add LeakReLUQuant OP for bug fix.

mindspore/nn/layer/quant.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""Quantization aware."""
+"""Quantization aware training."""
 
 from functools import partial
 import numpy as np
@@ -43,6 +43,7 @@ __all__ = [
     'Conv2dQuant',
     'DenseQuant',
     'ActQuant',
+    'LeakyReLUQuant',
     'HSwishQuant',
     'HSigmoidQuant',
     'TensorAddQuant',
@@ -349,7 +350,7 @@ class FakeQuantWithMinMax(Cell):
         self.maxq = Parameter(Tensor(max_array), name='quant_max', requires_grad=False)
 
         # init fake quant relative op
-        if per_channel:
+        if self.per_channel:
             quant_fun = partial(Q.FakeQuantPerChannel, channel_axis=self.channel_axis)
             ema_fun = partial(Q.MinMaxUpdatePerChannel, channel_axis=self.channel_axis)
         else:
@@ -369,7 +370,7 @@ class FakeQuantWithMinMax(Cell):
                                 num_bits=self.num_bits,
                                 symmetric=self.symmetric,
                                 narrow_range=self.narrow_range,
-                                quant_delay=quant_delay)
+                                quant_delay=self.quant_delay)
             self.fake_quant_train = quant_fun(training=True)
             self.fake_quant_infer = quant_fun(training=False)
 
@@ -832,7 +833,7 @@ class ActQuant(_QuantActivation):
         Tensor, with the same type and shape as the `x`.
 
     Examples:
-        >>> act_quant = nn.ActQuant(4, 1)
+        >>> act_quant = nn.ActQuant(nn.ReLU)
         >>> input_x = Tensor(np.array([[1, 2, -1], [-2, 0, -1]]), mindspore.float32)
         >>> result = act_quant(input_x)
     """
@@ -855,7 +856,7 @@ class ActQuant(_QuantActivation):
                                                   symmetric=symmetric,
                                                   narrow_range=narrow_range,
                                                   quant_delay=quant_delay)
-        self.act = activation()
+        self.act = activation
 
     def construct(self, x):
         x = self.act(x)
@@ -865,6 +866,75 @@ class ActQuant(_QuantActivation):
     def get_origin(self):
         return self.act
 
+class LeakyReLUQuant(_QuantActivation):
+    r"""
+    LeakyReLUQuant activation function. Add Fake Quant OP after HSwish OP.
+
+    For a more Detailed overview of HSwish op.
+
+    Args:
+        activation (Cell): Activation cell class.
+        ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
+        per_channel (bool):  Quantization granularity based on layer or on channel. Default: False.
+        num_bits (int): Quantization number bit, support 4 and 8bit. Default: 8.
+        symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
+        narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
+        quant_delay (int): Quantization delay parameters according by global step. Default: 0.
+
+    Inputs:
+        - **x** (Tensor) - The input of HSwishQuant.
+
+    Outputs:
+        Tensor, with the same type and shape as the `x`.
+
+    Examples:
+        >>> activation = nn.LeakyReLUQuant(nn.LeakyReLU())
+        >>> input = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
+        >>> result = activation(input)
+    """
+
+    def __init__(self,
+                 activation,
+                 ema_decay=0.999,
+                 per_channel=False,
+                 num_bits=8,
+                 symmetric=False,
+                 narrow_range=False,
+                 quant_delay=0):
+        super(LeakyReLUQuant, self).__init__()
+        self.fake_quant_act_before = FakeQuantWithMinMax(min_init=-6,
+                                                         max_init=6,
+                                                         ema=True,
+                                                         ema_decay=ema_decay,
+                                                         per_channel=per_channel,
+                                                         num_bits=num_bits,
+                                                         symmetric=symmetric,
+                                                         narrow_range=narrow_range,
+                                                         quant_delay=quant_delay)
+        self.fake_quant_act_after = FakeQuantWithMinMax(min_init=-6,
+                                                        max_init=6,
+                                                        ema=True,
+                                                        ema_decay=ema_decay,
+                                                        per_channel=per_channel,
+                                                        num_bits=num_bits,
+                                                        symmetric=symmetric,
+                                                        narrow_range=narrow_range,
+                                                        quant_delay=quant_delay)
+        if issubclass(activation.__class__, nn.LeakyReLU):
+            self.act = activation
+        else:
+            raise ValueError("Activation should be `nn.LeakyReLU`")
+
+    def construct(self, x):
+        x = self.fake_quant_act_before(x)
+        x = self.act(x)
+        x = self.fake_quant_act_after(x)
+        return x
+
+    def get_origin(self):
+        return self.act
+
+
 
 class HSwishQuant(_QuantActivation):
     r"""
@@ -888,9 +958,9 @@ class HSwishQuant(_QuantActivation):
         Tensor, with the same type and shape as the `x`.
 
     Examples:
-        >>> hswish_quant = nn.HSwishQuant(4, 1)
-        >>> input_x = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
-        >>> result = hswish_quant(input_x)
+        >>> activation = nn.HSwishQuant(nn.HSwish())
+        >>> input = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
+        >>> result = activation(input)
     """
 
     def __init__(self,
@@ -920,8 +990,8 @@ class HSwishQuant(_QuantActivation):
                                                         symmetric=symmetric,
                                                         narrow_range=narrow_range,
                                                         quant_delay=quant_delay)
-        if issubclass(activation, nn.HSwish):
-            self.act = activation()
+        if issubclass(activation.__class__, nn.HSwish):
+            self.act = activation
         else:
             raise ValueError("Activation should be `nn.HSwish`")
 
@@ -957,9 +1027,9 @@ class HSigmoidQuant(_QuantActivation):
         Tensor, with the same type and shape as the `x`.
 
     Examples:
-        >>> hsigmoid_quant = nn.HSigmoidQuant(4, 1)
-        >>> input_x = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
-        >>> result = hsigmoid_quant(input_x)
+        >>> activation = nn.HSigmoidQuant(nn.HSigmoid())
+        >>> input = Tensor(np.array([[1, 2, 1], [-2, 0, -1]]), mindspore.float32)
+        >>> result = activation(input)
     """
 
     def __init__(self,
@@ -989,8 +1059,8 @@ class HSigmoidQuant(_QuantActivation):
                                                         symmetric=symmetric,
                                                         narrow_range=narrow_range,
                                                         quant_delay=quant_delay)
-        if issubclass(activation, nn.HSigmoid):
-            self.act = activation()
+        if issubclass(activation.__class__, nn.HSigmoid):
+            self.act = activation
         else:
             raise ValueError("Activation should be `nn.HSigmoid`")
 

mindspore/ops/operations/_quant_ops.py

@@ -386,6 +386,8 @@ class FakeQuantPerChannel(PrimitiveWithInfer):
             raise ValueError(f"For '{self.name}' x rank should be in '{self.ascend_support_x_rank}'")
         if not self.is_ascend:
             validator.check_integer("x rank", len(x_shape), 1, Rel.GE, self.name)
+        if len(x_shape) == 1:
+            self.channel_axis = 0
         validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
         validator.check_integer(
             "min shape", min_shape[0], x_shape[self.channel_axis], Rel.EQ, self.name)

mindspore/train/quant/quant.py

@@ -35,8 +35,8 @@ from . import quant_utils
 
 _ACTIVATION_MAP = {nn.ReLU: quant.ActQuant,
                    nn.ReLU6: quant.ActQuant,
-                   nn.LeakyReLU: quant.ActQuant,
                    nn.Sigmoid: quant.ActQuant,
+                   nn.LeakyReLU: quant.LeakyReLUQuant,
                    nn.HSigmoid: quant.HSigmoidQuant,
                    nn.HSwish: quant.HSwishQuant}
 
@@ -167,32 +167,35 @@ class ConvertToQuantNetwork:
         convert Conv2d cell to quant cell
         """
         conv_inner = subcell.conv
-        if subcell.has_bn and self.bn_fold:
-            bn_inner = subcell.batchnorm
-            conv_inner = quant.Conv2dBatchNormQuant(conv_inner.in_channels,
-                                                    conv_inner.out_channels,
-                                                    kernel_size=conv_inner.kernel_size,
-                                                    stride=conv_inner.stride,
-                                                    pad_mode=conv_inner.pad_mode,
-                                                    padding=conv_inner.padding,
-                                                    dilation=conv_inner.dilation,
-                                                    group=conv_inner.group,
-                                                    eps=bn_inner.eps,
-                                                    quant_delay=self.weight_qdelay,
-                                                    freeze_bn=self.freeze_bn,
-                                                    per_channel=self.weight_channel,
-                                                    num_bits=self.weight_bits,
-                                                    fake=True,
-                                                    symmetric=self.weight_symmetric,
-                                                    narrow_range=self.weight_range)
-            # change original network BatchNormal OP parameters to quant network
-            conv_inner.gamma = subcell.batchnorm.gamma
-            conv_inner.beta = subcell.batchnorm.beta
-            conv_inner.moving_mean = subcell.batchnorm.moving_mean
-            conv_inner.moving_variance = subcell.batchnorm.moving_variance
-            del subcell.batchnorm
-            subcell.batchnorm = None
-            subcell.has_bn = False
+        if subcell.has_bn:
+            if self.bn_fold:
+                bn_inner = subcell.batchnorm
+                conv_inner = quant.Conv2dBatchNormQuant(conv_inner.in_channels,
+                                                        conv_inner.out_channels,
+                                                        kernel_size=conv_inner.kernel_size,
+                                                        stride=conv_inner.stride,
+                                                        pad_mode=conv_inner.pad_mode,
+                                                        padding=conv_inner.padding,
+                                                        dilation=conv_inner.dilation,
+                                                        group=conv_inner.group,
+                                                        eps=bn_inner.eps,
+                                                        quant_delay=self.weight_qdelay,
+                                                        freeze_bn=self.freeze_bn,
+                                                        per_channel=self.weight_channel,
+                                                        num_bits=self.weight_bits,
+                                                        fake=True,
+                                                        symmetric=self.weight_symmetric,
+                                                        narrow_range=self.weight_range)
+                # change original network BatchNormal OP parameters to quant network
+                conv_inner.gamma = subcell.batchnorm.gamma
+                conv_inner.beta = subcell.batchnorm.beta
+                conv_inner.moving_mean = subcell.batchnorm.moving_mean
+                conv_inner.moving_variance = subcell.batchnorm.moving_variance
+                del subcell.batchnorm
+                subcell.batchnorm = None
+                subcell.has_bn = False
+            else:
+                raise ValueError("Only support Batchnorm fold mode.")
         else:
             conv_inner = quant.Conv2dQuant(conv_inner.in_channels,
                                            conv_inner.out_channels,
@@ -259,7 +262,7 @@ class ConvertToQuantNetwork:
         act_class = activation.__class__
         if act_class not in _ACTIVATION_MAP:
             raise ValueError("Unsupported activation in auto quant: ", act_class)
-        return _ACTIVATION_MAP[act_class](activation=act_class,
+        return _ACTIVATION_MAP[act_class](activation=activation,
                                           num_bits=self.act_bits,
                                           quant_delay=self.act_qdelay,
                                           per_channel=self.act_channel,