Handling Data Type Mismatch between NumPy Arrays and PyTorch Tensors

In NumPy, the default float data type is float64, also known as double precision. In PyTorch, the default float data type is float32, also known as single precision. When we convert a NumPy array to a PyTorch tensor without explicitly specifying the data type, the original data type of the NumPy array is used, which might be different from the PyTorch default. This can cause issues when performing operations between tensors, such as matrix multiplication, which requires the same data types for all involved tensors.

Here’s an example to demonstrate this issue:

import numpy as np
import torch

# Create a NumPy array with float64 data type (default float data type in NumPy)
input_example_np = np.array([1.2, 2.3, 3.4, 4.5])

# Convert the NumPy array to a PyTorch tensor without specifying the data type
input_example = torch.tensor(input_example_np)
print(input_example.dtype)  # torch.float64

# Create another tensor with default data type in PyTorch (float32)
another_tensor = torch.tensor([1., 2., 3., 4.])
print(another_tensor.dtype)  # torch.float32

# Now try to perform matrix multiplication, which will raise an error due to different data types
try:
    result = torch.matmul(input_example, another_tensor)
except RuntimeError as e:
    print("Error:", e)  # Error: mat1 and mat2 must have the same dtype

# To fix this issue, specify the desired data type when converting the NumPy array:

# Convert the NumPy array to a PyTorch tensor with float32 data type
input_example = torch.tensor(input_example_np, dtype=torch.float)
print(input_example.dtype)  # torch.float32

# Now we can successfully perform matrix multiplication
result = torch.matmul(input_example, another_tensor)
print(result)

By explicitly specifying the dtype parameter when converting the NumPy array to a PyTorch tensor, we can avoid the “mat1 and mat2 must have the same dtype” error or “expected both vectors to have same dtype, but found Double and Float” error, and ensure that the tensors are compatible for mathematical operations.