2-train-gan-relook.ipynb

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     "text": [
      "2.6.2\n"
     ]
    }
   ],
   "source": [
    "from glob import glob\n",
    "import numpy as np\n",
    "from matplotlib import pylab as plt\n",
    "import cv2\n",
    "import time\n",
    "import os\n",
    "import torchvision.transforms as transforms\n",
    "import torch\n",
    "import tensorflow as tf\n",
    "print(tf.__version__)\n",
    "import tensorflow.keras.layers as layers\n",
    "from tensorflow.keras.models import Model\n",
    "import pandas as pd"
   ]
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   "source": [
    "df = pd.read_csv('../input/videopaths/datapaths.csv',header=0,index_col='Unnamed: 0')"
   ]
  },
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   "source": [
    "df.replace('audio-files-for-miraclvc1/ph1-13','audio-files-for-miraclvc1/ph1-1',inplace=True)\n",
    "for i in list(set(df['spectrograms'])):\n",
    "    if not os.path.exists('../input/'+i+'.jpg'):\n",
    "        df.replace(i,'audio-files-for-miraclvc1/ph1-1',inplace=True)"
   ]
  },
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   "source": [
    "class Aud2LipsDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self):\n",
    "        self.imgs = df\n",
    "    def __getitem__(self, idx):\n",
    "        # load lipimage,spectrogram and target:lipmovement image\n",
    "        lip_path = self.imgs['lippath'][idx]\n",
    "        spect_path='../input/'+self.imgs['spectrograms'][idx]+'.jpg'\n",
    "        lipmove_path=self.imgs['videopath'][idx]\n",
    "        #resize and load as tensor\n",
    "        lipimg=cv2.cvtColor(cv2.imread(lip_path), cv2.COLOR_BGR2RGB)\n",
    "        spectimg=cv2.cvtColor(cv2.imread(spect_path),cv2.COLOR_BGR2RGB)\n",
    "        lipmoveimg=cv2.cvtColor(cv2.imread(lipmove_path), cv2.COLOR_BGR2RGB)\n",
    "        lipimg = lipimg[250:400,150:400]\n",
    "        lipimg = cv2.resize(lipimg,(170,150))\n",
    "        spectT = cv2.resize(spectimg,(80, 150))\n",
    "        lipmoveimg=lipmoveimg[250:400,150:400]\n",
    "        \n",
    "        imageA = np.concatenate((lipimg,spectT),axis=1)\n",
    "        imageB = lipmoveimg\n",
    "        return imageA,imageB\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.imgs)\n",
    "dataset = Aud2LipsDataset()"
   ]
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   "source": [
    "def load_batch(batch_size):\n",
    "    #path1=sorted(glob('../input/building/facade/train_picture/*'))\n",
    "    #path2=sorted(glob('../input/building/facade/train_label/*'))\n",
    "    n_batches=int(len(df)/batch_size)\n",
    "  \n",
    "    for i in range(n_batches):\n",
    "        #batch1=path1[i*batch_size:(i+1)*batch_size]\n",
    "        #batch2=path2[i*batch_size:(i+1)*batch_size]\n",
    "        img_A,img_B=[],[]\n",
    "        for idx in range(i*batch_size,(i+1)*batch_size):\n",
    "            img1,img2 = dataset[idx]\n",
    "            img_A.append(img1)#picture\n",
    "            img_B.append(img2)#label\n",
    "      \n",
    "        img_A=np.array(img_A)/255\n",
    "        img_B=np.array(img_B)/255\n",
    "    \n",
    "        yield img_A,img_B #return generator"
   ]
  },
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   "source": [
    "class pix2pix():\n",
    "    def __init__(self):\n",
    "        self.img_rows= 150\n",
    "        self.img_cols= 250\n",
    "        self.channels=3\n",
    "        self.img_shape=(self.img_rows,self.img_cols,self.channels)\n",
    "    \n",
    "        patch=int(self.img_rows/(2**4)) # 16\n",
    "        self.disc_patch=(10,16,1)\n",
    "    \n",
    "        self.gf=64\n",
    "        self.df=64\n",
    "    \n",
    "        optimizer=tf.keras.optimizers.Adam(0.0002,0.5)\n",
    "    \n",
    "        self.discriminator=self.build_discriminator()\n",
    "        #self.discriminator.summary()\n",
    "        self.discriminator.compile(loss='binary_crossentropy',\n",
    "                              optimizer=optimizer)\n",
    "    \n",
    "        self.generator=self.build_generator()\n",
    "        #self.generator.summary()\n",
    "    \n",
    "        img_A=layers.Input(shape=self.img_shape)#picture--label\n",
    "        img_B=layers.Input(shape=self.img_shape)#label--real\n",
    "    \n",
    "        img=self.generator(img_A)\n",
    "    \n",
    "        self.discriminator.trainable=False\n",
    "    \n",
    "        valid=self.discriminator([img,img_A])\n",
    "    \n",
    "        self.combined=Model(img_A,valid)\n",
    "        self.combined.compile(loss='binary_crossentropy',\n",
    "                              optimizer=optimizer)\n",
    "    \n",
    "    def build_generator(self):\n",
    "        def conv2d(layer_input,filters,f_size=(4,4),bn=True):\n",
    "            d=layers.Conv2D(filters,kernel_size=f_size,strides=(2,2),padding='same')(layer_input)\n",
    "            d=layers.LeakyReLU(0.2)(d)\n",
    "            if bn:\n",
    "                d=layers.BatchNormalization()(d)\n",
    "            return d\n",
    "    \n",
    "        def deconv2d(layer_input,skip_input,filters,f_size=(4,4),dropout_rate=0):\n",
    "            u=layers.UpSampling2D((2,2))(layer_input)\n",
    "            u=layers.Conv2D(filters,kernel_size=f_size,strides=(1,1),padding='same',activation='relu')(u)\n",
    "            if dropout_rate:\n",
    "                u=layers.Dropout(dropout_rate)(u)\n",
    "            u=layers.BatchNormalization()(u)\n",
    "            u=layers.Concatenate()([u,skip_input])\n",
    "            return u\n",
    "    \n",
    "        d0=layers.Input(shape=self.img_shape)\n",
    "    \n",
    "        d1=conv2d(d0,self.gf,bn=False) \n",
    "        d1 = tf.keras.layers.Resizing(256,320)(d1)\n",
    "        d2=conv2d(d1,self.gf*2)\n",
    "        d2 = tf.keras.layers.Resizing(128,160)(d2)\n",
    "        d3=conv2d(d2,self.gf*4)  \n",
    "        d3 = tf.keras.layers.Resizing(64,80)(d3)\n",
    "        d4=conv2d(d3,self.gf*8) \n",
    "        d4 = tf.keras.layers.Resizing(32,40)(d4)\n",
    "        d5=conv2d(d4,self.gf*8)\n",
    "        d5 = tf.keras.layers.Resizing(16,20)(d5)\n",
    "        d6=conv2d(d5,self.gf*8)        \n",
    "    \n",
    "        d7=conv2d(d6,self.gf*8)         \n",
    "    \n",
    "        u1=deconv2d(d7,d6,self.gf*8,dropout_rate=0.5)\n",
    "        u2=deconv2d(u1,d5,self.gf*8,dropout_rate=0.5)   \n",
    "        u3=deconv2d(u2,d4,self.gf*8,dropout_rate=0.5)   \n",
    "        u4=deconv2d(u3,d3,self.gf*4)   \n",
    "        u5=deconv2d(u4,d2,self.gf*2)   \n",
    "        u6=deconv2d(u5,d1,self.gf)     \n",
    "        u7=layers.UpSampling2D((2,2))(u6)\n",
    "    \n",
    "        output_img=layers.Conv2D(self.channels,kernel_size=(4,4),strides=(1,1),padding='same',activation='tanh')(u7)\n",
    "        output_img = tf.keras.layers.Resizing(150,250)(output_img)\n",
    "        \n",
    "        return Model(d0,output_img)\n",
    "  \n",
    "    def build_discriminator(self):\n",
    "        def d_layer(layer_input,filters,f_size=(4,4),bn=True):\n",
    "            d=layers.Conv2D(filters,kernel_size=f_size,strides=(2,2),padding='same')(layer_input)\n",
    "            d=layers.LeakyReLU(0.2)(d)\n",
    "            if bn:\n",
    "                d=layers.BatchNormalization()(d)\n",
    "            return d\n",
    "    \n",
    "        img_A=layers.Input(shape=self.img_shape)\n",
    "        img_B=layers.Input(shape=self.img_shape)\n",
    "    \n",
    "        combined_imgs=layers.Concatenate(axis=-1)([img_A,img_B])\n",
    "    \n",
    "        d1=d_layer(combined_imgs,self.df,bn=False)\n",
    "        d2=d_layer(d1,self.df*2)\n",
    "        d3=d_layer(d2,self.df*4)\n",
    "        d4=d_layer(d3,self.df*8)\n",
    "    \n",
    "        validity=layers.Conv2D(1,kernel_size=(4,4),strides=(1,1),padding='same',activation='sigmoid')(d4)\n",
    "    \n",
    "        return Model([img_A,img_B],validity)\n",
    "  \n",
    "    def train(self,epochs,batch_size):\n",
    "        valid=np.ones((batch_size,)+self.disc_patch)\n",
    "        fake=np.zeros((batch_size,)+self.disc_patch)\n",
    "    \n",
    "        for epoch in range(epochs):\n",
    "            start=time.time()\n",
    "            for batch_i,(img_A,img_B) in enumerate(load_batch(batch_size)):\n",
    "                gen_imgs=self.generator.predict(img_A)\n",
    "        \n",
    "                d_loss_real = self.discriminator.train_on_batch([img_B, img_A], valid)\n",
    "                d_loss_fake = self.discriminator.train_on_batch([gen_imgs, img_A], fake)\n",
    "                d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
    "        \n",
    "                g_loss = self.combined.train_on_batch(img_A,valid)\n",
    "\n",
    "                if batch_i % 50 == 0:\n",
    "                    print (\"[Epoch %d] [Batch %d] [D loss: %f] [G loss: %f]\" % (epoch,\n",
    "                                                                                batch_i,\n",
    "                                                                                d_loss,\n",
    "                                                                                g_loss))\n",
    "                if batch_i % 1000 == 0:\n",
    "                    tf.keras.models.save_model(self.generator, 'trained-gan-model',\n",
    "                                               overwrite=True, include_optimizer=True,\n",
    "                                               save_format=None)\n",
    "            #self.sample_images(epoch)\n",
    "            print('Time for epoch {} is {} sec'.format(epoch,time.time()-start))\n",
    "      \n",
    "    def sample_images(self, epoch):\n",
    "        r, c = 1, 1\n",
    "        img_A, img_B = next(iter(load_batch(1)))\n",
    "        fake_A = self.generator.predict(img_A)\n",
    "\n",
    "        gen_imgs = np.concatenate([img_A, fake_A, img_B])\n",
    "\n",
    "        # Rescale images 0 - 1\n",
    "        gen_imgs = 0.5 * gen_imgs + 0.5\n",
    "\n",
    "        titles = ['Condition','Generated', 'Original']\n",
    "        fig, axs = plt.subplots(r, c)\n",
    "        cnt = 0\n",
    "        plt.imshow(gen_imgs[cnt])\n",
    "        plt.set_title(titles[i])\n",
    "        plt.axis('off')\n",
    "        \n",
    "        fig.savefig(\"./%d.png\" % (epoch))\n",
    "        plt.show()"
   ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-09 13:11:05.196627: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-09 13:11:05.197613: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-09 13:11:05.198276: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-09 13:11:05.199018: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-02-09 13:11:05.199702: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-09 13:11:05.200408: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-09 13:11:05.201024: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-09 13:11:09.324803: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-09 13:11:09.325612: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-09 13:11:09.326268: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ead from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-02-09 13:11:09.326853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14959 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-09 13:11:12.927081: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-09 13:11:17.761123: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-09 13:11:18.862435: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0] [Batch 0] [D loss: 1.413595] [G loss: 0.648292]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0] [Batch 50] [D loss: 0.091805] [G loss: 0.877723]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0] [Batch 100] [D loss: 0.030999] [G loss: 1.976720]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0] [Batch 150] [D loss: 0.009295] [G loss: 1.023681]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0] [Batch 200] [D loss: 0.003520] [G loss: 0.536047]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0] [Batch 250] [D loss: 0.002099] [G loss: 0.016790]\n"
     ]
    },
    {
     "name": "stdout",
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      "2022-02-09 18:20:45.619006: W tensorflow/core/data/root_dataset.cc:167] Optimization loop failed: Cancelled: Operation was cancelled\n"
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    },
    {
     "name": "stdout",
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     "text": [
      "Time for epoch 4 is 7797.879176616669 sec\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "2022-02-10 00:37:46.581213: W tensorflow/core/data/root_dataset.cc:167] Optimization loop failed: Cancelled: Operation was cancelled\n"
     ]
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     ]
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    {
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     "output_type": "stream",
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      "[Epoch 5] [Batch 3700] [D loss: 0.000000] [G loss: 0.138141]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[Epoch 5] [Batch 3750] [D loss: 0.000000] [G loss: 0.142240]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 5] [Batch 3800] [D loss: 0.000000] [G loss: 0.146524]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 5] [Batch 3850] [D loss: 0.000000] [G loss: 0.162105]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 5] [Batch 3900] [D loss: 0.000000] [G loss: 0.133268]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 5] [Batch 3950] [D loss: 0.000000] [G loss: 0.146536]\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    gan = pix2pix()\n",
    "    #gan.generator = tf.keras.models.load_model('../input/gan-aud2lips/trained-gan-model/', compile=True)\n",
    "    tf.keras.models.save_model(gan.generator, 'trained-gan-model', overwrite=True, include_optimizer=True, save_format=None)\n",
    "    gan.train(epochs=8, batch_size=4)"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "#### Loading Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fd57ec0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-08T12:44:25.00051Z",
     "iopub.status.busy": "2022-02-08T12:44:24.999738Z",
     "iopub.status.idle": "2022-02-08T12:44:37.761435Z",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#gan.generator = tf.keras.models.load_model('../input/gan-aud2lips/trained-gan-model/', compile=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5746194",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-09T13:05:04.594421Z",
     "iopub.status.busy": "2022-02-09T13:05:04.593721Z",
     "iopub.status.idle": "2022-02-09T13:05:04.863258Z",
     "shell.execute_reply": "2022-02-09T13:05:04.862485Z",
     "shell.execute_reply.started": "2022-02-09T13:05:04.594383Z"
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     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.imshow(dataset[4][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78920419",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-09T12:36:32.224307Z",
     "iopub.status.busy": "2022-02-09T12:36:32.223689Z",
     "iopub.status.idle": "2022-02-09T12:36:32.366022Z",
     "shell.execute_reply": "2022-02-09T12:36:32.365354Z",
     "shell.execute_reply.started": "2022-02-09T12:36:32.224265Z"
    },
    "papermill": {
     "duration": null,
     "end_time": null,
     "exception": null,
     "start_time": null,
     "status": "pending"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cf = dataset[4][0][None,]/255\n",
    "gan.generator.predict(cf)"
   ]
  }
 ],
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