2D_to_3D_predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TO DO: Create a dataloader that performs the transition to 3channel image, or figure out how to load in a 1 channel architecture\n",
    "Test: mixed precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import fastbook\n",
    "fastbook.setup_book()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from fastbook import *\n",
    "from fastai.vision.widgets import *\n",
    "import skimage\n",
    "from skimage import io as skio\n",
    "import numpy\n",
    "from PIL import Image, ImageEnhance\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "path =Path(\"D:/pytorch/data/2Dprojection_to_3D\")\n",
    "Path.BASE_PATH =path\n",
    "image_list = (path/'325Downsample_2D').ls()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert the input 16bit images into tensors from 0-1 using the min-max from the whole training batch. There could be issues here related to speckles and such"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Potential problem here, the 16bit images is squished into a narrow float range before being normalized out to 0-1\n",
    "#Attempting to use tensors for speed when scaling to 3D GB data\n",
    "\n",
    "#added - add 3 columns and reshape to a 3channel image to make it easier to use with the fastai model batching\n",
    "# image_tensors = [tensor(skimage.img_as_float( np.transpose( np.repeat( np.asarray(Image.open(o))[:, :, np.newaxis], 3, axis=2), (2,0,1)) )) for o in image_list]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# #normalize the dataset to min-max\n",
    "# stacked_images_tensor = torch.stack(image_tensors)\n",
    "# stacked_images_tensor.shape\n",
    "# max = torch.max(stacked_images_tensor)\n",
    "# min = torch.min(stacked_images_tensor)\n",
    "# #considering min*0.98 and max*0.98 to reduce error from speckles\n",
    "# normal_stacked_images_tensor = (stacked_images_tensor-min)/(max-min)\n",
    "# # df = pd.DataFrame(image1_tensor)\n",
    "# # df.style.set_properties(**{'font-size':'6pt'}).background_gradient('Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ##########################################\n",
    "# ## Replace this all with a better dataloader\n",
    "# ## Requires dummy files to be in place in the images folder\n",
    "# #########################################\n",
    "\n",
    "# numpy_array = [(o.numpy()*255).astype(numpy.uint8) for o in normal_stacked_images_tensor]\n",
    "\n",
    "# from tifffile import imsave\n",
    "# image_list_output = (path/'images').ls()\n",
    "# zippy = zip(image_list_output, (numpy_array))\n",
    "# #list(zippy)\n",
    "# [imsave(str(a),b) for a,b in zippy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PILImageGray(PILImage): _show_args,_open_args = {'cmap':'Gray'},{'mode': 'I;16'} # Used for loading RGBA input images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#model = torch.hub.load('mateuszbuda/brain-segmentation-pytorch', 'unet', in_channels=1, out_channels=1, init_features=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Due to IPython and Windows limitation, python multiprocessing isn't available now.\n",
      "So `number_workers` is changed to 0 to avoid getting stuck\n"
     ]
    }
   ],
   "source": [
    "#Notes - need the ImageBlock to take PILImageBW to not treat the incoming images as RGB\n",
    "\n",
    "import fastbook\n",
    "fastbook.setup_book()\n",
    "from fastbook import *\n",
    "fnames = get_image_files(path/\"325Downsample_2D\")\n",
    "def get_label_filename(name): return path/'325Downsample_3D'/f'{name.stem[0:-3]}.tif'\n",
    "def validation_split (r):  return os.path.basename(r)[0:-4].split(\"_\")[3] == \"R0003\" or os.path.basename(r)[0:-4].split(\"_\")[3] == \"R0006\"\n",
    "# def to_3_channels(x):\n",
    "#     return torch.cat((x, x, x), dim=1)\n",
    "item_tfms = transforms.Compose([\n",
    "    \n",
    "    transforms.ToTensor(),\n",
    "    transforms.ConvertImageDtype(torch.float),\n",
    "    transforms.Normalize(mean = 0.5, std=0.5),\n",
    "    transforms.RandomVerticalFlip(0.3),\n",
    "    transforms.RandomHorizontalFlip(0.3),\n",
    "    # transforms.Grayscale(1),\n",
    "    # transforms.Lambda(lambda a : a.unsqueeze_(0).expand_(1,325,325,325)),\n",
    "    # transforms.Lambda(lambda a : torch.cat_((a,a,a), dim=0)),\n",
    "\n",
    "    transforms.RandomApply([transforms.RandomRotation((90,90))], p=0.5)\n",
    "\n",
    "])\n",
    "\n",
    "dblock = DataBlock(blocks=(ImageBlock(cls=PILImageBW), ImageBlock),\n",
    "                  get_items = get_image_files,\n",
    "                  item_tfms=item_tfms,\n",
    "\n",
    "                  get_y= get_label_filename,\n",
    "                  splitter = FuncSplitter(validation_split)#,\n",
    "                  #batch_tfms =aug_transforms(size=(325,325,325))\n",
    "                  )\n",
    "dls = dblock.dataloaders(path/\"325Downsample_2D\", bs=8, shuffle=true)\n",
    "\n",
    "learn = unet_learner(dls, resnet34,n_in=1,n_out=325,loss_func=L1LossFlat(), lr=0.01).to_fp16()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method TfmdDL.show_batch of <fastai.data.core.TfmdDL object at 0x000001EF4D6313C0>>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(TensorImage([[[103, 110, 112,  ..., 108, 102, 111],\n",
      "         [112, 110, 117,  ..., 111, 109, 110],\n",
      "         [116, 122, 112,  ..., 116, 115, 104],\n",
      "         ...,\n",
      "         [117, 113, 105,  ..., 110, 112,  90],\n",
      "         [116, 109, 104,  ..., 102,  95,  87],\n",
      "         [118, 112, 103,  ..., 106, 106, 100]],\n",
      "\n",
      "        [[123, 128, 133,  ..., 121, 125, 124],\n",
      "         [114, 115, 115,  ..., 113, 120, 123],\n",
      "         [125, 124, 117,  ..., 118, 119, 133],\n",
      "         ...,\n",
      "         [131, 115,  92,  ..., 106,  95, 107],\n",
      "         [128, 114, 100,  ..., 112, 121, 120],\n",
      "         [118, 119, 118,  ..., 113, 125, 119]],\n",
      "\n",
      "        [[108, 105,  98,  ...,  87,  96,  89],\n",
      "         [108, 104, 101,  ...,  88,  95,  90],\n",
      "         [ 95,  87,  92,  ..., 101, 108,  92],\n",
      "         ...,\n",
      "         [102,  89, 105,  ..., 116, 120, 104],\n",
      "         [ 96,  91, 101,  ..., 102,  96,  94],\n",
      "         [ 91,  87,  83,  ...,  89,  83,  98]]]), TensorBase([[[-0.3481, -0.2250, -0.1892,  ..., -0.2573, -0.3699, -0.2025],\n",
      "         [-0.1908, -0.2319, -0.0996,  ..., -0.2089, -0.2429, -0.2297],\n",
      "         [-0.1270, -0.0185, -0.1860,  ..., -0.1250, -0.1482, -0.3298],\n",
      "         ...,\n",
      "         [-0.1139, -0.1805, -0.3157,  ..., -0.2233, -0.1919, -0.5757],\n",
      "         [-0.1295, -0.2468, -0.3291,  ..., -0.3682, -0.4858, -0.6138],\n",
      "         [-0.0959, -0.1876, -0.3474,  ..., -0.3015, -0.2964, -0.3892]],\n",
      "\n",
      "        [[ 0.1323,  0.2158,  0.2991,  ...,  0.0836,  0.1616,  0.1467],\n",
      "         [-0.0377, -0.0155, -0.0189,  ..., -0.0549,  0.0698,  0.1277],\n",
      "         [ 0.1694,  0.1458,  0.0222,  ...,  0.0327,  0.0556,  0.2932],\n",
      "         ...,\n",
      "         [ 0.2656, -0.0172, -0.4231,  ..., -0.1697, -0.3572, -0.1455],\n",
      "         [ 0.2095, -0.0284, -0.2739,  ..., -0.0594,  0.0958,  0.0755],\n",
      "         [ 0.0325,  0.0526,  0.0302,  ..., -0.0480,  0.1543,  0.0544]],\n",
      "\n",
      "        [[ 0.0880,  0.0429, -0.0938,  ..., -0.2749, -0.1251, -0.2389],\n",
      "         [ 0.0796,  0.0177, -0.0423,  ..., -0.2551, -0.1334, -0.2274],\n",
      "         [-0.1357, -0.2805, -0.1953,  ..., -0.0411,  0.0923, -0.1888],\n",
      "         ...,\n",
      "         [-0.0225, -0.2390,  0.0428,  ...,  0.2297,  0.3003,  0.0159],\n",
      "         [-0.1294, -0.2065, -0.0309,  ..., -0.0151, -0.1162, -0.1654],\n",
      "         [-0.2117, -0.2754, -0.3425,  ..., -0.2396, -0.3572, -0.0909]]]), TensorBase([[[-0.3481, -0.2250, -0.1892,  ..., -0.2573, -0.3699, -0.2025],\n",
      "         [-0.1908, -0.2319, -0.0996,  ..., -0.2089, -0.2429, -0.2297],\n",
      "         [-0.1270, -0.0185, -0.1860,  ..., -0.1250, -0.1482, -0.3298],\n",
      "         ...,\n",
      "         [-0.1139, -0.1805, -0.3157,  ..., -0.2233, -0.1919, -0.5757],\n",
      "         [-0.1295, -0.2468, -0.3291,  ..., -0.3682, -0.4858, -0.6138],\n",
      "         [-0.0959, -0.1876, -0.3474,  ..., -0.3015, -0.2964, -0.3892]],\n",
      "\n",
      "        [[ 0.1323,  0.2158,  0.2991,  ...,  0.0836,  0.1616,  0.1467],\n",
      "         [-0.0377, -0.0155, -0.0189,  ..., -0.0549,  0.0698,  0.1277],\n",
      "         [ 0.1694,  0.1458,  0.0222,  ...,  0.0327,  0.0556,  0.2932],\n",
      "         ...,\n",
      "         [ 0.2656, -0.0172, -0.4231,  ..., -0.1697, -0.3572, -0.1455],\n",
      "         [ 0.2095, -0.0284, -0.2739,  ..., -0.0594,  0.0958,  0.0755],\n",
      "         [ 0.0325,  0.0526,  0.0302,  ..., -0.0480,  0.1543,  0.0544]],\n",
      "\n",
      "        [[ 0.0880,  0.0429, -0.0938,  ..., -0.2749, -0.1251, -0.2389],\n",
      "         [ 0.0796,  0.0177, -0.0423,  ..., -0.2551, -0.1334, -0.2274],\n",
      "         [-0.1357, -0.2805, -0.1953,  ..., -0.0411,  0.0923, -0.1888],\n",
      "         ...,\n",
      "         [-0.0225, -0.2390,  0.0428,  ...,  0.2297,  0.3003,  0.0159],\n",
      "         [-0.1294, -0.2065, -0.0309,  ..., -0.0151, -0.1162, -0.1654],\n",
      "         [-0.2117, -0.2754, -0.3425,  ..., -0.2396, -0.3572, -0.0909]]]))\n"
     ]
    }
   ],
   "source": [
    "print(thing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Due to IPython and Windows limitation, python multiprocessing isn't available now.\n",
      "So `number_workers` is changed to 0 to avoid getting stuck\n"
     ]
    }
   ],
   "source": [
    "# fnames = get_image_files(path/\"325Downsample_2D\")\n",
    "# def get_label_filename(name): return path/'325Downsample_3D'/f'{name.stem[0:-3]}.tif'\n",
    "# def validation_split (r):  return os.path.basename(r)[0:-4].split(\"_\")[3] == \"R0003\" or os.path.basename(r)[0:-4].split(\"_\")[3] == \"R0006\"\n",
    "# dblock = DataBlock(blocks=(ImageBlock, ImageBlock),\n",
    "#                   get_items = get_image_files,\n",
    "#                   get_y= get_label_filename,\n",
    "#                   splitter = FuncSplitter(validation_split)\n",
    "#                   #batch_tfms =aug_transforms(size=(512,512))\n",
    "#                   )\n",
    "# dls = dblock.dataloaders(path, bs=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
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      "d:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\_tensor.py:1142: UserWarning: Using a target size (torch.Size([2535000])) that is different to the input size (torch.Size([274625000])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  ret = func(*args, **kwargs)\n"
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     "ename": "RuntimeError",
     "evalue": "The size of tensor a (274625000) must match the size of tensor b (2535000) at non-singleton dimension 0",
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\MichaelSNelson\\OneDrive - UW-Madison\\GitHub_clones\\zebrafish_2D\\2D_to_3D_predictor.ipynb Cell 15'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/MichaelSNelson/OneDrive%20-%20UW-Madison/GitHub_clones/zebrafish_2D/2D_to_3D_predictor.ipynb#ch0000011?line=0'>1</a>\u001b[0m learn\u001b[39m.\u001b[39;49mfine_tune(\u001b[39m50\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/MichaelSNelson/OneDrive%20-%20UW-Madison/GitHub_clones/zebrafish_2D/2D_to_3D_predictor.ipynb#ch0000011?line=1'>2</a>\u001b[0m learn\u001b[39m.\u001b[39munfreeze\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/MichaelSNelson/OneDrive%20-%20UW-Madison/GitHub_clones/zebrafish_2D/2D_to_3D_predictor.ipynb#ch0000011?line=2'>3</a>\u001b[0m learn\u001b[39m.\u001b[39mfit_one_cycle(\u001b[39m150\u001b[39m, lr_max\u001b[39m=\u001b[39m\u001b[39mslice\u001b[39m(\u001b[39m1e-6\u001b[39m, \u001b[39m5e-2\u001b[39m))\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\callback\\schedule.py:161\u001b[0m, in \u001b[0;36mfine_tune\u001b[1;34m(self, epochs, base_lr, freeze_epochs, lr_mult, pct_start, div, **kwargs)\u001b[0m\n\u001b[0;32m    159\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFine tune with `Learner.freeze` for `freeze_epochs`, then with `Learner.unfreeze` for `epochs`, using discriminative LR.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    160\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfreeze()\n\u001b[1;32m--> 161\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfit_one_cycle(freeze_epochs, \u001b[39mslice\u001b[39m(base_lr), pct_start\u001b[39m=\u001b[39m\u001b[39m0.99\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    162\u001b[0m base_lr \u001b[39m/\u001b[39m\u001b[39m=\u001b[39m \u001b[39m2\u001b[39m\n\u001b[0;32m    163\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39munfreeze()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\callback\\schedule.py:116\u001b[0m, in \u001b[0;36mfit_one_cycle\u001b[1;34m(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)\u001b[0m\n\u001b[0;32m    113\u001b[0m lr_max \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([h[\u001b[39m'\u001b[39m\u001b[39mlr\u001b[39m\u001b[39m'\u001b[39m] \u001b[39mfor\u001b[39;00m h \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mopt\u001b[39m.\u001b[39mhypers])\n\u001b[0;32m    114\u001b[0m scheds \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mlr\u001b[39m\u001b[39m'\u001b[39m: combined_cos(pct_start, lr_max\u001b[39m/\u001b[39mdiv, lr_max, lr_max\u001b[39m/\u001b[39mdiv_final),\n\u001b[0;32m    115\u001b[0m           \u001b[39m'\u001b[39m\u001b[39mmom\u001b[39m\u001b[39m'\u001b[39m: combined_cos(pct_start, \u001b[39m*\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmoms \u001b[39mif\u001b[39;00m moms \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m moms))}\n\u001b[1;32m--> 116\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfit(n_epoch, cbs\u001b[39m=\u001b[39;49mParamScheduler(scheds)\u001b[39m+\u001b[39;49mL(cbs), reset_opt\u001b[39m=\u001b[39;49mreset_opt, wd\u001b[39m=\u001b[39;49mwd)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:222\u001b[0m, in \u001b[0;36mLearner.fit\u001b[1;34m(self, n_epoch, lr, wd, cbs, reset_opt)\u001b[0m\n\u001b[0;32m    220\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mopt\u001b[39m.\u001b[39mset_hypers(lr\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlr \u001b[39mif\u001b[39;00m lr \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m lr)\n\u001b[0;32m    221\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_epoch \u001b[39m=\u001b[39m n_epoch\n\u001b[1;32m--> 222\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_with_events(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do_fit, \u001b[39m'\u001b[39;49m\u001b[39mfit\u001b[39;49m\u001b[39m'\u001b[39;49m, CancelFitException, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_end_cleanup)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:164\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[1;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_with_events\u001b[39m(\u001b[39mself\u001b[39m, f, event_type, ex, final\u001b[39m=\u001b[39mnoop):\n\u001b[1;32m--> 164\u001b[0m     \u001b[39mtry\u001b[39;00m: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbefore_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  f()\n\u001b[0;32m    165\u001b[0m     \u001b[39mexcept\u001b[39;00m ex: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_cancel_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m    166\u001b[0m     \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  final()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:213\u001b[0m, in \u001b[0;36mLearner._do_fit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_epoch):\n\u001b[0;32m    212\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mepoch\u001b[39m=\u001b[39mepoch\n\u001b[1;32m--> 213\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_with_events(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do_epoch, \u001b[39m'\u001b[39;49m\u001b[39mepoch\u001b[39;49m\u001b[39m'\u001b[39;49m, CancelEpochException)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:164\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[1;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_with_events\u001b[39m(\u001b[39mself\u001b[39m, f, event_type, ex, final\u001b[39m=\u001b[39mnoop):\n\u001b[1;32m--> 164\u001b[0m     \u001b[39mtry\u001b[39;00m: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbefore_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  f()\n\u001b[0;32m    165\u001b[0m     \u001b[39mexcept\u001b[39;00m ex: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_cancel_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m    166\u001b[0m     \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  final()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:207\u001b[0m, in \u001b[0;36mLearner._do_epoch\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    206\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_do_epoch\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m--> 207\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do_epoch_train()\n\u001b[0;32m    208\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_do_epoch_validate()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:199\u001b[0m, in \u001b[0;36mLearner._do_epoch_train\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    197\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_do_epoch_train\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    198\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdl \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdls\u001b[39m.\u001b[39mtrain\n\u001b[1;32m--> 199\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_with_events(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mall_batches, \u001b[39m'\u001b[39;49m\u001b[39mtrain\u001b[39;49m\u001b[39m'\u001b[39;49m, CancelTrainException)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:164\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[1;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_with_events\u001b[39m(\u001b[39mself\u001b[39m, f, event_type, ex, final\u001b[39m=\u001b[39mnoop):\n\u001b[1;32m--> 164\u001b[0m     \u001b[39mtry\u001b[39;00m: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbefore_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  f()\n\u001b[0;32m    165\u001b[0m     \u001b[39mexcept\u001b[39;00m ex: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_cancel_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m    166\u001b[0m     \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  final()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:170\u001b[0m, in \u001b[0;36mLearner.all_batches\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    168\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mall_batches\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    169\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_iter \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdl)\n\u001b[1;32m--> 170\u001b[0m     \u001b[39mfor\u001b[39;00m o \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdl): \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mone_batch(\u001b[39m*\u001b[39;49mo)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:195\u001b[0m, in \u001b[0;36mLearner.one_batch\u001b[1;34m(self, i, b)\u001b[0m\n\u001b[0;32m    193\u001b[0m b \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_set_device(b)\n\u001b[0;32m    194\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_split(b)\n\u001b[1;32m--> 195\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_with_events(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do_one_batch, \u001b[39m'\u001b[39;49m\u001b[39mbatch\u001b[39;49m\u001b[39m'\u001b[39;49m, CancelBatchException)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:164\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[1;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_with_events\u001b[39m(\u001b[39mself\u001b[39m, f, event_type, ex, final\u001b[39m=\u001b[39mnoop):\n\u001b[1;32m--> 164\u001b[0m     \u001b[39mtry\u001b[39;00m: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbefore_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  f()\n\u001b[0;32m    165\u001b[0m     \u001b[39mexcept\u001b[39;00m ex: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_cancel_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m    166\u001b[0m     \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  final()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:176\u001b[0m, in \u001b[0;36mLearner._do_one_batch\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    174\u001b[0m \u001b[39mself\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mafter_pred\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m    175\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39myb):\n\u001b[1;32m--> 176\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloss_grad \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mloss_func(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpred, \u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49myb)\n\u001b[0;32m    177\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloss \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloss_grad\u001b[39m.\u001b[39mclone()\n\u001b[0;32m    178\u001b[0m \u001b[39mself\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mafter_loss\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\losses.py:35\u001b[0m, in \u001b[0;36mBaseLoss.__call__\u001b[1;34m(self, inp, targ, **kwargs)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[39mif\u001b[39;00m targ\u001b[39m.\u001b[39mdtype \u001b[39min\u001b[39;00m [torch\u001b[39m.\u001b[39mint8, torch\u001b[39m.\u001b[39mint16, torch\u001b[39m.\u001b[39mint32]: targ \u001b[39m=\u001b[39m targ\u001b[39m.\u001b[39mlong()\n\u001b[0;32m     34\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflatten: inp \u001b[39m=\u001b[39m inp\u001b[39m.\u001b[39mview(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m,inp\u001b[39m.\u001b[39mshape[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]) \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mis_2d \u001b[39melse\u001b[39;00m inp\u001b[39m.\u001b[39mview(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m---> 35\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunc\u001b[39m.\u001b[39m\u001b[39m__call__\u001b[39m(inp, targ\u001b[39m.\u001b[39mview(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m) \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflatten \u001b[39melse\u001b[39;00m targ, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\modules\\module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1106\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1107\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1109\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1110\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1111\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\modules\\loss.py:96\u001b[0m, in \u001b[0;36mL1Loss.forward\u001b[1;34m(self, input, target)\u001b[0m\n\u001b[0;32m     95\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor, target: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m---> 96\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49ml1_loss(\u001b[39minput\u001b[39;49m, target, reduction\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mreduction)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\functional.py:3217\u001b[0m, in \u001b[0;36ml1_loss\u001b[1;34m(input, target, size_average, reduce, reduction)\u001b[0m\n\u001b[0;32m   3210\u001b[0m \u001b[39mr\u001b[39m\u001b[39m\"\"\"l1_loss(input, target, size_average=None, reduce=None, reduction='mean') -> Tensor\u001b[39;00m\n\u001b[0;32m   3211\u001b[0m \n\u001b[0;32m   3212\u001b[0m \u001b[39mFunction that takes the mean element-wise absolute value difference.\u001b[39;00m\n\u001b[0;32m   3213\u001b[0m \n\u001b[0;32m   3214\u001b[0m \u001b[39mSee :class:`~torch.nn.L1Loss` for details.\u001b[39;00m\n\u001b[0;32m   3215\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m   3216\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function_variadic(\u001b[39minput\u001b[39m, target):\n\u001b[1;32m-> 3217\u001b[0m     \u001b[39mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m   3218\u001b[0m         l1_loss, (\u001b[39minput\u001b[39;49m, target), \u001b[39minput\u001b[39;49m, target, size_average\u001b[39m=\u001b[39;49msize_average, reduce\u001b[39m=\u001b[39;49mreduce, reduction\u001b[39m=\u001b[39;49mreduction\n\u001b[0;32m   3219\u001b[0m     )\n\u001b[0;32m   3220\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (target\u001b[39m.\u001b[39msize() \u001b[39m==\u001b[39m \u001b[39minput\u001b[39m\u001b[39m.\u001b[39msize()):\n\u001b[0;32m   3221\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m   3222\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mUsing a target size (\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m) that is different to the input size (\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m). \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   3223\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mThis will likely lead to incorrect results due to broadcasting. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   3224\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mPlease ensure they have the same size.\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(target\u001b[39m.\u001b[39msize(), \u001b[39minput\u001b[39m\u001b[39m.\u001b[39msize()),\n\u001b[0;32m   3225\u001b[0m         stacklevel\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m,\n\u001b[0;32m   3226\u001b[0m     )\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\overrides.py:1394\u001b[0m, in \u001b[0;36mhandle_torch_function\u001b[1;34m(public_api, relevant_args, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1388\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\u001b[39m\"\u001b[39m\u001b[39mDefining your `__torch_function__ as a plain method is deprecated and \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   1389\u001b[0m                   \u001b[39m\"\u001b[39m\u001b[39mwill be an error in PyTorch 1.11, please define it as a classmethod.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   1390\u001b[0m                   \u001b[39mDeprecationWarning\u001b[39;00m)\n\u001b[0;32m   1392\u001b[0m \u001b[39m# Use `public_api` instead of `implementation` so __torch_function__\u001b[39;00m\n\u001b[0;32m   1393\u001b[0m \u001b[39m# implementations can do equality/identity comparisons.\u001b[39;00m\n\u001b[1;32m-> 1394\u001b[0m result \u001b[39m=\u001b[39m torch_func_method(public_api, types, args, kwargs)\n\u001b[0;32m   1396\u001b[0m \u001b[39mif\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNotImplemented\u001b[39m:\n\u001b[0;32m   1397\u001b[0m     \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\torch_core.py:341\u001b[0m, in \u001b[0;36mTensorBase.__torch_function__\u001b[1;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[0;32m    339\u001b[0m convert\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m\n\u001b[0;32m    340\u001b[0m \u001b[39mif\u001b[39;00m _torch_handled(args, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_opt, func): convert,types \u001b[39m=\u001b[39m \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m),(torch\u001b[39m.\u001b[39mTensor,)\n\u001b[1;32m--> 341\u001b[0m res \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m__torch_function__(func, types, args\u001b[39m=\u001b[39;49margs, kwargs\u001b[39m=\u001b[39;49mkwargs)\n\u001b[0;32m    342\u001b[0m \u001b[39mif\u001b[39;00m convert: res \u001b[39m=\u001b[39m convert(res)\n\u001b[0;32m    343\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(res, TensorBase): res\u001b[39m.\u001b[39mset_meta(\u001b[39mself\u001b[39m, as_copy\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\_tensor.py:1142\u001b[0m, in \u001b[0;36mTensor.__torch_function__\u001b[1;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[0;32m   1139\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mNotImplemented\u001b[39m\n\u001b[0;32m   1141\u001b[0m \u001b[39mwith\u001b[39;00m _C\u001b[39m.\u001b[39mDisableTorchFunction():\n\u001b[1;32m-> 1142\u001b[0m     ret \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1143\u001b[0m     \u001b[39mif\u001b[39;00m func \u001b[39min\u001b[39;00m get_default_nowrap_functions():\n\u001b[0;32m   1144\u001b[0m         \u001b[39mreturn\u001b[39;00m ret\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\functional.py:3230\u001b[0m, in \u001b[0;36ml1_loss\u001b[1;34m(input, target, size_average, reduce, reduction)\u001b[0m\n\u001b[0;32m   3227\u001b[0m \u001b[39mif\u001b[39;00m size_average \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mor\u001b[39;00m reduce \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m   3228\u001b[0m     reduction \u001b[39m=\u001b[39m _Reduction\u001b[39m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[1;32m-> 3230\u001b[0m expanded_input, expanded_target \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mbroadcast_tensors(\u001b[39minput\u001b[39;49m, target)\n\u001b[0;32m   3231\u001b[0m \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39m_C\u001b[39m.\u001b[39m_nn\u001b[39m.\u001b[39ml1_loss(expanded_input, expanded_target, _Reduction\u001b[39m.\u001b[39mget_enum(reduction))\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\functional.py:75\u001b[0m, in \u001b[0;36mbroadcast_tensors\u001b[1;34m(*tensors)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function(tensors):\n\u001b[0;32m     74\u001b[0m     \u001b[39mreturn\u001b[39;00m handle_torch_function(broadcast_tensors, tensors, \u001b[39m*\u001b[39mtensors)\n\u001b[1;32m---> 75\u001b[0m \u001b[39mreturn\u001b[39;00m _VF\u001b[39m.\u001b[39;49mbroadcast_tensors(tensors)\n",
      "\u001b[1;31mRuntimeError\u001b[0m: The size of tensor a (274625000) must match the size of tensor b (2535000) at non-singleton dimension 0"
     ]
    }
   ],
   "source": [
    "learn.fine_tune(50)\n",
    "learn.unfreeze\n",
    "learn.fit_one_cycle(150, lr_max=slice(1e-6, 5e-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.export('fish_yolk_segmentation_20220619.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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     "evalue": "Given groups=1, weight of size [64, 1, 7, 7], expected input[8, 3, 325, 325] to have 1 channels, but got 3 channels instead",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\MichaelSNelson\\OneDrive - UW-Madison\\GitHub_clones\\zebrafish_2D\\2D_to_3D_predictor.ipynb Cell 17'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/MichaelSNelson/OneDrive%20-%20UW-Madison/GitHub_clones/zebrafish_2D/2D_to_3D_predictor.ipynb#ch0000013?line=0'>1</a>\u001b[0m learn\u001b[39m.\u001b[39;49mfine_tune(\u001b[39m50\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/MichaelSNelson/OneDrive%20-%20UW-Madison/GitHub_clones/zebrafish_2D/2D_to_3D_predictor.ipynb#ch0000013?line=1'>2</a>\u001b[0m learn\u001b[39m.\u001b[39munfreeze\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/MichaelSNelson/OneDrive%20-%20UW-Madison/GitHub_clones/zebrafish_2D/2D_to_3D_predictor.ipynb#ch0000013?line=2'>3</a>\u001b[0m learn\u001b[39m.\u001b[39mfit_one_cycle(\u001b[39m150\u001b[39m, lr_max\u001b[39m=\u001b[39m\u001b[39mslice\u001b[39m(\u001b[39m1e-6\u001b[39m, \u001b[39m1e-3\u001b[39m))\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\callback\\schedule.py:161\u001b[0m, in \u001b[0;36mfine_tune\u001b[1;34m(self, epochs, base_lr, freeze_epochs, lr_mult, pct_start, div, **kwargs)\u001b[0m\n\u001b[0;32m    159\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFine tune with `Learner.freeze` for `freeze_epochs`, then with `Learner.unfreeze` for `epochs`, using discriminative LR.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    160\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfreeze()\n\u001b[1;32m--> 161\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfit_one_cycle(freeze_epochs, \u001b[39mslice\u001b[39m(base_lr), pct_start\u001b[39m=\u001b[39m\u001b[39m0.99\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    162\u001b[0m base_lr \u001b[39m/\u001b[39m\u001b[39m=\u001b[39m \u001b[39m2\u001b[39m\n\u001b[0;32m    163\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39munfreeze()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\callback\\schedule.py:116\u001b[0m, in \u001b[0;36mfit_one_cycle\u001b[1;34m(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)\u001b[0m\n\u001b[0;32m    113\u001b[0m lr_max \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([h[\u001b[39m'\u001b[39m\u001b[39mlr\u001b[39m\u001b[39m'\u001b[39m] \u001b[39mfor\u001b[39;00m h \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mopt\u001b[39m.\u001b[39mhypers])\n\u001b[0;32m    114\u001b[0m scheds \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mlr\u001b[39m\u001b[39m'\u001b[39m: combined_cos(pct_start, lr_max\u001b[39m/\u001b[39mdiv, lr_max, lr_max\u001b[39m/\u001b[39mdiv_final),\n\u001b[0;32m    115\u001b[0m           \u001b[39m'\u001b[39m\u001b[39mmom\u001b[39m\u001b[39m'\u001b[39m: combined_cos(pct_start, \u001b[39m*\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmoms \u001b[39mif\u001b[39;00m moms \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m moms))}\n\u001b[1;32m--> 116\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfit(n_epoch, cbs\u001b[39m=\u001b[39;49mParamScheduler(scheds)\u001b[39m+\u001b[39;49mL(cbs), reset_opt\u001b[39m=\u001b[39;49mreset_opt, wd\u001b[39m=\u001b[39;49mwd)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:222\u001b[0m, in \u001b[0;36mLearner.fit\u001b[1;34m(self, n_epoch, lr, wd, cbs, reset_opt)\u001b[0m\n\u001b[0;32m    220\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mopt\u001b[39m.\u001b[39mset_hypers(lr\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlr \u001b[39mif\u001b[39;00m lr \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m lr)\n\u001b[0;32m    221\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_epoch \u001b[39m=\u001b[39m n_epoch\n\u001b[1;32m--> 222\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_with_events(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do_fit, \u001b[39m'\u001b[39;49m\u001b[39mfit\u001b[39;49m\u001b[39m'\u001b[39;49m, CancelFitException, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_end_cleanup)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:164\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[1;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_with_events\u001b[39m(\u001b[39mself\u001b[39m, f, event_type, ex, final\u001b[39m=\u001b[39mnoop):\n\u001b[1;32m--> 164\u001b[0m     \u001b[39mtry\u001b[39;00m: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbefore_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  f()\n\u001b[0;32m    165\u001b[0m     \u001b[39mexcept\u001b[39;00m ex: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_cancel_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m    166\u001b[0m     \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  final()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:213\u001b[0m, in \u001b[0;36mLearner._do_fit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    211\u001b[0m \u001b[39mfor\u001b[39;00m epoch \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_epoch):\n\u001b[0;32m    212\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mepoch\u001b[39m=\u001b[39mepoch\n\u001b[1;32m--> 213\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_with_events(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do_epoch, \u001b[39m'\u001b[39;49m\u001b[39mepoch\u001b[39;49m\u001b[39m'\u001b[39;49m, CancelEpochException)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:164\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[1;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_with_events\u001b[39m(\u001b[39mself\u001b[39m, f, event_type, ex, final\u001b[39m=\u001b[39mnoop):\n\u001b[1;32m--> 164\u001b[0m     \u001b[39mtry\u001b[39;00m: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbefore_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  f()\n\u001b[0;32m    165\u001b[0m     \u001b[39mexcept\u001b[39;00m ex: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_cancel_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m    166\u001b[0m     \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  final()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:207\u001b[0m, in \u001b[0;36mLearner._do_epoch\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    206\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_do_epoch\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m--> 207\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do_epoch_train()\n\u001b[0;32m    208\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_do_epoch_validate()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:199\u001b[0m, in \u001b[0;36mLearner._do_epoch_train\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    197\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_do_epoch_train\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    198\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdl \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdls\u001b[39m.\u001b[39mtrain\n\u001b[1;32m--> 199\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_with_events(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mall_batches, \u001b[39m'\u001b[39;49m\u001b[39mtrain\u001b[39;49m\u001b[39m'\u001b[39;49m, CancelTrainException)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:164\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[1;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_with_events\u001b[39m(\u001b[39mself\u001b[39m, f, event_type, ex, final\u001b[39m=\u001b[39mnoop):\n\u001b[1;32m--> 164\u001b[0m     \u001b[39mtry\u001b[39;00m: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbefore_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  f()\n\u001b[0;32m    165\u001b[0m     \u001b[39mexcept\u001b[39;00m ex: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_cancel_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m    166\u001b[0m     \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  final()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:170\u001b[0m, in \u001b[0;36mLearner.all_batches\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    168\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mall_batches\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    169\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_iter \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdl)\n\u001b[1;32m--> 170\u001b[0m     \u001b[39mfor\u001b[39;00m o \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdl): \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mone_batch(\u001b[39m*\u001b[39;49mo)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:195\u001b[0m, in \u001b[0;36mLearner.one_batch\u001b[1;34m(self, i, b)\u001b[0m\n\u001b[0;32m    193\u001b[0m b \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_set_device(b)\n\u001b[0;32m    194\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_split(b)\n\u001b[1;32m--> 195\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_with_events(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do_one_batch, \u001b[39m'\u001b[39;49m\u001b[39mbatch\u001b[39;49m\u001b[39m'\u001b[39;49m, CancelBatchException)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:164\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[1;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_with_events\u001b[39m(\u001b[39mself\u001b[39m, f, event_type, ex, final\u001b[39m=\u001b[39mnoop):\n\u001b[1;32m--> 164\u001b[0m     \u001b[39mtry\u001b[39;00m: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbefore_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  f()\n\u001b[0;32m    165\u001b[0m     \u001b[39mexcept\u001b[39;00m ex: \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_cancel_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m    166\u001b[0m     \u001b[39mself\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mafter_\u001b[39m\u001b[39m{\u001b[39;00mevent_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m);  final()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\learner.py:173\u001b[0m, in \u001b[0;36mLearner._do_one_batch\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    172\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_do_one_batch\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m--> 173\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpred \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel(\u001b[39m*\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mxb)\n\u001b[0;32m    174\u001b[0m     \u001b[39mself\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mafter_pred\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m    175\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39myb):\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\modules\\module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1106\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1107\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1109\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1110\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1111\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\layers.py:407\u001b[0m, in \u001b[0;36mSequentialEx.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    405\u001b[0m \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlayers:\n\u001b[0;32m    406\u001b[0m     res\u001b[39m.\u001b[39morig \u001b[39m=\u001b[39m x\n\u001b[1;32m--> 407\u001b[0m     nres \u001b[39m=\u001b[39m l(res)\n\u001b[0;32m    408\u001b[0m     \u001b[39m# We have to remove res.orig to avoid hanging refs and therefore memory leaks\u001b[39;00m\n\u001b[0;32m    409\u001b[0m     res\u001b[39m.\u001b[39morig, nres\u001b[39m.\u001b[39morig \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m, \u001b[39mNone\u001b[39;00m\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\modules\\module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1106\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1107\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1109\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1110\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1111\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\modules\\container.py:141\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    139\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m):\n\u001b[0;32m    140\u001b[0m     \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[1;32m--> 141\u001b[0m         \u001b[39minput\u001b[39m \u001b[39m=\u001b[39m module(\u001b[39minput\u001b[39;49m)\n\u001b[0;32m    142\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39minput\u001b[39m\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\modules\\module.py:1110\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1106\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1107\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1108\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1109\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1110\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1111\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1112\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\modules\\conv.py:447\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 447\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_conv_forward(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\nn\\modules\\conv.py:443\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    439\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode \u001b[39m!=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mzeros\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m    440\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mconv2d(F\u001b[39m.\u001b[39mpad(\u001b[39minput\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode),\n\u001b[0;32m    441\u001b[0m                     weight, bias, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstride,\n\u001b[0;32m    442\u001b[0m                     _pair(\u001b[39m0\u001b[39m), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdilation, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgroups)\n\u001b[1;32m--> 443\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mconv2d(\u001b[39minput\u001b[39;49m, weight, bias, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstride,\n\u001b[0;32m    444\u001b[0m                 \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpadding, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdilation, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgroups)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\fastai\\torch_core.py:341\u001b[0m, in \u001b[0;36mTensorBase.__torch_function__\u001b[1;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[0;32m    339\u001b[0m convert\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m\n\u001b[0;32m    340\u001b[0m \u001b[39mif\u001b[39;00m _torch_handled(args, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_opt, func): convert,types \u001b[39m=\u001b[39m \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m),(torch\u001b[39m.\u001b[39mTensor,)\n\u001b[1;32m--> 341\u001b[0m res \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m__torch_function__(func, types, args\u001b[39m=\u001b[39;49margs, kwargs\u001b[39m=\u001b[39;49mkwargs)\n\u001b[0;32m    342\u001b[0m \u001b[39mif\u001b[39;00m convert: res \u001b[39m=\u001b[39m convert(res)\n\u001b[0;32m    343\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(res, TensorBase): res\u001b[39m.\u001b[39mset_meta(\u001b[39mself\u001b[39m, as_copy\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\fastai2\\lib\\site-packages\\torch\\_tensor.py:1142\u001b[0m, in \u001b[0;36mTensor.__torch_function__\u001b[1;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[0;32m   1139\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mNotImplemented\u001b[39m\n\u001b[0;32m   1141\u001b[0m \u001b[39mwith\u001b[39;00m _C\u001b[39m.\u001b[39mDisableTorchFunction():\n\u001b[1;32m-> 1142\u001b[0m     ret \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1143\u001b[0m     \u001b[39mif\u001b[39;00m func \u001b[39min\u001b[39;00m get_default_nowrap_functions():\n\u001b[0;32m   1144\u001b[0m         \u001b[39mreturn\u001b[39;00m ret\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Given groups=1, weight of size [64, 1, 7, 7], expected input[8, 3, 325, 325] to have 1 channels, but got 3 channels instead"
     ]
    }
   ],
   "source": [
    "learn.fine_tune(50)\n",
    "learn.unfreeze\n",
    "learn.fit_one_cycle(150, lr_max=slice(1e-6, 1e-3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "learn.export()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.unfreeze\n",
    "learn.fit_one_cycle(8, lr_max=slice(1e-6, 1e-3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fastai.learner.Learner at 0x256c3b0b520>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.load(\"morning_100\").to_fp16()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1440x1440 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.show_results(max_n=4, figsize=(20,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_steep = learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.test_utils import show_install\n",
    "show_install()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\pytorch\\data\\2D_FishYolk\\images\\embryo11_011.tif\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pick any image to get a prediction\n",
    "n=26\n",
    "fnames = get_image_files(path/\"images\")\n",
    "img = PILImage.create(fnames[n])\n",
    "zebrafish_classifier = load_learner(path/'export.pkl')\n",
    "#zebrafish_classifier.load('valid_loss_1')\n",
    "pred,pred_idx,probs = zebrafish_classifier.predict(fnames[n])\n",
    "#img2 =Image.fromarray(pred.numpy())\n",
    "#img2.show()\n",
    "print(fnames[n])\n",
    "_,axs = plt.subplots(1,3, figsize=(12,3))\n",
    "img.show(ctx=axs[0], title='image')\n",
    "pred.show(alpha=1, ctx=axs[1], vmin=0, vmax=3, title='mask')\n",
    "img.show(ctx=axs[2], title='superimposed')\n",
    "pred.show(ctx=axs[2], vmin=0, vmax=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87fab0ab37da4595b1b0b5673c24e43d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileUpload(value={}, description='Upload')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btn_upload= widgets.FileUpload()\n",
    "btn_upload\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PILImage mode=RGB size=512x512"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = PILImage.create(btn_upload.data[-1])\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c79b4ac055874c9aacb5e34f74602c4e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_output\n",
    "out_pl = widgets.Output()\n",
    "out_pl.clear_output()\n",
    "with out_pl: display(img.to_thumb(128,128))\n",
    "out_pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zebrafish_classifier = load_learner(path/'export.pkl')\n",
    "pred,pred_idx,probs = zebrafish_classifier.predict(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "out_pl = widgets.Output()\n",
    "out_pl_mask = widgets.Output()\n",
    "lbl_pred = widgets.Label()\n",
    "zebrafish_classifier = load_learner(path/'export.pkl')\n",
    "btn_run = widgets.Button(description='Process')\n",
    "def on_click_classify(change):\n",
    "    img = PILImage.create(btn_upload.data[-1])\n",
    "    out_pl.clear_output()\n",
    "    enhancer = ImageEnhance.Brightness(img)\n",
    "    factor = 10 \n",
    "    im_output = enhancer.enhance(factor)\n",
    "    with out_pl: display(im_output.to_thumb(128,128))\n",
    "        \n",
    "    pred,pred_idx,probs=zebrafish_classifier.predict(img)\n",
    "    with out_pl_mask: pred.show(alpha=1, vmin=0, vmax=3, title='mask')\n",
    "    \n",
    "    #lbl_pred.value = f'Predicted age: {age[0]};'\n",
    "\n",
    "btn_run.on_click(on_click_classify)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "btn_upload= widgets.FileUpload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1169a1a902f749e7b2b02d02934cad3f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Upload a grayscale zebrafish embryo image'), FileUpload(value={}, description='Upl…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "VBox([widgets.Label('Upload a grayscale zebrafish embryo image'), \n",
    "      btn_upload, btn_run, out_pl, out_pl_mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cam_img = PILImage.create(img_f)\n",
    "test_eq(cam_img.size, (512,512))\n",
    "tmask = Transform(PILMask.create)\n",
    "mask = tmask(lbl_f)\n",
    "test_eq(type(mask), PILMask)\n",
    "test_eq(mask.size, (512,512))\n",
    "_,axs = plt.subplots(1,3, figsize=(12,3))\n",
    "cam_img.show(ctx=axs[0], title='image')\n",
    "mask.show(alpha=1, ctx=axs[1], vmin=0, vmax=3, title='mask')\n",
    "cam_img.show(ctx=axs[2], title='superimposed')\n",
    "mask.show(ctx=axs[2], vmin=0, vmax=3);\n",
    "\n",
    "get_y_fn = lambda o: path/'labels'/f'{o.stem}_annotationLabels.tif'\n",
    "def get_label_filename(name, path): return path/'labels'/f'{name.stem}_annotationLabels.tif'"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "split_at_heading": true
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  "kernelspec": {
   "display_name": "Python 3.10.4 ('fastai2')",
   "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": "3.10.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "f69c838de7ce47136ebe027dd64b911371851962ad08f8b0aa77da2666fa0521"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}