pycharm安装pytorch报错 提示系列问题 torch 包找不到因为pip版本低，结果升级了pip从19.3到20.2 4又提示高版不支持torch安装

pycharm安装pytorch报错 提示系列问题  torch 包找不到因为pip版本低，结果升级了pip从19.3到20.2 4又提示高版不支持torch安装  

DEPRECATION: The -b/–build/–build-dir/–build-directory option is deprecated. pip 20.3 will remove support for this functionality. A possible replacement is use the TMPDIR/TEMP/TMP environment variable, possibly combined with –no-clean. You can find discussion regarding this at https://github.com/pypa/pip/issues/8333.

如上报错来自参考

https://www.jb51.net/article/194349.htm

参考有同学遇到类似的问题的解决方案

https://blog.csdn.net/weixin_/article/details/

ERROR: Could not find a version that satisfies the requirement torch==1.6.0 (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2)

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple

三  最终解决方案

是参考的如下的方式解决pycharm 导入pytorch问题的，真是周折

主要参考的如下连接的，将pycharm的system interceptor切换为anaconda的python.exe的环境变量

https://www.jb51.net/article/181954.htm

同时从上面的博文中也可以得出切换pycharm的环境变量的方法，可以使在pycharm中进行如下设置

之所以采用这种方案，是因为代码在本地启动的anaconda   jupyter notebook中是可以调用pytorch的，版本是1.1.0，但是在pycharm中就是安装不上，无论是1.1.0 还是0.41  1.6的版本都安装不上，分析可能是环境变量问题，

萌生了使用anaconda的环境变量 （主要是编译器）的想法，看到如上博文，参考更改了项目的解析器，代码中引用的torch包就找到了，不报找不到对应包的问题了

实验代码为NLP 的Seq2seq代码

# code by Tae Hwan Jung(Jeff Jung) @graykode, modify by wmathor import torch import numpy as np import torch.nn as nn import torch.utils.data as Data device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # S: Symbol that shows starting of decoding input # E: Symbol that shows starting of decoding output # ?: Symbol that will fill in blank sequence if current batch data size is short than n_step letter = [c for c in 'SE?abcdefghijklmnopqrstuvwxyz'] letter2idx = {n: i for i, n in enumerate(letter)} seq_data = [['man', 'women'], ['black', 'white'], ['king', 'queen'], ['girl', 'boy'], ['up', 'down'], ['high', 'low']] # Seq2Seq Parameter n_step = max([max(len(i), len(j)) for i, j in seq_data]) # max_len(=5) n_hidden = 128 n_class = len(letter2idx) # classfication problem batch_size = 3 #3 def make_data(seq_data): enc_input_all, dec_input_all, dec_output_all = [], [], [] for seq in seq_data: for i in range(2): seq[i] = seq[i] + '?' * (n_step - len(seq[i])) # 'man??', 'women' enc_input = [letter2idx[n] for n in (seq[0] + 'E')] # ['m', 'a', 'n', '?', '?', 'E'] dec_input = [letter2idx[n] for n in ('S' + seq[1])] # ['S', 'w', 'o', 'm', 'e', 'n'] dec_output = [letter2idx[n] for n in (seq[1] + 'E')] # ['w', 'o', 'm', 'e', 'n', 'E'] enc_input_all.append(np.eye(n_class)[enc_input]) dec_input_all.append(np.eye(n_class)[dec_input]) dec_output_all.append(dec_output) # not one-hot # make tensor return torch.Tensor(enc_input_all), torch.Tensor(dec_input_all), torch.LongTensor(dec_output_all) ''' enc_input_all: [6, n_step+1 (because of 'E'), n_class] dec_input_all: [6, n_step+1 (because of 'S'), n_class] dec_output_all: [6, n_step+1 (because of 'E')] ''' enc_input_all, dec_input_all, dec_output_all = make_data(seq_data) #4 class TranslateDataSet(Data.Dataset): def __init__(self, enc_input_all, dec_input_all, dec_output_all): self.enc_input_all = enc_input_all self.dec_input_all = dec_input_all self.dec_output_all = dec_output_all def __len__(self): # return dataset size return len(self.enc_input_all) def __getitem__(self, idx): return self.enc_input_all[idx], self.dec_input_all[idx], self.dec_output_all[idx] loader = Data.DataLoader(TranslateDataSet(enc_input_all, dec_input_all, dec_output_all), batch_size, True) #5 def make_data(seq_data): enc_input_all, dec_input_all, dec_output_all = [], [], [] for seq in seq_data: for i in range(2): seq[i] = seq[i] + '?' * (n_step - len(seq[i])) # 'man??', 'women' enc_input = [letter2idx[n] for n in (seq[0] + 'E')] # ['m', 'a', 'n', '?', '?', 'E'] dec_input = [letter2idx[n] for n in ('S' + seq[1])] # ['S', 'w', 'o', 'm', 'e', 'n'] dec_output = [letter2idx[n] for n in (seq[1] + 'E')] # ['w', 'o', 'm', 'e', 'n', 'E'] enc_input_all.append(np.eye(n_class)[enc_input]) dec_input_all.append(np.eye(n_class)[dec_input]) dec_output_all.append(dec_output) # not one-hot # make tensor return torch.Tensor(enc_input_all), torch.Tensor(dec_input_all), torch.LongTensor(dec_output_all) ''' enc_input_all: [6, n_step+1 (because of 'E'), n_class] dec_input_all: [6, n_step+1 (because of 'S'), n_class] dec_output_all: [6, n_step+1 (because of 'E')] ''' enc_input_all, dec_input_all, dec_output_all = make_data(seq_data) #6 class TranslateDataSet(Data.Dataset): def __init__(self, enc_input_all, dec_input_all, dec_output_all): self.enc_input_all = enc_input_all self.dec_input_all = dec_input_all self.dec_output_all = dec_output_all def __len__(self): # return dataset size return len(self.enc_input_all) def __getitem__(self, idx): return self.enc_input_all[idx], self.dec_input_all[idx], self.dec_output_all[idx] loader = Data.DataLoader(TranslateDataSet(enc_input_all, dec_input_all, dec_output_all), batch_size, True) #7 # Model class Seq2Seq(nn.Module): def __init__(self): super(Seq2Seq, self).__init__() self.encoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) # encoder self.decoder = nn.RNN(input_size=n_class, hidden_size=n_hidden, dropout=0.5) # decoder self.fc = nn.Linear(n_hidden, n_class) def forward(self, enc_input, enc_hidden, dec_input): # enc_input(=input_batch): [batch_size, n_step+1, n_class] # dec_inpu(=output_batch): [batch_size, n_step+1, n_class] enc_input = enc_input.transpose(0, 1) # enc_input: [n_step+1, batch_size, n_class] dec_input = dec_input.transpose(0, 1) # dec_input: [n_step+1, batch_size, n_class] # h_t : [num_layers(=1) * num_directions(=1), batch_size, n_hidden] _, h_t = self.encoder(enc_input, enc_hidden) # outputs : [n_step+1, batch_size, num_directions(=1) * n_hidden(=128)] outputs, _ = self.decoder(dec_input, h_t) model = self.fc(outputs) # model : [n_step+1, batch_size, n_class] return model model = Seq2Seq().to(device) criterion = nn.CrossEntropyLoss().to(device) optimizer = torch.optim.Adam(model.parameters(), lr=0.001) #8 for epoch in range(5000): for enc_input_batch, dec_input_batch, dec_output_batch in loader: # make hidden shape [num_layers * num_directions, batch_size, n_hidden] h_0 = torch.zeros(1, batch_size, n_hidden).to(device) (enc_input_batch, dec_intput_batch, dec_output_batch) = ( enc_input_batch.to(device), dec_input_batch.to(device), dec_output_batch.to(device)) # enc_input_batch : [batch_size, n_step+1, n_class] # dec_intput_batch : [batch_size, n_step+1, n_class] # dec_output_batch : [batch_size, n_step+1], not one-hot pred = model(enc_input_batch, h_0, dec_intput_batch) # pred : [n_step+1, batch_size, n_class] pred = pred.transpose(0, 1) # [batch_size, n_step+1(=6), n_class] loss = 0 for i in range(len(dec_output_batch)): # pred[i] : [n_step+1, n_class] # dec_output_batch[i] : [n_step+1] loss += criterion(pred[i], dec_output_batch[i]) if (epoch + 1) % 1000 == 0: print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss)) optimizer.zero_grad() loss.backward() optimizer.step() #9 # Test def translate(word): enc_input, dec_input, _ = make_data([[word, '?' * n_step]]) enc_input, dec_input = enc_input.to(device), dec_input.to(device) # make hidden shape [num_layers * num_directions, batch_size, n_hidden] print("enc_input=",enc_input, "dec_input=",dec_input,"n_hidden=",n_hidden) hidden = torch.zeros(1, 1, n_hidden).to(device) output = model(enc_input, hidden, dec_input) # output : [n_step+1, batch_size, n_class] print("output.data=",output.data) predict = output.data.max(2, keepdim=True)[1] # select n_class dimension print("predict=",predict) decoded = [letter[i] for i in predict] translated = ''.join(decoded[:decoded.index('E')]) return translated.replace('?', '') print('test') print('man ->', translate('man')) print('mans ->', translate('mans')) print('king ->', translate('king')) print('black ->', translate('black')) print('up ->', translate('up')) #10 #执行结果

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代码来自

https://github.com/wmathor/nlp-tutorial/blob/master/4-1.Seq2Seq/Seq2Seq_Torch.ipynb