04_train_phydnet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "from moving_mnist.models.phy_original import *\n",
    "from moving_mnist.models.seq2seq import TeacherForcing\n",
    "from moving_mnist.models.conv_rnn import StackUnstack, StackLoss, MultiImageDice\n",
    "from moving_mnist.data import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quadro RTX 8000\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    torch.cuda.set_device(0)\n",
    "    print(torch.cuda.get_device_name())\n",
    "    device = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train PhyDNet:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wil predict:\n",
    "- `n_in`: 5 images\n",
    "- `n_out`: 5 images  \n",
    "- `n_obj`: up to 3 objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('/home/tcapelle/SteadySun/moving_mnist')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path.cwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = Path.cwd()/'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = MovingMNIST(DATA_PATH, n_in=5, n_out=5, n_obj=[1,2], th=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tl = TfmdLists(range(25000), ImageTupleTransform(ds))\n",
    "valid_tl = TfmdLists(range(100), ImageTupleTransform(ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i=0\n",
    "# fat_tensor = torch.stack([torch.cat(train_tl[i][0], 0) for i in range(100)])\n",
    "\n",
    "# m,s = fat_tensor.mean(), fat_tensor.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = DataLoaders.from_dsets(train_tl, valid_tl, bs=64,#).cuda()\n",
    "                             after_batch=[Normalize.from_stats(*mnist_stats)]).cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse_loss = StackLoss(MSELossFlat(axis=1))\n",
    "metrics = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Left: Input, Right: Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x777.6 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = dls.one_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{tuple: [{moving_mnist.data.ImageSeq: [fastai.torch_core.TensorImageBW,\n",
       "    fastai.torch_core.TensorImageBW,\n",
       "    fastai.torch_core.TensorImageBW,\n",
       "    fastai.torch_core.TensorImageBW,\n",
       "    fastai.torch_core.TensorImageBW]},\n",
       "  {moving_mnist.data.ImageSeq: [fastai.torch_core.TensorImageBW,\n",
       "    fastai.torch_core.TensorImageBW,\n",
       "    fastai.torch_core.TensorImageBW,\n",
       "    fastai.torch_core.TensorImageBW,\n",
       "    fastai.torch_core.TensorImageBW]}]}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explode_types(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PhyDNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer  0 input dim  64  hidden dim  128\n",
      "layer  1 input dim  128  hidden dim  128\n",
      "layer  2 input dim  128  hidden dim  64\n"
     ]
    }
   ],
   "source": [
    "phycell =  PhyCell(input_shape=(16,16), input_dim=64, F_hidden_dims=[49], n_layers=1, kernel_size=(7,7)) \n",
    "convlstm = ConvLSTM(input_shape=(16,16), input_dim=64, hidden_dims=[128,128,64], n_layers=3, kernel_size=(3,3))   \n",
    "encoder =  EncoderRNN(phycell, convlstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = StackUnstack(PhyDNet(encoder, sigmoid=False, moment=True), dim=1).cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A handy callback to include the loss computed inside the model to the target loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class PHyCallback(Callback):\n",
    "    def after_pred(self):\n",
    "        self.learn.pred, self.loss_phy = self.pred\n",
    "    def after_loss(self):\n",
    "        self.learn.loss += self.loss_phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(dls, model, loss_func=mse_loss, metrics=metrics, \n",
    "                cbs=[TeacherForcing(10), PHyCallback()], opt_func=ranger)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "SuggestedLRs(lr_min=0.03019951581954956, lr_steep=0.0030199517495930195)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.258197</td>\n",
       "      <td>1.339348</td>\n",
       "      <td>02:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.068752</td>\n",
       "      <td>1.159314</td>\n",
       "      <td>02:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.977502</td>\n",
       "      <td>0.973215</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.911136</td>\n",
       "      <td>0.906120</td>\n",
       "      <td>02:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.866704</td>\n",
       "      <td>0.847074</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.817834</td>\n",
       "      <td>0.802413</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.779440</td>\n",
       "      <td>0.782506</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.758171</td>\n",
       "      <td>0.765073</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.741225</td>\n",
       "      <td>0.731223</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.724645</td>\n",
       "      <td>0.736586</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.710116</td>\n",
       "      <td>0.715392</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.700559</td>\n",
       "      <td>0.662308</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.677740</td>\n",
       "      <td>0.680897</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.667804</td>\n",
       "      <td>0.680016</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.658701</td>\n",
       "      <td>0.637257</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.646858</td>\n",
       "      <td>0.630494</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.635516</td>\n",
       "      <td>0.647589</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.631355</td>\n",
       "      <td>0.602225</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.614756</td>\n",
       "      <td>0.604910</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.605695</td>\n",
       "      <td>0.578041</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.595096</td>\n",
       "      <td>0.587606</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.583415</td>\n",
       "      <td>0.538316</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.576286</td>\n",
       "      <td>0.536290</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.569516</td>\n",
       "      <td>0.566520</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.567040</td>\n",
       "      <td>0.555454</td>\n",
       "      <td>02:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_flat_cos(25, 3e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p,t = learn.get_preds(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, torch.Size([100, 1, 64, 64]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(p), p[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_res(t, idx, argmax=False):\n",
    "    if argmax:\n",
    "        im_seq = ImageSeq.create([t[i][idx].argmax(0).unsqueeze(0) for i in range(5)], TensorMask)\n",
    "    else:\n",
    "        im_seq = ImageSeq.create([t[i][idx] for i in range(5)])\n",
    "    im_seq.show(figsize=(8,4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = random.randint(0,99)\n",
    "show_res(t,k)\n",
    "show_res(p,k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('models/phydnet.pth')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.save('phydnet')"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}