def __init__(self, recursions=1, stride=1, kernel_size=5, use_perceptual_loss=True, wgan=False, w_col=1, w_tex=0.001, w_per=0.1, gaussian=False, lpips_rot_flip=False, **kwargs): super(GeneratorLoss, self).__init__() self.pixel_loss = nn.L1Loss() self.color_filter = Filt...
smooth_l1_loss(reg_pred, reg_target, size_average=False, reduce=False) rloss = torch.div(torch.sum(rloss[np.where(class_target == 1)]), 16) #debug vis pos anchor loss = closs + rloss return closs, rloss, loss, reg_pred, reg_target, pos_index, neg_index ...
size_average(bool)- 当 reduce=True 时有效。为 True 时,返回的 loss 为平均值;为 False 时,返回的各样本的 loss 之和。 reduce(bool)- 返回值是否为标量,默认为 True。 ignore_index(int)- 忽略某一类别,不计算其 loss,其 loss 会为 0,并且,在采用 size_average 时,不会计算那一类的 loss,除的时...
class FunctionalSmoothL1LossMatcher(BaseMatcher): def generate_code(self, kwargs): if "size_average" in kwargs: size_average = kwargs.pop("size_average") if "True" in size_average: size_average = True elif "False" in size_average: size_average = False else: size_average = None else...
Default: False adaptive_avg_pool1d¶ torch.nn.functional.adaptive_avg_pool1d(input, output_size)→ Tensor¶ Applies a 1D adaptive average pooling over an input signal composed of several input planes. See AdaptiveAvgPool1d for details and output shape. Parameters output_size –the target ...
CLASS torch.nn.MSELoss(size_average=None, reduce=None, reduction=‘mean’) MSE : Means Square Error Reference: https://pytorch.org/docs/stable/nn.html#torch.nn.MSELoss... 查看原文 pytorch loss function 总结 。torch.nn.L1Loss(reduction=‘mean’) 参数:reduction-三个值,none: 不使用约简;me...
torch.nn.functional.avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)source对由几个输入平面组成的输入进行1D平均池化。 有关详细信息和输出形状,参考AvgPool1d参数:input – 输入的张量 (minibatch x in_channels x iW) kernel_size – 池化区域的大小,...
Default: ``False`` count_include_pad: when True, will include the zero-padding in the averaging calculation. Default: ``True`` divisor_override: if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: None """, ) avg_pool3d = _add...
head_bias (bool, optional)– If True, adds a bias term to the ‘head’ of the adaptive softmax. Default: False output is a Tensor of size N containing computed target log probabilities for each example loss is a Scalar representing the computed negative log likelihood loss input: (N...
torch.nn.functional.avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) → Tensor Applies 2D average-pooling operation in kH×kWkH \times kWkH×kW regions by step size sH×sWsH \times sWsH×sW steps. The number of outpu...