我目前正在使用PyTorch来训练神经网络。我使用的数据集是一个具有大量0的二进制分类数据集。我决定尝试使用PyTorch的交叉熵损失的weight参数.通过sklearn.utils.class_weight.compute_class_weight计算权重,得到[0.58479532, 3.44827586]的权重值。当我将这个class_weights张量添加到损失的weight参数中时(即, ...
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.per_tensor_symmetric) ...
# --- 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_...
loss_f_none = nn.CrossEntropyLoss(weight=None, reduction='none') loss_f_sum = nn.CrossEntropyLoss(weight=None, reduction='sum') loss_f_mean = nn.CrossEntropyLoss(weight=None, reduction='mean') # forward loss_none = loss_f_none(inputs, target) loss_sum = loss_f_sum(inputs, targe...
class_weights = torch.tensor([1 / i for i in df_agg_classes["proportion"].values], dtype=torch.float) model = MLP() criterion = torch.nn.CrossEntropyLoss(weight=class_weights) optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) ...
至此,例子中Linear层的weight的ShardedTensor就创建完成了。 Sharded_Linear执行逻辑 torch.distributed.shard是遵循SPMD模式来执行并行的,也就是从物理视角来看,每一张卡都会处理不同的数据。在后面的通信实现上,这个是需要注意的地方。 1. 首先要让torch函数可以使用ShardedTensor ...
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() ...
batch_size = 64 Din = 31 Dout = 33 weight = torch.randn(Dout, Din) print(f"weight shape = {weight.shape}") bias = torch.randn(Dout) x = torch.randn(batch_size, Din) compute_batch_jacobian = vmap(jacrev(predict, argnums=2), in_dims=(None, None, 0)) batch_jacobian0 = compu...
多次mask的累加通过PruningContainer的compute_mask比如想进一步结构化剪枝module.weight,对卷积的输出通道进行基于l2-nom剪枝,即第0维,对于conv1是个数为6,通过设置ln_structrued函数的参数n=2和dim = 0 prune.ln_structured(module, name="weight", amount=0.5, n=2, dim=0) ...
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...