Datasets
Models
Downloads: 1
Diff of
/scripts/InitializationNotebook.ipynb
[000000]
..
[aeb6cc]
Switch to side-by-side view
--- a +++ b/scripts/InitializationNotebook.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# David Ouyang 12/5/2019\n", + "\n", + "# Notebook which:\n", + "# 1. Downloads weights\n", + "# 2. Initializes model and imports weights\n", + "# 3. Performs test time evaluation of videos (already preprocessed with ConvertDICOMToAVI.ipynb)\n", + "\n", + "import re\n", + "import os, os.path\n", + "from os.path import splitext\n", + "import pydicom as dicom\n", + "import numpy as np\n", + "from pydicom.uid import UID, generate_uid\n", + "import shutil\n", + "from multiprocessing import dummy as multiprocessing\n", + "import time\n", + "import subprocess\n", + "import datetime\n", + "from datetime import date\n", + "import sys\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "from shutil import copy\n", + "import math\n", + "import torch\n", + "import torchvision\n", + "\n", + "sys.path.append(\"..\")\n", + "import echonet\n", + "\n", + "import wget \n", + "\n", + "#destinationFolder = \"/Users/davidouyang/Dropbox/Echo Research/CodeBase/Output\"\n", + "destinationFolder = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\Output\"\n", + "#videosFolder = \"/Users/davidouyang/Dropbox/Echo Research/CodeBase/a4c-video-dir\"\n", + "videosFolder = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\a4c-video-dir\"\n", + "#DestinationForWeights = \"/Users/davidouyang/Dropbox/Echo Research/CodeBase/EchoNetDynamic-Weights\"\n", + "DestinationForWeights = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\EchoNetDynamic-Weights\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The weights are at C:\\Users\\Windows\\Dropbox\\Echo Research\\CodeBase\\EchoNetDynamic-Weights\n", + "Segmentation Weights already present\n", + "EF Weights already present\n" + ] + } + ], + "source": [ + "# Download model weights\n", + "\n", + "if os.path.exists(DestinationForWeights):\n", + " print(\"The weights are at\", DestinationForWeights)\n", + "else:\n", + " print(\"Creating folder at \", DestinationForWeights, \" to store weights\")\n", + " os.mkdir(DestinationForWeights)\n", + " \n", + "segmentationWeightsURL = 'https://github.com/douyang/EchoNetDynamic/releases/download/v1.0.0/deeplabv3_resnet50_random.pt'\n", + "ejectionFractionWeightsURL = 'https://github.com/douyang/EchoNetDynamic/releases/download/v1.0.0/r2plus1d_18_32_2_pretrained.pt'\n", + "\n", + "\n", + "if not os.path.exists(os.path.join(DestinationForWeights, os.path.basename(segmentationWeightsURL))):\n", + " print(\"Downloading Segmentation Weights, \", segmentationWeightsURL,\" to \",os.path.join(DestinationForWeights,os.path.basename(segmentationWeightsURL)))\n", + " filename = wget.download(segmentationWeightsURL, out = DestinationForWeights)\n", + "else:\n", + " print(\"Segmentation Weights already present\")\n", + " \n", + "if not os.path.exists(os.path.join(DestinationForWeights, os.path.basename(ejectionFractionWeightsURL))):\n", + " print(\"Downloading EF Weights, \", ejectionFractionWeightsURL,\" to \",os.path.join(DestinationForWeights,os.path.basename(ejectionFractionWeightsURL)))\n", + " filename = wget.download(ejectionFractionWeightsURL, out = DestinationForWeights)\n", + "else:\n", + " print(\"EF Weights already present\")\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading weights from C:\\Users\\Windows\\Dropbox\\Echo Research\\CodeBase\\EchoNetDynamic-Weights\\r2plus1d_18_32_2_pretrained\n", + "cuda is available, original weights\n", + "external_test ['0X1A05DFFFCAFB253B.avi', '0X1A0A263B22CCD966.avi', '0X1A2A76BDB5B98BED.avi', '0X1A2C60147AF9FDAE.avi', '0X1A2E9496910EFF5B.avi', '0X1A3D565B371DC573.avi', '0X1A3E7BF1DFB132FB.avi', '0X1A5FAE3F9D37794E.avi', '0X1A6ACFE7B286DAFC.avi', '0X1A8D85542DBE8204.avi', '23_Apical_4_chamber_view.dcm.avi', '62_Apical_4_chamber_view.dcm.avi', '64_Apical_4_chamber_view.dcm.avi']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:10<00:00, 1.00s/it]\n", + "100%|████████████████████████████████████████████████████████| 13/13 [00:29<00:00, 2.26s/it, 3122.29 (3440.26) / 0.00]\n" + ] + } + ], + "source": [ + "# Initialize and Run EF model\n", + "\n", + "frames = 32\n", + "period = 1 #2\n", + "batch_size = 20\n", + "model = torchvision.models.video.r2plus1d_18(pretrained=False)\n", + "model.fc = torch.nn.Linear(model.fc.in_features, 1)\n", + "\n", + "\n", + "\n", + "print(\"loading weights from \", os.path.join(DestinationForWeights, \"r2plus1d_18_32_2_pretrained\"))\n", + "\n", + "if torch.cuda.is_available():\n", + " print(\"cuda is available, original weights\")\n", + " device = torch.device(\"cuda\")\n", + " model = torch.nn.DataParallel(model)\n", + " model.to(device)\n", + " checkpoint = torch.load(os.path.join(DestinationForWeights, os.path.basename(ejectionFractionWeightsURL)))\n", + " model.load_state_dict(checkpoint['state_dict'])\n", + "else:\n", + " print(\"cuda is not available, cpu weights\")\n", + " device = torch.device(\"cpu\")\n", + " checkpoint = torch.load(os.path.join(DestinationForWeights, os.path.basename(ejectionFractionWeightsURL)), map_location = \"cpu\")\n", + " state_dict_cpu = {k[7:]: v for (k, v) in checkpoint['state_dict'].items()}\n", + " model.load_state_dict(state_dict_cpu)\n", + "\n", + "\n", + "# try some random weights: final_r2+1d_model_regression_EF_sgd_skip1_32frames.pth.tar\n", + "# scp ouyangd@arthur2:~/Echo-Tracing-Analysis/final_r2+1d_model_regression_EF_sgd_skip1_32frames.pth.tar \"C:\\Users\\Windows\\Dropbox\\Echo Research\\CodeBase\\EchoNetDynamic-Weights\"\n", + "#Weights = \"final_r2+1d_model_regression_EF_sgd_skip1_32frames.pth.tar\"\n", + "\n", + "\n", + "output = os.path.join(destinationFolder, \"cedars_ef_output.csv\")\n", + "\n", + "ds = echonet.datasets.Echo(split = \"external_test\", external_test_location = videosFolder, crops=\"all\")\n", + "print(ds.split, ds.fnames)\n", + "\n", + "mean, std = echonet.utils.get_mean_and_std(ds)\n", + "\n", + "kwargs = {\"target_type\": \"EF\",\n", + " \"mean\": mean,\n", + " \"std\": std,\n", + " \"length\": frames,\n", + " \"period\": period,\n", + " }\n", + "\n", + "ds = echonet.datasets.Echo(split = \"external_test\", external_test_location = videosFolder, **kwargs, crops=\"all\")\n", + "\n", + "test_dataloader = torch.utils.data.DataLoader(ds, batch_size = 1, num_workers = 5, shuffle = True, pin_memory=(device.type == \"cuda\"))\n", + "loss, yhat, y = echonet.utils.video.run_epoch(model, test_dataloader, \"test\", None, device, save_all=True, blocks=25)\n", + "\n", + "with open(output, \"w\") as g:\n", + " for (filename, pred) in zip(ds.fnames, yhat):\n", + " for (i,p) in enumerate(pred):\n", + " g.write(\"{},{},{:.4f}\\n\".format(filename, i, p))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize and Run Segmentation model\n", + "\n", + "torch.cuda.empty_cache()\n", + "\n", + "\n", + "videosFolder = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\View Classification\\\\AppearsA4c\\\\Resized2\"\n", + "\n", + "def collate_fn(x):\n", + " x, f = zip(*x)\n", + " i = list(map(lambda t: t.shape[1], x))\n", + " x = torch.as_tensor(np.swapaxes(np.concatenate(x, 1), 0, 1))\n", + " return x, f, i\n", + "\n", + "dataloader = torch.utils.data.DataLoader(echonet.datasets.Echo(split=\"external_test\", external_test_location = videosFolder, target_type=[\"Filename\"], length=None, period=1, mean=mean, std=std),\n", + " batch_size=10, num_workers=0, shuffle=False, pin_memory=(device.type == \"cuda\"), collate_fn=collate_fn)\n", + "if not all([os.path.isfile(os.path.join(destinationFolder, \"labels\", os.path.splitext(f)[0] + \".npy\")) for f in dataloader.dataset.fnames]):\n", + " # Save segmentations for all frames\n", + " # Only run if missing files\n", + "\n", + " pathlib.Path(os.path.join(destinationFolder, \"labels\")).mkdir(parents=True, exist_ok=True)\n", + " block = 1024\n", + " model.eval()\n", + "\n", + " with torch.no_grad():\n", + " for (x, f, i) in tqdm.tqdm(dataloader):\n", + " x = x.to(device)\n", + " y = np.concatenate([model(x[i:(i + block), :, :, :])[\"out\"].detach().cpu().numpy() for i in range(0, x.shape[0], block)]).astype(np.float16)\n", + " start = 0\n", + " for (filename, offset) in zip(f, i):\n", + " np.save(os.path.join(destinationFolder, \"labels\", os.path.splitext(filename)[0]), y[start:(start + offset), 0, :, :])\n", + " start += offset\n", + " \n", + "dataloader = torch.utils.data.DataLoader(echonet.datasets.Echo(split=\"external_test\", external_test_location = videosFolder, target_type=[\"Filename\"], length=None, period=1, segmentation=os.path.join(destinationFolder, \"labels\")),\n", + " batch_size=1, num_workers=8, shuffle=False, pin_memory=False)\n", + "if not all(os.path.isfile(os.path.join(destinationFolder, \"videos\", f)) for f in dataloader.dataset.fnames):\n", + " pathlib.Path(os.path.join(destinationFolder, \"videos\")).mkdir(parents=True, exist_ok=True)\n", + " pathlib.Path(os.path.join(destinationFolder, \"size\")).mkdir(parents=True, exist_ok=True)\n", + " echonet.utils.latexify()\n", + " with open(os.path.join(destinationFolder, \"size.csv\"), \"w\") as g:\n", + " g.write(\"Filename,Frame,Size,ComputerSmall\\n\")\n", + " for (x, filename) in tqdm.tqdm(dataloader):\n", + " x = x.numpy()\n", + " for i in range(len(filename)):\n", + " img = x[i, :, :, :, :].copy()\n", + " logit = img[2, :, :, :].copy()\n", + " img[1, :, :, :] = img[0, :, :, :]\n", + " img[2, :, :, :] = img[0, :, :, :]\n", + " img = np.concatenate((img, img), 3)\n", + " img[0, :, :, 112:] = np.maximum(255. * (logit > 0), img[0, :, :, 112:])\n", + "\n", + " img = np.concatenate((img, np.zeros_like(img)), 2)\n", + " size = (logit > 0).sum(2).sum(1)\n", + " try:\n", + " trim_min = sorted(size)[round(len(size) ** 0.05)]\n", + " except:\n", + " import code; code.interact(local=dict(globals(), **locals()))\n", + " trim_max = sorted(size)[round(len(size) ** 0.95)]\n", + " trim_range = trim_max - trim_min\n", + " peaks = set(scipy.signal.find_peaks(-size, distance=20, prominence=(0.50 * trim_range))[0])\n", + " for (x, y) in enumerate(size):\n", + " g.write(\"{},{},{},{}\\n\".format(filename[0], x, y, 1 if x in peaks else 0))\n", + " fig = plt.figure(figsize=(size.shape[0] / 50 * 1.5, 3))\n", + " plt.scatter(np.arange(size.shape[0]) / 50, size, s=1)\n", + " ylim = plt.ylim()\n", + " for p in peaks:\n", + " plt.plot(np.array([p, p]) / 50, ylim, linewidth=1)\n", + " plt.ylim(ylim)\n", + " plt.title(os.path.splitext(filename[i])[0])\n", + " plt.xlabel(\"Seconds\")\n", + " plt.ylabel(\"Size (pixels)\")\n", + " plt.tight_layout()\n", + " plt.savefig(os.path.join(destinationFolder, \"size\", os.path.splitext(filename[i])[0] + \".pdf\"))\n", + " plt.close(fig)\n", + " size -= size.min()\n", + " size = size / size.max()\n", + " size = 1 - size\n", + " for (x, y) in enumerate(size):\n", + " img[:, :, int(round(115 + 100 * y)), int(round(x / len(size) * 200 + 10))] = 255.\n", + " interval = np.array([-3, -2, -1, 0, 1, 2, 3])\n", + " for a in interval:\n", + " for b in interval:\n", + " img[:, x, a + int(round(115 + 100 * y)), b + int(round(x / len(size) * 200 + 10))] = 255.\n", + " if x in peaks:\n", + " img[:, :, 200:225, b + int(round(x / len(size) * 200 + 10))] = 255.\n", + " echonet.utils.savevideo(os.path.join(destinationFolder, \"videos\", filename[i]), img.astype(np.uint8), 50) " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}