将以下模型转化为cfg格式class BlockA(nn.Module): def __init__(self, in_channels, out_channels, r, s=2): super().__init__() self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=s, padding=1) self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) self.conv3 = nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=s) self.act1 = nn.PReLU() self.act2 = nn.PReLU() self.bn1 = nn.BatchNorm1d(out_channels) self.bn2 = nn.BatchNorm1d(out_channels) self.bn3 = nn.BatchNorm1d(out_channels) self.dropout = nn.Dropout(r) def forward(self, x): i = self.conv3(x) i = self.bn3(i) x = self.conv1(x) x = self.bn1(x) x = self.act1(x) x = self.dropout(x) x = self.conv2(x) x = self.bn2(x) x = x+i x = self.act2(x) return x
时间: 2023-06-10 13:05:41 浏览: 123
回答:
以下是将该模型转化为cfg格式的代码:
[blockA]
type = "BlockA"
in_channels = ${in_channels}
out_channels = ${out_channels}
r = ${r}
s = ${s}
conv1_kernel_size = 3
conv1_stride = ${s}
conv1_padding = 1
conv2_kernel_size = 3
conv2_stride = 1
conv2_padding = 1
conv3_kernel_size = 1
conv3_stride = ${s}
act1_type = "PReLU"
act2_type = "PReLU"
bn1_type = "BatchNorm1d"
bn2_type = "BatchNorm1d"
bn3_type = "BatchNorm1d"
dropout_prob = ${r}
相关问题
下面的代码定义了VQTFNet模型,其中的model是由class tf_net定义的,使用pytorch_lightning修改下面的代码,要求VQTFNet模型在训练集上训练时tf_net的参数"t_timesteps"=303,在验证集上验证时"t_timesteps"=803 class VQTFNet(pl.LightningModule): def __init__(self, hparam): super(VQTFNet, self).__init__() self.hparam = hparam self.model = tf_net() class tf_net(nn.Module): def __init__( self, ): super(tf_net, self).__init__() cfg = { "t_timesteps": 303, "fc_dim": 128 }
下面是使用PyTorch Lightning修改后的代码,根据要求,在训练集上训练时tf_net的参数"t_timesteps"=303,在验证集上验证时"t_timesteps"=803:
```
import torch
import torch.nn as nn
import pytorch_lightning as pl
class VQTFNet(pl.LightningModule):
def __init__(self, hparam):
super(VQTFNet, self).__init__()
self.hparam = hparam
self.model = tf_net()
def forward(self, x):
return self.model(x)
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self.model(x)
loss = nn.MSELoss()(y_hat, y)
self.log('train_loss', loss, on_epoch=True)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_hat = self.model(x)
loss = nn.MSELoss()(y_hat, y)
self.log('val_loss', loss, on_epoch=True)
return loss
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.hparam['lr'])
return optimizer
class tf_net(nn.Module):
def __init__(
self,
t_timesteps=303,
fc_dim=128
):
super(tf_net, self).__init__()
self.t_timesteps = t_timesteps
self.fc_dim = fc_dim
self.fc = nn.Linear(self.t_timesteps, self.fc_dim)
def forward(self, x):
x = self.fc(x)
return x
```
在训练和验证时,我们可以使用 `Trainer` 类的 `fit` 和 `validate` 方法来训练和验证模型。为了在训练和验证过程中使用不同的超参数,我们可以在 `fit` 和 `validate` 方法中使用 `max_epochs` 和 `gpus` 参数来设置不同的值。例如,以下代码将模型在训练集上训练5个epoch,在验证集上验证1个epoch,同时在GPU上训练:
```
from pytorch_lightning import Trainer
model = VQTFNet({'lr': 1e-3})
trainer = Trainer(max_epochs=5, gpus=1)
trainer.fit(model, train_dataloader)
trainer.validate(model, val_dataloader)
```
class DoubleFastRCNNOutputLayers(nn.Module): def __init__( self, cfg, input_size, num_classes, cls_agnostic_bbox_reg, box_dim=4 ): super(DoubleFastRCNNOutputLayers, self).__init__() if not isinstance(input_size, int): input_size = np.prod(input_size) self.cls_score = nn.Linear(input_size, num_classes + 1) num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim) nn.init.normal_(self.cls_score.weight, std=0.01) nn.init.normal_(self.bbox_pred.weight, std=0.001) for l in [self.cls_score, self.bbox_pred]: nn.init.constant_(l.bias, 0) self._do_cls_dropout = cfg.MODEL.ROI_HEADS.CLS_DROPOUT self._dropout_ratio = cfg.MODEL.ROI_HEADS.DROPOUT_RATIO def forward(self, x_s, x_l): if x_s.dim() > 2: x_s = torch.flatten(x_s, start_dim=1) if x_l.dim() > 2: x_l = torch.flatten(x_l, start_dim=1) proposal_deltas = self.bbox_pred(x_l) if self._do_cls_dropout: x_s = F.dropout(x_s, self._dropout_ratio, training=self.training) scores = self.cls_score(x_s) return scores, proposal_deltas
这段代码是一个双输入的Fast R-CNN输出层的实现,其中包括一个分类得分层和一个边界框回归层。它接受两个输入x_s和x_l,分别代表短边和长边的特征。在前向传播时,它首先对输入进行扁平化处理,然后通过bbox_pred层获得边界框预测值,通过cls_score层获得分类得分。在进行分类得分的计算时,可以进行dropout操作来防止过拟合。最终,返回分类得分和边界框预测值。
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