[dygraph qat] Refine saving output scale to infer program (#31784)

* Refine saving output scale to infer program

python/paddle/fluid/contrib/slim/quantization/imperative/utils.py

@@ -30,7 +30,7 @@ op_real_in_out_name = {
     "swish": [["X"], ["Out"]],
 }
 
-supported_quant_layers_map = {
+quant_input_layers_map = {
     'Conv2D': paddle.nn.Conv2D,
     'Linear': paddle.nn.Linear,
     'AdaptiveAvgPool2D': paddle.nn.AdaptiveAvgPool2D,
@@ -58,8 +58,30 @@ fake_quantize_dequantize_types = [
     "fake_quantize_dequantize_moving_average_abs_max"
 ]
 
-out_scale_layers_list = (
-    paddle.nn.Conv2D, paddle.nn.Linear, paddle.nn.MaxPool2D,
-    paddle.nn.BatchNorm, paddle.nn.BatchNorm2D, paddle.nn.SyncBatchNorm,
-    paddle.nn.LeakyReLU, paddle.nn.PReLU, paddle.nn.ReLU, paddle.nn.ReLU6,
-    paddle.nn.Sigmoid, paddle.nn.Softmax, paddle.nn.Tanh, paddle.nn.Swish)
+quant_output_layers_map = {
+    'Conv2D': paddle.nn.Conv2D,
+    'Conv2DTranspose': paddle.nn.Conv2DTranspose,
+    'Linear': paddle.nn.Linear,
+    'AdaptiveAvgPool2D': paddle.nn.AdaptiveAvgPool2D,
+    'AdaptiveMaxPool2D': paddle.nn.AdaptiveMaxPool2D,
+    'AvgPool2D': paddle.nn.AvgPool2D,
+    'MaxPool2D': paddle.nn.MaxPool2D,
+    'BatchNorm': paddle.nn.BatchNorm,
+    'BatchNorm2D': paddle.nn.BatchNorm2D,
+    'SyncBatchNorm': paddle.nn.SyncBatchNorm,
+    'ELU': paddle.nn.ELU,
+    'GELU': paddle.nn.GELU,
+    'LeakyReLU': paddle.nn.LeakyReLU,
+    'PReLU': paddle.nn.PReLU,
+    'ReLU': paddle.nn.ReLU,
+    'ReLU6': paddle.nn.ReLU6,
+    'Sigmoid': paddle.nn.Sigmoid,
+    'Softmax': paddle.nn.Softmax,
+    'Tanh': paddle.nn.Tanh,
+    'Swish': paddle.nn.Swish,
+}
+
+weight_op_types = [
+    "conv2d", "depthwise_conv2d", "matmul", "conv2d_transpose",
+    "depthwise_conv2d_transpose"
+]

python/paddle/fluid/contrib/slim/tests/test_imperative_out_scale.py

@@ -33,7 +33,6 @@ from paddle.fluid.dygraph.container import Sequential
 from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
 from paddle.nn.layer import ReLU, LeakyReLU, Sigmoid, Softmax, PReLU
 from paddle.nn import Linear, Conv2D, Softmax, BatchNorm2D, MaxPool2D
-from paddle.fluid.dygraph.nn import Pool2D
 from paddle.fluid.log_helper import get_logger
 from paddle.fluid.dygraph import nn
 
@@ -131,8 +130,8 @@ class ImperativeLenet(fluid.dygraph.Layer):
                 bias_attr=False),
             BatchNorm2D(6),
             ReLU(),
-            Pool2D(
-                pool_size=2, pool_type='max', pool_stride=2),
+            MaxPool2D(
+                kernel_size=2, stride=2),
             Conv2D(
                 in_channels=6,
                 out_channels=16,
@@ -357,7 +356,6 @@ class TestImperativeOutSclae(unittest.TestCase):
                     "diff({}) at {}, dynamic loss = {}, static loss = {}".
                     format(diff, i, loss_d, loss_s))
                 break
-
         self.assertTrue(
             np.allclose(
                 np.array(dynamic_loss_rec),
@@ -398,10 +396,15 @@ class TestImperativeOutSclae(unittest.TestCase):
             if dynamic_ops[i].has_attr("out_threshold"):
                 op_count += 1
                 self.assertTrue(dynamic_ops[i].type == static_ops[i].type)
+                if dynamic_ops[i].attr("out_threshold") != static_ops[i].attr(
+                        "out_threshold"):
+                    _logger.info(dynamic_ops[i].attr("out_threshold"))
+                    _logger.info(static_ops[i].attr("out_threshold"))
                 self.assertTrue(dynamic_ops[i].attr("out_threshold") ==
                                 static_ops[i].attr("out_threshold"))
 
-        self.assertTrue(op_count == 13)
+        _logger.info("op_cout: {}".format(op_count))
+        self.assertTrue(op_count == 14)
 
 
 class TestSaveQuanztizedModelFromCheckPoint(unittest.TestCase):
@@ -470,7 +473,9 @@ class TestSaveQuanztizedModelFromCheckPoint(unittest.TestCase):
                 self.assertTrue(dynamic_ops[i].type == static_ops[i].type)
                 self.assertTrue(dynamic_ops[i].attr("out_threshold") ==
                                 static_ops[i].attr("out_threshold"))
-        self.assertTrue(op_count == 13)
+
+        _logger.info("op_cout: {}".format(op_count))
+        self.assertTrue(op_count == 14)
 
 
 class TestSaveQuantizedModel_Warning(unittest.TestCase):
@@ -490,8 +495,10 @@ class TestSaveQuantizedModel_Warning(unittest.TestCase):
                         shape=[None, 1, 28, 28], dtype='float32')
                 ])
 
-        warning_message = "Warning: No Layer of the model while to be saved contains the out_threshold attribute, " \
-                "so the generated inference model would not contain the out_threshold."
+        warning_message = "Warning: No Layer of the model while to be " \
+                          "saved contains the out_threshold attribute, so the " \
+                          "generated inference model would not contain the " \
+                          "out_threshold."
         num = get_vaild_warning_num(warning_message, w)
         assert num == 1