AI代码解释 default_dynamic_qconfig=QConfigDynamic(activation=default_dynamic_quant_observer,weight=default_weight_observer)default_dynamic_quant_observer=PlaceholderObserver.with_args(dtype=torch.float,compute_dtype=torch.quint8)default_weight_observer=MinMaxObserver.with_args(dtype=torch.qint8,qscheme=torch...
AI代码解释 classLLTM(torch.nn.Module):def__init__(self,input_features,state_size):super(LLTM,self).__init__()self.input_features=input_features self.state_size=state_size #3*state_sizeforinput gate,output gate and candidate cell gate.# input_features+state_size because we will multiplyw...
# --- compute by handidx = 0input_1 = inputs.detach().numpy()[idx] # [1, 2]target_1 = target.numpy()[idx] # [0]# 第一项x_class = input_1[target_1]# 第二项sigma_exp_x = np.sum(list(map(np.exp, input_1)))log_sigma_exp_x = np.log(sigma_exp_x)# 输出lossloss_...
# convolutional neural network (2 convolutional layers)class ConvNet(nn.Module):def __init__(self, num_classes=10):super(ConvNet, self).__init__()self.layer1 = nn.Sequential(nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),nn.BatchNorm2d(16),...
n_samples]#alpha=1.0-torch.exp(-nn.functional.relu(raw[...,3]+noise)*dists)# Compute weight for RGB of each sample along each ray. [n_rays, n_samples]# The higher the alpha, the lower subsequent weights are driven.weights=alpha*cumprod_exclusive(1.-alpha+1e-10)# Compute weighted ...
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。在推理过程中,激活被实时量化("动态")。这是所有方法中最简单的一种,它在torch. quantized.quantize_dynamic中只有一行API调用。但是目前只支持线性层和递归(LSTM, GRU, RNN)层的动态量化。 优点: 可产生更高的精度...
1报错内容:RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same版本: 1.0.0 with python 3.6.1原因:有部分变量未加载进入显存,注意,在如下情况 class model(nn.Module): def __init__(self): ...
fromtorch.optimimportAdam# Define the loss function with Classification Cross-Entropy loss and an optimizer with Adam optimizerloss_fn = nn.CrossEntropyLoss() optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001) 使用训练数据训练模型。
classMaskedConv2d(nn.Conv2d):def__init__(self, mask_type, *args, **kwargs):super(MaskedConv2d, self).__init__(*args, **kwargs)assertmask_typein('A','B') self.register_buffer('mask', self.weight.data.clone()) _, _, kH, kW = self.weight.size() ...
Usage/Cap|Memory-Usage|GPU-UtilComputeM.|test_1|||MIG M.|test_1||===+===+===|test_1||0Tesla T4On|00000000:00:1E.0Off|0|test_1||N/A23C P89W/70W|0MiB/15109MiB|0%Default|test_1|||N/A|test_1|+---+---+---+test_1|...