{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"%%bash\n# Check nvcc version\nnvcc -V\necho\n# Check GCC version\ngcc --version\necho\n# Check the version of torch and cuda packages\npip list | grep \"torch\\|cuda\"","metadata":{"id":"320cDWMGgsVb","outputId":"86ab66f9-6812-4ba0-80e4-de882ba8cca1","execution":{"iopub.status.busy":"2021-08-03T14:04:16.426997Z","iopub.execute_input":"2021-08-03T14:04:16.427376Z","iopub.status.idle":"2021-08-03T14:04:18.718635Z","shell.execute_reply.started":"2021-08-03T14:04:16.427291Z","shell.execute_reply":"2021-08-03T14:04:18.717624Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install mmcv-full==1.3.8 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html","metadata":{"id":"RG3ZA4TYhFUx","outputId":"fd3c8d2b-da23-43aa-a8b1-4dab01cfdfcc","_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-08-03T14:04:18.720501Z","iopub.execute_input":"2021-08-03T14:04:18.720856Z","iopub.status.idle":"2021-08-03T14:04:32.884153Z","shell.execute_reply.started":"2021-08-03T14:04:18.720811Z","shell.execute_reply":"2021-08-03T14:04:32.883184Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!rm -rf mmdetection\n!git clone https://github.com/open-mmlab/mmdetection.git\n!cd mmdetection && pip install -e .\n\n!pip install Pillow==7.0.0","metadata":{"id":"iJshzji7hHop","outputId":"7893d0dc-9fb0-46d2-c2b0-d95834125ff6","_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-08-03T14:04:32.886549Z","iopub.execute_input":"2021-08-03T14:04:32.88699Z","iopub.status.idle":"2021-08-03T14:05:10.790465Z","shell.execute_reply.started":"2021-08-03T14:04:32.88692Z","shell.execute_reply":"2021-08-03T14:05:10.789278Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Setup Weights & Biases","metadata":{}},{"cell_type":"code","source":"!pip install wandb --upgrade","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-08-04T08:54:25.626527Z","iopub.status.idle":"2021-08-04T08:54:25.627211Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\nTo connect the Kaggle Notebook and log in to Weights & Biases, we need to create an API key:\n\n1. New users can sign up for a Free Weights & Biases account for Research and Personal use from the https://wandb.ai/site page. Sign up process takes around 1-2 minutes.\n2. Now get the API key from https://wandb.ai/authorize.\n\nLogin to Weights & Biases from the notebook with the API key by using any of the two methods below:\n\n* Interative:\n 1. Run a cell with wandb.login(). It will ask for the API key, which can be copied and pasted to authenticate.\n\n* Kaggle Secrets:\n 1. The recommended way to use the API key is to use Kaggle Secrets to store the API key. From the top Menu on the Notebook Editor, click on 'Add-ons' and then, select 'Secrets'.\n 2. Select 'Add a new secret' and provide 'wandb_key' for label and it's value as the API key obtained from the previous steps.","metadata":{}},{"cell_type":"code","source":"import wandb\nfrom kaggle_secrets import UserSecretsClient\n\nuser_secrets = UserSecretsClient()\n\n# I have saved my API token with \"wandb_api\" as Label. \n# If you use some other Label make sure to change the same below. \nwandb_api = user_secrets.get_secret(\"wandbkey\") \n\nwandb.login(key=wandb_api)","metadata":{"execution":{"iopub.status.busy":"2021-08-04T08:54:25.628677Z","iopub.status.idle":"2021-08-04T08:54:25.629251Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Imports and Seed Everything","metadata":{}},{"cell_type":"code","source":"import sys\nsys.path.insert(0, \"./mmdetection\")\n\nimport os\n# Check Pytorch installation\nimport torch, torchvision\nprint(torch.__version__, torch.cuda.is_available())\n\n# Check mmcv installation\nfrom mmcv.ops import get_compiling_cuda_version, get_compiler_version\nprint(get_compiling_cuda_version())\nprint(get_compiler_version())\n\n# Check MMDetection installation\nfrom mmdet.apis import set_random_seed\n\n# Imports\nimport mmdet\nfrom mmdet.apis import set_random_seed\nfrom mmdet.datasets import build_dataset\nfrom mmdet.models import build_detector\nfrom mmdet.apis import train_detector\n\nimport random\nimport numpy as np\nfrom pathlib import Path","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:25.094144Z","iopub.execute_input":"2021-08-03T14:05:25.094532Z","iopub.status.idle":"2021-08-03T14:05:40.179244Z","shell.execute_reply.started":"2021-08-03T14:05:25.094491Z","shell.execute_reply":"2021-08-03T14:05:40.178304Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"global_seed = 111\n\ndef set_seed(seed=global_seed):\n \"\"\"Sets the random seeds.\"\"\"\n set_random_seed(seed, deterministic=False)\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n os.environ['PYTHONHASHSEED'] = str(seed)\n \nset_seed()","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:40.180731Z","iopub.execute_input":"2021-08-03T14:05:40.181183Z","iopub.status.idle":"2021-08-03T14:05:40.192713Z","shell.execute_reply.started":"2021-08-03T14:05:40.181136Z","shell.execute_reply":"2021-08-03T14:05:40.191755Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"🔨 Prepare the MMDetection Config","metadata":{}},{"cell_type":"code","source":"import os\n\nos.listdir('/kaggle/working/mmdetection/configs/vfnet/')","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:40.196644Z","iopub.execute_input":"2021-08-03T14:05:40.196917Z","iopub.status.idle":"2021-08-03T14:05:40.205334Z","shell.execute_reply.started":"2021-08-03T14:05:40.196891Z","shell.execute_reply":"2021-08-03T14:05:40.204003Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from mmcv import Config\n\n# cfg = Config.fromfile('/kaggle/working/mmdetection/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py')\n# cfg = Config.fromfile(\"/kaggle/working/mmdetection/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py\")\n# cfg = Config.fromfile(\"/kaggle/working/mmdetection/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py\")\n# baseline_cfg_path = \"/kaggle/working/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py\"\n# baseline_cfg_path = \"/kaggle/working/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py\"\nbaseline_cfg_path = \"/kaggle/working/mmdetection/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py\"\ncfg = Config.fromfile(baseline_cfg_path)","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:40.20759Z","iopub.execute_input":"2021-08-03T14:05:40.207953Z","iopub.status.idle":"2021-08-03T14:05:40.243567Z","shell.execute_reply.started":"2021-08-03T14:05:40.207915Z","shell.execute_reply":"2021-08-03T14:05:40.242811Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"General Training Settings\n","metadata":{}},{"cell_type":"code","source":"# model_name = 'vfnet_r50_fpn'\n# model_name = 'cascade_rcnn_r50_fpn'\nmodel_name = 'vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco'\nfold = 0\njob = 4\n\n# Folder to store model logs and weight files\njob_folder = f'/kaggle/working/job{job}_{model_name}_fold{fold}'\ncfg.work_dir = job_folder\n\n# Change the wnd username and project name below\nwnb_username = 'muhammad4hmed'\nwnb_project_name = 'siim-covid19-1'\n\n# Set seed thus the results are more reproducible\ncfg.seed = global_seed\n\nif not os.path.exists(job_folder):\n os.makedirs(job_folder)\n\nprint(\"Job folder:\", job_folder)","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:40.244813Z","iopub.execute_input":"2021-08-03T14:05:40.245172Z","iopub.status.idle":"2021-08-03T14:05:40.251938Z","shell.execute_reply.started":"2021-08-03T14:05:40.245137Z","shell.execute_reply":"2021-08-03T14:05:40.250916Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set the number of classes\n# for head in cfg.model.roi_head.bbox_head:\n# head.num_classes = 1\n# cfg.model.num_classes = 1\n# cfg.model.roi_head.bbox_head.num_classes = 1\ncfg.model.bbox_head.num_classes = 1\n\n# cfg.gpu_ids = range(1)\ncfg.gpu_ids = [0]\n\n# Setting pretrained model in the init_cfg which is required \n# for transfer learning as per the latest MMdetection update\ncfg.model.backbone.init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')\ncfg.model.backbone.init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')\ncfg.model.pop('pretrained', None)\n\ncfg.runner.max_epochs = 6 # Epochs for the runner that runs the workflow \ncfg.total_epochs = 6\n\n# Learning rate of optimizers. The LR is divided by 8 since the config file is originally for 8 GPUs\ncfg.optimizer.lr = 0.02/8\n\n## Learning rate scheduler config used to register LrUpdater hook\ncfg.lr_config = dict(\n policy='CosineAnnealing', # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9.\n by_epoch=False,\n warmup='linear', # The warmup policy, also support `exp` and `constant`.\n warmup_iters=500, # The number of iterations for warmup\n warmup_ratio=0.001, # The ratio of the starting learning rate used for warmup\n min_lr=1e-07)\n\n# config to register logger hook\ncfg.log_config.interval = 20 # Interval to print the log\n\n# Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.\ncfg.checkpoint_config.interval = 1 # The save interval is 1","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:40.253527Z","iopub.execute_input":"2021-08-03T14:05:40.254248Z","iopub.status.idle":"2021-08-03T14:05:40.264001Z","shell.execute_reply.started":"2021-08-03T14:05:40.254208Z","shell.execute_reply":"2021-08-03T14:05:40.263081Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Configure Datasets for Training and Evaluation","metadata":{}},{"cell_type":"code","source":"cfg.dataset_type = 'CocoDataset' # Dataset type, this will be used to define the dataset\ncfg.classes = (\"Covid_Abnormality\",)\n\ncfg.data.train.img_prefix = '/kaggle/input/siim-covid19-512-images-and-metadata/train' # Prefix of image path\ncfg.data.train.classes = cfg.classes\ncfg.data.train.ann_file = f'/kaggle/input/siim-covid19-coco-512x512-groupkfold/train_annotations_fold{fold}.json'\ncfg.data.train.type='CocoDataset'\n\ncfg.data.val.img_prefix = '/kaggle/input/siim-covid19-512-images-and-metadata/train' # Prefix of image path\ncfg.data.val.classes = cfg.classes\ncfg.data.val.ann_file = f'/kaggle/input/siim-covid19-coco-512x512-groupkfold/val_annotations_fold{fold}.json'\ncfg.data.val.type='CocoDataset'\n\ncfg.data.test.img_prefix = '/kaggle/input/siim-covid19-512-images-and-metadata/train' # Prefix of image path\ncfg.data.test.classes = cfg.classes\ncfg.data.test.ann_file = f'/kaggle/input/siim-covid19-coco-512x512-groupkfold/val_annotations_fold{fold}.json'\ncfg.data.test.type='CocoDataset'\n\ncfg.data.samples_per_gpu = 2 # Batch size of a single GPU used in testing\ncfg.data.workers_per_gpu = 2 # Worker to pre-fetch data for each single GPU","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:40.265371Z","iopub.execute_input":"2021-08-03T14:05:40.265746Z","iopub.status.idle":"2021-08-03T14:05:40.276816Z","shell.execute_reply.started":"2021-08-03T14:05:40.265706Z","shell.execute_reply":"2021-08-03T14:05:40.275865Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Setting Metric for Evaluation","metadata":{}},{"cell_type":"code","source":"# The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details.\ncfg.evaluation.metric = 'bbox' # Metrics used during evaluation\n\n# Set the epoch intervel to perform evaluation\ncfg.evaluation.interval = 1\n\n# Set the iou threshold of the mAP calculation during evaluation\ncfg.evaluation.iou_thrs = [0.5]\n\n# cfg.evaluation.save_best='bbox_mAP_50'","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:40.280024Z","iopub.execute_input":"2021-08-03T14:05:40.280934Z","iopub.status.idle":"2021-08-03T14:05:40.290181Z","shell.execute_reply.started":"2021-08-03T14:05:40.280899Z","shell.execute_reply":"2021-08-03T14:05:40.289123Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Prepare the Pre-processing & Augmentation Pipelines","metadata":{}},{"cell_type":"code","source":"albu_train_transforms = [\n dict(type='ShiftScaleRotate', shift_limit=0.0625,\n scale_limit=0.15, rotate_limit=15, p=0.4),\n dict(type='RandomBrightnessContrast', brightness_limit=0.2,\n contrast_limit=0.2, p=0.5),\n dict(type='IAAAffine', shear=(-10.0, 10.0), p=0.4),\n# dict(type='MixUp', p=0.2, lambd=0.5),\n dict(type=\"Blur\", p=1.0, blur_limit=7),\n dict(type='CLAHE', p=0.5),\n dict(type='Equalize', mode='cv', p=0.4),\n dict(\n type=\"OneOf\",\n transforms=[\n dict(type=\"GaussianBlur\", p=1.0, blur_limit=7),\n dict(type=\"MedianBlur\", p=1.0, blur_limit=7),\n ],\n p=0.4,\n ),\n \n# dict(type='MixUp', p=0.2, lambd=0.5),\n# dict(type='RandomRotate90', p=0.5),\n# dict(type='CLAHE', p=0.5),\n# dict(type='InvertImg', p=0.5),\n# dict(type='Equalize', mode='cv', p=0.4),\n# dict(type='MedianBlur', blur_limit=3, p=0.1)\n ]\n\n\ncfg.train_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Albu',\n transforms=albu_train_transforms,\n bbox_params=dict(\n type='BboxParams',\n format='pascal_voc',\n label_fields=['gt_labels'],\n min_visibility=0.0,\n filter_lost_elements=True),\n keymap=dict(img='image', gt_bboxes='bboxes'),\n update_pad_shape=False,\n skip_img_without_anno=True),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n]\ncfg.test_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:40.291563Z","iopub.execute_input":"2021-08-03T14:05:40.292062Z","iopub.status.idle":"2021-08-03T14:05:40.307804Z","shell.execute_reply.started":"2021-08-03T14:05:40.29202Z","shell.execute_reply":"2021-08-03T14:05:40.306676Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Weights & Biases Integration for Experiment Tracking and Logging","metadata":{}},{"cell_type":"code","source":"## 4, 8\n# cfg.log_level = 'DEBUG'\ncfg.log_config.hooks = [dict(type='TextLoggerHook'),\n dict(type='WandbLoggerHook',\n init_kwargs=dict(project=wnb_project_name,\n name=f'exp-{model_name}-fold{fold}-job{job}',\n entity=wnb_username))\n ]","metadata":{"execution":{"iopub.status.busy":"2021-08-03T14:05:40.309568Z","iopub.execute_input":"2021-08-03T14:05:40.310265Z","iopub.status.idle":"2021-08-03T14:05:40.318131Z","shell.execute_reply.started":"2021-08-03T14:05:40.310223Z","shell.execute_reply":"2021-08-03T14:05:40.317111Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Save Config File","metadata":{}},{"cell_type":"code","source":"cfg_path = f'{job_folder}/job{job}_{Path(baseline_cfg_path).name}'\nprint(cfg_path)\n\n# Save config file for inference later\ncfg.dump(cfg_path)\nprint(f'Config:\\n{cfg.pretty_text}')","metadata":{"id":"9C8s78L_hY2P","outputId":"5c532360-7684-42e1-bb2b-e59377576c2e","_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-08-03T14:05:40.31947Z","iopub.execute_input":"2021-08-03T14:05:40.319886Z","iopub.status.idle":"2021-08-03T14:05:42.705688Z","shell.execute_reply.started":"2021-08-03T14:05:40.319849Z","shell.execute_reply":"2021-08-03T14:05:42.704791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"🚀 Build Dataset and Start Training","metadata":{}},{"cell_type":"code","source":"model = build_detector(cfg.model,\n train_cfg=cfg.get('train_cfg'),\n test_cfg=cfg.get('test_cfg'))\nmodel.init_weights()","metadata":{"id":"o6u_0gZuhcUy","outputId":"557284a4-a687-474a-aa4d-2abbb8fb403c","execution":{"iopub.status.busy":"2021-08-03T14:05:42.708748Z","iopub.execute_input":"2021-08-03T14:05:42.709064Z","iopub.status.idle":"2021-08-03T14:06:23.527672Z","shell.execute_reply.started":"2021-08-03T14:05:42.709032Z","shell.execute_reply":"2021-08-03T14:06:23.526695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"datasets = [build_dataset(cfg.data.train)]","metadata":{"id":"om_JbWv9heIQ","outputId":"0f264828-5b49-4a30-b035-9635424cd2d1","execution":{"iopub.status.busy":"2021-08-03T14:06:23.529147Z","iopub.execute_input":"2021-08-03T14:06:23.52971Z","iopub.status.idle":"2021-08-03T14:06:23.638242Z","shell.execute_reply.started":"2021-08-03T14:06:23.529666Z","shell.execute_reply":"2021-08-03T14:06:23.637122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"https://wandb.ai/muhammad4hmed/siim-covid19-1","metadata":{}},{"cell_type":"code","source":"train_detector(model, datasets[0], cfg, distributed=False, validate=True)","metadata":{"id":"anIjmmhVhgKE","outputId":"9a97104b-5d08-4aa7-ce9c-be376e789ea5","execution":{"iopub.status.busy":"2021-08-03T14:06:23.63974Z","iopub.execute_input":"2021-08-03T14:06:23.640136Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Get the best epoch number\nimport json\nfrom collections import defaultdict\n\nlog_file = f'{job_folder}/None.log.json'\n\n# Source: mmdetection/tools/analysis_tools/analyze_logs.py \ndef load_json_logs(json_logs):\n # load and convert json_logs to log_dict, key is epoch, value is a sub dict\n # keys of sub dict is different metrics, e.g. memory, bbox_mAP\n # value of sub dict is a list of corresponding values of all iterations\n log_dicts = [dict() for _ in json_logs]\n for json_log, log_dict in zip(json_logs, log_dicts):\n with open(json_log, 'r') as log_file:\n for line in log_file:\n log = json.loads(line.strip())\n # skip lines without `epoch` field\n if 'epoch' not in log:\n continue\n epoch = log.pop('epoch')\n if epoch not in log_dict:\n log_dict[epoch] = defaultdict(list)\n for k, v in log.items():\n log_dict[epoch][k].append(v)\n return log_dicts\n\nlog_dict = load_json_logs([log_file])\n# [(print(inner['bbox_mAP']) for inner in item) for item in log_dict]\n# [print(item) for item in log_dict[0]]\nbest_epoch = np.argmax([item['bbox_mAP'][0] for item in log_dict[0].values()])+1\nbest_epoch","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_files = [f'{job_folder}/epoch_{best_epoch}.pth',\n cfg_path\n ]\n\n# Create a new wnb run for saving models as artifacts\nrun = wandb.init(project=wnb_project_name,\n name=f'models_files_{model_name}_fold{fold}_job{job}',\n entity=wnb_username,\n group='Artifact',\n job_type='model-files')\n\nartifact = wandb.Artifact(f'models_files_{model_name}_fold{fold}_job{job}', type='model')\n\nfor model_file in model_files:\n artifact.add_file(model_file)\n\nrun.log_artifact(artifact)\nrun.finish()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"📰 Inference and Visualize Output","metadata":{}},{"cell_type":"code","source":"import mmcv\nfrom mmdet.models import build_detector\nfrom mmcv.runner import load_checkpoint\nfrom mmcv.parallel import MMDataParallel\nfrom mmdet.datasets import build_dataloader, build_dataset\nfrom mmdet.apis import single_gpu_test\nfrom mmdet.apis import init_detector, inference_detector, show_result_pyplot\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom pathlib import Path\nimport cv2\nimport json","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with open(\"../input/siim-covid19-coco-512x512-groupkfold/val_annotations_fold0.json\") as f:\n val_ann = json.load(f)\nimagepaths = [item['file_name'] for item in val_ann['images'][:9]]\n\ndf_annotations = pd.read_csv('../input/siim-covid19-512-images-and-metadata/df_train_processed_meta.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def draw_bbox(image,\n box,\n label,\n color,\n label_size = 0.5,\n alpha_box = 0.3,\n alpha_label = 0.6):\n \n overlay_bbox = image.copy()\n overlay_label = image.copy()\n output = image.copy()\n\n text_width, text_height = cv2.getTextSize(label.upper(),\n cv2.FONT_HERSHEY_SIMPLEX, label_size, 1)[0]\n cv2.rectangle(overlay_bbox, (box[0], box[1]), (box[2], box[3]),\n color, -1)\n cv2.addWeighted(overlay_bbox, alpha_box, output, 1-alpha_box, 0, output)\n \n cv2.rectangle(overlay_label, (box[0], box[1]-7-text_height),\n (box[0]+text_width+2, box[1]), (0, 0, 0), -1)\n cv2.addWeighted(overlay_label, alpha_label, output, 1-alpha_label, 0, output)\n output = cv2.rectangle(output, (box[0], box[1]), (box[2], box[3]),\n color, 2)\n cv2.putText(output, label.upper(), (box[0], box[1]-5),\n cv2.FONT_HERSHEY_SIMPLEX, label_size, (255, 255, 255), 1, cv2.LINE_AA)\n return output","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"checkpoint = f'{job_folder}/epoch_{best_epoch}.pth'\n\nprint(\"Loading weights from:\", checkpoint)\ncfg = Config.fromfile(cfg_path)\nmodel = init_detector(cfg, checkpoint, device='cuda:0')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"new_size = (512, 512)\nimgs_path = \"/kaggle/input/siim-covid19-512-images-and-metadata/train\"\nthreshold = 0.45\n\nfig, axes = plt.subplots(3,3, figsize=(19,21))\nfig.subplots_adjust(hspace=0.2, wspace=0.2)\naxes = axes.ravel()\n\nresults_list = []\n\nfor idx, img_id in enumerate(imagepaths):\n img_path = os.path.join(imgs_path, img_id)\n img = cv2.imread(img_path)\n result = inference_detector(model, img_path)\n results_filtered = result[0][result[0][:, 4]>threshold]\n bboxes = results_filtered[:, :4]\n scores = results_filtered[:, 4] \n results_list.append(result[0])\n \n for box in bboxes:\n img = draw_bbox(img, list(np.int_(box)), \"Covid_Abnormality\",\n (255, 243, 0))\n\n axes[idx].imshow(img, cmap='gray')\n axes[idx].set_title(img_id, size=18, pad=30)\n axes[idx].set_xticklabels([])\n axes[idx].set_yticklabels([])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Interactively Visualize & Analyze Output in Dashboard","metadata":{}},{"cell_type":"code","source":"run = wandb.init(project=wnb_project_name,\n name=f'images-{model_name}-fold{fold}-job{job}',\n job_type='images')\n\nclass_id_to_label = {\n 1: \"pred_covid_abnormality\",\n 2: \"GT_covid_abnormality\"\n}\n\nwnb_images = []\n\nfor img_id, result in zip(imagepaths, results_list):\n \n bboxes = result[:, :4]\n scores = result[:, 4]\n ann_dict = {\"predictions\":{\n \"box_data\":[],\n \"class_labels\": class_id_to_label\n },\n \"ground_truth\":{\n \"box_data\":[],\n \"class_labels\": class_id_to_label\n }\n }\n\n for box, score in zip(bboxes, scores):\n single_data = {\n # one box expressed in the default relative/fractional domain\n \"position\": {\n \"minX\": round(float(box[0])/512, 3),\n \"maxX\": round(float(box[2])/512, 3),\n \"minY\": round(float(box[1])/512, 3),\n \"maxY\": round(float(box[3])/512, 3),\n },\n \"class_id\" : 1,\n \"box_caption\": class_id_to_label[1],\n \"scores\" : {\n \"confidence\": float(score),\n }\n }\n ann_dict[\"predictions\"][\"box_data\"].append(single_data)\n\n image_annotations = df_annotations[df_annotations.id==img_id.strip('.png')]\n\n for idxx, row in image_annotations[['xmin', 'ymin', 'xmax', 'ymax']].iterrows():\n single_data = {\n # one box expressed in the default relative/fractional domain\n \"position\": {\n \"minX\": round(float(row[0])/512, 3),\n \"maxX\": round(float(row[2])/512, 3),\n \"minY\": round(float(row[1])/512, 3),\n \"maxY\": round(float(row[3])/512, 3),\n },\n \"class_id\" : 2,\n \"box_caption\": class_id_to_label[2],\n \"scores\" : {\n \"confidence\": 1.0,\n }\n }\n ann_dict[\"ground_truth\"][\"box_data\"].append(single_data)\n\n image = cv2.imread(os.path.join(imgs_path, img_id))\n wnb_images.append(wandb.Image(image, boxes=ann_dict))\n \nwandb.log({f'images-{model_name}-fold{fold}-job{job}': wnb_images})\n\nrun.finish()\nrun","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"🚩 Click on the ⚙️ icon to change the bbox threshold\n\n🚩 Visit the training job run page and the images run page to see the interactive visualizations of training metrics and images in detail\n\n\n","metadata":{}},{"cell_type":"code","source":"!rm -rf mmdetection/","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Additional Resources\n\n 🔖 [MMDetection Documentation](https://mmdetection.readthedocs.io/en/latest/)\n\n 🔖 [MDetection Github Repository](https://github.com/open-mmlab/mmdetection)\n\n 🔖 [Weights & Biases Documentation](https://docs.wandb.ai/)\n\n 🔖 [Weights & Biases Github Repository](https://github.com/wandb/examples)\n","metadata":{}},{"cell_type":"markdown","source":"