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‎beginner_source/ptcheat.rst

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@@ -80,8 +80,8 @@ Distributed Training
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.. code-block:: python
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import torch.distributed as dist # distributed communication
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from multiprocessing import Process # memory sharing processes
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import torch.distributed as dist # distributed communication
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from torch.multiprocessing import Process # memory sharing processes
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See `distributed <https://pytorch.org/docs/stable/distributed.html>`__
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and
@@ -95,13 +95,13 @@ Creation
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.. code-block:: python
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torch.randn(*size) # tensor with independent N(0,1) entries
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torch.[ones|zeros](*size) # tensor with all 1's [or 0's]
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torch.Tensor(L) # create tensor from [nested] list or ndarray L
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x.clone() # clone of x
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with torch.no_grad(): # code wrap that stops autograd from tracking tensor history
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requires_grad=True # arg, when set to True, tracks computation
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# history for future derivative calculations
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x = torch.randn(*size) # tensor with independent N(0,1) entries
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x = torch.[ones|zeros](*size) # tensor with all 1's [or 0's]
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x = torch.tensor(L) # create tensor from [nested] list or ndarray L
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y = x.clone() # clone of x
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with torch.no_grad(): # code wrap that stops autograd from tracking tensor history
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requires_grad=True # arg, when set to True, tracks computation
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# history for future derivative calculations
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See `tensor <https://pytorch.org/docs/stable/tensors.html>`__
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@@ -110,14 +110,16 @@ Dimensionality
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.. code-block:: python
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x.size() # return tuple-like object of dimensions
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torch.cat(tensor_seq, dim=0) # concatenates tensors along dim
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x.view(a,b,...) # reshapes x into size (a,b,...)
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x.view(-1,a) # reshapes x into size (b,a) for some b
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x.transpose(a,b) # swaps dimensions a and b
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x.permute(*dims) # permutes dimensions
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x.unsqueeze(dim) # tensor with added axis
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x.unsqueeze(dim=2) # (a,b,c) tensor -> (a,b,1,c) tensor
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x.size() # return tuple-like object of dimensions
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x = torch.cat(tensor_seq, dim=0) # concatenates tensors along dim
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y = x.view(a,b,...) # reshapes x into size (a,b,...)
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y = x.view(-1,a) # reshapes x into size (b,a) for some b
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y = x.transpose(a,b) # swaps dimensions a and b
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y = x.permute(*dims) # permutes dimensions
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y = x.unsqueeze(dim) # tensor with added axis
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y = x.unsqueeze(dim=2) # (a,b,c) tensor -> (a,b,1,c) tensor
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y = x.squeeze() # removes all dimensions of size 1 (a,1,b,1) -> (a,b)
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y = x.squeeze(dim=1) # removes specified dimension of size 1 (a,1,b,1) -> (a,b,1)
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See `tensor <https://pytorch.org/docs/stable/tensors.html>`__
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@@ -127,9 +129,9 @@ Algebra
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.. code-block:: python
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A.mm(B) # matrix multiplication
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A.mv(x) # matrix-vector multiplication
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x.t() # matrix transpose
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ret = A.mm(B) # matrix multiplication
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ret = A.mv(x) # matrix-vector multiplication
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x = x.t() # matrix transpose
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See `math
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operations <https://pytorch.org/docs/stable/torch.html?highlight=mm#math-operations>`__
@@ -139,24 +141,24 @@ GPU Usage
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.. code-block:: python
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torch.cuda.is_available # check for cuda
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x.cuda() # move x's data from
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# CPU to GPU and return new object
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torch.cuda.is_available # check for cuda
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x = x.cuda() # move x's data from
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# CPU to GPU and return new object
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x.cpu() # move x's data from GPU to CPU
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# and return new object
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x = x.cpu() # move x's data from GPU to CPU
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# and return new object
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if not args.disable_cuda and torch.cuda.is_available(): # device agnostic code
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args.device = torch.device('cuda') # and modularity
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else: #
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args.device = torch.device('cpu') #
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if not args.disable_cuda and torch.cuda.is_available(): # device agnostic code
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args.device = torch.device('cuda') # and modularity
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else: #
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args.device = torch.device('cpu') #
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net.to(device) # recursively convert their
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# parameters and buffers to
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# device specific tensors
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net.to(device) # recursively convert their
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# parameters and buffers to
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# device specific tensors
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mytensor.to(device) # copy your tensors to a device
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# (gpu, cpu)
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x = x.to(device) # copy your tensors to a device
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# (gpu, cpu)
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See `cuda <https://pytorch.org/docs/stable/cuda.html>`__
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@@ -175,7 +177,7 @@ Deep Learning
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nn.MaxPoolXd(s) # X dimension pooling layer
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# (notation as above)
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nn.BatchNorm # batch norm layer
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nn.BatchNormXd # batch norm layer
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nn.RNN/LSTM/GRU # recurrent layers
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nn.Dropout(p=0.5, inplace=False) # dropout layer for any dimensional input
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nn.Dropout2d(p=0.5, inplace=False) # 2-dimensional channel-wise dropout
@@ -189,11 +191,15 @@ Loss Functions
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.. code-block:: python
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nn.X # where X is BCELoss, CrossEntropyLoss,
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# L1Loss, MSELoss, NLLLoss, SoftMarginLoss,
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# MultiLabelSoftMarginLoss, CosineEmbeddingLoss,
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# KLDivLoss, MarginRankingLoss, HingeEmbeddingLoss
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# or CosineEmbeddingLoss
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nn.X # where X is L1Loss, MSELoss, CrossEntropyLoss
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# CTCLoss, NLLLoss, PoissonNLLLoss,
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# KLDivLoss, BCELoss, BCEWithLogitsLoss,
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# MarginRankingLoss, HingeEmbeddingLoss,
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# MultiLabelMarginLoss, SmoothL1Loss,
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# SoftMarginLoss, MultiLabelSoftMarginLoss,
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# CosineEmbeddingLoss, MultiMarginLoss,
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# or TripletMarginLoss
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See `loss
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functions <https://pytorch.org/docs/stable/nn.html#loss-functions>`__
@@ -204,10 +210,10 @@ Activation Functions
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.. code-block:: python
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nn.X # where X is ReLU, ReLU6, ELU, SELU, PReLU, LeakyReLU,
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# Threshold, HardTanh, Sigmoid, Tanh,
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# LogSigmoid, Softplus, SoftShrink,
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# Softsign, TanhShrink, Softmin, Softmax,
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# Softmax2d or LogSoftmax
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# RReLu, CELU, GELU, Threshold, Hardshrink, HardTanh,
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# Sigmoid, LogSigmoid, Softplus, SoftShrink,
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# Softsign, Tanh, TanhShrink, Softmin, Softmax,
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# Softmax2d, LogSoftmax or AdaptiveSoftmaxWithLoss
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See `activation
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functions <https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity>`__
@@ -220,8 +226,8 @@ Optimizers
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opt = optim.x(model.parameters(), ...) # create optimizer
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opt.step() # update weights
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optim.X # where X is SGD, Adadelta, Adagrad, Adam,
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# SparseAdam, Adamax, ASGD,
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# LBFGS, RMSProp or Rprop
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# AdamW, SparseAdam, Adamax, ASGD,
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# LBFGS, RMSprop or Rprop
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See `optimizers <https://pytorch.org/docs/stable/optim.html>`__
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@@ -232,8 +238,10 @@ Learning rate scheduling
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scheduler = optim.X(optimizer,...) # create lr scheduler
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scheduler.step() # update lr at start of epoch
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optim.lr_scheduler.X # where X is LambdaLR, StepLR, MultiStepLR,
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# ExponentialLR or ReduceLROnPLateau
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optim.lr_scheduler.X # where X is LambdaLR, MultiplicativeLR,
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# StepLR, MultiStepLR, ExponentialLR,
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# CosineAnnealingLR, ReduceLROnPlateau, CyclicLR,
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# OneCycleLR, CosineAnnealingWarmRestarts,
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See `learning rate
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scheduler <https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate>`__
@@ -264,8 +272,8 @@ Dataloaders and DataSamplers
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sampler.Sampler(dataset,...) # abstract class dealing with
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# ways to sample from dataset
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sampler.XSampler where ... # Sequential, Random, Subset,
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# WeightedRandom or Distributed
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sampler.XSampler where ... # Sequential, Random, SubsetRandom,
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# WeightedRandom, Batch, Distributed
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See
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`dataloader <https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader>`__

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