pytorch 学习

tensor basics

1. Initialization of a Tensor
2. Tensor Mathematical Operations and Comparison
3. Tensor Indexing
4. Tensor Reshaping

import torch 
device = "cuda" if torch.cuda.is_available() else "cpu"  # Cuda to run on GPU!

1.Initialization of a Tensor

# Initializing a Tensor in this case of shape 2x3 (2 rows, 3 columns)
my_tensor = torch.tensor(
    [[1, 2, 3], [4, 5, 6]], dtype=torch.float32, device=device, requires_grad=True
)

# A few tensor attributes
print(
    f"Information about tensor: {my_tensor}"
)  # Prints data of the tensor, device and grad info
print(
    "Type of Tensor {my_tensor.dtype}"
)  # Prints dtype of the tensor (torch.float32, etc)
print(
    f"Device Tensor is on {my_tensor.device}"
)  # Prints cpu/cuda (followed by gpu number)
print(f"Shape of tensor {my_tensor.shape}")  # Prints shape, in this case 2x3
print(f"Requires gradient: {my_tensor.requires_grad}")  # Prints true/false

# Other common initialization methods (there exists a ton more)
x = torch.empty(size=(3, 3))  # Tensor of shape 3x3 with uninitialized data
x = torch.zeros((3, 3))  # Tensor of shape 3x3 with values of 0
x = torch.rand(
    (3, 3)
)  # Tensor of shape 3x3 with values from uniform distribution in interval [0,1)
x = torch.ones((3, 3))  # Tensor of shape 3x3 with values of 1
x = torch.eye(5, 5)  # Returns Identity Matrix I, (I <-> Eye), matrix of shape 2x3
x = torch.arange(
    start=0, end=5, step=1
)  # Tensor [0, 1, 2, 3, 4], note, can also do: torch.arange(11)
x = torch.linspace(start=0.1, end=1, steps=10)  # x = [0.1, 0.2, ..., 1]
x = torch.empty(size=(1, 5)).normal_(
    mean=0, std=1
)  # Normally distributed with mean=0, std=1
x = torch.empty(size=(1, 5)).uniform_(
    0, 1
)  # Values from a uniform distribution low=0, high=1
x = torch.diag(torch.ones(3))  # Diagonal matrix of shape 3x3

# How to make initialized tensors to other types (int, float, double)
# These will work even if you're on CPU or CUDA!
tensor = torch.arange(4)  # [0, 1, 2, 3] Initialized as int64 by default
print(f"Converted Boolean: {tensor.bool()}")  # Converted to Boolean: 1 if nonzero
print(f"Converted int16 {tensor.short()}")  # Converted to int16
print(
    f"Converted int64 {tensor.long()}"
)  # Converted to int64 (This one is very important, used super often)
print(f"Converted float16 {tensor.half()}")  # Converted to float16
print(
    f"Converted float32 {tensor.float()}"
)  # Converted to float32 (This one is very important, used super often)
print(f"Converted float64 {tensor.double()}")  # Converted to float64


# Array to Tensor conversion and vice-versa
import numpy as np

np_array = np.zeros((5, 5))
tensor = torch.from_numpy(np_array)
np_array_again = (
    tensor.numpy()
)  # np_array_again will be same as np_array (perhaps with numerical round offs)

2.Tensor Math & Comparison Operations

x = torch.tensor([1, 2, 3])
y = torch.tensor([9, 8, 7])

# -- Addition --
z1 = torch.empty(3)
torch.add(x, y, out=z1)  # This is one way
z2 = torch.add(x, y)  # This is another way
z = x + y  # This is my preferred way, simple and clean.

# -- Subtraction --
z = x - y  # We can do similarly as the preferred way of addition

# -- Division (A bit clunky) --
z = torch.true_divide(x, y)  # Will do element wise division if of equal shape

# -- Inplace Operations --
t = torch.zeros(3)

t.add_(x)  # Whenever we have operation followed by _ it will mutate the tensor in place
t += x  # Also inplace: t = t + x is not inplace, bit confusing.

# -- Exponentiation (Element wise if vector or matrices) --
z = x.pow(2)  # z = [1, 4, 9]
z = x ** 2  # z = [1, 4, 9]


# -- Simple Comparison --
z = x > 0  # Returns [True, True, True]
z = x < 0  # Returns [False, False, False]

# -- Matrix Multiplication --
x1 = torch.rand((2, 5))
x2 = torch.rand((5, 3))
x3 = torch.mm(x1, x2)  # Matrix multiplication of x1 and x2, out shape: 2x3
x3 = x1.mm(x2)  # Similar as line above

# -- Matrix Exponentiation --
matrix_exp = torch.rand(5, 5)
print(
    matrix_exp.matrix_power(3)
)  # is same as matrix_exp (mm) matrix_exp (mm) matrix_exp

# -- Element wise Multiplication --
z = x * y  # z = [9, 16, 21] = [1*9, 2*8, 3*7]

# -- Dot product --
z = torch.dot(x, y)  # Dot product, in this case z = 1*9 + 2*8 + 3*7

# -- Batch Matrix Multiplication --
batch = 32
n = 10
m = 20
p = 30
tensor1 = torch.rand((batch, n, m))
tensor2 = torch.rand((batch, m, p))
out_bmm = torch.bmm(tensor1, tensor2)  # Will be shape: (b x n x p)

# -- Example of broadcasting --
x1 = torch.rand((5, 5))
x2 = torch.ones((1, 5))
z = (
    x1 - x2
)  # Shape of z is 5x5: How? The 1x5 vector (x2) is subtracted for each row in the 5x5 (x1)
z = (
    x1 ** x2
)  # Shape of z is 5x5: How? Broadcasting! Element wise exponentiation for every row

# Other useful tensor operations
sum_x = torch.sum(
    x, dim=0
)  # Sum of x across dim=0 (which is the only dim in our case), sum_x = 6
values, indices = torch.max(x, dim=0)  # Can also do x.max(dim=0)
values, indices = torch.min(x, dim=0)  # Can also do x.min(dim=0)
abs_x = torch.abs(x)  # Returns x where abs function has been applied to every element
z = torch.argmax(x, dim=0)  # Gets index of the maximum value
z = torch.argmin(x, dim=0)  # Gets index of the minimum value
mean_x = torch.mean(x.float(), dim=0)  # mean requires x to be float
z = torch.eq(x, y)  # Element wise comparison, in this case z = [False, False, False]
sorted_y, indices = torch.sort(y, dim=0, descending=False)

z = torch.clamp(x, min=0)
# All values < 0 set to 0 and values > 0 unchanged (this is exactly ReLU function)
# If you want to values over max_val to be clamped, do torch.clamp(x, min=min_val, max=max_val)

x = torch.tensor([1, 0, 1, 1, 1], dtype=torch.bool)  # True/False values
z = torch.any(x)  # will return True, can also do x.any() instead of torch.any(x)
z = torch.all(
    x
)  # will return False (since not all are True), can also do x.all() instead of torch.all()

3. Tensor Reshaping

x = torch.arange(9)

# Let's say we want to reshape it to be 3x3
x_3x3 = x.view(3, 3)

# We can also do (view and reshape are very similar)
# and the differences are in simple terms (I'm no expert at this),
# is that view acts on contiguous tensors meaning if the
# tensor is stored contiguously in memory or not, whereas
# for reshape it doesn't matter because it will copy the
# tensor to make it contiguously stored, which might come
# with some performance loss.
x_3x3 = x.reshape(3, 3)

# If we for example do:
y = x_3x3.t()
print(
    y.is_contiguous()
)  # This will return False and if we try to use view now, it won't work!
# y.view(9) would cause an error, reshape however won't

# This is because in memory it was stored [0, 1, 2, ... 8], whereas now it's [0, 3, 6, 1, 4, 7, 2, 5, 8]
# The jump is no longer 1 in memory for one element jump (matrices are stored as a contiguous block, and
# using pointers to construct these matrices). This is a bit complicated and I need to explore this more
# as well, at least you know it's a problem to be cautious of! A solution is to do the following
print(y.contiguous().view(9))  # Calling .contiguous() before view and it works

# Moving on to another operation, let's say we want to add two tensors dimensions togethor
x1 = torch.rand(2, 5)
x2 = torch.rand(2, 5)
print(torch.cat((x1, x2), dim=0).shape)  # Shape: 4x5
print(torch.cat((x1, x2), dim=1).shape)  # Shape 2x10

# Let's say we want to unroll x1 into one long vector with 10 elements, we can do:
z = x1.view(-1)  # And -1 will unroll everything

# If we instead have an additional dimension and we wish to keep those as is we can do:
batch = 64
x = torch.rand((batch, 2, 5))
z = x.view(
    batch, -1
)  # And z.shape would be 64x10, this is very useful stuff and is used all the time

# Let's say we want to switch x axis so that instead of 64x2x5 we have 64x5x2
# I.e we want dimension 0 to stay, dimension 1 to become dimension 2, dimension 2 to become dimension 1
# Basically you tell permute where you want the new dimensions to be, torch.transpose is a special case
# of permute (why?)
z = x.permute(0, 2, 1)

# Splits x last dimension into chunks of 2 (since 5 is not integer div by 2) the last dimension
# will be smaller, so it will split it into two tensors: 64x2x3 and 64x2x2
z = torch.chunk(x, chunks=2, dim=1)
print(z[0].shape)
print(z[1].shape)

# Let's say we want to add an additional dimension
x = torch.arange(
    10
)  # Shape is [10], let's say we want to add an additional so we have 1x10
print(x.unsqueeze(0).shape)  # 1x10
print(x.unsqueeze(1).shape)  # 10x1

# Let's say we have x which is 1x1x10 and we want to remove a dim so we have 1x10
x = torch.arange(10).unsqueeze(0).unsqueeze(1)

# Perhaps unsurprisingly
z = x.squeeze(1)  # can also do .squeeze(0) both returns 1x10

That was some essential Tensor operations, hopefully you found it useful!