opencv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import torch\n",
    "from mobilenetv3 import MobileNetV3_Small\n",
    "from mobilenetv3 import MobileNetV3_Large\n",
    "from mobilenetv2 import MobileNetV2\n",
    "from mobilenetv1 import MobileNet\n",
    "\n",
    "from PIL import Image\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "from transforms.data_preprocessing import PredictionTransform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = cv2.dnn.readNetFromONNX(\"onnx/MobileNetV3_Large.onnx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_cv(path):\n",
    "    img = cv2.imread(path)\n",
    "    blob = cv2.dnn.blobFromImage(img, 1./128, size=(224, 224), mean=(127, 127, 127), swapRB=True, crop=False)\n",
    "    net.setInput(blob)\n",
    "    print(net.forward())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MobileNetV3_Large(n_class=7)\n",
    "state_dict = torch.load('./weights/mobilenet_50.pkl')\n",
    "model.load_state_dict(state_dict)\n",
    "model.eval()\n",
    "model.cuda()\n",
    "\n",
    "predict_transform = PredictionTransform([224, 224], np.array([127, 127, 127]), 128.0)\n",
    "\n",
    "def image_loader(image_name):\n",
    "    image = Image.open(image_name)    #.convert('RGB')\n",
    "    image = predict_transform(image).unsqueeze(0)\n",
    "    return image.cuda()\n",
    "\n",
    "def forward_torch(image_name):\n",
    "    inp = image_loader(image_name)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        out = model(inp).cpu()\n",
    "    print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-4.5641,  1.2246,  0.7689, -4.7141, -9.3101, -2.8519, 12.0943]])\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n"
     ]
    }
   ],
   "source": [
    "forward_torch(\"./sf6_03.jpg\")\n",
    "forward_cv(\"./sf6_03.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
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      "  12.08503  ]]\n",
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      "  12.08503  ]]\n",
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      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "[[-4.5987167  1.2692472  0.7589344 -4.751481  -9.331574  -2.81181\n",
      "  12.08503  ]]\n",
      "CPU times: user 1min 7s, sys: 298 ms, total: 1min 7s\n",
      "Wall time: 3.13 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(100):\n",
    "    forward_cv(\"./sf6_03.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.864622  -3.037331  -1.7247679 -6.5233693 -2.1903062 -2.1022515\n",
      "  16.48453  ]]\n",
      "tensor([[-4.8876, -3.0264, -1.7587, -6.5323, -2.1949, -2.0721, 16.5208]])\n"
     ]
    }
   ],
   "source": [
    "forward_cv(\"valdir/sf6/000517_1.jpg\")\n",
    "forward_torch(\"valdir/sf6/000517_1.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[285]\n"
     ]
    }
   ],
   "source": [
    "UnconnectedOutLayers = net.getUnconnectedOutLayers()\n",
    "for layer in UnconnectedOutLayers:\n",
    "    print(layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.getLayerNames()"
   ]
  }
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