self.attr2query = Linear(attr_size, embed_size)
时间: 2024-03-31 16:38:56 浏览: 27
这是一段PyTorch中的代码,其中`Linear`是一个全连接层,`attr_size`表示输入特征的大小,`embed_size`表示输出特征的大小。这一行代码的作用是将输入特征进行线性变换,将其转换为指定大小的特征。在深度学习中,这种线性变换通常是用于特征的降维或者升维,或者将不同特征之间进行组合。在一些模型中,这种线性变换是非常重要的,可以帮助模型更好地学习输入数据的表征。
相关问题
使用paddle将以下LeNet代码改为ResNet网络模型class LeNet(paddle.nn.Layer): def __init__(self): super(LeNet, self).__init__() # 创建卷积和池化层块,每个卷积层使用relu激活函数,后面跟着一个2x2的池化 self.conv1 = paddle.nn.Conv2D(3, 32, 3, 1, 1) self.relu1 = paddle.nn.ReLU() self.max_pool1 = paddle.nn.MaxPool2D(2, 2) self.conv2 = paddle.nn.Conv2D(32, 64, 3, 1, 1) self.relu2 = paddle.nn.ReLU() self.max_pool2 = paddle.nn.MaxPool2D(2, 2) self.avg_pool = AdaptiveAvgPool2D(1) self.linear= paddle.nn.Linear(64, 2) # 网络的前向计算过程 def forward(self, x): x = self.max_pool1(self.relu1(self.conv1(x))) x = self.max_pool2(self.relu2(self.conv2(x))) x = self.avg_pool(x) x = paddle.reshape(x, [x.shape[0],-1]) x = self.linear(x) return x paddle.Model(LeNet()).summary((-1,3,256,256))
以下是将LeNet代码改为ResNet网络模型的代码:
```
import paddle.nn.functional as F
class ResNet(paddle.nn.Layer):
def __init__(self, num_classes=2):
super(ResNet, self).__init__()
# 第一个卷积层,输出通道数为64,卷积核大小为7x7,步长为2,padding为3
self.conv1 = paddle.nn.Conv2D(3, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
self.bn1 = paddle.nn.BatchNorm2D(64)
self.relu = paddle.nn.ReLU()
self.max_pool = paddle.nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
# ResNet的主体部分,包括4个残差块
self.layer1 = self._make_layer(64, 3)
self.layer2 = self._make_layer(128, 4, stride=2)
self.layer3 = self._make_layer(256, 6, stride=2)
self.layer4 = self._make_layer(512, 3, stride=2)
# 全局平均池化层
self.avg_pool = paddle.nn.AdaptiveAvgPool2D((1, 1))
# 分类器
self.fc = paddle.nn.Linear(512, num_classes)
def _make_layer(self, channels, blocks, stride=1):
layers = []
# 下采样,对输入进行降维
downsample = None
if stride != 1 or self.in_channels != channels:
downsample = paddle.nn.Sequential(
paddle.nn.Conv2D(self.in_channels, channels, kernel_size=1, stride=stride, bias_attr=False),
paddle.nn.BatchNorm2D(channels)
)
layers.append(ResidualBlock(self.in_channels, channels, stride, downsample))
self.in_channels = channels
for _ in range(1, blocks):
layers.append(ResidualBlock(channels, channels))
return paddle.nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.max_pool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avg_pool(x)
x = paddle.flatten(x, 1)
x = self.fc(x)
return x
class ResidualBlock(paddle.nn.Layer):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = paddle.nn.Conv2D(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias_attr=False)
self.bn1 = paddle.nn.BatchNorm2D(out_channels)
self.relu = paddle.nn.ReLU()
self.conv2 = paddle.nn.Conv2D(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias_attr=False)
self.bn2 = paddle.nn.BatchNorm2D(out_channels)
self.downsample = downsample
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
paddle.Model(ResNet()).summary((-1,3,256,256))
```
这里我们定义了一个ResNet网络模型,包括一个卷积层,一个最大池化层,4个残差块,一个全局平均池化层和一个分类器。其中,每个残差块包含两个卷积层和一个跳跃连接,卷积层的输出通道数和步长根据网络深度不同而不同。在模型前向传播过程中,我们先使用一次卷积和池化操作,然后将特征图输入到各个残差块中,最后进行全局平均池化和分类。
td_s32 ret; ot_vpss_grp_attr grp_attr = { 0 }; ot_vpss_chn_attr chn_attr[OT_VPSS_MAX_PHYS_CHN_NUM] = { 0 }; td_bool chn_enable[OT_VPSS_MAX_PHYS_CHN_NUM] = { 0 }; if (vpss_chn >= OT_VPSS_MAX_PHYS_CHN_NUM) { sample_print("vpss_chn:%d invalid!\n", vpss_chn); return TD_FAILURE; } grp_attr.nr_en = TD_TRUE; grp_attr.ie_en = TD_TRUE; grp_attr.dci_en = TD_TRUE; grp_attr.nr_attr.compress_mode = OT_COMPRESS_MODE_FRAME; grp_attr.dei_mode = OT_VPSS_DEI_MODE_OFF; grp_attr.pixel_format = SAMPLE_PIXEL_FORMAT; grp_attr.frame_rate.src_frame_rate = -1; grp_attr.frame_rate.dst_frame_rate = -1; grp_attr.max_width = size->width; grp_attr.max_height = size->height; chn_enable[vpss_chn] = TD_TRUE; chn_attr[vpss_chn].chn_mode = OT_VPSS_CHN_MODE_AUTO; chn_attr[vpss_chn].width = size->width; chn_attr[vpss_chn].height = size->height; chn_attr[vpss_chn].pixel_format = OT_PIXEL_FORMAT_YVU_SEMIPLANAR_420; chn_attr[vpss_chn].frame_rate.src_frame_rate = -1; chn_attr[vpss_chn].frame_rate.dst_frame_rate = -1; chn_attr[vpss_chn].border_en = TD_TRUE; chn_attr[vpss_chn].border_attr.color = COLOR_RGB_BLUE; chn_attr[vpss_chn].border_attr.top_width = 2; /* 2 : border top width */ chn_attr[vpss_chn].border_attr.bottom_width = 2; /* 2 : border bottom width */ chn_attr[vpss_chn].border_attr.left_width = 2; /* 2 : border left width */ chn_attr[vpss_chn].border_attr.right_width = 2; /* 2 : border right width */ ret = sample_common_vpss_start(vpss_grp, chn_enable, &grp_attr, chn_attr, OT_VPSS_MAX_PHYS_CHN_NUM); if (ret != TD_SUCCESS) { sample_print("failed with %#x!\n", ret); return TD_FAILURE; } return TD_SUCCESS;
这段代码是关于视频处理的,主要是配置了一个 VPSS 组和一个 VPSS 通道。VPSS(Video Processing Subsystem)是专门用于视频处理的子系统,可以对视频进行降噪、锐化、缩放、颜色调整等操作。这里的代码中,首先判断了要配置的 VPSS 通道是否有效,然后设置了 VPSS 组和通道的属性,包括使能 NR(Noise Reduction)和 IE(Image Enhancement)等功能,压缩模式为帧压缩,DEI(Dynamic Edge Interpolation)模式为关闭,像素格式为 SAMPLE_PIXEL_FORMAT,帧率为 -1 不限制帧率,宽高为传入参数 size 的宽高,最后设置了边框的颜色和宽度,并启动了 VPSS 组和通道。
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