trainer_demo.ipynb

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     "text": [
      "error in estimating w: [ 0.15326214 -0.2172327 ]\n",
      "error in estimating b: [0.25929523]\n",
      "1.22366 sec\n"
     ]
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       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"p7f3f8e1fc5\">\n",
       "   <rect x=\"26.925\" y=\"7.2\" width=\"195.3\" height=\"138.6\"/>\n",
       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "from mxnet import autograd, context, gluon, image, init, npx, np\n",
    "from mxnet.gluon import nn\n",
    "import inspect\n",
    "import collections\n",
    "from matplotlib_inline import backend_inline\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "import random\n",
    "\n",
    "npx.set_np()\n",
    "\n",
    "class HyperParameters: #@save\n",
    "    def save_hyperparameters(self, ignore=[]):\n",
    "        raise NotImplemented\n",
    "        \n",
    "    def save_hyperparameters(self, ignore=[]):\n",
    "        \"\"\"Save function arguments into class attributes.\n",
    "    \n",
    "        Defined in :numref:`sec_utils`\"\"\"\n",
    "        frame = inspect.currentframe().f_back\n",
    "#         ident = (frame.f_code.co_filename, frame.f_lineno)\n",
    "#         print(frame)\n",
    "#         print(ident)\n",
    "        frame = inspect.currentframe().f_back\n",
    "        _, _, _, local_vars = inspect.getargvalues(frame)\n",
    "        self.hparams = {k:v for k, v in local_vars.items()\n",
    "                        if k not in set(ignore+['self']) and not k.startswith('_')}\n",
    "        for k, v in self.hparams.items():\n",
    "            setattr(self, k, v)\n",
    "\n",
    "            \n",
    "def add_to_class(Class):  #@save\n",
    "    def wrapper(obj):\n",
    "        setattr(Class, obj.__name__, obj)\n",
    "    return wrapper\n",
    "\n",
    "def cpu():\n",
    "    \"\"\"Defined in :numref:`sec_use_gpu`\"\"\"\n",
    "    return npx.cpu()\n",
    "def gpu(i=0):\n",
    "    \"\"\"Defined in :numref:`sec_use_gpu`\"\"\"\n",
    "    return npx.gpu(i)\n",
    "\n",
    "def snum_gpus():\n",
    "    \"\"\"Defined in :numref:`sec_use_gpu`\"\"\"\n",
    "    return npx.num_gpus()\n",
    "\n",
    "def try_gpu(i=0):\n",
    "    \"\"\"Return gpu(i) if exists, otherwise return cpu().\n",
    "    Defined in :numref:`sec_use_gpu`\"\"\"\n",
    "    if snum_gpus() >= i + 1:\n",
    "        return gpu(i)\n",
    "    return cpu()\n",
    "\n",
    "def try_all_gpus():\n",
    "    \"\"\"Return all available GPUs, or [cpu(),] if no GPU exists.\n",
    "    Defined in :numref:`sec_use_gpu`\"\"\"\n",
    "    return [gpu(i) for i in range(snum_gpus())]\n",
    "\n",
    "def use_svg_display():\n",
    "    \"\"\"Use the svg format to display a plot in Jupyter.\n",
    "    Defined in :numref:`sec_calculus`\"\"\"\n",
    "    backend_inline.set_matplotlib_formats('svg')\n",
    "\n",
    "\n",
    "class ProgressBoard(HyperParameters):  #@save\n",
    "    \"\"\"Plot data points in animation.\"\"\"\n",
    "    def __init__(self, xlabel=None, ylabel=None, xlim=None,\n",
    "                 ylim=None, xscale='linear', yscale='linear',\n",
    "                 ls=['-', '--', '-.', ':'], colors=['C0', 'C1', 'C2', 'C3'],\n",
    "                 fig=None, axes=None, figsize=(3.5, 2.5), display=True):\n",
    "        self.save_hyperparameters()\n",
    "\n",
    "    def draw(self, x, y, label, every_n=1):\n",
    "        raise NotImplemented\n",
    "    \n",
    "    def draw(self, x, y, label, every_n=1):\n",
    "        \"\"\"Defined in :numref:`sec_utils`\"\"\"\n",
    "        Point = collections.namedtuple('Point', ['x', 'y'])\n",
    "        if not hasattr(self, 'raw_points'):\n",
    "            self.raw_points = collections.OrderedDict()\n",
    "            self.data = collections.OrderedDict()\n",
    "        if label not in self.raw_points:\n",
    "            self.raw_points[label] = []\n",
    "            self.data[label] = []\n",
    "        points = self.raw_points[label]\n",
    "        line = self.data[label]\n",
    "        points.append(Point(x, y))\n",
    "        if len(points) != every_n:\n",
    "            return\n",
    "        mean = lambda x: sum(x) / len(x)\n",
    "        line.append(Point(mean([p.x for p in points]),\n",
    "                          mean([p.y for p in points])))\n",
    "        points.clear()\n",
    "        if not self.display:\n",
    "            return\n",
    "        use_svg_display()\n",
    "        \n",
    "        if self.fig is None:\n",
    "            self.fig = plt.figure(figsize=self.figsize)\n",
    "        plt_lines, labels = [], []\n",
    "        for (k, v), ls, color in zip(self.data.items(), self.ls, self.colors):\n",
    "            plt_lines.append(plt.plot([p.x for p in v], [p.y for p in v],\n",
    "                                          linestyle=ls, color=color)[0])\n",
    "            labels.append(k)\n",
    "        axes = self.axes if self.axes else plt.gca()\n",
    "        if self.xlim: axes.set_xlim(self.xlim)\n",
    "        if self.ylim: axes.set_ylim(self.ylim)\n",
    "        if not self.xlabel: self.xlabel = self.x\n",
    "        axes.set_xlabel(self.xlabel)\n",
    "        axes.set_ylabel(self.ylabel)\n",
    "        axes.set_xscale(self.xscale)\n",
    "        axes.set_yscale(self.yscale)\n",
    "        axes.legend(plt_lines, labels)\n",
    "        display.display(self.fig)\n",
    "        display.clear_output(wait=True)\n",
    "        \n",
    "\n",
    "class Module(nn.Block, HyperParameters):  #@save\n",
    "    def __init__(self, plot_train_per_epoch=2, plot_valid_per_epoch=1):\n",
    "        super().__init__()\n",
    "        self.save_hyperparameters()\n",
    "        self.board = ProgressBoard()\n",
    "    def loss(self, y_hat, y):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def forward(self, X):\n",
    "        assert hasattr(self, 'net'), 'Neural network is defined'\n",
    "        return self.net(X)\n",
    "\n",
    "    def plot(self, key, value, train):\n",
    "        \"\"\"Plot a point in animation.\"\"\"\n",
    "        assert hasattr(self, 'trainer'), 'Trainer is not inited'\n",
    "        self.board.xlabel = 'epoch'\n",
    "        if train:\n",
    "            x = self.trainer.train_batch_idx / \\\n",
    "                self.trainer.num_train_batches\n",
    "            n = self.trainer.num_train_batches / \\\n",
    "                self.plot_train_per_epoch\n",
    "        else:\n",
    "            x = self.trainer.epoch + 1\n",
    "            n = self.trainer.num_val_batches / \\\n",
    "                self.plot_valid_per_epoch\n",
    "        self.board.draw(x, value.asnumpy(), (\n",
    "            'train_' if train else 'val_') + key, every_n=int(n))\n",
    "        \n",
    "    def training_step(self, batch):\n",
    "        l = self.loss(self(*batch[:-1]), batch[-1])\n",
    "        self.plot('loss', l, train=True)\n",
    "        return l\n",
    "\n",
    "    def validation_step(self, batch):\n",
    "        l = self.loss(self(*batch[:-1]), batch[-1])\n",
    "        self.plot('loss', l, train=False)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def configure_optimizers(self):\n",
    "        \"\"\"Defined in :numref:`sec_classification`\"\"\"\n",
    "        params = self.parameters()\n",
    "        if isinstance(params, list):\n",
    "            return SGD(params, self.lr)\n",
    "        return gluon.Trainer(params, 'sgd', {'learning_rate': self.lr})\n",
    "\n",
    "    def get_scratch_params(self):\n",
    "        \"\"\"Defined in :numref:`sec_classification`\"\"\"\n",
    "        params = []\n",
    "        for attr in dir(self):\n",
    "            a = getattr(self, attr)\n",
    "            if isinstance(a, np.ndarray):\n",
    "                params.append(a)\n",
    "            if isinstance(a, Module):\n",
    "                params.extend(a.get_scratch_params())\n",
    "        return params\n",
    "    \n",
    "\n",
    "    def parameters(self):\n",
    "        \"\"\"Defined in :numref:`sec_classification`\"\"\"\n",
    "        params = self.collect_params()\n",
    "        return params if isinstance(params, gluon.parameter.ParameterDict) and len(\n",
    "            params.keys()) else self.get_scratch_params()\n",
    "\n",
    "    def set_scratch_params_device(self, device):\n",
    "        \"\"\"Defined in :numref:`sec_use_gpu`\"\"\"\n",
    "        for attr in dir(self):\n",
    "            a = getattr(self, attr)\n",
    "            if isinstance(a, np.ndarray):\n",
    "                with autograd.record():\n",
    "                    setattr(self, attr, a.as_in_ctx(device))\n",
    "                getattr(self, attr).attach_grad()\n",
    "            if isinstance(a, d2l.Module):\n",
    "                a.set_scratch_params_device(device)\n",
    "            if isinstance(a, list):\n",
    "                for elem in a:\n",
    "                    elem.set_scratch_params_device(device)\n",
    "        \n",
    "        \n",
    "class DataModule(HyperParameters):  #@save\n",
    "    def __init__(self, root='../data', num_workers=4):\n",
    "        self.save_hyperparameters()\n",
    "\n",
    "    def get_dataloader(self, train):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return self.get_dataloader(train=True)\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return self.get_dataloader(train=False)\n",
    "    \n",
    "    def get_tensorloader(self, tensors, train, indices=slice(0, None)):\n",
    "        \"\"\"Defined in :numref:`sec_synthetic-regression-data`\"\"\"\n",
    "        tensors = tuple(a[indices] for a in tensors)\n",
    "        dataset = gluon.data.ArrayDataset(*tensors)\n",
    "        return gluon.data.DataLoader(dataset, self.batch_size,\n",
    "                                     shuffle=train)\n",
    "    \n",
    "class Trainer(HyperParameters):  #@save\n",
    "    def __init__(self, max_epochs, num_gpus=0, gradient_clip_val=0):\n",
    "        self.save_hyperparameters()\n",
    "        assert num_gpus == 0, 'No GPU support yet'\n",
    "\n",
    "    def prepare_data(self, data):\n",
    "        self.train_dataloader = data.train_dataloader()\n",
    "        self.val_dataloader = data.val_dataloader()\n",
    "        self.num_train_batches = len(self.train_dataloader)\n",
    "        self.num_val_batches = (len(self.val_dataloader)\n",
    "                                if self.val_dataloader is not None else 0)\n",
    "\n",
    "    def prepare_model(self, model):\n",
    "        model.trainer = self\n",
    "        model.board.xlim = [0, self.max_epochs]\n",
    "        self.model = model\n",
    "\n",
    "    def fit(self, model, data):\n",
    "        self.prepare_data(data)\n",
    "        self.prepare_model(model)\n",
    "        self.optim = model.configure_optimizers()\n",
    "        self.epoch = 0\n",
    "        self.train_batch_idx = 0\n",
    "        self.val_batch_idx = 0\n",
    "        for self.epoch in range(self.max_epochs):\n",
    "            self.fit_epoch()\n",
    "\n",
    "    def fit_epoch(self):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def prepare_batch(self, batch):\n",
    "        \"\"\"Defined in :numref:`sec_linear_scratch`\"\"\"\n",
    "        return batch\n",
    "\n",
    "    def fit_epoch(self):\n",
    "        \"\"\"Defined in :numref:`sec_linear_scratch`\"\"\"\n",
    "        for batch in self.train_dataloader:\n",
    "            with autograd.record():\n",
    "                loss = self.model.training_step(self.prepare_batch(batch))\n",
    "            loss.backward()\n",
    "            if self.gradient_clip_val > 0:\n",
    "                self.clip_gradients(self.gradient_clip_val, self.model)\n",
    "            self.optim.step(1)\n",
    "            self.train_batch_idx += 1\n",
    "        if self.val_dataloader is None:\n",
    "            return\n",
    "        for batch in self.val_dataloader:\n",
    "            self.model.validation_step(self.prepare_batch(batch))\n",
    "            self.val_batch_idx += 1   \n",
    "    \n",
    "    def __init__(self, max_epochs, num_gpus=0, gradient_clip_val=0):\n",
    "        \"\"\"Defined in :numref:`sec_use_gpu`\"\"\"\n",
    "        self.save_hyperparameters()\n",
    "        num = 0;\n",
    "        if snum_gpus() - num_gpus >= 0 :\n",
    "            num = num_gpus\n",
    "        else:\n",
    "            num = snum_gpus()\n",
    "        self.gpus = [gpu(i) for i in range(num)]\n",
    "    \n",
    "    def prepare_batch(self, batch):\n",
    "        \"\"\"Defined in :numref:`sec_use_gpu`\"\"\"\n",
    "        if self.gpus:\n",
    "            batch = [d2l.to(a, self.gpus[0]) for a in batch]\n",
    "        return batch\n",
    "    \n",
    "    def prepare_model(self, model):\n",
    "        \"\"\"Defined in :numref:`sec_use_gpu`\"\"\"\n",
    "        model.trainer = self\n",
    "        model.board.xlim = [0, self.max_epochs]\n",
    "        if self.gpus:\n",
    "            model.collect_params().reset_ctx(self.gpus[0])\n",
    "            model.set_scratch_params_device(self.gpus[0])\n",
    "        self.model = model\n",
    "\n",
    "    def clip_gradients(self, grad_clip_val, model):\n",
    "        \"\"\"Defined in :numref:`sec_rnn-scratch`\"\"\"\n",
    "        params = model.parameters()\n",
    "        if not isinstance(params, list):\n",
    "            params = [p.data() for p in params.values()]\n",
    "        norm = math.sqrt(sum((p.grad ** 2).sum() for p in params))\n",
    "        if norm > grad_clip_val:\n",
    "            for param in params:\n",
    "                param.grad[:] *= grad_clip_val / norm\n",
    "                \n",
    "                \n",
    "class LinearRegressionScratch(Module):  #@save\n",
    "    def __init__(self, num_inputs, lr, sigma=0.01):\n",
    "        super().__init__()\n",
    "        self.save_hyperparameters()\n",
    "        self.w = np.random.normal(0, sigma, (num_inputs, 1))\n",
    "        self.b = np.zeros(1)\n",
    "        self.w.attach_grad()\n",
    "        self.b.attach_grad()\n",
    "        \n",
    "@add_to_class(LinearRegressionScratch)  #@save\n",
    "def forward(self, X):\n",
    "    \"\"\"The linear regression model.\"\"\"\n",
    "    return np.dot(X, self.w) + self.b\n",
    "\n",
    "\n",
    "@add_to_class(LinearRegressionScratch)  #@save\n",
    "def loss(self, y_hat, y):\n",
    "    l = (y_hat - y.reshape(y_hat.shape)) ** 2 / 2\n",
    "    return l.mean()\n",
    "\n",
    "\n",
    "class SGD(HyperParameters):  #@save\n",
    "    def __init__(self, params, lr):\n",
    "        \"\"\"Minibatch stochastic gradient descent.\"\"\"\n",
    "        self.save_hyperparameters()\n",
    "\n",
    "    def step(self, _):\n",
    "        for param in self.params:\n",
    "            param -= self.lr * param.grad\n",
    "            \n",
    "@add_to_class(LinearRegressionScratch)  #@save\n",
    "def configure_optimizers(self):\n",
    "    return SGD([self.w, self.b], self.lr)\n",
    "\n",
    "\n",
    "@add_to_class(Trainer)  #@save\n",
    "def prepare_batch(self, batch):\n",
    "    return batch\n",
    "\n",
    "@add_to_class(Trainer)  #@save\n",
    "def fit_epoch(self):\n",
    "    for batch in self.train_dataloader:\n",
    "        with autograd.record():\n",
    "            loss = self.model.training_step(self.prepare_batch(batch))\n",
    "        loss.backward()\n",
    "        if self.gradient_clip_val > 0:\n",
    "            self.clip_gradients(self.gradient_clip_val, self.model)\n",
    "        self.optim.step(1)\n",
    "        self.train_batch_idx += 1\n",
    "    if self.val_dataloader is None:\n",
    "        return\n",
    "    for batch in self.val_dataloader:\n",
    "        self.model.validation_step(self.prepare_batch(batch))\n",
    "        self.val_batch_idx += 1\n",
    "        \n",
    "class SyntheticRegressionData(DataModule):\n",
    "    \"\"\"Defined in :numref:`sec_synthetic-regression-data`\"\"\"\n",
    "    def __init__(self, w, b, noise=0.01, num_train=1000, num_val=1000,\n",
    "                 batch_size=32):\n",
    "        super().__init__()\n",
    "        self.save_hyperparameters()\n",
    "        n = num_train + num_val\n",
    "        self.X = np.random.randn(n, len(w))\n",
    "        noise = np.random.randn(n, 1) * noise\n",
    "        self.y = np.matmul(self.X, np.reshape(w, (-1, 1))) + b + noise\n",
    "\n",
    "    def get_dataloader(self, train):\n",
    "        \"\"\"Defined in :numref:`sec_synthetic-regression-data`\"\"\"\n",
    "        i = slice(0, self.num_train) if train else slice(self.num_train, None)\n",
    "        return self.get_tensorloader((self.X, self.y), train, i)\n",
    "        \n",
    "class Timer:  #@save\n",
    "    \"\"\"记录多次运行时间\"\"\"\n",
    "    def __init__(self):\n",
    "        self.times = []\n",
    "        self.start()\n",
    "\n",
    "    def start(self):\n",
    "        \"\"\"启动计时器\"\"\"\n",
    "        self.tik = time.time()\n",
    "\n",
    "    def stop(self):\n",
    "        \"\"\"停止计时器并将时间记录在列表中\"\"\"\n",
    "        self.times.append(time.time() - self.tik)\n",
    "        return self.times[-1]\n",
    "\n",
    "    def avg(self):\n",
    "        \"\"\"返回平均时间\"\"\"\n",
    "        return sum(self.times) / len(self.times)\n",
    "\n",
    "    def sum(self):\n",
    "        \"\"\"返回时间总和\"\"\"\n",
    "        return sum(self.times)\n",
    "\n",
    "    def cumsum(self):\n",
    "        \"\"\"返回累计时间\"\"\"\n",
    "        return np.array(self.times).cumsum().tolist()\n",
    "    \n",
    "timer = Timer()\n",
    "model = LinearRegressionScratch(2, lr=0.03)\n",
    "data = SyntheticRegressionData(w=np.array([2, -3.4]), b=4.2)\n",
    "trainer = Trainer(max_epochs=3)\n",
    "trainer.fit(model, data)\n",
    "print(f'error in estimating w: {data.w - model.w.reshape(data.w.shape)}')\n",
    "print(f'error in estimating b: {data.b - model.b}')\n",
    "print(f'{timer.stop():.5f} sec')\n",
    "\n",
    "# class LinearRegression(Module):  #@save\n",
    "#     def __init__(self, lr):\n",
    "#         super().__init__()\n",
    "#         self.save_hyperparameters()\n",
    "#         self.net = nn.Dense(1)\n",
    "#         self.net.initialize(init.Normal(sigma=0.01))\n",
    "        \n",
    "# @add_to_class(LinearRegression)  #@save\n",
    "# def forward(self, X):\n",
    "#     \"\"\"The linear regression model.\"\"\"\n",
    "#     return self.net(X)\n",
    "\n",
    "# @add_to_class(LinearRegression)  #@save\n",
    "# def loss(self, y_hat, y):\n",
    "#     fn = gluon.loss.L2Loss()\n",
    "#     return fn(y_hat, y).mean()\n",
    "\n",
    "# @add_to_class(LinearRegression)  #@save\n",
    "# def configure_optimizers(self):\n",
    "#     return gluon.Trainer(self.collect_params(),\n",
    "#                          'sgd', {'learning_rate': self.lr})\n",
    "\n",
    "# model = LinearRegression(lr=0.03)\n",
    "# data = SyntheticRegressionData(w=np.array([2, -3.4]), b=4.2)\n",
    "# trainer = Trainer(max_epochs=3)\n",
    "# trainer.fit(model, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19d3d721",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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