refactor: merge multiple project into one and create new project
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# AI School
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A repository containing all the code I wrote during my time at the AI school.
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0
.gitignore → d2l/.gitignore
vendored
0
.gitignore → d2l/.gitignore
vendored
3
d2l/README.md
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3
d2l/README.md
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# D2L Learning
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The code written with [Dive Into Deep Learning](https://zh.d2l.ai/) course.
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6
d2l/main.py
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6
d2l/main.py
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def main():
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print("Hello from learning!")
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if __name__ == "__main__":
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main()
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132
d2l/preliminaries/e1_ndarray.py
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132
d2l/preliminaries/e1_ndarray.py
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import torch
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def main():
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# print('===== Chapter 1 =====')
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# c1_introduction()
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# print('===== Chapter 2 =====')
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# c2_operator()
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# print('===== Chapter 3 =====')
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# c3_broadcast()
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# print('===== Chapter 4 =====')
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# c4_index_and_slice()
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# print('===== Chapter 5 =====')
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# c5_save_memory()
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print('===== Chapter 6 =====')
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c6_into_python_object()
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def c1_introduction():
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x = torch.arange(12)
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print(f'x: {x}')
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print(f'x.shape: {x.shape}')
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print(f'x.numel(): {x.numel()}')
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xs = x.reshape(3, 4)
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print(f'x.reshape: {xs}')
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xs = x.reshape(-1, 4)
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print(f'x.reshape auto 1: {xs}')
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xs = x.reshape(3, -1)
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print(f'x.reshape auto 2: {xs}')
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zeros = torch.zeros((2, 3, 4))
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print(f'zeros: {zeros}')
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ones = torch.ones((2, 3, 4))
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print(f'ones: {ones}')
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randoms = torch.randn(3, 4)
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print(f'randn: {randoms}')
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manual = torch.tensor([[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]], )
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print(f'manual: {manual}')
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# 看起来reshape第一个是行数
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manual = torch.tensor([2, 1, 4, 3, 1, 2, 3, 4, 4, 3, 2, 1]).reshape(3, -1)
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print(f'manual: {manual}')
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def c2_operator():
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# torch按类型自动决定dtype
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x = torch.tensor([1.0, 2, 4, 8])
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print(x.dtype)
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x = torch.tensor([1, 2, 4, 8])
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print(x.dtype)
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# 强制指定dtype
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x = torch.tensor([1, 2, 4, 8], dtype=torch.float32)
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y = torch.tensor([2, 2, 2, 2])
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print(f'x + y: {x + y}')
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print(f'x - y: {x - y}')
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print(f'x * y: {x * y}')
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print(f'x / y: {x / y}')
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print(f'x ** y: {x ** y}')
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print(f'exp(x): {torch.exp(x)}')
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x = torch.arange(12, dtype=torch.float32).reshape(3, 4)
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y = torch.tensor([[2, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]],
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dtype=torch.float32)
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xy_row = torch.cat((x, y), dim=0)
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xy_col = torch.cat((x, y), dim=1)
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print(f'Row Cat: {xy_row}')
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print(f'Column Cat: {xy_col}')
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xy_equal = x == y
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print(f'Equal Boolean: {xy_equal}')
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x_sum = x.sum()
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print(f'x.sum: {x_sum}')
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def c3_broadcast():
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a = torch.arange(3).reshape(3, -1)
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b = torch.arange(2).reshape(-1, 2)
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print(a)
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print(b)
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print(f'a + b: {a + b}')
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def c4_index_and_slice():
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x = torch.arange(12, dtype=torch.float32).reshape(3, 4)
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print(x)
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print(x[-1])
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print(x[1:3])
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print(x[0::2])
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print(x[:, 0::2])
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x[1, 2] = 9
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print(x)
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x[:, 0::2] = 0
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print(x)
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y = torch.arange(6).reshape(-1, 2)
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x[:, 0::2] = y
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print(x)
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def c5_save_memory():
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x = torch.arange(12, dtype=torch.float32).reshape(3, 4)
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y = torch.arange(12, dtype=torch.float32).reshape(3, 4)
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z = torch.zeros_like(x)
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z[:] = x + y
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print(z)
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z[:, :] = 0
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z[:] = x
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z += y
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print(z)
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def c6_into_python_object():
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x = torch.arange(12, dtype=torch.float32).reshape(3, 4)
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a = x.numpy()
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print(type(a))
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b = torch.tensor(a)
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print(type(b))
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x = torch.tensor([3.5])
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print(x)
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print(x.item())
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print(float(x))
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print(int(x))
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if __name__ == "__main__":
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main()
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26
d2l/pyproject.toml
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d2l/pyproject.toml
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[project]
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name = "learning"
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"datasets>=4.3.0",
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"matplotlib>=3.10.7",
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"numpy>=2.3.4",
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"torch>=2.9.0",
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"torchvision>=0.24.0",
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]
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[tool.uv.sources]
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torch = [
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{ index = "pytorch-cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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]
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torchvision = [
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{ index = "pytorch-cu126", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
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]
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[[tool.uv.index]]
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name = "pytorch-cu126"
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url = "https://download.pytorch.org/whl/cu126"
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explicit = true
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2205
d2l/uv.lock
generated
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2205
d2l/uv.lock
generated
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File diff suppressed because it is too large
Load Diff
10
dl-exp/.gitignore
vendored
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dl-exp/.gitignore
vendored
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# Python-generated files
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__pycache__/
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*.py[oc]
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build/
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dist/
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wheels/
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*.egg-info
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# Virtual environments
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.venv
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1
dl-exp/.python-version
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1
dl-exp/.python-version
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3.11
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@@ -66,8 +66,8 @@ def main():
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for t in range(2000):
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optimizer.zero_grad() #清空上一步的残余更新参数值
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prediction: torch.tensor = net(test_data.x) #喂给net训练数据x，输出预测值
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loss: torch.tensor = loss_func(prediction, test_data.y) #计算两者的误差
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prediction: torch.Tensor = net(test_data.x) #喂给net训练数据x，输出预测值
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loss: torch.Tensor = loss_func(prediction, test_data.y) #计算两者的误差
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loss.backward() #误差反向传播，计算参数更新值
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optimizer.step() #将参数更新值施加到net的parameters上
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[project]
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name = "experiment"
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name = "dlexperiment"
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version = "0.1.0"
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description = "Add your description here"
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description = "The code for deep learning experiment course."
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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0
uv.lock → dl-exp/uv.lock
generated
0
uv.lock → dl-exp/uv.lock
generated
0
dl/README.md
Normal file
0
dl/README.md
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1
mv-and-ip/.python-version
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1
mv-and-ip/.python-version
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3.11
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0
mv-and-ip/3.10.7
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0
mv-and-ip/3.10.7
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3
mv-and-ip/README.md
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3
mv-and-ip/README.md
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# Machine Vision and Image Process
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The code for Machine Vision and Image Process course.
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mv-and-ip/main.py
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mv-and-ip/main.py
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def main():
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print("Hello from mv-and-ip!")
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if __name__ == "__main__":
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main()
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11
mv-and-ip/pyproject.toml
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mv-and-ip/pyproject.toml
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[project]
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name = "mv-and-ip"
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version = "0.1.0"
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description = "Machine vision and image process course code."
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"matplotlib>=3.10.8",
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"numpy>=2.2.6",
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"opencv-python==4.12.0.88",
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]
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521
mv-and-ip/uv.lock
generated
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521
mv-and-ip/uv.lock
generated
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@@ -0,0 +1,521 @@
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version = 1
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revision = 2
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requires-python = ">=3.11"
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[[package]]
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name = "contourpy"
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version = "1.3.3"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "numpy" },
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]
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wheels = [
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Reference in New Issue
Block a user