class Expert(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim):super(Expert, self).__init__() self.layer1 = nn.Linear(input_dim, hidden_dim) self.layer2 = nn.Linear(hidden_dim, output_dim)def forward(self, x): x = ...
class GraphAttentionLayer(nn.Module): def __init__(self, in_features: int, out_features: int,n_heads: int, concat: bool = False, dropout: float = 0.4,leaky_relu_slope: float = 0.2):super(GraphAttentionLayer, self).__init__() self....
in_features:int,out_features:int,n_heads:int,concat:bool=False,dropout:float=0.4,leaky_relu_slope:float=0.2):super(GraphAttentionLayer,self).__init__()self.n_heads=n_heads # Numberofattention heads
在DNNs中,能够进行量化的是FP32权重(layer参数)和激活(layer输出)。量化权重可以减小模型尺寸,量化的激活通常会加快推理速度。例如,50层的ResNet网络有~ 2600万个权重参数,在前向传递中计算~ 1600万个激活。 动态量化(Post-Training Dynamic/Weight-only Quantization) 动态量化(PDQ)模型的权重是预先量化的。在推理...
input_dim=trained_experts[0].layer1.in_features self.gating=Gating(input_dim,num_experts)defforward(self,x):# Get the weights from the gating network weights=self.gating(x)# Calculate the expert outputs outputs=torch.stack([expert(x)forexpertinself.experts],dim=2)# Adjust the weights tenso...
(self, x): # Get the weights from the gating network weights = self.gating(x) # Calculate the expert outputs outputs = torch.stack( [expert(x) for expert in self.experts], dim=2) # Adjust the weights tensor shape to match the expert outputs weights = weights.unsqueeze(1).expand_as...
import utils model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT") dataset = PennFudanDataset('data/PennFudanPed', get_transform(train=True)) data_loader = torch.utils.data.DataLoader( dataset, batch_size=2, shuffle=True, num_workers=4, collate_fn=utils.collate_fn...
bn= model[1]#Get the number of weights of Batch Norm Layernum_bn_biases =bn.bias.numel()#Load the weightsbn_biases = torch.from_numpy(weights[ptr:ptr +num_bn_biases]) ptr+=num_bn_biases bn_weights= torch.from_numpy(weights[ptr: ptr +num_bn_biases]) ...
在第一章中,我们将首次接触 PyTorch,了解它是什么,解决了什么问题,以及它与其他深度学习框架的关系。第二章将带领我们进行一次旅行,让我们有机会玩玩已经在有趣任务上预训练的模型。第三章会更加严肃,教授 PyTorch 程序中使用的基本数据结构:张量。第四章将带领我们再次进行一次旅行,这次是跨越不同领域的数据如何表示...
之前记录过特征图的可视化:Pytorch实现特征图可视化,当时是利用IntermediateLayerGetter 实现的,但是有很大缺陷,只能获取到一级的子模块的特征图输出,无法获取内部二级子模块的输出。今天补充另一种Pytorch官方实现好的特征提取方式,非常好用! 特征图提取 前言一、Torch FX二、特征提取1.使用get_graph_node_names提取各个...