group01_AIA_Depression markes_recommendation.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":[{"cell_type":"markdown","source":"# Identifying Depression markers via social media,Facial expressions and building an early-stage recommendation engine\n\n### Sayantika Banik-12020067\n### Vaibhav Shah-12020080\n### Sahithi Koppula-12010034","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-04-10T04:47:55.772698Z","iopub.execute_input":"2022-04-10T04:47:55.773020Z","iopub.status.idle":"2022-04-10T04:47:55.812868Z","shell.execute_reply.started":"2022-04-10T04:47:55.772949Z","shell.execute_reply":"2022-04-10T04:47:55.812206Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/fer2013/fer2013.csv\n/kaggle/input/sample-image/5b9b44d02200005700d8d086.jpeg\n","output_type":"stream"}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"# Installation of necessary Libraries\n!pip install torch===1.7.1+torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html\n!pip install timm==0.4.12\n!pip install --upgrade torch torchvision\n","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:47:55.814311Z","iopub.execute_input":"2022-04-10T04:47:55.814577Z","iopub.status.idle":"2022-04-10T04:49:19.410280Z","shell.execute_reply.started":"2022-04-10T04:47:55.814543Z","shell.execute_reply":"2022-04-10T04:49:19.409475Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Looking in links: https://download.pytorch.org/whl/torch_stable.html\n\u001b[31mERROR: Could not find a version that satisfies the requirement torch===1.7.1+torchaudio===0.7.2 (from versions: 0.4.1, 0.4.1.post2, 1.0.0, 1.0.1, 1.0.1.post2, 1.1.0, 1.2.0, 1.2.0+cpu, 1.2.0+cu92, 1.3.0, 1.3.0+cpu, 1.3.0+cu100, 1.3.0+cu92, 1.3.1, 1.3.1+cpu, 1.3.1+cu100, 1.3.1+cu92, 1.4.0, 1.4.0+cpu, 1.4.0+cu100, 1.4.0+cu92, 1.5.0, 1.5.0+cpu, 1.5.0+cu101, 1.5.0+cu92, 1.5.1, 1.5.1+cpu, 1.5.1+cu101, 1.5.1+cu92, 1.6.0, 1.6.0+cpu, 1.6.0+cu101, 1.6.0+cu92, 1.7.0, 1.7.0+cpu, 1.7.0+cu101, 1.7.0+cu110, 1.7.0+cu92, 1.7.1, 1.7.1+cpu, 1.7.1+cu101, 1.7.1+cu110, 1.7.1+cu92, 1.7.1+rocm3.7, 1.7.1+rocm3.8, 1.8.0, 1.8.0+cpu, 1.8.0+cu101, 1.8.0+cu111, 1.8.0+rocm3.10, 1.8.0+rocm4.0.1, 1.8.1, 1.8.1+cpu, 1.8.1+cu101, 1.8.1+cu102, 1.8.1+cu111, 1.8.1+rocm3.10, 1.8.1+rocm4.0.1, 1.9.0, 1.9.0+cpu, 1.9.0+cu102, 1.9.0+cu111, 1.9.0+rocm4.0.1, 1.9.0+rocm4.1, 1.9.0+rocm4.2, 1.9.1, 1.9.1+cpu, 1.9.1+cu102, 1.9.1+cu111, 1.9.1+rocm4.0.1, 1.9.1+rocm4.1, 1.9.1+rocm4.2, 1.10.0, 1.10.0+cpu, 1.10.0+cu102, 1.10.0+cu111, 1.10.0+cu113, 1.10.0+rocm4.0.1, 1.10.0+rocm4.1, 1.10.0+rocm4.2, 1.10.1, 1.10.1+cpu, 1.10.1+cu102, 1.10.1+cu111, 1.10.1+cu113, 1.10.1+rocm4.0.1, 1.10.1+rocm4.1, 1.10.1+rocm4.2, 1.10.2, 1.10.2+cpu, 1.10.2+cu102, 1.10.2+cu111, 1.10.2+cu113, 1.10.2+rocm4.0.1, 1.10.2+rocm4.1, 1.10.2+rocm4.2, 1.11.0, 1.11.0+cpu, 1.11.0+cu102, 1.11.0+cu113, 1.11.0+cu115, 1.11.0+rocm4.3.1, 1.11.0+rocm4.5.2)\u001b[0m\n\u001b[31mERROR: No matching distribution found for torch===1.7.1+torchaudio===0.7.2\u001b[0m\nCollecting timm==0.4.12\n  Downloading timm-0.4.12-py3-none-any.whl (376 kB)\n     |████████████████████████████████| 376 kB 884 kB/s            \n\u001b[?25hRequirement already satisfied: torch>=1.4 in /opt/conda/lib/python3.7/site-packages (from timm==0.4.12) (1.9.1)\nRequirement already satisfied: torchvision in /opt/conda/lib/python3.7/site-packages (from timm==0.4.12) (0.10.1)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch>=1.4->timm==0.4.12) (4.1.1)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from torchvision->timm==0.4.12) (1.20.3)\nRequirement already satisfied: pillow>=5.3.0 in /opt/conda/lib/python3.7/site-packages (from torchvision->timm==0.4.12) (8.2.0)\nInstalling collected packages: timm\nSuccessfully installed timm-0.4.12\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\nRequirement already satisfied: torch in /opt/conda/lib/python3.7/site-packages (1.9.1)\nCollecting torch\n  Downloading torch-1.11.0-cp37-cp37m-manylinux1_x86_64.whl (750.6 MB)\n     |████████████████████████████████| 750.6 MB 9.2 kB/s             \n\u001b[?25hRequirement already satisfied: torchvision in /opt/conda/lib/python3.7/site-packages (0.10.1)\nCollecting torchvision\n  Downloading torchvision-0.12.0-cp37-cp37m-manylinux1_x86_64.whl (21.0 MB)\n     |████████████████████████████████| 21.0 MB 50.5 MB/s            \n\u001b[?25hRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch) (4.1.1)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from torchvision) (1.20.3)\nRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from torchvision) (2.26.0)\nRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.7/site-packages (from torchvision) (8.2.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision) (3.1)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision) (2021.10.8)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision) (1.26.7)\nRequirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision) (2.0.9)\nInstalling collected packages: torch, torchvision\n  Attempting uninstall: torch\n    Found existing installation: torch 1.9.1\n    Uninstalling torch-1.9.1:\n      Successfully uninstalled torch-1.9.1\n  Attempting uninstall: torchvision\n    Found existing installation: torchvision 0.10.1\n    Uninstalling torchvision-0.10.1:\n      Successfully uninstalled torchvision-0.10.1\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nfastai 2.5.3 requires torch<1.11,>=1.7.0, but you have torch 1.11.0 which is incompatible.\nallennlp 2.9.1 requires torch<1.11.0,>=1.6.0, but you have torch 1.11.0 which is incompatible.\nallennlp 2.9.1 requires torchvision<0.12.0,>=0.8.1, but you have torchvision 0.12.0 which is incompatible.\u001b[0m\nSuccessfully installed torch-1.11.0 torchvision-0.12.0\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch, os\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader, random_split\nfrom torchvision import transforms, utils\nfrom torchvision.transforms import ToTensor\nfrom torchvision.utils import make_grid","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:19.411748Z","iopub.execute_input":"2022-04-10T04:49:19.412145Z","iopub.status.idle":"2022-04-10T04:49:20.386044Z","shell.execute_reply.started":"2022-04-10T04:49:19.412105Z","shell.execute_reply":"2022-04-10T04:49:20.385308Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# loading the dataset\ndf = pd.read_csv('../input/fer2013/fer2013.csv') \n\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:20.391006Z","iopub.execute_input":"2022-04-10T04:49:20.392953Z","iopub.status.idle":"2022-04-10T04:49:25.273980Z","shell.execute_reply.started":"2022-04-10T04:49:20.392904Z","shell.execute_reply":"2022-04-10T04:49:25.272362Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"   emotion                                             pixels     Usage\n0        0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...  Training\n1        0  151 150 147 155 148 133 111 140 170 174 182 15...  Training\n2        2  231 212 156 164 174 138 161 173 182 200 106 38...  Training\n3        4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...  Training\n4        6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...  Training","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>emotion</th>\n      <th>pixels</th>\n      <th>Usage</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...</td>\n      <td>Training</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>151 150 147 155 148 133 111 140 170 174 182 15...</td>\n      <td>Training</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>231 212 156 164 174 138 161 173 182 200 106 38...</td>\n      <td>Training</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...</td>\n      <td>Training</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...</td>\n      <td>Training</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print('Total lenth of dataset: ', len(df.Usage), '\\n') # length of total dataframe\nprint('Total Categories we have: ', df.Usage.unique())\nprint('data in each category: ', '{ Training: ', len(df[df.Usage == 'Training']), ' PublicTest: ', len(df[df.Usage == 'PublicTest']), ' PrivateTest: ', len(df[df.Usage == 'PrivateTest']), '}\\n')\nprint('Total Expression Classes: ', df.emotion.unique(), '\\n')\nprint('pixel data type: ', type(df.pixels[0]))","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:25.276917Z","iopub.execute_input":"2022-04-10T04:49:25.277777Z","iopub.status.idle":"2022-04-10T04:49:25.322602Z","shell.execute_reply.started":"2022-04-10T04:49:25.277732Z","shell.execute_reply":"2022-04-10T04:49:25.321843Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Total lenth of dataset:  35887 \n\nTotal Categories we have:  ['Training' 'PublicTest' 'PrivateTest']\ndata in each category:  { Training:  28709  PublicTest:  3589  PrivateTest:  3589 }\n\nTotal Expression Classes:  [0 2 4 6 3 5 1] \n\npixel data type:  <class 'str'>\n","output_type":"stream"}]},{"cell_type":"code","source":"# Turning the pixels to a matrix of 48x48\ndef makemtrx(lst, n=48):\n    for i in range(0, 48*48, n):  \n        yield lst[i:i + n] \n\n# Function to show the images\ndef show(data):\n    pixel = [int(i) for i in data[1].split(' ')]\n    pixel = np.array(list(makemtrx(pixel)))\n    plt.imshow(pixel, cmap='gray')\n    plt.xlabel(f'Expression Class: {data[0]}')\n    plt.plot()\n\nfor i in range(7):\n    plt.figure()\n    show(df[df.emotion == i].values[0])","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:25.323844Z","iopub.execute_input":"2022-04-10T04:49:25.325393Z","iopub.status.idle":"2022-04-10T04:49:26.436362Z","shell.execute_reply.started":"2022-04-10T04:49:25.325349Z","shell.execute_reply":"2022-04-10T04:49:26.435598Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"#As the dataset has labels as numbers,Assigning those numbers the emotion category\nclasses = {\n    0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'\n}","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:26.437872Z","iopub.execute_input":"2022-04-10T04:49:26.438342Z","iopub.status.idle":"2022-04-10T04:49:26.443185Z","shell.execute_reply.started":"2022-04-10T04:49:26.438302Z","shell.execute_reply":"2022-04-10T04:49:26.442390Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"# Dataset prep for modeling\ndf_train = pd.concat([df[(df.Usage == 'Training')], df[df.Usage == 'PublicTest']], ignore_index=True).drop(['Usage'], axis=1)\ndf_test = df[df.Usage == 'PrivateTest'].drop(['Usage'], axis=1).reset_index().drop(['index'], 1)\n\n# differentiating between labels and images\ntrain_images = df_train.iloc[:, 1]\ntrain_labels = df_train.iloc[:, 0]\ntest_images = df_test.iloc[:, 1]\ntest_labels = df_test.iloc[:, 0]","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:26.444437Z","iopub.execute_input":"2022-04-10T04:49:26.444821Z","iopub.status.idle":"2022-04-10T04:49:26.498197Z","shell.execute_reply.started":"2022-04-10T04:49:26.444785Z","shell.execute_reply":"2022-04-10T04:49:26.497532Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n  This is separate from the ipykernel package so we can avoid doing imports until\n","output_type":"stream"}]},{"cell_type":"code","source":"# this is for the transformation of images to input into the model\ntrain_trfm = transforms.Compose(\n    [\n        transforms.ToPILImage(),\n        transforms.Grayscale(num_output_channels=1),\n        transforms.RandomCrop(48, padding=4, padding_mode='reflect'),\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        transforms.Normalize((0.5), (0.5), inplace=True)\n    ])\nval_trfm = transforms.Compose(\n    [\n        transforms.ToPILImage(),\n        transforms.Grayscale(num_output_channels=1),\n        transforms.ToTensor(),\n        transforms.Normalize((0.5), (0.5))\n    ])","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:26.500915Z","iopub.execute_input":"2022-04-10T04:49:26.501322Z","iopub.status.idle":"2022-04-10T04:49:26.657777Z","shell.execute_reply.started":"2022-04-10T04:49:26.501286Z","shell.execute_reply":"2022-04-10T04:49:26.657024Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"# Creating class for our dataset \nclass FERDataset(Dataset):\n    \n    def __init__(self, images, labels, transforms):\n        self.X = images\n        self.y = labels\n        self.transforms = transforms\n        \n    def __len__(self):\n        return len(self.X)\n    \n    def __getitem__(self, i):\n        data = [int(m) for m in self.X[i].split(' ')]\n        data = np.asarray(data).astype(np.uint8).reshape(48,48,1)\n        data = self.transforms(data)\n        label = self.y[i]\n        return (data, label)\n    \n# transformed data assignment\ntrain_data = FERDataset(train_images, train_labels, train_trfm)\nval_data = FERDataset(test_images, test_labels, val_trfm)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:26.659180Z","iopub.execute_input":"2022-04-10T04:49:26.659621Z","iopub.status.idle":"2022-04-10T04:49:26.669082Z","shell.execute_reply.started":"2022-04-10T04:49:26.659579Z","shell.execute_reply":"2022-04-10T04:49:26.668380Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"def show(data):\n    img, lbl = data\n    plt.figure()\n    plt.imshow(torch.squeeze(img), cmap='gray')\n    plt.xlabel(f'{classes[lbl]} ({lbl})')\n    plt.plot()\n    \nshow(train_data[8])","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:26.670458Z","iopub.execute_input":"2022-04-10T04:49:26.670871Z","iopub.status.idle":"2022-04-10T04:49:26.858417Z","shell.execute_reply.started":"2022-04-10T04:49:26.670836Z","shell.execute_reply":"2022-04-10T04:49:26.857663Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"random_seed = 42\ntorch.manual_seed(random_seed);","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:26.862031Z","iopub.execute_input":"2022-04-10T04:49:26.862332Z","iopub.status.idle":"2022-04-10T04:49:26.871596Z","shell.execute_reply.started":"2022-04-10T04:49:26.862297Z","shell.execute_reply":"2022-04-10T04:49:26.870798Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"batch_num = 400\n\ntrain_dl = DataLoader(train_data, batch_num, shuffle=True, num_workers=4, pin_memory=True)\nval_dl = DataLoader(val_data, batch_num*2, num_workers=4, pin_memory=True)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:26.875524Z","iopub.execute_input":"2022-04-10T04:49:26.877376Z","iopub.status.idle":"2022-04-10T04:49:26.887174Z","shell.execute_reply.started":"2022-04-10T04:49:26.877339Z","shell.execute_reply":"2022-04-10T04:49:26.886341Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n  cpuset_checked))\n","output_type":"stream"}]},{"cell_type":"code","source":"#Images of the data we are showing\ndef show_batch(dl):\n    for images, labels in dl:\n        fig, ax = plt.subplots(figsize=(12, 6))\n        ax.set_xticks([]); ax.set_yticks([])\n        ax.imshow(make_grid(images, nrow=16).permute(1, 2, 0))\n        break\n        \nshow_batch(train_dl)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:26.891112Z","iopub.execute_input":"2022-04-10T04:49:26.891584Z","iopub.status.idle":"2022-04-10T04:49:33.243294Z","shell.execute_reply.started":"2022-04-10T04:49:26.891550Z","shell.execute_reply":"2022-04-10T04:49:33.242557Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x432 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"def accuracy(outputs, labels):\n    _, preds = torch.max(outputs, dim=1)\n    return torch.tensor(torch.sum(preds==labels).item()/len(preds))\n\nclass FERBase(nn.Module):\n    \n    # batch from training dl\n    def training_step(self, batch):\n        images, labels = batch\n        out = self(images)                     # generates predictions by calling the training model\n        loss = F.cross_entropy(out, labels)    # calculates loss \n        return loss\n    \n    #  batch from validation dl\n    def validation_step(self, batch):\n        images, labels = batch\n        out = self(images)\n        loss = F.cross_entropy(out, labels)\n        acc = accuracy(out, labels)            #  accuracy function to measure the accuracy\n        return {'val_loss': loss.detach(), 'val_acc': acc}\n    \n    def validation_epoch_end(self, outputs):\n        batch_losses = [x['val_loss'] for x in outputs]\n        epoch_loss = torch.stack(batch_losses).mean()    # mean loss of the epoch batch\n        \n        batch_accs = [x['val_acc'] for x in outputs]\n        epoch_acc = torch.stack(batch_accs).mean()       # mean acc of the epoch batch\n        \n        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n    \n    def epoch_end(self, epoch, result):\n        print(\"Epoch [{}], last_lr: {:.5f}, train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n            epoch, result['lrs'][-1], result['train_loss'], result['val_loss'], result['val_acc']))\n        \n        ","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:33.244574Z","iopub.execute_input":"2022-04-10T04:49:33.244803Z","iopub.status.idle":"2022-04-10T04:49:33.259181Z","shell.execute_reply.started":"2022-04-10T04:49:33.244773Z","shell.execute_reply":"2022-04-10T04:49:33.258459Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"def conv_block(in_chnl, out_chnl, pool=False, padding=1):\n    layers = [\n        nn.Conv2d(in_chnl, out_chnl, kernel_size=3, padding=padding),\n        nn.BatchNorm2d(out_chnl),\n        nn.ReLU(inplace=True)]\n    if pool: layers.append(nn.MaxPool2d(2))\n    return nn.Sequential(*layers)\n\nclass FERModel(FERBase):\n    def __init__(self, in_chnls, num_cls):\n        super().__init__()\n        \n        self.conv1 = conv_block(in_chnls, 64, pool=True)           # 64x24x24 \n        self.conv2 = conv_block(64, 128, pool=True)                # 128x12x12\n        self.resnet1 = nn.Sequential(conv_block(128, 128), conv_block(128, 128))    # Resnet layer 1: includes 2 conv2d\n        \n        self.conv3 = conv_block(128, 256, pool=True)       # 256x6x6 \n        self.conv4 = conv_block(256, 512, pool=True)       # 512x3x3\n        self.resnet2 = nn.Sequential(conv_block(512, 512), conv_block(512, 512))    # Resnet layer 2: includes 2 conv2d\n        \n        self.classifier = nn.Sequential(nn.MaxPool2d(3),\n                                        nn.Flatten(),\n                                        nn.Linear(512, num_cls))    # num_cls\n        \n    def forward(self, xb):\n        out = self.conv1(xb)\n        out = self.conv2(out)\n        out = self.resnet1(out) + out\n        \n        out = self.conv3(out)\n        out = self.conv4(out)\n        out = self.resnet2(out) + out\n        \n        return self.classifier(out)\n        \nmodel = FERModel(1, 7)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:33.260799Z","iopub.execute_input":"2022-04-10T04:49:33.261127Z","iopub.status.idle":"2022-04-10T04:49:33.317703Z","shell.execute_reply.started":"2022-04-10T04:49:33.261092Z","shell.execute_reply":"2022-04-10T04:49:33.317053Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"for images, lbl in train_dl:\n    print('shape of image: ', images.shape)\n    out = model(images)\n    print('shape of output: ', out.shape)\n    print('Output: ', out[0])\n    break","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:33.319037Z","iopub.execute_input":"2022-04-10T04:49:33.319291Z","iopub.status.idle":"2022-04-10T04:49:41.535127Z","shell.execute_reply.started":"2022-04-10T04:49:33.319256Z","shell.execute_reply":"2022-04-10T04:49:41.534242Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"shape of image:  torch.Size([400, 1, 48, 48])\nshape of output:  torch.Size([400, 7])\nOutput:  tensor([-1.9475, -0.6722,  2.2308,  0.7485,  1.2213,  0.3419, -0.0255],\n       grad_fn=<SelectBackward0>)\n","output_type":"stream"}]},{"cell_type":"code","source":"def get_default_device():\n    \"\"\"Pick GPU if available, else CPU\"\"\"\n    if torch.cuda.is_available():\n        return torch.device('cuda')\n    else:\n        return torch.device('cpu')\n    \ndef to_device(data, device):\n    \"\"\"Move tensor(s) to chosen device\"\"\"\n    if isinstance(data, (list,tuple)):\n        return [to_device(x, device) for x in data]\n    return data.to(device, non_blocking=True)\n\nclass DeviceDataLoader():\n    \"\"\"Wrap a dataloader to move data to a device\"\"\"\n    def __init__(self, dl, device):\n        self.dl = dl\n        self.device = device\n        \n    def __iter__(self):\n        \"\"\"Yield a batch of data after moving it to device\"\"\"\n        for b in self.dl: \n            yield to_device(b, self.device)\n\n    def __len__(self):\n        \"\"\"Number of batches\"\"\"\n        return len(self.dl)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:41.536655Z","iopub.execute_input":"2022-04-10T04:49:41.537243Z","iopub.status.idle":"2022-04-10T04:49:41.549904Z","shell.execute_reply.started":"2022-04-10T04:49:41.537201Z","shell.execute_reply":"2022-04-10T04:49:41.547552Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"device = get_default_device()\ndevice","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:41.552186Z","iopub.execute_input":"2022-04-10T04:49:41.552744Z","iopub.status.idle":"2022-04-10T04:49:41.787099Z","shell.execute_reply.started":"2022-04-10T04:49:41.552701Z","shell.execute_reply":"2022-04-10T04:49:41.786360Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"device(type='cuda')"},"metadata":{}}]},{"cell_type":"code","source":"train_dl = DeviceDataLoader(train_dl, device)\nval_dl = DeviceDataLoader(val_dl, device)\nto_device(model, device);","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:41.788434Z","iopub.execute_input":"2022-04-10T04:49:41.788713Z","iopub.status.idle":"2022-04-10T04:49:41.803620Z","shell.execute_reply.started":"2022-04-10T04:49:41.788676Z","shell.execute_reply":"2022-04-10T04:49:41.802954Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"@torch.no_grad()    # this is for stopping the model from keeping track ofold parameters\ndef evaluate(model, val_loader):\n    # This function will evaluate the model and give back the val acc and loss\n    model.eval()\n    outputs = [model.validation_step(batch) for batch in val_loader]\n    return model.validation_epoch_end(outputs)\n\n# getting the current learning rate\ndef get_lr(optimizer):\n    for param_group in optimizer.param_groups:\n        return param_group['lr']\n\n# this fit function follows the intuition of 1cycle lr\ndef fit(epochs, max_lr, model, train_loader=train_dl, val_loader=val_dl, weight_decay=0, grad_clip=None, opt_func=torch.optim.Adam):\n    torch.cuda.empty_cache()\n    history = []    #keep track of the evaluation results\n    \n    # setting upcustom optimizer including weight decay\n    optimizer = opt_func(model.parameters(), max_lr, weight_decay=weight_decay)\n    # setting up 1cycle lr scheduler\n    sched = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr, epochs=epochs, steps_per_epoch=len(train_loader))\n    \n    for epoch in range(epochs):\n        # training\n        model.train()\n        train_losses = []\n        lrs = []\n        for batch in train_loader:\n            loss = model.training_step(batch)\n            train_losses.append(loss)\n            loss.backward()\n            \n            # gradient clipping\n            if grad_clip:\n                nn.utils.clip_grad_value_(model.parameters(), grad_clip)\n                \n            optimizer.step()\n            optimizer.zero_grad()\n            \n            # record the lr\n            lrs.append(get_lr(optimizer))\n            sched.step()\n            \n        #validation\n        result = evaluate(model, val_loader)\n        result['train_loss'] = torch.stack(train_losses).mean().item()\n        result['lrs'] = lrs\n        model.epoch_end(epoch, result)\n        history.append(result)\n    return history","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:41.804826Z","iopub.execute_input":"2022-04-10T04:49:41.805195Z","iopub.status.idle":"2022-04-10T04:49:41.816930Z","shell.execute_reply.started":"2022-04-10T04:49:41.805158Z","shell.execute_reply":"2022-04-10T04:49:41.816127Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"model = to_device(FERModel(1, 7), device)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:41.818215Z","iopub.execute_input":"2022-04-10T04:49:41.818482Z","iopub.status.idle":"2022-04-10T04:49:41.886120Z","shell.execute_reply.started":"2022-04-10T04:49:41.818435Z","shell.execute_reply":"2022-04-10T04:49:41.885471Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"evaluate(model, val_dl)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:41.887636Z","iopub.execute_input":"2022-04-10T04:49:41.887919Z","iopub.status.idle":"2022-04-10T04:49:45.273865Z","shell.execute_reply.started":"2022-04-10T04:49:41.887881Z","shell.execute_reply":"2022-04-10T04:49:45.273112Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"{'val_loss': 1.9597982168197632, 'val_acc': 0.043947942554950714}"},"metadata":{}}]},{"cell_type":"code","source":"max_lr = 0.001\ngrad_clip = 0.1\nweight_decay = 1e-4","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:45.277985Z","iopub.execute_input":"2022-04-10T04:49:45.278199Z","iopub.status.idle":"2022-04-10T04:49:45.282578Z","shell.execute_reply.started":"2022-04-10T04:49:45.278174Z","shell.execute_reply":"2022-04-10T04:49:45.281278Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"history = fit(32, max_lr, model, weight_decay=weight_decay, grad_clip=grad_clip)\n","metadata":{"execution":{"iopub.status.busy":"2022-04-10T04:49:45.284289Z","iopub.execute_input":"2022-04-10T04:49:45.284879Z","iopub.status.idle":"2022-04-10T05:10:11.898937Z","shell.execute_reply.started":"2022-04-10T04:49:45.284840Z","shell.execute_reply":"2022-04-10T05:10:11.898037Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"Epoch [0], last_lr: 0.00006, train_loss: 1.7253, val_loss: 1.5308, val_acc: 0.4167\nEpoch [1], last_lr: 0.00014, train_loss: 1.4541, val_loss: 1.3900, val_acc: 0.4712\nEpoch [2], last_lr: 0.00025, train_loss: 1.3322, val_loss: 1.3691, val_acc: 0.4909\nEpoch [3], last_lr: 0.00039, train_loss: 1.2532, val_loss: 1.2299, val_acc: 0.5357\nEpoch [4], last_lr: 0.00055, train_loss: 1.1873, val_loss: 1.1759, val_acc: 0.5584\nEpoch [5], last_lr: 0.00070, train_loss: 1.1371, val_loss: 1.2252, val_acc: 0.5419\nEpoch [6], last_lr: 0.00084, train_loss: 1.1035, val_loss: 1.1270, val_acc: 0.5826\nEpoch [7], last_lr: 0.00094, train_loss: 1.0573, val_loss: 1.2116, val_acc: 0.5589\nEpoch [8], last_lr: 0.00099, train_loss: 1.0296, val_loss: 1.0415, val_acc: 0.6078\nEpoch [9], last_lr: 0.00100, train_loss: 0.9998, val_loss: 1.1828, val_acc: 0.5655\nEpoch [10], last_lr: 0.00099, train_loss: 0.9678, val_loss: 1.0442, val_acc: 0.6153\nEpoch [11], last_lr: 0.00097, train_loss: 0.9358, val_loss: 1.1995, val_acc: 0.5540\nEpoch [12], last_lr: 0.00094, train_loss: 0.9131, val_loss: 1.1755, val_acc: 0.5655\nEpoch [13], last_lr: 0.00091, train_loss: 0.8840, val_loss: 1.0400, val_acc: 0.6224\nEpoch [14], last_lr: 0.00086, train_loss: 0.8555, val_loss: 1.1262, val_acc: 0.5826\nEpoch [15], last_lr: 0.00081, train_loss: 0.8326, val_loss: 0.9813, val_acc: 0.6434\nEpoch [16], last_lr: 0.00075, train_loss: 0.8105, val_loss: 0.9788, val_acc: 0.6421\nEpoch [17], last_lr: 0.00069, train_loss: 0.7767, val_loss: 1.0483, val_acc: 0.6333\nEpoch [18], last_lr: 0.00062, train_loss: 0.7523, val_loss: 0.9664, val_acc: 0.6562\nEpoch [19], last_lr: 0.00056, train_loss: 0.7259, val_loss: 1.0566, val_acc: 0.6224\nEpoch [20], last_lr: 0.00049, train_loss: 0.6938, val_loss: 1.0389, val_acc: 0.6426\nEpoch [21], last_lr: 0.00042, train_loss: 0.6649, val_loss: 0.9635, val_acc: 0.6676\nEpoch [22], last_lr: 0.00035, train_loss: 0.6287, val_loss: 0.9584, val_acc: 0.6776\nEpoch [23], last_lr: 0.00028, train_loss: 0.5913, val_loss: 0.9604, val_acc: 0.6700\nEpoch [24], last_lr: 0.00022, train_loss: 0.5566, val_loss: 0.9511, val_acc: 0.6808\nEpoch [25], last_lr: 0.00017, train_loss: 0.5213, val_loss: 0.9686, val_acc: 0.6738\nEpoch [26], last_lr: 0.00012, train_loss: 0.4893, val_loss: 0.9671, val_acc: 0.6906\nEpoch [27], last_lr: 0.00008, train_loss: 0.4491, val_loss: 0.9559, val_acc: 0.6918\nEpoch [28], last_lr: 0.00004, train_loss: 0.4229, val_loss: 0.9491, val_acc: 0.6927\nEpoch [29], last_lr: 0.00002, train_loss: 0.4019, val_loss: 0.9523, val_acc: 0.7002\nEpoch [30], last_lr: 0.00000, train_loss: 0.3891, val_loss: 0.9534, val_acc: 0.7002\nEpoch [31], last_lr: 0.00000, train_loss: 0.3828, val_loss: 0.9552, val_acc: 0.7032\n","output_type":"stream"}]},{"cell_type":"code","source":"def plot_losses(history):\n    train_losses = [x.get('train_loss') for x in history]\n    val_losses = [x['val_loss'] for x in history]\n    plt.plot(train_losses, '-bx')\n    plt.plot(val_losses, '-rx')\n    plt.xlabel('epoch')\n    plt.ylabel('loss')\n    plt.legend(['Training', 'Validation'])\n    plt.title('Loss vs. No. of epochs');\n    \ndef plot_lrs(history):\n    lrs = np.concatenate([x.get('lrs', []) for x in history])\n    plt.plot(lrs)\n    plt.xlabel('Batch no.')\n    plt.ylabel('Learning rate')\n    plt.title('Learning Rate vs. Batch no.');\n    \nplot_losses(history)\nplt.figure()\nplot_lrs(history)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T05:10:11.901835Z","iopub.execute_input":"2022-04-10T05:10:11.902232Z","iopub.status.idle":"2022-04-10T05:10:12.284999Z","shell.execute_reply.started":"2022-04-10T05:10:11.902195Z","shell.execute_reply":"2022-04-10T05:10:12.284326Z"},"trusted":true},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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wTdhzf7nG7948fsNtGgczUXDOrgdStA5qVcrpl45nJzCEi55cT4799mZiglPllDMUdu2p5g5GXlMHN6RxjGhMz19fTqmayKvXjGMbbuLufSleRRY95cJQ5ZQzFF7c95mAC4d0cnlSILbMV0TefmKNLbsKuLSl+az60Cp2yEZ41eWUMxRKSmrYNqCLZyamkK75o3cDifoHdstiZcnDWNj/gEufWk+e4osqZjwYQnFHJVPl+ewu6iMSSM7ux1KyDiuexIvXp7G+p37ueLVhbYCpAkbllDMEVNVpv60ie6tmjKyW6Lb4YSUE3omM+WSIazYVsh1b9gd9SY8WEIxR2zp1j2s2FbIpJGdGvyNjEfi1NQUHj5/AD9k5XPbu0tt7i8T8uySHHPE3p6/hSYxkZw7pL3boYSsC4a2Z09RKQ9+lklCoxX8/dz+lpxNyLKEYo7IvpIyPl2ew/hBbWkaa79GR+Oa47uyp6iMf3+TRfPGMdw9trfbIRlzROwvgTkiny7Pobiswm5k9JM/ntaT3UWlPPvtelLiY7niuC5uh2TMYbOEYo7Iuwu30qNVUwZ3aO52KGFBRHhgfD927DvI/Z9m0LZ5I07r29rtsIw5LAEdlBeRsSKyRkSyRGRyNa/Hisi7zuvzRaSz12v3OPvXiMjpddUpIqNFZLGILBWRH0SkeyDb1pCtzdvH0q17uHhYB+vv96PICOGpCYMZ0C6Bm6ctYenWPW6HZMxhCVhCEZFIYAowDkgFJopIapViVwO7VbU78ATwsHNsKjAB6AuMBZ4Rkcg66nwWuFRVBwFvA/8XqLY1dO8u3Ep0pHDu4HZuhxJ2GsVE8tKkYSTHx3LN1IVs3VXkdkjG+CyQZyjDgSxV3aCqpcA0YHyVMuOBqc72+8Bo8XzlHQ9MU9WDqroRyHLqq61OBZo52wnA9gC1q0ErLa/koyXbGNMnhcSmsW6HE5aS42N59YrhlFUok15dYHfTm5ARyITSDtjq9Tzb2VdtGVUtBwqBxFqOra3Oa4CZIpINXAY8VF1QInKtiKSLSPrOnTuPoFkN25eZeew6UGqD8QHWvVVTXrhsKNm7irnWbnw0ISKcbmy8DThDVdsDrwKPV1dIVV9Q1TRVTUtOTq7XAMPBh4uzSWkWywk97GcXaMd0TeTRCwewYOMu/vyflajajY8muAUyoWwDvL/Gtnf2VVtGRKLwdFUV1HJstftFJBkYqKrznf3vAsf6pxnmkF0HSvl2zU7GD2pHZIQNxteH8YPa8YdTujM9PZtXftzkdjjG1CqQCWUh0ENEuohIDJ5B9hlVyswAJjnbFwBfq+dr2AxggnMVWBegB7Cgljp3Awki0tOp61QgM4Bta5A+W76d8krlnEE2GF+fbhvTk9P7pvC3zzL4bq1105rgFbCE4oyJ3ATMwvPHfbqqrhKRB0TkbKfYy0CiiGQBtwOTnWNXAdOBDOAL4EZVraipTmf/74APRGQZnjGUOwPVtobqoyXb6JUST5828W6H0qBERAiPXzSIninx3PT2Ytbv3O92SMZUSxpyv2xaWpqmp6e7HUZI2FJQxAmPfsPdY3tzw0nd3A6nQcreXcT4f/9IQqNoPvr9cSQ0jnY7JNNAicgiVU2ruj+cBuVNAP1nqWf4a/ygti5H0nC1b9GY5y4bytbdRdz0zmLKKyrdDsmYX7CEYuqkqvxnyTZGdG1JW1uV0VXDOrfkwXP68f26fB6ZtcbtcIz5BUsopk7LswvZkH/ABuODxMXDOnLZiE68MHcDny3PcTscY/7LEoqp00dLthETGcG4/m3cDsU4/nxmKkM6NufO95exLm+f2+EYA1hCMXWoqFQ+XZ7DKb1bkdDIBoGDRUxUBM9cOpTGMZFc98Yi9pWUuR2SMZZQTO3mbywgf/9BzrbB+KDTOiGOf18yhM27irjjvWV2J71xnSUUU6vPlufQKDqSk3u1cjsUU40RXRO5Z1xvZq3K49nv1rsdjmngLKGYGpVXVPLFylxG92lFo5hIt8MxNbh6VBfOHNCGx2at4Yd1+W6HYxowSyimRvM37qLgQClnDrDB+GAmIjx8/gC6t2rKH95ZzPY9xW6HZBooSyimRp8uz6FxTCQnWXdX0GsSG8Vzvx1KaXklN729mDK76dG4wBKKqVZ5RSWzVuUyuk8KcdHW3RUKuiY35R/nD2Dxlj08Zjc9GhdYQjHVmrdhF7sOlPIbu/ckpJw9sC2XHtOR5+du4KvMPLfDMQ2MJRRTrc9WbKdJTCQn9bKFtELNn89MJbVNM/743jK22XiKqUeWUMyvlDlXd41Jte6uUBQXHcmUS4dQXqH8wcZTTD2yhGJ+Zd6GAnYXlVl3VwjrktSEh87vz+Ite3jUxlNMPbGEYn7ls+U5NI2N4oSe1t0Vys4c0Pa/k0h+mWHjKSbwLKGYX/jf1V2trLsrDPzpN33o29YznpK9u8jtcEyYs4RifiF98252F5Uxtm9rt0MxfhAXHcmUS4ZQUancMm2pLcplAsoSivmF2avyiImKsO6uMNI5qQl/O7cfizbv5umvs9wOx4QxSyjmv1SV2Rm5HN89iSaxUW6HY/xo/KB2nDekHU9/vY4FG3e5HY4JU5ZQzH+tzt1H9u5iTk1NcTsUEwAPjO9Hx5aNuXXaEgqLbP0U438+JRQRGSUiVzrbySLSJbBhGTfMXpWHCIzuYwklHDWNjeJfEwazY99B7vloua2fYvyuzoQiIvcCdwP3OLuigTcDGZRxx5zMXIZ0bEFyfKzboZgAGdihOX88rRczV+Ty7sKtbodjwowvZyjnAmcDBwBUdTsQH8igTP3btqeYldv2cpp1d4W9607oynHdE7n/kwyydux3OxwTRnxJKKXqOTdWABFpEtiQjBvmrMoF4DS7XDjsRUQIj180iLjoCG5+ZwkHyyvcDsmECV8SynQReR5oLiK/A74EXgpsWKa+zcnMo3urpnRJsu8LDUFKszgevWAgGTl7efhzm5rF+EedCUVVHwPeBz4AegF/UdWnAh2YqT+FRWXM27DLursamDGpKUwa2YlXftzIN6t3uB2OCQO+DMo/rKpzVPVOVb1DVeeIyMP1EZypH9+s2UFFpdrlwg3QPWf0oXfreO58fxn5+w+6HY4Jcb50eZ1azb5x/g7EuGd2Ri6t4mMZ2L6526GYehYXHcmTEwaxt7icyR+ssEuJzVGpMaGIyA0isgLoJSLLvR4bgeX1F6IJpJKyCr5ds5NTU1OIiBC3wzEu6N26GXeN7cWXmXl2KbE5KrWdobwNnAXMcP499Biqqr/1pXIRGSsia0QkS0QmV/N6rIi867w+X0Q6e712j7N/jYicXled4vE3EVkrIpkicrMvMTZ0P68voKi0wrq7GrirjuvCcd0TeeDTDDblH3A7HBOiakwoqlqoqptUdaKqbgaK8Vw63FREOtZVsYhEAlPwdI+lAhNFJLVKsauB3araHXgCeNg5NhWYAPQFxgLPiEhkHXVeAXQAeqtqH2CaLz+Ahm52Ri5NY6MY2S3R7VCMiyIihMcuHEhUhHDruzYrsTkyvgzKnyUi64CNwHfAJuBzH+oeDmSp6gZVLcXzB358lTLjganO9vvAaBERZ/80VT2oqhuBLKe+2uq8AXhAVSsBVNUuW6lDZaUyJ2MHJ/ZKJjbK1j5p6NokNOLBc/uzdOsepnyz3u1wTAjyZVD+QWAEsFZVuwCjgXk+HNcO8O6QzXb2VVtGVcuBQiCxlmNrq7MbcLGIpIvI5yLSo7qgRORap0z6zp07fWhG+FqydQ/5+w/a5cLmv84e2JZzBrXlqa/XsWTLbrfDMSHGl4RSpqoFQISIRKjqN0BagOM6ErFAiaqmAS8Cr1RXSFVfUNU0VU1LTm7Ya37MzsglOlI4uXcrt0MxQeT+8f1IiY/l9unLKCotdzscE0J8SSh7RKQpMBd4S0T+hTOvVx224RnTOKS9s6/aMiISBSQABbUcW1ud2cCHzvZHwAAfYmzQ5mTkMaJrIs3iot0OxQSRhEbR/POiQWwqOMCDn2W6HY4JIb4klPFAEXAb8AWwHs/VXnVZCPQQkS4iEoNnkH1GlTIzgEnO9gXA1868YTOACc5VYF2AHsCCOur8D3Cys30isNaHGBusrB372bDzgHV3mWqN7JbItcd35e35W/gqM8/tcEyIqDWhOFdVfaqqlaparqpTVfUppwusVs6YyE3ALCATmK6qq0TkARE52yn2MpAoIlnA7cBk59hVwHQgA08Su1FVK2qq06nrIeB8596ZfwDXHMbPocGZk+H5IzHGEoqpwe2n9aRPm2bc/cFyu4ve+ETqujNWRL4CzlPVwvoJqf6kpaVpenq622G44txnfqSiUplx0yi3QzFBbE3uPs769w+c0COJFy9Pw3MRpmnoRGSRM179C750ee0HVojIyyLy1KGH/0M09WXH3hKWbt1j3V2mTr1ax3P32N58mbmDaXYXvalDlA9lPuR/g90mDHyZuQNVODXV1j4xdbvy2M58s3oHD3ySwciuiXS2JQ5MDepMKKo6ta4yJrTMzsilU2JjeqY0dTsUEwIiIoRHLxzA6U/M5bbpS3nvupFERfrSuWEaGvutaGD2Hyznp6wCTu2TYv3hxmeH7qJfsmUPz35rd9Gb6llCaWC+W7OT0opKW+rXHLazB7bl7IFt+ddX61iRHXbX6Bg/sITSwMzJyKVlkxiGdmrhdigmBP11fD+SmsZy67tLKCmztejNL/kyOeQnIjKjyuMNEblFROLqI0jjH2UVlXy1egeje7ci0tY+MUcgoXE0j104kPU7D/DQ56vdDscEGV/OUDbguXT4ReexF9gH9HSemxAxf8Mu9pWUW3eXOSqjeiRxxbGdee2nTXy/rmFPsGp+yZeEcqyqXqKqnziP3wLDVPVGYEiA4zN+NCcjl7joCEZ1T3I7FBPiJo/rTbfkJtzx3jL2FJW6HY4JEr4klF8sqOVsH7re1H6TQoSqMicjjxN6JNMoxtY+MUcnLjqSJy8eTMH+Uv788aq6DzANgi8J5Y/ADyLyjYh8C3wP3CEiTfjf4lgmyK3avpfthSW21K/xm/7tE7h1TA8+Wbadj5dWnUjcNES+3Ng401msqreza42qljjbTwYqMONfs1flEiEwuo8lFOM/15/Yja9X7+DP/1nJsM4tadu8kdshGRf5etnwUDzruw8ELhKRywMXkgmE2Rl5DOvckpZNYtwOxYSRqMgIHr9oEOWVyp3vL6OysvbJZk148+Wy4TeAx4BRwDDnEYwrNpoabCkoYnXuPuvuMgHROakJfz4zlR+zCnjtp01uh2Nc5MvkkGlAqtY1z70JWrMzcgE4zSaDNAEyYVgHvszI46EvVnN8jyR6pMS7HZJxgS9dXisB+0sUwuZk5NG7dTwdExu7HYoJUyLCQ+cPoGlsFLe+u5TS8kq3QzIu8CWhJAEZIjLL+275QAdm/GPXgVIWbtpla5+YgEuOj+Uf5/Vn1fa9PPXVOrfDMS7wpcvrvkAHYQLnq8w8KhW7O97Ui9P7tuaitPY8820WJ/dOZminlm6HZOqRL5cNf1cfgZjAmJORR9uEOPq2beZ2KKaB+MtZffl5QwG3vbuMz285niaxvnxvNeGgxi4vEfnB+XefiOz1euwTkb31F6I5UsWlFcxdt5NTU23tE1N/msZG8fhFg9i6u4gHP8twOxxTj2pMKKo6yvk3XlWbeT3iVdW+7oaAH7LyKSmrtKV+Tb0b1rkl15/YjXcWbOXLjDy3wzH1xKcbG0UkUkTaikjHQ49AB2aO3uxVucTHRXFMV+vHNvXvtjE96dOmGZM/XE7+/oNuh2PqgS83Nv4ByAPmAJ85j08DHJc5SuUVlXyZmccpvVsRbet/GxfEREXw5MWD2Ftczj0frsBuZQt/vvyluQXopap9VbW/8xgQ6MDM0Vm0eTe7i8o43a7uMi7q1Tqeu8b2Yk5GHu+lZ7sdjgkwXxLKVsAWkA4xs1blERMVwYk9k90OxTRwVx3XhZFdE7n/k1VsKShyOxwTQL6u2PitiNwjIrcfegQ6MHPkVJXZGbkc3z3JLtk0rouIEB67aCARItw+fSkVNoFk2PIloWzBM34SA8R7PUyQyszZR/buYk7ra3fHm+DQrnkjHjinL+mbd/P83PVuh2MCpNavryISCfRU1UvrKR7jB7OctU/G2NonJoicM6gdX2bs4Ik5azmxZzJ92ya4HZLxs1rPUFS1AugkIraIRgiZnZFHWqeWJDaNdTsUY/5LRHjwnH60aBzDbe8upaSswu2QjJ/5Oobyo4j82cZQgt/WXUVk5uy17i4TlFo0ieHRCweyNm8/j81a43Y4xs98SSjr8dx3EoGNoQS9Wats7RMT3E7smczlIzvx0g8b+Skr3+1wjB/VmVBU9f7qHr5ULiJjRWSNiGSJyORqXo8VkXed1+eLSGev1+5x9q8RkdMPo86nRGS/L/GFo9m29okJAfeM60PXpCbc8d4yCovL3A7H+Ikvd8oni8ijIjJTRL4+9PDhuEhgCjAOSAUmikhqlWJXA7tVtTvwBPCwc2wqMAHPOvZjgWec6V9qrVNE0oAWdbY6TBXsP0j6pl02Vb0Jeo1iInni4kHk7TvIfTNWuR2O8RNfurzeAlYDXYD7gU3AQh+OGw5kqeoGVS0FpgHjq5QZD0x1tt8HRotnWtzxwDRVPaiqG4Esp74a63SSzaPAXT7EFpa+ytxBpcLpNn5iQsDADs25+ZQefLRkG58u3+52OMYPfEkoiar6MlCmqt+p6lXAKT4c1w7PXfaHZDv7qi2jquV47shPrOXY2uq8CZihqjm1BSUi14pIuoik79y504dmhI7ZGbm0a96I1DY2GbQJDTee3I2BHZrzp49WkltY4nY45ij5klAOdXDmiMhvRGQwEFTT14pIW+BC4Om6yqrqC6qapqppycnhMy3JgYPlzF2Xz2l9be0TEzqiIiN44qKBlJZXcuf7y2wCyRDnS0J5UEQSgD8CdwAvAbf5cNw2oIPX8/bOvmrLiEgUkAAU1HJsTfsHA92BLBHZBDQWkSwfYgwbc9fupLS80iaDNCGna3JT/vSbPny/Lp835m12OxxzFHy5yutTVS1U1ZWqerKqDlXVGT7UvRDoISJdnBsjJwBVj5sBTHK2LwC+Vs9XlBnABOcqsC5AD2BBTXWq6meq2lpVO6tqZ6DIGehvMGZn5NGicTRpnRrsNQkmhF16TEdO6pXM32dmkrWjwV6kGfJ8ucqrp4h8JSIrnecDROT/6jrOGRO5CZgFZALTVXWViDwgImc7xV4GEp2ziduByc6xq4DpQAbwBXCjqlbUVOfhNTn8lJZX8lVmHqP7pBBla5+YECQiPHL+AOKiI7l9+lLKKirdDskcAamrz1JEvgPuBJ5X1cHOvpWq2q8e4guotLQ0TU9PdzuMo/btmh1c8epCXp6Uxmibv8uEsM9X5HDDW4u5eXQPbj+1p9vhmBqIyCJVTau635evs41VdUGVfeX+Ccv4w+crcomPjWJUjyS3QzHmqIzr34bzhrRjyjdZLNmy2+1wzGHyJaHki0g3QAFE5AKg1ktzTf0pq6hkVkYuY1JTiI2KdDscY47afWf3pXWzOG6fvoyiUvvuGkp8SSg3As8DvUVkG3ArcH0ggzK+m7ehgD1FZYzrZ1d3mfDQLC6af140kE0FB/j7zEy3wzGHwZervDao6hggGeitqqOAcwMemfHJzBW5NImJ5ARb6teEkRFdE/nd8V15c94Wvlmzw+1wjI98viRIVQ+o6j7nqU1fHwTKKyqZvSqXU/qkEBdt3V0mvPzxtJ70bh3Pne8tZ+e+g26HY3xwpNeY2q3YQWDBxl0UHCjlDOvuMmEoNiqSf00YzL6SMm6fvpRKW4s+6B1pQrFPNgjMXJlDo+hITurVyu1QjAmIXq3jufesvny/Lp/n525wOxxThxrXlBeRfVSfOARoFLCIjE8qKpUvVuZxSu9WNIqx7i4TviYO78CPWfk8NnsNw7u0ZKjNBhG0ajxDUdV4VW1WzSNeVWtMRKZ+pG/aRf7+g4zrb91dJryJCP84vz9tEuK4+Z0ltiBXELN5OkLUzBU5xEZFcLJ1d5kGoFlcNE9PHEze3hImf7DcZiUOUpZQQlBFpfL5ylxO7JlMk1g7WTQNw+COLbjz9F58vjKXt+ZvcTscUw1LKCFo/sYCduw7yFkD27odijH16nfHd+XEnsk88GkGmTl73Q7HVGEJJQR9smw7jWMiGWMTQZoGJiJC+OdFA2neKJqb3l5sU7MEGUsoIaa0vJKZK3I5NTXFru4yDVJS01ievHgQG/IPcO/HDX71iqBiCSXEfL9uJ4XFZYwfZN1dpuE6tnsSN53cnfcWZfP+omy3wzEOSygh5uOl22neOJpR3W3uLtOw3TK6ByO7JvKnj1bYeEqQsIQSQopKy5mTkce4fm2IibKPzjRsUZERPDVxMAmNornhzUXsLbH7U9xmf5VCyJeZOyguq7DuLmMcyfGxTLl0CFt3F3PXe3Z/itssoYSQGUu307pZHMM7t3Q7FGOCxrDOLZk8tjdfrMrl5R82uh1Og2YJJUTsKSrlu7U7OGtgGyIibLJnY7xdc3wXxvZtzT8+X83CTbvcDqfBsoQSImauyKWsQjl7YDu3QzEm6IgIj1w4gA4tGnHjW4tt/RSXWEIJER8szqZHq6b0a9fM7VCMCUrN4qJ55tKhFBaXccu0JVTY+in1zhJKCNiwcz+LNu/mgqHtEbHuLmNqktq2GQ+e04+f1hfwyKzVbofT4NjMgiHgg8XZRAicO9i6u4ypy4VpHVi6dQ/Pf7eB1DbNGD/I/t/UFztDCXIVlcqHi7dxYs9kWjWLczscY0LCvWf1ZVjnFtz9wXJWbit0O5wGwxJKkPtpfT45hSVcMLSD26EYEzJioiJ45tKhtGgcw3VvLKJgvw3S1wdLKEHu/UXZNIuLYnQfW0jLmMORHB/LC5elkb//IL9/azFlFZVuhxT2LKEEsb0lZXyxMpezB7UlLtpmFjbmcPVvn8BD5/dn/sZdPPhphtvhhD0blA9iny7L4WB5JecPae92KMaErHMHt2fVtr289MNG+rZN4KJh1n0cKHaGEqRUlbfmb6Z363gGdWjudjjGhLTJ43ozqnsS//eflSzYaHfSB0pAE4qIjBWRNSKSJSKTq3k9VkTedV6fLyKdvV67x9m/RkROr6tOEXnL2b9SRF4RkehAti3QlmcXsmr7Xi49pqPde2LMUYqKjODflwymfYtGXPdGOpvyD7gdUlgKWEIRkUhgCjAOSAUmikhqlWJXA7tVtTvwBPCwc2wqMAHoC4wFnhGRyDrqfAvoDfQHGgHXBKpt9eHt+VtoHBPJOXbviTF+0bxxDK9cMQyAq15bSGGRTXfvb4E8QxkOZKnqBlUtBaYB46uUGQ9MdbbfB0aL5+v4eGCaqh5U1Y1AllNfjXWq6kx1AAuAkB142FtSxoxl2zl7YFvi40L6RMuYoNI5qQnPX5ZG9u5irnszndJyu/LLnwKZUNoBW72eZzv7qi2jquVAIZBYy7F11ul0dV0GfFFdUCJyrYiki0j6zp07D7NJ9eM/S7ZRXFbBpcd0cjsUY8LO8C4tefiC/szbsIs/fbTC1lDxo3AclH8GmKuq31f3oqq+oKppqpqWnBx8y+iqKm/N28KA9gn0b5/gdjjGhKVzB7fn5tE9eG9RNs9+t97tcMJGIBPKNsD7+rz2zr5qy4hIFJAAFNRybK11isi9QDJwu19a4IL0zbtZk7ePS4Z3dDsUY8LabWN6cPbAtjzyxRo+Xlr1T5M5EoFMKAuBHiLSRURi8Ayyz6hSZgYwydm+APjaGQOZAUxwrgLrAvTAMy5SY50icg1wOjBRVUO2Y/Tl7zfSvHG0TWhnTICJCI9cMIDhXVpyx3vL+H5dcHaBh5KAJRRnTOQmYBaQCUxX1VUi8oCInO0UexlIFJEsPGcVk51jVwHTgQw8YyE3qmpFTXU6dT0HpAA/i8hSEflLoNoWKFsKipiVkcslwzvSKMbujDcm0OKiI3nx8jS6JTfl+jcWsSLbJpI8GtKQB6TS0tI0PT3d7TD+6/5PVvHmvM38cPcppNjMwsbUm7y9JZz3zE+UlFXw/g3H0iWpidshBTURWaSqaVX3h+OgfEjaW1LG9IVbOXNAW0smxtSzlGZxvHH1cBS4/JX57NhX4nZIIckSSpCYvnArB0oruHpUF7dDMaZB6prclFeuGEbB/lImvbKQwmK78fFwWUIJAqXllbzyw0aGd2lJv3Z2qbAxbhnUoTnP/nYoWTv2ceWrC9h/sNztkEKKJZQg8OHibLYXlnDjyd3dDsWYBu/Ensk8PXEwy7ILufq1hRSXVrgdUsiwhOKy8opKnvl2PQPbJ3BCjyS3wzHGAGP7teHxiwayYNMurn0jnZIySyq+sITiso+XbmfLriJuOqWHzSpsTBAZP6gdj5w/gO/X5XPjW4tt3i8fWEJxUUWlMuXbLPq0acYYW+LXmKBzYVoHHjynH1+t3sHN7yyxZYTrYAnFRR8t2caGnQe4+ZTudnZiTJD67YhO/OXMVL5YlcsNby6y7q9aWEJxSUlZBY/PXsPA9gmM7dfa7XCMMbW4alQX/npOP77M3MHvXk+3gfoaWEJxyes/b2J7YQl3j+ttZyfGhIDLRnTikQsG8ENWPlfYJcXVsoTigsKiMqZ8s56TeiVzbDe7ssuYUHFRWgeevHgQ6Zt3c9nL823VxyosobjgiS/XsrekjLtO7+12KMaYwzR+UDumXDKEldsKufD5n9i+p9jtkIKGJZR6tnJbIa//vInfHtOJ1LbN3A7HGHMExvZrzdQrh5OzxzOp5JrcfW6HFBQsodSjykrlzx+vpGWTGO44rZfb4RhjjsKx3ZN497qRVKpywXM/MW9Dgdshuc4SSj16a8EWlmzZw/87ow8JjaPdDscYc5RS2zbjw98fS0qzOC5/eUGDX/nREko92Zh/gL9/lsnxPZI4d7CtxmhMuGjfojHvXz+SQR2ac8u0pTw6azWVlQ1znSlLKPWgvKKSP05fSnSk8OgFA+0yYWPCTPPGMbx5zTFMGNaBKd+s59o3FrGvpOFdAWYJpR78c85aFm/Zw1/P6UfrBFs8y5hwFBMVwT/O68/9Z/flmzU7OO+Zn9hccMDtsOqVJZQA+2JlDs9+u55LjunI+EHW1WVMOBMRJh3bmTeuGs7O/Qc58+kf+GJljtth1RtLKAG0PHsPf5y+jEEdmnPvWaluh2OMqSfHdk/ik5tG0TW5Kde/uZh7P17JwfLwn67FEkqAZO3YzxWvLqRFkxiev2wosVGRbodkjKlHHVo25r3rRnLNqC5M/Xkz5z/7Exvzw7sLzBJKAGRs38vEF+cRIfDm1ceQ0szGTYxpiGKiIvi/M1N56fI0tu4qZty/5vLajxvD9iowSyh+9vXqPC5+/meiI4R3fjeCzklN3A7JGOOyMakpzLr1BEZ0TeS+TzK45KV5bN1V5HZYfmcJxU8Ki8u49+OVXPVaOu1bNuaD3x9Lj5R4t8MyxgSJ1glxvHrFMB4+vz8rt+3l9Cfn8vIPGykPo0W7RDU8T718kZaWpunp6Ud8vKqydVcxHy7JZupPm9hTXMakkZ2ZPK43cdE2ZmKMqV727iL+9NFKvlu7k96t4/nrOf0Y1rml22H5TEQWqWrar/ZbQjn8hPLwF6v5eX0BOYXF5O09iAic2DOZO0/vRd+2CQGI1BgTblSVWatyeeCTDLYXlnDekHbccVov2jZv5HZodaopoUS5EUyoq6xU4uOi6JqUxMAOzTmldys6tGzsdljGmBAiIozt14YTeibz9NdZvPz9Rj5dnsPlIzpx48ndadEkxu0QD5udoRxFl5cxxvhL9u4invxyHR8uzqZJTBRXHNeZScd2JqlprNuh/Yp1eVXDEooxJtiszdvH47PXMisjl5jICC4Y2p7fHd81qK4YtYRSDUsoxphgtX7nfl76fgMfLNpGaUUlo7oncfGwDpzWN8X1G6UtoVTDEooxJtjt2FfCO/O3Mj19K9v2FNOicTRj+7VhbL/WjOyaSExU/d/94UpCEZGxwL+ASOAlVX2oyuuxwOvAUKAAuFhVNzmv3QNcDVQAN6vqrNrqFJEuwDQgEVgEXKaqpbXFZwnFGBMqKiuVH9fnMz09m68z8zhQWkF8XBQn9kxmRNdERnRNpFtyk3pZHqPeE4qIRAJrgVOBbGAhMFFVM7zK/B4YoKrXi8gE4FxVvVhEUoF3gOFAW+BLoKdzWLV1ish04ENVnSYizwHLVPXZ2mK0hGKMCUUlZRX8mJXPFytzmbtuJ3l7DwKQ2CSG1LbN6N06nl6tm9GxZWPaJMTRqlmsX7vJ3LhseDiQpaobnACmAeOBDK8y44H7nO33gX+LJ72OB6ap6kFgo4hkOfVRXZ0ikgmcAlzilJnq1FtrQjHGmFAUFx3J6D4pjO6TgqqyqaCI+RsKSN+8m9W5e5n682ZKy395B358bBRxMZE0io4kOlJ49YrhdEz07+0OgUwo7YCtXs+zgWNqKqOq5SJSiKfLqh0wr8qxhxYTqa7ORGCPqpZXU/4XRORa4FqAjh07Hl6LjDEmyIgIXZKa0CWpCROGe/6mlVdUsqmgiO17isktLCGnsITC4jKKyyooKaugtLwyIGMvDe7GRlV9AXgBPF1eLodjjDF+FxUZQfdWTeneqmm9vm8gLw/YBnTwet7e2VdtGRGJAhLwDM7XdGxN+wuA5k4dNb2XMcaYAApkQlkI9BCRLiISA0wAZlQpMwOY5GxfAHytnqsEZgATRCTWuXqrB7CgpjqdY75x6sCp8+MAts0YY0wVAevycsZEbgJm4bnE9xVVXSUiDwDpqjoDeBl4wxl034UnQeCUm45nAL8cuFFVKwCqq9N5y7uBaSLyILDEqdsYY0w9sRsb7bJhY4w5LDVdNmwLbBljjPELSyjGGGP8whKKMcYYv7CEYowxxi8a9KC8iOwENh/h4UlAvh/DCWbW1vDTUNoJ1tZA6KSqyVV3NuiEcjREJL26qxzCkbU1/DSUdoK1tT5Zl5cxxhi/sIRijDHGLyyhHLkX3A6gHllbw09DaSdYW+uNjaEYY4zxCztDMcYY4xeWUIwxxviFJZQjICJjRWSNiGSJyGS34zlaIrJJRFaIyFIRSXf2tRSROSKyzvm3hbNfROQpp+3LRWSIu9HXTkReEZEdIrLSa99ht01EJjnl14nIpOrey201tPU+EdnmfLZLReQMr9fucdq6RkRO99of1L/fItJBRL4RkQwRWSUitzj7w+5zraWtwfm5qqo9DuOBZ9r89UBXIAZYBqS6HddRtmkTkFRl3yPAZGd7MvCws30G8DkgwAhgvtvx19G2E4AhwMojbRvQEtjg/NvC2W7hdtt8bOt9wB3VlE11fndjgS7O73RkKPx+A22AIc52PLDWaU/Yfa61tDUoP1c7Qzl8w4EsVd2gqqXANGC8yzEFwnhgqrM9FTjHa//r6jEPz0qZbVyIzyeqOhfPWjveDrdtpwNzVHWXqu4G5gBjAx78YaqhrTUZD0xT1YOquhHIwvO7HfS/36qao6qLne19QCbQjjD8XGtpa01c/VwtoRy+dsBWr+fZ1P4BhwIFZovIIhG51tmXoqo5znYukOJsh0P7D7dtod7mm5yunlcOdQMRJm0Vkc7AYGA+Yf65VmkrBOHnagnFAIxS1SHAOOBGETnB+0X1nEuH5fXl4dw2x7NAN2AQkAP809Vo/EhEmgIfALeq6l7v18Ltc62mrUH5uVpCOXzbgA5ez9s7+0KWqm5z/t0BfITn9DjvUFeW8+8Op3g4tP9w2xaybVbVPFWtUNVK4EU8ny2EeFtFJBrPH9i3VPVDZ3dYfq7VtTVYP1dLKIdvIdBDRLqISAwwAZjhckxHTESaiEj8oW3gNGAlnjYduuplEvCxsz0DuNy5cmYEUOjVzRAqDrdts4DTRKSF07VwmrMv6FUZ3zoXz2cLnrZOEJFYEekC9AAWEAK/3yIiwMtApqo+7vVS2H2uNbU1aD9Xt69iCMUHnqtG1uK5auJPbsdzlG3piueKj2XAqkPtARKBr4B1wJdAS2e/AFOctq8A0txuQx3tewdPl0AZnn7jq4+kbcBVeAY4s4Ar3W7XYbT1Dacty/H8AWnjVf5PTlvXAOO89gf17zcwCk931nJgqfM4Ixw/11raGpSfq029Yowxxi+sy8sYY4xfWEIxxhjjF5ZQjDHG+IUlFGOMMX5hCcUYY4xfWEIx5iiISIUz2+syEVksIsfWUb65iPzeh3q/FZE0/0VqTOBZQjHm6BSr6iBVHQjcA/yjjvLNgToTijGhyBKKMf7TDNgNnrmXROQr56xlhYgcmtn1IaCbc1bzqFP2bqfMMhF5yKu+C0VkgYisFZHjq76ZiJzknMm8LyKrReQt585qRGS0iCxx6n1FRGID23RjIMrtAIwJcY1EZCkQh2ftilOc/SXAuaq6V0SSgHkiMgPPOh39VHUQgIiMwzON+DGqWiQiLb3qjlLV4c7iSfcCY6p5/8FAX2A78CNwnHgWSXsNGK2qa0XkdeAG4En/NduYX7MzFGOOzqEur9541tJ43TlLEODvIrIczzQg7fjfdOrexgCvqmoRgKp6r2dyaNLDRUDnGt5/gapmq2eSwKVOuV7ARlVd65SZimfxLWMCys5QjPETVf3ZORtJxjNvUjIwVFXLRGQTnrOYw3HQ+beCmv+vHvTarq2cMQFnZyjG+ImI9Maz1GoBkADscJLJyUAnp9g+PEu5HjIHuFJEGjt1eHd5Hak1QGcR6e48vwz4zg/1GlMr+zZjzNE5NIYCnm6uSapaISJvAZ+IyAogHVgNoKoFIvKjiKwEPlfVO0VkEJAuIqXATOD/HU1AqloiIlcC74lIFJ6py58DEJGXgOdUNf1o3sOY6thsw8YYY/zCuryMMcb4hSUUY4wxfmEJxRhjjF9YQjHGGOMXllCMMcb4hSUUY4wxfmEJxRhjjF/8f24i9JsDdKxNAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"##To further experiment with real time detection of emotions , created this model to run the python script on our local\ntorch.save(model.state_dict(), 'emotion_detection.pth')\n","metadata":{"execution":{"iopub.status.busy":"2022-04-10T05:10:12.286389Z","iopub.execute_input":"2022-04-10T05:10:12.286682Z","iopub.status.idle":"2022-04-10T05:10:12.347532Z","shell.execute_reply.started":"2022-04-10T05:10:12.286645Z","shell.execute_reply":"2022-04-10T05:10:12.346801Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Testing our Model on a sample  Fear image downloaded from the internet","metadata":{}},{"cell_type":"code","source":"#function to turn photos to tensor\ndef img2tensor(x):\n    transform = transforms.Compose(\n            [transforms.ToPILImage(),\n            transforms.Grayscale(num_output_channels=1),\n            transforms.ToTensor(),\n            transforms.Normalize((0.5), (0.5))])\n    return transform(x)\n\n# the model for predicting\nmodel = FERModel(1, 7)\nsoftmax = torch.nn.Softmax(dim=1)\nmodel.load_state_dict(torch.load('emotion_detection.pth', map_location=get_default_device()))\n\ndef predict(x):\n    out = model(img2tensor(img)[None])\n    scaled = softmax(out)\n    prob = torch.max(scaled).item()\n    label = classes[torch.argmax(scaled).item()]\n    return {'label': label, 'probability': prob}\n","metadata":{"execution":{"iopub.status.busy":"2022-04-10T05:11:10.647568Z","iopub.execute_input":"2022-04-10T05:11:10.648083Z","iopub.status.idle":"2022-04-10T05:11:10.741679Z","shell.execute_reply.started":"2022-04-10T05:11:10.648043Z","shell.execute_reply":"2022-04-10T05:11:10.741023Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"import cv2\n# Load an color image in grayscale\nimg = cv2.imread('../input/sample-image/5b9b44d02200005700d8d086.jpeg',0)\nresized = cv2.resize(img, (48, 48))   ","metadata":{"execution":{"iopub.status.busy":"2022-04-10T05:10:12.435959Z","iopub.execute_input":"2022-04-10T05:10:12.436513Z","iopub.status.idle":"2022-04-10T05:10:12.773619Z","shell.execute_reply.started":"2022-04-10T05:10:12.436459Z","shell.execute_reply":"2022-04-10T05:10:12.772895Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"# predict the mood\nimg = img2tensor(resized)\nprediction = predict(img)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T05:10:12.786552Z","iopub.execute_input":"2022-04-10T05:10:12.787047Z","iopub.status.idle":"2022-04-10T05:10:12.808465Z","shell.execute_reply.started":"2022-04-10T05:10:12.786975Z","shell.execute_reply":"2022-04-10T05:10:12.807821Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"prediction","metadata":{"execution":{"iopub.status.busy":"2022-04-10T05:10:12.809774Z","iopub.execute_input":"2022-04-10T05:10:12.810164Z","iopub.status.idle":"2022-04-10T05:10:12.815319Z","shell.execute_reply.started":"2022-04-10T05:10:12.810128Z","shell.execute_reply":"2022-04-10T05:10:12.814690Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"{'label': 'Fear', 'probability': 0.36630359292030334}"},"metadata":{}}]},{"cell_type":"code","source":"##The model has correctly predicted the image to be fear","metadata":{"execution":{"iopub.status.busy":"2022-04-10T07:12:35.497411Z","iopub.execute_input":"2022-04-10T07:12:35.498142Z","iopub.status.idle":"2022-04-10T07:12:35.501950Z","shell.execute_reply.started":"2022-04-10T07:12:35.498103Z","shell.execute_reply":"2022-04-10T07:12:35.501095Z"},"trusted":true},"execution_count":67,"outputs":[]},{"cell_type":"code","source":"prediction['probability']","metadata":{"execution":{"iopub.status.busy":"2022-04-10T05:54:30.872051Z","iopub.execute_input":"2022-04-10T05:54:30.872756Z","iopub.status.idle":"2022-04-10T05:54:30.878340Z","shell.execute_reply.started":"2022-04-10T05:54:30.872715Z","shell.execute_reply":"2022-04-10T05:54:30.877555Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"0.36630359292030334"},"metadata":{}}]},{"cell_type":"markdown","source":"## Facial expresions coupled with data from social media to predict the depression markers\n## We have used twitter data for the current project","metadata":{}},{"cell_type":"code","source":"! pip install text2emotion","metadata":{"execution":{"iopub.status.busy":"2022-04-10T05:10:12.827624Z","iopub.execute_input":"2022-04-10T05:10:12.828006Z","iopub.status.idle":"2022-04-10T05:10:20.826763Z","shell.execute_reply.started":"2022-04-10T05:10:12.827957Z","shell.execute_reply":"2022-04-10T05:10:20.825683Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stdout","text":"Collecting text2emotion\n  Downloading text2emotion-0.0.5-py3-none-any.whl (57 kB)\n     |████████████████████████████████| 57 kB 816 kB/s            \n\u001b[?25hRequirement already satisfied: emoji>=0.6.0 in /opt/conda/lib/python3.7/site-packages (from text2emotion) (1.7.0)\nRequirement already satisfied: nltk in /opt/conda/lib/python3.7/site-packages (from text2emotion) (3.2.4)\nRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from nltk->text2emotion) (1.16.0)\nInstalling collected packages: text2emotion\nSuccessfully installed text2emotion-0.0.5\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"import text2emotion as te\n#text_tweet = input()\ntext_tweet='I enjoy controlled loneliness. I like wandering around the city alone. I’m not afraid of coming back to an empty flat and lying down in an empty bed. I’m afraid of having no one to miss, of having no one to love'\nres = te.get_emotion(text_tweet)\n\nprint(res)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T06:14:10.650448Z","iopub.execute_input":"2022-04-10T06:14:10.650960Z","iopub.status.idle":"2022-04-10T06:14:10.859518Z","shell.execute_reply.started":"2022-04-10T06:14:10.650923Z","shell.execute_reply":"2022-04-10T06:14:10.858786Z"},"trusted":true},"execution_count":61,"outputs":[{"name":"stdout","text":"{'Happy': 0.22, 'Angry': 0.0, 'Surprise': 0.11, 'Sad': 0.44, 'Fear': 0.22}\n","output_type":"stream"}]},{"cell_type":"code","source":"type(res)","metadata":{"execution":{"iopub.status.busy":"2022-04-10T05:42:12.361957Z","iopub.execute_input":"2022-04-10T05:42:12.362572Z","iopub.status.idle":"2022-04-10T05:42:12.367204Z","shell.execute_reply.started":"2022-04-10T05:42:12.362497Z","shell.execute_reply":"2022-04-10T05:42:12.366547Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"dict"},"metadata":{}}]},{"cell_type":"markdown","source":"# Recommendation system\n","metadata":{}},{"cell_type":"markdown","source":"## From the facial expressions we are able to identify Fear as a significant emotion and from the tweets of that person we have identified her to be lonely /sad currently and generic recommendtaions to treat and overcome the emotional baggage early","metadata":{}},{"cell_type":"code","source":"\nif len(predict(img)) !=0 or len(res)!=0:\n    if res['Fear'] or res['Sad'] or res['Angry'] and Labels[label]=='Fear' or Labels[label]=='Angry' or Labels[label]=='Sad' or Labels[label]=='Disgust':\n        final_vector = res['Fear'] +res['Sad']+res['Angry']+ prediction['probability']\n        if final_vector >0.8:\n            if prediction['label']=='Fear':\n                print('Hi,Fear keeps us focused on the past or worried about the future. If we can acknowledge our fear, we can realize that right now we are okay. Right now, today, we are still alive, and our bodies are working marvelously. Our eyes can still see the beautiful sky. Our ears can still hear the voices of our loved ones.')\n                print('--------------')\n                print('Our Tailormade Recommendations to make the best of your day')\n                print('--------------')\n                print('Practice Mindulness' + ' ' \"https://www.mindful.org/mindfulness-how-to-do-it/\")\n                print('30 to 60 minutes of aerobic activity '+ ' ' \"https://30dayfitness.app/\")\n                print('Ensure proper sleep of 8 hours for atleast a week'+ ' '\"https://www.sleepfoundation.org/sleep-diary\")\n                print('Attend a meetup in your city' + \"  \"\"https://www.meetup.com/cities/in/hyderabad/outdoors-adventure/\")\n                print('checkout this happiness project blog' + '' \"https://gretchenrubin.com/2012/02/every-wednesday-is-tip-day-this-wednesday-four-tips-for-writing-your-personal-commandments-im-doing-a-happiness-pro/\")\n            if prediction['label']=='Angry':\n                print('“Holding on to anger is like grasping a hot coal with the intent of throwing it at someone else; you are the one who gets burned.”)\n                print('--------------')\n                print('Our Tailormade Recommendations to make the best of your day')\n                print('--------------')\n                print('start a gratitude journal' + \"https://greatergood.berkeley.edu/article/item/tips_for_keeping_a_gratitude_journal\")\n                print('Identify your anger triggers' +' '+\"https://www.verywellmind.com/anger-management-strategies-4178870\")\n                print('30 to 60 minutes of aerobic activity '+ ' ' \"https://30dayfitness.app/\")\n                print('Hangout with a friend '  +'https://www.stumagz.com/in/10-corners-to-hang-out-to-have-an-amusing-day-in-hyderabad-/')\n            \n        \n        \n        \n        ","metadata":{"execution":{"iopub.status.busy":"2022-04-10T07:06:33.532645Z","iopub.execute_input":"2022-04-10T07:06:33.533109Z","iopub.status.idle":"2022-04-10T07:06:33.559261Z","shell.execute_reply.started":"2022-04-10T07:06:33.533072Z","shell.execute_reply":"2022-04-10T07:06:33.558528Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stdout","text":"Hi,Fear keeps us focused on the past or worried about the future. If we can acknowledge our fear, we can realize that right now we are okay. Right now, today, we are still alive, and our bodies are working marvelously. Our eyes can still see the beautiful sky. Our ears can still hear the voices of our loved ones.\n--------------\nOur Tailormade Recommendations to make the best of your day\n--------------\nPractice Mindulness https://www.mindful.org/mindfulness-how-to-do-it/\n30 to 60 minutes of aerobic activity  https://30dayfitness.app/\nEnsure proper sleep of 8 hours for atleast a week https://www.sleepfoundation.org/sleep-diary\nAttend a meetup in your city  https://www.meetup.com/cities/in/hyderabad/outdoors-adventure/\ncheckout this happiness project bloghttps://gretchenrubin.com/2012/02/every-wednesday-is-tip-day-this-wednesday-four-tips-for-writing-your-personal-commandments-im-doing-a-happiness-pro/\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}