modify conv3d and conv3dtranspose for unet3d

model_zoo/official/cv/unet3d/README.md

@@ -102,7 +102,6 @@ python eval.py --data_url=/path/to/data/ --seg_url=/path/to/segment/ --ckpt_path
   │   ├──transform.py                 // handle dataset
   │   ├──convert_nifti.py             // convert dataset
   │   ├──loss.py                      // loss
-  │   ├──conv.py                      // conv components
   │   ├──utils.py                     // General components (callback function)
   │   ├──unet3d_model.py              // Unet3D model
   │   ├──unet3d_parts.py              // Unet3D part

model_zoo/official/cv/unet3d/src/conv.py

@@ -1,90 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# less required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-
-import mindspore.nn as nn
-from mindspore import Parameter
-from mindspore import dtype as mstype
-from mindspore.ops import operations as P
-from mindspore.ops.operations import nn_ops as nps
-from mindspore.common.initializer import initializer
-
-def weight_variable(shape):
-    init_value = initializer('Normal', shape, mstype.float32)
-    return Parameter(init_value)
-
-class Conv3D(nn.Cell):
-    def __init__(self,
-                 in_channel,
-                 out_channel,
-                 kernel_size,
-                 mode=1,
-                 pad_mode="valid",
-                 pad=0,
-                 stride=1,
-                 dilation=1,
-                 group=1,
-                 data_format="NCDHW",
-                 bias_init="zeros",
-                 has_bias=True):
-        super().__init__()
-        self.weight_shape = (out_channel, in_channel, kernel_size[0], kernel_size[1], kernel_size[2])
-        self.weight = weight_variable(self.weight_shape)
-        self.conv = nps.Conv3D(out_channel=out_channel, kernel_size=kernel_size, mode=mode, \
-                               pad_mode=pad_mode, pad=pad, stride=stride, dilation=dilation, \
-                               group=group, data_format=data_format)
-        self.bias_init = bias_init
-        self.has_bias = has_bias
-        self.bias_add = P.BiasAdd(data_format=data_format)
-        if self.has_bias:
-            self.bias = Parameter(initializer(self.bias_init, [out_channel]), name='bias')
-
-    def construct(self, x):
-        output = self.conv(x, self.weight)
-        if self.has_bias:
-            output = self.bias_add(output, self.bias)
-        return output
-
-class Conv3DTranspose(nn.Cell):
-    def __init__(self,
-                 in_channel,
-                 out_channel,
-                 kernel_size,
-                 mode=1,
-                 pad=0,
-                 stride=1,
-                 dilation=1,
-                 group=1,
-                 output_padding=0,
-                 data_format="NCDHW",
-                 bias_init="zeros",
-                 has_bias=True):
-        super().__init__()
-        self.weight_shape = (in_channel, out_channel, kernel_size[0], kernel_size[1], kernel_size[2])
-        self.weight = weight_variable(self.weight_shape)
-        self.conv_transpose = nps.Conv3DTranspose(in_channel=in_channel, out_channel=out_channel,\
-                                                  kernel_size=kernel_size, mode=mode, pad=pad, stride=stride, \
-                                                  dilation=dilation, group=group, output_padding=output_padding, \
-                                                  data_format=data_format)
-        self.bias_init = bias_init
-        self.has_bias = has_bias
-        self.bias_add = P.BiasAdd(data_format=data_format)
-        if self.has_bias:
-            self.bias = Parameter(initializer(self.bias_init, [out_channel]), name='bias')
-
-    def construct(self, x):
-        output = self.conv_transpose(x, self.weight)
-        if self.has_bias:
-            output = self.bias_add(output, self.bias)
-        return output

model_zoo/official/cv/unet3d/src/unet3d_parts.py

@@ -16,7 +16,6 @@
 import mindspore.nn as nn
 from mindspore import dtype as mstype
 from mindspore.ops import operations as P
-from src.conv import Conv3D, Conv3DTranspose
 
 class BatchNorm3d(nn.Cell):
     def __init__(self, num_features):
@@ -39,22 +38,22 @@ class ResidualUnit(nn.Cell):
         self.down = down
         self.in_channel = in_channel
         self.out_channel = out_channel
-        self.down_conv_1 = Conv3D(in_channel, out_channel, kernel_size=(3, 3, 3), \
+        self.down_conv_1 = nn.Conv3d(in_channel, out_channel, kernel_size=(3, 3, 3), \
-                                  pad_mode="pad", stride=self.stride, pad=1)
+                                     pad_mode="pad", stride=self.stride, padding=1)
         self.is_output = is_output
         if not is_output:
             self.batchNormal1 = BatchNorm3d(num_features=self.out_channel)
             self.relu1 = nn.PReLU()
         if self.down:
-            self.down_conv_2 = Conv3D(out_channel, out_channel, kernel_size=(3, 3, 3), \
+            self.down_conv_2 = nn.Conv3d(out_channel, out_channel, kernel_size=(3, 3, 3), \
-                                      pad_mode="pad", stride=1, pad=1)
+                                         pad_mode="pad", stride=1, padding=1)
             self.relu2 = nn.PReLU()
             if kernel_size[0] == 1:
-                self.residual = Conv3D(in_channel, out_channel, kernel_size=(1, 1, 1), \
+                self.residual = nn.Conv3d(in_channel, out_channel, kernel_size=(1, 1, 1), \
-                                       pad_mode="valid", stride=self.stride)
+                                          pad_mode="valid", stride=self.stride)
             else:
-                self.residual = Conv3D(in_channel, out_channel, kernel_size=(3, 3, 3), \
+                self.residual = nn.Conv3d(in_channel, out_channel, kernel_size=(3, 3, 3), \
-                                       pad_mode="pad", stride=self.stride, pad=1)
+                                          pad_mode="pad", stride=self.stride, padding=1)
             self.batchNormal2 = BatchNorm3d(num_features=self.out_channel)
 
 
@@ -93,9 +92,10 @@ class Up(nn.Cell):
         self.down_in_channel = down_in_channel
         self.out_channel = out_channel
         self.stride = stride
-        self.conv3d_transpose = Conv3DTranspose(in_channel=self.in_channel + self.down_in_channel, \
+        self.conv3d_transpose = nn.Conv3dTranspose(in_channels=self.in_channel + self.down_in_channel, \
-                                                pad=1, out_channel=self.out_channel, kernel_size=(3, 3, 3), \
+                                                   out_channels=self.out_channel, kernel_size=(3, 3, 3), \
-                                                stride=self.stride, output_padding=(1, 1, 1))
+                                                   pad_mode="pad", stride=self.stride, \
+                                                   output_padding=(1, 1, 1), padding=1)
 
         self.concat = P.Concat(axis=1)
         self.conv = ResidualUnit(self.out_channel, self.out_channel, stride=1, down=False, \