add detr model

training/benchmarks/detr/README.md

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+### 模型信息
+
+#### 模型介绍
+Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based global loss, which forces unique predictions via bipartite matching, and a Transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. Due to this parallel nature, DETR is very fast and efficient.
+#### 模型代码来源
+| repo    | commmit_id  | date |
+|  ----  | ----  |----  |
+| [detr](https://github.com/facebookresearch/detr) |3af9fa878e73b6894ce3596450a8d9b89d918ca9 |2023-2-7 05:12:31|
+
+This repository includes code is licensed under the Apache License, Version 2.0.
+
+Some of the files in this directory were modified by BAAI in 2023 to support FlagPerf.
+
+### 数据集
+#### 数据集下载地址
+  COCO2017数据集
+  COCO官网地址：https://cocodataset.org/
+
+#### 预处理
+
+这里以下载coco2017数据集为例，主要下载三个文件：
+- 2017 Train images [118K/18GB]：训练过程中使用到的所有图像文件
+- 2017 Val images [5K/1GB]：验证过程中使用到的所有图像文件
+- 2017 Train/Val annotations [241MB]：对应训练集和验证集的标注json文件
+都解压到coco2017文件夹下，可得到如下文件夹结构：
+
+```bash
+├── coco2017: # 数据集根目录
+     ├── train2017: # 所有训练图像文件夹(118287张)
+     ├── val2017: # 所有验证图像文件夹(5000张)
+     └── annotations: # 对应标注文件夹
+              ├── instances_train2017.json: # 对应目标检测、分割任务的训练集标注文件
+              ├── instances_val2017.json: # 对应目标检测、分割任务的验证集标注文件
+              ├── captions_train2017.json: # 对应图像描述的训练集标注文件
+              ├── captions_val2017.json: # 对应图像描述的验证集标注文件
+              ├── person_keypoints_train2017.json: # 对应人体关键点检测的训练集标注文件
+              └── person_keypoints_val2017.json: # 对应人体关键点检测的验证集标注文件夹
+```

training/benchmarks/detr/pytorch/config/__init__.py

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+from ._base import *
+from .mutable_params import mutable_params

training/benchmarks/detr/pytorch/config/_base.py

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+# case info
+vendor: str = None
+data_dir: str = None
+name: str = "detr"
+cudnn_benchmark: bool = False
+cudnn_deterministic: bool = True
+
+# =========================================================
+# data
+# =========================================================
+train_data: str = "train"
+eval_data: str = "val"
+
+
+# =========================================================
+# Model
+# =========================================================
+lr = 1e-4
+lr_backbone=1e-5
+lr_drop = 200
+weight_decay = 1e-4
+clip_max_norm = 0.1
+model_name = 'transformer'
+backbone = 'resnet50'
+dilation = False
+position_embedding = 'sine'
+enc_layers = 6
+dec_layers = 6
+dim_feedforward = 2048
+hidden_dim = 256
+dropout = 0.1
+nheads = 8
+num_queries = 100
+pre_norm = False
+masks = False
+
+
+# -----------------------------------------------------------------------------
+# Loss
+# -----------------------------------------------------------------------------
+#Disables auxiliary decoding losses (loss at each layer)
+aux_loss = True
+# Class coefficient in the matching cost
+set_cost_class = 1
+# L1 box coefficient in the matching cost
+set_cost_bbox = 5
+# giou box coefficient in the matching cost
+set_cost_giou = 2
+mask_loss_coef = 1
+dice_loss_coef = 1
+bbox_loss_coef = 5
+giou_loss_coef = 2
+# Relative classification weight of the no-object class
+eos_coef = 0.1
+
+
+# =========================================================
+# train && evaluate
+# =========================================================
+train_batch_size: int = 128
+eval_batch_size: int = 128
+
+target_mAP: float = 0.42
+
+start_epoch = 0
+epochs = 300
+
+do_train = True
+fp16 = False
+distributed: bool = True
+warmup = 0.1
+
+
+# =========================================================
+# utils
+# =========================================================
+dataset_file = 'coco'
+output_dir = ''
+data_dir = ''
+seed: int = 42
+dist_backend: str = 'nccl'
+num_workers: int = 1
+device : str = None
+resume =''
+
+# =========================================================
+# for driver
+# =========================================================
+local_rank: int = -1
+use_env: bool = True
+log_freq: int = 100
+print_freq: int = 100
+n_device: int = 1
+amp: bool = False
+sync_bn: bool = False
+gradient_accumulation_steps: int = 1
+
+
+# -----------------------------------------------------------------------------
+# distributed training parameters
+# -----------------------------------------------------------------------------
+local_rank: int = -1
+use_env: bool = True
+log_freq: int = 100
+print_freq: int = 100
+n_device: int = 1
+amp: bool = False
+sync_bn: bool = False
+gradient_accumulation_steps: int = 1

training/benchmarks/detr/pytorch/config/mutable_params.py

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+mutable_params = [
+    'vendor', 'data_dir', 'train_data', 'eval_data', 'lr', 'weight_decay', 'train_batch_size', 'eval_batch_size',
+    'do_train', 'fp16', 'distributed', 'warmup', 'dist_backend', 'num_workers',
+    'device',
+    'cudnn_benchmark',
+    'cudnn_deterministic',
+    'local_rank'
+]

training/benchmarks/detr/pytorch/dataloaders/__init__.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+import torch.utils.data
+import torchvision
+
+from .coco import build as build_coco
+
+
+def get_coco_api_from_dataset(dataset):
+    for _ in range(10):
+        # if isinstance(dataset, torchvision.datasets.CocoDetection):
+        #     break
+        if isinstance(dataset, torch.utils.data.Subset):
+            dataset = dataset.dataset
+    if isinstance(dataset, torchvision.datasets.CocoDetection):
+        return dataset.coco
+
+
+def build_dataset(image_set, args):
+    if args.dataset_file == 'coco':
+        return build_coco(image_set, args)
+    if args.dataset_file == 'coco_panoptic':
+        # add segmentation task in the future
+        pass
+    raise ValueError(f'dataset {args.dataset_file} not supported')

training/benchmarks/detr/pytorch/dataloaders/coco.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+COCO dataset which returns image_id for evaluation.
+
+Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
+"""
+from pathlib import Path
+
+import torch
+import torch.utils.data
+import torchvision
+from pycocotools import mask as coco_mask
+
+import dataloaders.transforms as T
+
+
+class CocoDetection(torchvision.datasets.CocoDetection):
+    def __init__(self, img_folder, ann_file, transforms, return_masks):
+        super(CocoDetection, self).__init__(img_folder, ann_file)
+        self._transforms = transforms
+        self.prepare = ConvertCocoPolysToMask(return_masks)
+
+    def __getitem__(self, idx):
+        img, target = super(CocoDetection, self).__getitem__(idx)
+        image_id = self.ids[idx]
+        target = {'image_id': image_id, 'annotations': target}
+        img, target = self.prepare(img, target)
+        if self._transforms is not None:
+            img, target = self._transforms(img, target)
+        return img, target
+
+
+def convert_coco_poly_to_mask(segmentations, height, width):
+    masks = []
+    for polygons in segmentations:
+        rles = coco_mask.frPyObjects(polygons, height, width)
+        mask = coco_mask.decode(rles)
+        if len(mask.shape) < 3:
+            mask = mask[..., None]
+        mask = torch.as_tensor(mask, dtype=torch.uint8)
+        mask = mask.any(dim=2)
+        masks.append(mask)
+    if masks:
+        masks = torch.stack(masks, dim=0)
+    else:
+        masks = torch.zeros((0, height, width), dtype=torch.uint8)
+    return masks
+
+
+class ConvertCocoPolysToMask(object):
+    def __init__(self, return_masks=False):
+        self.return_masks = return_masks
+
+    def __call__(self, image, target):
+        w, h = image.size
+
+        image_id = target["image_id"]
+        image_id = torch.tensor([image_id])
+
+        anno = target["annotations"]
+
+        anno = [obj for obj in anno if 'iscrowd' not in obj or obj['iscrowd'] == 0]
+
+        boxes = [obj["bbox"] for obj in anno]
+        # guard against no boxes via resizing
+        boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)
+        boxes[:, 2:] += boxes[:, :2]
+        boxes[:, 0::2].clamp_(min=0, max=w)
+        boxes[:, 1::2].clamp_(min=0, max=h)
+
+        classes = [obj["category_id"] for obj in anno]
+        classes = torch.tensor(classes, dtype=torch.int64)
+
+        if self.return_masks:
+            segmentations = [obj["segmentation"] for obj in anno]
+            masks = convert_coco_poly_to_mask(segmentations, h, w)
+
+        keypoints = None
+        if anno and "keypoints" in anno[0]:
+            keypoints = [obj["keypoints"] for obj in anno]
+            keypoints = torch.as_tensor(keypoints, dtype=torch.float32)
+            num_keypoints = keypoints.shape[0]
+            if num_keypoints:
+                keypoints = keypoints.view(num_keypoints, -1, 3)
+
+        keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
+        boxes = boxes[keep]
+        classes = classes[keep]
+        if self.return_masks:
+            masks = masks[keep]
+        if keypoints is not None:
+            keypoints = keypoints[keep]
+
+        target = {}
+        target["boxes"] = boxes
+        target["labels"] = classes
+        if self.return_masks:
+            target["masks"] = masks
+        target["image_id"] = image_id
+        if keypoints is not None:
+            target["keypoints"] = keypoints
+
+        # for conversion to coco api
+        area = torch.tensor([obj["area"] for obj in anno])
+        iscrowd = torch.tensor([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno])
+        target["area"] = area[keep]
+        target["iscrowd"] = iscrowd[keep]
+
+        target["orig_size"] = torch.as_tensor([int(h), int(w)])
+        target["size"] = torch.as_tensor([int(h), int(w)])
+
+        return image, target
+
+
+def make_coco_transforms(image_set):
+
+    normalize = T.Compose([
+        T.ToTensor(),
+        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+    scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
+
+    if image_set == 'train':
+        return T.Compose([
+            T.RandomHorizontalFlip(),
+            T.RandomSelect(
+                T.RandomResize(scales, max_size=1333),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=1333),
+                ])
+            ),
+            normalize,
+        ])
+
+    if image_set == 'val':
+        return T.Compose([
+            T.RandomResize([800], max_size=1333),
+            normalize,
+        ])
+
+    raise ValueError(f'unknown {image_set}')
+
+
+def build(image_set, args):
+    root = Path(args.data_dir)
+    assert root.exists(), f'provided COCO path {root} does not exist'
+    mode = 'instances'
+    PATHS = {
+        "train": (root / "train2017", root / "annotations" / f'{mode}_train2017.json'),
+        "val": (root / "val2017", root / "annotations" / f'{mode}_val2017.json'),
+    }
+
+    img_folder, ann_file = PATHS[image_set]
+    dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms(image_set), return_masks=args.masks)
+    return dataset