diff --git a/norch/__pycache__/tensor.cpython-38.pyc b/norch/__pycache__/tensor.cpython-38.pyc index 66a2518..3d69ca0 100644 Binary files a/norch/__pycache__/tensor.cpython-38.pyc and b/norch/__pycache__/tensor.cpython-38.pyc differ diff --git a/norch/nn/__pycache__/module.cpython-38.pyc b/norch/nn/__pycache__/module.cpython-38.pyc index f1442e1..7089e21 100644 Binary files a/norch/nn/__pycache__/module.cpython-38.pyc and b/norch/nn/__pycache__/module.cpython-38.pyc differ diff --git a/norch/tensor.py b/norch/tensor.py index 0883bb1..8231dca 100644 --- a/norch/tensor.py +++ b/norch/tensor.py @@ -591,4 +591,4 @@ class Tensor: self.grad = None self.grad_fn = None - return self \ No newline at end of file + return self \ No newline at end of file diff --git a/norch/tests.ipynb b/norch/tests.ipynb deleted file mode 100644 index ca51d7d..0000000 --- a/norch/tests.ipynb +++ /dev/null @@ -1,98 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0, 1, 2, 0, 2, 2] [2, 3, 3]\n", - "Tensor shape: [2, 3, 3]\n", - "Tensor created successfully\n", - "Tensor information:\n", - "Number of dimensions: 3\n", - "Shape: [2, 3, 3]\n", - "Data:\n", - "[0.00, 1.00, 2.00, 0.00, 2.00, 2.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00]\n" - ] - } - ], - "source": [ - "from tensor import Tensor\n", - "\n", - "data = [[0, 1, 2], [0, 2, 2], [0, 1, 2]]\n", - "tensor = Tensor(data)\n", - "print(\"Tensor shape:\", tensor.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tensor created successfully\n", - "Tensor information:\n", - "Number of dimensions: 2\n", - "Shape: [3, 3]\n", - "Data:\n", - "[0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90]\n" - ] - }, - { - "data": { - "text/plain": [ - "0.10000000149011612 0.20000000298023224 0.30000001192092896 \n", - "0.4000000059604645 0.5 0.6000000238418579 \n", - "0.699999988079071 0.800000011920929 0.8999999761581421" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "data = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n", - "shape = [3, 3]\n", - "tensor = Tensor(data, shape)\n", - "tensor\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/norch/utils/__init__.py b/norch/utils/__init__.py new file mode 100644 index 0000000..90f60fd --- /dev/null +++ b/norch/utils/__init__.py @@ -0,0 +1 @@ +from .utils import * \ No newline at end of file diff --git a/norch/utils/__pycache__/utils.cpython-38.pyc b/norch/utils/__pycache__/utils.cpython-38.pyc index 2285e44..5059707 100644 Binary files a/norch/utils/__pycache__/utils.cpython-38.pyc and b/norch/utils/__pycache__/utils.cpython-38.pyc differ diff --git a/norch/utils/utils.py b/norch/utils/utils.py index e977d6d..676af02 100644 --- a/norch/utils/utils.py +++ b/norch/utils/utils.py @@ -1,5 +1,5 @@ import random -import numpy as np +import torch def generate_random_list(shape): @@ -14,4 +14,27 @@ def generate_random_list(shape): return [random.uniform(-1, 1) for _ in range(shape[0])] else: return [generate_random_list(inner_shape) for _ in range(shape[0])] - \ No newline at end of file + + +def to_torch(custom_tensor): + shape = custom_tensor.shape + pytorch_tensor = torch.zeros(shape) + + def _iterate_indices(shape): + if len(shape) == 0: + yield () + else: + for index in range(shape[0]): + for sub_indices in _iterate_indices(shape[1:]): + yield (index,) + sub_indices + + # Iterate over all elements using the custom tensor's __getitem__ method + for indices in _iterate_indices(shape): + value = custom_tensor[indices] + pytorch_tensor[tuple(indices)] = value + + return pytorch_tensor + +def compare_torch(tensor1, tensor2, epsilon=1e-5): + diff = torch.abs(tensor1 - tensor2) + return torch.all(diff < epsilon) \ No newline at end of file diff --git a/profile.txt b/profile.txt deleted file mode 100644 index 6565bd8..0000000 --- a/profile.txt +++ /dev/null @@ -1,1135 +0,0 @@ -CPU Usage: 3.1% -Memory Usage: 86.9% -CPU Usage: 13.2% -Memory Usage: 87.0% -0 -tensor([0.6798695921897888,], device="cpu", requires_grad=True) -CPU Usage: 16.8% -Memory Usage: 87.1% -1 -tensor([0.6171554327011108,], device="cpu", requires_grad=True) -CPU Usage: 10.9% -Memory Usage: 87.1% -2 -tensor([0.617615818977356,], device="cpu", 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8.901], [9.012, 1.234], [2.345, 3.456]], [[4.567, 5.678], [6.789, 7.890], [8.901, 9.012]], @@ -29,6 +32,27 @@ if __name__ == "__main__": [[7.890, 8.901], [9.012, 1.234], [2.345, 3.456]] ], requires_grad=True) + print(a) + print(a[0, 2,0]) + + a = utils.to_torch(a) + + import torch + + b = torch.tensor([ + [[1.234, 2.123], [3.635, 4.456], [5.678, 6.789]], + [[7.890, 8.901], [9.012, 1.234], [2.345, 3.456]], + [[4.567, 5.678], [6.789, 7.890], [8.901, 9.012]], + [[1.234, 2.345], [3.456, 4.567], [5.678, 6.789]], + [[7.890, 8.901], [9.012, 1.234], [2.345, 3.456]] + ]) + + print(utils.torch_compare(a, b)) + + exit() + + """ + b = norch.Tensor([[ [1.234, 2.123, 1.5], [5.678, 6.789, 1.293], diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..fef67f5 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +from .test_operations import * \ No newline at end of file diff --git a/tests/test_operations.py b/tests/test_operations.py new file mode 100644 index 0000000..5d46c62 --- /dev/null +++ b/tests/test_operations.py @@ -0,0 +1,186 @@ +import unittest +import norch +from norch import utils +import torch + +class TestTensorOperations(unittest.TestCase): + def test_creation_and_conversion(self): + """ + Test creation and convertion of norch tensor to pytorch + """ + norch_tensor = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + torch_tensor = utils.to_torch(norch_tensor) + self.assertTrue(torch.is_tensor(torch_tensor)) + + def test_addition(self): + """ + Test addition two tensors: tensor1 + tensor2 + """ + norch_tensor1 = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + norch_tensor2 = norch.Tensor([[[1, 1], [1, 1]], [[1, 1], [1, 1]]]) + norch_result = norch_tensor1 + norch_tensor2 + torch_result = utils.to_torch(norch_result) + + torch_tensor1 = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + torch_tensor2 = torch.tensor([[[1, 1], [1, 1]], [[1, 1], [1, 1]]]) + torch_expected = torch_tensor1 + torch_tensor2 + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_subtraction(self): + """ + Test subtraction of two tensors: tensor1 - tensor2 + """ + norch_tensor1 = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + norch_tensor2 = norch.Tensor([[[1, 1], [1, 1]], [[1, 1], [1, 1]]]) + norch_result = norch_tensor1 - norch_tensor2 + torch_result = utils.to_torch(norch_result) + + torch_tensor1 = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + torch_tensor2 = torch.tensor([[[1, 1], [1, 1]], [[1, 1], [1, 1]]]) + torch_expected = torch_tensor1 - torch_tensor2 + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_division_by_scalar(self): + """ + Test division of a tensor by a scalar: tensor / scalar + """ + norch_tensor = norch.Tensor([[[2, 4], [6, -8]], [[10, 12], [14, 16]]]) + scalar = 2 + norch_result = norch_tensor / scalar + torch_result = utils.to_torch(norch_result) + + torch_tensor = torch.tensor([[[2, 4], [6, -8]], [[10, 12], [14, 16]]]) + torch_expected = torch_tensor / scalar + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_scalar_division_by_tensor(self): + """ + Test scalar division by a tensor: scalar / tensor + """ + scalar = 10 + norch_tensor = norch.Tensor([[[2, 4], [6, -8]], [[10, 12], [14, 16]]]) + norch_result = scalar / norch_tensor + torch_result = utils.to_torch(norch_result) + + torch_tensor = torch.tensor([[[2, 4], [6, -8]], [[10, 12], [14, 16]]]) + torch_expected = scalar / torch_tensor + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_matrix_multiplication(self): + """ + Test matrix multiplication: tensor1 @ tensor2 + """ + norch_tensor1 = norch.Tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) + norch_tensor2 = norch.Tensor([[[1, 0], [0, 1]], [[-1, 0], [0, -1]]]) + norch_result = norch_tensor1 @ norch_tensor2 + torch_result = utils.to_torch(norch_result) + + torch_tensor1 = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) + torch_tensor2 = torch.tensor([[[1, 0], [0, 1]], [[-1, 0], [0, -1]]]) + torch_expected = torch_tensor1 @ torch_tensor2 + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_elementwise_multiplication_by_scalar(self): + """ + Test elementwise multiplication of a tensor by a scalar: tensor * scalar + """ + norch_tensor = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + scalar = 2 + norch_result = norch_tensor * scalar + torch_result = utils.to_torch(norch_result) + + torch_tensor = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + torch_expected = torch_tensor * scalar + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_elementwise_multiplication_by_tensor(self): + """ + Test elementwise multiplication of two tensors: tensor1 * tensor2 + """ + norch_tensor1 = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + norch_tensor2 = norch.Tensor([[[2, 2], [2, 2]], [[2, 2], [2, 2]]]) + norch_result = norch_tensor1 * norch_tensor2 + torch_result = utils.to_torch(norch_result) + + torch_tensor1 = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + torch_tensor2 = torch.tensor([[[2, 2], [2, 2]], [[2, 2], [2, 2]]]) + torch_expected = torch_tensor1 * torch_tensor2 + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_reshape(self): + """ + Test reshaping of a tensor: tensor.reshape(shape) + """ + norch_tensor = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + new_shape = [2, 4] + norch_result = norch_tensor.reshape(new_shape) + torch_result = utils.to_torch(norch_result) + + torch_tensor = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + torch_expected = torch_tensor.reshape(new_shape) + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_transpose(self): + """ + Test transposition of a tensor: tensor.transpose(dim1, dim2) + """ + norch_tensor = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + dim1, dim2 = 0, 2 + norch_result = norch_tensor.transpose(dim1, dim2) + torch_result = utils.to_torch(norch_result) + + torch_tensor = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + torch_expected = torch_tensor.transpose(dim1, dim2) + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_logarithm(self): + """ + Test elementwise logarithm of a tensor: tensor.log() + """ + norch_tensor = norch.Tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) + norch_result = norch_tensor.log() + torch_result = utils.to_torch(norch_result) + + torch_tensor = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) + torch_expected = torch.log(torch_tensor) + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_sum(self): + """ + Test summation of a tensor: tensor.sum() + """ + norch_tensor = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + norch_result = norch_tensor.sum() + torch_result = utils.to_torch(norch_result) + + torch_tensor = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + torch_expected = torch.sum(torch_tensor) + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + def test_transpose_T(self): + """ + Test transposition of a tensor: tensor.T + """ + norch_tensor = norch.Tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + norch_result = norch_tensor.T + torch_result = utils.to_torch(norch_result) + + torch_tensor = torch.tensor([[[1, 2], [3, -4]], [[5, 6], [7, 8]]]) + torch_expected = torch.transpose(torch_tensor, 0, 2) + + self.assertTrue(utils.compare_torch(torch_result, torch_expected)) + + +if __name__ == '__main__': + unittest.main()