From b00a8e80a53daf3f7c5026bf4cf09069c8878ec0 Mon Sep 17 00:00:00 2001 From: xfr Date: Sat, 3 Aug 2019 14:18:57 +0800 Subject: [PATCH] after balance dataset --- Pesudo_label.ipynb | 120 +++++++++++ PrepareData.ipynb | 141 +++++++++---- Sampler.ipynb | 293 ++++++++++++++++++--------- configs/rscup/htc_ba.py | 307 +++++++++++++++++++++++++++++ configs/rscup/htc_ba_more.py | 307 +++++++++++++++++++++++++++++ configs/rscup/htc_sy_ft.py | 288 +++++++++++++++++++++++++++ mmdet/datasets/custom.py | 23 +++ mmdet/datasets/dataset_wrappers.py | 31 +++ mmdet/datasets/loader/sampler.py | 232 +++++++++++++++++++--- pipeline.ipynb | 185 ++++++++--------- 10 files changed, 1671 insertions(+), 256 deletions(-) create mode 100644 Pesudo_label.ipynb create mode 100644 configs/rscup/htc_ba.py create mode 100644 configs/rscup/htc_ba_more.py create mode 100644 configs/rscup/htc_sy_ft.py diff --git a/Pesudo_label.ipynb b/Pesudo_label.ipynb new file mode 100644 index 0000000..22da6fd --- /dev/null +++ b/Pesudo_label.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import mmcv\n", + "import cv2\n", + "import numpy as np\n", + "from tqdm import tqdm_notebook as tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "out_file = \"./result/test_postnms.pkl\"\n", + "ann = mmcv.load(out_file)\n", + "CLASSES = ['tennis-court', 'container-crane', 'storage-tank', 'baseball-diamond', 'plane', 'ground-track-field', 'helicopter', 'airport', 'harbor', 'ship', 'large-vehicle', 'swimming-pool', 'soccer-ball-field', 'roundabout', 'basketball-court', 'bridge', 'small-vehicle', 'helipad']" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_single_label(info, img_name, outdir, score_thresh=0.5):\n", + " bboxes = info['bbox']\n", + " polys = info['vis']\n", + " fp = open(outdir + \"{}.txt\".format(img_name), \"w\")\n", + " fp.write(\"xx\\nxx\\n\")\n", + " for i in range(len(CLASSES)):\n", + " class_name = CLASSES[i]\n", + " bbox = bboxes[i]\n", + " poly = polys[i]\n", + " if len(bbox) == 0:\n", + " continue\n", + " scores = bbox[:,4]\n", + " for p, score in zip(poly, scores):\n", + " if(score<0.5):\n", + " continue\n", + " rect = np.array(p).flatten()\n", + " rect = [str(x) for x in rect]\n", + " loc = \" \".join(rect)\n", + " out = loc + \" \" + class_name + \" 0\\n\"\n", + " fp.write(out)\n", + " fp.close()\n", + "\n", + "def generate_labels(ann, outdir, score_thresh=0.5):\n", + " img_names = ann.keys()\n", + " for img_name in tqdm(img_names):\n", + " generate_single_label(ann[img_name], img_name, outdir, score_thresh)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9a6eff1102494dd98e264c229d098380", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=780), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "generate_labels(ann, \"./result/pesudo/labelTxt/\", 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mm", + "language": "python", + "name": "mmdet" + }, + "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": 2 +} diff --git a/PrepareData.ipynb b/PrepareData.ipynb index fa27245..2e24ca3 100644 --- a/PrepareData.ipynb +++ b/PrepareData.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -48,10 +48,8 @@ }, { "cell_type": "code", - "execution_count": 150, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "def bbox_overlaps_py(boxes, query_boxes):\n", @@ -708,23 +706,92 @@ "increment_generate(datadir, \"./data/rscup/annotation/annos_rscup_val.json\", \"./data/rscup\", \"val\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### pesudo data" + ] + }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "[{'id': 0, 'name': 'tennis-court', 'supercategory': 'object'}, {'id': 1, 'name': 'container-crane', 'supercategory': 'object'}, {'id': 2, 'name': 'storage-tank', 'supercategory': 'object'}, {'id': 3, 'name': 'baseball-diamond', 'supercategory': 'object'}, {'id': 4, 'name': 'plane', 'supercategory': 'object'}, {'id': 5, 'name': 'ground-track-field', 'supercategory': 'object'}, {'id': 6, 'name': 'helicopter', 'supercategory': 'object'}, {'id': 7, 'name': 'airport', 'supercategory': 'object'}, {'id': 8, 'name': 'harbor', 'supercategory': 'object'}, {'id': 9, 'name': 'ship', 'supercategory': 'object'}, {'id': 10, 'name': 'large-vehicle', 'supercategory': 'object'}, {'id': 11, 'name': 'swimming-pool', 'supercategory': 'object'}, {'id': 12, 'name': 'soccer-ball-field', 'supercategory': 'object'}, {'id': 13, 'name': 'roundabout', 'supercategory': 'object'}, {'id': 14, 'name': 'basketball-court', 'supercategory': 'object'}, {'id': 15, 'name': 'bridge', 'supercategory': 'object'}, {'id': 16, 'name': 'small-vehicle', 'supercategory': 'object'}, {'id': 17, 'name': 'helipad', 'supercategory': 'object'}]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "857cc5db27d047fba6d49817e731d54d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=780), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "totol number 22510\n", "loading annotations into memory...\n", - "Done (t=0.15s)\n", + "Done (t=1.11s)\n", "creating index...\n", "index created!\n" ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "daf78e2369a04a66a1871eee0a38ea87", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=780), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "af6322d37d2044f3bb4c9b5fd12190e6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=780), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "totol number 23290\n" + ] } ], - "source": [] + "source": [ + "datadir = \"./result/pesudo/images\"\n", + "labeldir = \"./result/pesudo/labelTxt\"\n", + "scale_generate(datadir, labeldir, \"pesudo\",\"./data/trash/\", [0.5, 1])\n", + "increment_train_generate(datadir, labeldir, \"./data/trash/annotation/annos_rscup_pesudo.json\", \"./data/trash\", \"pesudo\")" + ] }, { "cell_type": "markdown", @@ -798,7 +865,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -806,7 +873,7 @@ "output_type": "stream", "text": [ "loading annotations into memory...\n", - "Done (t=6.40s)\n", + "Done (t=1.80s)\n", "creating index...\n", "index created!\n" ] @@ -819,12 +886,12 @@ "from pycocotools.coco import COCO # 载入 cocoz\n", "%matplotlib inline\n", "phase = \"train\"\n", - "coco=COCO(\"./data/rscup/annotation/annos_rscup_train.json\")" + "coco=COCO(\"./data/trash/annotation/annos_rscup_pesudo.json\")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -832,23 +899,23 @@ "output_type": "stream", "text": [ "[16]\n", - "{'license': 1, 'file_name': 'P1933_0.5_292.0_191.0_0part3.jpg', 'coco_url': 'xxx', 'height': 511.0, 'width': 511.0, 'date_captured': '2019-06-25', 'flickr_url': 'xxx', 'id': 42637}\n", - "132\n" + "{'license': 1, 'file_name': 'P5193_1_2496.0_1248.0_0part47.jpg', 'coco_url': 'xxx', 'height': 511, 'width': 511, 'date_captured': '2019-06-25', 'flickr_url': 'xxx', 'id': 17543}\n", + "4\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -865,7 +932,7 @@ "imgIds = coco.getImgIds(catIds=catIds)\n", "# imgIds = coco.getImgIds(imgIds=[335328])\n", "img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]\n", - "I = io.imread('./data/rscup/train/'+img['file_name'])\n", + "I = io.imread('./data/trash/pesudo/'+img['file_name'])\n", "plt.imshow(I)\n", "plt.axis('off')\n", "print(img)\n", @@ -882,39 +949,29 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[]\n", - "{'license': 1, 'file_name': 'P1780_0.5_832.0_1664.0_0part18.jpg', 'coco_url': 'xxx', 'height': 511, 'width': 511, 'date_captured': '2019-06-25', 'flickr_url': 'xxx', 'id': 40364}\n", - "22\n" + "[]\n" ] }, { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: './data/trash/train/P4544_1_23296.0_10400.0_0part946.jpg'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# imgIds = coco.getImgIds(imgIds=[335328])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoco\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadImgs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgIds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgIds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mI\u001b[0m \u001b[0;34m=\u001b[0m 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as_gray, plugin, flatten, **plugin_args)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mfile_or_url_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_plugin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'imread'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplugin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplugin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplugin_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m 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"\u001b[0;32m~/.conda/envs/mmdet/lib/python3.6/site-packages/skimage/io/_plugins/pil_plugin.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, dtype, img_num, **kwargs)\u001b[0m\n\u001b[1;32m 33\u001b[0m \"\"\"\n\u001b[1;32m 34\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpil_to_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_num\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './data/trash/train/P4544_1_23296.0_10400.0_0part946.jpg'" + ] } ], "source": [ diff --git a/Sampler.ipynb b/Sampler.ipynb index 6649021..398ecaa 100644 --- a/Sampler.ipynb +++ b/Sampler.ipynb @@ -2,10 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", "import mmcv\n", "from mmcv import Config\n", "from mmdet.datasets import build_dataset\n", @@ -14,115 +16,123 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "cfg = Config.fromfile(\"./configs/rscup/htc_libra.py\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = build_dataset(cfg.data.train)\n", + "mmcv.dump(train_dataset, \"dataset.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "loading annotations into memory...\n", - "Done (t=7.02s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.07s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.02s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.02s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.01s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.02s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.01s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.01s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.02s)\n", - "creating index...\n", - "index created!\n", - "loading annotations into memory...\n", - "Done (t=0.04s)\n", - "creating index...\n", - "index created!\n" + "15000\n" ] } ], "source": [ - "cfg = Config.fromfile(\"./configs/rscup/htc_libra.py\")\n", - "train_dataset = build_dataset(cfg.data.train)\n", + "train_dataset = mmcv.load(\"dataset.pkl\")\n", "data_loaders = [\n", " build_dataloader(\n", " train_dataset,\n", " cfg.data.imgs_per_gpu,\n", " cfg.data.workers_per_gpu,\n", - " dist=False)\n", + " dist=True)\n", "]" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 49, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n", - "torch.Size([6, 3, 512, 512])\n" + "ic| num_replicas: 4\n", + "ic| self.rank: 3\n", + "ic| self.num_replicas: 4\n" + ] + } + ], + "source": [ + "import sys\n", + "from functools import partial\n", + "from mmcv.runner import get_dist_info\n", + "from mmcv.parallel import collate\n", + "from torch.utils.data import DataLoader\n", + "sys.path.insert(0, \"/home/xfr/git_mm/mmdetection/mmdet/datasets/loader\")\n", + "from mmdet.datasets.loader.sampler import GroupSampler, DistributedGroupSampler, DistributedSampler\n", + "dataset = mmcv.load(\"dataset.pkl\")\n", + "num_gpus = 2\n", + "imgs_per_gpu = 6\n", + "num_workers=4\n", + "sampler = DistributedGroupSampler(dataset, imgs_per_gpu, 4, 3)\n", + "batch_size = num_gpus * imgs_per_gpu\n", + "data_loader = DataLoader(\n", + " dataset,\n", + " batch_size=batch_size,\n", + " sampler=sampler,\n", + " num_workers=num_workers,\n", + " collate_fn=partial(collate, samples_per_gpu=imgs_per_gpu),\n", + " pin_memory=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm_notebook as tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f825e7a0cef54cd4b52da3949878f3b4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=2500), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ic| self.num_replicas: 1\n", + "ic| self.rank: 0\n", + "ic| group_sizes: array([12483, 2517])\n", + "ic| num_samples: 15006\n", + "ic| self.num_replicas: 1\n", + "ic| total_size: 15006\n" ] }, { @@ -132,35 +142,134 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m 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221\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.conda/envs/mmdet/lib/python3.6/site-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 977\u001b[0m \"\"\", fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m 978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 979\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 980\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 981\u001b[0m \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.conda/envs/mmdet/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 575\u001b[0m \u001b[0;32massert\u001b[0m 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"\u001b[0;32m~/.conda/envs/mmdet/lib/python3.6/multiprocessing/queues.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeadline\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonotonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0;32mraise\u001b[0m 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\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 377\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 378\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ - "for item in data_loaders[0]:\n", - " print(item['img'].data[0].shape)" + "a = [0 for i in range(20)]\n", + "for item in tqdm(data_loaders[0]):\n", + " #print(item[\"img\"].data[0].shape)\n", + " for i in range(6): \n", + " m = item['gt_labels'].data[0][i]\n", + " for x in m:\n", + " a[x] += 1\n", + "print(a)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[autoreload of mmdet.datasets.custom failed: Traceback (most recent call last):\n", + " File \"/home/xfr/.conda/envs/mmdet/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 244, in check\n", + " superreload(m, reload, self.old_objects)\n", + " File \"/home/xfr/.conda/envs/mmdet/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 378, in superreload\n", + " module = reload(module)\n", + " File \"/home/xfr/.conda/envs/mmdet/lib/python3.6/imp.py\", line 315, in reload\n", + " return importlib.reload(module)\n", + " File \"/home/xfr/.conda/envs/mmdet/lib/python3.6/importlib/__init__.py\", line 166, in reload\n", + " _bootstrap._exec(spec, module)\n", + " File \"\", line 618, in _exec\n", + " File \"\", line 678, in exec_module\n", + " File \"\", line 219, in _call_with_frames_removed\n", + " File \"/home/xfr/git_mm/mmdetection/mmdet/datasets/custom.py\", line 16, in \n", + " class CustomDataset(Dataset):\n", + " File \"/home/xfr/git_mm/mmdetection/mmdet/utils/registry.py\", line 44, in register_module\n", + " self._register_module(cls)\n", + " File \"/home/xfr/git_mm/mmdetection/mmdet/utils/registry.py\", line 40, in _register_module\n", + " module_name, self.name))\n", + "KeyError: 'CustomDataset is already registered in dataset'\n", + "]\n" + ] + } + ], + "source": [ + "a= mmcv.load(\"./data/classes_indices.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "59237\n" + "47506\n", + "47453\n", + "47506\n", + "47492\n", + "47503\n", + "47498\n", + "47321\n", + "47494\n", + "47504\n", + "47504\n", + "47495\n", + "47506\n", + "47498\n", + "47501\n", + "47485\n", + "47505\n", + "47506\n", + "42358\n" + ] + }, + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m19\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + } + ], + "source": [ + "import numpy as np\n", + "for i in range(19):\n", + " print(np.max(a[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'train_dataset' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_indices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'train_dataset' is not defined" ] } ], "source": [ - "print(len(train_dataset.flag))" + "train_dataset.class_indices" ] }, { diff --git a/configs/rscup/htc_ba.py b/configs/rscup/htc_ba.py new file mode 100644 index 0000000..472310f --- /dev/null +++ b/configs/rscup/htc_ba.py @@ -0,0 +1,307 @@ +# model settings +fp16 = dict(loss_scale=512.) +# norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='HybridTaskCascade', + num_stages=3, + pretrained='modelzoo://resnet50', + interleaved=True, + mask_info_flow=True, + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + dcn=dict( + modulated=False, deformable_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + ), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_scales=[8], + anchor_ratios=[0.5, 1.0, 1.5, 2.5, 5.0, 7.0], + anchor_strides=[4, 8, 16, 32, 64], + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0], + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='SharedFCBBoxHead', + num_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=19, + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2], + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', + beta=1.0, + loss_weight=1.0)), + + dict( + type='SharedFCBBoxHead', + num_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=19, + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1], + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', + beta=1.0, + loss_weight=1.0)), + dict( + type='SharedFCBBoxHead', + num_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=19, + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067], + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', + beta=1.0, + loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='HTCMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=19, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))) +# model training and testing settings +train_cfg = dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_across_levels=False, + nms_pre=2000, + nms_post=2000, + max_num=2000, + nms_thr=0.7, + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.4, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + sampler=dict( + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3)), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1), + sampler=dict( + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3)), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + ignore_iof_thr=-1), + sampler=dict( + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3)), + mask_size=28, + pos_weight=-1, + debug=False) + ], + stage_loss_weights=[0.5, 1, 0.25]) +test_cfg = dict( + rpn=dict( + nms_across_levels=False, + nms_pre=1000, + nms_post=1000, + max_num=1000, + nms_thr=0.7, + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_thr=0.5), + max_per_img=1000, + mask_thr_binary=0.5), + keep_all_stages=False) +# dataset settings +dataset_type = 'CocoDataset' +data_root = './data/rscup/' +aug_root = "./data/rscup/aug/" +other_aug_root = "./data/rscup/otheraug/" +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +data = dict( + imgs_per_gpu=6, + workers_per_gpu=4, + train=dict( + type=dataset_type, + ann_file=(data_root + 'annotation/annos_rscup_train.json', + aug_root + 'annos_rscup_airport.json', + other_aug_root + "annos_rscup_baseball-diamond.json", + other_aug_root + "annos_rscup_basketball-court.json", + other_aug_root + "annos_rscup_container-crane.json", + other_aug_root + "annos_rscup_helicopter.json", + other_aug_root + "annos_rscup_helipad.json", + other_aug_root + "annos_rscup_helipad_ship.json", + other_aug_root + "annos_rscup_roundabout.json", + other_aug_root + "annos_rscup_soccer-ball-field_ground-track-field.json", + ), + img_prefix=(data_root + 'train/', + aug_root + "airport/", + other_aug_root + "baseball-diamond", + other_aug_root + "basketball-court", + other_aug_root + "container-crane", + other_aug_root + "helicopter", + other_aug_root + "helipad", + other_aug_root + "helipad_ship", + other_aug_root + "roundabout", + other_aug_root + "soccer-ball-field_ground-track-field"), + img_scale=(512, 512), + img_norm_cfg=img_norm_cfg, + size_divisor=32, + flip_ratio=0.5, + with_mask=True, + with_crowd=True, + with_label=True), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotation/annos_rscup_val.json', + img_prefix=data_root + 'val/', + img_scale=(512, 512), + img_norm_cfg=img_norm_cfg, + size_divisor=32, + flip_ratio=0, + with_mask=True, + with_crowd=True, + with_label=True), + test=dict( + type=dataset_type, + ann_file='./data/rscup/annotation/annos_rscup_val.json', + img_prefix='./data/rscup/val', + img_scale=(512, 512), + img_norm_cfg=img_norm_cfg, + size_divisor=32, + flip_ratio=0, + with_mask=True, + with_label=False, + test_mode=True)) +# optimizer +optimizer = dict(type='SGD', lr=9e-3, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[54, 70]) +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +# runtime settings +total_epochs = 81 +dist_params = dict(backend='nccl') +log_level = 'INFO' +work_dir = './work_dirs/htc_ba' +load_from = None +resume_from = None +workflow = [('train', 1)] diff --git a/configs/rscup/htc_ba_more.py b/configs/rscup/htc_ba_more.py new file mode 100644 index 0000000..2e52aa7 --- /dev/null +++ b/configs/rscup/htc_ba_more.py @@ -0,0 +1,307 @@ +# model settings +fp16 = dict(loss_scale=512.) +# norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='HybridTaskCascade', + num_stages=3, + pretrained='modelzoo://resnet50', + interleaved=True, + mask_info_flow=True, + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + dcn=dict( + modulated=False, deformable_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + ), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_scales=[8], + anchor_ratios=[0.5, 1.0, 1.5, 2.5, 5.0, 7.0], + anchor_strides=[4, 8, 16, 32, 64], + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0], + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='SharedFCBBoxHead', + num_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=19, + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2], + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', + beta=1.0, + loss_weight=1.0)), + + dict( + type='SharedFCBBoxHead', + num_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=19, + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1], + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', + beta=1.0, + loss_weight=1.0)), + dict( + type='SharedFCBBoxHead', + num_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=19, + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067], + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', + beta=1.0, + loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='HTCMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=19, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))) +# model training and testing settings +train_cfg = dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_across_levels=False, + nms_pre=2000, + nms_post=2000, + max_num=2000, + nms_thr=0.7, + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.4, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + sampler=dict( + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3)), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1), + sampler=dict( + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3)), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + ignore_iof_thr=-1), + sampler=dict( + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3)), + mask_size=28, + pos_weight=-1, + debug=False) + ], + stage_loss_weights=[0.5, 1, 0.25]) +test_cfg = dict( + rpn=dict( + nms_across_levels=False, + nms_pre=1000, + nms_post=1000, + max_num=1000, + nms_thr=0.7, + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_thr=0.5), + max_per_img=1000, + mask_thr_binary=0.5), + keep_all_stages=False) +# dataset settings +dataset_type = 'CocoDataset' +data_root = './data/rscup/' +aug_root = "./data/rscup/aug/" +other_aug_root = "./data/rscup/otheraug/" +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +data = dict( + imgs_per_gpu=6, + workers_per_gpu=4, + train=dict( + type=dataset_type, + ann_file=(data_root + 'annotation/annos_rscup_train.json', + aug_root + 'annos_rscup_airport.json', + other_aug_root + "annos_rscup_baseball-diamond.json", + other_aug_root + "annos_rscup_basketball-court.json", + other_aug_root + "annos_rscup_container-crane.json", + other_aug_root + "annos_rscup_helicopter.json", + other_aug_root + "annos_rscup_helipad.json", + other_aug_root + "annos_rscup_helipad_ship.json", + other_aug_root + "annos_rscup_roundabout.json", + other_aug_root + "annos_rscup_soccer-ball-field_ground-track-field.json", + ), + img_prefix=(data_root + 'train/', + aug_root + "airport/", + other_aug_root + "baseball-diamond", + other_aug_root + "basketball-court", + other_aug_root + "container-crane", + other_aug_root + "helicopter", + other_aug_root + "helipad", + other_aug_root + "helipad_ship", + other_aug_root + "roundabout", + other_aug_root + "soccer-ball-field_ground-track-field"), + img_scale=(512, 512), + img_norm_cfg=img_norm_cfg, + size_divisor=32, + flip_ratio=0.5, + with_mask=True, + with_crowd=True, + with_label=True), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotation/annos_rscup_val.json', + img_prefix=data_root + 'val/', + img_scale=(512, 512), + img_norm_cfg=img_norm_cfg, + size_divisor=32, + flip_ratio=0, + with_mask=True, + with_crowd=True, + with_label=True), + test=dict( + type=dataset_type, + ann_file='./data/rscup/annotation/annos_rscup_test.json', + img_prefix='./data/rscup/test', + img_scale=(512, 512), + img_norm_cfg=img_norm_cfg, + size_divisor=32, + flip_ratio=0, + with_mask=True, + with_label=False, + test_mode=True)) +# optimizer +optimizer = dict(type='SGD', lr=9e-3, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[42, 56, 60]) +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +# runtime settings +total_epochs = 64 +dist_params = dict(backend='nccl') +log_level = 'INFO' +work_dir = './work_dirs/htc_ba' +load_from = None +resume_from = None +workflow = [('train', 1)] diff --git a/configs/rscup/htc_sy_ft.py b/configs/rscup/htc_sy_ft.py new file mode 100644 index 0000000..7c59d79 --- /dev/null +++ b/configs/rscup/htc_sy_ft.py @@ -0,0 +1,288 @@ +# model settings +fp16 = dict(loss_scale=512.) +# norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='HybridTaskCascade', + num_stages=3, + pretrained='modelzoo://resnet50', + interleaved=True, + mask_info_flow=True, + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + dcn=dict( + modulated=False, deformable_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + ), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_scales=[8], + anchor_ratios=[0.5, 1.0, 1.5, 2.5, 5.0, 7.0], + anchor_strides=[4, 8, 16, 32, 64], + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0], + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='SharedFCBBoxHead', + num_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=19, + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2], + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', + beta=1.0, + loss_weight=1.0)), + + dict( + type='SharedFCBBoxHead', + num_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=19, + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1], + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', + beta=1.0, + loss_weight=1.0)), + dict( + type='SharedFCBBoxHead', + num_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=19, + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067], + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', + beta=1.0, + loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='HTCMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=19, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))) +# model training and testing settings +train_cfg = dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_across_levels=False, + nms_pre=2000, + nms_post=2000, + max_num=2000, + nms_thr=0.7, + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.4, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + sampler=dict( + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3)), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1), + sampler=dict( + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3)), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + ignore_iof_thr=-1), + sampler=dict( + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3)), + mask_size=28, + pos_weight=-1, + debug=False) + ], + stage_loss_weights=[0.5, 1, 0.25]) +test_cfg = dict( + rpn=dict( + nms_across_levels=False, + nms_pre=1000, + nms_post=1000, + max_num=1000, + nms_thr=0.7, + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_thr=0.5), + max_per_img=1000, + mask_thr_binary=0.5), + keep_all_stages=False) +# dataset settings +dataset_type = 'CocoDataset' +data_root = './data/rscup/' +aug_root = "./data/rscup/aug/" +other_aug_root = "./data/rscup/otheraug/" +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +data = dict( + imgs_per_gpu=6, + workers_per_gpu=4, + train=dict( + type=dataset_type, + ann_file=(data_root + 'annotation/annos_rscup_pesudo.json'), + img_prefix=(data_root + 'pesudo/'), + img_scale=(512, 512), + img_norm_cfg=img_norm_cfg, + size_divisor=32, + flip_ratio=0.5, + with_mask=True, + with_crowd=True, + with_label=True), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotation/annos_rscup_val.json', + img_prefix=data_root + 'val/', + img_scale=(512, 512), + img_norm_cfg=img_norm_cfg, + size_divisor=32, + flip_ratio=0, + with_mask=True, + with_crowd=True, + with_label=True), + test=dict( + type=dataset_type, + ann_file='./data/rscup/annotation/annos_rscup_test.json', + img_prefix='./data/rscup/test', + img_scale=(512, 512), + img_norm_cfg=img_norm_cfg, + size_divisor=32, + flip_ratio=0, + with_mask=True, + with_label=False, + test_mode=True)) +# optimizer +optimizer = dict(type='SGD', lr=9e-4, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[1]) +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +# runtime settings +total_epochs = 2 +dist_params = dict(backend='nccl') +log_level = 'INFO' +work_dir = './work_dirs/htc_sy_ft' +load_from = "./work_dirs/htc_sy/epoch_12.pth" +resume_from = None +workflow = [('train', 1)] diff --git a/mmdet/datasets/custom.py b/mmdet/datasets/custom.py index 89f3ef2..82cbc37 100644 --- a/mmdet/datasets/custom.py +++ b/mmdet/datasets/custom.py @@ -109,6 +109,7 @@ def __init__(self, # set group flag for the sampler if not self.test_mode: self._set_group_flag() + self._set_class_indices() # transforms self.img_transform = ImageTransform( size_divisor=self.size_divisor, **self.img_norm_cfg) @@ -130,6 +131,9 @@ def __init__(self, # augment image and bbox self.AUG = False + + + def __len__(self): return len(self.img_infos) @@ -161,6 +165,25 @@ def _set_group_flag(self): if img_info['width'] / img_info['height'] > 1: self.flag[i] = 1 + def _set_class_indices(self): + """Set flag according to image aspect ratio. + Images with aspect ratio greater than 1 will be set as group 1, + otherwise group 0. + """ + + print("generate class_indices") + self.class_indices = [[] for i in range(len(self.CLASSES))] + print(len(self.class_indices)) + for i in range(len(self)): + labels = self.get_ann_info(i)['labels'] + for label in labels: + self.class_indices[label-1].append(i) + for i in range(len(self.class_indices)): + self.class_indices[i] = np.array(self.class_indices[i]) + print("generate complete!") + + + def _rand_another(self, idx): pool = np.where(self.flag == self.flag[idx])[0] return np.random.choice(pool) diff --git a/mmdet/datasets/dataset_wrappers.py b/mmdet/datasets/dataset_wrappers.py index e749cb0..19256ab 100644 --- a/mmdet/datasets/dataset_wrappers.py +++ b/mmdet/datasets/dataset_wrappers.py @@ -4,6 +4,26 @@ from .registry import DATASETS +# @DATASETS.register_module +# class xxConcatDataset(_ConcatDataset): +# """A wrapper of concatenated dataset. +# +# Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but +# concat the group flag for image aspect ratio. +# +# Args: +# datasets (list[:obj:`Dataset`]): A list of datasets. +# """ +# +# def __init__(self, datasets): +# super(ConcatDataset, self).__init__(datasets) +# self.CLASSES = datasets[0].CLASSES +# if hasattr(datasets[0], 'flag'): +# flags = [] +# for i in range(0, len(datasets)): +# flags.append(datasets[i].flag) +# self.flag = np.concatenate(flags) + @DATASETS.register_module class ConcatDataset(_ConcatDataset): """A wrapper of concatenated dataset. @@ -24,6 +44,17 @@ def __init__(self, datasets): flags.append(datasets[i].flag) self.flag = np.concatenate(flags) + if hasattr(datasets[0], 'class_indices'): + lst = [[] for i in range(len(self.CLASSES))] + ind_offset = 0 + for i in range(0, len(datasets)): + for j in range(0, len(self.CLASSES)): + lst[j].append(datasets[i].class_indices[j] + ind_offset) + ind_offset += len(datasets[i]) + cls_to_ind = [[] for i in range(len(self.CLASSES))] + for i in range(0, len(self.CLASSES)): + cls_to_ind[i] = np.concatenate(lst[i]) + self.cls_to_ind = cls_to_ind @DATASETS.register_module class RepeatDataset(object): diff --git a/mmdet/datasets/loader/sampler.py b/mmdet/datasets/loader/sampler.py index c222eb2..a6a3c83 100644 --- a/mmdet/datasets/loader/sampler.py +++ b/mmdet/datasets/loader/sampler.py @@ -3,7 +3,7 @@ import math import torch import numpy as np - +from icecream import ic from mmcv.runner.utils import get_dist_info from torch.utils.data import Sampler from torch.utils.data import DistributedSampler as _DistributedSampler @@ -35,25 +35,86 @@ def __iter__(self): return iter(indices) +# class GroupSampler(Sampler): +# +# def __init__(self, dataset, samples_per_gpu=1): +# assert hasattr(dataset, 'flag') +# self.dataset = dataset +# self.samples_per_gpu = samples_per_gpu +# self.flag = dataset.flag.astype(np.int64) +# self.group_sizes = np.bincount(self.flag) +# self.num_samples = 0 +# for i, size in enumerate(self.group_sizes): +# self.num_samples += int(np.ceil( +# size / self.samples_per_gpu)) * self.samples_per_gpu +# +# def __iter__(self): +# indices = [] +# for i, size in enumerate(self.group_sizes): +# if size == 0: +# continue +# indice = np.where(self.flag == i)[0] +# assert len(indice) == size +# np.random.shuffle(indice) +# num_extra = int(np.ceil(size / self.samples_per_gpu) +# ) * self.samples_per_gpu - len(indice) +# indice = np.concatenate([indice, indice[:num_extra]]) +# indices.append(indice) +# indices = np.concatenate(indices) +# indices = [ +# indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu] +# for i in np.random.permutation( +# range(len(indices) // self.samples_per_gpu)) +# ] +# indices = np.concatenate(indices) +# indices = indices.astype(np.int64).tolist() +# assert len(indices) == self.num_samples +# return iter(indices) +# +# def __len__(self): +# return self.num_samples + + class GroupSampler(Sampler): - def __init__(self, dataset, samples_per_gpu=1): + def __init__(self, dataset, samples_per_gpu=1, samples_per_cls=1000,): + assert hasattr(dataset, 'cls_to_ind') assert hasattr(dataset, 'flag') self.dataset = dataset + + self.samples_per_cls = samples_per_cls + self.cls_to_ind = dataset.cls_to_ind + for i in range(len(self.cls_to_ind)): + self.cls_to_ind[i] = self.cls_to_ind[i].astype(np.int64) self.samples_per_gpu = samples_per_gpu self.flag = dataset.flag.astype(np.int64) - self.group_sizes = np.bincount(self.flag) - self.num_samples = 0 - for i, size in enumerate(self.group_sizes): - self.num_samples += int(np.ceil( - size / self.samples_per_gpu)) * self.samples_per_gpu + self.weight = np.ones(18) + self.weight[16] = 0.25 + self.weight[10] = 0.6 + self.weight[9] = 0.3 + self.weight = np.array(self.weight, np.int32) def __iter__(self): + indices = np.array([]) + for i in range(len(self.cls_to_ind)): + indices = np.concatenate([indices, np.random.choice(self.cls_to_ind[i], self.weight[ + i]*self.samples_per_cls)]) + np.random.shuffle(indices) + ind1 = indices.copy() + print(ind1.shape) + ind1 = np.array(ind1, np.int32) indices = [] - for i, size in enumerate(self.group_sizes): + flag = self.flag[ind1] + group_sizes = np.bincount(flag) + num_samples = 0 + for i, size in enumerate(group_sizes): + num_samples += int(np.ceil( + size / self.samples_per_gpu)) * self.samples_per_gpu + + for i, size in enumerate(group_sizes): if size == 0: continue - indice = np.where(self.flag == i)[0] + indice = np.where(flag == i)[0] assert len(indice) == size np.random.shuffle(indice) num_extra = int(np.ceil(size / self.samples_per_gpu) @@ -67,9 +128,9 @@ def __iter__(self): range(len(indices) // self.samples_per_gpu)) ] indices = np.concatenate(indices) - indices = indices.astype(np.int64).tolist() - assert len(indices) == self.num_samples - return iter(indices) + indices = torch.from_numpy(indices).long() + assert len(indices) == num_samples + return iter(ind1[indices]) def __len__(self): return self.num_samples @@ -94,7 +155,8 @@ def __init__(self, dataset, samples_per_gpu=1, num_replicas=None, - rank=None): + rank=None, + samples_per_cls=1000): _rank, _num_replicas = get_dist_info() if num_replicas is None: num_replicas = _num_replicas @@ -107,25 +169,46 @@ def __init__(self, self.epoch = 0 assert hasattr(self.dataset, 'flag') - self.flag = self.dataset.flag - self.group_sizes = np.bincount(self.flag) - - self.num_samples = 0 - for i, j in enumerate(self.group_sizes): - self.num_samples += int( - math.ceil(self.group_sizes[i] * 1.0 / self.samples_per_gpu / - self.num_replicas)) * self.samples_per_gpu - self.total_size = self.num_samples * self.num_replicas + assert hasattr(dataset, 'cls_to_ind') + self.samples_per_cls = samples_per_cls + self.cls_to_ind = dataset.cls_to_ind + for i in range(len(self.cls_to_ind)): + self.cls_to_ind[i] = self.cls_to_ind[i].astype(np.int64) + self.flag = self.dataset.flag.astype(np.int64) + self.weight = np.ones(18) + self.weight[16] = 0.25 + self.weight[10] = 0.6 + self.weight[9] = 0.3 + self.weight = np.array(self.weight, np.int32) + self.len = int(np.sum(self.weight*samples_per_cls)/self.num_replicas) def __iter__(self): # deterministically shuffle based on epoch g = torch.Generator() g.manual_seed(self.epoch) + np.random.seed(self.epoch) + indices = np.array([]) + for i in range(len(self.cls_to_ind)): + indices = np.concatenate([indices, np.random.choice(self.cls_to_ind[i], self.weight[i] * + self.samples_per_cls)]) + np.random.shuffle(indices) + ind1 = indices.copy() + ind1 = np.array(ind1, np.int32) + flag = self.flag[ind1] + group_sizes = np.bincount(flag) + num_samples = 0 indices = [] - for i, size in enumerate(self.group_sizes): + ic(group_sizes) + for i, j in enumerate(group_sizes): + num_samples += int( + math.ceil(group_sizes[i] * 1.0 / self.samples_per_gpu / + self.num_replicas)) * self.samples_per_gpu + total_size = num_samples * self.num_replicas + + for i, size in enumerate(group_sizes): if size > 0: - indice = np.where(self.flag == i)[0] + indice = np.where(flag == i)[0] assert len(indice) == size indice = indice[list(torch.randperm(int(size), generator=g))].tolist() @@ -136,7 +219,7 @@ def __iter__(self): indice += indice[:extra] indices += indice - assert len(indices) == self.total_size + assert len(indices) == total_size indices = [ indices[j] for i in list( @@ -147,14 +230,103 @@ def __iter__(self): ] # subsample - offset = self.num_samples * self.rank - indices = indices[offset:offset + self.num_samples] - assert len(indices) == self.num_samples + offset = num_samples * self.rank + ic(self.rank) + ic(offset) + ic(offset+num_samples) + indices = indices[offset:offset + num_samples] + assert len(indices) == num_samples - return iter(indices) + return iter(ind1[indices]) def __len__(self): - return self.num_samples + return self.len def set_epoch(self, epoch): self.epoch = epoch + + + +# class DistributedGroupSampler(Sampler): +# """Sampler that restricts data loading to a subset of the dataset. +# It is especially useful in conjunction with +# :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each +# process can pass a DistributedSampler instance as a DataLoader sampler, +# and load a subset of the original dataset that is exclusive to it. +# .. note:: +# Dataset is assumed to be of constant size. +# Arguments: +# dataset: Dataset used for sampling. +# num_replicas (optional): Number of processes participating in +# distributed training. +# rank (optional): Rank of the current process within num_replicas. +# """ +# +# def __init__(self, +# dataset, +# samples_per_gpu=1, +# num_replicas=None, +# rank=None): +# _rank, _num_replicas = get_dist_info() +# if num_replicas is None: +# num_replicas = _num_replicas +# if rank is None: +# rank = _rank +# self.dataset = dataset +# self.samples_per_gpu = samples_per_gpu +# self.num_replicas = num_replicas +# self.rank = rank +# self.epoch = 0 +# +# assert hasattr(self.dataset, 'flag') +# self.flag = self.dataset.flag +# self.group_sizes = np.bincount(self.flag) +# +# self.num_samples = 0 +# for i, j in enumerate(self.group_sizes): +# self.num_samples += int( +# math.ceil(self.group_sizes[i] * 1.0 / self.samples_per_gpu / +# self.num_replicas)) * self.samples_per_gpu +# self.total_size = self.num_samples * self.num_replicas +# +# def __iter__(self): +# # deterministically shuffle based on epoch +# g = torch.Generator() +# g.manual_seed(self.epoch) +# +# indices = [] +# for i, size in enumerate(self.group_sizes): +# if size > 0: +# indice = np.where(self.flag == i)[0] +# assert len(indice) == size +# indice = indice[list(torch.randperm(int(size), +# generator=g))].tolist() +# extra = int( +# math.ceil( +# size * 1.0 / self.samples_per_gpu / self.num_replicas) +# ) * self.samples_per_gpu * self.num_replicas - len(indice) +# indice += indice[:extra] +# indices += indice +# +# assert len(indices) == self.total_size +# +# indices = [ +# indices[j] for i in list( +# torch.randperm( +# len(indices) // self.samples_per_gpu, generator=g)) +# for j in range(i * self.samples_per_gpu, (i + 1) * +# self.samples_per_gpu) +# ] +# +# # subsample +# offset = self.num_samples * self.rank +# indices = indices[offset:offset + self.num_samples] +# assert len(indices) == self.num_samples +# +# return iter(indices) +# +# def __len__(self): +# return self.num_samples +# +# def set_epoch(self, epoch): +# self.epoch = epoch diff --git a/pipeline.ipynb b/pipeline.ipynb index 2101827..77b16a3 100644 --- a/pipeline.ipynb +++ b/pipeline.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -455,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -471,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -479,7 +479,7 @@ "output_type": "stream", "text": [ "loading annotations into memory...\n", - "Done (t=0.27s)\n", + "Done (t=0.14s)\n", "creating index...\n", "index created!\n", "loading annotations into memory...\n", @@ -491,7 +491,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "837a58fab4934c51985c5fb9588e7088", + "model_id": "3a9121e6dad14edeabe88104d7808570", "version_major": 2, "version_minor": 0 }, @@ -505,7 +505,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d1e665a47fe741ca81e67031e71c32bc", + "model_id": "1225088b002949bca561f1ab488f82f8", "version_major": 2, "version_minor": 0 }, @@ -519,7 +519,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38850b3095254be69364748500bb4567", + "model_id": "4bb481514c1d4722a06859f9b5855d59", "version_major": 2, "version_minor": 0 }, @@ -535,43 +535,43 @@ "output_type": "stream", "text": [ "tennis-court\n", - "1641\n", + "2168\n", "container-crane\n", - "166\n", + "1638\n", "storage-tank\n", - "5272\n", + "12474\n", "baseball-diamond\n", - "1007\n", + "3797\n", "plane\n", - "5723\n", + "8357\n", "ground-track-field\n", - "1413\n", + "3701\n", "helicopter\n", - "369\n", + "1458\n", "airport\n", - "166\n", + "297\n", "harbor\n", - "6916\n", + "23021\n", "ship\n", - "22358\n", + "37121\n", "large-vehicle\n", - "22937\n", + "22211\n", "swimming-pool\n", - "2298\n", + "2647\n", "soccer-ball-field\n", - "635\n", + "560\n", "roundabout\n", - "1780\n", + "6546\n", "basketball-court\n", - "770\n", + "1634\n", "bridge\n", - "5071\n", + "12743\n", "small-vehicle\n", - "53677\n", + "94228\n", "helipad\n", - "62\n", + "280\n", "loading annotations into memory...\n", - "Done (t=0.76s)\n", + "Done (t=0.63s)\n", "creating index...\n", "index created!\n" ] @@ -579,7 +579,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac8a52acd415407a86a8433250c403f6", + "model_id": "5f4c87c7f376401b8bce0d0136ff94b1", "version_major": 2, "version_minor": 0 }, @@ -594,25 +594,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "ap of tennis-court is 0.9424778881995032\n", - "ap of container-crane is 0.03826585397333197\n", - "ap of baseball-diamond is 0.7177681700926734\n", - "ap of ground-track-field is 0.7938305490042241\n", - "ap of storage-tank is 0.6472659062184729\n", - "ap of helicopter is 0.4568732141436756\n", - "ap of airport is 0.8265204840847956\n", - "ap of plane is 0.9157028043556301\n", - "ap of swimming-pool is 0.5720345885379551\n", - "ap of soccer-ball-field is 0.6240614306347341\n", - "ap of harbor is 0.7581349170640616\n", - "ap of roundabout is 0.6464088098417231\n", - "ap of basketball-court is 0.6673074170296184\n", - "ap of helipad is 0.125\n", - "ap of bridge is 0.4678420506961264\n", - "ap of large-vehicle is 0.7637899122542678\n", - "ap of ship is 0.8545456046773818\n", - "ap of small-vehicle is 0.4650249712217823\n", - "map is 0.6268252540016642\n" + "ap of tennis-court is 0.9374226196682458\n", + "ap of container-crane is 0.02476190476190476\n", + "ap of baseball-diamond is 0.674805449868746\n", + "ap of helicopter is 0.3801767727969691\n", + "ap of storage-tank is 0.5795471927924462\n", + "ap of ground-track-field is 0.7906871972044893\n", + "ap of airport is 0.7555911391947803\n", + "ap of plane is 0.9129288090381628\n", + "ap of swimming-pool is 0.5177042654792638\n", + "ap of soccer-ball-field is 0.5843615053908902\n", + "ap of roundabout is 0.5926198064686574\n", + "ap of basketball-court is 0.6493230219911726\n", + "ap of helipad is 0.022727272727272728\n", + "ap of bridge is 0.3758092032716739\n", + "ap of harbor is 0.6250773212473294\n", + "ap of large-vehicle is 0.5458630089239356\n", + "ap of ship is 0.8052994427474273\n", + "ap of small-vehicle is 0.3828733343302612\n", + "map is 0.5643099593279794\n" ] } ], @@ -765,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -773,7 +773,7 @@ "output_type": "stream", "text": [ "loading annotations into memory...\n", - "Done (t=0.13s)\n", + "Done (t=0.14s)\n", "creating index...\n", "index created!\n", "loading annotations into memory...\n", @@ -785,7 +785,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee6e194102504154b351e26cb723652d", + "model_id": "633ce6fdd7844a1d8f2f4c72ee51f2ad", "version_major": 2, "version_minor": 0 }, @@ -808,13 +808,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c5b617404d5e4f32869143f3d2feac19", + "model_id": "57485971a90547f7ab51f2c7b0b41869", "version_major": 2, "version_minor": 0 }, @@ -828,7 +828,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "77b6b62ea2154628bd25b7e4ba254054", + "model_id": "8e4708694dd148ac999375ff47518a46", "version_major": 2, "version_minor": 0 }, @@ -844,47 +844,48 @@ "output_type": "stream", "text": [ "tennis-court\n", - "3035\n", + "1380\n", "container-crane\n", - "2386\n", + "783\n", "storage-tank\n", - "12862\n", + "9184\n", "baseball-diamond\n", - "1169\n", + "564\n", "plane\n", - "2334\n", + "1862\n", "ground-track-field\n", - "2489\n", + "849\n", "helicopter\n", - "193\n", + "332\n", "airport\n", - "919\n", + "617\n", "harbor\n", - "8156\n", + "6840\n", "ship\n", - "38982\n", + "28544\n", "large-vehicle\n", - "16052\n", + "11997\n", "swimming-pool\n", - "3614\n", + "2252\n", "soccer-ball-field\n", - "1572\n", + "722\n", "roundabout\n", - "3944\n", + "2093\n", "basketball-court\n", - "1759\n", + "503\n", "bridge\n", - "11092\n", + "7617\n", "small-vehicle\n", - "56205\n", + "67680\n", "helipad\n", - "558\n" + "175\n" ] } ], "source": [ "ann = mmcv.load(out_file)\n", "ann = nms(ann, \"poly\", 0.5)\n", + "mmcv.dump(ann, \"./result/test_postnms.pkl\")\n", "generate_submit(ann, \"detection\", CLASSES)" ] }, @@ -897,13 +898,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab24e57f8c4c41ffaf4253522952ec54", + "model_id": "be002820e38944ca9628ca8bb66c13f5", "version_major": 2, "version_minor": 0 }, @@ -919,41 +920,41 @@ "output_type": "stream", "text": [ "tennis-court\n", - "3035\n", + "1380\n", "container-crane\n", - "2386\n", + "783\n", "storage-tank\n", - "12862\n", + "9184\n", "baseball-diamond\n", - "1169\n", + "564\n", "plane\n", - "2334\n", + "1862\n", "ground-track-field\n", - "2489\n", + "849\n", "helicopter\n", - "193\n", + "332\n", "airport\n", - "919\n", + "617\n", "harbor\n", - "8156\n", + "6840\n", "ship\n", - "38982\n", + "28544\n", "large-vehicle\n", - "16052\n", + "11997\n", "swimming-pool\n", - "3614\n", + "2252\n", "soccer-ball-field\n", - "1572\n", + "722\n", "roundabout\n", - "3944\n", + "2093\n", "basketball-court\n", - "1759\n", + "503\n", "bridge\n", - "11092\n", + "7617\n", "small-vehicle\n", - "56205\n", + "67680\n", "helipad\n", - "558\n" + "175\n" ] } ],