python callの役割は、オブジェクトをメソッドとして使用できるキー分析nn.Moduleソースコードです.

4169 ワード

コードの例

import torch.nn as nn

class LSTMClassifier(nn.Module):
    """
    This is the simple RNN model we will be using to perform Sentiment Analysis.
    """

    def __init__(self, embedding_dim, hidden_dim, vocab_size):
        """
        Initialize the model by settingg up the various layers.
        """
        super(LSTMClassifier, self).__init__()

        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim)
        self.dense = nn.Linear(in_features=hidden_dim, out_features=1)
        self.sig = nn.Sigmoid()
        
        self.word_dict = None

    def forward(self, x):
        """
        Perform a forward pass of our model on some input.
        """
        x = x.t()
        lengths = x[0,:]
        reviews = x[1:,:]
        embeds = self.embedding(reviews)
        lstm_out, _ = self.lstm(embeds)
        out = self.dense(lstm_out)
        out = out[lengths - 1, range(len(lengths))]
        return self.sig(out.squeeze())

def train(model, train_loader, epochs, optimizer, loss_fn, device): for epoch in range(1, epochs + 1): model.train() total_loss = 0 for batch in train_loader: batch_X, batch_y = batch print("batch_X.shape=",batch_X.shape) print("batch_y.shape=",batch_y.shape) batch_X = batch_X.to(device) batch_y = batch_y.to(device) # TODO: Complete this train method to train the model provided. optimizer.zero_grad()
# get predictions from model y_pred = model(batch_X)
# perform backprop loss = criterion(y_pred, batch_y) loss.backward() optimizer.step() total_loss += loss.data.item() print("Epoch: {}, BCELoss: {}".format(epoch, total_loss/len(train_loader)))

このクラスのLSTMClassifierオブジェクトmodelを使用して実際に呼び出されたのは、nn.Module()の__のためforward()です.call__メソッドでforwardメソッドが呼び出されました
#サードパーティがmodelコードを呼び出すシーンは次のとおりです.
import torch.optim as optim
from train.model import LSTMClassifier
device = torch.device("cuda"if torch.cuda.is_available() else "cpu")model = LSTMClassifier(32, 100, 5000).to(device) optimizer = optim.Adam(model.parameters(),lr=0.001) loss_fn = torch.nn.BCELoss()
train(model, train_sample_dl, 5, optimizer, loss_fn, device)

#上のクラスの親nn.Moduleの_call__メソッドではforwardメソッドが呼び出され、次のようになります.
def __call__(self, *input, **kwargs): for hook in self._forward_pre_hooks.values(): result = hook(self, input) if result is not None: if not isinstance(result, tuple): result = (result,) input = result if torch._C._get_tracing_state(): result = self._slow_forward(*input, **kwargs) else: result = self.forward(*input, **kwargs) for hook in self._forward_hooks.values(): hook_result = hook(self, input, result) if hook_result is not None: result = hook_result if len(self._backward_hooks) > 0: var = result while not isinstance(var, torch.Tensor): if isinstance(var, dict): var = next((v for v in var.values() if isinstance(v, torch.Tensor))) else: var = var[0] grad_fn = var.grad_fn if grad_fn is not None: for hook in self._backward_hooks.values(): wrapper = functools.partial(hook, self) functools.update_wrapper(wrapper, hook) grad_fn.register_hook(wrapper) return result

この現象を理解する鍵
__call__
_forward_pre_hooks
リファレンス
https://www.cnblogs.com/SBJBA/p/11355412.html
nn.Moduleメソッド逐次分析
https://blog.csdn.net/lishuiwang/article/details/104505675/
https://www.jb51.net/article/184033.htm
nn.Moduleソース
https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/module.py

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python __call__の役割は、オブジェクトをメソッドとして使用できるキー分析nn.Moduleソースコードです.

python callの役割は、オブジェクトをメソッドとして使用できるキー分析nn.Moduleソースコードです.