData"):
+    #         name = int8_prog.current_block().ops[value].desc.input("ResidualData")[0]
+    #         int8_prog.current_block().ops[value].desc.set_input("Scale_in_eltwise", ["{}_scale.output.test".format(name)])
     
     
     quantize_pos = get_quantization_op_pos(int8_prog)
 
     conv2_quantize_tmp = int8_prog.current_block().create_var(
         name="conv2_quantize_tmp",
-        dtype="float32",
-        persistable=True,
-        #shape= (np.array(fluid.global_scope().find_var('pool2d_0.tmp_0').get_tensor())).shape
+        dtype=core.VarDesc.VarType.UINT8,
+        # persistable=True,
+        # lod_level= 0,
+        # shape= shape
     )
 
     op = int8_prog.current_block()._insert_op(
-       index=quantize_pos[0],
+       index=quantize_pos[0] ,
         
-       type= "quantize",
+       type="quantize",
         
        inputs={"Input": int8_prog.current_block().ops[quantize_pos[0] - 1].output('Out')[0],
                "Scale": "{}_scale.input.test".format(conv_input_var_name[1])},
@@ -321,33 +320,34 @@ def eval(args):
     )
     op._set_attr("data_format", "NCHW")
     op._set_attr("use_mkldnn", 1)
-    int8_prog.current_block().ops[quantize_pos[0] + 1 ].desc.set_input("Input", ["conv2_quantize_tmp"])
-    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",
-        dtype="float32",
-        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,
+    # int8_prog.current_block().ops[quantize_pos[0] + 1 ].desc.set_input("Input", ["conv2_quantize_tmp"])
+    # 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",
+    #     dtype="float32",
+    #     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,
         
-       type= "dequantize",
+    #    type= "dequantize",
         
-       inputs={"Input": int8_prog.current_block().ops[dequantize_pos[0]].output('Out')[0],
-               "Scale": "{}_scale.output.test".format( int8_prog.current_block().ops[dequantize_pos[0]].output('Out')[0])},
+    #    inputs={"Input": int8_prog.current_block().ops[dequantize_pos[0]].output('Out')[0],
+    #            "Scale": "{}_scale.output.test".format( int8_prog.current_block().ops[dequantize_pos[0]].output('Out')[0])},
         
-       outputs={"Output": dequantize_tmp_var},
-    )
+    #    outputs={"Output": dequantize_tmp_var},
+    # )
 
-    int8_prog.current_block().ops[dequantize_pos[0] + 2].desc.set_input("X", ["dequantize_tmp_var"])
+    # 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
     # for i in int8_prog.current_block().ops:
     #     print '********'
     #     print i
@@ -362,9 +362,28 @@ def eval(args):
     #         print k, i.output(k)[0]
     # print conv_op_index
     # print dequantize_pos
-    if DEBUG:
-        dot(int8_prog)
-   
+    # sys.exit(0)
+    # if DEBUG:
+    #     dot(int8_prog)
+    # for i in int8_prog.current_block().ops:
+    #     print i
+    for batch_id, data in enumerate(val_reader()):
+        loss, acc1, acc5 = exe.run(int8_prog,
+                            fetch_list=fetch_list,
+                            feed=feeder.feed(data))
+        loss = np.mean(loss)
+        acc1 = np.mean(acc1)
+        acc5 = np.mean(acc5)
+        test_info[0].append(loss * len(data))
+        test_info[1].append(acc1 * len(data))
+        test_info[2].append(acc5 * len(data))
+        cnt += len(data)
+        if batch_id % 10 == 0:
+            print("Testbatch {0},loss {1}, "
+                    "acc1 {2},acc5 {3}".format(batch_id, \
+                    loss, acc1, acc5))
+            sys.stdout.flush()
+        break      
     with open("__model_quantized__", "wb") as f:
         f.write(int8_prog.desc.serialize_to_string())
 

paddle/fluid/operators/quantize_op.h

@@ -28,7 +28,10 @@ class QuantOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override{} 
+  void InferShape(framework::InferShapeContext* ctx) const override{
+       ctx->SetOutputDim("Output", ctx->GetInputDim("Input"));
+    ctx->ShareLoD("Input", /*->*/ "Output");
+  } 
 
  protected:
   framework::OpKernelType GetExpectedKernelType(