1

Basic Configuration

import torch
import torch.nn as nn
import torchvision
print(torch.__version__)
print(torch.version.cuda)
print(torch.backends.cudnn.version())
print(torch.cuda.get_device_name(0))

np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'

CUDA_VISIBLE_DEVICES=0,1 python train.py

torch.cuda.empty_cache()

nvidia-smi --gpu-reset -i [gpu_id]

tensor = torch.randn(3,4,5)
print(tensor.type())  # Data type
print(tensor.size())  # Tensor shape, a tuple
print(tensor.dim())   # Number of dimensions

# Before PyTorch 1.3, use comments
# Tensor[N, C, H, W]
images = torch.randn(32, 3, 56, 56)
images.sum(dim=1)
images.select(dim=1, index=0)

# After PyTorch 1.3
NCHW = ['N', 'C', 'H', 'W']
images = torch.randn(32, 3, 56, 56, names=NCHW)
images.sum('C')
images.select('C', index=0)

# Can also set like this
tensor = torch.rand(3,4,1,2,names=('C', 'N', 'H', 'W'))

# Use align_to to conveniently sort dimensions
tensor = tensor.align_to('N', 'C', 'H', 'W')

# Set default type, FloatTensor is much faster than DoubleTensor in pytorch
torch.set_default_tensor_type(torch.FloatTensor)

# Type conversion
tensor = tensor.cuda()
tensor = tensor.cpu()
tensor = tensor.float()
tensor = tensor.long()

ndarray = tensor.cpu().numpy()
tensor = torch.from_numpy(ndarray).float()
tensor = torch.from_numpy(ndarray.copy()).float() # If ndarray has negative stride.

# Tensors in pytorch default to [N, C, H, W] order, and the data range is [0,1], need to transpose and normalize
# torch.Tensor -> PIL.Image
image = PIL.Image.fromarray(torch.clamp(tensor*255, min=0, max=255).byte().permute(1,2,0).cpu().numpy())
image = torchvision.transforms.functional.to_pil_image(tensor)  # Equivalently way

# PIL.Image -> torch.Tensor
path = r'./figure.jpg'
tensor = torch.from_numpy(np.asarray(PIL.Image.open(path))).permute(2,0,1).float() / 255
tensor = torchvision.transforms.functional.to_tensor(PIL.Image.open(path)) # Equivalently way

image = PIL.Image.fromarray(ndarray.astype(np.uint8))
ndarray = np.asarray(PIL.Image.open(path))

value = torch.rand(1).item()

# When inputting a convolutional layer to a fully connected layer, tensor reshaping is usually required,
# Compared to torch.view, torch.reshape can automatically handle cases where the input tensor is non-contiguous.
tensor = torch.rand(2,3,4)
shape = (6, 4)
tensor = torch.reshape(tensor, shape)

tensor = tensor[torch.randperm(tensor.size(0))]  # Shuffle the first dimension

# Pytorch does not support negative step operations like tensor[::-1], horizontal flip can be achieved through tensor indexing
# Assuming the tensor dimension is [N, D, H, W].
tensor = tensor[:,:,:,torch.arange(tensor.size(3) - 1, -1, -1).long()]

# Operation | New/Shared memory | Still in computation graph |
tensor.clone() # | New | Yes |
tensor.detach() # | Shared | No |
tensor.detach().clone()() # | New | No |

'''Note that the difference between torch.cat and torch.stack is that torch.cat concatenates along the given dimension, while torch.stack adds a dimension. For example, when the parameter is 3 tensors of size 10x5, the result of torch.cat is a tensor of size 30x5, while the result of torch.stack is a tensor of size 3x10x5.''' 
tensor = torch.cat(list_of_tensors, dim=0)
tensor = torch.stack(list_of_tensors, dim=0)

# Pytorch labels default start from 0
tensor = torch.tensor([0, 2, 1, 3])
N = tensor.size(0)
num_classes = 4
one_hot = torch.zeros(N, num_classes).long()
one_hot.scatter_(dim=1, index=torch.unsqueeze(tensor, dim=1), src=torch.ones(N, num_classes).long())

torch.nonzero(tensor)               # index of non-zero elements
torch.nonzero(tensor==0)            # index of zero elements
torch.nonzero(tensor).size(0)       # number of non-zero elements
torch.nonzero(tensor == 0).size(0)  # number of zero elements

torch.allclose(tensor1, tensor2)  # float tensor
torch.equal(tensor1, tensor2)     # int tensor

# Expand tensor of shape 64*512 to shape 64*512*7*7.
tensor = torch.rand(64,512)
tensor = torch.reshape(tensor, (64, 512, 1, 1)).expand(64, 512, 7, 7)

# Matrix multiplication: (m*n) * (n*p) -> (m*p).
result = torch.mm(tensor1, tensor2)
# Batch matrix multiplication: (b*m*n) * (b*n*p) -> (b*m*p)
result = torch.bmm(tensor1, tensor2)
# Element-wise multiplication.
result = tensor1 * tensor2

dist = torch.sqrt(torch.sum((X1[:,None,:] - X2) ** 2, dim=2))

# convolutional neural network (2 convolutional layers)
class ConvNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(7*7*32, num_classes)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out

model = ConvNet(num_classes).to(device)

X = torch.reshape(N, D, H * W)                        # Assume X has shape N*D*H*W
X = torch.bmm(X, torch.transpose(X, 1, 2)) / (H * W)  # Bilinear pooling
assert X.size() == (N, D, D)
X = torch.reshape(X, (N, D * D))
X = torch.sign(X) * torch.sqrt(torch.abs(X) + 1e-5)   # Signed-sqrt normalization
X = torch.nn.functional.normalize(X)                  # L2 normalization

sync_bn = torch.nn.SyncBatchNorm(num_features, eps=1e-05, momentum=0.1, affine=True,
                                  track_running_stats=True)

def convertBNtoSyncBN(module, process_group=None):
    '''Recursively replace all BN layers to SyncBN layer.
    Args:        module[torch.nn.Module]. Network    '''
    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
        sync_bn = torch.nn.SyncBatchNorm(module.num_features, module.eps, module.momentum,
                                          module.affine, module.track_running_stats, process_group)
        sync_bn.running_mean = module.running_mean
        sync_bn.running_var = module.running_var
        if module.affine:
            sync_bn.weight = module.weight.clone().detach()
            sync_bn.bias = module.bias.clone().detach()
        return sync_bn
    else:
        for name, child_module in module.named_children():
            setattr(module, name, convertBNtoSyncBN(child_module, process_group=process_group))
        return module

class BN(torch.nn.Module):
    def __init__(self):
        ...
        self.register_buffer('running_mean', torch.zeros(num_features))

    def forward(self, X):
        ...
        self.running_mean += momentum * (current - self.running_mean)

num_parameters = sum(torch.numel(parameter) for parameter in model.parameters())

params = list(model.named_parameters())
(name, param) = params[28]
print(name)
print(param.grad)
print('-------------------------------------------------')
(name2, param2) = params[29]
print(name2)
print(param2.grad)
print('----------------------------------------------------')
(name1, param1) = params[30]
print(name1)
print(param1.grad)

# Common practice for initialization.
for layer in model.modules():
    if isinstance(layer, torch.nn.Conv2d):
        torch.nn.init.kaiming_normal_(layer.weight, mode='fan_out',
                                      nonlinearity='relu')
        if layer.bias is not None:
            torch.nn.init.constant_(layer.bias, val=0.0)
    elif isinstance(layer, torch.nn.BatchNorm2d):
        torch.nn.init.constant_(layer.weight, val=1.0)
        torch.nn.init.constant_(layer.bias, val=0.0)
    elif isinstance(layer, torch.nn.Linear):
        torch.nn.init.xavier_normal_(layer.weight)
        if layer.bias is not None:
            torch.nn.init.constant_(layer.bias, val=0.0)
# Initialization with given tensor.
layer.weight = torch.nn.Parameter(tensor)

# Extract the first two layers
new_model = nn.Sequential(*list(model.children())[:2])
# If you want to extract all convolutional layers from the model, you can do the following:
for layer in model.named_modules():
    if isinstance(layer[1], nn.Conv2d):
         conv_model.add_module(layer[0], layer[1])

model.load_state_dict(torch.load('model.pth'), strict=False)

model.load_state_dict(torch.load('model.pth', map_location='cpu'))

# model_new represents the new model
# model_saved represents another model, for example, a saved model imported using torch.load
model_new_dict = model_new.state_dict()
model_common_dict = {k:v for k, v in model_saved.items() if k in model_new_dict.keys()}
model_new_dict.update(model_common_dict)
model_new.load_state_dict(model_new_dict)

import os
import cv2
import numpy as np
from torch.utils.data import Dataset
from PIL import Image

def compute_mean_and_std(dataset):    # Input PyTorch dataset, output mean and std    mean_r = 0    mean_g = 0    mean_b = 0
    for img, _ in dataset:        img = np.asarray(img) # change PIL Image to numpy array        mean_b += np.mean(img[:, :, 0])        mean_g += np.mean(img[:, :, 1])        mean_r += np.mean(img[:, :, 2])
    mean_b /= len(dataset)    mean_g /= len(dataset)    mean_r /= len(dataset)
    diff_r = 0    diff_g = 0    diff_b = 0    N = 0
    for img, _ in dataset:        img = np.asarray(img)
        diff_b += np.sum(np.power(img[:, :, 0] - mean_b, 2))        diff_g += np.sum(np.power(img[:, :, 1] - mean_g, 2))        diff_r += np.sum(np.power(img[:, :, 2] - mean_r, 2))
        N += np.prod(img[:, :, 0].shape)
    std_b = np.sqrt(diff_b / N)    std_g = np.sqrt(diff_g / N)    std_r = np.sqrt(diff_r / N)
    mean = (mean_b.item() / 255.0, mean_g.item() / 255.0, mean_r.item() / 255.0)    std = (std_b.item() / 255.0, std_g.item() / 255.0, std_r.item() / 255.0)    return mean, std

import cv2
video = cv2.VideoCapture(mp4_path)
height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
fps = int(video.get(cv2.CAP_PROP_FPS))
video.release()

K = self._num_segments
if is_train:    if num_frames > K:        # Random index for each segment.        frame_indices = torch.randint(            high=num_frames // K, size=(K,), dtype=torch.long)        frame_indices += num_frames // K * torch.arange(K)    else:        frame_indices = torch.randint(            high=num_frames, size=(K - num_frames,), dtype=torch.long)        frame_indices = torch.sort(torch.cat((            torch.arange(num_frames), frame_indices)))[0]else:    if num_frames > K:        # Middle index for each segment.        frame_indices = num_frames / K // 2        frame_indices += num_frames // K * torch.arange(K)    else:        frame_indices = torch.sort(torch.cat((                                          torch.arange(num_frames), torch.arange(K - num_frames))))[0]
assert frame_indices.size() == (K,)
return [frame_indices[i] for i in range(K)]

train_transform = torchvision.transforms.Compose([
    torchvision.transforms.RandomResizedCrop(size=224,                                             scale=(0.08, 1.0)),
    torchvision.transforms.RandomHorizontalFlip(),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),                                     std=(0.229, 0.224, 0.225)),
])
val_transform = torchvision.transforms.Compose([
    torchvision.transforms.Resize(256),
    torchvision.transforms.CenterCrop(224),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),                                     std=(0.229, 0.224, 0.225)),])

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i ,(images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)
        # Backward and optimizer
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if (i+1) % 100 == 0:
            print('Epoch: [{}/{}], Step: [{}/{}], Loss: {}'                  .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# Test the model
model.eval()  # eval mode(batch norm uses moving mean/variance               #instead of mini-batch mean/variance)
with torch.no_grad():    correct = 0    total = 0    for images, labels in test_loader:        images = images.to(device)        labels = labels.to(device)        outputs = model(images)        _, predicted = torch.max(outputs.data, 1)        total += labels.size(0)        correct += (predicted == labels).sum().item()
    print('Test accuracy of the model on the 10000 test images: {} %'          .format(100 * correct / total))

class MyLoss(torch.nn.Module):
    def __init__(self):
        super(MyLoss, self).__init__()
    def forward(self, x, y):
        loss = torch.mean((x - y) ** 2)
        return loss

import torch
import torch.nn as nn

class LSR(nn.Module):
    def __init__(self, e=0.1, reduction='mean'):
        super().__init__()
        self.log_softmax = nn.LogSoftmax(dim=1)
        self.e = e
        self.reduction = reduction
    def _one_hot(self, labels, classes, value=1):
        """            Convert labels to one hot vectors
        Args:            labels: torch tensor in format [label1, label2, label3, ...]            classes: int, number of classes            value: label value in one hot vector, default to 1
        Returns:            return one hot format labels in shape [batchsize, classes]        """
        one_hot = torch.zeros(labels.size(0), classes)
        #labels and value_added  size must match        labels = labels.view(labels.size(0), -1)
        value_added = torch.Tensor(labels.size(0), 1).fill_(value)
        value_added = value_added.to(labels.device)
        one_hot = one_hot.to(labels.device)
        one_hot.scatter_add_(1, labels, value_added)
        return one_hot
    def _smooth_label(self, target, length, smooth_factor):
        """convert targets to one-hot format, and smooth        them.        Args:            target: target in form with [label1, label2, label_batchsize]            length: length of one-hot format(number of classes)            smooth_factor: smooth factor for label smooth
        Returns:            smoothed labels in one hot format        """
        one_hot = self._one_hot(target, length, value=1 - smooth_factor)
        one_hot += smooth_factor / (length - 1)
        return one_hot.to(target.device)
    def forward(self, x, target):
        if x.size(0) != target.size(0):
            raise ValueError('Expected input batchsize ({}) to match target batch_size({})'                    .format(x.size(0), target.size(0)))
        if x.dim() < 2:
            raise ValueError('Expected input tensor to have least 2 dimensions(got {})'                    .format(x.size(0)))
        if x.dim() != 2:
            raise ValueError('Only 2 dimension tensor are implemented, (got {})'                    .format(x.size()))

        smoothed_target = self._smooth_label(target, x.size(1), self.e)
        x = self.log_softmax(x)
        loss = torch.sum(- x * smoothed_target, dim=1)
        if self.reduction == 'none':
            return loss
        elif self.reduction == 'sum':
            return torch.sum(loss)
        elif self.reduction == 'mean':
            return torch.mean(loss)
        else:
            raise ValueError('unrecognized option, expect reduction to be one of none, mean, sum')

for images, labels in train_loader:    images, labels = images.cuda(), labels.cuda()    N = labels.size(0)    # C is the number of classes.    smoothed_labels = torch.full(size=(N, C), fill_value=0.1 / (C - 1)).cuda()    smoothed_labels.scatter_(dim=1, index=torch.unsqueeze(labels, dim=1), value=0.9)
    score = model(images)    log_prob = torch.nn.functional.log_softmax(score, dim=1)    loss = -torch.sum(log_prob * smoothed_labels) / N    optimizer.zero_grad()    loss.backward()    optimizer.step()

beta_distribution = torch.distributions.beta.Beta(alpha, alpha)
for images, labels in train_loader:    images, labels = images.cuda(), labels.cuda()
    # Mixup images and labels.    lambda_ = beta_distribution.sample([]).item()    index = torch.randperm(images.size(0)).cuda()    mixed_images = lambda_ * images + (1 - lambda_) * images[index, :]
    label_a, label_b = labels, labels[index]
    # Mixup loss.    scores = model(mixed_images)    loss = (lambda_ * loss_function(scores, label_a)            + (1 - lambda_) * loss_function(scores, label_b))    optimizer.zero_grad()    loss.backward()    optimizer.step()

l1_regularization = torch.nn.L1Loss(reduction='sum')
loss = ...  # Standard cross-entropy loss
for param in model.parameters():    loss += torch.sum(torch.abs(param))
loss.backward()

bias_list = (param for name, param in model.named_parameters() if name[-4:] == 'bias')
others_list = (param for name, param in model.named_parameters() if name[-4:] != 'bias')
parameters = [{'parameters': bias_list, 'weight_decay': 0},                              {'parameters': others_list}]
optimizer = torch.optim.SGD(parameters, lr=1e-2, momentum=0.9, weight_decay=1e-4)

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=20)

# If there is one global learning rate (which is the common case).
lr = next(iter(optimizer.param_groups))['lr']
# If there are multiple learning rates for different layers.
all_lr = []
for param_group in optimizer.param_groups:    all_lr.append(param_group['lr'])

# Reduce learning rate when validation accuracy plateau.
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', patience=5, verbose=True)
for t in range(0, 80):    train(...)    val(...)    scheduler.step(val_acc)
# Cosine annealing learning rate.
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=80)
# Reduce learning rate by 10 at given epochs.
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50, 70], gamma=0.1)
for t in range(0, 80):    scheduler.step()        train(...)    val(...)
# Learning rate warmup by 10 epochs.
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda t: t / 10)
for t in range(0, 10):    scheduler.step()    train(...)    val(...)

import torch
from torch.optim import SGD
from torch.optim.lr_scheduler import ExponentialLR, StepLR
model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
optimizer = SGD(model, 0.1)
scheduler1 = ExponentialLR(optimizer, gamma=0.9)
scheduler2 = StepLR(optimizer, step_size=3, gamma=0.1)
for epoch in range(4):    print(epoch, scheduler2.get_last_lr()[0])    optimizer.step()    scheduler1.step()    scheduler2.step()

pip install tensorboard
tensorboard --logdir=runs

from torch.utils.tensorboard import SummaryWriter
import numpy as np
writer = SummaryWriter()
for n_iter in range(100):    writer.add_scalar('Loss/train', np.random.random(), n_iter)    writer.add_scalar('Loss/test', np.random.random(), n_iter)    writer.add_scalar('Accuracy/train', np.random.random(), n_iter)    writer.add_scalar('Accuracy/test', np.random.random(), n_iter)

# VGG-16 relu5-3 feature.
model = torchvision.models.vgg16(pretrained=True).features[:-1]
# VGG-16 pool5 feature.
model = torchvision.models.vgg16(pretrained=True).features
# VGG-16 fc7 feature.
model = torchvision.models.vgg16(pretrained=True)
model.classifier = torch.nn.Sequential(*list(model.classifier.children())[:-3])
# ResNet GAP feature.
model = torchvision.models.resnet18(pretrained=True)
model = torch.nn.Sequential(collections.OrderedDict(    list(model.named_children())[:-1]))
with torch.no_grad():    model.eval()    conv_representation = model(image)

class FeatureExtractor(torch.nn.Module):    """Helper class to extract several convolution features from the given    pre-trained model.
    Attributes:        _model, torch.nn.Module.        _layers_to_extract, list<str> or set<str>
    Example:        >>> model = torchvision.models.resnet152(pretrained=True)        >>> model = torch.nn.Sequential(collections.OrderedDict(                list(model.named_children())[:-1]))        >>> conv_representation = FeatureExtractor(                pretrained_model=model,                layers_to_extract={'layer1', 'layer2', 'layer3', 'layer4'})(image)    """    def __init__(self, pretrained_model, layers_to_extract):        torch.nn.Module.__init__(self)        self._model = pretrained_model        self._model.eval()        self._layers_to_extract = set(layers_to_extract)
    def forward(self, x):        with torch.no_grad():            conv_representation = []            for name, layer in self._model.named_children():                x = layer(x)                if name in self._layers_to_extract:                    conv_representation.append(x)            return conv_representation

model = torchvision.models.resnet18(pretrained=True)
for param in model.parameters():    param.requires_grad = False
model.fc = nn.Linear(512, 100)  # Replace the last fc layer
optimizer = torch.optim.SGD(model.fc.parameters(), lr=1e-2, momentum=0.9, weight_decay=1e-4)

model = torchvision.models.resnet18(pretrained=True)
finetuned_parameters = list(map(id, model.fc.parameters()))
conv_parameters = (p for p in model.parameters() if id(p) not in finetuned_parameters)
parameters = [{'params': conv_parameters, 'lr': 1e-3},               {'params': model.fc.parameters()}]
optimizer = torch.optim.SGD(parameters, lr=1e-2, momentum=0.9, weight_decay=1e-4)

x = torch.nn.functional.relu(x, inplace=True)

with torch.autograd.profiler.profile(enabled=True, use_cuda=False) as profile:    ...
print(profile)
# Or run on the command line
python -m torch.utils.bottleneck main.py

# pip install torchsnooper
import torchsnooper
# For functions, use the @torchsnooper.snoop() decorator
# If not a function, use the with statement to activate TorchSnooper, wrapping the training loop in the with statement.
with torchsnooper.snoop():    # Original code