test2

Dockerfile

+# This is example dockerfile for Food Recognition Benchmark @ AIcrowd
+
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install needed apt packages
+COPY apt.txt apt.txt
+RUN apt -qq update && xargs -a apt.txt apt -qq install -y --no-install-recommends \
+ && rm -rf /var/cache/*
+
+# Create user home directory
+ENV USER aicrowd
+ENV HOME_DIR /home/$USER
+
+# Replace HOST_UID/HOST_GUID with your user / group id
+ENV HOST_UID 1001
+ENV HOST_GID 1001
+
+# Use bash as default shell, rather than sh
+ENV SHELL /bin/bash
+
+# Set up user
+RUN adduser --disabled-password \
+    --gecos "Default user" \
+    --uid ${HOST_UID} \
+    ${USER}
+USER ${USER}
+WORKDIR ${HOME_DIR}
+ENV CONDA_DIR ${HOME_DIR}/.conda
+ENV PATH ${CONDA_DIR}/bin:${PATH}
+
+# Download miniconda for python
+RUN wget -nv -O miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh \
+ && bash miniconda.sh -b -p ${CONDA_DIR} \
+ && . ${CONDA_DIR}/etc/profile.d/conda.sh \
+ && rm -rf miniconda.sh \
+ && conda install pytorch==1.5.1 torchvision==0.6.1 cudatoolkit=10.1 -c pytorch \
+ && conda clean -a -y
+ENV FORCE_CUDA="1"
+
+# Install needed pypi packages
+USER ${USER}
+RUN pip install numpy cython --no-cache-dir
+COPY --chown=1001:1001 requirements.txt requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# (optional)
+# (either add below as dependency in requirements.txt or uncomment below line)
+# RUN pip install -U git+https://github.com/open-mmlab/mmdetection.git@v2.3.0
+
+# Copy user files
+COPY --chown=1001:1001 . ${HOME_DIR}

aicrowd.json

@@ -3,5 +3,6 @@
   "authors" : ["aicrowd-bot"],
   "description" : "Food Recognition Benchmark Submission",
   "license" : "MIT",
-  "gpu": false
-}
+  "gpu": true,
+  "debug": true 
\ No newline at end of file
+}

run.sh

 #!/bin/bash
-python predict.py
+python test.py ./configs/solo.py ./models/epoch_20.pth --json_out $AICROWD_PREDICTIONS_OUTPUT_PATH
+

test.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import time
+import warnings
+
+import mmcv
+import torch
+from mmcv import Config, DictAction
+from mmcv.cnn import fuse_conv_bn
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
+                         wrap_fp16_model)
+
+from mmdet.apis import multi_gpu_test, single_gpu_test
+from mmdet.datasets import (build_dataloader, build_dataset,
+                            replace_ImageToTensor)
+from mmdet.models import build_detector
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDet test (and eval) a model')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument(
+        '--work-dir',
+        help='the directory to save the file containing evaluation metrics')
+    parser.add_argument('--out', help='output result file in pickle format')
+    parser.add_argument(
+        '--fuse-conv-bn',
+        action='store_true',
+        help='Whether to fuse conv and bn, this will slightly increase'
+        'the inference speed')
+    parser.add_argument(
+        '--format-only',
+        action='store_true',
+        help='Format the output results without perform evaluation. It is'
+        'useful when you want to format the result to a specific format and '
+        'submit it to the test server')
+    parser.add_argument(
+        '--eval',
+        type=str,
+        nargs='+',
+        help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
+        ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
+    parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument(
+        '--show-dir', help='directory where painted images will be saved')
+    parser.add_argument(
+        '--show-score-thr',
+        type=float,
+        default=0.3,
+        help='score threshold (default: 0.3)')
+    parser.add_argument(
+        '--gpu-collect',
+        action='store_true',
+        help='whether to use gpu to collect results.')
+    parser.add_argument(
+        '--tmpdir',
+        help='tmp directory used for collecting results from multiple '
+        'workers, available when gpu-collect is not specified')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--options',
+        nargs='+',
+        action=DictAction,
+        help='custom options for evaluation, the key-value pair in xxx=yyy '
+        'format will be kwargs for dataset.evaluate() function (deprecate), '
+        'change to --eval-options instead.')
+    parser.add_argument(
+        '--eval-options',
+        nargs='+',
+        action=DictAction,
+        help='custom options for evaluation, the key-value pair in xxx=yyy '
+        'format will be kwargs for dataset.evaluate() function')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+
+    if args.options and args.eval_options:
+        raise ValueError(
+            '--options and --eval-options cannot be both '
+            'specified, --options is deprecated in favor of --eval-options')
+    if args.options:
+        warnings.warn('--options is deprecated in favor of --eval-options')
+        args.eval_options = args.options
+    return args
+
+
+def main():
+    args = parse_args()
+
+    assert args.out or args.eval or args.format_only or args.show \
+        or args.show_dir, \
+        ('Please specify at least one operation (save/eval/format/show the '
+         'results / save the results) with the argument "--out", "--eval"'
+         ', "--format-only", "--show" or "--show-dir"')
+
+    if args.eval and args.format_only:
+        raise ValueError('--eval and --format_only cannot be both specified')
+
+    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
+        raise ValueError('The output file must be a pkl file.')
+
+    cfg = Config.fromfile(args.config)
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+    # import modules from string list.
+    if cfg.get('custom_imports', None):
+        from mmcv.utils import import_modules_from_strings
+        import_modules_from_strings(**cfg['custom_imports'])
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
+
+    cfg.model.pretrained = None
+    if cfg.model.get('neck'):
+        if isinstance(cfg.model.neck, list):
+            for neck_cfg in cfg.model.neck:
+                if neck_cfg.get('rfp_backbone'):
+                    if neck_cfg.rfp_backbone.get('pretrained'):
+                        neck_cfg.rfp_backbone.pretrained = None
+        elif cfg.model.neck.get('rfp_backbone'):
+            if cfg.model.neck.rfp_backbone.get('pretrained'):
+                cfg.model.neck.rfp_backbone.pretrained = None
+
+    # in case the test dataset is concatenated
+    samples_per_gpu = 1
+    if isinstance(cfg.data.test, dict):
+        cfg.data.test.test_mode = True
+        samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
+        if samples_per_gpu > 1:
+            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
+            cfg.data.test.pipeline = replace_ImageToTensor(
+                cfg.data.test.pipeline)
+    elif isinstance(cfg.data.test, list):
+        for ds_cfg in cfg.data.test:
+            ds_cfg.test_mode = True
+        samples_per_gpu = max(
+            [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test])
+        if samples_per_gpu > 1:
+            for ds_cfg in cfg.data.test:
+                ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
+
+    # init distributed env first, since logger depends on the dist info.
+    if args.launcher == 'none':
+        distributed = False
+    else:
+        distributed = True
+        init_dist(args.launcher, **cfg.dist_params)
+
+    rank, _ = get_dist_info()
+    # allows not to create
+    if args.work_dir is not None and rank == 0:
+        mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
+        timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+        json_file = osp.join(args.work_dir, f'eval_{timestamp}.json')
+
+    # build the dataloader
+    dataset = build_dataset(cfg.data.test)
+    data_loader = build_dataloader(
+        dataset,
+        samples_per_gpu=samples_per_gpu,
+        workers_per_gpu=cfg.data.workers_per_gpu,
+        dist=distributed,
+        shuffle=False)
+
+    # build the model and load checkpoint
+    cfg.model.train_cfg = None
+    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
+    fp16_cfg = cfg.get('fp16', None)
+    if fp16_cfg is not None:
+        wrap_fp16_model(model)
+    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
+    if args.fuse_conv_bn:
+        model = fuse_conv_bn(model)
+    # old versions did not save class info in checkpoints, this walkaround is
+    # for backward compatibility
+    if 'CLASSES' in checkpoint.get('meta', {}):
+        model.CLASSES = checkpoint['meta']['CLASSES']
+    else:
+        model.CLASSES = dataset.CLASSES
+
+    if not distributed:
+        model = MMDataParallel(model, device_ids=[0])
+        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
+                                  args.show_score_thr)
+    else:
+        model = MMDistributedDataParallel(
+            model.cuda(),
+            device_ids=[torch.cuda.current_device()],
+            broadcast_buffers=False)
+        outputs = multi_gpu_test(model, data_loader, args.tmpdir,
+                                 args.gpu_collect)
+
+    rank, _ = get_dist_info()
+    if rank == 0:
+        if args.out:
+            print(f'\nwriting results to {args.out}')
+            mmcv.dump(outputs, args.out)
+        kwargs = {} if args.eval_options is None else args.eval_options
+        if args.format_only:
+            dataset.format_results(outputs, **kwargs)
+        if args.eval:
+            eval_kwargs = cfg.get('evaluation', {}).copy()
+            # hard-code way to remove EvalHook args
+            for key in [
+                    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
+                    'rule'
+            ]:
+                eval_kwargs.pop(key, None)
+            eval_kwargs.update(dict(metric=args.eval, **kwargs))
+            metric = dataset.evaluate(outputs, **eval_kwargs)
+            print(metric)
+            metric_dict = dict(config=args.config, metric=metric)
+            if args.work_dir is not None and rank == 0:
+                mmcv.dump(metric_dict, json_file)
+
+
+if __name__ == '__main__':
+    main()