Using_Json_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fastai\n",
    "from fastai.vision import *\n",
    "from fastai.widgets import *\n",
    "from fastai.callbacks import *\n",
    "from fastai.vision.gan import *\n",
    "\n",
    "from fastai.metrics import accuracy_thresh, top_k_accuracy, error_rate, FBeta, root_mean_squared_error, mean_squared_error, mean_absolute_error\n",
    "\n",
    "from torchvision.models import vgg16_bn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path('./data/0418_Combined')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = get_transforms(do_flip=True, flip_vert=True, max_rotate=0.77, max_zoom=1.07,\n",
    "                   max_lighting=0.2, max_warp=0.2, p_affine=0.2,\n",
    "                   p_lighting=0.2, xtra_tfms=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ImageDataBunch;\n",
       "\n",
       "Train: LabelList (2345 items)\n",
       "x: ImageList\n",
       "Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296)\n",
       "y: CategoryList\n",
       "000024463,000024463,000024463,000024463,000024463\n",
       "Path: data\\0418_Combined;\n",
       "\n",
       "Valid: LabelList (425 items)\n",
       "x: ImageList\n",
       "Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296)\n",
       "y: CategoryList\n",
       "000024463,000024463,000024463,000024463,000024464\n",
       "Path: data\\0418_Combined;\n",
       "\n",
       "Test: LabelList (379 items)\n",
       "x: ImageList\n",
       "Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296)\n",
       "y: EmptyLabelList\n",
       ",,,,\n",
       "Path: data\\0418_Combined"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = ImageDataBunch.from_folder(path, ds_tfms=tfms, bs=64, size=296, test='test')\n",
    "data.normalize(imagenet_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.resnet50, pretrained=True, metrics=[accuracy], callback_fns=ShowGraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Learner(data=ImageDataBunch;\n",
       "\n",
       "Train: LabelList (2345 items)\n",
       "x: ImageList\n",
       "Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296)\n",
       "y: CategoryList\n",
       "000024463,000024463,000024463,000024463,000024463\n",
       "Path: data\\0418_Combined;\n",
       "\n",
       "Valid: LabelList (425 items)\n",
       "x: ImageList\n",
       "Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296)\n",
       "y: CategoryList\n",
       "000024463,000024463,000024463,000024463,000024464\n",
       "Path: data\\0418_Combined;\n",
       "\n",
       "Test: LabelList (379 items)\n",
       "x: ImageList\n",
       "Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296),Image (3, 296, 296)\n",
       "y: EmptyLabelList\n",
       ",,,,\n",
       "Path: data\\0418_Combined, model=Sequential(\n",
       "  (0): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU(inplace)\n",
       "    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (4): 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)\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)\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)\n",
       "      )\n",
       "    )\n",
       "    (5): 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)\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)\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)\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)\n",
       "      )\n",
       "    )\n",
       "    (6): 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)\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)\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)\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)\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)\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)\n",
       "      )\n",
       "    )\n",
       "    (7): 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)\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)\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)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): AdaptiveConcatPool2d(\n",
       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "    )\n",
       "    (1): Flatten()\n",
       "    (2): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.25)\n",
       "    (4): Linear(in_features=4096, out_features=512, bias=True)\n",
       "    (5): ReLU(inplace)\n",
       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.5)\n",
       "    (8): Linear(in_features=512, out_features=92, bias=True)\n",
       "  )\n",
       "), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of CrossEntropyLoss(), metrics=[<function accuracy at 0x000002A85B1889D8>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=WindowsPath('data/0418_Combined'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False), <class 'fastai.train.ShowGraph'>], callbacks=[], layer_groups=[Sequential(\n",
       "  (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): ReLU(inplace)\n",
       "  (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (4): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (8): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (9): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (10): ReLU(inplace)\n",
       "  (11): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (13): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (14): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (15): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (16): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (17): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (19): ReLU(inplace)\n",
       "  (20): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (21): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (22): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (23): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (24): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (25): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (26): ReLU(inplace)\n",
       "  (27): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (28): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (29): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (30): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (31): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (32): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (33): ReLU(inplace)\n",
       "  (34): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (36): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (37): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (38): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (39): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (40): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (42): ReLU(inplace)\n",
       "  (43): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (44): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (45): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (46): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (47): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (48): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (49): ReLU(inplace)\n",
       "  (50): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (51): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (52): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (53): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (54): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (55): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (56): ReLU(inplace)\n",
       "), Sequential(\n",
       "  (0): Conv2d(512, 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",
       "  (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (4): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (8): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (9): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (10): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (11): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (13): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (14): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (15): ReLU(inplace)\n",
       "  (16): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (17): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (18): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (19): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (20): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (21): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (22): ReLU(inplace)\n",
       "  (23): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (25): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (26): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (27): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (28): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (29): ReLU(inplace)\n",
       "  (30): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (31): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (32): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (33): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (34): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (35): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (36): ReLU(inplace)\n",
       "  (37): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (38): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (39): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (40): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (41): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (42): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (43): ReLU(inplace)\n",
       "  (44): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (45): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (46): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "  (47): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (48): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (49): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (50): ReLU(inplace)\n",
       "  (51): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "  (52): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (53): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (54): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (55): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (56): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (57): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (58): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (59): ReLU(inplace)\n",
       "  (60): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (61): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (62): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "  (63): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (64): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "  (65): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (66): ReLU(inplace)\n",
       "), Sequential(\n",
       "  (0): AdaptiveAvgPool2d(output_size=1)\n",
       "  (1): AdaptiveMaxPool2d(output_size=1)\n",
       "  (2): Flatten()\n",
       "  (3): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (4): Dropout(p=0.25)\n",
       "  (5): Linear(in_features=4096, out_features=512, bias=True)\n",
       "  (6): ReLU(inplace)\n",
       "  (7): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (8): Dropout(p=0.5)\n",
       "  (9): Linear(in_features=512, out_features=92, bias=True)\n",
       ")], add_time=True, silent=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load file\n",
    "learn.load('resnet50_json92_40epoch_0896471')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "image/png": 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\n",
      "text/plain": [
       "Image (3, 600, 337)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test image\n",
    "img_path = './data/0418_Combined/test_one.jpg'\n",
    "img = open_image(img_path)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category 681800513"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get prediction\n",
    "#In this case we have only trained the model to identify corresponding numbers, in this case 681800513\n",
    "prediction, indicies, losses = learn.predict(img)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#However this can be expanded by using a corresponding json file that has all the info about what 681800513 is by loading\n",
    "#json file\n",
    "prediction, indice, losses = learn.predict(img)\n",
    "with open('json_92_version2.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction #1\n",
    "pred_info = cat_to_name[str(prediction)]\n",
    "pred_name = pred_info['name']\n",
    "pred_shape = pred_info['shape']\n",
    "pred_color = pred_info['color']\n",
    "pred_mark = pred_info['marking']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Category 681800513, 'Lisinopril 5mg', 'round', 'pink', '5')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now get the prediction as well as corresponding values\n",
    "prediction, pred_name, pred_shape, pred_color, pred_mark"
   ]
  }
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