train-squeeze.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision import datasets\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "from torch.autograd import Variable\n",
    "\n",
    "from tqdm import tqdm\n",
    "from mobilenetv3 import *\n",
    "from mobilenetv2 import MobileNetV2\n",
    "from mobilenetv1 import MobileNet\n",
    "import torchvision.models as models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# small\n",
    "# net_small = mobilenetv3(mode='small')\n",
    "# state_dict = torch.load('mobilenetv3_small_67.4.pth.tar')\n",
    "# net_small.load_state_dict(state_dict)\n",
    "\n",
    "# large\n",
    "# net_large = mobilenetv3(mode='large')\n",
    "#net_large = mobilenetv3(mode='large')\n",
    "\n",
    "#x = torch.randn(2,3,224,224)\n",
    "#y = net_large(x)\n",
    "#print(y.size())\n",
    "#print(y)\n",
    "import os \n",
    "os.system(\"rm ./weights/*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Parameter' object has no attribute 'key'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-2a55a8b3a9d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequires_grad\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Parameter' object has no attribute 'key'"
     ]
    }
   ],
   "source": [
    "model = models.squeezenet1_1(pretrained=False, num_classes=2)\n",
    "#model.classifier[1] = nn.Conv2d(in_channels=512, out_channels=2, kernel_size=(1, 1), stride=(1, 1))\n",
    "model.train()\n",
    "\n",
    "param_grads = []\n",
    "for param in model.parameters():\n",
    "    if param.requires_grad:\n",
    "        param_grads.append(param)\n",
    "model.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                 std=[0.229, 0.224, 0.225])\n",
    "#normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "#                                 std=[1, 1, 1])\n",
    "\n",
    "input_size = 224\n",
    "batch_size = 16\n",
    "n_worker = 4\n",
    "\n",
    "train_dir = './traindir'\n",
    "train_datasets = datasets.ImageFolder(\n",
    "        train_dir, transforms.Compose([\n",
    "        transforms.RandomResizedCrop(input_size),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        normalize,\n",
    "    ]))\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    train_datasets,\n",
    "    batch_size=batch_size, shuffle=True,\n",
    "    num_workers=n_worker, pin_memory=True)\n",
    "\n",
    "val_dir = './valdir'\n",
    "val_datasets = datasets.ImageFolder(\n",
    "        val_dir, transforms.Compose([\n",
    "        transforms.Resize(int(input_size/0.875)),\n",
    "        transforms.CenterCrop(input_size),\n",
    "        transforms.ToTensor(),\n",
    "        normalize,\n",
    "    ]))\n",
    "\n",
    "val_dataloader = torch.utils.data.DataLoader(\n",
    "    val_datasets,\n",
    "    batch_size=batch_size, shuffle=False,\n",
    "    num_workers=n_worker, pin_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([16, 2]) torch.Size([16])\n",
      "torch.Size([8, 2]) torch.Size([8])\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-6b1766e476b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0meval_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0meval_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_y\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mval_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m#tqdm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m             \u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    538\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    539\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 540\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shutdown_workers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    541\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_shutdown_workers\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    631\u001b[0m                 \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m                 \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__del__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/multiprocessing/process.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_pid\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a child process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a started process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m             \u001b[0m_children\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiscard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/multiprocessing/popen_fork.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m     48\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0;31m# This shouldn't block if wait() returned successfully.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWNOHANG\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/multiprocessing/popen_fork.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, flag)\u001b[0m\n\u001b[1;32m     26\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m                     \u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m                     \u001b[0;31m# Child process not yet created. See #1731717\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "total_epoch = 51\n",
    "learning_rate = 0.05\n",
    "save_every = 10\n",
    "log_every = 1\n",
    "\n",
    "# --------------------训练过程---------------------------------\n",
    "if torch.cuda.is_available():\n",
    "    model.cuda()\n",
    "\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, 50, 0.1)\n",
    "loss_func = nn.CrossEntropyLoss()\n",
    "\n",
    "Loss_list = []\n",
    "Accuracy_list = []\n",
    "\n",
    "for epoch in range(total_epoch):\n",
    "    #print(' ====== ====== ====== epoch {}'.format(epoch))\n",
    "    # training-----------------------------\n",
    "    train_loss = 0.\n",
    "    train_acc = 0.\n",
    "    model.train()\n",
    "    for batch_x, batch_y in (train_dataloader):  #tqdm\n",
    "        batch_x, batch_y = Variable(batch_x).cuda(), Variable(batch_y).cuda()\n",
    "        out = model(batch_x)\n",
    "        print(out.shape, batch_y.shape)\n",
    "        loss = loss_func(out, batch_y)\n",
    "        train_loss += loss.item()  #loss.data[0]\n",
    "        pred = torch.max(out, 1)[1]\n",
    "        train_correct = (pred == batch_y).sum()\n",
    "        train_acc += train_correct.item()  #data[0]\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    #print('Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len(\n",
    "        #train_datasets)), train_acc / (len(train_datasets))))\n",
    "\n",
    "    # evaluation--------------------------------\n",
    "    model.eval()\n",
    "    eval_loss = 0.\n",
    "    eval_acc = 0.\n",
    "    for batch_x, batch_y in (val_dataloader):  #tqdm\n",
    "        with torch.no_grad():\n",
    "            batch_x, batch_y = Variable(batch_x).cuda(), Variable(batch_y).cuda()\n",
    "            out = model(batch_x)\n",
    "            loss = loss_func(out, batch_y)\n",
    "            eval_loss += loss.item()  #.data[0]\n",
    "            pred = torch.max(out, 1)[1]\n",
    "            num_correct = (pred == batch_y).sum()\n",
    "            eval_acc += num_correct.item()  #.data[0]\n",
    "    #print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(\n",
    "        #val_datasets)), eval_acc / (len(val_datasets))))\n",
    "\n",
    "    Loss_list.append(eval_loss / (len(val_datasets)))\n",
    "    Accuracy_list.append(100 * eval_acc / (len(val_datasets)))\n",
    "\n",
    "    if epoch % save_every == 0:\n",
    "        print('Save model at epoch %d'%epoch)\n",
    "        # save model\n",
    "        torch.save(model.state_dict(), 'weights/mobilenet_%d.pkl'%epoch)\n",
    "        #torch.save(model, 'weights/mobilenetv2_%d.pkl'%epoch)\n",
    "\n",
    "    if epoch % log_every == 0:\n",
    "        print('====== epoch {}'.format(epoch))\n",
    "        #print('epoch {}, Train Loss: {:.6f}'.format((epoch), train_loss / (len(trainDataset))))\n",
    "        print('      Train Loss: {:.6f}, Acc: {:.6f}'.format(train_loss / (len(\n",
    "                train_datasets)), train_acc / (len(train_datasets))))\n",
    "        print('      Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(\n",
    "                val_datasets)), eval_acc / (len(val_datasets))))\n",
    "\n",
    "    scheduler.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "transf = transforms.Compose([\n",
    "         transforms.Resize(256),\n",
    "         transforms.CenterCrop(224),\n",
    "         transforms.ToTensor(),\n",
    "         #transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "         #                     std=[0.229, 0.224, 0.225]),\n",
    "         ])\n",
    "\n",
    "def image_loader(image_name):\n",
    "    image = Image.open(image_name).convert('RGB')\n",
    "    image = transf(image).unsqueeze(0)\n",
    "    #print(np.array(image)[0][0][0][:10])\n",
    "    return image.cuda()\n",
    "\n",
    "def forward(image_name):\n",
    "    inp = image_loader(image_name)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        out = model(inp).cpu()\n",
    "    print(out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.7085, -1.6978]])\n",
      "tensor([[ 1.9104, -1.9086]])\n",
      "tensor([[ 0.6705, -0.6266]])\n",
      "tensor([[ 1.8742, -1.8742]])\n",
      "tensor([[-3.5147,  3.2813]])\n",
      "tensor([[-3.7945,  3.5505]])\n",
      "tensor([[-4.2304,  3.9035]])\n",
      "tensor([[-2.9786,  2.7847]])\n"
     ]
    }
   ],
   "source": [
    "forward(\"valdir/greenlight/000771_4.jpg\")\n",
    "forward(\"valdir/greenlight/000774_3.jpg\")\n",
    "forward(\"valdir/greenlight/000849_2.jpg\")\n",
    "forward(\"valdir/greenlight/000840_2.jpg\")\n",
    "forward(\"valdir/pointer/000650_1.jpg\")\n",
    "forward(\"valdir/pointer/000656_1.jpg\")\n",
    "forward(\"valdir/pointer/000692_2.jpg\")\n",
    "forward(\"valdir/pointer/000722_1.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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