refactor: merge multiple project into one and create new project

d2l/.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

d2l/.python-version

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+3.11

d2l/README.md

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+# D2L Learning
+
+The code written with [Dive Into Deep Learning](https://zh.d2l.ai/) course.

d2l/main.py

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+def main():
+    print("Hello from learning!")
+
+
+if __name__ == "__main__":
+    main()

d2l/preliminaries/e1_ndarray.py

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+import torch
+
+
+def main():
+    # print('===== Chapter 1 =====')
+    # c1_introduction()
+    # print('===== Chapter 2 =====')
+    # c2_operator()
+    # print('===== Chapter 3 =====')
+    # c3_broadcast()
+    # print('===== Chapter 4 =====')
+    # c4_index_and_slice()
+    # print('===== Chapter 5 =====')
+    # c5_save_memory()
+    print('===== Chapter 6 =====')
+    c6_into_python_object()
+
+
+def c1_introduction():
+    x = torch.arange(12)
+    print(f'x: {x}')
+    print(f'x.shape: {x.shape}')
+    print(f'x.numel(): {x.numel()}')
+    xs = x.reshape(3, 4)
+    print(f'x.reshape: {xs}')
+    xs = x.reshape(-1, 4)
+    print(f'x.reshape auto 1: {xs}')
+    xs = x.reshape(3, -1)
+    print(f'x.reshape auto 2: {xs}')
+
+    zeros = torch.zeros((2, 3, 4))
+    print(f'zeros: {zeros}')
+    ones = torch.ones((2, 3, 4))
+    print(f'ones: {ones}')
+
+    randoms = torch.randn(3, 4)
+    print(f'randn: {randoms}')
+
+    manual = torch.tensor([[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]], )
+    print(f'manual: {manual}')
+    # 看起来reshape第一个是行数
+    manual = torch.tensor([2, 1, 4, 3, 1, 2, 3, 4, 4, 3, 2, 1]).reshape(3, -1)
+    print(f'manual: {manual}')
+
+
+def c2_operator():
+    # torch按类型自动决定dtype
+    x = torch.tensor([1.0, 2, 4, 8])
+    print(x.dtype)
+    x = torch.tensor([1, 2, 4, 8])
+    print(x.dtype)
+
+    # 强制指定dtype
+    x = torch.tensor([1, 2, 4, 8], dtype=torch.float32)
+    y = torch.tensor([2, 2, 2, 2])
+    print(f'x + y: {x + y}')
+    print(f'x - y: {x - y}')
+    print(f'x * y: {x * y}')
+    print(f'x / y: {x / y}')
+    print(f'x ** y: {x ** y}')
+
+    print(f'exp(x): {torch.exp(x)}')
+
+    x = torch.arange(12, dtype=torch.float32).reshape(3, 4)
+    y = torch.tensor([[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]],
+                     dtype=torch.float32)
+    xy_row = torch.cat((x, y), dim=0)
+    xy_col = torch.cat((x, y), dim=1)
+    print(f'Row Cat: {xy_row}')
+    print(f'Column Cat: {xy_col}')
+
+    xy_equal = x == y
+    print(f'Equal Boolean: {xy_equal}')
+
+    x_sum = x.sum()
+    print(f'x.sum: {x_sum}')
+
+
+def c3_broadcast():
+    a = torch.arange(3).reshape(3, -1)
+    b = torch.arange(2).reshape(-1, 2)
+    print(a)
+    print(b)
+    print(f'a + b: {a + b}')
+
+
+def c4_index_and_slice():
+    x = torch.arange(12, dtype=torch.float32).reshape(3, 4)
+    print(x)
+    print(x[-1])
+    print(x[1:3])
+    print(x[0::2])
+    print(x[:, 0::2])
+
+    x[1, 2] = 9
+    print(x)
+    x[:, 0::2] = 0
+    print(x)
+    y = torch.arange(6).reshape(-1, 2)
+    x[:, 0::2] = y
+    print(x)
+
+
+def c5_save_memory():
+    x = torch.arange(12, dtype=torch.float32).reshape(3, 4)
+    y = torch.arange(12, dtype=torch.float32).reshape(3, 4)
+    z = torch.zeros_like(x)
+    z[:] = x + y
+    print(z)
+
+    z[:, :] = 0
+    z[:] = x
+    z += y
+    print(z)
+
+
+def c6_into_python_object():
+    x = torch.arange(12, dtype=torch.float32).reshape(3, 4)
+    a = x.numpy()
+    print(type(a))
+    b = torch.tensor(a)
+    print(type(b))
+
+    x = torch.tensor([3.5])
+    print(x)
+    print(x.item())
+    print(float(x))
+    print(int(x))
+
+
+if __name__ == "__main__":
+    main()

d2l/pyproject.toml

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+[project]
+name = "learning"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "datasets>=4.3.0",
+    "matplotlib>=3.10.7",
+    "numpy>=2.3.4",
+  "torch>=2.9.0",
+  "torchvision>=0.24.0",
+]
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch-cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+]
+torchvision = [
+  { index = "pytorch-cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cu126"
+url = "https://download.pytorch.org/whl/cu126"
+explicit = true