add Conv2dBnFoldQuantOneConv

mindspore/nn/layer/combined.py

@@ -97,7 +97,7 @@ class Conv2dBnAct(Cell):
                  weight_init='normal',
                  bias_init='zeros',
                  has_bn=False,
-                 momentum=0.9,
+                 momentum=0.997,
                  eps=1e-5,
                  activation=None,
                  alpha=0.2,

mindspore/nn/layer/quant.py

@@ -34,6 +34,7 @@ from ...ops.operations import _quant_ops as Q
 
 __all__ = [
     'FakeQuantWithMinMaxObserver',
+    'Conv2dBnFoldQuantOneConv',
     'Conv2dBnFoldQuant',
     'Conv2dBnWithoutFoldQuant',
     'Conv2dQuant',
@@ -330,6 +331,220 @@ QuantConfig = namedtuple("QuantConfig", ['weight', 'activation'])
 quant_config_default = QuantConfig(weight=FakeQuantWithMinMaxObserver, activation=FakeQuantWithMinMaxObserver)
 
 
+class Conv2dBnFoldQuantOneConv(Cell):
+    r"""
+    2D convolution with BatchNormal op folded construct.
+
+    This part is a more detailed overview of Conv2d op.
+
+    Args:
+        in_channels (int): The number of input channel :math:`C_{in}`.
+        out_channels (int): The number of output channel :math:`C_{out}`.
+        kernel_size (Union[int, tuple]): Specifies the height and width of the 2D convolution window.
+        stride (int): Specifies stride for all spatial dimensions with the same value.
+        pad_mode (str): Specifies padding mode. The optional values are "same", "valid", "pad". Default: "same".
+        padding (int): Implicit paddings on both sides of the input. Default: 0.
+        eps (float): Parameters for BatchNormal. Default: 1e-5.
+        momentum (float): Parameters for BatchNormal op. Default: 0.997.
+        has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
+        weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            convolution kernel. Default: 'normal'.
+        bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            bias vector. Default: 'zeros'.
+        beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            beta vector. Default: 'zeros'.
+        gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            gamma vector. Default: 'ones'.
+        mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            mean vector. Default: 'zeros'.
+        var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            variance vector. Default: 'ones'.
+        fake (bool): Whether Conv2dBnFoldQuant Cell adds FakeQuantWithMinMaxObserver. Default: True.
+        quant_config (QuantConfig): Configs the oberser types and quant configs of weight and activation. Default:
+            both set to default FakeQuantWithMinMaxObserver.
+        quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
+
+    Inputs:
+        - **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`.
+
+    Outputs:
+        Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
+
+    Examples:
+        >>> qconfig = compression.quant.create_quant_config()
+        >>> conv2d_bnfold = nn.Conv2dBnFoldQuant(1, 6, kernel_size=(2, 2), stride=(1, 1), pad_mode="valid",
+        >>>                                      quant_config=qconfig)
+        >>> input = Tensor(np.random.randint(-2, 2, (2, 1, 3, 3)), mindspore.float32)
+        >>> result = conv2d_bnfold(input)
+        >>> result.shape
+        (2, 6, 2, 2)
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 pad_mode='same',
+                 padding=0,
+                 dilation=1,
+                 group=1,
+                 eps=1e-5,
+                 momentum=0.997,
+                 has_bias=False,
+                 weight_init='normal',
+                 bias_init='zeros',
+                 beta_init='zeros',
+                 gamma_init='ones',
+                 mean_init='zeros',
+                 var_init='ones',
+                 fake=True,
+                 quant_config=quant_config_default,
+                 quant_dtype=QuantDtype.INT8):
+        """Initialize Conv2dBnFoldQuant layer"""
+        super(Conv2dBnFoldQuantOneConv, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = twice(kernel_size)
+        self.stride = twice(stride)
+        self.pad_mode = pad_mode
+        self.padding = padding
+        self.dilation = twice(dilation)
+        self.group = group
+        self.eps = eps
+        self.momentum = momentum
+        self.has_bias = has_bias
+        self.fake = fake
+        self.quant_config = quant_config
+        self.quant_dtype = quant_dtype
+        self.is_gpu = context.get_context('device_target') == "GPU"
+        self.is_Ascend = context.get_context('device_target') == "Ascend"
+        if context.get_context("enable_ge"):
+            self.is_ge_backend = True
+        else:
+            self.is_ge_backend = False
+
+        # initialize convolution op and Parameter
+        if context.get_context('device_target') == "Ascend" and group > 1:
+            Validator.check_equal_int(group, in_channels, 'group')
+            Validator.check_equal_int(group, out_channels, 'group')
+            self.conv = P.DepthwiseConv2dNative(channel_multiplier=1,
+                                                kernel_size=self.kernel_size,
+                                                pad_mode=pad_mode,
+                                                pad=padding,
+                                                stride=self.stride,
+                                                dilation=self.dilation)
+            weight_shape = [1, in_channels, *self.kernel_size]
+            channel_axis = 1
+        else:
+            self.conv = P.Conv2D(out_channel=out_channels,
+                                 kernel_size=self.kernel_size,
+                                 pad_mode=pad_mode,
+                                 pad=padding,
+                                 stride=self.stride,
+                                 dilation=self.dilation,
+                                 group=group)
+            weight_shape = [out_channels, in_channels // group, *self.kernel_size]
+            channel_axis = 0
+        self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
+        self.bias_add = P.BiasAdd()
+        if Validator.check_bool(has_bias):
+            self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
+        else:
+            self.bias = None
+
+        # initialize BatchNorm Parameter
+        self.gamma = Parameter(initializer(gamma_init, [out_channels]), name='gamma')
+        self.beta = Parameter(initializer(beta_init, [out_channels]), name='beta')
+        self.moving_mean = Parameter(initializer(mean_init, [out_channels]), name='moving_mean', requires_grad=False)
+        self.moving_variance = Parameter(initializer(var_init, [out_channels]), name='moving_variance',
+                                         requires_grad=False)
+
+        # initialize fake ops
+        self.fake_quant_weight = quant_config.weight(min_init=-6,
+                                                     max_init=6,
+                                                     ema=False,
+                                                     channel_axis=channel_axis,
+                                                     num_channels=out_channels,
+                                                     quant_dtype=quant_dtype)
+        if self.is_graph_mode and (self.is_ge_backend or self.is_ascend):
+            self.bn_train = P.BatchNorm(is_training=True,
+                                        epsilon=self.eps)
+        elif self.is_gpu:
+            self.bn_train = P.FusedBatchNormEx(mode=1,
+                                               epsilon=self.eps,
+                                               momentum=self.momentum,
+                                               data_format=self.format)
+        else:
+            self.bn_train = P.FusedBatchNorm(mode=1,
+                                             epsilon=self.eps,
+                                             momentum=self.momentum)
+        self.bn_infer = P.BatchNorm(is_training=False, epsilon=self.eps, data_format=self.format)
+        data_parallel_strategy = ((1,), (1,))
+        data_parallel_strategy_one = ((1,), ())
+        self.sub_mean = P.Sub().shard(data_parallel_strategy)
+        self.sub_var = P.Sub().shard(data_parallel_strategy)
+        self.mul_mean = P.Mul().shard(data_parallel_strategy_one)
+        self.mul_var = P.Mul().shard(data_parallel_strategy_one)
+        self.assign_sub_mean = P.AssignSub().shard(data_parallel_strategy)
+        self.assign_sub_var = P.AssignSub().shard(data_parallel_strategy)
+        self.one = Tensor(1, mstype.int32)
+        self.reshape = P.Reshape()
+
+    def extend_repr(self):
+        s = 'in_channels={}, out_channels={}, kernel_size={}, stride={}, ' \
+            'pad_mode={}, padding={}, dilation={}, group={}, ' \
+            'fake={}, freeze_bn={}, momentum={}, quant_delay={}'.format(self.in_channels, self.out_channels,
+                                                                        self.kernel_size, self.stride,
+                                                                        self.pad_mode, self.padding, self.dilation,
+                                                                        self.group,
+                                                                        self.fake, self.freeze_bn, self.momentum,
+                                                                        self.fake_quant_weight.quant_delay)
+        return s
+
+    def construct(self, x):
+        running_std = P.Sqrt()(P.TensorAdd()(self.moving_variance, self.eps))
+        scale_factor = self.gamma / running_std
+        weight = self.weight * scale_factor
+        if self.channel_axis:
+            scale_factor = self.reshape(scale_factor, (1, -1, 1, 1))
+        else:
+            scale_factor = self.reshape(scale_factor, (-1, 1, 1, 1))
+        if self.fake:
+            weight = self.fake_quant_weight(weight)
+        conv = self.conv(x, weight)
+        scale_factor = self.reshape(scale_factor, (1, -1, 1, 1))
+        conv_orig = conv / scale_factor
+        if self.training:
+            if not self.is_gpu:
+                out, batch_mean, batch_var, _, _ = self.bn_train(conv_orig,
+                                                                 self.gamma,
+                                                                 self.beta,
+                                                                 None,
+                                                                 None)
+
+                mean_sub = self.sub_mean(self.moving_mean, batch_mean)
+                temp_mean = self.mul_mean(mean_sub, self.momentum)
+                mean_sub2 = self.sub_var(self.moving_variance, batch_var)
+                temp_variance = self.mul_var(mean_sub2, self.momentum)
+                out = F.depend(out, self.assign_sub_mean(self.moving_mean, temp_mean))
+                out = F.depend(out, self.assign_sub_var(self.moving_variance, temp_variance))
+            else:
+                out = self.bn_train(conv_orig,
+                                    self.gamma,
+                                    self.beta,
+                                    self.moving_mean,
+                                    self.moving_variance)[0]
+        else:
+            out = self.bn_infer(conv_orig,
+                                self.gamma,
+                                self.beta,
+                                self.moving_mean,
+                                self.moving_variance)[0]
+
+        return out
+
+
 class Conv2dBnFoldQuant(Cell):
     r"""
     2D convolution with BatchNormal op folded construct.
@@ -627,7 +842,7 @@ class Conv2dBnWithoutFoldQuant(Cell):
                                                      channel_axis=channel_axis,
                                                      num_channels=out_channels,
                                                      quant_dtype=quant_dtype)
-        self.batchnorm = BatchNorm2d(out_channels, eps=eps, momentum=momentum)
+        self.batchnorm = BatchNorm2d(out_channels, eps=eps, momentum=1-momentum)
 
     def construct(self, x):
         weight = self.fake_quant_weight(self.weight)