解释每一句 def __init__(self, layers=[3, 6, 6, 3], channels=[64, 128, 256, 512, 1024], act='swish', return_idx=[0, 1, 2, 3, 4], depth_wise=False, use_large_stem=False, width_mult=1.0, depth_mult=1.0, trt=False): super(CSPResNet, self).__init__() channels = [max(round(c * width_mult), 1) for c in channels] layers = [max(round(l * depth_mult), 1) for l in layers] act = get_act_fn( act, trt=trt) if act is None or isinstance(act, (str, dict)) else act

时间: 2023-06-16 12:02:38 浏览: 41
这段代码是一个 Python 类的初始化函数，用于初始化 CSPResNet 模型的各种参数。 - `layers`：一个列表，表示 CSPResNet 模型中每个 CSPBlock 中的残差块数量。默认值为 [3, 6, 6, 3]，表示第一个 CSPBlock 中有 3 个残差块，第二个 CSPBlock 中有 6 个残差块，以此类推。 - `channels`：一个列表，表示 CSPResNet 模型中每个 CSPBlock 中的输出通道数。默认值为 [64, 128, 256, 512, 1024]，表示第一个 CSPBlock 输出通道数为 64，第二个 CSPBlock 输出通道数为 128，以此类推。 - `act`：一个字符串或字典，表示 CSPResNet 模型中使用的激活函数。默认值为 'swish'，表示使用 Swish 激活函数。 - `return_idx`：一个列表，表示 CSPResNet 模型中每个 CSPBlock 输出的特征图的索引。默认值为 [0, 1, 2, 3, 4]，表示第一个 CSPBlock 输出的特征图索引为 0，第二个 CSPBlock 输出的特征图索引为 1，以此类推。 - `depth_wise`：一个布尔值，表示 CSPResNet 模型中是否使用深度可分离卷积。默认值为 False，表示不使用深度可分离卷积。 - `use_large_stem`：一个布尔值，表示 CSPResNet 模型中是否使用大的卷积核来进行特征提取。默认值为 False，表示不使用大的卷积核。 - `width_mult`：一个浮点数，表示 CSPResNet 模型中每个 CSPBlock 的通道数缩放因子。默认值为 1.0，表示不进行通道数缩放。 - `depth_mult`：一个浮点数，表示 CSPResNet 模型中每个 CSPBlock 的残差块数量缩放因子。默认值为 1.0，表示不进行残差块数量缩放。 - `trt`：一个布尔值，表示 CSPResNet 模型中是否使用 TensorRT 进行加速。默认值为 False，表示不使用 TensorRT 进行加速。 在初始化函数中，先调用父类的初始化函数，然后对 `channels` 和 `layers` 进行缩放，最后调用 `get_act_fn` 函数获取激活函数。如果 `act` 是字符串或字典类型，则根据其值获取相应的激活函数；否则直接使用传入的激活函数。

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把这段代码里的location_embedding_dim去掉class my_GAT(torch.nn.Module): def __init__(self, in_channels, out_channels, n_heads, location_embedding_dim, filters_1, filters_2, dropout): super(my_GAT, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.n_heads = n_heads location_embedding_dim = 0 self.filters_1 = filters_1 self.filters_2 = filters_2 self.dropout = dropout self.location_embedding_dim = location_embedding_dim self.setup_layers() def setup_layers(self): self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1) self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads + self.location_embedding_dim, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False) def forward(self, edge_indices, features, location_embedding): features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_1(features, edge_indices) features = torch.nn.functional.relu(features) features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training) features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_2(features, edge_indices) return features

def __init__(self, in_channels, out_channels, n_heads, filters_1, filters_2, dropout): super(my_GAT, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.n_heads ...

class DenseBlock(nn.Module): def __init__(self, in_channels, growth_rate, num_layers): super(DenseBlock, self).__init__() self.layers = nn.ModuleList([Bottleneck(in_channels + i * growth_rate, growth_rate) for i in range(num_layers)]) def forward(self, x): for layer in self.layers: x = layer(x) return x

在这段代码中，self.layers定义了一个由num_layers个Bottleneck块组成的列表，每个Bottleneck块的输入通道数为in_channels + i * growth_rate，其中i表示Bottleneck块在DenseBlock中的位置，growth_rate表示每个...

class DnCNN(nn.Module): def __init__(self, channels, num_of_layers=17): super(DnCNN, self).__init__() kernel_size = 3 padding = 1 features = 64 layers = [] layers.append(nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False)) layers.append(nn.ReLU(inplace=True)) for _ in range(num_of_layers-2): layers.append(nn.Conv2d(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False)) layers.append(nn.BatchNorm2d(features)) layers.append(nn.ReLU(inplace=True)) layers.append(nn.Conv2d(in_channels=features, out_channels=channels, kernel_size=kernel_size, padding=padding, bias=False)) self.dncnn = nn.Sequential(*layers) def forward(self, x): out = self.dncnn(x) return out怎么改为训练集输出[16,3,50,50]，评估集输出[1,3,256,256]

def __init__(self, channels, num_of_layers=17): super(DnCNN, self).__init__() kernel_size = 3 padding = 1 features = 64 layers = [] layers.append(nn.Conv2d(in_channels=channels, out_channels=...

class Net4CNN(torch.nn.Module): def __init__(self, output_size, hidden_size, layers, channels, embedding_size): super().__init__() self.features = CNN4Backbone(hidden_size, channels, layers, max_pool_factor=4 // layers) self.classifier = torch.nn.Linear(embedding_size, output_size, bias=True) maml_init_(self.classifier) self.hidden_size = hidden_size def forward(self, x): x = self.features(x) x = self.classifier(x) return x 如何在这里添加ECA模块

def __init__(self, output_size, hidden_size, layers, channels, embedding_size): super().__init__() self.features = CNN4Backbone(hidden_size, channels, layers, max_pool_factor=4 // layers) self.eca...

class BasicBlock2D(nn.Module): expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super(BasicBlock2D, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.shortcut = nn.Sequential() if stride != 1 or in_channels != self.expansion * out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * out_channels) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out # 定义二维ResNet-18模型 class ResNet18_2D(nn.Module): def __init__(self, num_classes=1000): super(ResNet18_2D, self).__init__() self.in_channels = 64 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1) self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2) self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2) self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 , 512) def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) # print(out.shape) out = out.view(out.size(0), -1) out = self.fc(out) return out改为用稀疏表示替换全连接层

layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) ...

class TemporalModel(nn.Module): def __init__( self, in_channels, receptive_field, input_shape, start_out_channels=64, extra_in_channels=0, n_spatial_layers_between_temporal_layers=0, use_pyramid_pooling=True): super().__init__() self.receptive_field = receptive_field n_temporal_layers = receptive_field - 1 h, w = input_shape modules = [] block_in_channels = in_channels block_out_channels = start_out_channels for _ in range(n_temporal_layers): if use_pyramid_pooling: use_pyramid_pooling = True pool_sizes = [(2, h, w)] else: use_pyramid_pooling = False pool_sizes = None temporal = TemporalBlock( block_in_channels, block_out_channels, use_pyramid_pooling=use_pyramid_pooling, pool_sizes=pool_sizes, ) spatial = [ Bottleneck3D(block_out_channels, block_out_channels, kernel_size=(1, 3, 3)) for _ in range(n_spatial_layers_between_temporal_layers) ] temporal_spatial_layers = nn.Sequential(temporal, *spatial) modules.extend(temporal_spatial_layers) block_in_channels = block_out_channels block_out_channels += extra_in_channels self.out_channels = block_in_channels self.model = nn.Sequential(*modules) def forward(self, x): # Reshape input tensor to (batch, C, time, H, W) x = x.permute(0, 2, 1, 3, 4) x = self.model(x) x = x.permute(0, 2, 1, 3, 4).contiguous() return x[:, (self.receptive_field - 1):]是如何一步步前向传播的？

首先，输入张量x的形状为(batch_size, in_channels, sequence_length, height, width)。 然后，我们将x的维度从(sequence_length, batch_size, in_channels, height, width)改变为(batch_size, sequence_length, in...

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class DnCNN(nn.Module): def init(self, channels, num_of_layers=17): super(DnCNN, self).init() kernel_size = 3 padding = 1 features = 64 layers = [] layers.append(nn.Conv2d(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, bias=False))) layers.append(nn.ReLU(inplace=True)) for _ in range(num_of_layers-2): layers.append(nn.Conv2d(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, bias=False))) layers.append(nn.BatchNorm2d(features)) layers.append(nn.ReLU(inplace=True)) layers.append(nn.Conv2d(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, bias=False))) self.dncnn = nn.Sequential(*layers) def forward(self, x): out = self.dncnn(x) return out怎么改为训练集输出[16,3,50,50]，评估集输出[1,3,256,256]

def __init__(self, channels, num_of_layers=17): super(DnCNN, self).__init__() kernel_size = 3 padding = 1 features = 64 layers = [] layers.append(nn.Conv2d(in_channels=16, out_channels=64, ...

class SelfAttention(nn.Module): def __init__(self,in_c,out_c,fm_sz,pos_bias = False): super(SelfAttention,self).__init__() self.w_q = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_k = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.w_v = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pos_code = self.__getPosCode(fm_sz,out_c) self.softmax = nn.Softmax(dim = 2) self.pos_bias = pos_bias 改写为twensorflow形式

def __init__(self, in_c, out_c, fm_sz, pos_bias=False): super(SelfAttention, self).__init__() self.w_q = keras.layers.Conv2D(filters=out_c, kernel_size=1, input_shape=(None, None, in_c)) self.w_k ...

class Positional_GAT(torch.nn.Module): def __init__(self, in_channels, out_channels, n_heads, location_embedding_dim, filters_1, filters_2, dropout): super(Positional_GAT, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.n_heads = n_heads self.filters_1 = filters_1 self.filters_2 = filters_2 self.dropout = dropout self.location_embedding_dim = location_embedding_dim self.setup_layers() def setup_layers(self): self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1) self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads + self.location_embedding_dim, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False) def forward(self, edge_indices, features, location_embedding): features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_1(features, edge_indices) features = torch.nn.functional.relu(features) features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training) features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_2(features, edge_indices) return features

这段代码实现了一个名为Positional_GAT的模型，它基于GAT（Graph Attention Network）模型，并添加了位置嵌入（location embedding）来考虑节点在图中的位置信息。具体来说，该模型包含一个GATConv层（表示第一层GAT...

class CellTrack_GNN(EedgePath_MPNN): def __init__(self, in_channels: int, hidden_channels: int, in_edge_channels: int, hidden_edge_channels_linear: int, hidden_edge_channels_conv: int, num_layers: int, num_nodes_features: int, dropout: float = 0.0, act: Optional[Callable] = ReLU(inplace=True), norm: Optional[torch.nn.Module] = None, jk: str = 'last', **kwargs): super().__init__(in_channels, hidden_channels, in_edge_channels, hidden_edge_channels_linear, num_layers, dropout, act, norm, jk) assert in_edge_channels == hidden_edge_channels_linear[-1] in_edge_dims = in_edge_channels + num_nodes_features * in_channels + 1 self.convs.append(PDNConv(in_channels, hidden_channels, in_edge_channels, hidden_edge_channels_conv, **kwargs)) self.fcs.append(MLP(in_edge_dims, hidden_edge_channels_linear, dropout_p=dropout)) for _ in range(1, num_layers): self.convs.append( PDNConv(hidden_channels, hidden_channels, in_edge_channels, hidden_edge_channels_conv, **kwargs)) self.fcs.append(MLP(in_edge_dims, hidden_edge_channels_linear, dropout_p=dropout))

- self.convs.append(PDNConv(in_channels, hidden_channels, in_edge_channels, hidden_edge_channels_conv, **kwargs))将一个PDNConv层对象添加到self.convs列表中，用于对节点特征进行卷积操作。 - self.fcs....

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这段代码实现了一个名为 TemporalBlock 的神经网络模块，该模块包括以下层: - 3 个卷积路径: 2x3x3 卷积、1x3x3 卷积和 1x1x1 卷积 - dropout 层 - skip 连接 - 可选的 spatio-temporal pyramid pooling 层 - 最后...

class EnhancedResidual(nn.Module): def __init__(self,in_c,out_c,fm_sz,net_type = 'ta'): super(EnhancedResidual,self).__init__() self.net_type = net_type self.conv1 = nn.Sequential( nn.Conv2d(in_channels = in_c,out_channels = in_c,kernel_size = 3,padding = 1), nn.BatchNorm2d(in_c), nn.ReLU(), ) self.conv2 = nn.Sequential( nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 3,padding = 1), nn.BatchNorm2d(out_c), nn.ReLU(), ) self.botneck = nn.Conv2d(in_channels = in_c,out_channels = out_c,kernel_size = 1) self.pool = nn.MaxPool2d(kernel_size = 2,stride = 2) if net_type == 'ta': self.spa = SpatialAttention() self.ca = ChannelAttention(in_planes = in_c,ratio = in_c) self.sa = MultiHeadSelfAttention(in_c = in_c,out_c = in_c // 4,head_n = 4,fm_sz = fm_sz) elif net_type == 'sa': self.sa = MultiHeadSelfAttention(in_c = in_c,out_c = out_c // 4,head_n = 4,fm_sz = fm_sz) elif net_type == 'cbam': self.spa = SpatialAttention() self.ca = ChannelAttention(in_planes = in_c,ratio = in_c) 改写为tensorflow形式

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这段代码是一个使用UNet3plus架构实现的语义分割的自动编码器。下面是对代码的解释： - UNet3plus类继承自tnn.Module，用于构建UNET3自动编码器。 - 构造函数__init__接受一些参数，包括类别数(n_classes)...

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