Delete preprocess

preprocess/README.txt

@@ -1 +0,0 @@
-We support ONNX for humanparsing now

preprocess/humanparsing/datasets/datasets.py

@@ -1,201 +0,0 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
-"""
-@Author  :   Peike Li
-@Contact :   peike.li@yahoo.com
-@File    :   datasets.py
-@Time    :   8/4/19 3:35 PM
-@Desc    :
-@License :   This source code is licensed under the license found in the
-             LICENSE file in the root directory of this source tree.
-"""
-
-import os
-import numpy as np
-import random
-import torch
-import cv2
-from torch.utils import data
-from utils.transforms import get_affine_transform
-
-
-class LIPDataSet(data.Dataset):
-    def __init__(self, root, dataset, crop_size=[473, 473], scale_factor=0.25,
-                 rotation_factor=30, ignore_label=255, transform=None):
-        self.root = root
-        self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0]
-        self.crop_size = np.asarray(crop_size)
-        self.ignore_label = ignore_label
-        self.scale_factor = scale_factor
-        self.rotation_factor = rotation_factor
-        self.flip_prob = 0.5
-        self.transform = transform
-        self.dataset = dataset
-
-        list_path = os.path.join(self.root, self.dataset + '_id.txt')
-        train_list = [i_id.strip() for i_id in open(list_path)]
-
-        self.train_list = train_list
-        self.number_samples = len(self.train_list)
-
-    def __len__(self):
-        return self.number_samples
-
-    def _box2cs(self, box):
-        x, y, w, h = box[:4]
-        return self._xywh2cs(x, y, w, h)
-
-    def _xywh2cs(self, x, y, w, h):
-        center = np.zeros((2), dtype=np.float32)
-        center[0] = x + w * 0.5
-        center[1] = y + h * 0.5
-        if w > self.aspect_ratio * h:
-            h = w * 1.0 / self.aspect_ratio
-        elif w < self.aspect_ratio * h:
-            w = h * self.aspect_ratio
-        scale = np.array([w * 1.0, h * 1.0], dtype=np.float32)
-        return center, scale
-
-    def __getitem__(self, index):
-        train_item = self.train_list[index]
-
-        im_path = os.path.join(self.root, self.dataset + '_images', train_item + '.jpg')
-        parsing_anno_path = os.path.join(self.root, self.dataset + '_segmentations', train_item + '.png')
-
-        im = cv2.imread(im_path, cv2.IMREAD_COLOR)
-        h, w, _ = im.shape
-        parsing_anno = np.zeros((h, w), dtype=np.long)
-
-        # Get person center and scale
-        person_center, s = self._box2cs([0, 0, w - 1, h - 1])
-        r = 0
-
-        if self.dataset != 'test':
-            # Get pose annotation
-            parsing_anno = cv2.imread(parsing_anno_path, cv2.IMREAD_GRAYSCALE)
-            if self.dataset == 'train' or self.dataset == 'trainval':
-                sf = self.scale_factor
-                rf = self.rotation_factor
-                s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
-                r = np.clip(np.random.randn() * rf, -rf * 2, rf * 2) if random.random() <= 0.6 else 0
-
-                if random.random() <= self.flip_prob:
-                    im = im[:, ::-1, :]
-                    parsing_anno = parsing_anno[:, ::-1]
-                    person_center[0] = im.shape[1] - person_center[0] - 1
-                    right_idx = [15, 17, 19]
-                    left_idx = [14, 16, 18]
-                    for i in range(0, 3):
-                        right_pos = np.where(parsing_anno == right_idx[i])
-                        left_pos = np.where(parsing_anno == left_idx[i])
-                        parsing_anno[right_pos[0], right_pos[1]] = left_idx[i]
-                        parsing_anno[left_pos[0], left_pos[1]] = right_idx[i]
-
-        trans = get_affine_transform(person_center, s, r, self.crop_size)
-        input = cv2.warpAffine(
-            im,
-            trans,
-            (int(self.crop_size[1]), int(self.crop_size[0])),
-            flags=cv2.INTER_LINEAR,
-            borderMode=cv2.BORDER_CONSTANT,
-            borderValue=(0, 0, 0))
-
-        if self.transform:
-            input = self.transform(input)
-
-        meta = {
-            'name': train_item,
-            'center': person_center,
-            'height': h,
-            'width': w,
-            'scale': s,
-            'rotation': r
-        }
-
-        if self.dataset == 'val' or self.dataset == 'test':
-            return input, meta
-        else:
-            label_parsing = cv2.warpAffine(
-                parsing_anno,
-                trans,
-                (int(self.crop_size[1]), int(self.crop_size[0])),
-                flags=cv2.INTER_NEAREST,
-                borderMode=cv2.BORDER_CONSTANT,
-                borderValue=(255))
-
-            label_parsing = torch.from_numpy(label_parsing)
-
-            return input, label_parsing, meta
-
-
-class LIPDataValSet(data.Dataset):
-    def __init__(self, root, dataset='val', crop_size=[473, 473], transform=None, flip=False):
-        self.root = root
-        self.crop_size = crop_size
-        self.transform = transform
-        self.flip = flip
-        self.dataset = dataset
-        self.root = root
-        self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0]
-        self.crop_size = np.asarray(crop_size)
-
-        list_path = os.path.join(self.root, self.dataset + '_id.txt')
-        val_list = [i_id.strip() for i_id in open(list_path)]
-
-        self.val_list = val_list
-        self.number_samples = len(self.val_list)
-
-    def __len__(self):
-        return len(self.val_list)
-
-    def _box2cs(self, box):
-        x, y, w, h = box[:4]
-        return self._xywh2cs(x, y, w, h)
-
-    def _xywh2cs(self, x, y, w, h):
-        center = np.zeros((2), dtype=np.float32)
-        center[0] = x + w * 0.5
-        center[1] = y + h * 0.5
-        if w > self.aspect_ratio * h:
-            h = w * 1.0 / self.aspect_ratio
-        elif w < self.aspect_ratio * h:
-            w = h * self.aspect_ratio
-        scale = np.array([w * 1.0, h * 1.0], dtype=np.float32)
-
-        return center, scale
-
-    def __getitem__(self, index):
-        val_item = self.val_list[index]
-        # Load training image
-        im_path = os.path.join(self.root, self.dataset + '_images', val_item + '.jpg')
-        im = cv2.imread(im_path, cv2.IMREAD_COLOR)
-        h, w, _ = im.shape
-        # Get person center and scale
-        person_center, s = self._box2cs([0, 0, w - 1, h - 1])
-        r = 0
-        trans = get_affine_transform(person_center, s, r, self.crop_size)
-        input = cv2.warpAffine(
-            im,
-            trans,
-            (int(self.crop_size[1]), int(self.crop_size[0])),
-            flags=cv2.INTER_LINEAR,
-            borderMode=cv2.BORDER_CONSTANT,
-            borderValue=(0, 0, 0))
-        input = self.transform(input)
-        flip_input = input.flip(dims=[-1])
-        if self.flip:
-            batch_input_im = torch.stack([input, flip_input])
-        else:
-            batch_input_im = input
-
-        meta = {
-            'name': val_item,
-            'center': person_center,
-            'height': h,
-            'width': w,
-            'scale': s,
-            'rotation': r
-        }
-
-        return batch_input_im, meta

preprocess/humanparsing/datasets/simple_extractor_dataset.py

@@ -1,89 +0,0 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
-"""
-@Author  :   Peike Li
-@Contact :   peike.li@yahoo.com
-@File    :   dataset.py
-@Time    :   8/30/19 9:12 PM
-@Desc    :   Dataset Definition
-@License :   This source code is licensed under the license found in the
-             LICENSE file in the root directory of this source tree.
-"""
-
-import os
-import pdb
-
-import cv2
-import numpy as np
-from PIL import Image
-from torch.utils import data
-from utils.transforms import get_affine_transform
-
-
-class SimpleFolderDataset(data.Dataset):
-    def __init__(self, root, input_size=[512, 512], transform=None):
-        self.root = root
-        self.input_size = input_size
-        self.transform = transform
-        self.aspect_ratio = input_size[1] * 1.0 / input_size[0]
-        self.input_size = np.asarray(input_size)
-        self.is_pil_image = False
-        if isinstance(root, Image.Image):
-            self.file_list = [root]
-            self.is_pil_image = True
-        elif os.path.isfile(root):
-            self.file_list = [os.path.basename(root)]
-            self.root = os.path.dirname(root)
-        else:
-            self.file_list = os.listdir(self.root)
-
-    def __len__(self):
-        return len(self.file_list)
-
-    def _box2cs(self, box):
-        x, y, w, h = box[:4]
-        return self._xywh2cs(x, y, w, h)
-
-    def _xywh2cs(self, x, y, w, h):
-        center = np.zeros((2), dtype=np.float32)
-        center[0] = x + w * 0.5
-        center[1] = y + h * 0.5
-        if w > self.aspect_ratio * h:
-            h = w * 1.0 / self.aspect_ratio
-        elif w < self.aspect_ratio * h:
-            w = h * self.aspect_ratio
-        scale = np.array([w, h], dtype=np.float32)
-        return center, scale
-
-    def __getitem__(self, index):
-        if self.is_pil_image:
-            img = np.asarray(self.file_list[index])[:, :, [2, 1, 0]]
-        else:
-            img_name = self.file_list[index]
-            img_path = os.path.join(self.root, img_name)
-            img = cv2.imread(img_path, cv2.IMREAD_COLOR)
-        h, w, _ = img.shape
-
-        # Get person center and scale
-        person_center, s = self._box2cs([0, 0, w - 1, h - 1])
-        r = 0
-        trans = get_affine_transform(person_center, s, r, self.input_size)
-        input = cv2.warpAffine(
-            img,
-            trans,
-            (int(self.input_size[1]), int(self.input_size[0])),
-            flags=cv2.INTER_LINEAR,
-            borderMode=cv2.BORDER_CONSTANT,
-            borderValue=(0, 0, 0))
-
-        input = self.transform(input)
-        meta = {
-            'center': person_center,
-            'height': h,
-            'width': w,
-            'scale': s,
-            'rotation': r
-        }
-
-        return input, meta

preprocess/humanparsing/datasets/target_generation.py

@@ -1,40 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-
-def generate_edge_tensor(label, edge_width=3):
-    label = label.type(torch.cuda.FloatTensor)
-    if len(label.shape) == 2:
-        label = label.unsqueeze(0)
-    n, h, w = label.shape
-    edge = torch.zeros(label.shape, dtype=torch.float).cuda()
-    # right
-    edge_right = edge[:, 1:h, :]
-    edge_right[(label[:, 1:h, :] != label[:, :h - 1, :]) & (label[:, 1:h, :] != 255)
-               & (label[:, :h - 1, :] != 255)] = 1
-
-    # up
-    edge_up = edge[:, :, :w - 1]
-    edge_up[(label[:, :, :w - 1] != label[:, :, 1:w])
-            & (label[:, :, :w - 1] != 255)
-            & (label[:, :, 1:w] != 255)] = 1
-
-    # upright
-    edge_upright = edge[:, :h - 1, :w - 1]
-    edge_upright[(label[:, :h - 1, :w - 1] != label[:, 1:h, 1:w])
-                 & (label[:, :h - 1, :w - 1] != 255)
-                 & (label[:, 1:h, 1:w] != 255)] = 1
-
-    # bottomright
-    edge_bottomright = edge[:, :h - 1, 1:w]
-    edge_bottomright[(label[:, :h - 1, 1:w] != label[:, 1:h, :w - 1])
-                     & (label[:, :h - 1, 1:w] != 255)
-                     & (label[:, 1:h, :w - 1] != 255)] = 1
-
-    kernel = torch.ones((1, 1, edge_width, edge_width), dtype=torch.float).cuda()
-    with torch.no_grad():
-        edge = edge.unsqueeze(1)
-        edge = F.conv2d(edge, kernel, stride=1, padding=1)
-    edge[edge!=0] = 1
-    edge = edge.squeeze()
-    return edge

preprocess/humanparsing/mhp_extension/coco_style_annotation_creator/human_to_coco.py

@@ -1,166 +0,0 @@
-import argparse
-import datetime
-import json
-import os
-from PIL import Image
-import numpy as np
-
-import pycococreatortools
-
-
-def get_arguments():
-    parser = argparse.ArgumentParser(description="transform mask annotation to coco annotation")
-    parser.add_argument("--dataset", type=str, default='CIHP', help="name of dataset (CIHP, MHPv2 or VIP)")
-    parser.add_argument("--json_save_dir", type=str, default='../data/msrcnn_finetune_annotations',
-                        help="path to save coco-style annotation json file")
-    parser.add_argument("--use_val", type=bool, default=False,
-                        help="use train+val set for finetuning or not")
-    parser.add_argument("--train_img_dir", type=str, default='../data/instance-level_human_parsing/Training/Images',
-                        help="train image path")
-    parser.add_argument("--train_anno_dir", type=str,
-                        default='../data/instance-level_human_parsing/Training/Human_ids',
-                        help="train human mask path")
-    parser.add_argument("--val_img_dir", type=str, default='../data/instance-level_human_parsing/Validation/Images',
-                        help="val image path")
-    parser.add_argument("--val_anno_dir", type=str,
-                        default='../data/instance-level_human_parsing/Validation/Human_ids',
-                        help="val human mask path")
-    return parser.parse_args()
-
-
-def main(args):
-    INFO = {
-        "description": args.split_name + " Dataset",
-        "url": "",
-        "version": "",
-        "year": 2019,
-        "contributor": "xyq",
-        "date_created": datetime.datetime.utcnow().isoformat(' ')
-    }
-
-    LICENSES = [
-        {
-            "id": 1,
-            "name": "",
-            "url": ""
-        }
-    ]
-
-    CATEGORIES = [
-        {
-            'id': 1,
-            'name': 'person',
-            'supercategory': 'person',
-        },
-    ]
-
-    coco_output = {
-        "info": INFO,
-        "licenses": LICENSES,
-        "categories": CATEGORIES,
-        "images": [],
-        "annotations": []
-    }
-
-    image_id = 1
-    segmentation_id = 1
-
-    for image_name in os.listdir(args.train_img_dir):
-        image = Image.open(os.path.join(args.train_img_dir, image_name))
-        image_info = pycococreatortools.create_image_info(
-            image_id, image_name, image.size
-        )
-        coco_output["images"].append(image_info)
-
-        human_mask_name = os.path.splitext(image_name)[0] + '.png'
-        human_mask = np.asarray(Image.open(os.path.join(args.train_anno_dir, human_mask_name)))
-        human_gt_labels = np.unique(human_mask)
-
-        for i in range(1, len(human_gt_labels)):
-            category_info = {'id': 1, 'is_crowd': 0}
-            binary_mask = np.uint8(human_mask == i)
-            annotation_info = pycococreatortools.create_annotation_info(
-                segmentation_id, image_id, category_info, binary_mask,
-                image.size, tolerance=10
-            )
-            if annotation_info is not None:
-                coco_output["annotations"].append(annotation_info)
-
-            segmentation_id += 1
-        image_id += 1
-
-    if not os.path.exists(args.json_save_dir):
-        os.makedirs(args.json_save_dir)
-    if not args.use_val:
-        with open('{}/{}_train.json'.format(args.json_save_dir, args.split_name), 'w') as output_json_file:
-            json.dump(coco_output, output_json_file)
-    else:
-        for image_name in os.listdir(args.val_img_dir):
-            image = Image.open(os.path.join(args.val_img_dir, image_name))
-            image_info = pycococreatortools.create_image_info(
-                image_id, image_name, image.size
-            )
-            coco_output["images"].append(image_info)
-
-            human_mask_name = os.path.splitext(image_name)[0] + '.png'
-            human_mask = np.asarray(Image.open(os.path.join(args.val_anno_dir, human_mask_name)))
-            human_gt_labels = np.unique(human_mask)
-
-            for i in range(1, len(human_gt_labels)):
-                category_info = {'id': 1, 'is_crowd': 0}
-                binary_mask = np.uint8(human_mask == i)
-                annotation_info = pycococreatortools.create_annotation_info(
-                    segmentation_id, image_id, category_info, binary_mask,
-                    image.size, tolerance=10
-                )
-                if annotation_info is not None:
-                    coco_output["annotations"].append(annotation_info)
-
-                segmentation_id += 1
-            image_id += 1
-
-        with open('{}/{}_trainval.json'.format(args.json_save_dir, args.split_name), 'w') as output_json_file:
-            json.dump(coco_output, output_json_file)
-
-    coco_output_val = {
-        "info": INFO,
-        "licenses": LICENSES,
-        "categories": CATEGORIES,
-        "images": [],
-        "annotations": []
-    }
-
-    image_id_val = 1
-    segmentation_id_val = 1
-
-    for image_name in os.listdir(args.val_img_dir):
-        image = Image.open(os.path.join(args.val_img_dir, image_name))
-        image_info = pycococreatortools.create_image_info(
-            image_id_val, image_name, image.size
-        )
-        coco_output_val["images"].append(image_info)
-
-        human_mask_name = os.path.splitext(image_name)[0] + '.png'
-        human_mask = np.asarray(Image.open(os.path.join(args.val_anno_dir, human_mask_name)))
-        human_gt_labels = np.unique(human_mask)
-
-        for i in range(1, len(human_gt_labels)):
-            category_info = {'id': 1, 'is_crowd': 0}
-            binary_mask = np.uint8(human_mask == i)
-            annotation_info = pycococreatortools.create_annotation_info(
-                segmentation_id_val, image_id_val, category_info, binary_mask,
-                image.size, tolerance=10
-            )
-            if annotation_info is not None:
-                coco_output_val["annotations"].append(annotation_info)
-
-            segmentation_id_val += 1
-        image_id_val += 1
-
-    with open('{}/{}_val.json'.format(args.json_save_dir, args.split_name), 'w') as output_json_file_val:
-        json.dump(coco_output_val, output_json_file_val)
-
-
-if __name__ == "__main__":
-    args = get_arguments()
-    main(args)

preprocess/humanparsing/mhp_extension/coco_style_annotation_creator/pycococreatortools.py

@@ -1,114 +0,0 @@
-import re
-import datetime
-import numpy as np
-from itertools import groupby
-from skimage import measure
-from PIL import Image
-from pycocotools import mask
-
-convert = lambda text: int(text) if text.isdigit() else text.lower()
-natrual_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]
-
-
-def resize_binary_mask(array, new_size):
-    image = Image.fromarray(array.astype(np.uint8) * 255)
-    image = image.resize(new_size)
-    return np.asarray(image).astype(np.bool_)
-
-
-def close_contour(contour):
-    if not np.array_equal(contour[0], contour[-1]):
-        contour = np.vstack((contour, contour[0]))
-    return contour
-
-
-def binary_mask_to_rle(binary_mask):
-    rle = {'counts': [], 'size': list(binary_mask.shape)}
-    counts = rle.get('counts')
-    for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order='F'))):
-        if i == 0 and value == 1:
-            counts.append(0)
-        counts.append(len(list(elements)))
-
-    return rle
-
-
-def binary_mask_to_polygon(binary_mask, tolerance=0):
-    """Converts a binary mask to COCO polygon representation
-    Args:
-        binary_mask: a 2D binary numpy array where '1's represent the object
-        tolerance: Maximum distance from original points of polygon to approximated
-            polygonal chain. If tolerance is 0, the original coordinate array is returned.
-    """
-    polygons = []
-    # pad mask to close contours of shapes which start and end at an edge
-    padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
-    contours = measure.find_contours(padded_binary_mask, 0.5)
-    contours = np.subtract(contours, 1)
-    for contour in contours:
-        contour = close_contour(contour)
-        contour = measure.approximate_polygon(contour, tolerance)
-        if len(contour) < 3:
-            continue
-        contour = np.flip(contour, axis=1)
-        segmentation = contour.ravel().tolist()
-        # after padding and subtracting 1 we may get -0.5 points in our segmentation 
-        segmentation = [0 if i < 0 else i for i in segmentation]
-        polygons.append(segmentation)
-
-    return polygons
-
-
-def create_image_info(image_id, file_name, image_size,
-                      date_captured=datetime.datetime.utcnow().isoformat(' '),
-                      license_id=1, coco_url="", flickr_url=""):
-    image_info = {
-        "id": image_id,
-        "file_name": file_name,
-        "width": image_size[0],
-        "height": image_size[1],
-        "date_captured": date_captured,
-        "license": license_id,
-        "coco_url": coco_url,
-        "flickr_url": flickr_url
-    }
-
-    return image_info
-
-
-def create_annotation_info(annotation_id, image_id, category_info, binary_mask,
-                           image_size=None, tolerance=2, bounding_box=None):
-    if image_size is not None:
-        binary_mask = resize_binary_mask(binary_mask, image_size)
-
-    binary_mask_encoded = mask.encode(np.asfortranarray(binary_mask.astype(np.uint8)))
-
-    area = mask.area(binary_mask_encoded)
-    if area < 1:
-        return None
-
-    if bounding_box is None:
-        bounding_box = mask.toBbox(binary_mask_encoded)
-
-    if category_info["is_crowd"]:
-        is_crowd = 1
-        segmentation = binary_mask_to_rle(binary_mask)
-    else:
-        is_crowd = 0
-        segmentation = binary_mask_to_polygon(binary_mask, tolerance)
-        if not segmentation:
-            return None
-
-    annotation_info = {
-        "id": annotation_id,
-        "image_id": image_id,
-        "category_id": category_info["id"],
-        "iscrowd": is_crowd,
-        "area": area.tolist(),
-        "bbox": bounding_box.tolist(),
-        "segmentation": segmentation,
-        "width": binary_mask.shape[1],
-        "height": binary_mask.shape[0],
-    }
-
-    return annotation_info

preprocess/humanparsing/mhp_extension/coco_style_annotation_creator/test_human2coco_format.py

@@ -1,74 +0,0 @@
-import argparse
-import datetime
-import json
-import os
-from PIL import Image
-
-import pycococreatortools
-
-
-def get_arguments():
-    parser = argparse.ArgumentParser(description="transform mask annotation to coco annotation")
-    parser.add_argument("--dataset", type=str, default='CIHP', help="name of dataset (CIHP, MHPv2 or VIP)")
-    parser.add_argument("--json_save_dir", type=str, default='../data/CIHP/annotations',
-                        help="path to save coco-style annotation json file")
-    parser.add_argument("--test_img_dir", type=str, default='../data/CIHP/Testing/Images',
-                        help="test image path")
-    return parser.parse_args()
-
-args = get_arguments()
-
-INFO = {
-    "description": args.dataset + "Dataset",
-    "url": "",
-    "version": "",
-    "year": 2020,
-    "contributor": "yunqiuxu",
-    "date_created": datetime.datetime.utcnow().isoformat(' ')
-}
-
-LICENSES = [
-    {
-        "id": 1,
-        "name": "",
-        "url": ""
-    }
-]
-
-CATEGORIES = [
-    {
-        'id': 1,
-        'name': 'person',
-        'supercategory': 'person',
-    },
-]
-
-
-def main(args):
-    coco_output = {
-        "info": INFO,
-        "licenses": LICENSES,
-        "categories": CATEGORIES,
-        "images": [],
-        "annotations": []
-    }
-
-    image_id = 1
-
-    for image_name in os.listdir(args.test_img_dir):
-        image = Image.open(os.path.join(args.test_img_dir, image_name))
-        image_info = pycococreatortools.create_image_info(
-            image_id, image_name, image.size
-        )
-        coco_output["images"].append(image_info)
-        image_id += 1
-
-    if not os.path.exists(os.path.join(args.json_save_dir)):
-        os.mkdir(os.path.join(args.json_save_dir))
-
-    with open('{}/{}.json'.format(args.json_save_dir, args.dataset), 'w') as output_json_file:
-        json.dump(coco_output, output_json_file)
-
-
-if __name__ == "__main__":
-    main(args)

preprocess/humanparsing/mhp_extension/detectron2/.circleci/config.yml

@@ -1,179 +0,0 @@
-# Python CircleCI 2.0 configuration file
-#
-# Check https://circleci.com/docs/2.0/language-python/ for more details
-#
-version: 2
-
-# -------------------------------------------------------------------------------------
-# Environments to run the jobs in
-# -------------------------------------------------------------------------------------
-cpu: &cpu
-  docker:
-    - image: circleci/python:3.6.8-stretch
-  resource_class: medium
-
-gpu: &gpu
-  machine:
-    image: ubuntu-1604:201903-01
-    docker_layer_caching: true
-  resource_class: gpu.small
-
-# -------------------------------------------------------------------------------------
-# Re-usable commands
-# -------------------------------------------------------------------------------------
-install_python: &install_python
-  - run:
-      name: Install Python
-      working_directory: ~/
-      command: |
-        pyenv install 3.6.1
-        pyenv global 3.6.1
-
-setup_venv: &setup_venv
-  - run:
-      name: Setup Virtual Env
-      working_directory: ~/
-      command: |
-        python -m venv ~/venv
-        echo ". ~/venv/bin/activate" >> $BASH_ENV
-        . ~/venv/bin/activate
-        python --version
-        which python
-        which pip
-        pip install --upgrade pip
-
-install_dep: &install_dep
-  - run:
-      name: Install Dependencies
-      command: |
-        pip install --progress-bar off -U 'git+https://github.com/facebookresearch/fvcore'
-        pip install --progress-bar off cython opencv-python
-        pip install --progress-bar off 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
-        pip install --progress-bar off torch torchvision
-
-install_detectron2: &install_detectron2
-  - run:
-      name: Install Detectron2
-      command: |
-        gcc --version
-        pip install -U --progress-bar off -e .[dev]
-        python -m detectron2.utils.collect_env
-
-install_nvidia_driver: &install_nvidia_driver
-  - run:
-      name: Install nvidia driver
-      working_directory: ~/
-      command: |
-        wget -q 'https://s3.amazonaws.com/ossci-linux/nvidia_driver/NVIDIA-Linux-x86_64-430.40.run'
-        sudo /bin/bash ./NVIDIA-Linux-x86_64-430.40.run -s --no-drm
-        nvidia-smi
-
-run_unittests: &run_unittests
-  - run:
-      name: Run Unit Tests
-      command: |
-        python -m unittest discover -v -s tests
-
-# -------------------------------------------------------------------------------------
-# Jobs to run
-# -------------------------------------------------------------------------------------
-jobs:
-  cpu_tests:
-    <<: *cpu
-
-    working_directory: ~/detectron2
-
-    steps:
-      - checkout
-      - <<: *setup_venv
-
-      # Cache the venv directory that contains dependencies
-      - restore_cache:
-          keys:
-            - cache-key-{{ .Branch }}-ID-20200425
-
-      - <<: *install_dep
-
-      - save_cache:
-          paths:
-            - ~/venv
-          key: cache-key-{{ .Branch }}-ID-20200425
-
-      - <<: *install_detectron2
-
-      - run:
-          name: isort
-          command: |
-            isort -c -sp .
-      - run:
-          name: black
-          command: |
-            black --check -l 100 .
-      - run:
-          name: flake8
-          command: |
-            flake8 .
-
-      - <<: *run_unittests
-
-  gpu_tests:
-    <<: *gpu
-
-    working_directory: ~/detectron2
-
-    steps:
-      - checkout
-      - <<: *install_nvidia_driver
-
-      - run:
-          name: Install nvidia-docker
-          working_directory: ~/
-          command: |
-            curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
-            distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
-            curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
-            sudo tee /etc/apt/sources.list.d/nvidia-docker.list
-            sudo apt-get update && sudo apt-get install -y nvidia-docker2
-            # reload the docker daemon configuration
-            sudo pkill -SIGHUP dockerd
-
-      - run:
-          name: Launch docker
-          working_directory: ~/detectron2/docker
-          command: |
-            nvidia-docker build -t detectron2:v0 -f Dockerfile-circleci .
-            nvidia-docker run -itd --name d2 detectron2:v0
-            docker exec -it d2 nvidia-smi
-
-      - run:
-          name: Build Detectron2
-          command: |
-            docker exec -it d2 pip install 'git+https://github.com/facebookresearch/fvcore'
-            docker cp ~/detectron2 d2:/detectron2
-            # This will build d2 for the target GPU arch only
-            docker exec -it d2 pip install -e /detectron2
-            docker exec -it d2 python3 -m detectron2.utils.collect_env
-            docker exec -it d2 python3 -c 'import torch; assert(torch.cuda.is_available())'
-
-      - run:
-          name: Run Unit Tests
-          command: |
-            docker exec -e CIRCLECI=true -it d2 python3 -m unittest discover -v -s /detectron2/tests
-
-workflows:
-  version: 2
-  regular_test:
-    jobs:
-      - cpu_tests
-      - gpu_tests
-
-  #nightly_test:
-    #jobs:
-      #- gpu_tests
-    #triggers:
-      #- schedule:
-          #cron: "0 0 * * *"
-          #filters:
-            #branches:
-              #only:
-                #- master

preprocess/humanparsing/mhp_extension/detectron2/.clang-format

@@ -1,85 +0,0 @@
-AccessModifierOffset: -1
-AlignAfterOpenBracket: AlwaysBreak
-AlignConsecutiveAssignments: false
-AlignConsecutiveDeclarations: false
-AlignEscapedNewlinesLeft: true
-AlignOperands:   false
-AlignTrailingComments: false
-AllowAllParametersOfDeclarationOnNextLine: false
-AllowShortBlocksOnASingleLine: false
-AllowShortCaseLabelsOnASingleLine: false
-AllowShortFunctionsOnASingleLine: Empty
-AllowShortIfStatementsOnASingleLine: false
-AllowShortLoopsOnASingleLine: false
-AlwaysBreakAfterReturnType: None
-AlwaysBreakBeforeMultilineStrings: true
-AlwaysBreakTemplateDeclarations: true
-BinPackArguments: false
-BinPackParameters: false
-BraceWrapping:
-  AfterClass:      false
-  AfterControlStatement: false
-  AfterEnum:       false
-  AfterFunction:   false
-  AfterNamespace:  false
-  AfterObjCDeclaration: false
-  AfterStruct:     false
-  AfterUnion:      false
-  BeforeCatch:     false
-  BeforeElse:      false
-  IndentBraces:    false
-BreakBeforeBinaryOperators: None
-BreakBeforeBraces: Attach
-BreakBeforeTernaryOperators: true
-BreakConstructorInitializersBeforeComma: false
-BreakAfterJavaFieldAnnotations: false
-BreakStringLiterals: false
-ColumnLimit:     80
-CommentPragmas:  '^ IWYU pragma:'
-ConstructorInitializerAllOnOneLineOrOnePerLine: true
-ConstructorInitializerIndentWidth: 4
-ContinuationIndentWidth: 4
-Cpp11BracedListStyle: true
-DerivePointerAlignment: false
-DisableFormat:   false
-ForEachMacros:   [ FOR_EACH, FOR_EACH_ENUMERATE, FOR_EACH_KV, FOR_EACH_R, FOR_EACH_RANGE, ]
-IncludeCategories:
-  - Regex:           '^<.*\.h(pp)?>'
-    Priority:        1
-  - Regex:           '^<.*'
-    Priority:        2
-  - Regex:           '.*'
-    Priority:        3
-IndentCaseLabels: true
-IndentWidth:     2
-IndentWrappedFunctionNames: false
-KeepEmptyLinesAtTheStartOfBlocks: false
-MacroBlockBegin: ''
-MacroBlockEnd:   ''
-MaxEmptyLinesToKeep: 1
-NamespaceIndentation: None
-ObjCBlockIndentWidth: 2
-ObjCSpaceAfterProperty: false
-ObjCSpaceBeforeProtocolList: false
-PenaltyBreakBeforeFirstCallParameter: 1
-PenaltyBreakComment: 300
-PenaltyBreakFirstLessLess: 120
-PenaltyBreakString: 1000
-PenaltyExcessCharacter: 1000000
-PenaltyReturnTypeOnItsOwnLine: 200
-PointerAlignment: Left
-ReflowComments:  true
-SortIncludes:    true
-SpaceAfterCStyleCast: false
-SpaceBeforeAssignmentOperators: true
-SpaceBeforeParens: ControlStatements
-SpaceInEmptyParentheses: false
-SpacesBeforeTrailingComments: 1
-SpacesInAngles:  false
-SpacesInContainerLiterals: true
-SpacesInCStyleCastParentheses: false
-SpacesInParentheses: false
-SpacesInSquareBrackets: false
-Standard:        Cpp11
-TabWidth:        8
-UseTab:          Never

preprocess/humanparsing/mhp_extension/detectron2/.flake8

@@ -1,9 +0,0 @@
-# This is an example .flake8 config, used when developing *Black* itself.
-# Keep in sync with setup.cfg which is used for source packages.
-
-[flake8]
-ignore = W503, E203, E221, C901, C408, E741
-max-line-length = 100
-max-complexity = 18
-select = B,C,E,F,W,T4,B9
-exclude = build,__init__.py

preprocess/humanparsing/mhp_extension/detectron2/.github/CODE_OF_CONDUCT.md

@@ -1,5 +0,0 @@
-# Code of Conduct
-
-Facebook has adopted a Code of Conduct that we expect project participants to adhere to.
-Please read the [full text](https://code.fb.com/codeofconduct/)
-so that you can understand what actions will and will not be tolerated.

preprocess/humanparsing/mhp_extension/detectron2/.github/CONTRIBUTING.md

@@ -1,49 +0,0 @@
-# Contributing to detectron2
-
-## Issues
-We use GitHub issues to track public bugs and questions.
-Please make sure to follow one of the
-[issue templates](https://github.com/facebookresearch/detectron2/issues/new/choose)
-when reporting any issues.
-
-Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## Pull Requests
-We actively welcome your pull requests.
-
-However, if you're adding any significant features (e.g. > 50 lines), please
-make sure to have a corresponding issue to discuss your motivation and proposals,
-before sending a PR. We do not always accept new features, and we take the following
-factors into consideration:
-
-1. Whether the same feature can be achieved without modifying detectron2.
-Detectron2 is designed so that you can implement many extensions from the outside, e.g.
-those in [projects](https://github.com/facebookresearch/detectron2/tree/master/projects).
-If some part is not as extensible, you can also bring up the issue to make it more extensible.
-2. Whether the feature is potentially useful to a large audience, or only to a small portion of users.
-3. Whether the proposed solution has a good design / interface.
-4. Whether the proposed solution adds extra mental/practical overhead to users who don't
-   need such feature.
-5. Whether the proposed solution breaks existing APIs.
-
-When sending a PR, please do:
-
-1. If a PR contains multiple orthogonal changes, split it to several PRs.
-2. If you've added code that should be tested, add tests.
-3. For PRs that need experiments (e.g. adding a new model or new methods),
-	 you don't need to update model zoo, but do provide experiment results in the description of the PR.
-4. If APIs are changed, update the documentation.
-5. Make sure your code lints with `./dev/linter.sh`.
-
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Facebook's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## License
-By contributing to detectron2, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.

preprocess/humanparsing/mhp_extension/detectron2/.github/ISSUE_TEMPLATE.md

@@ -1,5 +0,0 @@
-
-Please select an issue template from
-https://github.com/facebookresearch/detectron2/issues/new/choose .
-
-Otherwise your issue will be closed.

preprocess/humanparsing/mhp_extension/detectron2/.github/ISSUE_TEMPLATE/bugs.md

@@ -1,36 +0,0 @@
----
-name: "🐛 Bugs"
-about: Report bugs in detectron2
-title: Please read & provide the following
-
----
-
-## Instructions To Reproduce the 🐛 Bug:
-
-1. what changes you made (`git diff`) or what code you wrote
-```
-<put diff or code here>
-```
-2. what exact command you run:
-3. what you observed (including __full logs__):
-```
-<put logs here>
-```
-4. please simplify the steps as much as possible so they do not require additional resources to
-	 run, such as a private dataset.
-
-## Expected behavior:
-
-If there are no obvious error in "what you observed" provided above,
-please tell us the expected behavior.
-
-## Environment:
-
-Provide your environment information using the following command:
-```
-wget -nc -q https://github.com/facebookresearch/detectron2/raw/master/detectron2/utils/collect_env.py && python collect_env.py
-```
-
-If your issue looks like an installation issue / environment issue,
-please first try to solve it yourself with the instructions in
-https://detectron2.readthedocs.io/tutorials/install.html#common-installation-issues

preprocess/humanparsing/mhp_extension/detectron2/.github/ISSUE_TEMPLATE/config.yml

@@ -1,9 +0,0 @@
-# require an issue template to be chosen
-blank_issues_enabled: false
-
-# Unexpected behaviors & bugs are split to two templates.
-# When they are one template, users think "it's not a bug" and don't choose the template.
-#
-# But the file name is still "unexpected-problems-bugs.md" so that old references
-# to this issue template still works.
-# It's ok since this template should be a superset of "bugs.md" (unexpected behaviors is a superset of bugs)

preprocess/humanparsing/mhp_extension/detectron2/.github/ISSUE_TEMPLATE/feature-request.md

@@ -1,31 +0,0 @@
----
-name: "\U0001F680Feature Request"
-about: Submit a proposal/request for a new detectron2 feature
-
----
-
-## 🚀 Feature
-A clear and concise description of the feature proposal.
-
-
-## Motivation & Examples
-
-Tell us why the feature is useful.
-
-Describe what the feature would look like, if it is implemented.
-Best demonstrated using **code examples** in addition to words.
-
-## Note
-
-We only consider adding new features if they are relevant to many users.
-
-If you request implementation of research papers --
-we only consider papers that have enough significance and prevalance in the object detection field.
-
-We do not take requests for most projects in the `projects/` directory,
-because they are research code release that is mainly for other researchers to reproduce results.
-
-Instead of adding features inside detectron2,
-you can implement many features by [extending detectron2](https://detectron2.readthedocs.io/tutorials/extend.html).
-The [projects/](https://github.com/facebookresearch/detectron2/tree/master/projects/) directory contains many of such examples.
-

preprocess/humanparsing/mhp_extension/detectron2/.github/ISSUE_TEMPLATE/questions-help-support.md

@@ -1,26 +0,0 @@
----
-name: "❓How to do something?"
-about: How to do something using detectron2? What does an API do?
-
----
-
-## ❓ How to do something using detectron2
-
-Describe what you want to do, including:
-1. what inputs you will provide, if any:
-2. what outputs you are expecting:
-
-## ❓ What does an API do and how to use it?
-Please link to which API or documentation you're asking about from
-https://detectron2.readthedocs.io/
-
-
-NOTE:
-
-1. Only general answers are provided.
-   If you want to ask about "why X did not work", please use the
-   [Unexpected behaviors](https://github.com/facebookresearch/detectron2/issues/new/choose) issue template.
-
-2. About how to implement new models / new dataloader / new training logic, etc., check documentation first.
-
-3. We do not answer general machine learning / computer vision questions that are not specific to detectron2, such as how a model works, how to improve your training/make it converge, or what algorithm/methods can be used to achieve X.

preprocess/humanparsing/mhp_extension/detectron2/.github/ISSUE_TEMPLATE/unexpected-problems-bugs.md

@@ -1,45 +0,0 @@
----
-name: "Unexpected behaviors"
-about: Run into unexpected behaviors when using detectron2
-title: Please read & provide the following
-
----
-
-If you do not know the root cause of the problem, and wish someone to help you, please
-post according to this template:
-
-## Instructions To Reproduce the Issue:
-
-1. what changes you made (`git diff`) or what code you wrote
-```
-<put diff or code here>
-```
-2. what exact command you run:
-3. what you observed (including __full logs__):
-```
-<put logs here>
-```
-4. please simplify the steps as much as possible so they do not require additional resources to
-	 run, such as a private dataset.
-
-## Expected behavior:
-
-If there are no obvious error in "what you observed" provided above,
-please tell us the expected behavior.
-
-If you expect the model to converge / work better, note that we do not give suggestions
-on how to train a new model.
-Only in one of the two conditions we will help with it:
-(1) You're unable to reproduce the results in detectron2 model zoo.
-(2) It indicates a detectron2 bug.
-
-## Environment:
-
-Provide your environment information using the following command:
-```
-wget -nc -q https://github.com/facebookresearch/detectron2/raw/master/detectron2/utils/collect_env.py && python collect_env.py
-```
-
-If your issue looks like an installation issue / environment issue,
-please first try to solve it yourself with the instructions in
-https://detectron2.readthedocs.io/tutorials/install.html#common-installation-issues

preprocess/humanparsing/mhp_extension/detectron2/.github/pull_request_template.md

@@ -1,9 +0,0 @@
-Thanks for your contribution!
-
-If you're sending a large PR (e.g., >50 lines),
-please open an issue first about the feature / bug, and indicate how you want to contribute.
-
-Before submitting a PR, please run `dev/linter.sh` to lint the code.
-
-See https://detectron2.readthedocs.io/notes/contributing.html#pull-requests
-about how we handle PRs.

preprocess/humanparsing/mhp_extension/detectron2/.gitignore

@@ -1,46 +0,0 @@
-# output dir
-output
-instant_test_output
-inference_test_output
-
-
-*.jpg
-*.png
-*.txt
-*.json
-*.diff
-
-# compilation and distribution
-__pycache__
-_ext
-*.pyc
-*.so
-detectron2.egg-info/
-build/
-dist/
-wheels/
-
-# pytorch/python/numpy formats
-*.pth
-*.pkl
-*.npy
-
-# ipython/jupyter notebooks
-*.ipynb
-**/.ipynb_checkpoints/
-
-# Editor temporaries
-*.swn
-*.swo
-*.swp
-*~
-
-# editor settings
-.idea
-.vscode
-
-# project dirs
-/detectron2/model_zoo/configs
-/datasets
-/projects/*/datasets
-/models

preprocess/humanparsing/mhp_extension/detectron2/GETTING_STARTED.md

@@ -1,79 +0,0 @@
-## Getting Started with Detectron2
-
-This document provides a brief intro of the usage of builtin command-line tools in detectron2.
-
-For a tutorial that involves actual coding with the API,
-see our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
-which covers how to run inference with an
-existing model, and how to train a builtin model on a custom dataset.
-
-For more advanced tutorials, refer to our [documentation](https://detectron2.readthedocs.io/tutorials/extend.html).
-
-
-### Inference Demo with Pre-trained Models
-
-1. Pick a model and its config file from
-	[model zoo](MODEL_ZOO.md),
-	for example, `mask_rcnn_R_50_FPN_3x.yaml`.
-2. We provide `demo.py` that is able to run builtin standard models. Run it with:
-```
-cd demo/
-python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
-  --input input1.jpg input2.jpg \
-  [--other-options]
-  --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
-```
-The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation.
-This command will run the inference and show visualizations in an OpenCV window.
-
-For details of the command line arguments, see `demo.py -h` or look at its source code
-to understand its behavior. Some common arguments are:
-* To run __on your webcam__, replace `--input files` with `--webcam`.
-* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
-* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`.
-* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.
-
-
-### Training & Evaluation in Command Line
-
-We provide a script in "tools/{,plain_}train_net.py", that is made to train
-all the configs provided in detectron2.
-You may want to use it as a reference to write your own training script.
-
-To train a model with "train_net.py", first
-setup the corresponding datasets following
-[datasets/README.md](./datasets/README.md),
-then run:
-```
-cd tools/
-./train_net.py --num-gpus 8 \
-	--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
-```
-
-The configs are made for 8-GPU training.
-To train on 1 GPU, you may need to [change some parameters](https://arxiv.org/abs/1706.02677), e.g.:
-```
-./train_net.py \
-	--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
-	--num-gpus 1 SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
-```
-
-For most models, CPU training is not supported.
-
-To evaluate a model's performance, use
-```
-./train_net.py \
-	--config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
-	--eval-only MODEL.WEIGHTS /path/to/checkpoint_file
-```
-For more options, see `./train_net.py -h`.
-
-### Use Detectron2 APIs in Your Code
-
-See our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
-to learn how to use detectron2 APIs to:
-1. run inference with an existing model
-2. train a builtin model on a custom dataset
-
-See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/master/projects)
-for more ways to build your project on detectron2.

preprocess/humanparsing/mhp_extension/detectron2/INSTALL.md

@@ -1,184 +0,0 @@
-## Installation
-
-Our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
-has step-by-step instructions that install detectron2.
-The [Dockerfile](docker)
-also installs detectron2 with a few simple commands.
-
-### Requirements
-- Linux or macOS with Python ≥ 3.6
-- PyTorch ≥ 1.4
-- [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
-	You can install them together at [pytorch.org](https://pytorch.org) to make sure of this.
-- OpenCV, optional, needed by demo and visualization
-- pycocotools: `pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'`
-
-
-### Build Detectron2 from Source
-
-gcc & g++ ≥ 5 are required. [ninja](https://ninja-build.org/) is recommended for faster build.
-After having them, run:
-```
-python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
-# (add --user if you don't have permission)
-
-# Or, to install it from a local clone:
-git clone https://github.com/facebookresearch/detectron2.git
-python -m pip install -e detectron2
-
-# Or if you are on macOS
-# CC=clang CXX=clang++ python -m pip install -e .
-```
-
-To __rebuild__ detectron2 that's built from a local clone, use `rm -rf build/ **/*.so` to clean the
-old build first. You often need to rebuild detectron2 after reinstalling PyTorch.
-
-### Install Pre-Built Detectron2 (Linux only)
-```
-# for CUDA 10.1:
-python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/index.html
-```
-You can replace cu101 with "cu{100,92}" or "cpu".
-
-Note that:
-1. Such installation has to be used with certain version of official PyTorch release.
-   See [releases](https://github.com/facebookresearch/detectron2/releases) for requirements.
-   It will not work with a different version of PyTorch or a non-official build of PyTorch.
-2. Such installation is out-of-date w.r.t. master branch of detectron2. It may not be
-	 compatible with the master branch of a research project that uses detectron2 (e.g. those in
-	 [projects](projects) or [meshrcnn](https://github.com/facebookresearch/meshrcnn/)).
-
-### Common Installation Issues
-
-If you met issues using the pre-built detectron2, please uninstall it and try building it from source.
-
-Click each issue for its solutions:
-
-<details>
-<summary>
-Undefined torch/aten/caffe2 symbols, or segmentation fault immediately when running the library.
-</summary>
-<br/>
-
-This usually happens when detectron2 or torchvision is not
-compiled with the version of PyTorch you're running.
-
-Pre-built torchvision or detectron2 has to work with the corresponding official release of pytorch.
-If the error comes from a pre-built torchvision, uninstall torchvision and pytorch and reinstall them
-following [pytorch.org](http://pytorch.org). So the versions will match.
-
-If the error comes from a pre-built detectron2, check [release notes](https://github.com/facebookresearch/detectron2/releases)
-to see the corresponding pytorch version required for each pre-built detectron2.
-
-If the error comes from detectron2 or torchvision that you built manually from source,
-remove files you built (`build/`, `**/*.so`) and rebuild it so it can pick up the version of pytorch currently in your environment.
-
-If you cannot resolve this problem, please include the output of `gdb -ex "r" -ex "bt" -ex "quit" --args python -m detectron2.utils.collect_env`
-in your issue.
-</details>
-
-<details>
-<summary>
-Undefined C++ symbols (e.g. `GLIBCXX`) or C++ symbols not found.
-</summary>
-<br/>
-Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime.
-
-This often happens with old anaconda.
-Try `conda update libgcc`. Then rebuild detectron2.
-
-The fundamental solution is to run the code with proper C++ runtime.
-One way is to use `LD_PRELOAD=/path/to/libstdc++.so`.
-
-</details>
-
-<details>
-<summary>
-"Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available".
-</summary>
-<br/>
-CUDA is not found when building detectron2.
-You should make sure
-
-```
-python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'
-```
-
-print valid outputs at the time you build detectron2.
-
-Most models can run inference (but not training) without GPU support. To use CPUs, set `MODEL.DEVICE='cpu'` in the config.
-</details>
-
-<details>
-<summary>
-"invalid device function" or "no kernel image is available for execution".
-</summary>
-<br/>
-Two possibilities:
-
-* You build detectron2 with one version of CUDA but run it with a different version.
-
-  To check whether it is the case,
-  use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
-	In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
-	to contain cuda libraries of the same version.
-
-	When they are inconsistent,
-	you need to either install a different build of PyTorch (or build by yourself)
-	to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
-
-* Detectron2 or PyTorch/torchvision is not built for the correct GPU architecture (compute compatibility).
-
-	The GPU architecture for PyTorch/detectron2/torchvision is available in the "architecture flags" in
-	`python -m detectron2.utils.collect_env`.
-
-	The GPU architecture flags of detectron2/torchvision by default matches the GPU model detected
-	during compilation. This means the compiled code may not work on a different GPU model.
-	To overwrite the GPU architecture for detectron2/torchvision, use `TORCH_CUDA_ARCH_LIST` environment variable during compilation.
-
-	For example, `export TORCH_CUDA_ARCH_LIST=6.0,7.0` makes it compile for both P100s and V100s.
-	Visit [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus) to find out
-	the correct compute compatibility number for your device.
-
-</details>
-
-<details>
-<summary>
-Undefined CUDA symbols; cannot open libcudart.so; other nvcc failures.
-</summary>
-<br/>
-The version of NVCC you use to build detectron2 or torchvision does
-not match the version of CUDA you are running with.
-This often happens when using anaconda's CUDA runtime.
-
-Use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
-In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
-to contain cuda libraries of the same version.
-
-When they are inconsistent,
-you need to either install a different build of PyTorch (or build by yourself)
-to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
-</details>
-
-
-<details>
-<summary>
-"ImportError: cannot import name '_C'".
-</summary>
-<br/>
-Please build and install detectron2 following the instructions above.
-
-If you are running code from detectron2's root directory, `cd` to a different one.
-Otherwise you may not import the code that you installed.
-</details>
-
-<details>
-<summary>
-ONNX conversion segfault after some "TraceWarning".
-</summary>
-<br/>
-The ONNX package is compiled with too old compiler.
-
-Please build and install ONNX from its source code using a compiler
-whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`).
-</details>

preprocess/humanparsing/mhp_extension/detectron2/LICENSE

@@ -1,201 +0,0 @@
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preprocess/humanparsing/mhp_extension/detectron2/MODEL_ZOO.md

@@ -1,903 +0,0 @@
-# Detectron2 Model Zoo and Baselines
-
-## Introduction
-
-This file documents a large collection of baselines trained
-with detectron2 in Sep-Oct, 2019.
-All numbers were obtained on [Big Basin](https://engineering.fb.com/data-center-engineering/introducing-big-basin-our-next-generation-ai-hardware/)
-servers with 8 NVIDIA V100 GPUs & NVLink. The software in use were PyTorch 1.3, CUDA 9.2, cuDNN 7.4.2 or 7.6.3.
-You can access these models from code using [detectron2.model_zoo](https://detectron2.readthedocs.io/modules/model_zoo.html) APIs.
-
-In addition to these official baseline models, you can find more models in [projects/](projects/).
-
-#### How to Read the Tables
-* The "Name" column contains a link to the config file. Running `tools/train_net.py` with this config file
-	and 8 GPUs will reproduce the model.
-* Training speed is averaged across the entire training.
-	We keep updating the speed with latest version of detectron2/pytorch/etc.,
-	so they might be different from the `metrics` file.
-	Training speed for multi-machine jobs is not provided.
-* Inference speed is measured by `tools/train_net.py --eval-only`, or [inference_on_dataset()](https://detectron2.readthedocs.io/modules/evaluation.html#detectron2.evaluation.inference_on_dataset),
-  with batch size 1 in detectron2 directly.
-	Measuring it with your own code will likely introduce other overhead.
-  Actual deployment in production should in general be faster than the given inference
-  speed due to more optimizations.
-* The *model id* column is provided for ease of reference.
-  To check downloaded file integrity, any model on this page contains its md5 prefix in its file name.
-* Training curves and other statistics can be found in `metrics` for each model.
-
-#### Common Settings for COCO Models
-* All COCO models were trained on `train2017` and evaluated on `val2017`.
-* The default settings are __not directly comparable__ with Detectron's standard settings.
-  For example, our default training data augmentation uses scale jittering in addition to horizontal flipping.
-
-  To make fair comparisons with Detectron's settings, see
-  [Detectron1-Comparisons](configs/Detectron1-Comparisons/) for accuracy comparison,
-  and [benchmarks](https://detectron2.readthedocs.io/notes/benchmarks.html)
-  for speed comparison.
-* For Faster/Mask R-CNN, we provide baselines based on __3 different backbone combinations__:
-  * __FPN__: Use a ResNet+FPN backbone with standard conv and FC heads for mask and box prediction,
-    respectively. It obtains the best
-    speed/accuracy tradeoff, but the other two are still useful for research.
-  * __C4__: Use a ResNet conv4 backbone with conv5 head. The original baseline in the Faster R-CNN paper.
-  * __DC5__ (Dilated-C5): Use a ResNet conv5 backbone with dilations in conv5, and standard conv and FC heads
-    for mask and box prediction, respectively.
-    This is used by the Deformable ConvNet paper.
-* Most models are trained with the 3x schedule (~37 COCO epochs).
-  Although 1x models are heavily under-trained, we provide some ResNet-50 models with the 1x (~12 COCO epochs)
-  training schedule for comparison when doing quick research iteration.
-
-#### ImageNet Pretrained Models
-
-We provide backbone models pretrained on ImageNet-1k dataset.
-These models have __different__ format from those provided in Detectron: we do not fuse BatchNorm into an affine layer.
-* [R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl): converted copy of [MSRA's original ResNet-50](https://github.com/KaimingHe/deep-residual-networks) model.
-* [R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl): converted copy of [MSRA's original ResNet-101](https://github.com/KaimingHe/deep-residual-networks) model.
-* [X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl): ResNeXt-101-32x8d model trained with Caffe2 at FB.
-
-Pretrained models in Detectron's format can still be used. For example:
-* [X-152-32x8d-IN5k.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl):
-  ResNeXt-152-32x8d model trained on ImageNet-5k with Caffe2 at FB (see ResNeXt paper for details on ImageNet-5k).
-* [R-50-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47261647/R-50-GN.pkl):
-  ResNet-50 with Group Normalization.
-* [R-101-GN.pkl](https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/47592356/R-101-GN.pkl):
-  ResNet-101 with Group Normalization.
-
-Torchvision's ResNet models can be used after converted by [this script](tools/convert-torchvision-to-d2.py).
-
-#### License
-
-All models available for download through this document are licensed under the
-[Creative Commons Attribution-ShareAlike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/).
-
-### COCO Object Detection Baselines
-
-#### Faster R-CNN:
-<!--
-(fb only) To update the table in vim:
-1. Remove the old table: d}
-2. Copy the below command to the place of the table
-3. :.!bash
-
-./gen_html_table.py --config 'COCO-Detection/faster*50*'{1x,3x}'*' 'COCO-Detection/faster*101*' --name R50-C4 R50-DC5 R50-FPN R50-C4 R50-DC5 R50-FPN R101-C4 R101-DC5 R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: faster_rcnn_R_50_C4_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml">R50-C4</a></td>
-<td align="center">1x</td>
-<td align="center">0.551</td>
-<td align="center">0.102</td>
-<td align="center">4.8</td>
-<td align="center">35.7</td>
-<td align="center">137257644</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/model_final_721ade.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_1x/137257644/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_DC5_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml">R50-DC5</a></td>
-<td align="center">1x</td>
-<td align="center">0.380</td>
-<td align="center">0.068</td>
-<td align="center">5.0</td>
-<td align="center">37.3</td>
-<td align="center">137847829</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_1x/137847829/model_final_51d356.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_1x/137847829/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.210</td>
-<td align="center">0.038</td>
-<td align="center">3.0</td>
-<td align="center">37.9</td>
-<td align="center">137257794</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_C4_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml">R50-C4</a></td>
-<td align="center">3x</td>
-<td align="center">0.543</td>
-<td align="center">0.104</td>
-<td align="center">4.8</td>
-<td align="center">38.4</td>
-<td align="center">137849393</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_3x/137849393/model_final_f97cb7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_C4_3x/137849393/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_DC5_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml">R50-DC5</a></td>
-<td align="center">3x</td>
-<td align="center">0.378</td>
-<td align="center">0.070</td>
-<td align="center">5.0</td>
-<td align="center">39.0</td>
-<td align="center">137849425</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_3x/137849425/model_final_68d202.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_DC5_3x/137849425/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.209</td>
-<td align="center">0.038</td>
-<td align="center">3.0</td>
-<td align="center">40.2</td>
-<td align="center">137849458</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_101_C4_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml">R101-C4</a></td>
-<td align="center">3x</td>
-<td align="center">0.619</td>
-<td align="center">0.139</td>
-<td align="center">5.9</td>
-<td align="center">41.1</td>
-<td align="center">138204752</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_C4_3x/138204752/model_final_298dad.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_C4_3x/138204752/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_101_DC5_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml">R101-DC5</a></td>
-<td align="center">3x</td>
-<td align="center">0.452</td>
-<td align="center">0.086</td>
-<td align="center">6.1</td>
-<td align="center">40.6</td>
-<td align="center">138204841</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_DC5_3x/138204841/model_final_3e0943.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_DC5_3x/138204841/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_101_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.286</td>
-<td align="center">0.051</td>
-<td align="center">4.1</td>
-<td align="center">42.0</td>
-<td align="center">137851257</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_FPN_3x/137851257/model_final_f6e8b1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_101_FPN_3x/137851257/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_X_101_32x8d_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.638</td>
-<td align="center">0.098</td>
-<td align="center">6.7</td>
-<td align="center">43.0</td>
-<td align="center">139173657</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/model_final_68b088.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-#### RetinaNet:
-<!--
-./gen_html_table.py --config 'COCO-Detection/retina*50*' 'COCO-Detection/retina*101*' --name R50 R50 R101 --fields lr_sched train_speed inference_speed mem box_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: retinanet_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml">R50</a></td>
-<td align="center">1x</td>
-<td align="center">0.200</td>
-<td align="center">0.055</td>
-<td align="center">3.9</td>
-<td align="center">36.5</td>
-<td align="center">137593951</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/137593951/model_final_b796dc.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/137593951/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: retinanet_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml">R50</a></td>
-<td align="center">3x</td>
-<td align="center">0.201</td>
-<td align="center">0.055</td>
-<td align="center">3.9</td>
-<td align="center">37.9</td>
-<td align="center">137849486</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/137849486/model_final_4cafe0.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/137849486/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: retinanet_R_101_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml">R101</a></td>
-<td align="center">3x</td>
-<td align="center">0.280</td>
-<td align="center">0.068</td>
-<td align="center">5.1</td>
-<td align="center">39.9</td>
-<td align="center">138363263</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/138363263/model_final_59f53c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/138363263/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-#### RPN & Fast R-CNN:
-<!--
-./gen_html_table.py --config 'COCO-Detection/rpn*' 'COCO-Detection/fast_rcnn*' --name "RPN R50-C4" "RPN R50-FPN" "Fast R-CNN R50-FPN" --fields lr_sched train_speed inference_speed mem box_AP prop_AR
--->
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">prop.<br/>AR</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: rpn_R_50_C4_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/rpn_R_50_C4_1x.yaml">RPN R50-C4</a></td>
-<td align="center">1x</td>
-<td align="center">0.130</td>
-<td align="center">0.034</td>
-<td align="center">1.5</td>
-<td align="center"></td>
-<td align="center">51.6</td>
-<td align="center">137258005</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_C4_1x/137258005/model_final_450694.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_C4_1x/137258005/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: rpn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/rpn_R_50_FPN_1x.yaml">RPN R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.186</td>
-<td align="center">0.032</td>
-<td align="center">2.7</td>
-<td align="center"></td>
-<td align="center">58.0</td>
-<td align="center">137258492</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_FPN_1x/137258492/model_final_02ce48.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/rpn_R_50_FPN_1x/137258492/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: fast_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml">Fast R-CNN R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.140</td>
-<td align="center">0.029</td>
-<td align="center">2.6</td>
-<td align="center">37.8</td>
-<td align="center"></td>
-<td align="center">137635226</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-### COCO Instance Segmentation Baselines with Mask R-CNN
-<!--
-./gen_html_table.py --config 'COCO-InstanceSegmentation/mask*50*'{1x,3x}'*' 'COCO-InstanceSegmentation/mask*101*' --name R50-C4 R50-DC5 R50-FPN R50-C4 R50-DC5 R50-FPN R101-C4 R101-DC5 R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP
--->
-
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_C4_1x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml">R50-C4</a></td>
-<td align="center">1x</td>
-<td align="center">0.584</td>
-<td align="center">0.110</td>
-<td align="center">5.2</td>
-<td align="center">36.8</td>
-<td align="center">32.2</td>
-<td align="center">137259246</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x/137259246/model_final_9243eb.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x/137259246/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_DC5_1x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml">R50-DC5</a></td>
-<td align="center">1x</td>
-<td align="center">0.471</td>
-<td align="center">0.076</td>
-<td align="center">6.5</td>
-<td align="center">38.3</td>
-<td align="center">34.2</td>
-<td align="center">137260150</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x/137260150/model_final_4f86c3.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x/137260150/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">38.6</td>
-<td align="center">35.2</td>
-<td align="center">137260431</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/model_final_a54504.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_C4_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml">R50-C4</a></td>
-<td align="center">3x</td>
-<td align="center">0.575</td>
-<td align="center">0.111</td>
-<td align="center">5.2</td>
-<td align="center">39.8</td>
-<td align="center">34.4</td>
-<td align="center">137849525</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_DC5_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml">R50-DC5</a></td>
-<td align="center">3x</td>
-<td align="center">0.470</td>
-<td align="center">0.076</td>
-<td align="center">6.5</td>
-<td align="center">40.0</td>
-<td align="center">35.9</td>
-<td align="center">137849551</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/model_final_84107b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x/137849551/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">41.0</td>
-<td align="center">37.2</td>
-<td align="center">137849600</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_C4_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml">R101-C4</a></td>
-<td align="center">3x</td>
-<td align="center">0.652</td>
-<td align="center">0.145</td>
-<td align="center">6.3</td>
-<td align="center">42.6</td>
-<td align="center">36.7</td>
-<td align="center">138363239</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/model_final_a2914c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x/138363239/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_DC5_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml">R101-DC5</a></td>
-<td align="center">3x</td>
-<td align="center">0.545</td>
-<td align="center">0.092</td>
-<td align="center">7.6</td>
-<td align="center">41.9</td>
-<td align="center">37.3</td>
-<td align="center">138363294</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x/138363294/model_final_0464b7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x/138363294/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.340</td>
-<td align="center">0.056</td>
-<td align="center">4.6</td>
-<td align="center">42.9</td>
-<td align="center">38.6</td>
-<td align="center">138205316</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/model_final_a3ec72.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x/138205316/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_X_101_32x8d_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.690</td>
-<td align="center">0.103</td>
-<td align="center">7.2</td>
-<td align="center">44.3</td>
-<td align="center">39.5</td>
-<td align="center">139653917</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/model_final_2d9806.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x/139653917/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-### COCO Person Keypoint Detection Baselines with Keypoint R-CNN
-<!--
-./gen_html_table.py --config 'COCO-Keypoints/*50*' 'COCO-Keypoints/*101*'  --name R50-FPN R50-FPN R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP keypoint_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">kp.<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: keypoint_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.315</td>
-<td align="center">0.072</td>
-<td align="center">5.0</td>
-<td align="center">53.6</td>
-<td align="center">64.0</td>
-<td align="center">137261548</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x/137261548/model_final_04e291.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x/137261548/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: keypoint_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml">R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.316</td>
-<td align="center">0.066</td>
-<td align="center">5.0</td>
-<td align="center">55.4</td>
-<td align="center">65.5</td>
-<td align="center">137849621</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: keypoint_rcnn_R_101_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml">R101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.390</td>
-<td align="center">0.076</td>
-<td align="center">6.1</td>
-<td align="center">56.4</td>
-<td align="center">66.1</td>
-<td align="center">138363331</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x/138363331/model_final_997cc7.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x/138363331/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: keypoint_rcnn_X_101_32x8d_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml">X101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.738</td>
-<td align="center">0.121</td>
-<td align="center">8.7</td>
-<td align="center">57.3</td>
-<td align="center">66.0</td>
-<td align="center">139686956</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x/139686956/model_final_5ad38f.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x/139686956/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-### COCO Panoptic Segmentation Baselines with Panoptic FPN
-<!--
-./gen_html_table.py --config 'COCO-PanopticSegmentation/*50*' 'COCO-PanopticSegmentation/*101*'  --name R50-FPN R50-FPN R101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP PQ
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">PQ</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: panoptic_fpn_R_50_1x -->
- <tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.304</td>
-<td align="center">0.053</td>
-<td align="center">4.8</td>
-<td align="center">37.6</td>
-<td align="center">34.7</td>
-<td align="center">39.4</td>
-<td align="center">139514544</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/model_final_dbfeb4.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_1x/139514544/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: panoptic_fpn_R_50_3x -->
- <tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml">R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.302</td>
-<td align="center">0.053</td>
-<td align="center">4.8</td>
-<td align="center">40.0</td>
-<td align="center">36.5</td>
-<td align="center">41.5</td>
-<td align="center">139514569</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/model_final_c10459.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_50_3x/139514569/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: panoptic_fpn_R_101_3x -->
- <tr><td align="left"><a href="configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml">R101-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.392</td>
-<td align="center">0.066</td>
-<td align="center">6.0</td>
-<td align="center">42.4</td>
-<td align="center">38.5</td>
-<td align="center">43.0</td>
-<td align="center">139514519</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x/139514519/model_final_cafdb1.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x/139514519/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-### LVIS Instance Segmentation Baselines with Mask R-CNN
-
-Mask R-CNN baselines on the [LVIS dataset](https://lvisdataset.org), v0.5.
-These baselines are described in Table 3(c) of the [LVIS paper](https://arxiv.org/abs/1908.03195).
-
-NOTE: the 1x schedule here has the same amount of __iterations__ as the COCO 1x baselines.
-They are roughly 24 epochs of LVISv0.5 data.
-The final results of these configs have large variance across different runs.
-
-<!--
-./gen_html_table.py --config 'LVIS-InstanceSegmentation/mask*50*' 'LVIS-InstanceSegmentation/mask*101*' --name R50-FPN R101-FPN X101-FPN --fields lr_sched train_speed inference_speed mem box_AP mask_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.292</td>
-<td align="center">0.107</td>
-<td align="center">7.1</td>
-<td align="center">23.6</td>
-<td align="center">24.4</td>
-<td align="center">144219072</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/144219072/model_final_571f7c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVIS-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/144219072/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_101_FPN_1x -->
- <tr><td align="left"><a href="configs/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x.yaml">R101-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.371</td>
-<td align="center">0.114</td>
-<td align="center">7.8</td>
-<td align="center">25.6</td>
-<td align="center">25.9</td>
-<td align="center">144219035</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x/144219035/model_final_824ab5.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVIS-InstanceSegmentation/mask_rcnn_R_101_FPN_1x/144219035/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_X_101_32x8d_FPN_1x -->
- <tr><td align="left"><a href="configs/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x.yaml">X101-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.712</td>
-<td align="center">0.151</td>
-<td align="center">10.2</td>
-<td align="center">26.7</td>
-<td align="center">27.1</td>
-<td align="center">144219108</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x/144219108/model_final_5e3439.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/LVIS-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x/144219108/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-
-### Cityscapes & Pascal VOC Baselines
-
-Simple baselines for
-* Mask R-CNN on Cityscapes instance segmentation (initialized from COCO pre-training, then trained on Cityscapes fine annotations only)
-* Faster R-CNN on PASCAL VOC object detection (trained on VOC 2007 train+val + VOC 2012 train+val, tested on VOC 2007 using 11-point interpolated AP)
-
-<!--
-./gen_html_table.py --config 'Cityscapes/*' 'PascalVOC-Detection/*' --name "R50-FPN, Cityscapes" "R50-C4, VOC" --fields train_speed inference_speed mem box_AP box_AP50 mask_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">box<br/>AP50</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_FPN -->
- <tr><td align="left"><a href="configs/Cityscapes/mask_rcnn_R_50_FPN.yaml">R50-FPN, Cityscapes</a></td>
-<td align="center">0.240</td>
-<td align="center">0.078</td>
-<td align="center">4.4</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center">36.5</td>
-<td align="center">142423278</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Cityscapes/mask_rcnn_R_50_FPN/142423278/model_final_af9cf5.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Cityscapes/mask_rcnn_R_50_FPN/142423278/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: faster_rcnn_R_50_C4 -->
- <tr><td align="left"><a href="configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml">R50-C4, VOC</a></td>
-<td align="center">0.537</td>
-<td align="center">0.081</td>
-<td align="center">4.8</td>
-<td align="center">51.9</td>
-<td align="center">80.3</td>
-<td align="center"></td>
-<td align="center">142202221</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PascalVOC-Detection/faster_rcnn_R_50_C4/142202221/model_final_b1acc2.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PascalVOC-Detection/faster_rcnn_R_50_C4/142202221/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-
-### Other Settings
-
-Ablations for Deformable Conv and Cascade R-CNN:
-
-<!--
-./gen_html_table.py --config 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml' 'Misc/*R_50_FPN_1x_dconv*' 'Misc/cascade*1x.yaml' 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' 'Misc/*R_50_FPN_3x_dconv*' 'Misc/cascade*3x.yaml' --name "Baseline R50-FPN" "Deformable Conv" "Cascade R-CNN" "Baseline R50-FPN" "Deformable Conv" "Cascade R-CNN"  --fields lr_sched train_speed inference_speed mem box_AP mask_AP
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml">Baseline R50-FPN</a></td>
-<td align="center">1x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">38.6</td>
-<td align="center">35.2</td>
-<td align="center">137260431</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/model_final_a54504.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x/137260431/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_1x_dconv_c3-c5 -->
- <tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5.yaml">Deformable Conv</a></td>
-<td align="center">1x</td>
-<td align="center">0.342</td>
-<td align="center">0.048</td>
-<td align="center">3.5</td>
-<td align="center">41.5</td>
-<td align="center">37.5</td>
-<td align="center">138602867</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5/138602867/model_final_65c703.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5/138602867/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_R_50_FPN_1x -->
- <tr><td align="left"><a href="configs/Misc/cascade_mask_rcnn_R_50_FPN_1x.yaml">Cascade R-CNN</a></td>
-<td align="center">1x</td>
-<td align="center">0.317</td>
-<td align="center">0.052</td>
-<td align="center">4.0</td>
-<td align="center">42.1</td>
-<td align="center">36.4</td>
-<td align="center">138602847</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_1x/138602847/model_final_e9d89b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_1x/138602847/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">Baseline R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">41.0</td>
-<td align="center">37.2</td>
-<td align="center">137849600</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x_dconv_c3-c5 -->
- <tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5.yaml">Deformable Conv</a></td>
-<td align="center">3x</td>
-<td align="center">0.349</td>
-<td align="center">0.047</td>
-<td align="center">3.5</td>
-<td align="center">42.7</td>
-<td align="center">38.5</td>
-<td align="center">144998336</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5/144998336/model_final_821d0b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5/144998336/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml">Cascade R-CNN</a></td>
-<td align="center">3x</td>
-<td align="center">0.328</td>
-<td align="center">0.053</td>
-<td align="center">4.0</td>
-<td align="center">44.3</td>
-<td align="center">38.5</td>
-<td align="center">144998488</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/model_final_480dd8.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-Ablations for normalization methods, and a few models trained from scratch following [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883).
-(Note: The baseline uses `2fc` head while the others use [`4conv1fc` head](https://arxiv.org/abs/1803.08494))
-<!--
-./gen_html_table.py --config 'COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' 'Misc/mask*50_FPN_3x_gn.yaml' 'Misc/mask*50_FPN_3x_syncbn.yaml' 'Misc/scratch*' --name "Baseline R50-FPN" "GN" "SyncBN" "GN (from scratch)" "GN (from scratch)" "SyncBN (from scratch)" --fields lr_sched train_speed inference_speed mem box_AP mask_AP
-   -->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">lr<br/>sched</th>
-<th valign="bottom">train<br/>time<br/>(s/iter)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_R_50_FPN_3x -->
- <tr><td align="left"><a href="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml">Baseline R50-FPN</a></td>
-<td align="center">3x</td>
-<td align="center">0.261</td>
-<td align="center">0.043</td>
-<td align="center">3.4</td>
-<td align="center">41.0</td>
-<td align="center">37.2</td>
-<td align="center">137849600</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x_gn -->
- <tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_3x_gn.yaml">GN</a></td>
-<td align="center">3x</td>
-<td align="center">0.356</td>
-<td align="center">0.069</td>
-<td align="center">7.3</td>
-<td align="center">42.6</td>
-<td align="center">38.6</td>
-<td align="center">138602888</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_gn/138602888/model_final_dc5d9e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_gn/138602888/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: mask_rcnn_R_50_FPN_3x_syncbn -->
- <tr><td align="left"><a href="configs/Misc/mask_rcnn_R_50_FPN_3x_syncbn.yaml">SyncBN</a></td>
-<td align="center">3x</td>
-<td align="center">0.371</td>
-<td align="center">0.053</td>
-<td align="center">5.5</td>
-<td align="center">41.9</td>
-<td align="center">37.8</td>
-<td align="center">169527823</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_syncbn/169527823/model_final_3b3c51.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/mask_rcnn_R_50_FPN_3x_syncbn/169527823/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: scratch_mask_rcnn_R_50_FPN_3x_gn -->
- <tr><td align="left"><a href="configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml">GN (from scratch)</a></td>
-<td align="center">3x</td>
-<td align="center">0.400</td>
-<td align="center">0.069</td>
-<td align="center">9.8</td>
-<td align="center">39.9</td>
-<td align="center">36.6</td>
-<td align="center">138602908</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/model_final_01ca85.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn/138602908/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: scratch_mask_rcnn_R_50_FPN_9x_gn -->
- <tr><td align="left"><a href="configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml">GN (from scratch)</a></td>
-<td align="center">9x</td>
-<td align="center">N/A</td>
-<td align="center">0.070</td>
-<td align="center">9.8</td>
-<td align="center">43.7</td>
-<td align="center">39.6</td>
-<td align="center">183808979</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn/183808979/model_final_da7b4c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn/183808979/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: scratch_mask_rcnn_R_50_FPN_9x_syncbn -->
- <tr><td align="left"><a href="configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml">SyncBN (from scratch)</a></td>
-<td align="center">9x</td>
-<td align="center">N/A</td>
-<td align="center">0.055</td>
-<td align="center">7.2</td>
-<td align="center">43.6</td>
-<td align="center">39.3</td>
-<td align="center">184226666</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn/184226666/model_final_5ce33e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn/184226666/metrics.json">metrics</a></td>
-</tr>
-</tbody></table>
-
-
-A few very large models trained for a long time, for demo purposes. They are trained using multiple machines:
-
-<!--
-./gen_html_table.py --config 'Misc/panoptic_*dconv*' 'Misc/cascade_*152*' --name "Panoptic FPN R101" "Mask R-CNN X152" --fields inference_speed mem box_AP mask_AP PQ
-# manually add TTA results
--->
-
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">PQ</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: panoptic_fpn_R_101_dconv_cascade_gn_3x -->
- <tr><td align="left"><a href="configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml">Panoptic FPN R101</a></td>
-<td align="center">0.107</td>
-<td align="center">11.4</td>
-<td align="center">47.4</td>
-<td align="center">41.3</td>
-<td align="center">46.1</td>
-<td align="center">139797668</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x/139797668/model_final_be35db.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x/139797668/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv -->
- <tr><td align="left"><a href="configs/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv.yaml">Mask R-CNN X152</a></td>
-<td align="center">0.242</td>
-<td align="center">15.1</td>
-<td align="center">50.2</td>
-<td align="center">44.0</td>
-<td align="center"></td>
-<td align="center">18131413</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv/18131413/model_0039999_e76410.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv/18131413/metrics.json">metrics</a></td>
-</tr>
-<!-- ROW: TTA cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv -->
- <tr><td align="left">above + test-time aug.</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center">51.9</td>
-<td align="center">45.9</td>
-<td align="center"></td>
-<td align="center"></td>
-<td align="center"></td>
-</tr>
-</tbody></table>

preprocess/humanparsing/mhp_extension/detectron2/README.md

@@ -1,56 +0,0 @@
-<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
-
-Detectron2 is Facebook AI Research's next generation software system
-that implements state-of-the-art object detection algorithms.
-It is a ground-up rewrite of the previous version,
-[Detectron](https://github.com/facebookresearch/Detectron/),
-and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
-
-<div align="center">
-  <img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
-</div>
-
-### What's New
-* It is powered by the [PyTorch](https://pytorch.org) deep learning framework.
-* Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
-* Can be used as a library to support [different projects](projects/) on top of it.
-  We'll open source more research projects in this way.
-* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
-
-See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
-to see more demos and learn about detectron2.
-
-## Installation
-
-See [INSTALL.md](INSTALL.md).
-
-## Quick Start
-
-See [GETTING_STARTED.md](GETTING_STARTED.md),
-or the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5).
-
-Learn more at our [documentation](https://detectron2.readthedocs.org).
-And see [projects/](projects/) for some projects that are built on top of detectron2.
-
-## Model Zoo and Baselines
-
-We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
-
-
-## License
-
-Detectron2 is released under the [Apache 2.0 license](LICENSE).
-
-## Citing Detectron2
-
-If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
-
-```BibTeX
-@misc{wu2019detectron2,
-  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
-                  Wan-Yen Lo and Ross Girshick},
-  title =        {Detectron2},
-  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
-  year =         {2019}
-}
-```

preprocess/humanparsing/mhp_extension/detectron2/configs/Base-RCNN-C4.yaml

@@ -1,18 +0,0 @@
-MODEL:
-  META_ARCHITECTURE: "GeneralizedRCNN"
-  RPN:
-    PRE_NMS_TOPK_TEST: 6000
-    POST_NMS_TOPK_TEST: 1000
-  ROI_HEADS:
-    NAME: "Res5ROIHeads"
-DATASETS:
-  TRAIN: ("coco_2017_train",)
-  TEST: ("coco_2017_val",)
-SOLVER:
-  IMS_PER_BATCH: 16
-  BASE_LR: 0.02
-  STEPS: (60000, 80000)
-  MAX_ITER: 90000
-INPUT:
-  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
-VERSION: 2

preprocess/humanparsing/mhp_extension/detectron2/configs/Base-RCNN-DilatedC5.yaml

@@ -1,31 +0,0 @@
-MODEL:
-  META_ARCHITECTURE: "GeneralizedRCNN"
-  RESNETS:
-    OUT_FEATURES: ["res5"]
-    RES5_DILATION: 2
-  RPN:
-    IN_FEATURES: ["res5"]
-    PRE_NMS_TOPK_TEST: 6000
-    POST_NMS_TOPK_TEST: 1000
-  ROI_HEADS:
-    NAME: "StandardROIHeads"
-    IN_FEATURES: ["res5"]
-  ROI_BOX_HEAD:
-    NAME: "FastRCNNConvFCHead"
-    NUM_FC: 2
-    POOLER_RESOLUTION: 7
-  ROI_MASK_HEAD:
-    NAME: "MaskRCNNConvUpsampleHead"
-    NUM_CONV: 4
-    POOLER_RESOLUTION: 14
-DATASETS:
-  TRAIN: ("coco_2017_train",)
-  TEST: ("coco_2017_val",)
-SOLVER:
-  IMS_PER_BATCH: 16
-  BASE_LR: 0.02
-  STEPS: (60000, 80000)
-  MAX_ITER: 90000
-INPUT:
-  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
-VERSION: 2

preprocess/humanparsing/mhp_extension/detectron2/configs/Base-RCNN-FPN.yaml

@@ -1,42 +0,0 @@
-MODEL:
-  META_ARCHITECTURE: "GeneralizedRCNN"
-  BACKBONE:
-    NAME: "build_resnet_fpn_backbone"
-  RESNETS:
-    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
-  FPN:
-    IN_FEATURES: ["res2", "res3", "res4", "res5"]
-  ANCHOR_GENERATOR:
-    SIZES: [[32], [64], [128], [256], [512]]  # One size for each in feature map
-    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]  # Three aspect ratios (same for all in feature maps)
-  RPN:
-    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
-    PRE_NMS_TOPK_TRAIN: 2000  # Per FPN level
-    PRE_NMS_TOPK_TEST: 1000  # Per FPN level
-    # Detectron1 uses 2000 proposals per-batch,
-    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
-    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
-    POST_NMS_TOPK_TRAIN: 1000
-    POST_NMS_TOPK_TEST: 1000
-  ROI_HEADS:
-    NAME: "StandardROIHeads"
-    IN_FEATURES: ["p2", "p3", "p4", "p5"]
-  ROI_BOX_HEAD:
-    NAME: "FastRCNNConvFCHead"
-    NUM_FC: 2
-    POOLER_RESOLUTION: 7
-  ROI_MASK_HEAD:
-    NAME: "MaskRCNNConvUpsampleHead"
-    NUM_CONV: 4
-    POOLER_RESOLUTION: 14
-DATASETS:
-  TRAIN: ("coco_2017_train",)
-  TEST: ("coco_2017_val",)
-SOLVER:
-  IMS_PER_BATCH: 16
-  BASE_LR: 0.02
-  STEPS: (60000, 80000)
-  MAX_ITER: 90000
-INPUT:
-  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
-VERSION: 2

preprocess/humanparsing/mhp_extension/detectron2/configs/Base-RetinaNet.yaml

@@ -1,24 +0,0 @@
-MODEL:
-  META_ARCHITECTURE: "RetinaNet"
-  BACKBONE:
-    NAME: "build_retinanet_resnet_fpn_backbone"
-  RESNETS:
-    OUT_FEATURES: ["res3", "res4", "res5"]
-  ANCHOR_GENERATOR:
-    SIZES: !!python/object/apply:eval ["[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]"]
-  FPN:
-    IN_FEATURES: ["res3", "res4", "res5"]
-  RETINANET:
-    IOU_THRESHOLDS: [0.4, 0.5]
-    IOU_LABELS: [0, -1, 1]
-DATASETS:
-  TRAIN: ("coco_2017_train",)
-  TEST: ("coco_2017_val",)
-SOLVER:
-  IMS_PER_BATCH: 16
-  BASE_LR: 0.01  # Note that RetinaNet uses a different default learning rate
-  STEPS: (60000, 80000)
-  MAX_ITER: 90000
-INPUT:
-  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
-VERSION: 2

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml

@@ -1,17 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  LOAD_PROPOSALS: True
-  RESNETS:
-    DEPTH: 50
-  PROPOSAL_GENERATOR:
-    NAME: "PrecomputedProposals"
-DATASETS:
-  TRAIN: ("coco_2017_train",)
-  PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_train_box_proposals_21bc3a.pkl", )
-  TEST: ("coco_2017_val",)
-  PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", )
-DATALOADER:
-  # proposals are part of the dataset_dicts, and take a lot of RAM
-  NUM_WORKERS: 2

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-DilatedC5.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-DilatedC5.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-DilatedC5.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml

@@ -1,13 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  MASK_ON: False
-  WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
-  PIXEL_STD: [57.375, 57.120, 58.395]
-  RESNETS:
-    STRIDE_IN_1X1: False  # this is a C2 model
-    NUM_GROUPS: 32
-    WIDTH_PER_GROUP: 8
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml

@@ -1,8 +0,0 @@
-_BASE_: "../Base-RetinaNet.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml

@@ -1,5 +0,0 @@
-_BASE_: "../Base-RetinaNet.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml

@@ -1,8 +0,0 @@
-_BASE_: "../Base-RetinaNet.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/rpn_R_50_C4_1x.yaml

@@ -1,10 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  META_ARCHITECTURE: "ProposalNetwork"
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-  RPN:
-    PRE_NMS_TOPK_TEST: 12000
-    POST_NMS_TOPK_TEST: 2000

preprocess/humanparsing/mhp_extension/detectron2/configs/COCO-Detection/rpn_R_50_FPN_1x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  META_ARCHITECTURE: "ProposalNetwork"
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-  RPN:
-    POST_NMS_TOPK_TEST: 2000