Pytorch

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资源

• The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. [github]

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Tools

• fitlog [url]

[Bryce1010]

collate_fn() https://discuss.pytorch.org/t/how-to-create-a-dataloader-with-variable-size-input/8278/2 By default, torch stacks the input image to from a tensor of size NCH*W, so every image in the batch must have the same height and width. In order to load a batch with variable size input image, we have to use our own collate_fn which is used to pack a batch of images.

For image classification, the input to collate_fn is a list of with size batch_size. Each element is a tuple where the first element is the input image(a torch.FloatTensor) and the second element is the image label which is simply an int. Because the samples in a batch have different size, we can store these samples in a list ans store the corresponding labels in torch.LongTensor. Then we put the image list and the label tensor into a list and return the result.

here is a very simple snippet to demonstrate how to write a custom collate_fn:

import torch
from torch.utils.data import DataLoader
from torchvision import transforms
import torchvision.datasets as datasets
import matplotlib.pyplot as plt

# a simple custom collate function, just to show the idea
def my_collate(batch):
    data = [item[0] for item in batch]
    target = [item[1] for item in batch]
    target = torch.LongTensor(target)
    return [data, target]

def show_image_batch(img_list, title=None):
    num = len(img_list)
    fig = plt.figure()
    for i in range(num):
        ax = fig.add_subplot(1, num, i+1)
        ax.imshow(img_list[i].numpy().transpose([1,2,0]))
        ax.set_title(title[i])

    plt.show()

#  do not do randomCrop to show that the custom collate_fn can handle images of different size
train_transforms = transforms.Compose([transforms.Scale(size = 224),
                                       transforms.ToTensor(),
                                       ])

# change root to valid dir in your system, see ImageFolder documentation for more info
train_dataset = datasets.ImageFolder(root="/hd1/jdhao/toyset",
                                     transform=train_transforms)

trainset = DataLoader(dataset=train_dataset,
                      batch_size=4,
                      shuffle=True,
                      collate_fn=my_collate, # use custom collate function here
                      pin_memory=True)

trainiter = iter(trainset)
imgs, labels = trainiter.next()

# print(type(imgs), type(labels))
show_image_batch(imgs, title=[train_dataset.classes[x] for x in labels])

[Bryce1010]

PyTorch 提速 [github]

预处理提速

IO提速

训练策略

推理加速

[Bryce1010]

a list of torch -> torch

torch.cat([x,x],0)
torch.stack(a)

tensor -> PIL Image

img=transforms.ToPILImage(mode='')(img).convert()

torch

• [ref]

• torch.stack(tensors, dim=0, out=None) → Tensor

  将张量的序列沿一个新的维度连接起来

• torch.``unsqueeze(input, dim, out=None) → Tensor

• torch.``squeeze(input, dim=None, out=None) → Tensor

• torch.``reshape(input, shape) → Tensor

• torch.``topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor)

  >>> x = torch.arange(1., 6.)
  >>> x
  tensor([ 1.,  2.,  3.,  4.,  5.])
  >>> torch.topk(x, 3)
  torch.return_types.topk(values=tensor([5., 4., 3.]), indices=tensor([4, 3, 2]))

torch.nn

torch.nn

​ torch.nn.Module

• add_module(name, module)

• apply(fn)

• children()

• cpu()

• cuda(device=None)

• eval()

• train(mode=True)

• float()

• half()

• forward(*input)

• load_state_dict(state_dict, strict=True)

• modules()

• named_children()

• named_modules(memo=None, prefix='')

• parameters(recurse=True)

• register_backward_hook(hook)

  hook(module, grad_input, grad_output) -> Tensor or None

• register_forward_hook(hook)

  hook(module, input, output) -> None or modified output

• requires_grad_(requires_grad=True)

• state_dict(destination=None, prefix='', keep_vars=False)

• to(args, kwargs*)

  to(device=None, dtype=None, non_blocking=False)
  to(dtype, non_blocking=False)
  to(tensor, non_blocking=False)

torch.nn.Parameter

class Parameter(torch.Tensor):
"""
A kind of Tensor that is to be considered a module parameter.
Arguments:  
    data(Tensor): parameter tensor.  
    requires_grad(bool,optional)
"""
    def __new__(cls, data=None, requires_grad=True):
    def __deepcopy__(self, memo): # 重写deepcopy()
    def __repr__(self): 

torch.nn.Module

所有网络模型的基类

• add_module(name,module)

  往当前模块增加子模块

  • name (string) – name of the child module. The child module can be accessed from this module using the given name
  • module (Module) – child module to be added to the module.

• apply(fn)

  经常用来初始化函数

  >>> def init_weights(m):
  >>>     print(m)
  >>>     if type(m) == nn.Linear:
  >>>         m.weight.data.fill_(1.0)
  >>>         print(m.weight)
  >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
  >>> net.apply(init_weights)

• buffers(recurse=True)

  >>> for buf in model.buffers():
  >>>     print(type(buf.data), buf.size())
  <class 'torch.FloatTensor'> (20L,)
  <class 'torch.FloatTensor'> (20L, 1L, 5L, 5L)

torch.optim

torch.optim是实现了不同的优化算法的python包，除了支持大量经常使用的算法外，结构也很丰富，所以可以轻易地接入后续的开发升级。

optimizer的参数首先需要一个可迭代的参数对象，然后可以个性化的设置lr，weight_decay，等参数；

如果你在使用GPU运算，还需要使用optimizer.cuda()将优化器放到显存中，保证优化的参数和优化器在统一的位置。

• 构建一个optimizer

  optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
  optimizer = optim.Adam([var1, var2], lr=0.0001)

• 预先设置参数可选项

  optimizer还可以通过dict的key_word个性化的设置优化器的参数；

  optim.SGD([
                {'params': model.base.parameters()},
                {'params': model.classifier.parameters(), 'lr': 1e-3}
            ], lr=1e-2, momentum=0.9)

• 如何更新参数？optimizer.step()

  optimizer可以通过backward之后使用step()函数来更新网络参数，可以有两种使用方法：

  optimizer.step()

  for input, target in dataset:
    optimizer.zero_grad()
    output = model(input)
    loss = loss_fn(output, target)
    loss.backward()
    optimizer.step()

  optimizer.step(closure)

  for input, target in dataset:
    def closure():
        optimizer.zero_grad()
        output = model(input)
        loss = loss_fn(output, target)
        loss.backward()
        return loss
    optimizer.step(closure)

torch.optim.Optimizer(params, defaults)

• load_state_dict(state_dict)

• state_dict()

• step(closure)

• zero_grad()

torch.optim.SGD(params, lr=, momentum=0, dampening=0, weight_decay=0, nesterov=False)

• params (iterable) – iterable of parameters to optimize or dicts defining parameter groups
• lr (python:float) – learning rate
• momentum (*python:float*, optional) – momentum factor (default: 0)
• weight_decay (*python:float*, optional) – weight decay (L2 penalty) (default: 0)
• dampening (*python:float*, optional) – dampening for momentum (default: 0)
• nesterov (*bool*, optional) – enables Nesterov momentum (default: False)

如何调整学习率？ torch.optim.lr_scheduler

• torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, verbose=False, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0, eps=1e-08)

  >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
  >>> scheduler = ReduceLROnPlateau(optimizer, 'min')
  >>> for epoch in range(10):
  >>>     train(...)
  >>>     val_loss = validate(...)
  >>>     # Note that step should be called after validate()
  >>>     scheduler.step(val_loss)

Pretrained models for Pytorch

rep:

• [github]

model_name = 'nasnetalarge' # could be fbresnet152 or inceptionresnetv2
model = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
model.eval()

pretrained model API

• model.input_size

• model.input_space

• model.input_range

• model.mean

• model.std

• model.features

  Method which is used to extract the features from the image.

  print(input_224.size())            # (1,3,224,224)
  output = model.features(input_224) 
  print(output.size())               # (1,2048,1,1)
  
  # print(input_448.size())          # (1,3,448,448)
  output = model.features(input_448)
  # print(output.size())             # (1,2048,7,7)

• model.logits

  Method which is used to classify the features from the image.

  output = model.features(input_224) 
  print(output.size())               # (1,2048, 1, 1)
  output = model.logits(output)
  print(output.size())               # (1,1000)

• model.forward

  Method used to call model.features and model.logits. It can be overwritten as desired.

  # Without model.__call__
  output = model.forward(input_224)
  print(output.size())      # (1,1000)
  
  # With model.__call__
  output = model(input_224)
  print(output.size())      # (1,1000)

• model.last_linear

  Attribut of type nn.Linear. This module is the last one to be called during the forward pass.

  • Can be replaced by an adapted nn.Linear for fine tuning.
  • Can be replaced by pretrained.utils.Identity for features extraction.

  print(input_224.size())            # (1,3,224,224)
  output = model.features(input_224) 
  print(output.size())               # (1,2048,1,1)
  output = model.logits(output)
  print(output.size())               # (1,1000)
  
  # fine tuning
  dim_feats = model.last_linear.in_features # =2048
  nb_classes = 4
  model.last_linear = nn.Linear(dim_feats, nb_classes)
  output = model(input_224)
  print(output.size())               # (1,4)
  
  # features extraction
  model.last_linear = pretrained.utils.Identity()
  output = model(input_224)
  print(output.size())               # (1,2048)

Dataset

chainer.dataset.DatasetMixin

[url]

DatasetMixin provides the __getitem__() operator. The default implementation uses get_example() to extract each example, and combines the results into a list. This mixin makes it easy to implement a new dataset that does not support efficient slicing.

• __getitem__(index)

  Returns an example or a sequence of examples.

  It implements the standard Python indexing and one-dimensional integer array indexing. It uses the get_example() method by default, but it may be overridden by the implementation to, for example, improve the slicing performance.

  >>> import numpy
  >>> from chainer import dataset
  >>> class SimpleDataset(dataset.DatasetMixin):
  ...     def __init__(self, values):
  ...         self.values = values
  ...     def __len__(self):
  ...         return len(self.values)
  ...     def get_example(self, i):
  ...         return self.values[i]
  ...
  >>> ds = SimpleDataset([0, 1, 2, 3, 4, 5])
  >>> ds[1]   # Access by int
  1
  >>> ds[1:3]  # Access by slice
  [1, 2]
  >>> ds[[4, 0]]  # Access by one-dimensional integer list
  [4, 0]
  >>> index = numpy.arange(3)
  >>> ds[index]  # Access by one-dimensional integer numpy.ndarray
  [0, 1, 2]

• __len__()

  Return the number of datapoint.

• get_example(i)

  Returns the i-th example.

  Implementations should override it. It should raise IndexError if the index is invalid.

Ignite

[Homepage] [blog] [documens]

[Learning and testing neural networks on PyTorch using Ignite]

Ignite 如何工作？

Engine

通过Engine循环提供数据和执行进程函数；

例如执行一个监督学习任务：

训练过程

def update_model(trainer,batch):
    model.train()
    optimizer.zero_grad()
    x,y=prepare_batch.next(batch)
    y_pred=model(x)
    loss=loss_fn(y_pred,y)
    loss.backward()
    optimizer.step()
    return loss.item()
trainer=Engine(update_model)
trainer.run(data,max_epoch=100)

测试过程

from ignite.engine import Engine

total_loss = []
def compute_metrics(_, batch):
    x, y = batch
    model.eval()
    with torch.no_grad():
        y_pred = model(x)
        loss = criterion(y_pred, y)
        total_loss.append(loss.item())

    return loss.item()

evaluator = Engine(compute_metrics)
evaluator.run(data, max_epochs=1)
print(f”Loss: {torch.tensor(total_loss).mean()}”)

Events & Handlers

Ignite的Events & Handlers设计结构如下所示：

fire_event(Events.STARTED)

while epoch < max_epochs:
    fire_event(Events.EPOCH_STARTED)
    # run once on data
    for batch in data:
        fire_event(Events.ITERATION_STARTED)

        output = process_function(batch)

        fire_event(Events.ITERATION_COMPLETED)
    fire_event(Events.EPOCH_COMPLETED)
fire_event(Events.COMPLETED)

如何调用Events & Handlers？

train_loader = …
model = …
optimizer = …
criterion = ...
lr_scheduler = …

def process_function(engine, batch):
    # … user function to update model weights

trainer = Engine(process_function)

@trainer.on(Events.STARTED)
def setup_logging_folder(_):
    # create a folder for the run
    # set up some run dependent variables

@trainer.on(Events.ITERATION_COMPLETED)
def update_lr(engine):
    lr_scheduler.step()

trainer.run(train_loader, max_epochs=50)

# --------------------------

trainer=Engine(update_model)
trainer.add_event_hander(
    Events.STARTED,
    lambda engine:print("strating training")
)
# or
@trainer.on(Events.STARTED)
def on_training_started(engine):
    print("Another message of start training")

# attach handler with args,kwargs
mydata=[1,2,3,4]
def on_train_ended(engine,data):
    print("Training is ended.mydata={}".format(data))

trainer.add_event_handler(
    Events.COMPLETED,on_training_ended,my_data
)

Built-in evetns filtering

• run the validation every 5 epochs,

• store a checkpoint every 1000 iterations,

• change a variable on 20th epoch,

• log gradients on the first 10 iterations.

• etc.

  每五个epoch做一次validation

  @trainer.on(Events.EPOCH_COMPLETED(every=5))
  def run_validation(_):
    # run validation

  在第20个epoch改变训练变量的值

  @trainer.on(Events.EPOCH_COMPLETED(every=5))
  def run_validation(_):
    # run validation

  custom filtering method

  def first_x_iters(_, event):
    if event < 10:
        return True
    return False
  
  @trainer.on(Events.ITERATION_COMPLETED(event_filter=first_x_iters))
  def log_gradients(_):
  # …

Out-of-box features

Visualisation loggers

supports Tensorboard, Visdom, MLflow, Polyaxo

• Checkpoint : to save training checkpoints (composed of trainer, model(s), optimizer(s), lr scheduler(s), etc) to save best models (by validation score)
• EarlyStopping\: stops the training if no progress is done (by validation score)
• TerminateOnNan: stops the training if NaN is encountered
• Optimizer Parameters Scheduling: concatenate, add a warm-up, setup linear or cosine annealing, linear piecewise scheduling of any optimizer parameter (lr, momentum, betas, …)

Metrics

Ignite also provides a list of out-of-the-box metrics for various tasks: Precision, Recall, Accuracy, Confusion Matrix, IoU etc, ~20 regression metrics

from ignite.metrics import Accuracy

def compute_predictions(_, batch):
    # …
    return y_pred, y_true

evaluator = Engine(compute_predictions)
metric = Accuracy()
metric.attach(evaluator, "val_accuracy")
evaluator.run(val_loader)
> evaluator.state.metrics[“val_accuracy”] = 0.98765

precision = Precision(average=False)
recall = Recall(average=False)
F1_per_class = (precision * recall * 2 / (precision + recall))
F1_mean = F1_per_class.mean()  # torch mean method
F1_mean.attach(engine, "F1")

Go here and here to see the full list of available metrics.

Optimizer’s parameter scheduling

• Save best validation

  to_save{'model':classifier,'optimizer':optimizer,'scheduler':scheduler,'trainer':trainer}
  def score_function(engine):
    val_recall=engine.state.metrics['recall']
    return val_recall
   checkpoint_handler=Checkpoint(to_save,DiskSaver(outdir,create_dir=True),n_saved=2,filename_prefix='best',score_function=score_function,score_name='recall',global_step_transform=global_step_from_engine(trainer))
  
   #trainer.add_event_handler(Events.EPOCH_COMPLETED,ModelSnapshotHandler((predictor,filepath=outdir/'model_{count:06}.pth',interval=5))
  evaluator.add_event_handler(Events.COMPLETED,checkpoint_handler)    

• Early stopping

  from ignite.handlers 
  import EarlyStopping 
  early_stopping = EarlyStopping(patience=10, score_function=score_function, trainer=trainer) 
  val_evaluator.add_event_handler(Events.EPOCH_COMPLETED, early_stopping) 
  

• TTA

  [kaggle kernel]