Source code for d2l.torch
Open the notebook in Colab
Open the notebook in Colab
Open the notebook in Colab
Open the notebook in Colab

DATA_HUB = dict()

import numpy as np
import torch
import torchvision
from PIL import Image
from torch import nn
from torch.nn import functional as F
from torch.utils import data
from torchvision import transforms

nn_Module = nn.Module

#################   WARNING   ################
# The below part is generated automatically through:
#    d2lbook build lib
# Don't edit it directly

import collections
import hashlib
import inspect
import math
import os
import random
import re
import shutil
import sys
import tarfile
import time
import zipfile
from collections import defaultdict
import gym
import pandas as pd
import requests
from IPython import display
from matplotlib import pyplot as plt
from matplotlib_inline import backend_inline
from scipy.spatial import distance_matrix

d2l = sys.modules[__name__]

import numpy as np
import torch
import torchvision
from PIL import Image
from torch import nn
from torch.nn import functional as F
from torchvision import transforms

def use_svg_display():
    """Use the svg format to display a plot in Jupyter.

    Defined in :numref:`sec_calculus`"""

def set_figsize(figsize=(3.5, 2.5)):
    """Set the figure size for matplotlib.

    Defined in :numref:`sec_calculus`"""
    d2l.plt.rcParams['figure.figsize'] = figsize

def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
    """Set the axes for matplotlib.

    Defined in :numref:`sec_calculus`"""
    axes.set_xlabel(xlabel), axes.set_ylabel(ylabel)
    axes.set_xscale(xscale), axes.set_yscale(yscale)
    axes.set_xlim(xlim),     axes.set_ylim(ylim)
    if legend:

def plot(X, Y=None, xlabel=None, ylabel=None, legend=[], xlim=None,
         ylim=None, xscale='linear', yscale='linear',
         fmts=('-', 'm--', 'g-.', 'r:'), figsize=(3.5, 2.5), axes=None):
    """Plot data points.

    Defined in :numref:`sec_calculus`"""

    def has_one_axis(X):  # True if `X` (tensor or list) has 1 axis
        return (hasattr(X, "ndim") and X.ndim == 1 or isinstance(X, list)
                and not hasattr(X[0], "__len__"))

    if has_one_axis(X): X = [X]
    if Y is None:
        X, Y = [[]] * len(X), X
    elif has_one_axis(Y):
        Y = [Y]
    if len(X) != len(Y):
        X = X * len(Y)

    if axes is None: axes = d2l.plt.gca()
    for x, y, fmt in zip(X, Y, fmts):
        axes.plot(x,y,fmt) if len(x) else axes.plot(y,fmt)
    set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend)

[docs]def add_to_class(Class): """Defined in :numref:`sec_oo-design`""" def wrapper(obj): setattr(Class, obj.__name__, obj) return wrapper
[docs]class HyperParameters: def save_hyperparameters(self, ignore=[]): """Defined in :numref:`sec_oo-design`""" raise NotImplemented
[docs] def save_hyperparameters(self, ignore=[]): """Save function arguments into class attributes. Defined in :numref:`sec_utils`""" frame = inspect.currentframe().f_back _, _, _, local_vars = inspect.getargvalues(frame) self.hparams = {k:v for k, v in local_vars.items() if k not in set(ignore+['self']) and not k.startswith('_')} for k, v in self.hparams.items(): setattr(self, k, v)
[docs]class ProgressBoard(d2l.HyperParameters): """Plot data points in animation. Defined in :numref:`sec_oo-design`""" def __init__(self, xlabel=None, ylabel=None, xlim=None, ylim=None, xscale='linear', yscale='linear', ls=['-', '--', '-.', ':'], colors=['C0', 'C1', 'C2', 'C3'], fig=None, axes=None, figsize=(3.5, 2.5), display=True): self.save_hyperparameters() def draw(self, x, y, label, every_n=1): raise NotImplemented
[docs] def draw(self, x, y, label, every_n=1): """Defined in :numref:`sec_utils`""" Point = collections.namedtuple('Point', ['x', 'y']) if not hasattr(self, 'raw_points'): self.raw_points = collections.OrderedDict() = collections.OrderedDict() if label not in self.raw_points: self.raw_points[label] = [][label] = [] points = self.raw_points[label] line =[label] points.append(Point(x, y)) if len(points) != every_n: return mean = lambda x: sum(x) / len(x) line.append(Point(mean([p.x for p in points]), mean([p.y for p in points]))) points.clear() if not self.display: return d2l.use_svg_display() if self.fig is None: self.fig = d2l.plt.figure(figsize=self.figsize) plt_lines, labels = [], [] for (k, v), ls, color in zip(,, self.colors): plt_lines.append(d2l.plt.plot([p.x for p in v], [p.y for p in v], linestyle=ls, color=color)[0]) labels.append(k) axes = self.axes if self.axes else d2l.plt.gca() if self.xlim: axes.set_xlim(self.xlim) if self.ylim: axes.set_ylim(self.ylim) if not self.xlabel: self.xlabel = self.x axes.set_xlabel(self.xlabel) axes.set_ylabel(self.ylabel) axes.set_xscale(self.xscale) axes.set_yscale(self.yscale) axes.legend(plt_lines, labels) display.display(self.fig) display.clear_output(wait=True)
[docs]class Module(d2l.nn_Module, d2l.HyperParameters): """Defined in :numref:`sec_oo-design`""" def __init__(self, plot_train_per_epoch=2, plot_valid_per_epoch=1): super().__init__() self.save_hyperparameters() self.board = ProgressBoard()
[docs] def loss(self, y_hat, y): raise NotImplementedError
[docs] def forward(self, X): assert hasattr(self, 'net'), 'Neural network is defined' return
[docs] def plot(self, key, value, train): """Plot a point in animation.""" assert hasattr(self, 'trainer'), 'Trainer is not inited' self.board.xlabel = 'epoch' if train: x = self.trainer.train_batch_idx / \ self.trainer.num_train_batches n = self.trainer.num_train_batches / \ self.plot_train_per_epoch else: x = self.trainer.epoch + 1 n = self.trainer.num_val_batches / \ self.plot_valid_per_epoch self.board.draw(x, d2l.numpy(, d2l.cpu())), ('train_' if train else 'val_') + key, every_n=int(n))
[docs] def training_step(self, batch): l = self.loss(self(*batch[:-1]), batch[-1]) self.plot('loss', l, train=True) return l
[docs] def validation_step(self, batch): l = self.loss(self(*batch[:-1]), batch[-1]) self.plot('loss', l, train=False)
def configure_optimizers(self): raise NotImplementedError
[docs] def configure_optimizers(self): """Defined in :numref:`sec_classification`""" return torch.optim.SGD(self.parameters(),
[docs] def apply_init(self, inputs, init=None): """Defined in :numref:`sec_lazy_init`""" self.forward(*inputs) if init is not None:
[docs]class DataModule(d2l.HyperParameters): """Defined in :numref:`subsec_oo-design-models`""" def __init__(self, root='../data', num_workers=4): self.save_hyperparameters()
[docs] def get_dataloader(self, train): raise NotImplementedError
[docs] def train_dataloader(self): return self.get_dataloader(train=True)
[docs] def val_dataloader(self): return self.get_dataloader(train=False)
[docs] def get_tensorloader(self, tensors, train, indices=slice(0, None)): """Defined in :numref:`sec_synthetic-regression-data`""" tensors = tuple(a[indices] for a in tensors) dataset =*tensors) return, self.batch_size, shuffle=train)
[docs]class Trainer(d2l.HyperParameters): """Defined in :numref:`subsec_oo-design-models`""" def __init__(self, max_epochs, num_gpus=0, gradient_clip_val=0): self.save_hyperparameters() assert num_gpus == 0, 'No GPU support yet'
[docs] def prepare_data(self, data): self.train_dataloader = data.train_dataloader() self.val_dataloader = data.val_dataloader() self.num_train_batches = len(self.train_dataloader) self.num_val_batches = (len(self.val_dataloader) if self.val_dataloader is not None else 0)
def prepare_model(self, model): model.trainer = self model.board.xlim = [0, self.max_epochs] self.model = model
[docs] def fit(self, model, data): self.prepare_data(data) self.prepare_model(model) self.optim = model.configure_optimizers() self.epoch = 0 self.train_batch_idx = 0 self.val_batch_idx = 0 for self.epoch in range(self.max_epochs): self.fit_epoch()
def fit_epoch(self): raise NotImplementedError def prepare_batch(self, batch): """Defined in :numref:`sec_linear_scratch`""" return batch
[docs] def fit_epoch(self): """Defined in :numref:`sec_linear_scratch`""" self.model.train() for batch in self.train_dataloader: loss = self.model.training_step(self.prepare_batch(batch)) self.optim.zero_grad() with torch.no_grad(): loss.backward() if self.gradient_clip_val > 0: # To be discussed later self.clip_gradients(self.gradient_clip_val, self.model) self.optim.step() self.train_batch_idx += 1 if self.val_dataloader is None: return self.model.eval() for batch in self.val_dataloader: with torch.no_grad(): self.model.validation_step(self.prepare_batch(batch)) self.val_batch_idx += 1
def __init__(self, max_epochs, num_gpus=0, gradient_clip_val=0): """Defined in :numref:`sec_use_gpu`""" self.save_hyperparameters() self.gpus = [d2l.gpu(i) for i in range(min(num_gpus, d2l.num_gpus()))]
[docs] def prepare_batch(self, batch): """Defined in :numref:`sec_use_gpu`""" if self.gpus: batch = [, self.gpus[0]) for a in batch] return batch
[docs] def prepare_model(self, model): """Defined in :numref:`sec_use_gpu`""" model.trainer = self model.board.xlim = [0, self.max_epochs] if self.gpus:[0]) self.model = model
[docs] def clip_gradients(self, grad_clip_val, model): """Defined in :numref:`sec_rnn-scratch`""" params = [p for p in model.parameters() if p.requires_grad] norm = torch.sqrt(sum(torch.sum((p.grad ** 2)) for p in params)) if norm > grad_clip_val: for param in params: param.grad[:] *= grad_clip_val / norm
[docs]class SyntheticRegressionData(d2l.DataModule): """Defined in :numref:`sec_synthetic-regression-data`""" def __init__(self, w, b, noise=0.01, num_train=1000, num_val=1000, batch_size=32): super().__init__() self.save_hyperparameters() n = num_train + num_val self.X = d2l.randn(n, len(w)) noise = d2l.randn(n, 1) * noise self.y = d2l.matmul(self.X, d2l.reshape(w, (-1, 1))) + b + noise
[docs] def get_dataloader(self, train): """Defined in :numref:`sec_synthetic-regression-data`""" i = slice(0, self.num_train) if train else slice(self.num_train, None) return self.get_tensorloader((self.X, self.y), train, i)
[docs]class LinearRegressionScratch(d2l.Module): """Defined in :numref:`sec_linear_scratch`""" def __init__(self, num_inputs, lr, sigma=0.01): super().__init__() self.save_hyperparameters() self.w = d2l.normal(0, sigma, (num_inputs, 1), requires_grad=True) self.b = d2l.zeros(1, requires_grad=True)
[docs] def forward(self, X): """The linear regression model. Defined in :numref:`sec_linear_scratch`""" return d2l.matmul(X, self.w) + self.b
[docs] def loss(self, y_hat, y): """Defined in :numref:`sec_linear_scratch`""" l = (y_hat - y) ** 2 / 2 return d2l.reduce_mean(l)
[docs] def configure_optimizers(self): """Defined in :numref:`sec_linear_scratch`""" return SGD([self.w, self.b],
[docs]class SGD(d2l.HyperParameters): """Defined in :numref:`sec_linear_scratch`""" def __init__(self, params, lr): """Minibatch stochastic gradient descent.""" self.save_hyperparameters()
[docs] def step(self): for param in self.params: param -= * param.grad
[docs] def zero_grad(self): for param in self.params: if param.grad is not None: param.grad.zero_()
[docs]class LinearRegression(d2l.Module): """Defined in :numref:`sec_linear_concise`""" def __init__(self, lr): super().__init__() self.save_hyperparameters() = nn.LazyLinear(1), 0.01)
[docs] def forward(self, X): """The linear regression model. Defined in :numref:`sec_linear_concise`""" return
[docs] def loss(self, y_hat, y): """Defined in :numref:`sec_linear_concise`""" fn = nn.MSELoss() return fn(y_hat, y)
[docs] def configure_optimizers(self): """Defined in :numref:`sec_linear_concise`""" return torch.optim.SGD(self.parameters(),
[docs] def get_w_b(self): """Defined in :numref:`sec_linear_concise`""" return (,
[docs]class FashionMNIST(d2l.DataModule): """Defined in :numref:`sec_fashion_mnist`""" def __init__(self, batch_size=64, resize=(28, 28)): super().__init__() self.save_hyperparameters() trans = transforms.Compose([transforms.Resize(resize), transforms.ToTensor()]) self.train = torchvision.datasets.FashionMNIST( root=self.root, train=True, transform=trans, download=True) self.val = torchvision.datasets.FashionMNIST( root=self.root, train=False, transform=trans, download=True)
[docs] def text_labels(self, indices): """Return text labels. Defined in :numref:`sec_fashion_mnist`""" labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot'] return [labels[int(i)] for i in indices]
[docs] def get_dataloader(self, train): """Defined in :numref:`sec_fashion_mnist`""" data = self.train if train else self.val return, self.batch_size, shuffle=train, num_workers=self.num_workers)
[docs] def visualize(self, batch, nrows=1, ncols=8, labels=[]): """Defined in :numref:`sec_fashion_mnist`""" X, y = batch if not labels: labels = self.text_labels(y) d2l.show_images(X.squeeze(1), nrows, ncols, titles=labels)
def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5): """Plot a list of images. Defined in :numref:`sec_fashion_mnist`""" raise NotImplementedError
[docs]class Classifier(d2l.Module): """Defined in :numref:`sec_classification`"""
[docs] def validation_step(self, batch): Y_hat = self(*batch[:-1]) self.plot('loss', self.loss(Y_hat, batch[-1]), train=False) self.plot('acc', self.accuracy(Y_hat, batch[-1]), train=False)
[docs] def accuracy(self, Y_hat, Y, averaged=True): """Compute the number of correct predictions. Defined in :numref:`sec_classification`""" Y_hat = d2l.reshape(Y_hat, (-1, Y_hat.shape[-1])) preds = d2l.astype(d2l.argmax(Y_hat, axis=1), Y.dtype) compare = d2l.astype(preds == d2l.reshape(Y, -1), d2l.float32) return d2l.reduce_mean(compare) if averaged else compare
[docs] def loss(self, Y_hat, Y, averaged=True): """Defined in :numref:`sec_softmax_concise`""" Y_hat = d2l.reshape(Y_hat, (-1, Y_hat.shape[-1])) Y = d2l.reshape(Y, (-1,)) return F.cross_entropy( Y_hat, Y, reduction='mean' if averaged else 'none')
[docs] def layer_summary(self, X_shape): """Defined in :numref:`sec_lenet`""" X = d2l.randn(*X_shape) for layer in X = layer(X) print(layer.__class__.__name__, 'output shape:\t', X.shape)
class SoftmaxRegression(d2l.Classifier): """Defined in :numref:`sec_softmax_concise`""" def __init__(self, num_outputs, lr): super().__init__() self.save_hyperparameters() = nn.Sequential(nn.Flatten(), nn.LazyLinear(num_outputs)) def forward(self, X): return
[docs]def cpu(): """Defined in :numref:`sec_use_gpu`""" return torch.device('cpu')
[docs]def gpu(i=0): """Defined in :numref:`sec_use_gpu`""" return torch.device(f'cuda:{i}')
[docs]def num_gpus(): """Defined in :numref:`sec_use_gpu`""" return torch.cuda.device_count()
def try_gpu(i=0): """Return gpu(i) if exists, otherwise return cpu(). Defined in :numref:`sec_use_gpu`""" if num_gpus() >= i + 1: return gpu(i) return cpu() def try_all_gpus(): """Return all available GPUs, or [cpu(),] if no GPU exists. Defined in :numref:`sec_use_gpu`""" return [gpu(i) for i in range(num_gpus())] def corr2d(X, K): """Compute 2D cross-correlation. Defined in :numref:`sec_conv_layer`""" h, w = K.shape Y = d2l.zeros((X.shape[0] - h + 1, X.shape[1] - w + 1)) for i in range(Y.shape[0]): for j in range(Y.shape[1]): Y[i, j] = d2l.reduce_sum((X[i: i + h, j: j + w] * K)) return Y def init_cnn(module): """Initialize weights for CNNs. Defined in :numref:`sec_lenet`""" if type(module) == nn.Linear or type(module) == nn.Conv2d: nn.init.xavier_uniform_(module.weight) class LeNet(d2l.Classifier): """Defined in :numref:`sec_lenet`""" def __init__(self, lr=0.1, num_classes=10): super().__init__() self.save_hyperparameters() = nn.Sequential( nn.LazyConv2d(6, kernel_size=5, padding=2), nn.Sigmoid(), nn.AvgPool2d(kernel_size=2, stride=2), nn.LazyConv2d(16, kernel_size=5), nn.Sigmoid(), nn.AvgPool2d(kernel_size=2, stride=2), nn.Flatten(), nn.LazyLinear(120), nn.Sigmoid(), nn.LazyLinear(84), nn.Sigmoid(), nn.LazyLinear(num_classes)) class Residual(nn.Module): """The Residual block of ResNet.""" def __init__(self, num_channels, use_1x1conv=False, strides=1): super().__init__() self.conv1 = nn.LazyConv2d(num_channels, kernel_size=3, padding=1, stride=strides) self.conv2 = nn.LazyConv2d(num_channels, kernel_size=3, padding=1) if use_1x1conv: self.conv3 = nn.LazyConv2d(num_channels, kernel_size=1, stride=strides) else: self.conv3 = None self.bn1 = nn.LazyBatchNorm2d() self.bn2 = nn.LazyBatchNorm2d() def forward(self, X): Y = F.relu(self.bn1(self.conv1(X))) Y = self.bn2(self.conv2(Y)) if self.conv3: X = self.conv3(X) Y += X return F.relu(Y) class ResNeXtBlock(nn.Module): """The ResNeXt block. Defined in :numref:`subsec_residual-blks`""" def __init__(self, num_channels, groups, bot_mul, use_1x1conv=False, strides=1): super().__init__() bot_channels = int(round(num_channels * bot_mul)) self.conv1 = nn.LazyConv2d(bot_channels, kernel_size=1, stride=1) self.conv2 = nn.LazyConv2d(bot_channels, kernel_size=3, stride=strides, padding=1, groups=bot_channels//groups) self.conv3 = nn.LazyConv2d(num_channels, kernel_size=1, stride=1) self.bn1 = nn.LazyBatchNorm2d() self.bn2 = nn.LazyBatchNorm2d() self.bn3 = nn.LazyBatchNorm2d() if use_1x1conv: self.conv4 = nn.LazyConv2d(num_channels, kernel_size=1, stride=strides) self.bn4 = nn.LazyBatchNorm2d() else: self.conv4 = None def forward(self, X): Y = F.relu(self.bn1(self.conv1(X))) Y = F.relu(self.bn2(self.conv2(Y))) Y = self.bn3(self.conv3(Y)) if self.conv4: X = self.bn4(self.conv4(X)) return F.relu(Y + X) class TimeMachine(d2l.DataModule): """Defined in :numref:`sec_text-sequence`""" def _download(self): fname = + 'timemachine.txt', self.root, '090b5e7e70c295757f55df93cb0a180b9691891a') with open(fname) as f: return def _preprocess(self, text): """Defined in :numref:`sec_text-sequence`""" return re.sub('[^A-Za-z]+', ' ', text).lower() def _tokenize(self, text): """Defined in :numref:`sec_text-sequence`""" return list(text) def build(self, raw_text, vocab=None): """Defined in :numref:`sec_text-sequence`""" tokens = self._tokenize(self._preprocess(raw_text)) if vocab is None: vocab = Vocab(tokens) corpus = [vocab[token] for token in tokens] return corpus, vocab def __init__(self, batch_size, num_steps, num_train=10000, num_val=5000): """Defined in :numref:`subsec_perplexity`""" super(d2l.TimeMachine, self).__init__() self.save_hyperparameters() corpus, self.vocab = array = d2l.tensor([corpus[i:i+num_steps+1] for i in range(len(corpus)-num_steps)]) self.X, self.Y = array[:,:-1], array[:,1:] def get_dataloader(self, train): """Defined in :numref:`subsec_partitioning-seqs`""" idx = slice(0, self.num_train) if train else slice( self.num_train, self.num_train + self.num_val) return self.get_tensorloader([self.X, self.Y], train, idx) class Vocab: """Vocabulary for text.""" def __init__(self, tokens=[], min_freq=0, reserved_tokens=[]): """Defined in :numref:`sec_text-sequence`""" # Flatten a 2D list if needed if tokens and isinstance(tokens[0], list): tokens = [token for line in tokens for token in line] # Count token frequencies counter = collections.Counter(tokens) self.token_freqs = sorted(counter.items(), key=lambda x: x[1], reverse=True) # The list of unique tokens self.idx_to_token = list(sorted(set(['<unk>'] + reserved_tokens + [ token for token, freq in self.token_freqs if freq >= min_freq]))) self.token_to_idx = {token: idx for idx, token in enumerate(self.idx_to_token)} def __len__(self): return len(self.idx_to_token) def __getitem__(self, tokens): if not isinstance(tokens, (list, tuple)): return self.token_to_idx.get(tokens, self.unk) return [self.__getitem__(token) for token in tokens] def to_tokens(self, indices): if hasattr(indices, '__len__') and len(indices) > 1: return [self.idx_to_token[int(index)] for index in indices] return self.idx_to_token[indices] @property def unk(self): # Index for the unknown token return self.token_to_idx['<unk>'] class RNNScratch(d2l.Module): """Defined in :numref:`sec_rnn-scratch`""" def __init__(self, num_inputs, num_hiddens, sigma=0.01): super().__init__() self.save_hyperparameters() self.W_xh = nn.Parameter( d2l.randn(num_inputs, num_hiddens) * sigma) self.W_hh = nn.Parameter( d2l.randn(num_hiddens, num_hiddens) * sigma) self.b_h = nn.Parameter(d2l.zeros(num_hiddens)) def forward(self, inputs, state=None): """Defined in :numref:`sec_rnn-scratch`""" if state is None: # Initial state with shape: (batch_size, num_hiddens) state = d2l.zeros((inputs.shape[1], self.num_hiddens), device=inputs.device) else: state, = state outputs = [] for X in inputs: # Shape of inputs: (num_steps, batch_size, num_inputs) state = d2l.tanh(d2l.matmul(X, self.W_xh) + d2l.matmul(state, self.W_hh) + self.b_h) outputs.append(state) return outputs, state def check_len(a, n): """Defined in :numref:`sec_rnn-scratch`""" assert len(a) == n, f'list\'s len {len(a)} != expected length {n}' def check_shape(a, shape): """Defined in :numref:`sec_rnn-scratch`""" assert a.shape == shape, \ f'tensor\'s shape {a.shape} != expected shape {shape}' class RNNLMScratch(d2l.Classifier): """Defined in :numref:`sec_rnn-scratch`""" def __init__(self, rnn, vocab_size, lr=0.01): super().__init__() self.save_hyperparameters() self.init_params() def init_params(self): self.W_hq = nn.Parameter( d2l.randn( self.rnn.num_hiddens, self.vocab_size) * self.rnn.sigma) self.b_q = nn.Parameter(d2l.zeros(self.vocab_size)) def training_step(self, batch): l = self.loss(self(*batch[:-1]), batch[-1]) self.plot('ppl', d2l.exp(l), train=True) return l def validation_step(self, batch): l = self.loss(self(*batch[:-1]), batch[-1]) self.plot('ppl', d2l.exp(l), train=False) def one_hot(self, X): """Defined in :numref:`sec_rnn-scratch`""" # Output shape: (num_steps, batch_size, vocab_size) return F.one_hot(X.T, self.vocab_size).type(torch.float32) def output_layer(self, rnn_outputs): """Defined in :numref:`sec_rnn-scratch`""" outputs = [d2l.matmul(H, self.W_hq) + self.b_q for H in rnn_outputs] return d2l.stack(outputs, 1) def forward(self, X, state=None): """Defined in :numref:`sec_rnn-scratch`""" embs = self.one_hot(X) rnn_outputs, _ = self.rnn(embs, state) return self.output_layer(rnn_outputs) def predict(self, prefix, num_preds, vocab, device=None): """Defined in :numref:`sec_rnn-scratch`""" state, outputs = None, [vocab[prefix[0]]] for i in range(len(prefix) + num_preds - 1): X = d2l.tensor([[outputs[-1]]], device=device) embs = self.one_hot(X) rnn_outputs, state = self.rnn(embs, state) if i < len(prefix) - 1: # Warm-up period outputs.append(vocab[prefix[i + 1]]) else: # Predict `num_preds` steps Y = self.output_layer(rnn_outputs) outputs.append(int(d2l.reshape(d2l.argmax(Y, axis=2), 1))) return ''.join([vocab.idx_to_token[i] for i in outputs]) class RNN(d2l.Module): """Defined in :numref:`sec_rnn-concise`""" def __init__(self, num_inputs, num_hiddens): super().__init__() self.save_hyperparameters() self.rnn = nn.RNN(num_inputs, num_hiddens) def forward(self, inputs, H=None): return self.rnn(inputs, H) class RNNLM(d2l.RNNLMScratch): """Defined in :numref:`sec_rnn-concise`""" def init_params(self): self.linear = nn.LazyLinear(self.vocab_size) def output_layer(self, hiddens): return d2l.swapaxes(self.linear(hiddens), 0, 1) class GRU(d2l.RNN): """Defined in :numref:`sec_deep_rnn`""" def __init__(self, num_inputs, num_hiddens, num_layers, dropout=0): d2l.Module.__init__(self) self.save_hyperparameters() self.rnn = nn.GRU(num_inputs, num_hiddens, num_layers, dropout=dropout) class MTFraEng(d2l.DataModule): """Defined in :numref:`sec_machine_translation`""" def _download(self): d2l.extract( d2l.DATA_URL+'', self.root, '94646ad1522d915e7b0f9296181140edcf86a4f5')) with open(self.root + '/fra-eng/fra.txt', encoding='utf-8') as f: return def _preprocess(self, text): """Defined in :numref:`sec_machine_translation`""" # Replace non-breaking space with space text = text.replace('\u202f', ' ').replace('\xa0', ' ') # Insert space between words and punctuation marks no_space = lambda char, prev_char: char in ',.!?' and prev_char != ' ' out = [' ' + char if i > 0 and no_space(char, text[i - 1]) else char for i, char in enumerate(text.lower())] return ''.join(out) def _tokenize(self, text, max_examples=None): """Defined in :numref:`sec_machine_translation`""" src, tgt = [], [] for i, line in enumerate(text.split('\n')): if max_examples and i > max_examples: break parts = line.split('\t') if len(parts) == 2: # Skip empty tokens src.append([t for t in f'{parts[0]} <eos>'.split(' ') if t]) tgt.append([t for t in f'{parts[1]} <eos>'.split(' ') if t]) return src, tgt def __init__(self, batch_size, num_steps=9, num_train=512, num_val=128): """Defined in :numref:`sec_machine_translation`""" super(MTFraEng, self).__init__() self.save_hyperparameters() self.arrays, self.src_vocab, self.tgt_vocab = self._build_arrays( self._download()) def _build_arrays(self, raw_text, src_vocab=None, tgt_vocab=None): """Defined in :numref:`sec_machine_translation`""" def _build_array(sentences, vocab, is_tgt=False): pad_or_trim = lambda seq, t: ( seq[:t] if len(seq) > t else seq + ['<pad>'] * (t - len(seq))) sentences = [pad_or_trim(s, self.num_steps) for s in sentences] if is_tgt: sentences = [['<bos>'] + s for s in sentences] if vocab is None: vocab = d2l.Vocab(sentences, min_freq=2) array = d2l.tensor([vocab[s] for s in sentences]) valid_len = d2l.reduce_sum( d2l.astype(array != vocab['<pad>'], d2l.int32), 1) return array, vocab, valid_len src, tgt = self._tokenize(self._preprocess(raw_text), self.num_train + self.num_val) src_array, src_vocab, src_valid_len = _build_array(src, src_vocab) tgt_array, tgt_vocab, _ = _build_array(tgt, tgt_vocab, True) return ((src_array, tgt_array[:,:-1], src_valid_len, tgt_array[:,1:]), src_vocab, tgt_vocab) def get_dataloader(self, train): """Defined in :numref:`subsec_loading-seq-fixed-len`""" idx = slice(0, self.num_train) if train else slice(self.num_train, None) return self.get_tensorloader(self.arrays, train, idx) def build(self, src_sentences, tgt_sentences): """Defined in :numref:`subsec_loading-seq-fixed-len`""" raw_text = '\n'.join([src + '\t' + tgt for src, tgt in zip( src_sentences, tgt_sentences)]) arrays, _, _ = self._build_arrays( raw_text, self.src_vocab, self.tgt_vocab) return arrays def show_list_len_pair_hist(legend, xlabel, ylabel, xlist, ylist): """Plot the histogram for list length pairs. Defined in :numref:`sec_machine_translation`""" d2l.set_figsize() _, _, patches = d2l.plt.hist( [[len(l) for l in xlist], [len(l) for l in ylist]]) d2l.plt.xlabel(xlabel) d2l.plt.ylabel(ylabel) for patch in patches[1].patches: patch.set_hatch('/') d2l.plt.legend(legend) class Encoder(nn.Module): """The base encoder interface for the encoder-decoder architecture.""" def __init__(self): super().__init__() # Later there can be additional arguments (e.g., length excluding padding) def forward(self, X, *args): raise NotImplementedError class Decoder(nn.Module): """The base decoder interface for the encoder-decoder architecture. Defined in :numref:`sec_encoder-decoder`""" def __init__(self): super().__init__() # Later there can be additional arguments (e.g., length excluding padding) def init_state(self, enc_all_outputs, *args): raise NotImplementedError def forward(self, X, state): raise NotImplementedError class EncoderDecoder(d2l.Classifier): """The base class for the encoder-decoder architecture. Defined in :numref:`sec_encoder-decoder`""" def __init__(self, encoder, decoder): super().__init__() self.encoder = encoder self.decoder = decoder def forward(self, enc_X, dec_X, *args): enc_all_outputs = self.encoder(enc_X, *args) dec_state = self.decoder.init_state(enc_all_outputs, *args) # Return decoder output only return self.decoder(dec_X, dec_state)[0] def predict_step(self, batch, device, num_steps, save_attention_weights=False): """Defined in :numref:`sec_seq2seq_training`""" batch = [, device) for a in batch] src, tgt, src_valid_len, _ = batch enc_all_outputs = self.encoder(src, src_valid_len) dec_state = self.decoder.init_state(enc_all_outputs, src_valid_len) outputs, attention_weights = [d2l.expand_dims(tgt[:, 0], 1), ], [] for _ in range(num_steps): Y, dec_state = self.decoder(outputs[-1], dec_state) outputs.append(d2l.argmax(Y, 2)) # Save attention weights (to be covered later) if save_attention_weights: attention_weights.append(self.decoder.attention_weights) return d2l.concat(outputs[1:], 1), attention_weights def init_seq2seq(module): """Initialize weights for Seq2Seq. Defined in :numref:`sec_seq2seq`""" if type(module) == nn.Linear: nn.init.xavier_uniform_(module.weight) if type(module) == nn.GRU: for param in module._flat_weights_names: if "weight" in param: nn.init.xavier_uniform_(module._parameters[param]) class Seq2SeqEncoder(d2l.Encoder): """The RNN encoder for sequence to sequence learning. Defined in :numref:`sec_seq2seq`""" def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0): super().__init__() self.embedding = nn.Embedding(vocab_size, embed_size) self.rnn = d2l.GRU(embed_size, num_hiddens, num_layers, dropout) self.apply(init_seq2seq) def forward(self, X, *args): # X shape: (batch_size, num_steps) embs = self.embedding(d2l.astype(d2l.transpose(X), d2l.int64)) # embs shape: (num_steps, batch_size, embed_size) outputs, state = self.rnn(embs) # outputs shape: (num_steps, batch_size, num_hiddens) # state shape: (num_layers, batch_size, num_hiddens) return outputs, state class Seq2Seq(d2l.EncoderDecoder): """Defined in :numref:`sec_seq2seq_decoder`""" def __init__(self, encoder, decoder, tgt_pad, lr): super().__init__(encoder, decoder) self.save_hyperparameters() def validation_step(self, batch): Y_hat = self(*batch[:-1]) self.plot('loss', self.loss(Y_hat, batch[-1]), train=False) def configure_optimizers(self): # Adam optimizer is used here return torch.optim.Adam(self.parameters(), def bleu(pred_seq, label_seq, k): """Compute the BLEU. Defined in :numref:`sec_seq2seq_training`""" pred_tokens, label_tokens = pred_seq.split(' '), label_seq.split(' ') len_pred, len_label = len(pred_tokens), len(label_tokens) score = math.exp(min(0, 1 - len_label / len_pred)) for n in range(1, min(k, len_pred) + 1): num_matches, label_subs = 0, collections.defaultdict(int) for i in range(len_label - n + 1): label_subs[' '.join(label_tokens[i: i + n])] += 1 for i in range(len_pred - n + 1): if label_subs[' '.join(pred_tokens[i: i + n])] > 0: num_matches += 1 label_subs[' '.join(pred_tokens[i: i + n])] -= 1 score *= math.pow(num_matches / (len_pred - n + 1), math.pow(0.5, n)) return score def show_heatmaps(matrices, xlabel, ylabel, titles=None, figsize=(2.5, 2.5), cmap='Reds'): """Show heatmaps of matrices. Defined in :numref:`sec_queries-keys-values`""" d2l.use_svg_display() num_rows, num_cols, _, _ = matrices.shape fig, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize, sharex=True, sharey=True, squeeze=False) for i, (row_axes, row_matrices) in enumerate(zip(axes, matrices)): for j, (ax, matrix) in enumerate(zip(row_axes, row_matrices)): pcm = ax.imshow(d2l.numpy(matrix), cmap=cmap) if i == num_rows - 1: ax.set_xlabel(xlabel) if j == 0: ax.set_ylabel(ylabel) if titles: ax.set_title(titles[j]) fig.colorbar(pcm, ax=axes, shrink=0.6); def masked_softmax(X, valid_lens): """Perform softmax operation by masking elements on the last axis. Defined in :numref:`sec_attention-scoring-functions`""" # X: 3D tensor, valid_lens: 1D or 2D tensor def _sequence_mask(X, valid_len, value=0): maxlen = X.size(1) mask = torch.arange((maxlen), dtype=torch.float32, device=X.device)[None, :] < valid_len[:, None] X[~mask] = value return X if valid_lens is None: return nn.functional.softmax(X, dim=-1) else: shape = X.shape if valid_lens.dim() == 1: valid_lens = torch.repeat_interleave(valid_lens, shape[1]) else: valid_lens = valid_lens.reshape(-1) # On the last axis, replace masked elements with a very large negative # value, whose exponentiation outputs 0 X = _sequence_mask(X.reshape(-1, shape[-1]), valid_lens, value=-1e6) return nn.functional.softmax(X.reshape(shape), dim=-1) class DotProductAttention(nn.Module): """Scaled dot product attention. Defined in :numref:`subsec_batch_dot`""" def __init__(self, dropout, num_heads=None): super().__init__() self.dropout = nn.Dropout(dropout) self.num_heads = num_heads # To be covered later # Shape of queries: (batch_size, no. of queries, d) # Shape of keys: (batch_size, no. of key-value pairs, d) # Shape of values: (batch_size, no. of key-value pairs, value dimension) # Shape of valid_lens: (batch_size,) or (batch_size, no. of queries) def forward(self, queries, keys, values, valid_lens=None, window_mask=None): d = queries.shape[-1] # Swap the last two dimensions of keys with keys.transpose(1, 2) scores = torch.bmm(queries, keys.transpose(1, 2)) / math.sqrt(d) if window_mask is not None: # To be covered later num_windows = window_mask.shape[0] n, num_queries, num_kv_pairs = scores.shape # Shape of window_mask: (num_windows, no. of queries, # no. of key-value pairs) scores = d2l.reshape( scores, (n//(num_windows*self.num_heads), num_windows, self.num_heads, num_queries, num_kv_pairs )) + d2l.expand_dims( d2l.expand_dims(window_mask, 1), 0) scores = d2l.reshape(scores, (n, num_queries, num_kv_pairs)) self.attention_weights = masked_softmax(scores, valid_lens) return torch.bmm(self.dropout(self.attention_weights), values) class AdditiveAttention(nn.Module): """Additive attention. Defined in :numref:`subsec_batch_dot`""" def __init__(self, num_hiddens, dropout, **kwargs): super(AdditiveAttention, self).__init__(**kwargs) self.W_k = nn.LazyLinear(num_hiddens, bias=False) self.W_q = nn.LazyLinear(num_hiddens, bias=False) self.w_v = nn.LazyLinear(1, bias=False) self.dropout = nn.Dropout(dropout) def forward(self, queries, keys, values, valid_lens): queries, keys = self.W_q(queries), self.W_k(keys) # After dimension expansion, shape of queries: (batch_size, no. of # queries, 1, num_hiddens) and shape of keys: (batch_size, 1, no. of # key-value pairs, num_hiddens). Sum them up with broadcasting features = queries.unsqueeze(2) + keys.unsqueeze(1) features = torch.tanh(features) # There is only one output of self.w_v, so we remove the last # one-dimensional entry from the shape. Shape of scores: (batch_size, # no. of queries, no. of key-value pairs) scores = self.w_v(features).squeeze(-1) self.attention_weights = masked_softmax(scores, valid_lens) # Shape of values: (batch_size, no. of key-value pairs, value # dimension) return torch.bmm(self.dropout(self.attention_weights), values) class AttentionDecoder(d2l.Decoder): """The base attention-based decoder interface. Defined in :numref:`sec_seq2seq_attention`""" def __init__(self): super().__init__() @property def attention_weights(self): raise NotImplementedError class MultiHeadAttention(d2l.Module): """Multi-head attention. Defined in :numref:`sec_multihead-attention`""" def __init__(self, num_hiddens, num_heads, dropout, bias=False, **kwargs): super().__init__() self.num_heads = num_heads self.attention = d2l.DotProductAttention(dropout, num_heads) self.W_q = nn.LazyLinear(num_hiddens, bias=bias) self.W_k = nn.LazyLinear(num_hiddens, bias=bias) self.W_v = nn.LazyLinear(num_hiddens, bias=bias) self.W_o = nn.LazyLinear(num_hiddens, bias=bias) def forward(self, queries, keys, values, valid_lens, window_mask=None): # Shape of queries, keys, or values: # (batch_size, no. of queries or key-value pairs, num_hiddens) # Shape of valid_lens: (batch_size,) or (batch_size, no. of queries) # After transposing, shape of output queries, keys, or values: # (batch_size * num_heads, no. of queries or key-value pairs, # num_hiddens / num_heads) queries = self.transpose_qkv(self.W_q(queries)) keys = self.transpose_qkv(self.W_k(keys)) values = self.transpose_qkv(self.W_v(values)) if valid_lens is not None: # On axis 0, copy the first item (scalar or vector) for num_heads # times, then copy the next item, and so on valid_lens = torch.repeat_interleave( valid_lens, repeats=self.num_heads, dim=0) # Shape of output: (batch_size * num_heads, no. of queries, # num_hiddens / num_heads) output = self.attention(queries, keys, values, valid_lens, window_mask) # Shape of output_concat: (batch_size, no. of queries, num_hiddens) output_concat = self.transpose_output(output) return self.W_o(output_concat) def transpose_qkv(self, X): """Transposition for parallel computation of multiple attention heads. Defined in :numref:`sec_multihead-attention`""" # Shape of input X: (batch_size, no. of queries or key-value pairs, # num_hiddens). Shape of output X: (batch_size, no. of queries or # key-value pairs, num_heads, num_hiddens / num_heads) X = X.reshape(X.shape[0], X.shape[1], self.num_heads, -1) # Shape of output X: (batch_size, num_heads, no. of queries or key-value # pairs, num_hiddens / num_heads) X = X.permute(0, 2, 1, 3) # Shape of output: (batch_size * num_heads, no. of queries or key-value # pairs, num_hiddens / num_heads) return X.reshape(-1, X.shape[2], X.shape[3]) def transpose_output(self, X): """Reverse the operation of transpose_qkv. Defined in :numref:`sec_multihead-attention`""" X = X.reshape(-1, self.num_heads, X.shape[1], X.shape[2]) X = X.permute(0, 2, 1, 3) return X.reshape(X.shape[0], X.shape[1], -1) class PositionalEncoding(nn.Module): """Positional encoding. Defined in :numref:`sec_self-attention-and-positional-encoding`""" def __init__(self, num_hiddens, dropout, max_len=1000): super().__init__() self.dropout = nn.Dropout(dropout) # Create a long enough P self.P = d2l.zeros((1, max_len, num_hiddens)) X = d2l.arange(max_len, dtype=torch.float32).reshape( -1, 1) / torch.pow(10000, torch.arange( 0, num_hiddens, 2, dtype=torch.float32) / num_hiddens) self.P[:, :, 0::2] = torch.sin(X) self.P[:, :, 1::2] = torch.cos(X) def forward(self, X): X = X + self.P[:, :X.shape[1], :].to(X.device) return self.dropout(X) class PositionWiseFFN(nn.Module): """Positionwise feed-forward network. Defined in :numref:`sec_transformer`""" def __init__(self, ffn_num_hiddens, ffn_num_outputs): super().__init__() self.dense1 = nn.LazyLinear(ffn_num_hiddens) self.relu = nn.ReLU() self.dense2 = nn.LazyLinear(ffn_num_outputs) def forward(self, X): return self.dense2(self.relu(self.dense1(X))) class AddNorm(nn.Module): """Residual connection followed by layer normalization. Defined in :numref:`subsec_positionwise-ffn`""" def __init__(self, norm_shape, dropout): super().__init__() self.dropout = nn.Dropout(dropout) self.ln = nn.LayerNorm(norm_shape) def forward(self, X, Y): return self.ln(self.dropout(Y) + X) class TransformerEncoderBlock(nn.Module): """Transformer encoder block. Defined in :numref:`subsec_positionwise-ffn`""" def __init__(self, num_hiddens, ffn_num_hiddens, num_heads, dropout, use_bias=False): super().__init__() self.attention = d2l.MultiHeadAttention(num_hiddens, num_heads, dropout, use_bias) self.addnorm1 = AddNorm(num_hiddens, dropout) self.ffn = PositionWiseFFN(ffn_num_hiddens, num_hiddens) self.addnorm2 = AddNorm(num_hiddens, dropout) def forward(self, X, valid_lens): Y = self.addnorm1(X, self.attention(X, X, X, valid_lens)) return self.addnorm2(Y, self.ffn(Y)) class TransformerEncoder(d2l.Encoder): """Transformer encoder. Defined in :numref:`subsec_transformer-encoder`""" def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens, num_heads, num_blks, dropout, use_bias=False): super().__init__() self.num_hiddens = num_hiddens self.embedding = nn.Embedding(vocab_size, num_hiddens) self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout) self.blks = nn.Sequential() for i in range(num_blks): self.blks.add_module("block"+str(i), TransformerEncoderBlock( num_hiddens, ffn_num_hiddens, num_heads, dropout, use_bias)) def forward(self, X, valid_lens): # Since positional encoding values are between -1 and 1, the embedding # values are multiplied by the square root of the embedding dimension # to rescale before they are summed up X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens)) self.attention_weights = [None] * len(self.blks) for i, blk in enumerate(self.blks): X = blk(X, valid_lens) self.attention_weights[ i] = blk.attention.attention.attention_weights return X def annotate(text, xy, xytext): d2l.plt.gca().annotate(text, xy=xy, xytext=xytext, arrowprops=dict(arrowstyle='->')) def train_2d(trainer, steps=20, f_grad=None): """Optimize a 2D objective function with a customized trainer. Defined in :numref:`subsec_gd-learningrate`""" # `s1` and `s2` are internal state variables that will be used in Momentum, adagrad, RMSProp x1, x2, s1, s2 = -5, -2, 0, 0 results = [(x1, x2)] for i in range(steps): if f_grad: x1, x2, s1, s2 = trainer(x1, x2, s1, s2, f_grad) else: x1, x2, s1, s2 = trainer(x1, x2, s1, s2) results.append((x1, x2)) print(f'epoch {i + 1}, x1: {float(x1):f}, x2: {float(x2):f}') return results def show_trace_2d(f, results): """Show the trace of 2D variables during optimization. Defined in :numref:`subsec_gd-learningrate`""" d2l.set_figsize() d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e') x1, x2 = d2l.meshgrid(d2l.arange(-5.5, 1.0, 0.1), d2l.arange(-3.0, 1.0, 0.1)) d2l.plt.contour(x1, x2, f(x1, x2), colors='#1f77b4') d2l.plt.xlabel('x1') d2l.plt.ylabel('x2') class Timer: """Record multiple running times.""" def __init__(self): """Defined in :numref:`sec_minibatch_sgd`""" self.times = [] self.start() def start(self): """Start the timer.""" self.tik = time.time() def stop(self): """Stop the timer and record the time in a list.""" self.times.append(time.time() - self.tik) return self.times[-1] def avg(self): """Return the average time.""" return sum(self.times) / len(self.times) def sum(self): """Return the sum of time.""" return sum(self.times) def cumsum(self): """Return the accumulated time.""" return np.array(self.times).cumsum().tolist() d2l.DATA_HUB['airfoil'] = (d2l.DATA_URL + 'airfoil_self_noise.dat', '76e5be1548fd8222e5074cf0faae75edff8cf93f') def get_data_ch11(batch_size=10, n=1500): """Defined in :numref:`sec_minibatches`""" data = np.genfromtxt('airfoil'), dtype=np.float32, delimiter='\t') data = torch.from_numpy((data - data.mean(axis=0)) / data.std(axis=0)) data_iter = d2l.load_array((data[:n, :-1], data[:n, -1]), batch_size, is_train=True) return data_iter, data.shape[1]-1 def train_ch11(trainer_fn, states, hyperparams, data_iter, feature_dim, num_epochs=2): """Defined in :numref:`sec_minibatches`""" # Initialization w = torch.normal(mean=0.0, std=0.01, size=(feature_dim, 1), requires_grad=True) b = torch.zeros((1), requires_grad=True) net, loss = lambda X: d2l.linreg(X, w, b), d2l.squared_loss # Train animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[0, num_epochs], ylim=[0.22, 0.35]) n, timer = 0, d2l.Timer() for _ in range(num_epochs): for X, y in data_iter: l = loss(net(X), y).mean() l.backward() trainer_fn([w, b], states, hyperparams) n += X.shape[0] if n % 200 == 0: timer.stop() animator.add(n/X.shape[0]/len(data_iter), (d2l.evaluate_loss(net, data_iter, loss),)) timer.start() print(f'loss: {animator.Y[0][-1]:.3f}, {timer.sum()/num_epochs:.3f} sec/epoch') return timer.cumsum(), animator.Y[0] def train_concise_ch11(trainer_fn, hyperparams, data_iter, num_epochs=4): """Defined in :numref:`sec_minibatches`""" # Initialization net = nn.Sequential(nn.Linear(5, 1)) def init_weights(module): if type(module) == nn.Linear: torch.nn.init.normal_(module.weight, std=0.01) net.apply(init_weights) optimizer = trainer_fn(net.parameters(), **hyperparams) loss = nn.MSELoss(reduction='none') animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[0, num_epochs], ylim=[0.22, 0.35]) n, timer = 0, d2l.Timer() for _ in range(num_epochs): for X, y in data_iter: optimizer.zero_grad() out = net(X) y = y.reshape(out.shape) l = loss(out, y) l.mean().backward() optimizer.step() n += X.shape[0] if n % 200 == 0: timer.stop() # `MSELoss` computes squared error without the 1/2 factor animator.add(n/X.shape[0]/len(data_iter), (d2l.evaluate_loss(net, data_iter, loss) / 2,)) timer.start() print(f'loss: {animator.Y[0][-1]:.3f}, {timer.sum()/num_epochs:.3f} sec/epoch') class Benchmark: """For measuring running time.""" def __init__(self, description='Done'): """Defined in :numref:`sec_hybridize`""" self.description = description def __enter__(self): self.timer = d2l.Timer() return self def __exit__(self, *args): print(f'{self.description}: {self.timer.stop():.4f} sec') def split_batch(X, y, devices): """Split `X` and `y` into multiple devices. Defined in :numref:`sec_multi_gpu`""" assert X.shape[0] == y.shape[0] return (nn.parallel.scatter(X, devices), nn.parallel.scatter(y, devices)) def resnet18(num_classes, in_channels=1): """A slightly modified ResNet-18 model. Defined in :numref:`sec_multi_gpu_concise`""" def resnet_block(in_channels, out_channels, num_residuals, first_block=False): blk = [] for i in range(num_residuals): if i == 0 and not first_block: blk.append(d2l.Residual(out_channels, use_1x1conv=True, strides=2)) else: blk.append(d2l.Residual(out_channels)) return nn.Sequential(*blk) # This model uses a smaller convolution kernel, stride, and padding and # removes the max-pooling layer net = nn.Sequential( nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1), nn.BatchNorm2d(64), nn.ReLU()) net.add_module("resnet_block1", resnet_block(64, 64, 2, first_block=True)) net.add_module("resnet_block2", resnet_block(64, 128, 2)) net.add_module("resnet_block3", resnet_block(128, 256, 2)) net.add_module("resnet_block4", resnet_block(256, 512, 2)) net.add_module("global_avg_pool", nn.AdaptiveAvgPool2d((1,1))) net.add_module("fc", nn.Sequential(nn.Flatten(), nn.Linear(512, num_classes))) return net def train_batch_ch13(net, X, y, loss, trainer, devices): """Train for a minibatch with multiple GPUs (defined in Chapter 13). Defined in :numref:`sec_image_augmentation`""" if isinstance(X, list): # Required for BERT fine-tuning (to be covered later) X = [[0]) for x in X] else: X =[0]) y =[0]) net.train() trainer.zero_grad() pred = net(X) l = loss(pred, y) l.sum().backward() trainer.step() train_loss_sum = l.sum() train_acc_sum = d2l.accuracy(pred, y) return train_loss_sum, train_acc_sum def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices=d2l.try_all_gpus()): """Train a model with multiple GPUs (defined in Chapter 13). Defined in :numref:`sec_image_augmentation`""" timer, num_batches = d2l.Timer(), len(train_iter) animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1], legend=['train loss', 'train acc', 'test acc']) net = nn.DataParallel(net, device_ids=devices).to(devices[0]) for epoch in range(num_epochs): # Sum of training loss, sum of training accuracy, no. of examples, # no. of predictions metric = d2l.Accumulator(4) for i, (features, labels) in enumerate(train_iter): timer.start() l, acc = train_batch_ch13( net, features, labels, loss, trainer, devices) metric.add(l, acc, labels.shape[0], labels.numel()) timer.stop() if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1: animator.add(epoch + (i + 1) / num_batches, (metric[0] / metric[2], metric[1] / metric[3], None)) test_acc = d2l.evaluate_accuracy_gpu(net, test_iter) animator.add(epoch + 1, (None, None, test_acc)) print(f'loss {metric[0] / metric[2]:.3f}, train acc ' f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}') print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on ' f'{str(devices)}') d2l.DATA_HUB['hotdog'] = (d2l.DATA_URL + '', 'fba480ffa8aa7e0febbb511d181409f899b9baa5') def box_corner_to_center(boxes): """Convert from (upper-left, lower-right) to (center, width, height). Defined in :numref:`sec_bbox`""" x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3] cx = (x1 + x2) / 2 cy = (y1 + y2) / 2 w = x2 - x1 h = y2 - y1 boxes = d2l.stack((cx, cy, w, h), axis=-1) return boxes def box_center_to_corner(boxes): """Convert from (center, width, height) to (upper-left, lower-right). Defined in :numref:`sec_bbox`""" cx, cy, w, h = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3] x1 = cx - 0.5 * w y1 = cy - 0.5 * h x2 = cx + 0.5 * w y2 = cy + 0.5 * h boxes = d2l.stack((x1, y1, x2, y2), axis=-1) return boxes def bbox_to_rect(bbox, color): """Convert bounding box to matplotlib format. Defined in :numref:`sec_bbox`""" # Convert the bounding box (upper-left x, upper-left y, lower-right x, # lower-right y) format to the matplotlib format: ((upper-left x, # upper-left y), width, height) return d2l.plt.Rectangle( xy=(bbox[0], bbox[1]), width=bbox[2]-bbox[0], height=bbox[3]-bbox[1], fill=False, edgecolor=color, linewidth=2) def multibox_prior(data, sizes, ratios): """Generate anchor boxes with different shapes centered on each pixel. Defined in :numref:`sec_anchor`""" in_height, in_width = data.shape[-2:] device, num_sizes, num_ratios = data.device, len(sizes), len(ratios) boxes_per_pixel = (num_sizes + num_ratios - 1) size_tensor = d2l.tensor(sizes, device=device) ratio_tensor = d2l.tensor(ratios, device=device) # Offsets are required to move the anchor to the center of a pixel. Since # a pixel has height=1 and width=1, we choose to offset our centers by 0.5 offset_h, offset_w = 0.5, 0.5 steps_h = 1.0 / in_height # Scaled steps in y axis steps_w = 1.0 / in_width # Scaled steps in x axis # Generate all center points for the anchor boxes center_h = (torch.arange(in_height, device=device) + offset_h) * steps_h center_w = (torch.arange(in_width, device=device) + offset_w) * steps_w shift_y, shift_x = torch.meshgrid(center_h, center_w, indexing='ij') shift_y, shift_x = shift_y.reshape(-1), shift_x.reshape(-1) # Generate `boxes_per_pixel` number of heights and widths that are later # used to create anchor box corner coordinates (xmin, xmax, ymin, ymax) w = * torch.sqrt(ratio_tensor[0]), sizes[0] * torch.sqrt(ratio_tensor[1:])))\ * in_height / in_width # Handle rectangular inputs h = / torch.sqrt(ratio_tensor[0]), sizes[0] / torch.sqrt(ratio_tensor[1:]))) # Divide by 2 to get half height and half width anchor_manipulations = torch.stack((-w, -h, w, h)).T.repeat( in_height * in_width, 1) / 2 # Each center point will have `boxes_per_pixel` number of anchor boxes, so # generate a grid of all anchor box centers with `boxes_per_pixel` repeats out_grid = torch.stack([shift_x, shift_y, shift_x, shift_y], dim=1).repeat_interleave(boxes_per_pixel, dim=0) output = out_grid + anchor_manipulations return output.unsqueeze(0) def show_bboxes(axes, bboxes, labels=None, colors=None): """Show bounding boxes. Defined in :numref:`sec_anchor`""" def make_list(obj, default_values=None): if obj is None: obj = default_values elif not isinstance(obj, (list, tuple)): obj = [obj] return obj labels = make_list(labels) colors = make_list(colors, ['b', 'g', 'r', 'm', 'c']) for i, bbox in enumerate(bboxes): color = colors[i % len(colors)] rect = d2l.bbox_to_rect(d2l.numpy(bbox), color) axes.add_patch(rect) if labels and len(labels) > i: text_color = 'k' if color == 'w' else 'w' axes.text(rect.xy[0], rect.xy[1], labels[i], va='center', ha='center', fontsize=9, color=text_color, bbox=dict(facecolor=color, lw=0)) def box_iou(boxes1, boxes2): """Compute pairwise IoU across two lists of anchor or bounding boxes. Defined in :numref:`sec_anchor`""" box_area = lambda boxes: ((boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])) # Shape of `boxes1`, `boxes2`, `areas1`, `areas2`: (no. of boxes1, 4), # (no. of boxes2, 4), (no. of boxes1,), (no. of boxes2,) areas1 = box_area(boxes1) areas2 = box_area(boxes2) # Shape of `inter_upperlefts`, `inter_lowerrights`, `inters`: (no. of # boxes1, no. of boxes2, 2) inter_upperlefts = torch.max(boxes1[:, None, :2], boxes2[:, :2]) inter_lowerrights = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) inters = (inter_lowerrights - inter_upperlefts).clamp(min=0) # Shape of `inter_areas` and `union_areas`: (no. of boxes1, no. of boxes2) inter_areas = inters[:, :, 0] * inters[:, :, 1] union_areas = areas1[:, None] + areas2 - inter_areas return inter_areas / union_areas def assign_anchor_to_bbox(ground_truth, anchors, device, iou_threshold=0.5): """Assign closest ground-truth bounding boxes to anchor boxes. Defined in :numref:`sec_anchor`""" num_anchors, num_gt_boxes = anchors.shape[0], ground_truth.shape[0] # Element x_ij in the i-th row and j-th column is the IoU of the anchor # box i and the ground-truth bounding box j jaccard = box_iou(anchors, ground_truth) # Initialize the tensor to hold the assigned ground-truth bounding box for # each anchor anchors_bbox_map = torch.full((num_anchors,), -1, dtype=torch.long, device=device) # Assign ground-truth bounding boxes according to the threshold max_ious, indices = torch.max(jaccard, dim=1) anc_i = torch.nonzero(max_ious >= iou_threshold).reshape(-1) box_j = indices[max_ious >= iou_threshold] anchors_bbox_map[anc_i] = box_j col_discard = torch.full((num_anchors,), -1) row_discard = torch.full((num_gt_boxes,), -1) for _ in range(num_gt_boxes): max_idx = torch.argmax(jaccard) # Find the largest IoU box_idx = (max_idx % num_gt_boxes).long() anc_idx = (max_idx / num_gt_boxes).long() anchors_bbox_map[anc_idx] = box_idx jaccard[:, box_idx] = col_discard jaccard[anc_idx, :] = row_discard return anchors_bbox_map def offset_boxes(anchors, assigned_bb, eps=1e-6): """Transform for anchor box offsets. Defined in :numref:`subsec_labeling-anchor-boxes`""" c_anc = d2l.box_corner_to_center(anchors) c_assigned_bb = d2l.box_corner_to_center(assigned_bb) offset_xy = 10 * (c_assigned_bb[:, :2] - c_anc[:, :2]) / c_anc[:, 2:] offset_wh = 5 * d2l.log(eps + c_assigned_bb[:, 2:] / c_anc[:, 2:]) offset = d2l.concat([offset_xy, offset_wh], axis=1) return offset def multibox_target(anchors, labels): """Label anchor boxes using ground-truth bounding boxes. Defined in :numref:`subsec_labeling-anchor-boxes`""" batch_size, anchors = labels.shape[0], anchors.squeeze(0) batch_offset, batch_mask, batch_class_labels = [], [], [] device, num_anchors = anchors.device, anchors.shape[0] for i in range(batch_size): label = labels[i, :, :] anchors_bbox_map = assign_anchor_to_bbox( label[:, 1:], anchors, device) bbox_mask = ((anchors_bbox_map >= 0).float().unsqueeze(-1)).repeat( 1, 4) # Initialize class labels and assigned bounding box coordinates with # zeros class_labels = torch.zeros(num_anchors, dtype=torch.long, device=device) assigned_bb = torch.zeros((num_anchors, 4), dtype=torch.float32, device=device) # Label classes of anchor boxes using their assigned ground-truth # bounding boxes. If an anchor box is not assigned any, we label its # class as background (the value remains zero) indices_true = torch.nonzero(anchors_bbox_map >= 0) bb_idx = anchors_bbox_map[indices_true] class_labels[indices_true] = label[bb_idx, 0].long() + 1 assigned_bb[indices_true] = label[bb_idx, 1:] # Offset transformation offset = offset_boxes(anchors, assigned_bb) * bbox_mask batch_offset.append(offset.reshape(-1)) batch_mask.append(bbox_mask.reshape(-1)) batch_class_labels.append(class_labels) bbox_offset = torch.stack(batch_offset) bbox_mask = torch.stack(batch_mask) class_labels = torch.stack(batch_class_labels) return (bbox_offset, bbox_mask, class_labels) def offset_inverse(anchors, offset_preds): """Predict bounding boxes based on anchor boxes with predicted offsets. Defined in :numref:`subsec_labeling-anchor-boxes`""" anc = d2l.box_corner_to_center(anchors) pred_bbox_xy = (offset_preds[:, :2] * anc[:, 2:] / 10) + anc[:, :2] pred_bbox_wh = d2l.exp(offset_preds[:, 2:] / 5) * anc[:, 2:] pred_bbox = d2l.concat((pred_bbox_xy, pred_bbox_wh), axis=1) predicted_bbox = d2l.box_center_to_corner(pred_bbox) return predicted_bbox def nms(boxes, scores, iou_threshold): """Sort confidence scores of predicted bounding boxes. Defined in :numref:`subsec_predicting-bounding-boxes-nms`""" B = torch.argsort(scores, dim=-1, descending=True) keep = [] # Indices of predicted bounding boxes that will be kept while B.numel() > 0: i = B[0] keep.append(i) if B.numel() == 1: break iou = box_iou(boxes[i, :].reshape(-1, 4), boxes[B[1:], :].reshape(-1, 4)).reshape(-1) inds = torch.nonzero(iou <= iou_threshold).reshape(-1) B = B[inds + 1] return d2l.tensor(keep, device=boxes.device) def multibox_detection(cls_probs, offset_preds, anchors, nms_threshold=0.5, pos_threshold=0.009999999): """Predict bounding boxes using non-maximum suppression. Defined in :numref:`subsec_predicting-bounding-boxes-nms`""" device, batch_size = cls_probs.device, cls_probs.shape[0] anchors = anchors.squeeze(0) num_classes, num_anchors = cls_probs.shape[1], cls_probs.shape[2] out = [] for i in range(batch_size): cls_prob, offset_pred = cls_probs[i], offset_preds[i].reshape(-1, 4) conf, class_id = torch.max(cls_prob[1:], 0) predicted_bb = offset_inverse(anchors, offset_pred) keep = nms(predicted_bb, conf, nms_threshold) # Find all non-`keep` indices and set the class to background all_idx = torch.arange(num_anchors, dtype=torch.long, device=device) combined =, all_idx)) uniques, counts = combined.unique(return_counts=True) non_keep = uniques[counts == 1] all_id_sorted =, non_keep)) class_id[non_keep] = -1 class_id = class_id[all_id_sorted] conf, predicted_bb = conf[all_id_sorted], predicted_bb[all_id_sorted] # Here `pos_threshold` is a threshold for positive (non-background) # predictions below_min_idx = (conf < pos_threshold) class_id[below_min_idx] = -1 conf[below_min_idx] = 1 - conf[below_min_idx] pred_info =, conf.unsqueeze(1), predicted_bb), dim=1) out.append(pred_info) return d2l.stack(out) d2l.DATA_HUB['banana-detection'] = ( d2l.DATA_URL + '', '5de26c8fce5ccdea9f91267273464dc968d20d72') def read_data_bananas(is_train=True): """Read the banana detection dataset images and labels. Defined in :numref:`sec_object-detection-dataset`""" data_dir = d2l.download_extract('banana-detection') csv_fname = os.path.join(data_dir, 'bananas_train' if is_train else 'bananas_val', 'label.csv') csv_data = pd.read_csv(csv_fname) csv_data = csv_data.set_index('img_name') images, targets = [], [] for img_name, target in csv_data.iterrows(): images.append( os.path.join(data_dir, 'bananas_train' if is_train else 'bananas_val', 'images', f'{img_name}'))) # Here `target` contains (class, upper-left x, upper-left y, # lower-right x, lower-right y), where all the images have the same # banana class (index 0) targets.append(list(target)) return images, torch.tensor(targets).unsqueeze(1) / 256 class BananasDataset( """A customized dataset to load the banana detection dataset. Defined in :numref:`sec_object-detection-dataset`""" def __init__(self, is_train): self.features, self.labels = read_data_bananas(is_train) print('read ' + str(len(self.features)) + (f' training examples' if is_train else f' validation examples')) def __getitem__(self, idx): return (self.features[idx].float(), self.labels[idx]) def __len__(self): return len(self.features) def load_data_bananas(batch_size): """Load the banana detection dataset. Defined in :numref:`sec_object-detection-dataset`""" train_iter =, batch_size, shuffle=True) val_iter =, batch_size) return train_iter, val_iter d2l.DATA_HUB['voc2012'] = (d2l.DATA_URL + 'VOCtrainval_11-May-2012.tar', '4e443f8a2eca6b1dac8a6c57641b67dd40621a49') def read_voc_images(voc_dir, is_train=True): """Read all VOC feature and label images. Defined in :numref:`sec_semantic_segmentation`""" txt_fname = os.path.join(voc_dir, 'ImageSets', 'Segmentation', 'train.txt' if is_train else 'val.txt') mode = with open(txt_fname, 'r') as f: images = features, labels = [], [] for i, fname in enumerate(images): features.append( voc_dir, 'JPEGImages', f'{fname}.jpg'))) labels.append( voc_dir, 'SegmentationClass' ,f'{fname}.png'), mode)) return features, labels VOC_COLORMAP = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]] VOC_CLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor'] def voc_colormap2label(): """Build the mapping from RGB to class indices for VOC labels. Defined in :numref:`sec_semantic_segmentation`""" colormap2label = torch.zeros(256 ** 3, dtype=torch.long) for i, colormap in enumerate(VOC_COLORMAP): colormap2label[ (colormap[0] * 256 + colormap[1]) * 256 + colormap[2]] = i return colormap2label def voc_label_indices(colormap, colormap2label): """Map any RGB values in VOC labels to their class indices. Defined in :numref:`sec_semantic_segmentation`""" colormap = colormap.permute(1, 2, 0).numpy().astype('int32') idx = ((colormap[:, :, 0] * 256 + colormap[:, :, 1]) * 256 + colormap[:, :, 2]) return colormap2label[idx] def voc_rand_crop(feature, label, height, width): """Randomly crop both feature and label images. Defined in :numref:`sec_semantic_segmentation`""" rect = torchvision.transforms.RandomCrop.get_params( feature, (height, width)) feature = torchvision.transforms.functional.crop(feature, *rect) label = torchvision.transforms.functional.crop(label, *rect) return feature, label class VOCSegDataset( """A customized dataset to load the VOC dataset. Defined in :numref:`sec_semantic_segmentation`""" def __init__(self, is_train, crop_size, voc_dir): self.transform = torchvision.transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) self.crop_size = crop_size features, labels = read_voc_images(voc_dir, is_train=is_train) self.features = [self.normalize_image(feature) for feature in self.filter(features)] self.labels = self.filter(labels) self.colormap2label = voc_colormap2label() print('read ' + str(len(self.features)) + ' examples') def normalize_image(self, img): return self.transform(img.float() / 255) def filter(self, imgs): return [img for img in imgs if ( img.shape[1] >= self.crop_size[0] and img.shape[2] >= self.crop_size[1])] def __getitem__(self, idx): feature, label = voc_rand_crop(self.features[idx], self.labels[idx], *self.crop_size) return (feature, voc_label_indices(label, self.colormap2label)) def __len__(self): return len(self.features) def load_data_voc(batch_size, crop_size): """Load the VOC semantic segmentation dataset. Defined in :numref:`sec_semantic_segmentation`""" voc_dir = d2l.download_extract('voc2012', os.path.join( 'VOCdevkit', 'VOC2012')) num_workers = d2l.get_dataloader_workers() train_iter = VOCSegDataset(True, crop_size, voc_dir), batch_size, shuffle=True, drop_last=True, num_workers=num_workers) test_iter = VOCSegDataset(False, crop_size, voc_dir), batch_size, drop_last=True, num_workers=num_workers) return train_iter, test_iter d2l.DATA_HUB['cifar10_tiny'] = (d2l.DATA_URL + '', '2068874e4b9a9f0fb07ebe0ad2b29754449ccacd') def read_csv_labels(fname): """Read `fname` to return a filename to label dictionary. Defined in :numref:`sec_kaggle_cifar10`""" with open(fname, 'r') as f: # Skip the file header line (column name) lines = f.readlines()[1:] tokens = [l.rstrip().split(',') for l in lines] return dict(((name, label) for name, label in tokens)) def copyfile(filename, target_dir): """Copy a file into a target directory. Defined in :numref:`sec_kaggle_cifar10`""" os.makedirs(target_dir, exist_ok=True) shutil.copy(filename, target_dir) def reorg_train_valid(data_dir, labels, valid_ratio): """Split the validation set out of the original training set. Defined in :numref:`sec_kaggle_cifar10`""" # The number of examples of the class that has the fewest examples in the # training dataset n = collections.Counter(labels.values()).most_common()[-1][1] # The number of examples per class for the validation set n_valid_per_label = max(1, math.floor(n * valid_ratio)) label_count = {} for train_file in os.listdir(os.path.join(data_dir, 'train')): label = labels[train_file.split('.')[0]] fname = os.path.join(data_dir, 'train', train_file) copyfile(fname, os.path.join(data_dir, 'train_valid_test', 'train_valid', label)) if label not in label_count or label_count[label] < n_valid_per_label: copyfile(fname, os.path.join(data_dir, 'train_valid_test', 'valid', label)) label_count[label] = label_count.get(label, 0) + 1 else: copyfile(fname, os.path.join(data_dir, 'train_valid_test', 'train', label)) return n_valid_per_label def reorg_test(data_dir): """Organize the testing set for data loading during prediction. Defined in :numref:`sec_kaggle_cifar10`""" for test_file in os.listdir(os.path.join(data_dir, 'test')): copyfile(os.path.join(data_dir, 'test', test_file), os.path.join(data_dir, 'train_valid_test', 'test', 'unknown')) d2l.DATA_HUB['dog_tiny'] = (d2l.DATA_URL + '', '0cb91d09b814ecdc07b50f31f8dcad3e81d6a86d') d2l.DATA_HUB['ptb'] = (d2l.DATA_URL + '', '319d85e578af0cdc590547f26231e4e31cdf1e42') def read_ptb(): """Load the PTB dataset into a list of text lines. Defined in :numref:`sec_word2vec_data`""" data_dir = d2l.download_extract('ptb') # Read the training set with open(os.path.join(data_dir, 'ptb.train.txt')) as f: raw_text = return [line.split() for line in raw_text.split('\n')] def subsample(sentences, vocab): """Subsample high-frequency words. Defined in :numref:`sec_word2vec_data`""" # Exclude unknown tokens ('<unk>') sentences = [[token for token in line if vocab[token] != vocab.unk] for line in sentences] counter = collections.Counter([ token for line in sentences for token in line]) num_tokens = sum(counter.values()) # Return True if `token` is kept during subsampling def keep(token): return(random.uniform(0, 1) < math.sqrt(1e-4 / counter[token] * num_tokens)) return ([[token for token in line if keep(token)] for line in sentences], counter) def get_centers_and_contexts(corpus, max_window_size): """Return center words and context words in skip-gram. Defined in :numref:`sec_word2vec_data`""" centers, contexts = [], [] for line in corpus: # To form a "center word--context word" pair, each sentence needs to # have at least 2 words if len(line) < 2: continue centers += line for i in range(len(line)): # Context window centered at `i` window_size = random.randint(1, max_window_size) indices = list(range(max(0, i - window_size), min(len(line), i + 1 + window_size))) # Exclude the center word from the context words indices.remove(i) contexts.append([line[idx] for idx in indices]) return centers, contexts class RandomGenerator: """Randomly draw among {1, ..., n} according to n sampling weights.""" def __init__(self, sampling_weights): """Defined in :numref:`sec_word2vec_data`""" # Exclude self.population = list(range(1, len(sampling_weights) + 1)) self.sampling_weights = sampling_weights self.candidates = [] self.i = 0 def draw(self): if self.i == len(self.candidates): # Cache `k` random sampling results self.candidates = random.choices( self.population, self.sampling_weights, k=10000) self.i = 0 self.i += 1 return self.candidates[self.i - 1] def get_negatives(all_contexts, vocab, counter, K): """Return noise words in negative sampling. Defined in :numref:`sec_word2vec_data`""" # Sampling weights for words with indices 1, 2, ... (index 0 is the # excluded unknown token) in the vocabulary sampling_weights = [counter[vocab.to_tokens(i)]**0.75 for i in range(1, len(vocab))] all_negatives, generator = [], RandomGenerator(sampling_weights) for contexts in all_contexts: negatives = [] while len(negatives) < len(contexts) * K: neg = generator.draw() # Noise words cannot be context words if neg not in contexts: negatives.append(neg) all_negatives.append(negatives) return all_negatives def batchify(data): """Return a minibatch of examples for skip-gram with negative sampling. Defined in :numref:`sec_word2vec_data`""" max_len = max(len(c) + len(n) for _, c, n in data) centers, contexts_negatives, masks, labels = [], [], [], [] for center, context, negative in data: cur_len = len(context) + len(negative) centers += [center] contexts_negatives += [context + negative + [0] * (max_len - cur_len)] masks += [[1] * cur_len + [0] * (max_len - cur_len)] labels += [[1] * len(context) + [0] * (max_len - len(context))] return (d2l.reshape(d2l.tensor(centers), (-1, 1)), d2l.tensor( contexts_negatives), d2l.tensor(masks), d2l.tensor(labels)) def load_data_ptb(batch_size, max_window_size, num_noise_words): """Download the PTB dataset and then load it into memory. Defined in :numref:`subsec_word2vec-minibatch-loading`""" num_workers = d2l.get_dataloader_workers() sentences = read_ptb() vocab = d2l.Vocab(sentences, min_freq=10) subsampled, counter = subsample(sentences, vocab) corpus = [vocab[line] for line in subsampled] all_centers, all_contexts = get_centers_and_contexts( corpus, max_window_size) all_negatives = get_negatives( all_contexts, vocab, counter, num_noise_words) class PTBDataset( def __init__(self, centers, contexts, negatives): assert len(centers) == len(contexts) == len(negatives) self.centers = centers self.contexts = contexts self.negatives = negatives def __getitem__(self, index): return (self.centers[index], self.contexts[index], self.negatives[index]) def __len__(self): return len(self.centers) dataset = PTBDataset(all_centers, all_contexts, all_negatives) data_iter =, batch_size, shuffle=True, collate_fn=batchify, num_workers=num_workers) return data_iter, vocab d2l.DATA_HUB['glove.6b.50d'] = (d2l.DATA_URL + '', '0b8703943ccdb6eb788e6f091b8946e82231bc4d') d2l.DATA_HUB['glove.6b.100d'] = (d2l.DATA_URL + '', 'cd43bfb07e44e6f27cbcc7bc9ae3d80284fdaf5a') d2l.DATA_HUB['glove.42b.300d'] = (d2l.DATA_URL + '', 'b5116e234e9eb9076672cfeabf5469f3eec904fa') d2l.DATA_HUB['wiki.en'] = (d2l.DATA_URL + '', 'c1816da3821ae9f43899be655002f6c723e91b88') class TokenEmbedding: """Token Embedding.""" def __init__(self, embedding_name): """Defined in :numref:`sec_synonyms`""" self.idx_to_token, self.idx_to_vec = self._load_embedding( embedding_name) self.unknown_idx = 0 self.token_to_idx = {token: idx for idx, token in enumerate(self.idx_to_token)} def _load_embedding(self, embedding_name): idx_to_token, idx_to_vec = ['<unk>'], [] data_dir = d2l.download_extract(embedding_name) # GloVe website: # fastText website: with open(os.path.join(data_dir, 'vec.txt'), 'r') as f: for line in f: elems = line.rstrip().split(' ') token, elems = elems[0], [float(elem) for elem in elems[1:]] # Skip header information, such as the top row in fastText if len(elems) > 1: idx_to_token.append(token) idx_to_vec.append(elems) idx_to_vec = [[0] * len(idx_to_vec[0])] + idx_to_vec return idx_to_token, d2l.tensor(idx_to_vec) def __getitem__(self, tokens): indices = [self.token_to_idx.get(token, self.unknown_idx) for token in tokens] vecs = self.idx_to_vec[d2l.tensor(indices)] return vecs def __len__(self): return len(self.idx_to_token) def get_tokens_and_segments(tokens_a, tokens_b=None): """Get tokens of the BERT input sequence and their segment IDs. Defined in :numref:`sec_bert`""" tokens = ['<cls>'] + tokens_a + ['<sep>'] # 0 and 1 are marking segment A and B, respectively segments = [0] * (len(tokens_a) + 2) if tokens_b is not None: tokens += tokens_b + ['<sep>'] segments += [1] * (len(tokens_b) + 1) return tokens, segments class BERTEncoder(nn.Module): """BERT encoder. Defined in :numref:`subsec_bert_input_rep`""" def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens, num_heads, num_blks, dropout, max_len=1000, **kwargs): super(BERTEncoder, self).__init__(**kwargs) self.token_embedding = nn.Embedding(vocab_size, num_hiddens) self.segment_embedding = nn.Embedding(2, num_hiddens) self.blks = nn.Sequential() for i in range(num_blks): self.blks.add_module(f"{i}", d2l.TransformerEncoderBlock( num_hiddens, ffn_num_hiddens, num_heads, dropout, True)) # In BERT, positional embeddings are learnable, thus we create a # parameter of positional embeddings that are long enough self.pos_embedding = nn.Parameter(torch.randn(1, max_len, num_hiddens)) def forward(self, tokens, segments, valid_lens): # Shape of `X` remains unchanged in the following code snippet: # (batch size, max sequence length, `num_hiddens`) X = self.token_embedding(tokens) + self.segment_embedding(segments) X = X + self.pos_embedding[:, :X.shape[1], :] for blk in self.blks: X = blk(X, valid_lens) return X class MaskLM(nn.Module): """The masked language model task of BERT. Defined in :numref:`subsec_bert_input_rep`""" def __init__(self, vocab_size, num_hiddens, **kwargs): super(MaskLM, self).__init__(**kwargs) self.mlp = nn.Sequential(nn.LazyLinear(num_hiddens), nn.ReLU(), nn.LayerNorm(num_hiddens), nn.LazyLinear(vocab_size)) def forward(self, X, pred_positions): num_pred_positions = pred_positions.shape[1] pred_positions = pred_positions.reshape(-1) batch_size = X.shape[0] batch_idx = torch.arange(0, batch_size) # Suppose that `batch_size` = 2, `num_pred_positions` = 3, then # `batch_idx` is `torch.tensor([0, 0, 0, 1, 1, 1])` batch_idx = torch.repeat_interleave(batch_idx, num_pred_positions) masked_X = X[batch_idx, pred_positions] masked_X = masked_X.reshape((batch_size, num_pred_positions, -1)) mlm_Y_hat = self.mlp(masked_X) return mlm_Y_hat class NextSentencePred(nn.Module): """The next sentence prediction task of BERT. Defined in :numref:`subsec_mlm`""" def __init__(self, **kwargs): super(NextSentencePred, self).__init__(**kwargs) self.output = nn.LazyLinear(2) def forward(self, X): # `X` shape: (batch size, `num_hiddens`) return self.output(X) class BERTModel(nn.Module): """The BERT model. Defined in :numref:`subsec_nsp`""" def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens, num_heads, num_blks, dropout, max_len=1000): super(BERTModel, self).__init__() self.encoder = BERTEncoder(vocab_size, num_hiddens, ffn_num_hiddens, num_heads, num_blks, dropout, max_len=max_len) self.hidden = nn.Sequential(nn.LazyLinear(num_hiddens), nn.Tanh()) self.mlm = MaskLM(vocab_size, num_hiddens) self.nsp = NextSentencePred() def forward(self, tokens, segments, valid_lens=None, pred_positions=None): encoded_X = self.encoder(tokens, segments, valid_lens) if pred_positions is not None: mlm_Y_hat = self.mlm(encoded_X, pred_positions) else: mlm_Y_hat = None # The hidden layer of the MLP classifier for next sentence prediction. # 0 is the index of the '<cls>' token nsp_Y_hat = self.nsp(self.hidden(encoded_X[:, 0, :])) return encoded_X, mlm_Y_hat, nsp_Y_hat d2l.DATA_HUB['wikitext-2'] = ( '' '', '3c914d17d80b1459be871a5039ac23e752a53cbe') def _read_wiki(data_dir): """Defined in :numref:`sec_bert-dataset`""" file_name = os.path.join(data_dir, 'wiki.train.tokens') with open(file_name, 'r') as f: lines = f.readlines() # Uppercase letters are converted to lowercase ones paragraphs = [line.strip().lower().split(' . ') for line in lines if len(line.split(' . ')) >= 2] random.shuffle(paragraphs) return paragraphs def _get_next_sentence(sentence, next_sentence, paragraphs): """Defined in :numref:`sec_bert-dataset`""" if random.random() < 0.5: is_next = True else: # `paragraphs` is a list of lists of lists next_sentence = random.choice(random.choice(paragraphs)) is_next = False return sentence, next_sentence, is_next def _get_nsp_data_from_paragraph(paragraph, paragraphs, vocab, max_len): """Defined in :numref:`sec_bert-dataset`""" nsp_data_from_paragraph = [] for i in range(len(paragraph) - 1): tokens_a, tokens_b, is_next = _get_next_sentence( paragraph[i], paragraph[i + 1], paragraphs) # Consider 1 '<cls>' token and 2 '<sep>' tokens if len(tokens_a) + len(tokens_b) + 3 > max_len: continue tokens, segments = d2l.get_tokens_and_segments(tokens_a, tokens_b) nsp_data_from_paragraph.append((tokens, segments, is_next)) return nsp_data_from_paragraph def _replace_mlm_tokens(tokens, candidate_pred_positions, num_mlm_preds, vocab): """Defined in :numref:`sec_bert-dataset`""" # For the input of a masked language model, make a new copy of tokens and # replace some of them by '<mask>' or random tokens mlm_input_tokens = [token for token in tokens] pred_positions_and_labels = [] # Shuffle for getting 15% random tokens for prediction in the masked # language modeling task random.shuffle(candidate_pred_positions) for mlm_pred_position in candidate_pred_positions: if len(pred_positions_and_labels) >= num_mlm_preds: break masked_token = None # 80% of the time: replace the word with the '<mask>' token if random.random() < 0.8: masked_token = '<mask>' else: # 10% of the time: keep the word unchanged if random.random() < 0.5: masked_token = tokens[mlm_pred_position] # 10% of the time: replace the word with a random word else: masked_token = random.choice(vocab.idx_to_token) mlm_input_tokens[mlm_pred_position] = masked_token pred_positions_and_labels.append( (mlm_pred_position, tokens[mlm_pred_position])) return mlm_input_tokens, pred_positions_and_labels def _get_mlm_data_from_tokens(tokens, vocab): """Defined in :numref:`subsec_prepare_mlm_data`""" candidate_pred_positions = [] # `tokens` is a list of strings for i, token in enumerate(tokens): # Special tokens are not predicted in the masked language modeling # task if token in ['<cls>', '<sep>']: continue candidate_pred_positions.append(i) # 15% of random tokens are predicted in the masked language modeling task num_mlm_preds = max(1, round(len(tokens) * 0.15)) mlm_input_tokens, pred_positions_and_labels = _replace_mlm_tokens( tokens, candidate_pred_positions, num_mlm_preds, vocab) pred_positions_and_labels = sorted(pred_positions_and_labels, key=lambda x: x[0]) pred_positions = [v[0] for v in pred_positions_and_labels] mlm_pred_labels = [v[1] for v in pred_positions_and_labels] return vocab[mlm_input_tokens], pred_positions, vocab[mlm_pred_labels] def _pad_bert_inputs(examples, max_len, vocab): """Defined in :numref:`subsec_prepare_mlm_data`""" max_num_mlm_preds = round(max_len * 0.15) all_token_ids, all_segments, valid_lens, = [], [], [] all_pred_positions, all_mlm_weights, all_mlm_labels = [], [], [] nsp_labels = [] for (token_ids, pred_positions, mlm_pred_label_ids, segments, is_next) in examples: all_token_ids.append(torch.tensor(token_ids + [vocab['<pad>']] * ( max_len - len(token_ids)), dtype=torch.long)) all_segments.append(torch.tensor(segments + [0] * ( max_len - len(segments)), dtype=torch.long)) # `valid_lens` excludes count of '<pad>' tokens valid_lens.append(torch.tensor(len(token_ids), dtype=torch.float32)) all_pred_positions.append(torch.tensor(pred_positions + [0] * ( max_num_mlm_preds - len(pred_positions)), dtype=torch.long)) # Predictions of padded tokens will be filtered out in the loss via # multiplication of 0 weights all_mlm_weights.append( torch.tensor([1.0] * len(mlm_pred_label_ids) + [0.0] * ( max_num_mlm_preds - len(pred_positions)), dtype=torch.float32)) all_mlm_labels.append(torch.tensor(mlm_pred_label_ids + [0] * ( max_num_mlm_preds - len(mlm_pred_label_ids)), dtype=torch.long)) nsp_labels.append(torch.tensor(is_next, dtype=torch.long)) return (all_token_ids, all_segments, valid_lens, all_pred_positions, all_mlm_weights, all_mlm_labels, nsp_labels) class _WikiTextDataset( """Defined in :numref:`subsec_prepare_mlm_data`""" def __init__(self, paragraphs, max_len): # Input `paragraphs[i]` is a list of sentence strings representing a # paragraph; while output `paragraphs[i]` is a list of sentences # representing a paragraph, where each sentence is a list of tokens paragraphs = [d2l.tokenize( paragraph, token='word') for paragraph in paragraphs] sentences = [sentence for paragraph in paragraphs for sentence in paragraph] self.vocab = d2l.Vocab(sentences, min_freq=5, reserved_tokens=[ '<pad>', '<mask>', '<cls>', '<sep>']) # Get data for the next sentence prediction task examples = [] for paragraph in paragraphs: examples.extend(_get_nsp_data_from_paragraph( paragraph, paragraphs, self.vocab, max_len)) # Get data for the masked language model task examples = [(_get_mlm_data_from_tokens(tokens, self.vocab) + (segments, is_next)) for tokens, segments, is_next in examples] # Pad inputs (self.all_token_ids, self.all_segments, self.valid_lens, self.all_pred_positions, self.all_mlm_weights, self.all_mlm_labels, self.nsp_labels) = _pad_bert_inputs( examples, max_len, self.vocab) def __getitem__(self, idx): return (self.all_token_ids[idx], self.all_segments[idx], self.valid_lens[idx], self.all_pred_positions[idx], self.all_mlm_weights[idx], self.all_mlm_labels[idx], self.nsp_labels[idx]) def __len__(self): return len(self.all_token_ids) def load_data_wiki(batch_size, max_len): """Load the WikiText-2 dataset. Defined in :numref:`subsec_prepare_mlm_data`""" num_workers = d2l.get_dataloader_workers() data_dir = d2l.download_extract('wikitext-2', 'wikitext-2') paragraphs = _read_wiki(data_dir) train_set = _WikiTextDataset(paragraphs, max_len) train_iter =, batch_size, shuffle=True, num_workers=num_workers) return train_iter, train_set.vocab def _get_batch_loss_bert(net, loss, vocab_size, tokens_X, segments_X, valid_lens_x, pred_positions_X, mlm_weights_X, mlm_Y, nsp_y): """Defined in :numref:`sec_bert-pretraining`""" # Forward pass _, mlm_Y_hat, nsp_Y_hat = net(tokens_X, segments_X, valid_lens_x.reshape(-1), pred_positions_X) # Compute masked language model loss mlm_l = loss(mlm_Y_hat.reshape(-1, vocab_size), mlm_Y.reshape(-1)) *\ mlm_weights_X.reshape(-1, 1) mlm_l = mlm_l.sum() / (mlm_weights_X.sum() + 1e-8) # Compute next sentence prediction loss nsp_l = loss(nsp_Y_hat, nsp_y) l = mlm_l + nsp_l return mlm_l, nsp_l, l d2l.DATA_HUB['aclImdb'] = (d2l.DATA_URL + 'aclImdb_v1.tar.gz', '01ada507287d82875905620988597833ad4e0903') def read_imdb(data_dir, is_train): """Read the IMDb review dataset text sequences and labels. Defined in :numref:`sec_sentiment`""" data, labels = [], [] for label in ('pos', 'neg'): folder_name = os.path.join(data_dir, 'train' if is_train else 'test', label) for file in os.listdir(folder_name): with open(os.path.join(folder_name, file), 'rb') as f: review ='utf-8').replace('\n', '') data.append(review) labels.append(1 if label == 'pos' else 0) return data, labels def load_data_imdb(batch_size, num_steps=500): """Return data iterators and the vocabulary of the IMDb review dataset. Defined in :numref:`sec_sentiment`""" data_dir = d2l.download_extract('aclImdb', 'aclImdb') train_data = read_imdb(data_dir, True) test_data = read_imdb(data_dir, False) train_tokens = d2l.tokenize(train_data[0], token='word') test_tokens = d2l.tokenize(test_data[0], token='word') vocab = d2l.Vocab(train_tokens, min_freq=5) train_features = torch.tensor([d2l.truncate_pad( vocab[line], num_steps, vocab['<pad>']) for line in train_tokens]) test_features = torch.tensor([d2l.truncate_pad( vocab[line], num_steps, vocab['<pad>']) for line in test_tokens]) train_iter = d2l.load_array((train_features, torch.tensor(train_data[1])), batch_size) test_iter = d2l.load_array((test_features, torch.tensor(test_data[1])), batch_size, is_train=False) return train_iter, test_iter, vocab def predict_sentiment(net, vocab, sequence): """Predict the sentiment of a text sequence. Defined in :numref:`sec_sentiment_rnn`""" sequence = torch.tensor(vocab[sequence.split()], device=d2l.try_gpu()) label = torch.argmax(net(sequence.reshape(1, -1)), dim=1) return 'positive' if label == 1 else 'negative' d2l.DATA_HUB['SNLI'] = ( '', '9fcde07509c7e87ec61c640c1b2753d9041758e4') def read_snli(data_dir, is_train): """Read the SNLI dataset into premises, hypotheses, and labels. Defined in :numref:`sec_natural-language-inference-and-dataset`""" def extract_text(s): # Remove information that will not be used by us s = re.sub('\\(', '', s) s = re.sub('\\)', '', s) # Substitute two or more consecutive whitespace with space s = re.sub('\\s{2,}', ' ', s) return s.strip() label_set = {'entailment': 0, 'contradiction': 1, 'neutral': 2} file_name = os.path.join(data_dir, 'snli_1.0_train.txt' if is_train else 'snli_1.0_test.txt') with open(file_name, 'r') as f: rows = [row.split('\t') for row in f.readlines()[1:]] premises = [extract_text(row[1]) for row in rows if row[0] in label_set] hypotheses = [extract_text(row[2]) for row in rows if row[0] in label_set] labels = [label_set[row[0]] for row in rows if row[0] in label_set] return premises, hypotheses, labels class SNLIDataset( """A customized dataset to load the SNLI dataset. Defined in :numref:`sec_natural-language-inference-and-dataset`""" def __init__(self, dataset, num_steps, vocab=None): self.num_steps = num_steps all_premise_tokens = d2l.tokenize(dataset[0]) all_hypothesis_tokens = d2l.tokenize(dataset[1]) if vocab is None: self.vocab = d2l.Vocab(all_premise_tokens + all_hypothesis_tokens, min_freq=5, reserved_tokens=['<pad>']) else: self.vocab = vocab self.premises = self._pad(all_premise_tokens) self.hypotheses = self._pad(all_hypothesis_tokens) self.labels = torch.tensor(dataset[2]) print('read ' + str(len(self.premises)) + ' examples') def _pad(self, lines): return torch.tensor([d2l.truncate_pad( self.vocab[line], self.num_steps, self.vocab['<pad>']) for line in lines]) def __getitem__(self, idx): return (self.premises[idx], self.hypotheses[idx]), self.labels[idx] def __len__(self): return len(self.premises) def load_data_snli(batch_size, num_steps=50): """Download the SNLI dataset and return data iterators and vocabulary. Defined in :numref:`sec_natural-language-inference-and-dataset`""" num_workers = d2l.get_dataloader_workers() data_dir = d2l.download_extract('SNLI') train_data = read_snli(data_dir, True) test_data = read_snli(data_dir, False) train_set = SNLIDataset(train_data, num_steps) test_set = SNLIDataset(test_data, num_steps, train_set.vocab) train_iter =, batch_size, shuffle=True, num_workers=num_workers) test_iter =, batch_size, shuffle=False, num_workers=num_workers) return train_iter, test_iter, train_set.vocab def predict_snli(net, vocab, premise, hypothesis): """Predict the logical relationship between the premise and hypothesis. Defined in :numref:`sec_natural-language-inference-attention`""" net.eval() premise = torch.tensor(vocab[premise], device=d2l.try_gpu()) hypothesis = torch.tensor(vocab[hypothesis], device=d2l.try_gpu()) label = torch.argmax(net([premise.reshape((1, -1)), hypothesis.reshape((1, -1))]), dim=1) return 'entailment' if label == 0 else 'contradiction' if label == 1 \ else 'neutral' def rbfkernel(x1, x2, ls=4.): dist = distance_matrix(np.expand_dims(x1, 1), np.expand_dims(x2, 1)) return np.exp(-(1. / ls / 2) * (dist ** 2)) class HPOTrainer(d2l.Trainer): """Defined in :numref:`sec_definition_hpo`""" def validation_error(self): self.model.eval() accuracy = 0 val_batch_idx = 0 for batch in self.val_dataloader: with torch.no_grad(): x, y = self.prepare_batch(batch) y_hat = self.model(x) accuracy += self.model.accuracy(y_hat, y) val_batch_idx += 1 return 1 - accuracy / val_batch_idx class HPOSearcher(d2l.HyperParameters): """Defined in :numref:`sec_api_hpo`""" def sample_configuration() -> dict: raise NotImplementedError def update(self, config: dict, error: float, additional_info=None): pass class RandomSearcher(HPOSearcher): """Defined in :numref:`sec_api_hpo`""" def __init__(self, config_space: dict, initial_config=None): self.save_hyperparameters() def sample_configuration(self) -> dict: if self.initial_config is not None: result = self.initial_config self.initial_config = None else: result = { name: domain.rvs() for name, domain in self.config_space.items() } return result class HPOScheduler(d2l.HyperParameters): """Defined in :numref:`sec_api_hpo`""" def suggest(self) -> dict: raise NotImplementedError def update(self, config: dict, error: float, info=None): raise NotImplementedError class BasicScheduler(HPOScheduler): """Defined in :numref:`sec_api_hpo`""" def __init__(self, searcher: HPOSearcher): self.save_hyperparameters() def suggest(self) -> dict: return self.searcher.sample_configuration() def update(self, config: dict, error: float, info=None): self.searcher.update(config, error, additional_info=info) class HPOTuner(d2l.HyperParameters): """Defined in :numref:`sec_api_hpo`""" def __init__(self, scheduler: HPOScheduler, objective: callable): self.save_hyperparameters() # Bookeeping results for plotting self.incumbent = None self.incumbent_error = None self.incumbent_trajectory = [] self.cumulative_runtime = [] self.current_runtime = 0 self.records = [] def run(self, number_of_trials): for i in range(number_of_trials): start_time = time.time() config = self.scheduler.suggest() print(f"Trial {i}: config = {config}") error = self.objective(**config) error = float(d2l.numpy(error.cpu())) self.scheduler.update(config, error) runtime = time.time() - start_time self.bookkeeping(config, error, runtime) print(f" error = {error}, runtime = {runtime}") def bookkeeping(self, config: dict, error: float, runtime: float): """Defined in :numref:`sec_api_hpo`""" self.records.append({"config": config, "error": error, "runtime": runtime}) # Check if the last hyperparameter configuration performs better # than the incumbent if self.incumbent is None or self.incumbent_error > error: self.incumbent = config self.incumbent_error = error # Add current best observed performance to the optimization trajectory self.incumbent_trajectory.append(self.incumbent_error) # Update runtime self.current_runtime += runtime self.cumulative_runtime.append(self.current_runtime) def hpo_objective_lenet(learning_rate, batch_size, max_epochs=10): """Defined in :numref:`sec_api_hpo`""" model = d2l.LeNet(lr=learning_rate, num_classes=10) trainer = d2l.HPOTrainer(max_epochs=max_epochs, num_gpus=1) data = d2l.FashionMNIST(batch_size=batch_size) model.apply_init([next(iter(data.get_dataloader(True)))[0]], d2l.init_cnn), data=data) validation_error = trainer.validation_error() return validation_error class SuccessiveHalvingScheduler(d2l.HPOScheduler): """Defined in :numref:`sec_mf_hpo`""" def __init__(self, searcher, eta, r_min, r_max, prefact=1): self.save_hyperparameters() # Compute K, which is later used to determine the number of configurations self.K = int(np.log(r_max / r_min) / np.log(eta)) # Define the rungs self.rung_levels = [r_min * eta ** k for k in range(self.K + 1)] if r_max not in self.rung_levels: # The final rung should be r_max self.rung_levels.append(r_max) self.K += 1 # Bookkeeping self.observed_error_at_rungs = defaultdict(list) self.all_observed_error_at_rungs = defaultdict(list) # Our processing queue self.queue = [] def suggest(self): """Defined in :numref:`sec_mf_hpo_sh`""" if len(self.queue) == 0: # Start a new round of successive halving # Number of configurations for the first rung: n0 = int(self.prefact * self.eta ** self.K) for _ in range(n0): config = self.searcher.sample_configuration() config["max_epochs"] = self.r_min # Set r = r_min self.queue.append(config) # Return an element from the queue return self.queue.pop() def update(self, config: dict, error: float, info=None): """Defined in :numref:`sec_mf_hpo_sh`""" ri = int(config["max_epochs"]) # Rung r_i # Update our searcher, e.g if we use Bayesian optimization later self.searcher.update(config, error, additional_info=info) self.all_observed_error_at_rungs[ri].append((config, error)) if ri < self.r_max: # Bookkeeping self.observed_error_at_rungs[ri].append((config, error)) # Determine how many configurations should be evaluated on this rung ki = self.K - self.rung_levels.index(ri) ni = int(self.prefact * self.eta ** ki) # If we observed all configuration on this rung r_i, we estimate the # top 1 / eta configuration, add them to queue and promote them for # the next rung r_{i+1} if len(self.observed_error_at_rungs[ri]) >= ni: kiplus1 = ki - 1 niplus1 = int(self.prefact * self.eta ** kiplus1) best_performing_configurations = self.get_top_n_configurations( rung_level=ri, n=niplus1 ) riplus1 = self.rung_levels[self.K - kiplus1] # r_{i+1} # Queue may not be empty: insert new entries at the beginning self.queue = [ dict(config, max_epochs=riplus1) for config in best_performing_configurations ] + self.queue self.observed_error_at_rungs[ri] = [] # Reset def get_top_n_configurations(self, rung_level, n): """Defined in :numref:`sec_mf_hpo_sh`""" rung = self.observed_error_at_rungs[rung_level] if not rung: return [] sorted_rung = sorted(rung, key=lambda x: x[1]) return [x[0] for x in sorted_rung[:n]] def update_D(X, Z, net_D, net_G, loss, trainer_D): """Update discriminator. Defined in :numref:`sec_basic_gan`""" batch_size = X.shape[0] ones = torch.ones((batch_size,), device=X.device) zeros = torch.zeros((batch_size,), device=X.device) trainer_D.zero_grad() real_Y = net_D(X) fake_X = net_G(Z) # Do not need to compute gradient for `net_G`, detach it from # computing gradients. fake_Y = net_D(fake_X.detach()) loss_D = (loss(real_Y, ones.reshape(real_Y.shape)) + loss(fake_Y, zeros.reshape(fake_Y.shape))) / 2 loss_D.backward() trainer_D.step() return loss_D def update_G(Z, net_D, net_G, loss, trainer_G): """Update generator. Defined in :numref:`sec_basic_gan`""" batch_size = Z.shape[0] ones = torch.ones((batch_size,), device=Z.device) trainer_G.zero_grad() # We could reuse `fake_X` from `update_D` to save computation fake_X = net_G(Z) # Recomputing `fake_Y` is needed since `net_D` is changed fake_Y = net_D(fake_X) loss_G = loss(fake_Y, ones.reshape(fake_Y.shape)) loss_G.backward() trainer_G.step() return loss_G d2l.DATA_HUB['pokemon'] = (d2l.DATA_URL + '', 'c065c0e2593b8b161a2d7873e42418bf6a21106c') def frozen_lake(seed): """Defined in :numref:`sec_utils`""" # See to learn more about this env # How to process env.P.items is adpated from env = gym.make('FrozenLake-v1', is_slippery=False) env.seed(seed) env.action_space.np_random.seed(seed) env.action_space.seed(seed) env_info = {} env_info['desc'] = env.desc # 2D array specifying what each grid item means env_info['num_states'] = env.nS # Number of observations/states or obs/state dim env_info['num_actions'] = env.nA # Number of actions or action dim # Define indices for (transition probability, nextstate, reward, done) tuple env_info['trans_prob_idx'] = 0 # Index of transition probability entry env_info['nextstate_idx'] = 1 # Index of next state entry env_info['reward_idx'] = 2 # Index of reward entry env_info['done_idx'] = 3 # Index of done entry env_info['mdp'] = {} env_info['env'] = env for (s, others) in env.P.items(): # others(s) = {a0: [ (p(s'|s,a0), s', reward, done),...], a1:[...], ...} for (a, pxrds) in others.items(): # pxrds is [(p1,next1,r1,d1),(p2,next2,r2,d2),..]. # e.g. [(0.3, 0, 0, False), (0.3, 0, 0, False), (0.3, 4, 1, False)] env_info['mdp'][(s,a)] = pxrds return env_info def make_env(name ='', seed=0): """Defined in :numref:`sec_utils`""" # Input parameters: # name: specifies a gym environment. # For Value iteration, only FrozenLake-v1 is supported. if name == 'FrozenLake-v1': return frozen_lake(seed) else: raise ValueError("%s env is not supported in this Notebook") def show_value_function_progress(env_desc, V, pi): """Defined in :numref:`sec_utils`""" # This function visualizes how value and policy changes over time. # V: [num_iters, num_states] # pi: [num_iters, num_states] # How to visualize value function is adapted (but changed) from: num_iters = V.shape[0] fig, ax = plt.subplots(figsize=(15, 15)) for k in range(V.shape[0]): plt.subplot(4, 4, k + 1) plt.imshow(V[k].reshape(4,4), cmap="bone") ax = plt.gca() ax.set_xticks(np.arange(0, 5)-.5, minor=True) ax.set_yticks(np.arange(0, 5)-.5, minor=True) ax.grid(which="minor", color="w", linestyle='-', linewidth=3) ax.tick_params(which="minor", bottom=False, left=False) ax.set_xticks([]) ax.set_yticks([]) # LEFT action: 0, DOWN action: 1 # RIGHT action: 2, UP action: 3 action2dxdy = {0:(-.25, 0),1: (0, .25), 2:(0.25, 0),3: (-.25, 0)} for y in range(4): for x in range(4): action = pi[k].reshape(4,4)[y, x] dx, dy = action2dxdy[action] if env_desc[y,x].decode() == 'H': ax.text(x, y, str(env_desc[y,x].decode()), ha="center", va="center", color="y", size=20, fontweight='bold') elif env_desc[y,x].decode() == 'G': ax.text(x, y, str(env_desc[y,x].decode()), ha="center", va="center", color="w", size=20, fontweight='bold') else: ax.text(x, y, str(env_desc[y,x].decode()), ha="center", va="center", color="g", size=15, fontweight='bold') # No arrow for cells with G and H labels if env_desc[y,x].decode() != 'G' and env_desc[y,x].decode() != 'H': ax.arrow(x, y, dx, dy, color='r', head_width=0.2, head_length=0.15) ax.set_title("Step = " + str(k + 1), fontsize=20) fig.tight_layout() def show_Q_function_progress(env_desc, V_all, pi_all): """Defined in :numref:`sec_utils`""" # This function visualizes how value and policy changes over time. # V: [num_iters, num_states] # pi: [num_iters, num_states] # We want to only shows few values num_iters_all = V_all.shape[0] num_iters = num_iters_all // 10 vis_indx = np.arange(0, num_iters_all, num_iters).tolist() vis_indx.append(num_iters_all - 1) V = np.zeros((len(vis_indx), V_all.shape[1])) pi = np.zeros((len(vis_indx), V_all.shape[1])) for c, i in enumerate(vis_indx): V[c] = V_all[i] pi[c] = pi_all[i] num_iters = V.shape[0] fig, ax = plt.subplots(figsize=(15, 15)) for k in range(V.shape[0]): plt.subplot(4, 4, k + 1) plt.imshow(V[k].reshape(4,4), cmap="bone") ax = plt.gca() ax.set_xticks(np.arange(0, 5)-.5, minor=True) ax.set_yticks(np.arange(0, 5)-.5, minor=True) ax.grid(which="minor", color="w", linestyle='-', linewidth=3) ax.tick_params(which="minor", bottom=False, left=False) ax.set_xticks([]) ax.set_yticks([]) # LEFT action: 0, DOWN action: 1 # RIGHT action: 2, UP action: 3 action2dxdy = {0:(-.25, 0),1:(0, .25), 2:(0.25, 0),3:(-.25, 0)} for y in range(4): for x in range(4): action = pi[k].reshape(4,4)[y, x] dx, dy = action2dxdy[action] if env_desc[y,x].decode() == 'H': ax.text(x, y, str(env_desc[y,x].decode()), ha="center", va="center", color="y", size=20, fontweight='bold') elif env_desc[y,x].decode() == 'G': ax.text(x, y, str(env_desc[y,x].decode()), ha="center", va="center", color="w", size=20, fontweight='bold') else: ax.text(x, y, str(env_desc[y,x].decode()), ha="center", va="center", color="g", size=15, fontweight='bold') # No arrow for cells with G and H labels if env_desc[y,x].decode() != 'G' and env_desc[y,x].decode() != 'H': ax.arrow(x, y, dx, dy, color='r', head_width=0.2, head_length=0.15) ax.set_title("Step = " + str(vis_indx[k] + 1), fontsize=20) fig.tight_layout() def load_array(data_arrays, batch_size, is_train=True): """Construct a PyTorch data iterator. Defined in :numref:`sec_utils`""" dataset =*data_arrays) return, batch_size, shuffle=is_train) def synthetic_data(w, b, num_examples): """Generate y = Xw + b + noise. Defined in :numref:`sec_utils`""" X = d2l.normal(0, 1, (num_examples, len(w))) y = d2l.matmul(X, w) + b y += d2l.normal(0, 0.01, y.shape) return X, d2l.reshape(y, (-1, 1)) def sgd(params, lr, batch_size): """Minibatch stochastic gradient descent. Defined in :numref:`sec_utils`""" with torch.no_grad(): for param in params: param -= lr * param.grad / batch_size param.grad.zero_() def get_dataloader_workers(): """Use 4 processes to read the data. Defined in :numref:`sec_utils`""" return 4 def load_data_fashion_mnist(batch_size, resize=None): """Download the Fashion-MNIST dataset and then load it into memory. Defined in :numref:`sec_utils`""" trans = [transforms.ToTensor()] if resize: trans.insert(0, transforms.Resize(resize)) trans = transforms.Compose(trans) mnist_train = torchvision.datasets.FashionMNIST( root="../data", train=True, transform=trans, download=True) mnist_test = torchvision.datasets.FashionMNIST( root="../data", train=False, transform=trans, download=True) return (, batch_size, shuffle=True, num_workers=get_dataloader_workers()),, batch_size, shuffle=False, num_workers=get_dataloader_workers())) def evaluate_accuracy_gpu(net, data_iter, device=None): """Compute the accuracy for a model on a dataset using a GPU. Defined in :numref:`sec_utils`""" if isinstance(net, nn.Module): net.eval() # Set the model to evaluation mode if not device: device = next(iter(net.parameters())).device # No. of correct predictions, no. of predictions metric = d2l.Accumulator(2) with torch.no_grad(): for X, y in data_iter: if isinstance(X, list): # Required for BERT Fine-tuning (to be covered later) X = [ for x in X] else: X = y = metric.add(d2l.accuracy(net(X), y), d2l.size(y)) return metric[0] / metric[1] def train_ch6(net, train_iter, test_iter, num_epochs, lr, device): """Train a model with a GPU (defined in Chapter 6). Defined in :numref:`sec_utils`""" def init_weights(m): if type(m) == nn.Linear or type(m) == nn.Conv2d: nn.init.xavier_uniform_(m.weight) net.apply(init_weights) print('training on', device) optimizer = torch.optim.SGD(net.parameters(), lr=lr) loss = nn.CrossEntropyLoss() animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], legend=['train loss', 'train acc', 'test acc']) timer, num_batches = d2l.Timer(), len(train_iter) for epoch in range(num_epochs): # Sum of training loss, sum of training accuracy, no. of examples metric = d2l.Accumulator(3) net.train() for i, (X, y) in enumerate(train_iter): timer.start() optimizer.zero_grad() X, y =, y_hat = net(X) l = loss(y_hat, y) l.backward() optimizer.step() with torch.no_grad(): metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0]) timer.stop() train_l = metric[0] / metric[2] train_acc = metric[1] / metric[2] if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1: animator.add(epoch + (i + 1) / num_batches, (train_l, train_acc, None)) test_acc = evaluate_accuracy_gpu(net, test_iter) animator.add(epoch + 1, (None, None, test_acc)) print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, ' f'test acc {test_acc:.3f}') print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec ' f'on {str(device)}') def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5): """Plot a list of images. Defined in :numref:`sec_utils`""" figsize = (num_cols * scale, num_rows * scale) _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize) axes = axes.flatten() for i, (ax, img) in enumerate(zip(axes, imgs)): try: img = d2l.numpy(img) except: pass ax.imshow(img) ax.axes.get_xaxis().set_visible(False) ax.axes.get_yaxis().set_visible(False) if titles: ax.set_title(titles[i]) return axes def linreg(X, w, b): """The linear regression model. Defined in :numref:`sec_utils`""" return d2l.matmul(X, w) + b def squared_loss(y_hat, y): """Squared loss. Defined in :numref:`sec_utils`""" return (y_hat - d2l.reshape(y, y_hat.shape)) ** 2 / 2 def get_fashion_mnist_labels(labels): """Return text labels for the Fashion-MNIST dataset. Defined in :numref:`sec_utils`""" text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot'] return [text_labels[int(i)] for i in labels] class Animator: """For plotting data in animation.""" def __init__(self, xlabel=None, ylabel=None, legend=None, xlim=None, ylim=None, xscale='linear', yscale='linear', fmts=('-', 'm--', 'g-.', 'r:'), nrows=1, ncols=1, figsize=(3.5, 2.5)): """Defined in :numref:`sec_utils`""" # Incrementally plot multiple lines if legend is None: legend = [] d2l.use_svg_display() self.fig, self.axes = d2l.plt.subplots(nrows, ncols, figsize=figsize) if nrows * ncols == 1: self.axes = [self.axes, ] # Use a lambda function to capture arguments self.config_axes = lambda: d2l.set_axes( self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend) self.X, self.Y, self.fmts = None, None, fmts def add(self, x, y): # Add multiple data points into the figure if not hasattr(y, "__len__"): y = [y] n = len(y) if not hasattr(x, "__len__"): x = [x] * n if not self.X: self.X = [[] for _ in range(n)] if not self.Y: self.Y = [[] for _ in range(n)] for i, (a, b) in enumerate(zip(x, y)): if a is not None and b is not None: self.X[i].append(a) self.Y[i].append(b) self.axes[0].cla() for x, y, fmt in zip(self.X, self.Y, self.fmts): self.axes[0].plot(x, y, fmt) self.config_axes() display.display(self.fig) display.clear_output(wait=True) class Accumulator: """For accumulating sums over `n` variables.""" def __init__(self, n): """Defined in :numref:`sec_utils`""" = [0.0] * n def add(self, *args): = [a + float(b) for a, b in zip(, args)] def reset(self): = [0.0] * len( def __getitem__(self, idx): return[idx] def accuracy(y_hat, y): """Compute the number of correct predictions. Defined in :numref:`sec_utils`""" if len(y_hat.shape) > 1 and y_hat.shape[1] > 1: y_hat = d2l.argmax(y_hat, axis=1) cmp = d2l.astype(y_hat, y.dtype) == y return float(d2l.reduce_sum(d2l.astype(cmp, y.dtype))) def download(url, folder='../data', sha1_hash=None): """Download a file to folder and return the local filepath. Defined in :numref:`sec_utils`""" if not url.startswith('http'): # For back compatability url, sha1_hash = DATA_HUB[url] os.makedirs(folder, exist_ok=True) fname = os.path.join(folder, url.split('/')[-1]) # Check if hit cache if os.path.exists(fname) and sha1_hash: sha1 = hashlib.sha1() with open(fname, 'rb') as f: while True: data = if not data: break sha1.update(data) if sha1.hexdigest() == sha1_hash: return fname # Download print(f'Downloading {fname} from {url}...') r = requests.get(url, stream=True, verify=True) with open(fname, 'wb') as f: f.write(r.content) return fname def extract(filename, folder=None): """Extract a zip/tar file into folder. Defined in :numref:`sec_utils`""" base_dir = os.path.dirname(filename) _, ext = os.path.splitext(filename) assert ext in ('.zip', '.tar', '.gz'), 'Only support zip/tar files.' if ext == '.zip': fp = zipfile.ZipFile(filename, 'r') else: fp =, 'r') if folder is None: folder = base_dir fp.extractall(folder) def download_extract(name, folder=None): """Download and extract a zip/tar file. Defined in :numref:`sec_utils`""" fname = download(name) base_dir = os.path.dirname(fname) data_dir, ext = os.path.splitext(fname) if ext == '.zip': fp = zipfile.ZipFile(fname, 'r') elif ext in ('.tar', '.gz'): fp =, 'r') else: assert False, 'Only zip/tar files can be extracted.' fp.extractall(base_dir) return os.path.join(base_dir, folder) if folder else data_dir def tokenize(lines, token='word'): """Split text lines into word or character tokens. Defined in :numref:`sec_utils`""" assert token in ('word', 'char'), 'Unknown token type: ' + token return [line.split() if token == 'word' else list(line) for line in lines] def evaluate_loss(net, data_iter, loss): """Evaluate the loss of a model on the given dataset. Defined in :numref:`sec_utils`""" metric = d2l.Accumulator(2) # Sum of losses, no. of examples for X, y in data_iter: out = net(X) y = d2l.reshape(y, out.shape) l = loss(out, y) metric.add(d2l.reduce_sum(l), d2l.size(l)) return metric[0] / metric[1] def grad_clipping(net, theta): """Clip the gradient. Defined in :numref:`sec_utils`""" if isinstance(net, nn.Module): params = [p for p in net.parameters() if p.requires_grad] else: params = net.params norm = torch.sqrt(sum(torch.sum((p.grad ** 2)) for p in params)) if norm > theta: for param in params: param.grad[:] *= theta / norm d2l.DATA_HUB['fra-eng'] = (d2l.DATA_URL + '', '94646ad1522d915e7b0f9296181140edcf86a4f5') def read_data_nmt(): """Load the English-French dataset. Defined in :numref:`sec_utils`""" data_dir = d2l.download_extract('fra-eng') with open(os.path.join(data_dir, 'fra.txt'), 'r', encoding='utf-8') as f: return def preprocess_nmt(text): """Preprocess the English-French dataset. Defined in :numref:`sec_utils`""" def no_space(char, prev_char): return char in set(',.!?') and prev_char != ' ' # Replace non-breaking space with space, and convert uppercase letters to # lowercase ones text = text.replace('\u202f', ' ').replace('\xa0', ' ').lower() # Insert space between words and punctuation marks out = [' ' + char if i > 0 and no_space(char, text[i - 1]) else char for i, char in enumerate(text)] return ''.join(out) def tokenize_nmt(text, num_examples=None): """Tokenize the English-French dataset. Defined in :numref:`sec_utils`""" source, target = [], [] for i, line in enumerate(text.split('\n')): if num_examples and i > num_examples: break parts = line.split('\t') if len(parts) == 2: source.append(parts[0].split(' ')) target.append(parts[1].split(' ')) return source, target def truncate_pad(line, num_steps, padding_token): """Truncate or pad sequences. Defined in :numref:`sec_utils`""" if len(line) > num_steps: return line[:num_steps] # Truncate return line + [padding_token] * (num_steps - len(line)) # Pad def build_array_nmt(lines, vocab, num_steps): """Transform text sequences of machine translation into minibatches. Defined in :numref:`sec_utils`""" lines = [vocab[l] for l in lines] lines = [l + [vocab['<eos>']] for l in lines] array = d2l.tensor([truncate_pad( l, num_steps, vocab['<pad>']) for l in lines]) valid_len = d2l.reduce_sum( d2l.astype(array != vocab['<pad>'], d2l.int32), 1) return array, valid_len def load_data_nmt(batch_size, num_steps, num_examples=600): """Return the iterator and the vocabularies of the translation dataset. Defined in :numref:`sec_utils`""" text = preprocess_nmt(read_data_nmt()) source, target = tokenize_nmt(text, num_examples) src_vocab = d2l.Vocab(source, min_freq=2, reserved_tokens=['<pad>', '<bos>', '<eos>']) tgt_vocab = d2l.Vocab(target, min_freq=2, reserved_tokens=['<pad>', '<bos>', '<eos>']) src_array, src_valid_len = build_array_nmt(source, src_vocab, num_steps) tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, num_steps) data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len) data_iter = d2l.load_array(data_arrays, batch_size) return data_iter, src_vocab, tgt_vocab def sequence_mask(X, valid_len, value=0): """Mask irrelevant entries in sequences. Defined in :numref:`sec_utils`""" maxlen = X.size(1) mask = torch.arange((maxlen), dtype=torch.float32, device=X.device)[None, :] < valid_len[:, None] X[~mask] = value return X class MaskedSoftmaxCELoss(nn.CrossEntropyLoss): """The softmax cross-entropy loss with masks. Defined in :numref:`sec_utils`""" # `pred` shape: (`batch_size`, `num_steps`, `vocab_size`) # `label` shape: (`batch_size`, `num_steps`) # `valid_len` shape: (`batch_size`,) def forward(self, pred, label, valid_len): weights = torch.ones_like(label) weights = sequence_mask(weights, valid_len) self.reduction='none' unweighted_loss = super(MaskedSoftmaxCELoss, self).forward( pred.permute(0, 2, 1), label) weighted_loss = (unweighted_loss * weights).mean(dim=1) return weighted_loss def train_seq2seq(net, data_iter, lr, num_epochs, tgt_vocab, device): """Train a model for sequence to sequence. Defined in :numref:`sec_utils`""" def xavier_init_weights(m): if type(m) == nn.Linear: nn.init.xavier_uniform_(m.weight) if type(m) == nn.GRU: for param in m._flat_weights_names: if "weight" in param: nn.init.xavier_uniform_(m._parameters[param]) net.apply(xavier_init_weights) optimizer = torch.optim.Adam(net.parameters(), lr=lr) loss = MaskedSoftmaxCELoss() net.train() animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[10, num_epochs]) for epoch in range(num_epochs): timer = d2l.Timer() metric = d2l.Accumulator(2) # Sum of training loss, no. of tokens for batch in data_iter: optimizer.zero_grad() X, X_valid_len, Y, Y_valid_len = [ for x in batch] bos = torch.tensor([tgt_vocab['<bos>']] * Y.shape[0], device=device).reshape(-1, 1) dec_input = d2l.concat([bos, Y[:, :-1]], 1) # Teacher forcing Y_hat, _ = net(X, dec_input, X_valid_len) l = loss(Y_hat, Y, Y_valid_len) l.sum().backward() # Make the loss scalar for `backward` d2l.grad_clipping(net, 1) num_tokens = Y_valid_len.sum() optimizer.step() with torch.no_grad(): metric.add(l.sum(), num_tokens) if (epoch + 1) % 10 == 0: animator.add(epoch + 1, (metric[0] / metric[1],)) print(f'loss {metric[0] / metric[1]:.3f}, {metric[1] / timer.stop():.1f} ' f'tokens/sec on {str(device)}') def predict_seq2seq(net, src_sentence, src_vocab, tgt_vocab, num_steps, device, save_attention_weights=False): """Predict for sequence to sequence. Defined in :numref:`sec_utils`""" # Set `net` to eval mode for inference net.eval() src_tokens = src_vocab[src_sentence.lower().split(' ')] + [ src_vocab['<eos>']] enc_valid_len = torch.tensor([len(src_tokens)], device=device) src_tokens = d2l.truncate_pad(src_tokens, num_steps, src_vocab['<pad>']) # Add the batch axis enc_X = torch.unsqueeze( torch.tensor(src_tokens, dtype=torch.long, device=device), dim=0) enc_outputs = net.encoder(enc_X, enc_valid_len) dec_state = net.decoder.init_state(enc_outputs, enc_valid_len) # Add the batch axis dec_X = torch.unsqueeze(torch.tensor( [tgt_vocab['<bos>']], dtype=torch.long, device=device), dim=0) output_seq, attention_weight_seq = [], [] for _ in range(num_steps): Y, dec_state = net.decoder(dec_X, dec_state) # We use the token with the highest prediction likelihood as input # of the decoder at the next time step dec_X = Y.argmax(dim=2) pred = dec_X.squeeze(dim=0).type(torch.int32).item() # Save attention weights (to be covered later) if save_attention_weights: attention_weight_seq.append(net.decoder.attention_weights) # Once the end-of-sequence token is predicted, the generation of the # output sequence is complete if pred == tgt_vocab['<eos>']: break output_seq.append(pred) return ' '.join(tgt_vocab.to_tokens(output_seq)), attention_weight_seq # Alias defined in config.ini nn_Module = nn.Module ones_like = torch.ones_like ones = torch.ones zeros_like = torch.zeros_like zeros = torch.zeros tensor = torch.tensor arange = torch.arange meshgrid = torch.meshgrid sin = torch.sin sinh = torch.sinh cos = torch.cos cosh = torch.cosh tanh = torch.tanh linspace = torch.linspace exp = torch.exp log = torch.log normal = torch.normal rand = torch.rand randn = torch.randn matmul = torch.matmul int32 = torch.int32 int64 = torch.int64 float32 = torch.float32 concat = stack = torch.stack abs = torch.abs eye = torch.eye sigmoid = torch.sigmoid batch_matmul = torch.bmm numpy = lambda x, *args, **kwargs: x.detach().numpy(*args, **kwargs) size = lambda x, *args, **kwargs: x.numel(*args, **kwargs) reshape = lambda x, *args, **kwargs: x.reshape(*args, **kwargs) to = lambda x, *args, **kwargs:*args, **kwargs) reduce_sum = lambda x, *args, **kwargs: x.sum(*args, **kwargs) argmax = lambda x, *args, **kwargs: x.argmax(*args, **kwargs) astype = lambda x, *args, **kwargs: x.type(*args, **kwargs) transpose = lambda x, *args, **kwargs: x.t(*args, **kwargs) reduce_mean = lambda x, *args, **kwargs: x.mean(*args, **kwargs) expand_dims = lambda x, *args, **kwargs: x.unsqueeze(*args, **kwargs) swapaxes = lambda x, *args, **kwargs: x.swapaxes(*args, **kwargs) repeat = lambda x, *args, **kwargs: x.repeat(*args, **kwargs)