Step 6 - Inferencing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "efc76dd8-7477-4a9e-a1f0-f3b871dda2bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torchvision import models\n",
    "import torchvision.models.efficientnet as e\n",
    "import torchvision.models.resnet as r\n",
    "import torch.nn as nn\n",
    "\n",
    "WEIGHTS = r.resnet50(pretrained= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a11a0968-1441-4c70-a739-3e305182b037",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use GPUs if available\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26445fcb-96b4-42f5-80eb-e6ba48671307",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = WEIGHTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "634c8c91-56be-46d9-b7db-2f14c8116026",
   "metadata": {},
   "outputs": [],
   "source": [
    "WEIGHTS.fc = nn.Linear(2048, 82)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c375f32c-ade3-4554-81ff-01f0269254dd",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(\"./models/xfer-learning-10eps.pkl\", map_location=torch.device('cpu'))) # cpu\n",
    "# model.load_state_dict(torch.load(\"./models/xfer-learning-10eps.pkl\")) # gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59c96a1c-4296-4451-96c8-27cb620ddafc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=82, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "688d6b4b-6d22-4f97-a46d-77d636196dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=82, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "206ace63-a448-43d2-8bba-fcf32d319c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77761a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ['adho-mukha-svanasana',\n",
    " 'adho-mukha-vriksasana',\n",
    " 'adho-mukha-vrksasana',\n",
    " 'ananda-balasana',\n",
    " 'anjaneyasana',\n",
    " 'ardha-chandrasana',\n",
    " 'ardha-matsyendrasana',\n",
    " 'ardha-pincha-mayurasana',\n",
    " 'ardha-uttanasana',\n",
    " 'astavakrasana',\n",
    " 'baddha-konasana',\n",
    " 'bakasana',\n",
    " 'balasana',\n",
    " 'bharadvajasana-i',\n",
    " 'bhujangasana',\n",
    " 'bhujapidasana',\n",
    " 'bitilasana',\n",
    " 'camatkarasana',\n",
    " 'chakravakasana',\n",
    " 'chaturanga-dandasana',\n",
    " 'dandasana',\n",
    " 'dhanurasana',\n",
    " 'dwi-pada-viparita-dandasana',\n",
    " 'eka-pada-koundinyanasana-i',\n",
    " 'eka-pada-koundinyanasana-ii',\n",
    " 'eka-pada-rajakapotasana',\n",
    " 'eka-pada-rajakapotasana-ii',\n",
    " 'garudasana',\n",
    " 'gomukhasana',\n",
    " 'halasana',\n",
    " 'kapotasana',\n",
    " 'makarasana',\n",
    " 'malasana',\n",
    " 'marjaryasana',\n",
    " 'matsyasana',\n",
    " 'mayurasana',\n",
    " 'natarajasana',\n",
    " 'padmasana',\n",
    " 'paripurna-navasana',\n",
    " 'parivrtta-trikonasana',\n",
    " 'parsva-bakasana',\n",
    " 'pasasana',\n",
    " 'paschimottanasana',\n",
    " 'phalakasana',\n",
    " 'prasarita-padottanasana',\n",
    " 'purvottanasana',\n",
    " 'salabhasana',\n",
    " 'salamba-bhujangasana',\n",
    " 'salamba-sarvangasana',\n",
    " 'salamba-sirsasana',\n",
    " 'savasana',\n",
    " 'setu-bandha-sarvangasana',\n",
    " 'sukhasana',\n",
    " 'supta-baddha-konasana',\n",
    " 'supta-matsyendrasana',\n",
    " 'supta-padangusthasana',\n",
    " 'supta-virasana',\n",
    " 'tadasana',\n",
    " 'tittibhasana',\n",
    " 'tolasana',\n",
    " 'upavistha-konasana',\n",
    " 'urdhva-dhanurasana',\n",
    " 'urdhva-mukha-svanasana',\n",
    " 'urdhva-prasarita-eka-padasana',\n",
    " 'ustrasana',\n",
    " 'utkatasana',\n",
    " 'uttana-shishosana',\n",
    " 'uttanasana',\n",
    " 'utthita-hasta-padangustasana',\n",
    " 'utthita-hasta-padangusthasana',\n",
    " 'utthita-parsvakonasana',\n",
    " 'utthita-trikonasana',\n",
    " 'vajrasana',\n",
    " 'vasisthasana',\n",
    " 'viparita-karani',\n",
    " 'virabhadrasana-i',\n",
    " 'virabhadrasana-ii',\n",
    " 'virabhadrasana-iii',\n",
    " 'virasana',\n",
    " 'vriksasana',\n",
    " 'vrischikasana',\n",
    " 'vrksasana']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a0afc70-4048-4413-9410-a3ca26c58771",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-06-26 02:51:18--  https://cdn.yogajournal.com/wp-content/uploads/2021/11/Chair-Pose_Andrew-Clark_2400x1350.jpg\n",
      "Resolving cdn.yogajournal.com (cdn.yogajournal.com)... 2620:12a:8001::1, 2620:12a:8000::1, 23.185.0.1\n",
      "Connecting to cdn.yogajournal.com (cdn.yogajournal.com)|2620:12a:8001::1|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 122229 (119K) [image/jpeg]\n",
      "Saving to: ‘utkatasana.jpg’\n",
      "\n",
      "utkatasana.jpg      100%[===================>] 119.36K   327KB/s    in 0.4s    \n",
      "\n",
      "2023-06-26 02:51:19 (327 KB/s) - ‘utkatasana.jpg’ saved [122229/122229]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O utkatasana.jpg https://cdn.yogajournal.com/wp-content/uploads/2021/11/Chair-Pose_Andrew-Clark_2400x1350.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d794142f-a23e-4dd4-a955-7c41b21cb83d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_prediction(filepath):\n",
    "    img = Image.open(filepath)\n",
    "    transformer = torchvision.transforms.Compose([\n",
    "        transforms.Resize(size = (224, 224)),\n",
    "        transforms.ToTensor(),\n",
    "    ])\n",
    "    img_normalized = transformer(img).float()\n",
    "    img_normalized = img_normalized.unsqueeze_(0)\n",
    "    img_normalized = img_normalized.to(device)\n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        output = model(img_normalized)\n",
    "        print(output)\n",
    "        index = output.data.cpu().numpy().argmax()\n",
    "        print(index)\n",
    "        class_name = classes[index]\n",
    "        print(class_name)\n",
    "    # model.predict(\"inference.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "af4bf532-efe2-4218-a255-ce2984562fb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 2.0362e+01,  8.7758e+00,  2.5990e+01, -2.9330e+00,  3.2544e+01,\n",
      "          1.8206e+01,  1.8363e+01,  1.8943e+01,  1.2351e+01, -2.7124e+00,\n",
      "          1.3963e+01,  8.9649e+00,  7.9687e+00, -2.6835e+00,  1.5603e+01,\n",
      "         -1.1861e+01,  1.4418e+01,  2.7999e+00,  1.4389e+01,  1.8544e+00,\n",
      "          2.2805e+01,  7.8263e+00, -5.6727e+00,  2.3732e+01, -1.2077e+00,\n",
      "          4.0473e+00,  1.7596e+01, -6.5749e+00,  9.2365e+00,  2.4551e-01,\n",
      "          9.5675e+00,  9.4257e-01,  1.8222e+01,  1.2343e+01,  7.3041e+00,\n",
      "          4.2306e+00,  1.4498e+01,  2.6587e+01,  2.5676e-03,  1.6729e+01,\n",
      "          1.0110e+01,  1.2438e+01, -6.7664e-01,  1.3709e+01,  5.0021e+00,\n",
      "          1.0081e+01,  2.0208e+01,  7.7553e+00,  8.7585e+00,  2.1111e+01,\n",
      "          6.9942e+00, -1.3129e+01,  1.4525e+00,  1.1531e+01,  1.6870e+01,\n",
      "          1.9985e+01, -1.5103e+00,  2.1000e+01,  7.9652e+00, -9.2480e+00,\n",
      "          9.9091e+00,  9.4411e+00,  1.1737e+01,  3.7601e+00,  1.4518e+01,\n",
      "          4.8273e+01,  9.6724e+00,  2.7491e+00,  1.5818e+01,  1.9703e+01,\n",
      "          1.8779e+01,  2.2124e+01, -1.9151e+01,  1.5957e+01,  1.9937e+00,\n",
      "          3.4927e+01,  1.8460e+01,  2.0069e+01,  8.2630e+00,  1.2758e+01,\n",
      "          9.3417e+00,  4.2592e+00]])\n",
      "65\n",
      "utkatasana\n"
     ]
    }
   ],
   "source": [
    "make_prediction(\"utkatasana.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a4b9b93-f760-4b00-9826-3dfacc2dfa00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-06-26 02:52:20--  https://www.gaia.com/wp-content/uploads/article-migration-image-1920x1080_TheLungePose-768x432.jpg\n",
      "Resolving www.gaia.com (www.gaia.com)... 2600:9000:20be:ce00:1a:585:f0c0:93a1, 2600:9000:20be:1000:1a:585:f0c0:93a1, 2600:9000:20be:7a00:1a:585:f0c0:93a1, ...\n",
      "Connecting to www.gaia.com (www.gaia.com)|2600:9000:20be:ce00:1a:585:f0c0:93a1|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 14771 (14K) [image/jpeg]\n",
      "Saving to: ‘anjaneyasana.jpg’\n",
      "\n",
      "anjaneyasana.jpg    100%[===================>]  14.42K  --.-KB/s    in 0s      \n",
      "\n",
      "2023-06-26 02:52:20 (112 MB/s) - ‘anjaneyasana.jpg’ saved [14771/14771]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O anjaneyasana.jpg https://www.gaia.com/wp-content/uploads/article-migration-image-1920x1080_TheLungePose-768x432.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "06e753ec-21eb-4e8f-9524-4c27fa382062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.5236e+01,  8.0259e+00,  2.6459e+01, -1.0775e+00,  3.5886e+01,\n",
      "          2.4685e+01,  1.8597e+01,  1.6425e+01,  1.4232e+01,  4.6247e+00,\n",
      "          1.7175e+01,  6.5085e+00,  1.0635e+01,  8.7915e+00,  7.5874e+00,\n",
      "         -7.5887e+00,  9.0901e+00,  7.4321e+00,  8.1009e+00,  3.2302e+00,\n",
      "          2.6351e+01,  6.1425e+00, -7.9081e+00,  2.4856e+01, -4.5391e+00,\n",
      "          4.2609e+00,  2.1660e+01,  1.4044e+01,  2.0342e+01, -3.0697e+00,\n",
      "          3.7611e+00,  2.2480e+00,  1.7758e+01,  6.2440e-03, -3.6745e+00,\n",
      "         -8.7582e-01,  1.8399e+01,  3.0638e+01,  7.8047e-01,  1.1023e+01,\n",
      "          1.1027e+01,  6.5704e+00, -4.2489e+00,  6.4812e+00, -7.4802e+00,\n",
      "          1.2971e+00,  2.0726e+01,  3.4673e+00,  1.5981e+01,  1.5659e+01,\n",
      "         -4.0335e-01, -1.9818e+01,  8.0764e+00,  7.8856e+00,  1.8976e+01,\n",
      "          1.1928e+01, -4.4164e+00,  2.8538e+01,  6.5533e+00,  9.2675e-01,\n",
      "          4.2739e+00,  2.0787e+00,  1.1945e+01,  1.0361e+01,  7.4317e+00,\n",
      "          4.9208e+01,  1.2048e+01,  8.6380e+00,  2.0614e+01,  2.1010e+01,\n",
      "          2.2473e+01,  1.9639e+01, -2.5295e+01,  2.1117e+01,  7.1009e+00,\n",
      "          3.2069e+01,  1.8446e+01,  2.4533e+01,  4.6254e+00,  2.4976e+01,\n",
      "          1.1859e+01,  1.0438e+01]])\n",
      "65\n",
      "utkatasana\n"
     ]
    }
   ],
   "source": [
    "make_prediction(\"anjaneyasana.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "70b18f14-d2b6-4ecb-b067-f810ef3dbaad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-06-26 02:52:31--  https://www.fitsri.com/wp-content/uploads/2020/10/firefly-pose-tittibhasana.jpg\n",
      "Resolving www.fitsri.com (www.fitsri.com)... 2606:4700:3032::6815:2495, 2606:4700:3036::ac43:c31f, 104.21.36.149, ...\n",
      "Connecting to www.fitsri.com (www.fitsri.com)|2606:4700:3032::6815:2495|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 49481 (48K) [image/jpeg]\n",
      "Saving to: ‘tittibhasana.jpg’\n",
      "\n",
      "tittibhasana.jpg    100%[===================>]  48.32K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2023-06-26 02:52:33 (2.13 MB/s) - ‘tittibhasana.jpg’ saved [49481/49481]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O tittibhasana.jpg https://www.fitsri.com/wp-content/uploads/2020/10/firefly-pose-tittibhasana.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e900ad36-1d63-4c6f-a9e9-389d596c9f23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 8.2112e+00,  8.7616e+00,  1.5270e+01, -6.1950e-01,  1.9923e+01,\n",
      "          6.2621e+00,  9.0548e+00,  1.4827e+01,  2.4554e+01,  3.2438e+01,\n",
      "          6.1196e-01,  2.1270e+01,  1.9677e+01, -9.8215e+00, -9.2576e-03,\n",
      "          2.5986e+01,  1.1885e+01, -1.2349e+00,  2.8453e+00,  1.6129e+00,\n",
      "          2.2684e+01,  1.8447e+01, -1.1348e+01,  2.6245e+01,  2.0871e-01,\n",
      "          1.2638e+01,  2.7487e+01,  1.7719e+01,  6.8021e-01,  8.6236e+00,\n",
      "          1.0883e+01, -1.9406e+00,  2.6463e+01,  1.6720e+01,  4.5426e+00,\n",
      "          3.1014e+00,  1.8195e+01,  1.4181e+01,  3.6028e+00,  1.7946e+01,\n",
      "          3.1038e+01,  8.4566e+00,  8.2092e+00,  1.6080e+01,  6.5669e+00,\n",
      "          4.2980e+00,  2.8039e+01,  5.3482e+00, -1.2073e+00,  1.6287e+01,\n",
      "         -3.2971e+00, -7.9597e+00,  2.8037e+00,  7.3924e+00,  1.6023e+01,\n",
      "          1.0417e+01,  1.3744e+01,  1.8815e+01,  2.3062e+01, -3.4037e+00,\n",
      "          1.3689e+01,  8.0551e+00,  4.6605e+00,  2.8781e+00,  6.9166e+00,\n",
      "          2.6657e+01,  1.1837e+01,  2.9188e+01,  2.8453e+01,  1.9541e+01,\n",
      "          1.6931e+01,  1.2968e+01, -2.3817e+01,  1.4389e+01,  1.1253e+00,\n",
      "          1.2541e+01, -1.0766e+00,  8.8193e+00,  1.0214e+01,  7.7332e+00,\n",
      "          1.9533e+01,  6.9553e+00]])\n",
      "9\n",
      "astavakrasana\n"
     ]
    }
   ],
   "source": [
    "make_prediction(\"tittibhasana.jpg\")"
   ]
  }
 ],
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