2-pipeline-build-gan-relook.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/theayushanand/2-pipeline-build-gan-relook?scriptVersionId=87470634\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"code","source":"from glob import glob\nimport numpy as np\nfrom matplotlib import pylab as plt\nimport cv2\nimport time\nimport os\nimport torchvision.transforms as transforms\nimport torch\nimport tensorflow as tf\nprint(tf.__version__)\nimport tensorflow.keras.layers as layers\nfrom tensorflow.keras.models import Model\nimport pandas as pd","metadata":{"execution":{"iopub.status.busy":"2022-02-10T10:54:33.275429Z","iopub.execute_input":"2022-02-10T10:54:33.275711Z","iopub.status.idle":"2022-02-10T10:54:40.593543Z","shell.execute_reply.started":"2022-02-10T10:54:33.275682Z","shell.execute_reply":"2022-02-10T10:54:40.592818Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"2.6.2\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.read_csv('../input/videopaths/datapaths.csv',header=0,index_col='Unnamed: 0')\ndf.replace('audio-files-for-miraclvc1/ph1-13','audio-files-for-miraclvc1/ph1-1',inplace=True)\nfor 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)","metadata":{"execution":{"iopub.status.busy":"2022-02-10T10:54:40.59506Z","iopub.execute_input":"2022-02-10T10:54:40.595585Z","iopub.status.idle":"2022-02-10T10:54:42.414534Z","shell.execute_reply.started":"2022-02-10T10:54:40.595553Z","shell.execute_reply":"2022-02-10T10:54:42.413749Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","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        imageA = np.concatenate((lipimg,spectT),axis=1)\n        imageB = lipmoveimg\n        return imageA,imageB\n    def __len__(self):\n        return len(self.imgs)\ndataset = Aud2LipsDataset()","metadata":{"execution":{"iopub.status.busy":"2022-02-10T10:54:42.415892Z","iopub.execute_input":"2022-02-10T10:54:42.416145Z","iopub.status.idle":"2022-02-10T10:54:42.425365Z","shell.execute_reply.started":"2022-02-10T10:54:42.416108Z","shell.execute_reply":"2022-02-10T10:54:42.424542Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"def load_batch(batch_size):\n    n_batches=int(len(df)/batch_size)\n    for i in range(n_batches):\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)/127.5-1\n        img_B=np.array(img_B)/127.5-1\n    \n        yield img_A,img_B #return generator","metadata":{"execution":{"iopub.status.busy":"2022-02-10T10:54:53.878334Z","iopub.execute_input":"2022-02-10T10:54:53.878844Z","iopub.status.idle":"2022-02-10T10:54:53.884658Z","shell.execute_reply.started":"2022-02-10T10:54:53.878805Z","shell.execute_reply":"2022-02-10T10:54:53.883965Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","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        optimizerD = tf.keras.optimizers.Adam(0.0004,0.5)\n        optimizerG = tf.keras.optimizers.Adam(0.0001,0.5)\n        \n        self.discriminator=self.build_discriminator()\n        #self.discriminator.summary()\n        self.discriminator.compile(loss='binary_crossentropy',\n                              optimizer=optimizerD)\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=optimizerG)\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=1):\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                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        (o,t) = dataset[3180]\n        o=o[None,]\n        outi_image = gan.generator.predict(o)\n        plt.imshow(outi_image.squeeze())\n        plt.savefig(\"./%d.png\" % (epoch))\n        plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-10T11:27:05.94535Z","iopub.execute_input":"2022-02-10T11:27:05.945693Z","iopub.status.idle":"2022-02-10T11:27:05.982532Z","shell.execute_reply.started":"2022-02-10T11:27:05.945659Z","shell.execute_reply":"2022-02-10T11:27:05.981795Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"markdown","source":"## Loading Model","metadata":{}},{"cell_type":"code","source":"if __name__ == '__main__':\n    gan = pix2pix()\n    gan.generator = tf.keras.models.load_model('../input/gan-aud2lips-v2/trained-gan-model/', compile=True)\n    gan.train(1,4)","metadata":{"execution":{"iopub.status.busy":"2022-02-10T11:27:08.171757Z","iopub.execute_input":"2022-02-10T11:27:08.172011Z","iopub.status.idle":"2022-02-10T11:29:26.718266Z","shell.execute_reply.started":"2022-02-10T11:27:08.171983Z","shell.execute_reply":"2022-02-10T11:29:26.717082Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"[Epoch 0] [Batch 0] [D loss: 1.953321] [G loss: 0.654430]\n","output_type":"stream"},{"name":"stderr","text":"2022-02-10 11:27:20.349113: 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","output_type":"stream"},{"name":"stdout","text":"[Epoch 0] [Batch 50] [D loss: 0.004473] [G loss: 0.202539]\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_34/503962520.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mgan\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpix2pix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../input/gan-aud2lips-v2/trained-gan-model/'\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"tf.constant\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m   \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36m_constant_eager_impl\u001b[0;34m(ctx, value, dtype, shape, verify_shape)\u001b[0m\n\u001b[1;32m    306\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_constant_eager_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverify_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m   \u001b[0;34m\"\"\"Creates a constant on the current device.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m   \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_to_eager_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m    104\u001b[0m       \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m   \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"(o,t) = dataset[0]\no=o[None,]\nout_image = gan.generator.predict(o)\nplt.imshow(out_image.squeeze())","metadata":{"execution":{"iopub.status.busy":"2022-02-10T11:30:05.341799Z","iopub.execute_input":"2022-02-10T11:30:05.342422Z","iopub.status.idle":"2022-02-10T11:30:05.69216Z","shell.execute_reply.started":"2022-02-10T11:30:05.342349Z","shell.execute_reply":"2022-02-10T11:30:05.691458Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7fb8c1e9d6d0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}