mean_pooling函数的示例。PyTorch是一个流行的深度学习库,它提供了丰富的神经网络构建块,包括各种池化层。 1. 导入所需的库 首先,我们需要导入PyTorch库。 python import torch import torch.nn as nn 2. 准备输入数据 为了演示池化操作,我们需要定义一个四维张量,其形状为(batch_size, channels, height, width...
F0_mean_pooling=False ): speaker_id = self.spk2id.__dict__.get(speaker) if not speaker_id and type(speaker) is int: if len(self.spk2id.__dict__) >= speaker: speaker_id = speaker sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0) c, f0, uv = self.get...
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from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded =...
import torch.nn as nn class MeanHead(nn.Module): def __init__(self, input_size, output_size=1): super(MeanHead, self).__init__() self.pooling = nn.AdaptiveAvgPool2d((1, 1)) #使用自适应平均池化将输入的特征平均为一个值 self.fc = nn.Linear(input_size, output_size) #全连接层...
import logging import torch import torch.nn as nn import torch.nn.functional as F logger = logging.getLogger(__name__) def mish(x): """Mish: A Self Regularized Non-Monotonic Neural Activation Function (https://arxiv.org/abs/1908.08681)""" return x * torch.tanh(F.softplus(x)) class ...
I've recently been optimising some of my code & have found that global average and max pooling seem to be very slow. Furthermore, there seems to be a great difference in speed between different implementations To Reproduce nb_cycles = 1000 x = torch.FloatTensor(np.random.rand(512, 128, ...
SE-modules are integrated with a pre-activation ResNet-50 which follows the setup in fb.resnet.torch: Repository. TensorFlow. Simple Tensorflow implementation of SENets using Cifar10: Repository. MatConvNet. All the released SENets are imported into MatConvNet: Repository. MXNet. SE-modules ...
parser.add_argument('-fmp', '--f0_mean_pooling', type=bool, default=False, help='是否对F0使用均值滤波器(池化),对部分哑音可能有改善。注意,启动该选项会导致推理速度下降,默认关闭') parser.add_argument('-fmp', '--f0_mean_pooling', type=bool, default=False, help='是否对F0使用均值滤波器...
sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0) c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter) c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker, f0_filter,F0_mean_pooling) if "half" in...