pytorch_classify resnet18.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import torchvision.models as models\n",
    "import pickle, gzip, math, torch, matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from torch import tensor\n",
    "import numpy as np\n",
    "from torch_lr_finder import LRFinder\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixiedust database opened successfully\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <div style=\"margin:10px\">\n",
       "            <a href=\"https://github.com/ibm-watson-data-lab/pixiedust\" target=\"_new\">\n",
       "                <img src=\"https://github.com/ibm-watson-data-lab/pixiedust/raw/master/docs/_static/pd_icon32.png\" style=\"float:left;margin-right:10px\"/>\n",
       "            </a>\n",
       "            <span>Pixiedust version 1.1.18</span>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pixiedust\n",
    "import torch\n",
    "from torch import nn\n",
    "import pathlib\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformtrain= transforms.Compose([\n",
    "    transforms.Resize((124,124)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),              #convert the value to tensor\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                 std=[0.229, 0.224, 0.225])         # convert all the value form -1 to 1 for all RGB\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformvalid= transforms.Compose([\n",
    "    transforms.Resize((124,124)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    #convert the value to tensor\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                 std=[0.229, 0.224, 0.225])         # convert all the value form -1 to 1 for all RGB\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.resnet18(pretrained=True).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "traindata=datasets.ImageFolder('seg_train/seg_train' , transform=transformtrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "valdata=datasets.ImageFolder('seg_test/seg_test', transform=transformvalid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainloader=DataLoader(traindata, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    " #next(iter(trainloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "valloader=DataLoader(valdata,batch_size=64,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(valloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes=['buildings','forest','glacier','mountain','sea','street']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "noclass=len(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for param in model.parameters():\n",
    "    param.requires_grad=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for param in model.parameters():\n",
    "#     print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "      (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): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fc=nn.Linear(model.fc.in_features,noclass).to(device)\n",
    "criterion=nn.CrossEntropyLoss()\n",
    "optimizer=torch.optim.Adam(model.parameters(),lr=0.000001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ab176a2e76f4f5d970aae896536e4b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping early, the loss has diverged\n",
      "Learning rate search finished. See the graph with {finder_name}.plot()\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_finder = LRFinder(model, optimizer, criterion, device=\"cuda\")\n",
    "lr_finder.range_test(trainloader, end_lr=150, num_iter=100)\n",
    "lr_finder.plot() # to inspect the loss-learning rate graph\n",
    "lr_finder.reset() # to reset the model and optimizer to their initial state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# optimizer=torch.optim.Adam(model.parameters(),lr=0.00001)\n",
    "# lr_finder = LRFinder(model, optimizer, criterion, device=\"cuda\")\n",
    "# lr_finder.range_test(trainloader, val_loader=valloader, end_lr=100, num_iter=100, step_mode=\"linear\")\n",
    "# lr_finder.plot(log_lr=False)\n",
    "# lr_finder.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer=torch.optim.Adam(model.parameters(),lr=0.0005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer.zero_grad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(out, yb): \n",
    "    return (torch.argmax(out, dim=1)==yb).float().mean()\n",
    "#%%pixie_debugger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(epochs, model, loss_func, opt, train_dl, valid_dl):\n",
    "    tloss=[]\n",
    "    vloss=[]\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        # Handle batchnorm / dropout\n",
    "        model.train()\n",
    "        tot_train=0\n",
    "#         print(model.training)\n",
    "        for xb,yb in train_dl:\n",
    "            xb, yb = xb.to(device), yb.to(device)\n",
    "            loss = loss_func(model(xb), yb)\n",
    "            tot_train+=loss\n",
    "            loss.backward()\n",
    "            opt.step()\n",
    "            opt.zero_grad()\n",
    "        nt=len(train_dl)\n",
    "        \n",
    "        \n",
    "        model.eval()\n",
    "#         print(model.training)\n",
    "        with torch.no_grad():\n",
    "            tot_loss,tot_acc = 0.,0.\n",
    "            for xb,yb in valid_dl:\n",
    "                xb, yb = xb.to(device), yb.to(device)\n",
    "                pred = model(xb)\n",
    "                tot_loss += loss_func(pred, yb)\n",
    "                tot_acc  += accuracy (pred,yb)\n",
    "        nv = len(valid_dl)\n",
    "        tloss.append(tot_train/nt)\n",
    "        vloss.append(tot_loss/nv)\n",
    "        print(epoch,tot_train/nt, tot_loss/nv, tot_acc/nv)\n",
    "    return tloss, vloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(0.8280, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.4978, device='cuda:0') tensor(0.8432, device='cuda:0')\n",
      "1 tensor(0.4557, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.4229, device='cuda:0') tensor(0.8604, device='cuda:0')\n",
      "2 tensor(0.4056, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.4007, device='cuda:0') tensor(0.8605, device='cuda:0')\n",
      "3 tensor(0.3756, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.3811, device='cuda:0') tensor(0.8699, device='cuda:0')\n",
      "4 tensor(0.3537, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.3577, device='cuda:0') tensor(0.8774, device='cuda:0')\n",
      "5 tensor(0.3474, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.3652, device='cuda:0') tensor(0.8703, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "ltrain,lval = fit(6, model, criterion, optimizer, trainloader, valloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor(0.8280, device='cuda:0', grad_fn=<DivBackward0>),\n",
       " tensor(0.4557, device='cuda:0', grad_fn=<DivBackward0>),\n",
       " tensor(0.4056, device='cuda:0', grad_fn=<DivBackward0>),\n",
       " tensor(0.3756, device='cuda:0', grad_fn=<DivBackward0>),\n",
       " tensor(0.3537, device='cuda:0', grad_fn=<DivBackward0>),\n",
       " tensor(0.3474, device='cuda:0', grad_fn=<DivBackward0>)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ltrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'C:/Users/Iconsense/Documents/virat/abhi'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-9a297b706ba4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"C:/Users/Iconsense/Documents/virat/abhi\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\abhishek\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[0;32m    325\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 327\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    328\u001b[0m         \u001b[0m_legacy_save\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    329\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\abhishek\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36m_open_file_like\u001b[1;34m(name_or_buffer, mode)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0m_is_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'w'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\abhishek\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, name, mode)\u001b[0m\n\u001b[0;32m    191\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_opener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_open_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'C:/Users/Iconsense/Documents/virat/abhi'"
     ]
    }
   ],
   "source": [
    "path=\"C:/Users/Iconsense/Documents/virat/abhi\"\n",
    "torch.save(model.state_dict(), path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'C:/Users/Iconsense/Documents/virat/abhi'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-1966c44cd414>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcheckpoint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'model_state_dict'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# modelB.load_state_dict(checkpoint['modelB_state_dict'])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'optimizer_state_dict'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# optimizerB.load_state_dict(checkpoint['optimizerB_state_dict'])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\abhishek\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[0;32m    523\u001b[0m         \u001b[0mpickle_load_args\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'encoding'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'utf-8'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 525\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    526\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0m_is_zipfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    527\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0m_open_zipfile_reader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\abhishek\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36m_open_file_like\u001b[1;34m(name_or_buffer, mode)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0m_is_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'w'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\abhishek\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, name, mode)\u001b[0m\n\u001b[0;32m    191\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0m_open_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_opener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_open_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'C:/Users/Iconsense/Documents/virat/abhi'"
     ]
    }
   ],
   "source": [
    "checkpoint = torch.load(path)\n",
    "model.load_state_dict(checkpoint['model_state_dict'])\n",
    "# modelB.load_state_dict(checkpoint['modelB_state_dict'])\n",
    "optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
    "# optimizerB.load_state_dict(checkpoint['optimizerB_state_dict'])\n",
    "\n",
    "# modelA.eval()\n",
    "# modelB.eval()\n",
    "# # - or -\n",
    "model.train()\n",
    "# modelB.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor(0.4978, device='cuda:0'),\n",
       " tensor(0.4229, device='cuda:0'),\n",
       " tensor(0.4007, device='cuda:0'),\n",
       " tensor(0.3811, device='cuda:0'),\n",
       " tensor(0.3577, device='cuda:0'),\n",
       " tensor(0.3652, device='cuda:0')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tloss=np.array(losstrain)\n",
    "# vloss=np.array(lossval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ltrain, label='Training loss', color='green')\n",
    "plt.plot(lval, label='Validation loss', color ='black')\n",
    "plt.legend(frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.open('seg_pred/seg_pred/3966.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "nimg = np.array(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1ef35fabb08>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(nimg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "nimg = transformvalid(img).unsqueeze(0).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 124, 124])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nimg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model(nimg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, tpredict = torch.max(prediction.data, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sea'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes[tpredict[0].item()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "      (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): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=6, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"optimizer = torch.optim.Adam([{'params': model.layer4.parameters()},\\n                {'params': model.layer1.parameters(), 'lr': 1e-2},\\n                {'params': model.layer2.parameters(), 'lr': 1e-4},\\n            \\n            ], lr=1e-1)\""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for param in model.parameters():\n",
    "    param.requires_grad = True\n",
    "optimizer=torch.optim.Adam(model.parameters(),lr=0.00000001)   \n",
    "\"\"\"optimizer = torch.optim.Adam([{'params': model.layer4.parameters()},\n",
    "                {'params': model.layer1.parameters(), 'lr': 1e-2},\n",
    "                {'params': model.layer2.parameters(), 'lr': 1e-4},\n",
    "            \n",
    "            ], lr=1e-1)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9aef25d98e634d69b28a6bb24024123c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping early, the loss has diverged\n",
      "Learning rate search finished. See the graph with {finder_name}.plot()\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_finder = LRFinder(model, optimizer, criterion, device=\"cuda\")\n",
    "lr_finder.range_test(trainloader, end_lr=100, num_iter=100)\n",
    "lr_finder.plot() # to inspect the loss-learning rate graph\n",
    "lr_finder.reset() # to reset the model and optimizer to their initial state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in model.parameters():\n",
    "    param.requires_grad = True\n",
    "    \n",
    "optimizer = torch.optim.Adam([{'params': model.layer4.parameters()},\n",
    "                {'params': model.layer1.parameters(), 'lr': 5e-7},\n",
    "                 {'params': model.layer2.parameters(), 'lr': 1e-6},\n",
    "                {'params': model.layer3.parameters(), 'lr': 1e-5}\n",
    "            ], lr=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(epochs, model, loss_func, opt, train_dl, valid_dl):\n",
    "    tloss=[]\n",
    "    vloss=[]\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        # Handle batchnorm / dropout\n",
    "        model.train()\n",
    "        tot_train=0\n",
    "#         print(model.training)\n",
    "        for xb,yb in train_dl:\n",
    "            xb, yb = xb.to(device), yb.to(device)\n",
    "            loss = loss_func(model(xb), yb)\n",
    "            tot_train+=loss\n",
    "            loss.backward()\n",
    "            opt.step()\n",
    "            opt.zero_grad()\n",
    "        nt=len(train_dl)\n",
    "        \n",
    "        \n",
    "        model.eval()\n",
    "#         print(model.training)\n",
    "        with torch.no_grad():\n",
    "            tot_loss,tot_acc = 0.,0.\n",
    "            for xb,yb in valid_dl:\n",
    "                xb, yb = xb.to(device), yb.to(device)\n",
    "                pred = model(xb)\n",
    "                tot_loss += loss_func(pred, yb)\n",
    "                tot_acc  += accuracy (pred,yb)\n",
    "        nv = len(valid_dl)\n",
    "        tloss.append(tot_train/nt)\n",
    "        vloss.append(tot_loss/nv)\n",
    "        print(epoch,tot_train/nt, tot_loss/nv, tot_acc/nv)\n",
    "    return tloss, vloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 tensor(0.0353, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.2867, device='cuda:0') tensor(0.9277, device='cuda:0')\n",
      "1 tensor(0.0212, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.3138, device='cuda:0') tensor(0.9247, device='cuda:0')\n",
      "2 tensor(0.0189, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.3912, device='cuda:0') tensor(0.9130, device='cuda:0')\n",
      "3 tensor(0.0213, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.3768, device='cuda:0') tensor(0.9201, device='cuda:0')\n",
      "4 tensor(0.0161, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.3603, device='cuda:0') tensor(0.9233, device='cuda:0')\n",
      "5 tensor(0.0170, device='cuda:0', grad_fn=<DivBackward0>) tensor(0.3613, device='cuda:0') tensor(0.9261, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "ltrain,lval = fit(6, model, criterion, optimizer, trainloader, valloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "# criterion = nn.CrossEntropyLoss()\n",
    "# optimizer=torch.optim.Adam(model.parameters(),lr=0.000001)\n",
    "# lr_finder = LRFinder(model, optimizer, criterion, device=\"cuda\")\n",
    "# lr_finder.range_test(trainloader, val_loader=valloader, end_lr=1, num_iter=100, step_mode=\"linear\")\n",
    "# lr_finder.plot(log_lr=False)\n",
    "# lr_finder.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for param in model.features.parameters():\n",
    "#     param.requires_grad = True\n",
    "    \n",
    "# optimizer = torch.optim.Adam([{'params': model.classifier.parameters()},\n",
    "#                 {'params': model.features.parameters(), 'lr': 0.00001}\n",
    "#             ], lr=0.002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(ltrain, label='Training loss', color='green')\n",
    "plt.plot(lval, label='Validation loss', color ='black')\n",
    "plt.legend(frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}