比较与tf.nn.conv2d的差异
tf.nn.conv2d
tf.nn.conv2d(
input,
filters,
strides,
padding,
data_format='NHWC',
dilations=None,
name=None
) -> Tensor
更多内容详见tf.nn.conv2d。
mindspore.nn.Conv2d
class mindspore.nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros',
data_format='NCHW'
)(x) -> Tensor
更多内容详见mindspore.nn.Conv2d。
差异对比
TensorFlow:对输入Tensor计算二维卷积,通常情况下,输入大小为 \(\left(N, C_{\mathrm{in}}, H, W\right)\) 、输出大小为 \(\left(N, C_{\text {out }}, H_{\text {out }}, W_{\text {out }}\right)\) 的输出值可以描述为: \(\operatorname{out}\left(N_{i}, C_{\text {out }_{j}}\right)=\operatorname{bias}\left(C_{\text {out }_{j}}\right)+\sum_{k=0}^{C_{i n}-1} \text { weight }\left(C_{\text {out }_{j}}, k\right) \star \operatorname{input}\left(N_{i}, k\right)\) 其中,\(\star\) 为2D cross-correlation 算子,\(N\) 是batch size,\(C\) 是通道数量,\(H\) 和 \(W\) 分别是特征层的高度和宽度。
MindSpore:与TensorFlow实现的功能基本一致,但部分参数结构、支持维度、默认值不同。MindSpore和TensorFlow的填充模式都包含了’same’、’valid’,但MindSpore相较于TensorFlow多了’pad’(零填充)。
分类 |
子类 |
TensorFlow |
MindSpore |
差异 |
---|---|---|---|---|
参数 |
参数1 |
input |
x |
功能一致,参数名不同 |
参数2 |
filters |
kernel_size |
功能一致,参数名不同,数据结构不同 |
|
参数3 |
strides |
stride |
功能一致,参数名不同,支持维度不同,默认值不同 |
|
参数4 |
padding |
pad_mode |
功能一致,参数名不同,可选项不同,默认值不同 |
|
参数5 |
data_format |
data_format |
功能一致,默认值不同 |
|
参数6 |
dilations |
dilation |
功能一致,参数名不同,支持维度不同,默认值不同 |
|
参数7 |
name |
- |
不涉及 |
|
参数8 |
- |
in_channels |
输入Tensor的空间维度 |
|
参数9 |
- |
out_channels |
输出Tensor的空间维度 |
|
参数10 |
- |
padding |
输入的高度和宽度方向上填充的数量 |
|
参数11 |
- |
group |
将过滤器拆分为组 |
|
参数12 |
- |
has_bias |
是否添加偏置参数 |
|
参数13 |
- |
weight_init |
权重参数的初始化方法 |
|
参数14 |
- |
bias_init |
偏置参数的初始化方法 |
代码示例1
TensorFlow的参数data_format默认值为’NHWC’,表示输入和输出的Tensor格式为[batchsize,in_height,in_width,in_channels]。MindSpore的参数data_format默认值为’NCHW’,表示输入和输出的Tensor格式为[batchsize,in_channels,in_height,in_width]。MindSpore的’NHWC’数据格式只能在GPU上使用,其它平台上,当输入数据格式为’NHWC’时,可以使用ops.transpose将数据格式修改为’NCHW’再进行卷积操作,最后将结果再通过ops.transpose转化为’NHWC’。
# TensorFlow
import tensorflow as tf
import numpy as np
x_ = tf.ones((1, 3, 3, 5))
x = tf.convert_to_tensor(x_, dtype=tf.float32)
filters_ = tf.ones((2, 2, 5, 1))
filters = tf.convert_to_tensor(filters_, dtype=tf.float32)
output = tf.nn.conv2d(x, filters, strides=1, padding='SAME').shape
print(output)
# (1, 3, 3, 1)
# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.nn as nn
import mindspore.ops as ops
import numpy as np
x_ = np.ones((1, 3, 3, 5))
x_NHWC = Tensor(x_, mindspore.float32)
x = ops.transpose(x_NHWC, (0, 3, 1, 2))
net = nn.Conv2d(5, 1, 2, stride=1, pad_mode='same')
output = ops.transpose(net(x), (0, 2, 3, 1)).shape
print(output)
# (1, 3, 3, 1)
代码示例2
TensorFlow的参数filters是一个四维Tensor,包括[filter_height,filter_width,in_channels,out_channels],即[卷积核的高度,卷积核的宽度,图像通道数,卷积核个数]。MindSpore的参数kernel_size为整型或两个整型的tuple,一个整数表示卷积核的高度和宽度均为该值。两个整数的tuple分别表示卷积核的高度和宽度。
# TensorFlow
import tensorflow as tf
import numpy as np
x_ = tf.ones((1, 4, 4, 5))
x = tf.convert_to_tensor(x_, dtype=tf.float32)
filters_ = tf.ones((2, 3, 5, 1))
filters = tf.convert_to_tensor(filters_, dtype=tf.float32)
output = tf.nn.conv2d(x, filters, strides=1, padding='VALID').shape
print(output)
# (1, 3, 2, 1)
# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.nn as nn
import mindspore.ops as ops
import numpy as np
x_ = np.ones((1, 4, 4, 5))
x_NHWC = Tensor(x_, mindspore.float32)
x = ops.transpose(x_NHWC, (0, 3, 1, 2))
net = nn.Conv2d(5, 1, (2, 3), stride=1, pad_mode='valid')
output = ops.transpose(net(x), (0, 2, 3, 1)).shape
print(output)
# (1, 3, 2, 1)
代码示例3
TensorFlow的参数strides是一个一维向量,长度可以为1、2、4,表示卷积时每一维的步长。一个整数表示在高度和宽度方向的移动步长均为该值,两个整数分别表示在高度和宽度方向的移动步长,剩下两维移动步长默认为1,此参数无默认值。MindSpore的参数stride为整型或两个整型的tuple。一个整数表示在高度和宽度方向的移动步长均为该值。两个整数的tuple分别表示在高度和宽度方向的移动步长,参数默认值为1。
# TensorFlow
import tensorflow as tf
import numpy as np
x_ = tf.ones((1, 4, 4, 5))
x = tf.convert_to_tensor(x_, dtype=tf.float32)
filters_ = tf.ones((2, 3, 5, 1))
filters = tf.convert_to_tensor(filters_, dtype=tf.float32)
output = tf.nn.conv2d(x, filters, strides=[1,1,1,1], padding='VALID').shape
print(output)
# (1, 3, 2, 1)
# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.nn as nn
import mindspore.ops as ops
import numpy as np
x_ = np.ones((1, 4, 4, 5))
x_NHWC = Tensor(x_, mindspore.float32)
x = ops.transpose(x_NHWC, (0, 3, 1, 2))
net = nn.Conv2d(5, 1, (2, 3), pad_mode='valid')
output = ops.transpose(net(x), (0, 2, 3, 1)).shape
print(output)
# (1, 3, 2, 1)
代码示例4
TensorFlow的参数dilations是一个一维向量,长度可以为1、2、4,表示卷积核膨胀尺寸,在H和C维度上的值必须为1。MindSpore的参数dilation为整型或两个整型的tuple。
# TensorFlow
import tensorflow as tf
import numpy as np
x_ = tf.ones((1, 6, 6, 5))
x = tf.convert_to_tensor(x_, dtype=tf.float32)
filters_ = tf.ones((2, 3, 5, 1))
filters = tf.convert_to_tensor(filters_, dtype=tf.float32)
output = tf.nn.conv2d(x, filters, strides=1, dilations=[1,2,2,1], padding='VALID').shape
print(output)
# (1, 4, 2, 1)
# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.nn as nn
import mindspore.ops as ops
import numpy as np
x_ = np.ones((1, 6, 6, 5))
x_NHWC = Tensor(x_, mindspore.float32)
x = ops.transpose(x_NHWC, (0, 3, 1, 2))
net = nn.Conv2d(5, 1, (2, 3), dilation=(2,2), pad_mode='valid')
output = ops.transpose(net(x), (0, 2, 3, 1)).shape
print(output)
# (1, 4, 2, 1)
代码示例5
TensorFlow的参数padding表示填充模式,没有默认值。MindSpore的参数pad_mode默认值为’same’。
# TensorFlow
import tensorflow as tf
import numpy as np
x_ = tf.ones((1, 4, 4, 5))
x = tf.convert_to_tensor(x_, dtype=tf.float32)
filters_ = tf.ones((2, 3, 5, 1))
filters = tf.convert_to_tensor(filters_, dtype=tf.float32)
output = tf.nn.conv2d(x, filters, strides=1, padding='SAME').shape
print(output)
# (1, 4, 4, 1)
# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.nn as nn
import mindspore.ops as ops
import numpy as np
x_ = np.ones((1, 4, 4, 5))
x_NHWC = Tensor(x_, mindspore.float32)
x = ops.transpose(x_NHWC, (0, 3, 1, 2))
net = nn.Conv2d(5, 1, (2, 3), stride=1)
output = ops.transpose(net(x), (0, 2, 3, 1)).shape
print(output)
# (1, 4, 4, 1)