!11712 replace DepthWiseConv with nn.Conv2D

From: @yuchaojie
Reviewed-by: @kingxian,@c_34
Signed-off-by: @kingxian

RELEASE.md

@@ -20,22 +20,22 @@ Previously the kernel size and pad mode attrs of pooling ops are named "ksize" a
 <td>
 
 ```python
->>> from mindspore.ops import operations as P
+>>> import mindspore.ops as ops
 >>>
->>> avg_pool = P.AvgPool(ksize=2, padding='same')
->>> max_pool = P.MaxPool(ksize=2, padding='same')
->>> max_pool_with_argmax = P.MaxPoolWithArgmax(ksize=2, padding='same')
+>>> avg_pool = ops.AvgPool(ksize=2, padding='same')
+>>> max_pool = ops.MaxPool(ksize=2, padding='same')
+>>> max_pool_with_argmax = ops.MaxPoolWithArgmax(ksize=2, padding='same')
 ```
 
 </td>
 <td>
 
 ```python
->>> from mindspore.ops import operations as P
+>>> import mindspore.ops as ops
 >>>
->>> avg_pool = P.AvgPool(kernel_size=2, pad_mode='same')
->>> max_pool = P.MaxPool(kernel_size=2, pad_mode='same')
->>> max_pool_with_argmax = P.MaxPoolWithArgmax(kernel_size=2, pad_mode='same')
+>>> avg_pool = ops.AvgPool(kernel_size=2, pad_mode='same')
+>>> max_pool = ops.MaxPool(kernel_size=2, pad_mode='same')
+>>> max_pool_with_argmax = ops.MaxPoolWithArgmax(kernel_size=2, pad_mode='same')
 ```
 
 </td>

mindspore/ops/operations/nn_ops.py

@@ -18,6 +18,7 @@
 import math
 import operator
 from functools import reduce, partial
+from mindspore import log as logger
 from mindspore._checkparam import _check_3d_int_or_tuple
 import numpy as np
 from ... import context
@@ -1476,6 +1477,8 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
                  dilation=1,
                  group=1):
         """Initialize DepthwiseConv2dNative"""
+        logger.warning("WARN_DEPRECATED: The usage of DepthwiseConv2dNative is deprecated."
+                       " Please use nn.Conv2D.")
         self.init_prim_io_names(inputs=['x', 'w'], outputs=['output'])
         self.kernel_size = _check_positive_int_or_tuple('kernel_size', kernel_size, self.name)
         self.stride = _check_positive_int_or_tuple('stride', stride, self.name)

model_zoo/official/cv/centerface/README.md

@@ -102,7 +102,7 @@ step1: prepare pretrained model: train a mobilenet_v2 model by mindspore or use
 #        The key/cell/module name must as follow, otherwise you need to modify "name_map" function:
 #            --mindspore: as the same as mobilenet_v2_key.ckpt
 #            --pytorch: same as official pytorch model(e.g., official mobilenet_v2-b0353104.pth)
-python torch_to_ms_mobilenetv2.py --ckpt_fn=./mobilenet_v2_key.ckpt --pt_fn=./mobilenet_v2-b0353104.pth --out_ckpt_fn=./mobilenet_v2.ckpt
+python convert_weight_mobilenetv2.py --ckpt_fn=./mobilenet_v2_key.ckpt --pt_fn=./mobilenet_v2-b0353104.pth --out_ckpt_fn=./mobilenet_v2.ckpt
 ```
 
 step2: prepare user rank_table
@@ -120,7 +120,7 @@ step3: train
 cd scripts;
 # prepare data_path, use symbolic link
 ln -sf [USE_DATA_DIR] dataset
-# check you dir to make sure your datas are in the right path
+# check you dir to make sure your data are in the right path
 ls ./dataset/centerface # data path
 ls ./dataset/centerface/annotations/train.json # annot_path
 ls ./dataset/centerface/images/train/images # img_dir
@@ -147,7 +147,7 @@ python setup.py install; # used for eval
 cd -; #cd ../../scripts;
 mkdir ./output
 mkdir ./output/centerface
-# check you dir to make sure your datas are in the right path
+# check you dir to make sure your data are in the right path
 ls ./dataset/images/val/images/ # data path
 ls ./dataset/centerface/ground_truth/val.mat # annot_path
 ```
@@ -195,7 +195,7 @@ sh eval_all.sh
         │   ├──lr_scheduler.py           // learning rate scheduler
         │   ├──mobile_v2.py              // modified mobilenet_v2 backbone
         │   ├──utils.py                  // auxiliary functions for train, to log and preload
-        │   ├──var_init.py               // weight initilization
+        │   ├──var_init.py               // weight initialization
         │   ├──convert_weight_mobilenetv2.py   // convert pretrained backbone to mindspore
         │   ├──convert_weight.py               // CenterFace model convert to mindspore
         └── dependency                   // third party codes: MIT License
@@ -414,7 +414,7 @@ After testing, you can find many txt file save the box information and scores,
 open it you can see:
 
 ```python
-646.3 189.1 42.1 51.8 0.747 # left top hight weight score
+646.3 189.1 42.1 51.8 0.747 # left top height weight score
 157.4 408.6 43.1 54.1 0.667
 120.3 212.4 38.7 42.8 0.650
 ...
@@ -553,7 +553,7 @@ CenterFace on 3.2K images(The annotation and data format must be the same as wid
 # [Description of Random Situation](#contents)
 
 In dataset.py, we set the seed inside ```create_dataset``` function.
-In var_init.py, we set seed for weight initilization
+In var_init.py, we set seed for weight initialization
 
 # [ModelZoo Homepage](#contents)
 

model_zoo/official/cv/centerface/src/convert_weight.py

@@ -133,11 +133,6 @@ def pt_to_ckpt(pt, ckpt, out_path):
         parameter = state_dict_torch[key]
         parameter = parameter.numpy()
 
-        # depwise conv pytorch[cout, 1, k , k] -> ms[1, cin, k , k], cin = cout
-        if state_dict_ms[name_relate[key]].data.shape != parameter.shape:
-            parameter = parameter.transpose(1, 0, 2, 3)
-            print('ms=', state_dict_ms[name_relate[key]].data.shape, 'pytorch=', parameter.shape, 'name=', key)
-
         param_dict['name'] = name_relate[key]
         param_dict['data'] = Tensor(parameter)
         new_params_list.append(param_dict)
@@ -158,13 +153,6 @@ def ckpt_to_pt(pt, ckpt, out_path):
         name = name_relate[key]
         parameter = state_dict_ms[name].data
         parameter = parameter.asnumpy()
-        if state_dict_ms[name_relate[key]].data.shape != state_dict_torch[key].numpy().shape:
-            print('before ms=', state_dict_ms[name_relate[key]].data.shape, 'pytorch=',
-                  state_dict_torch[key].numpy().shape, 'name=', key)
-            parameter = parameter.transpose(1, 0, 2, 3)
-            print('after ms=', state_dict_ms[name_relate[key]].data.shape, 'pytorch=',
-                  state_dict_torch[key].numpy().shape, 'name=', key)
-
         state_dict[key] = torch.from_numpy(parameter)
 
     save_model(out_path, epoch=0, model=None, optimizer=None, state_dict=state_dict)

model_zoo/official/cv/centerface/src/convert_weight_mobilenetv2.py

@@ -120,12 +120,6 @@ def pt_to_ckpt(pt, ckpt, out_ckpt):
         parameter = state_dict_torch[key]
         parameter = parameter.numpy()
 
-        # depwise conv pytorch[cout, 1, k , k] -> ms[1, cin, k , k], cin = cout
-        if state_dict_ms[name_relate[key]].data.shape != parameter.shape:
-            parameter = parameter.transpose(1, 0, 2, 3)
-            print('ms=', state_dict_ms[name_relate[key]].data.shape, 'pytorch=', parameter.shape, 'name=', key)
-
-
         param_dict['name'] = name_relate[key]
         param_dict['data'] = Tensor(parameter)
         new_params_list.append(param_dict)

model_zoo/official/cv/centerface/src/mobile_v2.py

@@ -17,12 +17,10 @@
 import mindspore.nn as nn
 from mindspore.ops import operations as P
 from mindspore.ops.operations import TensorAdd
-from mindspore import Parameter
-from mindspore.common.initializer import initializer
 
 from src.var_init import KaimingNormal
 
-__all__ = ['MobileNetV2', 'mobilenet_v2', 'DepthWiseConv']
+__all__ = ['MobileNetV2', 'mobilenet_v2']
 
 def _make_divisible(v, divisor, min_value=None):
     """
@@ -43,32 +41,6 @@ def _make_divisible(v, divisor, min_value=None):
         new_v += divisor
     return new_v
 
-class DepthWiseConv(nn.Cell):
-    """
-    Depthwise convolution
-    """
-    def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
-        super(DepthWiseConv, self).__init__()
-        self.has_bias = has_bias
-        self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier, kernel_size=kernel_size,
-                                                      stride=stride, pad_mode=pad_mode, pad=pad)
-        self.bias_add = P.BiasAdd()
-
-        weight_shape = [channel_multiplier, in_planes, kernel_size, kernel_size]
-        self.weight = Parameter(initializer(KaimingNormal(mode='fan_out'), weight_shape))
-
-        if has_bias:
-            bias_shape = [channel_multiplier * in_planes]
-            self.bias = Parameter(initializer('zeros', bias_shape))
-        else:
-            self.bias = None
-
-    def construct(self, x):
-        output = self.depthwise_conv(x, self.weight)
-        if self.has_bias:
-            output = self.bias_add(output, self.bias)
-        return output
-
 
 class ConvBNReLU(nn.Cell):
     """
@@ -81,7 +53,8 @@ class ConvBNReLU(nn.Cell):
             conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode="pad", padding=padding,
                              has_bias=False)
         else:
-            conv = DepthWiseConv(in_planes, kernel_size, stride, pad_mode="pad", pad=padding)
+            conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode="pad", padding=padding,
+                             has_bias=False, group=groups, weight_init=KaimingNormal(mode='fan_out'))
 
         layers = [conv, nn.BatchNorm2d(out_planes).add_flags_recursive(fp32=True), nn.ReLU6()]  #, momentum=0.9
         self.features = nn.SequentialCell(layers)

model_zoo/official/cv/centerface/src/utils.py

@@ -24,8 +24,6 @@ import numpy as np
 from mindspore.train.serialization import load_checkpoint
 import mindspore.nn as nn
 
-from src.mobile_v2 import DepthWiseConv
-
 def load_backbone(net, ckpt_path, args):
     """
     Load backbone
@@ -52,7 +50,7 @@ def load_backbone(net, ckpt_path, args):
     for name, cell in net.cells_and_names():
         if name.startswith(centerface_backbone_prefix):
             name = name.replace(centerface_backbone_prefix, mobilev2_backbone_prefix)
-            if isinstance(cell, (nn.Conv2d, nn.Dense, DepthWiseConv)):
+            if isinstance(cell, (nn.Conv2d, nn.Dense)):
                 name, replace_name, replace_idx = replace_names(name, replace_name, replace_idx)
                 mobilev2_weight = '{}.weight'.format(name)
                 mobilev2_bias = '{}.bias'.format(name)

model_zoo/official/cv/centerface/train.py

@@ -33,6 +33,7 @@ from mindspore.train.callback import ModelCheckpoint, RunContext
 from mindspore.train.callback import _InternalCallbackParam, CheckpointConfig
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.profiler.profiling import Profiler
+from mindspore.common import set_seed
 
 from src.utils import get_logger
 from src.utils import AverageMeter
@@ -47,6 +48,7 @@ from src.config import ConfigCenterface
 from src.centerface import CenterFaceWithLossCell, TrainingWrapper
 from src.dataset import GetDataLoader
 
+set_seed(1)
 dev_id = int(os.getenv('DEVICE_ID'))
 context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=False,
                     device_target="Ascend", save_graphs=False, device_id=dev_id, reserve_class_name_in_scope=False)
@@ -130,7 +132,7 @@ if __name__ == "__main__":
         args.rank = get_rank()
         args.group_size = get_group_size()
 
-    # select for master rank save ckpt or all rank save, compatiable for model parallel
+    # select for master rank save ckpt or all rank save, compatible for model parallel
     args.rank_save_ckpt_flag = 0
     if args.is_save_on_master:
         if args.rank == 0:

model_zoo/official/cv/efficientnet/src/efficientnet.py

@@ -20,10 +20,8 @@ from copy import deepcopy
 
 import mindspore as ms
 import mindspore.nn as nn
-from mindspore import context, ms_function
-from mindspore.common.initializer import (Normal, One, Uniform, Zero,
-                                          initializer)
-from mindspore.common.parameter import Parameter
+from mindspore import ms_function
+from mindspore.common.initializer import (Normal, One, Uniform, Zero)
 from mindspore.ops import operations as P
 from mindspore.ops.composite import clip_by_value
 
@@ -224,13 +222,7 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
             # activation fn
             key = op[0]
             v = op[1:]
-            if v == 're':
-                print('not support')
-            elif v == 'r6':
-                print('not support')
-            elif v == 'hs':
-                print('not support')
-            elif v == 'sw':
+            if v in ('re', 'r6', 'hs', 'sw'):
                 print('not support')
             else:
                 continue
@@ -459,28 +451,6 @@ class BlockBuilder(nn.Cell):
         return self.layer(x)
 
 
-class DepthWiseConv(nn.Cell):
-    def __init__(self, in_planes, kernel_size, stride):
-        super(DepthWiseConv, self).__init__()
-        platform = context.get_context("device_target")
-        weight_shape = [1, kernel_size, in_planes]
-        weight_init = _initialize_weight_goog(shape=weight_shape)
-        if platform == "GPU":
-            self.depthwise_conv = P.Conv2D(out_channel=in_planes * 1, kernel_size=kernel_size,
-                                           stride=stride, pad_mode="same", group=in_planes)
-            self.weight = Parameter(initializer(
-                weight_init, [in_planes * 1, 1, kernel_size, kernel_size]))
-        else:
-            self.depthwise_conv = P.DepthwiseConv2dNative(
-                channel_multiplier=1, kernel_size=kernel_size, stride=stride, pad_mode='same',)
-            self.weight = Parameter(initializer(
-                weight_init, [1, in_planes, kernel_size, kernel_size]))
-
-    def construct(self, x):
-        x = self.depthwise_conv(x, self.weight)
-        return x
-
-
 class DropConnect(nn.Cell):
     def __init__(self, drop_connect_rate=0., seed0=0, seed1=0):
         super(DropConnect, self).__init__()
@@ -540,7 +510,9 @@ class DepthwiseSeparableConv(nn.Cell):
         self.has_pw_act = pw_act
         self.act_fn = act_fn
         self.drop_connect_rate = drop_connect_rate
-        self.conv_dw = DepthWiseConv(in_chs, dw_kernel_size, stride)
+        self.conv_dw = nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride, pad_mode="same",
+                                 has_bias=False, group=in_chs,
+                                 weight_init=_initialize_weight_goog(shape=[1, dw_kernel_size, in_chs]))
         self.bn1 = _fused_bn(in_chs, **bn_args)
 
         #
@@ -595,7 +567,9 @@ class InvertedResidual(nn.Cell):
         if self.shuffle_type is not None and isinstance(exp_kernel_size, list):
             self.shuffle = None
 
-        self.conv_dw = DepthWiseConv(mid_chs, dw_kernel_size, stride)
+        self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride, pad_mode="same",
+                                 has_bias=False, group=mid_chs,
+                                 weight_init=_initialize_weight_goog(shape=[1, dw_kernel_size, mid_chs]))
         self.bn2 = _fused_bn(mid_chs, **bn_args)
 
         if self.has_se:

model_zoo/research/cv/ssd_ghostnet/src/ssd_ghostnet.py

@@ -20,13 +20,12 @@ import numpy as np
 import mindspore.common.dtype as mstype
 import mindspore as ms
 import mindspore.nn as nn
-from mindspore import Parameter, context, Tensor
+from mindspore import context, Tensor
 from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from mindspore.communication.management import get_group_size
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.ops import composite as C
-from mindspore.common.initializer import initializer
 
 
 def _make_divisible(x, divisor=4):
@@ -44,8 +43,8 @@ def _bn(channel):
 
 
 def _last_conv2d(in_channel, out_channel, kernel_size=3, stride=1, pad_mod='same', pad=0):
-    depthwise_conv = DepthwiseConv(
-        in_channel, kernel_size, stride, pad_mode='same', pad=pad)
+    depthwise_conv = nn.Conv2d(in_channel, in_channel, kernel_size, stride, pad_mode='same', padding=pad,
+                               has_bias=False, group=in_channel, weight_init='ones')
     conv = _conv2d(in_channel, out_channel, kernel_size=1)
     return nn.SequentialCell([depthwise_conv, _bn(in_channel), nn.ReLU6(), conv])
 
@@ -75,8 +74,8 @@ class ConvBNReLU(nn.Cell):
             conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='same',
                              padding=padding)
         else:
-            conv = DepthwiseConv(in_planes, kernel_size,
-                                 stride, pad_mode='same', pad=padding)
+            conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='same', padding=padding,
+                             has_bias=False, group=groups, weight_init='ones')
         layers = [conv, _bn(out_planes)]
         if use_act:
             layers.append(Activation(act_type))
@@ -87,52 +86,6 @@ class ConvBNReLU(nn.Cell):
         return output
 
 
-class DepthwiseConv(nn.Cell):
-    """
-    Depthwise Convolution warpper definition.
-
-    Args:
-        in_planes (int): Input channel.
-        kernel_size (int): Input kernel size.
-        stride (int): Stride size.
-        pad_mode (str): pad mode in (pad, same, valid)
-        channel_multiplier (int): Output channel multiplier
-        has_bias (bool): has bias or not
-
-    Returns:
-        Tensor, output tensor.
-
-    Examples:
-        >>> DepthwiseConv(16, 3, 1, 'pad', 1, channel_multiplier=1)
-    """
-
-    def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
-        super(DepthwiseConv, self).__init__()
-        self.has_bias = has_bias
-        self.in_channels = in_planes
-        self.channel_multiplier = channel_multiplier
-        self.out_channels = in_planes * channel_multiplier
-        self.kernel_size = (kernel_size, kernel_size)
-        self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier,
-                                                      kernel_size=self.kernel_size,
-                                                      stride=stride, pad_mode=pad_mode, pad=pad)
-        self.bias_add = P.BiasAdd()
-        weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
-        self.weight = Parameter(initializer('ones', weight_shape), name="weight")
-
-        if has_bias:
-            bias_shape = [channel_multiplier * in_planes]
-            self.bias = Parameter(initializer('zeros', bias_shape), name="bias")
-        else:
-            self.bias = None
-
-    def construct(self, x):
-        output = self.depthwise_conv(x, self.weight)
-        if self.has_bias:
-            output = self.bias_add(output, self.bias)
-        return output
-
-
 class MyHSigmoid(nn.Cell):
     def __init__(self):
         super(MyHSigmoid, self).__init__()

model_zoo/research/cv/tinynet/src/tinynet.py

@@ -20,9 +20,8 @@ from copy import deepcopy
 import mindspore.nn as nn
 import mindspore.common.dtype as mstype
 from mindspore.ops import operations as P
-from mindspore.common.initializer import Normal, Zero, One, initializer, Uniform
-from mindspore import context, ms_function
-from mindspore.common.parameter import Parameter
+from mindspore.common.initializer import Normal, Zero, One, Uniform
+from mindspore import ms_function
 from mindspore import Tensor
 
 # Imagenet constant values
@@ -244,13 +243,7 @@ def _decode_block_str(block_str, depth_multiplier=1.0):
             # activation fn
             key = op[0]
             v = op[1:]
-            if v == 're':
-                print('not support')
-            elif v == 'r6':
-                print('not support')
-            elif v == 'hs':
-                print('not support')
-            elif v == 'sw':
+            if v in ('re', 'r6', 'hs', 'sw'):
                 print('not support')
             else:
                 continue
@@ -485,40 +478,6 @@ class BlockBuilder(nn.Cell):
         return self.layer(x)
 
 
-class DepthWiseConv(nn.Cell):
-    """depth-wise convolution"""
-
-    def __init__(self, in_planes, kernel_size, stride):
-        super(DepthWiseConv, self).__init__()
-        platform = context.get_context("device_target")
-        weight_shape = [1, kernel_size, in_planes]
-        weight_init = _initialize_weight_goog(shape=weight_shape)
-
-        if platform == "GPU":
-            self.depthwise_conv = P.Conv2D(out_channel=in_planes*1,
-                                           kernel_size=kernel_size,
-                                           stride=stride,
-                                           pad=int(kernel_size/2),
-                                           pad_mode="pad",
-                                           group=in_planes)
-
-            self.weight = Parameter(initializer(weight_init,
-                                                [in_planes*1, 1, kernel_size, kernel_size]))
-
-        else:
-            self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=1,
-                                                          kernel_size=kernel_size,
-                                                          stride=stride, pad_mode='pad',
-                                                          pad=int(kernel_size/2))
-
-            self.weight = Parameter(initializer(weight_init,
-                                                [1, in_planes, kernel_size, kernel_size]))
-
-    def construct(self, x):
-        x = self.depthwise_conv(x, self.weight)
-        return x
-
-
 class DropConnect(nn.Cell):
     """drop connect implementation"""
 
@@ -584,7 +543,9 @@ class DepthwiseSeparableConv(nn.Cell):
         self.has_pw_act = pw_act
         self.act_fn = Swish()
         self.drop_connect_rate = drop_connect_rate
-        self.conv_dw = DepthWiseConv(in_chs, dw_kernel_size, stride)
+        self.conv_dw = nn.Conv2d(in_chs, in_chs, dw_kernel_size, stride, pad_mode="pad",
+                                 padding=int(dw_kernel_size/2), has_bias=False, group=in_chs,
+                                 weight_init=_initialize_weight_goog(shape=[1, dw_kernel_size, in_chs]))
         self.bn1 = _fused_bn(in_chs, **bn_args)
 
         if self.has_se:
@@ -640,7 +601,9 @@ class InvertedResidual(nn.Cell):
         if self.shuffle_type is not None and isinstance(exp_kernel_size, list):
             self.shuffle = None
 
-        self.conv_dw = DepthWiseConv(mid_chs, dw_kernel_size, stride)
+        self.conv_dw = nn.Conv2d(mid_chs, mid_chs, dw_kernel_size, stride, pad_mode="pad",
+                                 padding=int(dw_kernel_size/2), has_bias=False, group=mid_chs,
+                                 weight_init=_initialize_weight_goog(shape=[1, dw_kernel_size, mid_chs]))
         self.bn2 = _fused_bn(mid_chs, **bn_args)
 
         if self.has_se:

model_zoo/research/nlp/dscnn/src/ds_cnn.py

@@ -16,34 +16,6 @@
 import math
 
 import mindspore.nn as nn
-from mindspore.ops import operations as P
-from mindspore.common.initializer import initializer
-from mindspore import Parameter
-
-
-class DepthWiseConv(nn.Cell):
-    '''Build DepthWise conv.'''
-    def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
-        super(DepthWiseConv, self).__init__()
-        self.has_bias = has_bias
-        self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier, kernel_size=kernel_size,
-                                                      stride=stride, pad_mode=pad_mode, pad=pad)
-        self.bias_add = P.BiasAdd()
-
-        weight_shape = [channel_multiplier, in_planes, kernel_size[0], kernel_size[1]]
-        self.weight = Parameter(initializer('ones', weight_shape))
-
-        if has_bias:
-            bias_shape = [channel_multiplier * in_planes]
-            self.bias = Parameter(initializer('zeros', bias_shape))
-        else:
-            self.bias = None
-
-    def construct(self, x):
-        output = self.depthwise_conv(x, self.weight)
-        if self.has_bias:
-            output = self.bias_add(output, self.bias)
-        return output
 
 
 class DSCNN(nn.Cell):
@@ -85,8 +57,9 @@ class DSCNN(nn.Cell):
                 seq_cell.append(nn.BatchNorm2d(num_features=conv_feat[layer_no], momentum=0.98))
                 in_channel = conv_feat[layer_no]
             else:
-                seq_cell.append(DepthWiseConv(in_planes=in_channel, kernel_size=(conv_kt[layer_no], conv_kf[layer_no]),
-                                              stride=(conv_st[layer_no], conv_sf[layer_no]), pad_mode='same', pad=0))
+                seq_cell.append(nn.Conv2d(in_channel, in_channel, kernel_size=(conv_kt[layer_no], conv_kf[layer_no]),
+                                          stride=(conv_st[layer_no], conv_sf[layer_no]), pad_mode='same',
+                                          has_bias=False, group=in_channel, weight_init='ones'))
                 seq_cell.append(nn.BatchNorm2d(num_features=in_channel, momentum=0.98))
                 seq_cell.append(nn.ReLU())
                 seq_cell.append(nn.Conv2d(in_channels=in_channel, out_channels=conv_feat[layer_no], kernel_size=(1, 1),