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add nn api examples #10

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77b8cac
upload nn
tomzw11 Jan 8, 2025
ed2c02f
first update
tomzw11 Jan 9, 2025
f528ee3
update nn
tomzw11 Jan 9, 2025
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remove checkpoints
tomzw11 Jan 9, 2025
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93 changes: 93 additions & 0 deletions api-examples/nn/nn.bceloss.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"from torch import Tensor\n",
"from torch import nn"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.8536, grad_fn=<BinaryCrossEntropyBackward0>)\n"
]
}
],
"source": [
"# BCELoss\n",
"m = nn.Sigmoid()\n",
"input_np = np.random.rand(3,2)\n",
"target_np = np.random.rand(3,2)\n",
"output = nn.BCELoss()(m(Tensor(input)), Tensor(target))\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.71798706\n"
]
}
],
"source": [
"import mindspore as ms\n",
"from mindspore import nn\n",
"from mindspore import Tensor\n",
"\n",
"m = nn.Sigmoid()\n",
"loss = nn.BCELoss(weight=None, reduction='mean')\n",
"input = ms.Tensor(input_np, ms.float32)\n",
"target = ms.Tensor(target_np, ms.float32)\n",
"output = loss(m(input), target)\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 默认reduction=mean下输出与PyTorch似乎不一致\n",
"- mint.nn.BCELoss待验证，CPU上不支持"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
131 changes: 131 additions & 0 deletions api-examples/nn/nn.celldict.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"from torch import Tensor\n",
"from torch import nn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ModuleDict(\n",
" (cond2d): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1))\n",
")\n",
"ModuleDict(\n",
" (relu): ReLU()\n",
" (dict1): ModuleDict(\n",
" (cond2d): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1))\n",
" )\n",
")\n"
]
}
],
"source": [
"# moduledict\n",
"moduledict1 = nn.ModuleDict({'cond2d': nn.Conv2d(10,10,3)})\n",
"moduledict2 = nn.ModuleDict({'relu': nn.ReLU(), 'dict1': moduledict1})\n",
"print(moduledict1)\n",
"print(moduledict2)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CellDict<\n",
" (cond2d): Conv2d<input_channels=10, output_channels=10, kernel_size=(3, 3), stride=(1, 1), pad_mode=same, padding=0, dilation=(1, 1), group=1, has_bias=False, weight_init=<mindspore.common.initializer.HeUniform object at 0x000001D5E2CC5A10>, bias_init=None, format=NCHW>\n",
" >\n"
]
}
],
"source": [
"import mindspore as ms\n",
"from mindspore import nn\n",
"celldict1 = nn.CellDict({'cond2d': nn.Conv2d(10,10,3)})\n",
"print(celldict1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**MindSpore支持CellDict(Cell)**\n",
"- 打印出的结构没有缩进；weight_init过于详细；\n",
"- 建议默认参数不打印，提供选择开关（print_all）"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "For 'CellDict', the type of cell can not be CellDict, CellList or SequentialCell, but got CellDict.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m celldict2 \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mCellDict({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m'\u001b[39m: nn\u001b[38;5;241m.\u001b[39mReLU(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdict1\u001b[39m\u001b[38;5;124m'\u001b[39m: celldict1})\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(celldict2)\n",
"File \u001b[1;32m~\\anaconda3\\envs\\tutorial\\Lib\\site-packages\\mindspore\\nn\\layer\\container.py:583\u001b[0m, in \u001b[0;36mCellDict.__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 581\u001b[0m Cell\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, auto_prefix)\n\u001b[0;32m 582\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdate(args[\u001b[38;5;241m0\u001b[39m])\n",
"File \u001b[1;32m~\\anaconda3\\envs\\tutorial\\Lib\\site-packages\\mindspore\\nn\\layer\\container.py:720\u001b[0m, in \u001b[0;36mCellDict.update\u001b[1;34m(self, cells)\u001b[0m\n\u001b[0;32m 718\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(cells, (OrderedDict, CellDict, abc\u001b[38;5;241m.\u001b[39mMapping)):\n\u001b[0;32m 719\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, cell \u001b[38;5;129;01min\u001b[39;00m cells\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m--> 720\u001b[0m \u001b[38;5;28mself\u001b[39m[key] \u001b[38;5;241m=\u001b[39m cell\n\u001b[0;32m 721\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 722\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mid\u001b[39m, k_v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(cells):\n",
"File \u001b[1;32m~\\anaconda3\\envs\\tutorial\\Lib\\site-packages\\mindspore\\nn\\layer\\container.py:590\u001b[0m, in \u001b[0;36mCellDict.__setitem__\u001b[1;34m(self, key, cell)\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__setitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, cell):\n\u001b[0;32m 589\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key)\n\u001b[1;32m--> 590\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_cell_type(cell)\n\u001b[0;32m 591\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_cell_para_name(key, cell)\n\u001b[0;32m 592\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cells[key] \u001b[38;5;241m=\u001b[39m cell\n",
"File \u001b[1;32m~\\anaconda3\\envs\\tutorial\\Lib\\site-packages\\mindspore\\nn\\layer\\container.py:631\u001b[0m, in \u001b[0;36mCellDict._validate_cell_type\u001b[1;34m(self, cell)\u001b[0m\n\u001b[0;32m 628\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFor \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcls_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, the type of cell should be Cell, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 629\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(cell)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(cell, (CellDict, CellList, SequentialCell)):\n\u001b[1;32m--> 631\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFor \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcls_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, the type of cell can not be CellDict, CellList or SequentialCell, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(cell)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[1;31mTypeError\u001b[0m: For 'CellDict', the type of cell can not be CellDict, CellList or SequentialCell, but got CellDict."
]
}
],
"source": [
"celldict2 = nn.CellDict({'relu': nn.ReLU(), 'dict1': celldict1})\n",
"print(celldict2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**MindSpore不支持CellDict(CellDict)**\n",
"- PyTorch支持CellDict/SequentialCell/CellList嵌套"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
113 changes: 113 additions & 0 deletions api-examples/nn/nn.dense.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"from torch import Tensor\n",
"from torch import nn"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([128, 30])\n"
]
}
],
"source": [
"# Linear\n",
"input = np.random.randn(128, 20)\n",
"output = nn.Linear(20,30)(Tensor(input))\n",
"print(output.size())"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(128, 30)\n"
]
}
],
"source": [
"import jax\n",
"import flax.linen as nn\n",
"from jax import numpy as jnp\n",
"from jax import random\n",
"\n",
"input = jnp.ones((128,20))\n",
"dense = nn.Dense(features=30)\n",
"variables = dense.init(random.key(42), input)\n",
"output = dense.apply(variables, input)\n",
"print(output.shape)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(128, 30)\n"
]
}
],
"source": [
"import mindspore as ms\n",
"from mindspore import nn\n",
"from mindspore import Tensor\n",
"output = nn.Dense(20,30)(Tensor(input, dtype=ms.float32))\n",
"print(output.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**MindSpore Dense用法与输出与PyTorch一致**\n",
"- MindSpore的命名与jax保持一致（Dense）\n",
"- jax需要额外定义参数"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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