Basic functions using pytorch

Torch Core

This module contains all the basic functions we need in other modules of the fastai library (split with core that contains the ones not requiring pytorch). Its documentation can easily be skipped at a first read, unless you want to know what a given function does.

Global constants

AdamW = partial(optim.Adam, betas=(0.9,0.99))

bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)

defaults.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

If you are trying to make fastai run on the CPU, simply change the default device: defaults.device = torch.device('cpu').

Alternatively, if not using wildcard imports: fastai.torch_core.defaults.device = torch.device('cpu').

Conversion functions

batch_to_half[source][test]

batch_to_half(b:Collection[Tensor]) → Collection[Tensor]

Tests found for batch_to_half:

  • pytest -sv tests/test_fp16.py::test_batch_to_half [source]

To run tests please refer to this guide.

Set the input of batch b to half precision.

flatten_model[source][test]

flatten_model(m)

No tests found for <lambda>. To contribute a test please refer to this guide and this discussion.

Flattens all the layers of m into an array. This allows for easy access to the layers of the model and allows you to manipulate the model as if it was an array.

m = simple_cnn([3,6,12])
m
Sequential(
  (0): Sequential(
    (0): Conv2d(3, 6, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (1): ReLU(inplace)
  )
  (1): Sequential(
    (0): Conv2d(6, 12, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (1): ReLU(inplace)
  )
  (2): Sequential(
    (0): AdaptiveAvgPool2d(output_size=1)
    (1): Flatten()
  )
)
flatten_model(m)
[Conv2d(3, 6, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
 ReLU(inplace),
 Conv2d(6, 12, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
 ReLU(inplace),
 AdaptiveAvgPool2d(output_size=1),
 Flatten()]
show_doc(flatten_check)

flatten_check[source][test]

flatten_check(out:Tensor, targ:Tensor) → Tensor

No tests found for flatten_check. To contribute a test please refer to this guide and this discussion.

Check that out and targ have the same number of elements and flatten them.

model2half[source][test]

model2half(model:Module) → Module

Tests found for model2half:

  • pytest -sv tests/test_fp16.py::test_model2half [source]
  • pytest -sv tests/test_fp16.py::test_model2half_forward [source]

To run tests please refer to this guide.

Convert model to half precision except the batchnorm layers.

Converting model parameters to half precision allows us to leverage fast FP16 arithmetic which can speed up the computations by 2-8 times. It also reduces memory consumption allowing us to train deeper models.

Note: Batchnorm layers are not converted to half precision as that may lead to instability in training.

m = simple_cnn([3,6,12], bn=True)

def show_params_dtype(state_dict):
    """Simple function to pretty print the dtype of the model params"""
    for wt_name, param in state_dict.items():
        print("{:<30}: {}".format(wt_name, str(param.dtype)))
    print()    

print("dtypes of model parameters before model2half: ")
show_params_dtype(m.state_dict())

# Converting model to half precision
m_half = model2half(m)

print("dtypes of model parameters after model2half: ")
show_params_dtype(m_half.state_dict())
dtypes of model parameters before model2half: 
0.0.weight                    : torch.float32
0.2.weight                    : torch.float32
0.2.bias                      : torch.float32
0.2.running_mean              : torch.float32
0.2.running_var               : torch.float32
0.2.num_batches_tracked       : torch.int64
1.0.weight                    : torch.float32
1.0.bias                      : torch.float32

dtypes of model parameters after model2half: 
0.0.weight                    : torch.float16
0.2.weight                    : torch.float32
0.2.bias                      : torch.float32
0.2.running_mean              : torch.float32
0.2.running_var               : torch.float32
0.2.num_batches_tracked       : torch.int64
1.0.weight                    : torch.float16
1.0.bias                      : torch.float16

np2model_tensor[source][test]

np2model_tensor(a)

Tests found for np2model_tensor:

  • pytest -sv tests/test_torch_core.py::test_np2model_tensor [source]

To run tests please refer to this guide.

Tranform numpy array a to a tensor of the same type.

It is a wrapper on top of Pytorch's torch.as_tensor which converts numpy array to torch tensor, and additionally attempts to map all floats to torch.float32 and all integers to torch.int64 for consistencies in model data. Below is an example demonstrating it's functionality for floating number, similar functionality applies to integer as well.

a1 = np.ones((2, 3)).astype(np.float16)
a2 = np.ones((2, 3)).astype(np.float32)
a3 = np.ones((2, 3)).astype(np.float64)

b1 = np2model_tensor(a1) # Maps to torch.float32
b2 = np2model_tensor(a2) # Maps to torch.float32
b3 = np2model_tensor(a3) # Maps to torch.float32

print(f"Datatype of as': {a1.dtype}, {a2.dtype}, {a3.dtype}")
print(f"Datatype of bs': {b1.dtype}, {b2.dtype}, {b3.dtype}")
Datatype of as': float16, float32, float64
Datatype of bs': torch.float32, torch.float32, torch.float32

requires_grad[source][test]

requires_grad(m:Module, b:Optional[bool]=None) → Optional[bool]

Tests found for requires_grad:

  • pytest -sv tests/test_torch_core.py::test_requires_grad [source]
  • pytest -sv tests/test_torch_core.py::test_requires_grad_set [source]

Some other tests where requires_grad is used:

  • pytest -sv tests/test_torch_core.py::test_set_bn_eval [source]

To run tests please refer to this guide.

If b is not set return requires_grad of first param, else set requires_grad on all params as b

Performs both getting and setting of requires_grad parameter of the tensors, which decided whether to accumulate gradients or not.

  • If b is None: The function gets the requires_grad for the model parameter, to be more specific it returns the requires_grad of the first element in the model.

  • Else if b is passed (a boolean value), requires_grad of all parameters of the model is set to b.

# Any Pytorch model
m = simple_cnn([3, 6, 12], bn=True)

# Get the requires_grad of model
print("requires_grad of model: {}".format(requires_grad(m)))

# Set requires_grad of all params in model to false
requires_grad(m, False)

# Get the requires_grad of model
print("requires_grad of model: {}".format(requires_grad(m)))
requires_grad of model: True
requires_grad of model: False

tensor[source][test]

tensor(x:Any, *rest) → Tensor

Tests found for tensor:

  • pytest -sv tests/test_torch_core.py::test_tensor_with_list [source]
  • pytest -sv tests/test_torch_core.py::test_tensor_with_ndarray [source]
  • pytest -sv tests/test_torch_core.py::test_tensor_with_tensor [source]

Some other tests where tensor is used:

  • pytest -sv tests/test_torch_core.py::test_to_cpu [source]
  • pytest -sv tests/test_torch_core.py::test_to_data [source]
  • pytest -sv tests/test_torch_core.py::test_to_detach [source]
  • pytest -sv tests/test_torch_core.py::test_to_float [source]
  • pytest -sv tests/test_torch_core.py::test_to_half [source]

To run tests please refer to this guide.

Like torch.as_tensor, but handle lists too, and can pass multiple vector elements directly.

Handy function when you want to convert any list type object to tensor, initialize your weights manually, and other similar cases.

NB: When passing multiple vectors, all vectors must be of same dimensions. (Obvious but can be forgotten sometimes)

# Conversion from any numpy array
b = tensor(np.array([1, 2, 3]))
print(b, type(b))

# Passing as multiple parameters
b = tensor(1, 2, 3)
print(b, type(b))

# Passing a single list
b = tensor([1, 2, 3])
print(b, type(b))

# Can work with multiple vectors / lists
b = tensor([1, 2], [3, 4])
print(b, type(b))
tensor([1, 2, 3]) <class 'torch.Tensor'>
tensor([1, 2, 3]) <class 'torch.Tensor'>
tensor([1, 2, 3]) <class 'torch.Tensor'>
tensor([[1, 2],
        [3, 4]]) <class 'torch.Tensor'>

to_cpu[source][test]

to_cpu(b:ItemsList)

Tests found for to_cpu:

  • pytest -sv tests/test_torch_core.py::test_to_cpu [source]

To run tests please refer to this guide.

Recursively map lists of tensors in b to the cpu.

A wrapper on top of Pytorch's torch.Tensor.cpu() function, which creates and returns a copy of a tensor or even a list of tensors in the CPU. As described in Pytorch's docs, if the tensor or list of tensor is already on the CPU, the exact data is returned and no copy is made.

Useful to convert all the list of parameters of the model to CPU in a single call.

if torch.cuda.is_available():
    a = [torch.randn((1, 1)).cuda() for i in range(3)]
    print(a)
    print("Id of tensors in a: ")
    for i in a: print(id(i))
    
    # Getting a CPU version of the tensors in GPU
    b = to_cpu(a)
    print(b)
    print("Id of tensors in b:")
    for i in b: print(id(i))
    
    # Trying to perform to_cpu on a list of tensor already in CPU
    c = to_cpu(b)
    print(c)
    # The tensors in c has exact id as that of b. No copy performed.
    print("Id of tensors in c:")
    for i in c: print(id(i))
[tensor([[-0.0729]], device='cuda:0'), tensor([[0.6379]], device='cuda:0'), tensor([[-1.1639]], device='cuda:0')]
Id of tensors in a: 
140198250940456
140198119936216
140198119936432
[tensor([[-0.0729]]), tensor([[0.6379]]), tensor([[-1.1639]])]
Id of tensors in b:
140198119936792
140198119936864
140198119937656
[tensor([[-0.0729]]), tensor([[0.6379]]), tensor([[-1.1639]])]
Id of tensors in c:
140198119936792
140198119936864
140198119937656

to_data[source][test]

to_data(b:ItemsList)

Tests found for to_data:

  • pytest -sv tests/test_torch_core.py::test_to_data [source]

To run tests please refer to this guide.

Recursively map lists of items in b to their wrapped data.

Returns the data attribute from the object or collection of objects that inherits from ItemBase class. Useful to examine the exact values of the data, could be used to work with the data outside of fastai classes.

# Default example examined

from fastai import *
from fastai.vision import *

path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)

# Examin the labels
ys = list(data.y)
print("Category display names: ", [ys[0], ys[-1]])

print("Unique classes internally represented as: ", to_data([ys[0], ys[-1]]))
Category display names:  [Category 7, Category 3]
Unique classes internally represented as:  [1, 0]

to_detach[source][test]

to_detach(b:Tensors, cpu:bool=True)

Tests found for to_detach:

  • pytest -sv tests/test_torch_core.py::test_to_detach [source]

To run tests please refer to this guide.

Recursively detach lists of tensors in b; put them on the CPU if cpu=True.

to_device[source][test]

to_device(b:Tensors, device:device)

No tests found for to_device. To contribute a test please refer to this guide and this discussion.

Recursively put b on device.

to_half[source][test]

to_half(b:Collection[Tensor]) → Collection[Tensor]

Tests found for to_half:

  • pytest -sv tests/test_fp16.py::test_to_half [source]
  • pytest -sv tests/test_torch_core.py::test_to_half [source]

To run tests please refer to this guide.

Recursively map lists of tensors in b to FP16.

Converts the tensor or list of FP16, resulting in less memory consumption and faster computations with the tensor. It does not convert torch.int types to half precision.

a1 = torch.tensor([1, 2], dtype=torch.int64)
a2 = torch.tensor([1, 2], dtype=torch.int32)
a3 = torch.tensor([1, 2], dtype=torch.int16)
a4 = torch.tensor([1, 2], dtype=torch.float64)
a5 = torch.tensor([1, 2], dtype=torch.float32)
a6 = torch.tensor([1, 2], dtype=torch.float16)

print("dtype of as: ", a1.dtype, a2.dtype, a3.dtype, a4.dtype, a5.dtype, a6.dtype, sep="\t")

b1, b2, b3, b4, b5, b6 = to_half([a1, a2, a3, a4, a5, a6])

print("dtype of bs: ", b1.dtype, b2.dtype, b3.dtype, b4.dtype, b5.dtype, b6.dtype, sep="\t")
dtype of as: 	torch.int64	torch.int32	torch.int16	torch.float64	torch.float32	torch.float16
dtype of bs: 	torch.int64	torch.int32	torch.int16	torch.float16	torch.float16	torch.float16

to_np[source][test]

to_np(x)

Tests found for to_np:

  • pytest -sv tests/test_torch_core.py::test_to_np [source]

To run tests please refer to this guide.

Convert a tensor to a numpy array.

Internally puts the data to CPU, and converts to numpy.ndarray equivalent of torch.tensor by calling torch.Tensor.numpy().

a = torch.tensor([1, 2], dtype=torch.float64)

if torch.cuda.is_available():
    a = a.cuda()

print(a, type(a), a.device)

b = to_np(a)

print(b, type(b))
tensor([1., 2.], device='cuda:0', dtype=torch.float64) <class 'torch.Tensor'> cuda:0
[1. 2.] <class 'numpy.ndarray'>

try_int[source][test]

try_int(o:Any) → Any

No tests found for try_int. To contribute a test please refer to this guide and this discussion.

Try to convert o to int, default to o if not possible.

# Converts floating point numbers to integer
print(try_int(12.5), type(try_int(12.5)))

# This is a Rank-1 ndarray, which ideally should not be converted to int 
print(try_int(np.array([1.5])), try_int(np.array([1.5])).dtype)

# Numpy array with a single elements are converted to int
print(try_int(np.array(1.5)), type(try_int(np.array(1.5))))

print(try_int(torch.tensor(2.5)), type(try_int(torch.tensor(2.5))))

# Strings are not converted to int (of course)
print(try_int("12.5"), type(try_int("12.5")))
12 <class 'int'>
[1.5] float64
1 <class 'int'>
2 <class 'int'>
12.5 <class 'str'>
show_doc(to_float)

to_float[source][test]

to_float(b:Collection[Tensor]) → Collection[Tensor]

Tests found for to_float:

  • pytest -sv tests/test_torch_core.py::test_to_float [source]

To run tests please refer to this guide.

Recursively map lists of tensors in b to FP16.

Functions to deal with model initialization

apply_init[source][test]

apply_init(m, init_func:LayerFunc)

Tests found for apply_init:

  • pytest -sv tests/test_torch_core.py::test_apply_init [source]

To run tests please refer to this guide.

Initialize all non-batchnorm layers of m with init_func.

apply_leaf[source][test]

apply_leaf(m:Module, f:LayerFunc)

Tests found for apply_leaf:

  • pytest -sv tests/test_torch_core.py::test_apply_init [source]

To run tests please refer to this guide.

Apply f to children of m.

cond_init[source][test]

cond_init(m:Module, init_func:LayerFunc)

No tests found for cond_init. To contribute a test please refer to this guide and this discussion.

Initialize the non-batchnorm layers of m with init_func.

in_channels[source][test]

in_channels(m:Module) → List[int]

Tests found for in_channels:

  • pytest -sv tests/test_torch_core.py::test_in_channels [source]
  • pytest -sv tests/test_torch_core.py::test_in_channels_no_weights [source]

To run tests please refer to this guide.

Return the shape of the first weight layer in m.

init_default[source][test]

init_default(m:Module, func:LayerFunc='kaiming_normal_') → Module

No tests found for init_default. To contribute a test please refer to this guide and this discussion.

Initialize m weights with func and set bias to 0.

Functions to get information of a model

children[source][test]

children(m:Module) → ModuleList

Tests found for children:

  • pytest -sv tests/test_torch_core.py::test_children [source]

To run tests please refer to this guide.

Get children of m.

children_and_parameters[source][test]

children_and_parameters(m:Module)

No tests found for children_and_parameters. To contribute a test please refer to this guide and this discussion.

Return the children of m and its direct parameters not registered in modules.

first_layer[source][test]

first_layer(m:Module) → Module

Tests found for first_layer:

  • pytest -sv tests/test_torch_core.py::test_first_layer [source]

To run tests please refer to this guide.

Retrieve first layer in a module m.

last_layer[source][test]

last_layer(m:Module) → Module

Tests found for last_layer:

  • pytest -sv tests/test_torch_core.py::test_last_layer [source]

To run tests please refer to this guide.

Retrieve last layer in a module m.

num_children[source][test]

num_children(m:Module) → int

Tests found for num_children:

  • pytest -sv tests/test_torch_core.py::test_num_children [source]

To run tests please refer to this guide.

Get number of children modules in m.

one_param[source][test]

one_param(m:Module) → Tensor

No tests found for one_param. To contribute a test please refer to this guide and this discussion.

Return the first parameter of m.

range_children[source][test]

range_children(m:Module) → Iterator[int]

Tests found for range_children:

  • pytest -sv tests/test_torch_core.py::test_range_children [source]

To run tests please refer to this guide.

Return iterator of len of children of m.

trainable_params[source][test]

trainable_params(m:Module) → ParamList

No tests found for trainable_params. To contribute a test please refer to this guide and this discussion.

Return list of trainable params in m.

Functions to deal with BatchNorm layers

bn2float[source][test]

bn2float(module:Module) → Module

No tests found for bn2float. To contribute a test please refer to this guide and this discussion.

If module is batchnorm don't use half precision.

set_bn_eval[source][test]

set_bn_eval(m:Module)

Tests found for set_bn_eval:

  • pytest -sv tests/test_torch_core.py::test_set_bn_eval [source]

To run tests please refer to this guide.

Set bn layers in eval mode for all recursive children of m.

split_no_wd_params[source][test]

split_no_wd_params(layer_groups:ModuleList) → List[List[Parameter]]

Tests found for split_no_wd_params:

  • pytest -sv tests/test_torch_core.py::test_split_no_wd_params [source]

To run tests please refer to this guide.

Separate the parameters in layer_groups between no_wd_types and bias (bias_types) from the rest.

This is used by the optimizer to determine which params should be applied weight decay when using the option bn_wd=False is used in a Learner.

Functions to get random tensors

log_uniform[source][test]

log_uniform(low, high, size:Optional[List[int]]=None) → FloatOrTensor

No tests found for log_uniform. To contribute a test please refer to this guide and this discussion.

Draw 1 or shape=size random floats from uniform dist: min=log(low), max=log(high).

log_uniform(0.5,2,(8,))
tensor([1.3422, 1.7174, 1.7097, 0.9742, 1.8986, 1.4985, 1.8341, 0.7608])

rand_bool[source][test]

rand_bool(p:float, size:Optional[List[int]]=None) → BoolOrTensor

No tests found for rand_bool. To contribute a test please refer to this guide and this discussion.

Draw 1 or shape=size random booleans (True occuring with probability p).

rand_bool(0.5, 8)
tensor([0, 0, 1, 0, 0, 0, 1, 0], dtype=torch.uint8)

uniform[source][test]

uniform(low:Number, high:Number=None, size:Optional[List[int]]=None) → FloatOrTensor

No tests found for uniform. To contribute a test please refer to this guide and this discussion.

Draw 1 or shape=size random floats from uniform dist: min=low, max=high.

uniform(0,1,(8,))
tensor([0.6441, 0.4143, 0.6510, 0.4366, 0.7045, 0.0803, 0.8638, 0.1684])

uniform_int[source][test]

uniform_int(low:int, high:int, size:Optional[List[int]]=None) → IntOrTensor

No tests found for uniform_int. To contribute a test please refer to this guide and this discussion.

Generate int or tensor size of ints between low and high (included).

uniform_int(0,2,(8,))
tensor([1, 1, 2, 1, 0, 2, 1, 1])

Other functionality

class Module[source][test]

Module() :: PrePostInitMeta :: Module

No tests found for Module. To contribute a test please refer to this guide and this discussion.

Same as nn.Module, but no need for subclasses to call super().__init__

class _T(Module):
    "my doc string"
    def __init__(self): self.f = nn.Linear(2,1)
    def forward(self,x): return self.f(x)

t = _T()
t(tensor(1.,2))
tensor([-0.6151], grad_fn=<AddBackward0>)

class ModelOnCPU[source][test]

ModelOnCPU(model:Module)

No tests found for ModelOnCPU. To contribute a test please refer to this guide and this discussion.

A context manager to evaluate model on the CPU inside.

class NoneReduceOnCPU[source][test]

NoneReduceOnCPU(loss_func:LossFunction)

Tests found for NoneReduceOnCPU:

  • pytest -sv tests/test_torch_core.py::test_none_reduce_on_cpu [source]

To run tests please refer to this guide.

A context manager to evaluate loss_func with none reduce and weights on the CPU inside.

class ParameterModule[source][test]

ParameterModule(p:Parameter) :: PrePostInitMeta :: Module

No tests found for ParameterModule. To contribute a test please refer to this guide and this discussion.

Register a lone parameter p in a module.

data_collate[source][test]

data_collate(batch:ItemsList) → Tensor

No tests found for data_collate. To contribute a test please refer to this guide and this discussion.

Convert batch items to tensor data.

get_model[source][test]

get_model(model:Module)

No tests found for get_model. To contribute a test please refer to this guide and this discussion.

Return the model maybe wrapped inside model.

grab_idx[source][test]

grab_idx(x, i, batch_first:bool=True)

No tests found for grab_idx. To contribute a test please refer to this guide and this discussion.

Grab the i-th batch in x, batch_first stating the batch dimension.

logit[source][test]

logit(x:Tensor) → Tensor

No tests found for logit. To contribute a test please refer to this guide and this discussion.

Logit of x, clamped to avoid inf.

logit_[source][test]

logit_(x:Tensor) → Tensor

No tests found for logit_. To contribute a test please refer to this guide and this discussion.

Inplace logit of x, clamped to avoid inf

model_type[source][test]

model_type(dtype)

Tests found for model_type:

  • pytest -sv tests/test_torch_core.py::test_model_type [source]

To run tests please refer to this guide.

Return the torch type corresponding to dtype.

np_address[source][test]

np_address(x:ndarray) → int

Tests found for np_address:

  • pytest -sv tests/test_torch_core.py::test_np_address [source]

Some other tests where np_address is used:

  • pytest -sv tests/test_torch_core.py::test_tensor_with_ndarray [source]

To run tests please refer to this guide.

Address of x in memory.

split_model[source][test]

split_model(model:Module=None, splits:Collection[Union[Module, ModuleList]]=None)

Tests found for split_model:

  • pytest -sv tests/test_torch_core.py::test_split_model [source]

To run tests please refer to this guide.

Split model according to the layers in splits.

If splits are layers, the model is split at those (not included) sequentially. If want_idxs is True, the corresponding indexes are returned. If splits are lists of layers, the model is split according to those.

split_model_idx[source][test]

split_model_idx(model:Module, idxs:Collection[int]) → ModuleList

No tests found for split_model_idx. To contribute a test please refer to this guide and this discussion.

Split model according to the indexes in idxs.

trange_of[source][test]

trange_of(x)

Tests found for trange_of:

  • pytest -sv tests/test_torch_core.py::test_trange_of [source]

To run tests please refer to this guide.

Create a tensor from range_of(x).