unet_inference.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import torch\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_path = Path('/tmp/am_segm/inference/Crops of Exp9_FD')\n",
    "# data_path = Path('/opt/data/am_segm/inference/Crops of FDtest Exp8A')\n",
    "data_path = Path('/opt/data/am_segm/inference/Jul19 test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import segmentation_models_pytorch as smp\n",
    "\n",
    "from am_segm.preprocess import convert_to_tiles, stitch_tiles_at_path\n",
    "from am_segm.dataset import load_ds, valid_transform\n",
    "from am_segm.utils import predict_save, overlay_images_with_masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting images to tiles\n",
      "Splitting /opt/data/am_segm/inference/Jul19 test/source/Vitaly-test-AM_cropped/source.tiff\n",
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    },
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     "output_type": "stream",
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      "Save tile: /opt/data/am_segm/inference/Jul19 test/source_tiles/Vitaly-test-AM_cropped/source/224.png\n"
     ]
    }
   ],
   "source": [
    "convert_to_tiles(data_path / 'source')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = smp.Unet(encoder_name='se_resnext50_32x4d', encoder_weights=None, decoder_use_batchnorm=True)\n",
    "model.load_state_dict(torch.load('unet.pt', map_location=device))\n",
    "model.to(device);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = load_ds(data_path / 'source_tiles', valid_transform())\n",
    "len(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vitaly-test-AM_cropped\n",
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    },
    {
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      "/opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped/mask/220.png\n",
      "/opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped/source/221.png\n",
      "/opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped/mask/221.png\n",
      "/opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped/source/222.png\n",
      "/opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped/mask/222.png\n",
      "/opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped/source/223.png\n",
      "/opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped/mask/223.png\n",
      "/opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped/source/224.png\n",
      "/opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped/mask/224.png\n",
      "CPU times: user 1h 7min 8s, sys: 7min 17s, total: 1h 14min 25s\n",
      "Wall time: 6min 43s\n"
     ]
    }
   ],
   "source": [
    "%time predict_save(model, ds, data_path / 'pseudo_tiles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stitching tiles at /opt/data/am_segm/inference/Jul19 test/pseudo_tiles/Vitaly-test-AM_cropped\n",
      "Saved stitched image to /opt/data/am_segm/inference/Jul19 test/pseudo_tiles_stitched/Vitaly-test-AM_cropped/source.tiff\n",
      "Saved stitched image to /opt/data/am_segm/inference/Jul19 test/pseudo_tiles_stitched/Vitaly-test-AM_cropped/mask.tiff\n"
     ]
    }
   ],
   "source": [
    "stitch_tiles_at_path(data_path / 'pseudo_tiles', data_path / 'source_tiles',\n",
    "                     overwrite=True, image_ext='tiff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overlaying: /opt/data/am_segm/inference/Jul19 test/pseudo_tiles_stitched/Vitaly-test-AM_cropped\n"
     ]
    }
   ],
   "source": [
    "overlay_images_with_masks(data_path / 'pseudo_tiles_stitched', image_ext='tiff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "am_segm",
   "language": "python",
   "name": "am_segm"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}