[dgraph qat] Refine calculating output scale of dygraph qat (#31710)

* Refine calculating output scale of dygraph qat, test=develop

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

@@ -499,6 +499,10 @@ class QuantizedNoweightLayer(layers.Layer):
 
     def forward(self, input):
         quant_input = self._fake_quant_input(input)
+        # TODO (jc): support ops that have several inputs
+        if isinstance(input, list):
+            assert len(input) == 1, \
+                "The QuantizedNoweightLayer should only have one input."
         return self._layer.forward(quant_input)
 
 

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

@@ -12,12 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from paddle.nn import Linear, Conv2D
-from paddle.fluid.dygraph.nn import Pool2D
-from paddle.nn.layer.activation import ReLU, LeakyReLU, Sigmoid, ReLU6
-from paddle.nn.layer.activation import Tanh, Softmax, PReLU, Swish
+import paddle
 
-_op_real_in_out_name = {
+op_real_in_out_name = {
     "conv2d": [["Input", "Filter"], ["Output"]],
     "depthwise_conv2d": [["Input", "Filter"], ["Output"]],
     "pool2d": [["X"], ["Out"]],
@@ -33,14 +30,30 @@ _op_real_in_out_name = {
     "swish": [["X"], ["Out"]],
 }
 
-_quant_layers_map = {
-    'Conv2D': Conv2D,
-    'Linear': Linear,
-    'Pool2D': Pool2D,
-    'ReLU': ReLU,
-    'LeakyReLU': LeakyReLU,
-    'ReLU6': ReLU6,
-    'Softmax': Softmax,
-    'Tanh': Tanh,
-    'Swish': Swish
+supported_quant_layers_map = {
+    'Conv2D': paddle.nn.Conv2D,
+    'Linear': paddle.nn.Linear,
+    'AdaptiveAvgPool2D': paddle.nn.AdaptiveAvgPool2D,
+    'AdaptiveMaxPool2D': paddle.nn.AdaptiveMaxPool2D,
+    'AvgPool2D': paddle.nn.AvgPool2D,
+    'MaxPool2D': paddle.nn.MaxPool2D,
+    'Hardswish': paddle.nn.Hardswish,
+    'LeakyReLU': paddle.nn.LeakyReLU,
+    'PReLU': paddle.nn.PReLU,
+    'ReLU': paddle.nn.ReLU,
+    'ReLU6': paddle.nn.ReLU6,
+    'Sigmoid': paddle.nn.Sigmoid,
+    'Softmax': paddle.nn.Softmax,
+    'Swish': paddle.nn.Swish,
+    'Tanh': paddle.nn.Tanh,
+    'Hardswish': paddle.nn.Hardswish,
+    'BatchNorm': paddle.nn.BatchNorm,
+    'GroupNorm': paddle.nn.GroupNorm,
+    'LayerNorm': paddle.nn.LayerNorm,
 }
+
+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)

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

@@ -191,8 +191,8 @@ class TestImperativeAddQuantDequant(unittest.TestCase):
             weight_quantize_type='abs_max',
             activation_quantize_type='moving_average_abs_max',
             quantizable_layer_type=[
-                'Conv2D', 'Linear', 'ReLU', 'Pool2D', 'LeakyReLU', 'ReLU6',
-                'Tanh', 'Swish'
+                'Conv2D', 'Linear', 'ReLU', 'LeakyReLU', 'ReLU6', 'Tanh',
+                'Swish'
             ])
 
         with fluid.dygraph.guard():