discord.py 相关问题

我想将两个火炬模型加载到我的不和谐应用程序中。当我启动命令 python main.py 时，收到错误 AttributeError: Can't get attribute 'Generator' on 我想将两个火炬模型加载到我的不和谐应用程序中。当我启动命令 python main.py 时，收到错误 AttributeError: Can't get attribute 'Generator' on <module '__main__' from '/home/mle/Devel/gan-discord-bot/main.py'>。但是当我运行 bot.py 脚本时，加载工作正常。这是我使用的脚本主.py import bot if __name__=='__main__': bot.run_discord_bot() bot.py import discord import torch from torchvision.utils import save_image from model import * # the models have been trained on gpus netG = torch.load(netG_name, map_location=torch.device('cpu')) netD = torch.load(netD_name, map_location=torch.device('cpu')) netG.eval() netD.eval() async def generate_image(user_message, is_private): try: z = torch.randn(1, nz, 1, 1) torch_image = netG(z) save_image(torch_image, 'tmp.png') image = discord.File('tmp.png') await user_message.author.send(file=image) if is_private else await user_message.channel.send(file=image) except Exception as e: print(e) def run_discord_bot(): TOKEN = 'MyToken' intents = discord.Intents.default() intents.message_content = True client = discord.Client(intents=intents) @client.event async def on_ready(): print(f'{client.user} is now running!') @client.event async def on_message(message): if message.author == client.user: return username = str(message.author) user_message = str(message.content) channel = str(message.channel) if str.find(user_message, '! generate') != -1: await generate_image(user_message, is_private=False) client.run(TOKEN) 模型.py 我想要加载的两个网络是 GAN 的生成器和判别器。使用的参数在以下脚本中： from torch import nn # Spatial size of training images. All images will be resized to this # size using a transformer. image_size = 64 # Number of channels in the training images. For color images this is 3 nc = 3 # Size of z latent vector (i.e. size of generator input) nz = 100 # Size of feature maps in generator ngf = 128 # Size of feature maps in discriminator ndf = 128 # saved models netG_name = "netG128.pt" netD_name = "netD128.pt" class Generator(nn.Module): def __init__(self, ngpu): super(Generator, self).__init__() self.ngpu = ngpu self.main = nn.Sequential( # input is Z, going into a convolution nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False), nn.BatchNorm2d(ngf * 8), nn.ReLU(True), # state size. ``(ngf*8) x 4 x 4`` nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 4), nn.ReLU(True), # state size. ``(ngf*4) x 8 x 8`` nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 2), nn.ReLU(True), # state size. ``(ngf*2) x 16 x 16`` nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf), nn.ReLU(True), # state size. ``(ngf) x 32 x 32`` nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False), # state size. ``(nc) x 64 x 64`` nn.Tanh() ) def forward(self, input): return self.main(input) p_dropout = 0.5 bias_discriminator = False class Discriminator(nn.Module): def __init__(self, ngpu): super(Discriminator, self).__init__() self.ngpu = ngpu self.main = nn.Sequential( # input is ``(nc) x 64 x 64`` nn.Conv2d(nc, ndf, 4, 2, 1, bias=bias_discriminator), nn.LeakyReLU(0.2, inplace=True), # state size. ``(ndf) x 32 x 32`` nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=bias_discriminator), nn.BatchNorm2d(ndf * 2), nn.LeakyReLU(0.2, inplace=True), nn.Dropout(p=p_dropout), # state size. ``(ndf*2) x 16 x 16`` nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=bias_discriminator), nn.BatchNorm2d(ndf * 4), nn.LeakyReLU(0.2, inplace=True), nn.Dropout(p=p_dropout), # state size. ``(ndf*4) x 8 x 8`` nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=bias_discriminator), nn.BatchNorm2d(ndf * 8), nn.LeakyReLU(0.2, inplace=True), nn.Dropout(p=p_dropout), # state size. ``(ndf*8) x 4 x 4`` nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=bias_discriminator), nn.Sigmoid() ) def forward(self, input): return self.main(input) 减少点冻结输出 discord==2.3.2 discord.py==2.3.2 torch==2.1.0 torchvision==0.16.0 通过在generate_image_function中加载模型，它可以工作。但这不是最佳的这是更新的bot.py： import discord import torch import torchvision from torchvision.utils import save_image from torchvision.transforms import InterpolationMode import torchvision.transforms as transforms from model import * async def generate_image(message, user_message, is_private): try: netG = torch.load(netG_name, map_location=torch.device('cpu')) netD = torch.load(netD_name, map_location=torch.device('cpu')) netG.eval() netD.eval() z = torch.randn(1, nz, 1, 1) torch_image = netG(z) """ transformations = transforms.Compose([ torchvision.transforms.Resize( size=256, antialias = True, interpolation=InterpolationMode.BILINEAR ), transforms.ToPILImage() ]) torch_image = transformations(torch_image) torch_image.save("tmp.png") """ resize = torchvision.transforms.Resize( size=256, antialias = True, interpolation=InterpolationMode.BILINEAR ) torch_image = resize(torch_image) save_image(torch_image, 'tmp.png', normalize=True) image = discord.File('tmp.png') await message.author.send(file=image) if is_private else await message.channel.send(file=image) except Exception as e: print(e) def run_discord_bot(): TOKEN = 'MTE4MzQ0MTI1ODczNjI2MzIxOQ.G1Xv0k.igv_KI-1oHRKaY5631Mi8ANBczWwMxeqDFXecU' intents = discord.Intents.default() intents.message_content = True client = discord.Client(intents=intents) @client.event async def on_ready(): print(f'{client.user} is now running!') @client.event async def on_message(message): if message.author == client.user: return username = str(message.author) user_message = str(message.content) channel = str(message.channel) if str.find(user_message, '! generate') != -1: await generate_image(message, user_message, is_private=False) client.run(TOKEN)

pytorch discord.py torchvision

回答 1 投票 0

从 guild.fetch_members() 获取列表

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discord.py

回答 2 投票 0

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我正在用我的discord 机器人摆弄 OpenAI 的 Assistants API，总的来说，它相对简单。但是我不知道如何调用异步函数。当

python discord.py openai-api pycord

回答 1 投票 0

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python discord discord.py

回答 2 投票 0

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