Character_recog_Inference_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Character recog Inference .ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySvMptVsP-Y8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "33363141-266e-47cb-c90f-913f13447cdd"
      },
      "source": [
        "!cp '/content/drive/My Drive/Projects/Nishikita Competition/data.zip' /content/\n",
        "!unzip /content/data.zip\n",
        "!unzip /content/train.zip\n",
        "!unzip /content/test.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiK9B2o8lkEt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "5db7d3e7-368b-44d7-9928-a0c5d6651e14"
      },
      "source": [
        "!pip install -U --pre efficientnet\n",
        "!pip install tf2cv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting efficientnet\n",
            "  Downloading https://files.pythonhosted.org/packages/28/91/67848a143b54c331605bfba5fd31cf4e9db13d2e429d103fe807acc3bcf4/efficientnet-1.1.0-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: keras-applications<=1.0.8,>=1.0.7 in /usr/local/lib/python3.6/dist-packages (from efficientnet) (1.0.8)\n",
            "Requirement already satisfied, skipping upgrade: scikit-image in /usr/local/lib/python3.6/dist-packages (from efficientnet) (0.16.2)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (2.10.0)\n",
            "Requirement already satisfied, skipping upgrade: networkx>=2.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (2.4)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (3.2.2)\n",
            "Requirement already satisfied, skipping upgrade: imageio>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (2.4.1)\n",
            "Requirement already satisfied, skipping upgrade: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (1.1.1)\n",
            "Requirement already satisfied, skipping upgrade: pillow>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (7.0.0)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->efficientnet) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from h5py->keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->scikit-image->efficientnet) (4.4.2)\n",
            "Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (1.2.0)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (0.10.0)\n",
            "Installing collected packages: efficientnet\n",
            "Successfully installed efficientnet-1.1.0\n",
            "Collecting tf2cv\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a2/c8/0274bdbcd9504287406cf2fe3fd465a3b2e80fb53335f7512374bae92f3e/tf2cv-0.0.11-py2.py3-none-any.whl (340kB)\n",
            "\u001b[K     |████████████████████████████████| 348kB 4.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from tf2cv) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tf2cv) (1.18.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->tf2cv) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->tf2cv) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->tf2cv) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->tf2cv) (2.9)\n",
            "Installing collected packages: tf2cv\n",
            "Successfully installed tf2cv-0.0.11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cILmBveoQImV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import tensorflow \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D,GlobalMaxPooling2D,Dense,concatenate,Input\n",
        "from tensorflow.keras.models import Model,Sequential,load_model\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.resnet import ResNet101,ResNet50,ResNet152\n",
        "from tensorflow.keras.utils import Sequence \n",
        "from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.callbacks import *\n",
        "import efficientnet.tfkeras as efn\n",
        "import math\n",
        "import tf2cv\n",
        "from tf2cv.model_provider import get_model as tf2cv_get_model\n",
        "from albumentations import (\n",
        "    Compose, HorizontalFlip, CLAHE, HueSaturationValue,\n",
        "    RandomBrightness, RandomContrast, RandomGamma,\n",
        "    ToFloat, ShiftScaleRotate,Cutout\n",
        ")\n",
        "#@title Imports\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_datasets as tfds\n",
        "import time\n",
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pathlib\n",
        "from tqdm import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6Xw0j0OQN1E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dir='/content/'\n",
        "classes_map={\n",
        "    0: 'Male-Aristocrat', 1: 'Male-Samurai', 2: 'Male-Animal', 3: 'Male-Common People',\n",
        "4: 'Female-Aristocrat', 5: 'Female-Samurai', 6: 'Female-Animal' , 7: 'Woman-common people'\n",
        "}\n",
        "\n",
        "img_size=256\n",
        "batch_size=64\n",
        "epochs=10\n",
        "n_classes=len(classes_map.items())\n",
        "lr=1e-2\n",
        "model_dir='/content/drive/My Drive/Projects/Nishikita Competition/Models/bitransferresnet.hdf5'\n",
        "model_dirs=['/content/drive/My Drive/Projects/Nishikita Competition/Models/definitive.hdf5','/content/drive/My Drive/Projects/Nishikita Competition/Models/bitransferresnet.hdf5','/content/drive/My Drive/Projects/Nishikita Competition/Models/bitresnet101.hdf5','/content/drive/My Drive/Projects/Nishikita Competition/Models/bitresnet50*3.hdf5','/content/drive/My Drive/Projects/Nishikita Competition/Models/bitresnet101*3.hdf5']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrPALbZ4QP6N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6493a961-8a04-4dcd-d777-7a264d7cd048"
      },
      "source": [
        "train_df=pd.read_csv(data_dir+'sample_submission.csv')\n",
        "train_df['image']=train_df['image'].apply(lambda x:data_dir+'test/'+x)\n",
        "train_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image</th>\n",
              "      <th>gender_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/test/n_00000.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/test/n_00004.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/test/n_00009.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/test/n_00026.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/test/n_00030.jpg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       image  gender_status\n",
              "0  /content/test/n_00000.jpg              0\n",
              "1  /content/test/n_00004.jpg              0\n",
              "2  /content/test/n_00009.jpg              0\n",
              "3  /content/test/n_00026.jpg              0\n",
              "4  /content/test/n_00030.jpg              0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7j4j2GaiQZXW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.utils import Sequence \n",
        "\n",
        "class AnimeData(Sequence):\n",
        "  def __init__(self,x_set,batch_size=batch_size):\n",
        "    self.x=x_set\n",
        "    self.batch_size=batch_size \n",
        "  def __getitem__(self,step):\n",
        "    start=step*self.batch_size \n",
        "    end=(step+1)*self.batch_size \n",
        "    x_batch=self.x[start:end]\n",
        "    x_batch=self.data_gen(x_batch)\n",
        "    return x_batch/255.0\n",
        "  \n",
        "  def data_gen(self,batch_x):\n",
        "    x_batch=np.empty((batch_x.shape[0],img_size,img_size,3))\n",
        "    for i in range(batch_x.shape[0]):\n",
        "      x=cv2.imread(batch_x[i])\n",
        "      x_batch[i]=x\n",
        "    return np.array(x_batch)\n",
        "  \n",
        "  def __len__(self):\n",
        "    return math.ceil(len(self.x) / self.batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czREMo1zRfBu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXSgEaYRUMjG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#All model building functions defined here. \n",
        "def get_efficientnet(shape=(img_size,img_size,3),weights='noisy-student',trainable_base=False,name='b7'):\n",
        "  if name=='b7':\n",
        "    base=efn.EfficientNetB7(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b6':\n",
        "    base=efn.EfficientNetB6(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b5':\n",
        "    base=efn.EfficientNetB5(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b4':\n",
        "    base=efn.EfficientNetB4(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b3':\n",
        "    base=efn.EfficientNetB3(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b2':\n",
        "    base=efn.EfficientNetB2(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='b1':\n",
        "    base=efn.EfficientNetB1(weights=weights,input_shape=shape,include_top=False)\n",
        "  elif name=='l2':\n",
        "    base=efn.EfficientNetL2(weights=weights,input_shape=shape,include_top=False)\n",
        "  base.trainable=trainable_base\n",
        "  avg_pool=GlobalAveragePooling2D()(base.output)\n",
        "  #max_pool=GlobalMaxPooling2D()(base.output)\n",
        "  #out=concatenate([avg_pool,max_pool])\n",
        "  #avg_pool=Dense(1024,activation='relu')(avg_pool)\n",
        "  out=Dense(n_classes,activation='softmax')(avg_pool)\n",
        "  model=Model(inputs=[base.input],outputs=[out])\n",
        "  return model\n",
        "\n",
        "\n",
        "\n",
        "def get_tf2cv_model(input_shape=(img_size,img_size,3),trainable_base=False):\n",
        "  net=tf2cv_get_model('senet154',pretrained=True)\n",
        "  net.trainable=trainable_base\n",
        "  inp=Input(input_shape)\n",
        "  model_out=net.layers[-2](inp)\n",
        "  model_out=GlobalAveragePooling2D()(model_out)\n",
        "  model_out=Dense(n_classes,activation='softmax')(model_out)\n",
        "  model=Model(inputs=[inp],outputs=[model_out])\n",
        "  return model\n",
        "\n",
        "\n",
        "def get_bitresnet(input_shape=(img_size,img_size,3),trainable_base=False,variant='50'):\n",
        "  # Load model into KerasLayer\n",
        "  inp=Input(shape=input_shape)\n",
        "  if variant=='50':\n",
        "    model_url = \"https://tfhub.dev/google/bit/m-r50x1/1\"\n",
        "  elif variant=='101':\n",
        "    model_url=\"https://tfhub.dev/google/bit/m-r101x1/1\"\n",
        "  elif variant=='50*3':\n",
        "    model_url=\"https://tfhub.dev/google/bit/m-r50x3/1\"\n",
        "  elif variant=='101*3':\n",
        "    model_url=\"https://tfhub.dev/google/bit/s-r101x3/1\"\n",
        "  module = hub.KerasLayer(model_url)\n",
        "  model_out=module(inp)\n",
        "  model_out=Dense(n_classes,activation='softmax')(model_out)\n",
        "  model=Model(inputs=[inp],outputs=[model_out])\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEJJIlGGQ0E4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Averaging Ensemble (A Little crude)\n",
        "test_gen=AnimeData(train_df.image.values,batch_size=1)\n",
        "prediction_full=np.empty((len(train_df),n_classes,len(model_dirs)))\n",
        "for i,path in tqdm(enumerate(model_dirs)):\n",
        "  if i==0:\n",
        "    variant='b7'\n",
        "  elif i==1:\n",
        "    variant='50'\n",
        "  elif i==2:\n",
        "    variant='101'\n",
        "  elif i==3:\n",
        "    variant='50*3'\n",
        "  elif i==4:\n",
        "    variant='101*3'\n",
        "  if variant in [f'b{i+1}' for i in range(7)]:\n",
        "    model=get_efficientnet(name=variant)\n",
        "  else:\n",
        "    model=get_bitresnet(variant=variant)\n",
        "  model.load_weights(path)\n",
        "  preds=model.predict(test_gen,verbose=1)\n",
        "  prediction_full[:,:,i]=preds \n",
        "\n",
        "predictions_full=np.average(prediction_full,axis=-1)\n",
        "preds=predictions_full.argmax(axis=-1)\n",
        "sample=pd.read_csv('sample_submission.csv')\n",
        "train_df['image']=sample['image']\n",
        "train_df['gender_status']=preds\n",
        "train_df.to_csv('submit.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}