Add files using upload-large-folder tool

RAVE-main/annotator/clipvision/__init__.py

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+import os
+import torch
+
+from modules import devices
+from modules.modelloader import load_file_from_url
+from annotator.annotator_path import models_path
+from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor
+
+
+config_clip_g = {
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_size": 1664,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 48,
+  "patch_size": 14,
+  "projection_dim": 1280,
+  "torch_dtype": "float32"
+}
+
+config_clip_h = {
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_size": 1280,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 32,
+  "patch_size": 14,
+  "projection_dim": 1024,
+  "torch_dtype": "float32"
+}
+
+config_clip_vitl = {
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 1024,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "patch_size": 14,
+  "projection_dim": 768,
+  "torch_dtype": "float32"
+}
+
+configs = {
+    'clip_g': config_clip_g,
+    'clip_h': config_clip_h,
+    'clip_vitl': config_clip_vitl,
+}
+
+downloads = {
+    'clip_vitl': 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin',
+    'clip_g': 'https://huggingface.co/lllyasviel/Annotators/resolve/main/clip_g.pth',
+    'clip_h': 'https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/pytorch_model.bin'
+}
+
+
+clip_vision_h_uc = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'clip_vision_h_uc.data')
+clip_vision_h_uc = torch.load(clip_vision_h_uc)['uc']
+
+
+class ClipVisionDetector:
+    def __init__(self, config):
+        assert config in downloads
+        self.download_link = downloads[config]
+        self.model_path = os.path.join(models_path, 'clip_vision')
+        self.file_name = config + '.pth'
+        self.config = configs[config]
+        self.device = devices.get_device_for("controlnet")
+        os.makedirs(self.model_path, exist_ok=True)
+        file_path = os.path.join(self.model_path, self.file_name)
+        if not os.path.exists(file_path):
+            load_file_from_url(url=self.download_link, model_dir=self.model_path, file_name=self.file_name)
+        config = CLIPVisionConfig(**self.config)
+        self.model = CLIPVisionModelWithProjection(config)
+        self.processor = CLIPImageProcessor(crop_size=224,
+                                            do_center_crop=True,
+                                            do_convert_rgb=True,
+                                            do_normalize=True,
+                                            do_resize=True,
+                                            image_mean=[0.48145466, 0.4578275, 0.40821073],
+                                            image_std=[0.26862954, 0.26130258, 0.27577711],
+                                            resample=3,
+                                            size=224)
+
+        sd = torch.load(file_path, map_location=torch.device('cpu'))
+        self.model.load_state_dict(sd, strict=False)
+        del sd
+
+        self.model.eval()
+        self.model.cpu()
+
+    def unload_model(self):
+        if self.model is not None:
+            self.model.to('meta')
+
+    def __call__(self, input_image):
+        with torch.no_grad():
+            clip_vision_model = self.model.cpu()
+            feat = self.processor(images=input_image, return_tensors="pt")
+            feat['pixel_values'] = feat['pixel_values'].cpu()
+            result = clip_vision_model(**feat, output_hidden_states=True)
+            result['hidden_states'] = [v.to(devices.get_device_for("controlnet")) for v in result['hidden_states']]
+            result = {k: v.to(devices.get_device_for("controlnet")) if isinstance(v, torch.Tensor) else v for k, v in result.items()}
+        return result

RAVE-main/annotator/leres/pix2pix/options/__init__.py

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+"""This package options includes option modules: training options, test options, and basic options (used in both training and test)."""

RAVE-main/annotator/leres/pix2pix/options/base_options.py

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+import argparse
+import os
+from ...pix2pix.util import util
+# import torch
+from ...pix2pix import models
+# import pix2pix.data
+import numpy as np
+
+class BaseOptions():
+    """This class defines options used during both training and test time.
+
+    It also implements several helper functions such as parsing, printing, and saving the options.
+    It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
+    """
+
+    def __init__(self):
+        """Reset the class; indicates the class hasn't been initailized"""
+        self.initialized = False
+
+    def initialize(self, parser):
+        """Define the common options that are used in both training and test."""
+        # basic parameters
+        parser.add_argument('--dataroot', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
+        parser.add_argument('--name', type=str, default='void', help='mahdi_unet_new, scaled_unet')
+        parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU')
+        parser.add_argument('--checkpoints_dir', type=str, default='./pix2pix/checkpoints', help='models are saved here')
+        # model parameters
+        parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. [cycle_gan | pix2pix | test | colorization]')
+        parser.add_argument('--input_nc', type=int, default=2, help='# of input image channels: 3 for RGB and 1 for grayscale')
+        parser.add_argument('--output_nc', type=int, default=1, help='# of output image channels: 3 for RGB and 1 for grayscale')
+        parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
+        parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')
+        parser.add_argument('--netD', type=str, default='basic', help='specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
+        parser.add_argument('--netG', type=str, default='resnet_9blocks', help='specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]')
+        parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
+        parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]')
+        parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]')
+        parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
+        parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
+        # dataset parameters
+        parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
+        parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
+        parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
+        parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
+        parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
+        parser.add_argument('--load_size', type=int, default=672, help='scale images to this size')
+        parser.add_argument('--crop_size', type=int, default=672, help='then crop to this size')
+        parser.add_argument('--max_dataset_size', type=int, default=10000, help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
+        parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]')
+        parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
+        parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
+        # additional parameters
+        parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
+        parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
+        parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
+        parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
+
+        parser.add_argument('--data_dir', type=str, required=False,
+                            help='input files directory images can be .png .jpg .tiff')
+        parser.add_argument('--output_dir', type=str, required=False,
+                            help='result dir. result depth will be png. vides are JMPG as avi')
+        parser.add_argument('--savecrops', type=int, required=False)
+        parser.add_argument('--savewholeest', type=int, required=False)
+        parser.add_argument('--output_resolution', type=int, required=False,
+                            help='0 for no restriction 1 for resize to input size')
+        parser.add_argument('--net_receptive_field_size', type=int, required=False)
+        parser.add_argument('--pix2pixsize', type=int, required=False)
+        parser.add_argument('--generatevideo', type=int, required=False)
+        parser.add_argument('--depthNet', type=int, required=False, help='0: midas 1:strurturedRL')
+        parser.add_argument('--R0', action='store_true')
+        parser.add_argument('--R20', action='store_true')
+        parser.add_argument('--Final', action='store_true')
+        parser.add_argument('--colorize_results', action='store_true')
+        parser.add_argument('--max_res', type=float, default=np.inf)
+
+        self.initialized = True
+        return parser
+
+    def gather_options(self):
+        """Initialize our parser with basic options(only once).
+        Add additional model-specific and dataset-specific options.
+        These options are defined in the <modify_commandline_options> function
+        in model and dataset classes.
+        """
+        if not self.initialized:  # check if it has been initialized
+            parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+            parser = self.initialize(parser)
+
+        # get the basic options
+        opt, _ = parser.parse_known_args()
+
+        # modify model-related parser options
+        model_name = opt.model
+        model_option_setter = models.get_option_setter(model_name)
+        parser = model_option_setter(parser, self.isTrain)
+        opt, _ = parser.parse_known_args()  # parse again with new defaults
+
+        # modify dataset-related parser options
+        # dataset_name = opt.dataset_mode
+        # dataset_option_setter = pix2pix.data.get_option_setter(dataset_name)
+        # parser = dataset_option_setter(parser, self.isTrain)
+
+        # save and return the parser
+        self.parser = parser
+        #return parser.parse_args() #EVIL
+        return opt
+
+    def print_options(self, opt):
+        """Print and save options
+
+        It will print both current options and default values(if different).
+        It will save options into a text file / [checkpoints_dir] / opt.txt
+        """
+        message = ''
+        message += '----------------- Options ---------------\n'
+        for k, v in sorted(vars(opt).items()):
+            comment = ''
+            default = self.parser.get_default(k)
+            if v != default:
+                comment = '\t[default: %s]' % str(default)
+            message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
+        message += '----------------- End -------------------'
+        print(message)
+
+        # save to the disk
+        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
+        util.mkdirs(expr_dir)
+        file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
+        with open(file_name, 'wt') as opt_file:
+            opt_file.write(message)
+            opt_file.write('\n')
+
+    def parse(self):
+        """Parse our options, create checkpoints directory suffix, and set up gpu device."""
+        opt = self.gather_options()
+        opt.isTrain = self.isTrain   # train or test
+
+        # process opt.suffix
+        if opt.suffix:
+            suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
+            opt.name = opt.name + suffix
+
+        #self.print_options(opt)
+
+        # set gpu ids
+        str_ids = opt.gpu_ids.split(',')
+        opt.gpu_ids = []
+        for str_id in str_ids:
+            id = int(str_id)
+            if id >= 0:
+                opt.gpu_ids.append(id)
+        #if len(opt.gpu_ids) > 0:
+        #    torch.cuda.set_device(opt.gpu_ids[0])
+
+        self.opt = opt
+        return self.opt

RAVE-main/annotator/leres/pix2pix/options/test_options.py

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+from .base_options import BaseOptions
+
+
+class TestOptions(BaseOptions):
+    """This class includes test options.
+
+    It also includes shared options defined in BaseOptions.
+    """
+
+    def initialize(self, parser):
+        parser = BaseOptions.initialize(self, parser)  # define shared options
+        parser.add_argument('--aspect_ratio', type=float, default=1.0, help='aspect ratio of result images')
+        parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
+        # Dropout and Batchnorm has different behavioir during training and test.
+        parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')
+        parser.add_argument('--num_test', type=int, default=50, help='how many test images to run')
+        # rewrite devalue values
+        parser.set_defaults(model='pix2pix4depth')
+        # To avoid cropping, the load_size should be the same as crop_size
+        parser.set_defaults(load_size=parser.get_default('crop_size'))
+        self.isTrain = False
+        return parser

RAVE-main/annotator/normalbae/LICENSE

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+MIT License
+
+Copyright (c) 2022 Caroline Chan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

RAVE-main/annotator/normalbae/__init__.py

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+import os
+import types
+import torch
+import numpy as np
+
+from einops import rearrange
+from .models.NNET import NNET
+from modules import devices
+from annotator.annotator_path import models_path
+import torchvision.transforms as transforms
+
+
+# load model
+def load_checkpoint(fpath, model):
+    ckpt = torch.load(fpath, map_location='cpu')['model']
+
+    load_dict = {}
+    for k, v in ckpt.items():
+        if k.startswith('module.'):
+            k_ = k.replace('module.', '')
+            load_dict[k_] = v
+        else:
+            load_dict[k] = v
+
+    model.load_state_dict(load_dict)
+    return model
+
+
+class NormalBaeDetector:
+    model_dir = os.path.join(models_path, "normal_bae")
+
+    def __init__(self):
+        self.model = None
+        self.device = devices.get_device_for("controlnet")
+
+    def load_model(self):
+        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/scannet.pt"
+        modelpath = os.path.join(self.model_dir, "scannet.pt")
+        if not os.path.exists(modelpath):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=self.model_dir)
+        args = types.SimpleNamespace()
+        args.mode = 'client'
+        args.architecture = 'BN'
+        args.pretrained = 'scannet'
+        args.sampling_ratio = 0.4
+        args.importance_ratio = 0.7
+        model = NNET(args)
+        model = load_checkpoint(modelpath, model)
+        model.eval()
+        self.model = model.to(self.device)
+        self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    def unload_model(self):
+        if self.model is not None:
+            self.model.cpu()
+
+    def __call__(self, input_image):
+        if self.model is None:
+            self.load_model()
+
+        self.model.to(self.device)
+        assert input_image.ndim == 3
+        image_normal = input_image
+        with torch.no_grad():
+            image_normal = torch.from_numpy(image_normal).float().to(self.device)
+            image_normal = image_normal / 255.0
+            image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
+            image_normal = self.norm(image_normal)
+
+            normal = self.model(image_normal)
+            normal = normal[0][-1][:, :3]
+            # d = torch.sum(normal ** 2.0, dim=1, keepdim=True) ** 0.5
+            # d = torch.maximum(d, torch.ones_like(d) * 1e-5)
+            # normal /= d
+            normal = ((normal + 1) * 0.5).clip(0, 1)
+
+            normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
+            normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)
+
+            return normal_image

RAVE-main/annotator/normalbae/models/NNET.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .submodules.encoder import Encoder
+from .submodules.decoder import Decoder
+
+
+class NNET(nn.Module):
+    def __init__(self, args):
+        super(NNET, self).__init__()
+        self.encoder = Encoder()
+        self.decoder = Decoder(args)
+
+    def get_1x_lr_params(self):  # lr/10 learning rate
+        return self.encoder.parameters()
+
+    def get_10x_lr_params(self):  # lr learning rate
+        return self.decoder.parameters()
+
+    def forward(self, img, **kwargs):
+        return self.decoder(self.encoder(img), **kwargs)

RAVE-main/annotator/normalbae/models/baseline.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .submodules.submodules import UpSampleBN, norm_normalize
+
+
+# This is the baseline encoder-decoder we used in the ablation study
+class NNET(nn.Module):
+    def __init__(self, args=None):
+        super(NNET, self).__init__()
+        self.encoder = Encoder()
+        self.decoder = Decoder(num_classes=4)
+
+    def forward(self, x, **kwargs):
+        out = self.decoder(self.encoder(x), **kwargs)
+
+        # Bilinearly upsample the output to match the input resolution
+        up_out = F.interpolate(out, size=[x.size(2), x.size(3)], mode='bilinear', align_corners=False)
+        
+        # L2-normalize the first three channels / ensure positive value for concentration parameters (kappa)
+        up_out = norm_normalize(up_out) 
+        return up_out
+
+    def get_1x_lr_params(self):  # lr/10 learning rate
+        return self.encoder.parameters()
+
+    def get_10x_lr_params(self):  # lr learning rate
+        modules = [self.decoder]
+        for m in modules:
+            yield from m.parameters()
+
+
+# Encoder
+class Encoder(nn.Module):
+    def __init__(self):
+        super(Encoder, self).__init__()
+
+        basemodel_name = 'tf_efficientnet_b5_ap'
+        basemodel = torch.hub.load('rwightman/gen-efficientnet-pytorch', basemodel_name, pretrained=True)
+
+        # Remove last layer
+        basemodel.global_pool = nn.Identity()
+        basemodel.classifier = nn.Identity()
+
+        self.original_model = basemodel
+
+    def forward(self, x):
+        features = [x]
+        for k, v in self.original_model._modules.items():
+            if (k == 'blocks'):
+                for ki, vi in v._modules.items():
+                    features.append(vi(features[-1]))
+            else:
+                features.append(v(features[-1]))
+        return features
+
+
+# Decoder (no pixel-wise MLP, no uncertainty-guided sampling)
+class Decoder(nn.Module):
+    def __init__(self, num_classes=4):
+        super(Decoder, self).__init__()
+        self.conv2 = nn.Conv2d(2048, 2048, kernel_size=1, stride=1, padding=0)
+        self.up1 = UpSampleBN(skip_input=2048 + 176, output_features=1024)
+        self.up2 = UpSampleBN(skip_input=1024 + 64, output_features=512)
+        self.up3 = UpSampleBN(skip_input=512 + 40, output_features=256)
+        self.up4 = UpSampleBN(skip_input=256 + 24, output_features=128)
+        self.conv3 = nn.Conv2d(128, num_classes, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, features):
+        x_block0, x_block1, x_block2, x_block3, x_block4 = features[4], features[5], features[6], features[8], features[11]
+        x_d0 = self.conv2(x_block4)
+        x_d1 = self.up1(x_d0, x_block3)
+        x_d2 = self.up2(x_d1, x_block2)
+        x_d3 = self.up3(x_d2, x_block1)
+        x_d4 = self.up4(x_d3, x_block0)
+        out = self.conv3(x_d4)
+        return out
+
+
+if __name__ == '__main__':
+    model = Baseline()
+    x = torch.rand(2, 3, 480, 640)
+    out = model(x)
+    print(out.shape)

RAVE-main/annotator/normalbae/models/submodules/decoder.py

@@ -0,0 +1,202 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .submodules import UpSampleBN, UpSampleGN, norm_normalize, sample_points
+
+
+class Decoder(nn.Module):
+    def __init__(self, args):
+        super(Decoder, self).__init__()
+
+        # hyper-parameter for sampling
+        self.sampling_ratio = args.sampling_ratio
+        self.importance_ratio = args.importance_ratio
+
+        # feature-map
+        self.conv2 = nn.Conv2d(2048, 2048, kernel_size=1, stride=1, padding=0)
+        if args.architecture == 'BN':
+            self.up1 = UpSampleBN(skip_input=2048 + 176, output_features=1024)
+            self.up2 = UpSampleBN(skip_input=1024 + 64, output_features=512)
+            self.up3 = UpSampleBN(skip_input=512 + 40, output_features=256)
+            self.up4 = UpSampleBN(skip_input=256 + 24, output_features=128)
+
+        elif args.architecture == 'GN':
+            self.up1 = UpSampleGN(skip_input=2048 + 176, output_features=1024)
+            self.up2 = UpSampleGN(skip_input=1024 + 64, output_features=512)
+            self.up3 = UpSampleGN(skip_input=512 + 40, output_features=256)
+            self.up4 = UpSampleGN(skip_input=256 + 24, output_features=128)
+
+        else:
+            raise Exception('invalid architecture')
+
+        # produces 1/8 res output
+        self.out_conv_res8 = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
+
+        # produces 1/4 res output
+        self.out_conv_res4 = nn.Sequential(
+            nn.Conv1d(512 + 4, 128, kernel_size=1), nn.ReLU(),
+            nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(),
+            nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(),
+            nn.Conv1d(128, 4, kernel_size=1),
+        )
+
+        # produces 1/2 res output
+        self.out_conv_res2 = nn.Sequential(
+            nn.Conv1d(256 + 4, 128, kernel_size=1), nn.ReLU(),
+            nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(),
+            nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(),
+            nn.Conv1d(128, 4, kernel_size=1),
+        )
+
+        # produces 1/1 res output
+        self.out_conv_res1 = nn.Sequential(
+            nn.Conv1d(128 + 4, 128, kernel_size=1), nn.ReLU(),
+            nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(),
+            nn.Conv1d(128, 128, kernel_size=1), nn.ReLU(),
+            nn.Conv1d(128, 4, kernel_size=1),
+        )
+
+    def forward(self, features, gt_norm_mask=None, mode='test'):
+        x_block0, x_block1, x_block2, x_block3, x_block4 = features[4], features[5], features[6], features[8], features[11]
+
+        # generate feature-map
+
+        x_d0 = self.conv2(x_block4)                     # x_d0 : [2, 2048, 15, 20]      1/32 res
+        x_d1 = self.up1(x_d0, x_block3)                 # x_d1 : [2, 1024, 30, 40]      1/16 res
+        x_d2 = self.up2(x_d1, x_block2)                 # x_d2 : [2, 512, 60, 80]       1/8 res
+        x_d3 = self.up3(x_d2, x_block1)                 # x_d3: [2, 256, 120, 160]      1/4 res
+        x_d4 = self.up4(x_d3, x_block0)                 # x_d4: [2, 128, 240, 320]      1/2 res
+
+        # 1/8 res output
+        out_res8 = self.out_conv_res8(x_d2)             # out_res8: [2, 4, 60, 80]      1/8 res output
+        out_res8 = norm_normalize(out_res8)             # out_res8: [2, 4, 60, 80]      1/8 res output
+
+        ################################################################################################################
+        # out_res4
+        ################################################################################################################
+
+        if mode == 'train':
+            # upsampling ... out_res8: [2, 4, 60, 80] -> out_res8_res4: [2, 4, 120, 160]
+            out_res8_res4 = F.interpolate(out_res8, scale_factor=2, mode='bilinear', align_corners=True)
+            B, _, H, W = out_res8_res4.shape
+
+            # samples: [B, 1, N, 2]
+            point_coords_res4, rows_int, cols_int = sample_points(out_res8_res4.detach(), gt_norm_mask,
+                                                                  sampling_ratio=self.sampling_ratio,
+                                                                  beta=self.importance_ratio)
+
+            # output (needed for evaluation / visualization)
+            out_res4 = out_res8_res4
+
+            # grid_sample feature-map
+            feat_res4 = F.grid_sample(x_d2, point_coords_res4, mode='bilinear', align_corners=True)  # (B, 512, 1, N)
+            init_pred = F.grid_sample(out_res8, point_coords_res4, mode='bilinear', align_corners=True)  # (B, 4, 1, N)
+            feat_res4 = torch.cat([feat_res4, init_pred], dim=1)  # (B, 512+4, 1, N)
+
+            # prediction (needed to compute loss)
+            samples_pred_res4 = self.out_conv_res4(feat_res4[:, :, 0, :])  # (B, 4, N)
+            samples_pred_res4 = norm_normalize(samples_pred_res4)  # (B, 4, N) - normalized
+
+            for i in range(B):
+                out_res4[i, :, rows_int[i, :], cols_int[i, :]] = samples_pred_res4[i, :, :]
+
+        else:
+            # grid_sample feature-map
+            feat_map = F.interpolate(x_d2, scale_factor=2, mode='bilinear', align_corners=True)
+            init_pred = F.interpolate(out_res8, scale_factor=2, mode='bilinear', align_corners=True)
+            feat_map = torch.cat([feat_map, init_pred], dim=1)  # (B, 512+4, H, W)
+            B, _, H, W = feat_map.shape
+
+            # try all pixels
+            out_res4 = self.out_conv_res4(feat_map.view(B, 512 + 4, -1))  # (B, 4, N)
+            out_res4 = norm_normalize(out_res4)  # (B, 4, N) - normalized
+            out_res4 = out_res4.view(B, 4, H, W)
+            samples_pred_res4 = point_coords_res4 = None
+
+        ################################################################################################################
+        # out_res2
+        ################################################################################################################
+
+        if mode == 'train':
+
+            # upsampling ... out_res4: [2, 4, 120, 160] -> out_res4_res2: [2, 4, 240, 320]
+            out_res4_res2 = F.interpolate(out_res4, scale_factor=2, mode='bilinear', align_corners=True)
+            B, _, H, W = out_res4_res2.shape
+
+            # samples: [B, 1, N, 2]
+            point_coords_res2, rows_int, cols_int = sample_points(out_res4_res2.detach(), gt_norm_mask,
+                                                                  sampling_ratio=self.sampling_ratio,
+                                                                  beta=self.importance_ratio)
+
+            # output (needed for evaluation / visualization)
+            out_res2 = out_res4_res2
+
+            # grid_sample feature-map
+            feat_res2 = F.grid_sample(x_d3, point_coords_res2, mode='bilinear', align_corners=True)  # (B, 256, 1, N)
+            init_pred = F.grid_sample(out_res4, point_coords_res2, mode='bilinear', align_corners=True)  # (B, 4, 1, N)
+            feat_res2 = torch.cat([feat_res2, init_pred], dim=1)  # (B, 256+4, 1, N)
+
+            # prediction (needed to compute loss)
+            samples_pred_res2 = self.out_conv_res2(feat_res2[:, :, 0, :])  # (B, 4, N)
+            samples_pred_res2 = norm_normalize(samples_pred_res2)  # (B, 4, N) - normalized
+
+            for i in range(B):
+                out_res2[i, :, rows_int[i, :], cols_int[i, :]] = samples_pred_res2[i, :, :]
+
+        else:
+            # grid_sample feature-map
+            feat_map = F.interpolate(x_d3, scale_factor=2, mode='bilinear', align_corners=True)
+            init_pred = F.interpolate(out_res4, scale_factor=2, mode='bilinear', align_corners=True)
+            feat_map = torch.cat([feat_map, init_pred], dim=1)  # (B, 512+4, H, W)
+            B, _, H, W = feat_map.shape
+
+            out_res2 = self.out_conv_res2(feat_map.view(B, 256 + 4, -1))  # (B, 4, N)
+            out_res2 = norm_normalize(out_res2)  # (B, 4, N) - normalized
+            out_res2 = out_res2.view(B, 4, H, W)
+            samples_pred_res2 = point_coords_res2 = None
+
+        ################################################################################################################
+        # out_res1
+        ################################################################################################################
+
+        if mode == 'train':
+            # upsampling ... out_res4: [2, 4, 120, 160] -> out_res4_res2: [2, 4, 240, 320]
+            out_res2_res1 = F.interpolate(out_res2, scale_factor=2, mode='bilinear', align_corners=True)
+            B, _, H, W = out_res2_res1.shape
+
+            # samples: [B, 1, N, 2]
+            point_coords_res1, rows_int, cols_int = sample_points(out_res2_res1.detach(), gt_norm_mask,
+                                                                  sampling_ratio=self.sampling_ratio,
+                                                                  beta=self.importance_ratio)
+
+            # output (needed for evaluation / visualization)
+            out_res1 = out_res2_res1
+
+            # grid_sample feature-map
+            feat_res1 = F.grid_sample(x_d4, point_coords_res1, mode='bilinear', align_corners=True)  # (B, 128, 1, N)
+            init_pred = F.grid_sample(out_res2, point_coords_res1, mode='bilinear', align_corners=True)  # (B, 4, 1, N)
+            feat_res1 = torch.cat([feat_res1, init_pred], dim=1)  # (B, 128+4, 1, N)
+
+            # prediction (needed to compute loss)
+            samples_pred_res1 = self.out_conv_res1(feat_res1[:, :, 0, :])  # (B, 4, N)
+            samples_pred_res1 = norm_normalize(samples_pred_res1)  # (B, 4, N) - normalized
+
+            for i in range(B):
+                out_res1[i, :, rows_int[i, :], cols_int[i, :]] = samples_pred_res1[i, :, :]
+
+        else:
+            # grid_sample feature-map
+            feat_map = F.interpolate(x_d4, scale_factor=2, mode='bilinear', align_corners=True)
+            init_pred = F.interpolate(out_res2, scale_factor=2, mode='bilinear', align_corners=True)
+            feat_map = torch.cat([feat_map, init_pred], dim=1)  # (B, 512+4, H, W)
+            B, _, H, W = feat_map.shape
+
+            out_res1 = self.out_conv_res1(feat_map.view(B, 128 + 4, -1))  # (B, 4, N)
+            out_res1 = norm_normalize(out_res1)  # (B, 4, N) - normalized
+            out_res1 = out_res1.view(B, 4, H, W)
+            samples_pred_res1 = point_coords_res1 = None
+
+        return [out_res8, out_res4, out_res2, out_res1], \
+               [out_res8, samples_pred_res4, samples_pred_res2, samples_pred_res1], \
+               [None, point_coords_res4, point_coords_res2, point_coords_res1]
+

RAVE-main/annotator/normalbae/models/submodules/efficientnet_repo/.gitignore

@@ -0,0 +1,109 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# pytorch stuff
+*.pth
+*.onnx
+*.pb
+
+trained_models/
+.fuse_hidden*

RAVE-main/annotator/normalbae/models/submodules/efficientnet_repo/caffe2_validate.py

@@ -0,0 +1,138 @@
+""" Caffe2 validation script
+
+This script is created to verify exported ONNX models running in Caffe2
+It utilizes the same PyTorch dataloader/processing pipeline for a
+fair comparison against the originals.
+
+Copyright 2020 Ross Wightman
+"""
+import argparse
+import numpy as np
+from caffe2.python import core, workspace, model_helper
+from caffe2.proto import caffe2_pb2
+from data import create_loader, resolve_data_config, Dataset
+from utils import AverageMeter
+import time
+
+parser = argparse.ArgumentParser(description='Caffe2 ImageNet Validation')
+parser.add_argument('data', metavar='DIR',
+                    help='path to dataset')
+parser.add_argument('--c2-prefix', default='', type=str, metavar='NAME',
+                    help='caffe2 model pb name prefix')
+parser.add_argument('--c2-init', default='', type=str, metavar='PATH',
+                    help='caffe2 model init .pb')
+parser.add_argument('--c2-predict', default='', type=str, metavar='PATH',
+                    help='caffe2 model predict .pb')
+parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
+                    help='number of data loading workers (default: 2)')
+parser.add_argument('-b', '--batch-size', default=256, type=int,
+                    metavar='N', help='mini-batch size (default: 256)')
+parser.add_argument('--img-size', default=None, type=int,
+                    metavar='N', help='Input image dimension, uses model default if empty')
+parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
+                    help='Override mean pixel value of dataset')
+parser.add_argument('--std', type=float,  nargs='+', default=None, metavar='STD',
+                    help='Override std deviation of of dataset')
+parser.add_argument('--crop-pct', type=float, default=None, metavar='PCT',
+                    help='Override default crop pct of 0.875')
+parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
+                    help='Image resize interpolation type (overrides model)')
+parser.add_argument('--tf-preprocessing', dest='tf_preprocessing', action='store_true',
+                    help='use tensorflow mnasnet preporcessing')
+parser.add_argument('--print-freq', '-p', default=10, type=int,
+                    metavar='N', help='print frequency (default: 10)')
+
+
+def main():
+    args = parser.parse_args()
+    args.gpu_id = 0
+    if args.c2_prefix:
+        args.c2_init = args.c2_prefix + '.init.pb'
+        args.c2_predict = args.c2_prefix + '.predict.pb'
+
+    model = model_helper.ModelHelper(name="validation_net", init_params=False)
+
+    # Bring in the init net from init_net.pb
+    init_net_proto = caffe2_pb2.NetDef()
+    with open(args.c2_init, "rb") as f:
+        init_net_proto.ParseFromString(f.read())
+    model.param_init_net = core.Net(init_net_proto)
+
+    # bring in the predict net from predict_net.pb
+    predict_net_proto = caffe2_pb2.NetDef()
+    with open(args.c2_predict, "rb") as f:
+        predict_net_proto.ParseFromString(f.read())
+    model.net = core.Net(predict_net_proto)
+
+    data_config = resolve_data_config(None, args)
+    loader = create_loader(
+        Dataset(args.data, load_bytes=args.tf_preprocessing),
+        input_size=data_config['input_size'],
+        batch_size=args.batch_size,
+        use_prefetcher=False,
+        interpolation=data_config['interpolation'],
+        mean=data_config['mean'],
+        std=data_config['std'],
+        num_workers=args.workers,
+        crop_pct=data_config['crop_pct'],
+        tensorflow_preprocessing=args.tf_preprocessing)
+
+    # this is so obvious, wonderful interface </sarcasm>
+    input_blob = model.net.external_inputs[0]
+    output_blob = model.net.external_outputs[0]
+
+    if True:
+        device_opts = None
+    else:
+        # CUDA is crashing, no idea why, awesome error message, give it a try for kicks
+        device_opts = core.DeviceOption(caffe2_pb2.PROTO_CUDA, args.gpu_id)
+        model.net.RunAllOnGPU(gpu_id=args.gpu_id, use_cudnn=True)
+        model.param_init_net.RunAllOnGPU(gpu_id=args.gpu_id, use_cudnn=True)
+
+    model.param_init_net.GaussianFill(
+        [], input_blob.GetUnscopedName(),
+        shape=(1,) + data_config['input_size'], mean=0.0, std=1.0)
+    workspace.RunNetOnce(model.param_init_net)
+    workspace.CreateNet(model.net, overwrite=True)
+
+    batch_time = AverageMeter()
+    top1 = AverageMeter()
+    top5 = AverageMeter()
+    end = time.time()
+    for i, (input, target) in enumerate(loader):
+        # run the net and return prediction
+        caffe2_in = input.data.numpy()
+        workspace.FeedBlob(input_blob, caffe2_in, device_opts)
+        workspace.RunNet(model.net, num_iter=1)
+        output = workspace.FetchBlob(output_blob)
+
+        # measure accuracy and record loss
+        prec1, prec5 = accuracy_np(output.data, target.numpy())
+        top1.update(prec1.item(), input.size(0))
+        top5.update(prec5.item(), input.size(0))
+
+        # measure elapsed time
+        batch_time.update(time.time() - end)
+        end = time.time()
+
+        if i % args.print_freq == 0:
+            print('Test: [{0}/{1}]\t'
+                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {rate_avg:.3f}/s, {ms_avg:.3f} ms/sample) \t'
+                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
+                  'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
+                i, len(loader), batch_time=batch_time, rate_avg=input.size(0) / batch_time.avg,
+                ms_avg=100 * batch_time.avg / input.size(0), top1=top1, top5=top5))
+
+    print(' * Prec@1 {top1.avg:.3f} ({top1a:.3f}) Prec@5 {top5.avg:.3f} ({top5a:.3f})'.format(
+        top1=top1, top1a=100-top1.avg, top5=top5, top5a=100.-top5.avg))
+
+
+def accuracy_np(output, target):
+    max_indices = np.argsort(output, axis=1)[:, ::-1]
+    top5 = 100 * np.equal(max_indices[:, :5], target[:, np.newaxis]).sum(axis=1).mean()
+    top1 = 100 * np.equal(max_indices[:, 0], target).mean()
+    return top1, top5
+
+
+if __name__ == '__main__':
+    main()

RAVE-main/annotator/normalbae/models/submodules/efficientnet_repo/geffnet/activations/__init__.py

@@ -0,0 +1,137 @@
+from geffnet import config
+from geffnet.activations.activations_me import *
+from geffnet.activations.activations_jit import *
+from geffnet.activations.activations import *
+import torch
+
+_has_silu = 'silu' in dir(torch.nn.functional)
+
+_ACT_FN_DEFAULT = dict(
+    silu=F.silu if _has_silu else swish,
+    swish=F.silu if _has_silu else swish,
+    mish=mish,
+    relu=F.relu,
+    relu6=F.relu6,
+    sigmoid=sigmoid,
+    tanh=tanh,
+    hard_sigmoid=hard_sigmoid,
+    hard_swish=hard_swish,
+)
+
+_ACT_FN_JIT = dict(
+    silu=F.silu if _has_silu else swish_jit,
+    swish=F.silu if _has_silu else swish_jit,
+    mish=mish_jit,
+)
+
+_ACT_FN_ME = dict(
+    silu=F.silu if _has_silu else swish_me,
+    swish=F.silu if _has_silu else swish_me,
+    mish=mish_me,
+    hard_swish=hard_swish_me,
+    hard_sigmoid_jit=hard_sigmoid_me,
+)
+
+_ACT_LAYER_DEFAULT = dict(
+    silu=nn.SiLU if _has_silu else Swish,
+    swish=nn.SiLU if _has_silu else Swish,
+    mish=Mish,
+    relu=nn.ReLU,
+    relu6=nn.ReLU6,
+    sigmoid=Sigmoid,
+    tanh=Tanh,
+    hard_sigmoid=HardSigmoid,
+    hard_swish=HardSwish,
+)
+
+_ACT_LAYER_JIT = dict(
+    silu=nn.SiLU if _has_silu else SwishJit,
+    swish=nn.SiLU if _has_silu else SwishJit,
+    mish=MishJit,
+)
+
+_ACT_LAYER_ME = dict(
+    silu=nn.SiLU if _has_silu else SwishMe,
+    swish=nn.SiLU if _has_silu else SwishMe,
+    mish=MishMe,
+    hard_swish=HardSwishMe,
+    hard_sigmoid=HardSigmoidMe
+)
+
+_OVERRIDE_FN = dict()
+_OVERRIDE_LAYER = dict()
+
+
+def add_override_act_fn(name, fn):
+    global _OVERRIDE_FN
+    _OVERRIDE_FN[name] = fn
+
+
+def update_override_act_fn(overrides):
+    assert isinstance(overrides, dict)
+    global _OVERRIDE_FN
+    _OVERRIDE_FN.update(overrides)
+
+
+def clear_override_act_fn():
+    global _OVERRIDE_FN
+    _OVERRIDE_FN = dict()
+
+
+def add_override_act_layer(name, fn):
+    _OVERRIDE_LAYER[name] = fn
+
+
+def update_override_act_layer(overrides):
+    assert isinstance(overrides, dict)
+    global _OVERRIDE_LAYER
+    _OVERRIDE_LAYER.update(overrides)
+
+
+def clear_override_act_layer():
+    global _OVERRIDE_LAYER
+    _OVERRIDE_LAYER = dict()
+
+
+def get_act_fn(name='relu'):
+    """ Activation Function Factory
+    Fetching activation fns by name with this function allows export or torch script friendly
+    functions to be returned dynamically based on current config.
+    """
+    if name in _OVERRIDE_FN:
+        return _OVERRIDE_FN[name]
+    use_me = not (config.is_exportable() or config.is_scriptable() or config.is_no_jit())
+    if use_me and name in _ACT_FN_ME:
+        # If not exporting or scripting the model, first look for a memory optimized version
+        # activation with custom autograd, then fallback to jit scripted, then a Python or Torch builtin
+        return _ACT_FN_ME[name]
+    if config.is_exportable() and name in ('silu', 'swish'):
+        # FIXME PyTorch SiLU doesn't ONNX export, this is a temp hack
+        return swish
+    use_jit = not (config.is_exportable() or config.is_no_jit())
+    # NOTE: export tracing should work with jit scripted components, but I keep running into issues
+    if use_jit and name in _ACT_FN_JIT:  # jit scripted models should be okay for export/scripting
+        return _ACT_FN_JIT[name]
+    return _ACT_FN_DEFAULT[name]
+
+
+def get_act_layer(name='relu'):
+    """ Activation Layer Factory
+    Fetching activation layers by name with this function allows export or torch script friendly
+    functions to be returned dynamically based on current config.
+    """
+    if name in _OVERRIDE_LAYER:
+        return _OVERRIDE_LAYER[name]
+    use_me = not (config.is_exportable() or config.is_scriptable() or config.is_no_jit())
+    if use_me and name in _ACT_LAYER_ME:
+        return _ACT_LAYER_ME[name]
+    if config.is_exportable() and name in ('silu', 'swish'):
+        # FIXME PyTorch SiLU doesn't ONNX export, this is a temp hack
+        return Swish
+    use_jit = not (config.is_exportable() or config.is_no_jit())
+    # NOTE: export tracing should work with jit scripted components, but I keep running into issues
+    if use_jit and name in _ACT_FN_JIT:  # jit scripted models should be okay for export/scripting
+        return _ACT_LAYER_JIT[name]
+    return _ACT_LAYER_DEFAULT[name]
+
+

RAVE-main/annotator/normalbae/models/submodules/efficientnet_repo/geffnet/activations/activations.py

@@ -0,0 +1,102 @@
+""" Activations
+
+A collection of activations fn and modules with a common interface so that they can
+easily be swapped. All have an `inplace` arg even if not used.
+
+Copyright 2020 Ross Wightman
+"""
+from torch import nn as nn
+from torch.nn import functional as F
+
+
+def swish(x, inplace: bool = False):
+    """Swish - Described originally as SiLU (https://arxiv.org/abs/1702.03118v3)
+    and also as Swish (https://arxiv.org/abs/1710.05941).
+
+    TODO Rename to SiLU with addition to PyTorch
+    """
+    return x.mul_(x.sigmoid()) if inplace else x.mul(x.sigmoid())
+
+
+class Swish(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(Swish, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return swish(x, self.inplace)
+
+
+def mish(x, inplace: bool = False):
+    """Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
+    """
+    return x.mul(F.softplus(x).tanh())
+
+
+class Mish(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(Mish, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return mish(x, self.inplace)
+
+
+def sigmoid(x, inplace: bool = False):
+    return x.sigmoid_() if inplace else x.sigmoid()
+
+
+# PyTorch has this, but not with a consistent inplace argmument interface
+class Sigmoid(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(Sigmoid, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return x.sigmoid_() if self.inplace else x.sigmoid()
+
+
+def tanh(x, inplace: bool = False):
+    return x.tanh_() if inplace else x.tanh()
+
+
+# PyTorch has this, but not with a consistent inplace argmument interface
+class Tanh(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(Tanh, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return x.tanh_() if self.inplace else x.tanh()
+
+
+def hard_swish(x, inplace: bool = False):
+    inner = F.relu6(x + 3.).div_(6.)
+    return x.mul_(inner) if inplace else x.mul(inner)
+
+
+class HardSwish(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(HardSwish, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return hard_swish(x, self.inplace)
+
+
+def hard_sigmoid(x, inplace: bool = False):
+    if inplace:
+        return x.add_(3.).clamp_(0., 6.).div_(6.)
+    else:
+        return F.relu6(x + 3.) / 6.
+
+
+class HardSigmoid(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(HardSigmoid, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, x):
+        return hard_sigmoid(x, self.inplace)
+
+

RAVE-main/annotator/normalbae/models/submodules/efficientnet_repo/geffnet/activations/activations_jit.py

@@ -0,0 +1,79 @@
+""" Activations (jit)
+
+A collection of jit-scripted activations fn and modules with a common interface so that they can
+easily be swapped. All have an `inplace` arg even if not used.
+
+All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
+currently work across in-place op boundaries, thus performance is equal to or less than the non-scripted
+versions if they contain in-place ops.
+
+Copyright 2020 Ross Wightman
+"""
+
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+__all__ = ['swish_jit', 'SwishJit', 'mish_jit', 'MishJit',
+           'hard_sigmoid_jit', 'HardSigmoidJit', 'hard_swish_jit', 'HardSwishJit']
+
+
+@torch.jit.script
+def swish_jit(x, inplace: bool = False):
+    """Swish - Described originally as SiLU (https://arxiv.org/abs/1702.03118v3)
+    and also as Swish (https://arxiv.org/abs/1710.05941).
+
+    TODO Rename to SiLU with addition to PyTorch
+    """
+    return x.mul(x.sigmoid())
+
+
+@torch.jit.script
+def mish_jit(x, _inplace: bool = False):
+    """Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
+    """
+    return x.mul(F.softplus(x).tanh())
+
+
+class SwishJit(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(SwishJit, self).__init__()
+
+    def forward(self, x):
+        return swish_jit(x)
+
+
+class MishJit(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(MishJit, self).__init__()
+
+    def forward(self, x):
+        return mish_jit(x)
+
+
+@torch.jit.script
+def hard_sigmoid_jit(x, inplace: bool = False):
+    # return F.relu6(x + 3.) / 6.
+    return (x + 3).clamp(min=0, max=6).div(6.)  # clamp seems ever so slightly faster?
+
+
+class HardSigmoidJit(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(HardSigmoidJit, self).__init__()
+
+    def forward(self, x):
+        return hard_sigmoid_jit(x)
+
+
+@torch.jit.script
+def hard_swish_jit(x, inplace: bool = False):
+    # return x * (F.relu6(x + 3.) / 6)
+    return x * (x + 3).clamp(min=0, max=6).div(6.)  # clamp seems ever so slightly faster?
+
+
+class HardSwishJit(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(HardSwishJit, self).__init__()
+
+    def forward(self, x):
+        return hard_swish_jit(x)

RAVE-main/annotator/normalbae/models/submodules/efficientnet_repo/geffnet/activations/activations_me.py

@@ -0,0 +1,174 @@
+""" Activations (memory-efficient w/ custom autograd)
+
+A collection of activations fn and modules with a common interface so that they can
+easily be swapped. All have an `inplace` arg even if not used.
+
+These activations are not compatible with jit scripting or ONNX export of the model, please use either
+the JIT or basic versions of the activations.
+
+Copyright 2020 Ross Wightman
+"""
+
+import torch
+from torch import nn as nn
+from torch.nn import functional as F
+
+
+__all__ = ['swish_me', 'SwishMe', 'mish_me', 'MishMe',
+           'hard_sigmoid_me', 'HardSigmoidMe', 'hard_swish_me', 'HardSwishMe']
+
+
+@torch.jit.script
+def swish_jit_fwd(x):
+    return x.mul(torch.sigmoid(x))
+
+
+@torch.jit.script
+def swish_jit_bwd(x, grad_output):
+    x_sigmoid = torch.sigmoid(x)
+    return grad_output * (x_sigmoid * (1 + x * (1 - x_sigmoid)))
+
+
+class SwishJitAutoFn(torch.autograd.Function):
+    """ torch.jit.script optimised Swish w/ memory-efficient checkpoint
+    Inspired by conversation btw Jeremy Howard & Adam Pazske
+    https://twitter.com/jeremyphoward/status/1188251041835315200
+
+    Swish - Described originally as SiLU (https://arxiv.org/abs/1702.03118v3)
+    and also as Swish (https://arxiv.org/abs/1710.05941).
+
+    TODO Rename to SiLU with addition to PyTorch
+    """
+
+    @staticmethod
+    def forward(ctx, x):
+        ctx.save_for_backward(x)
+        return swish_jit_fwd(x)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        x = ctx.saved_tensors[0]
+        return swish_jit_bwd(x, grad_output)
+
+
+def swish_me(x, inplace=False):
+    return SwishJitAutoFn.apply(x)
+
+
+class SwishMe(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(SwishMe, self).__init__()
+
+    def forward(self, x):
+        return SwishJitAutoFn.apply(x)
+
+
+@torch.jit.script
+def mish_jit_fwd(x):
+    return x.mul(torch.tanh(F.softplus(x)))
+
+
+@torch.jit.script
+def mish_jit_bwd(x, grad_output):
+    x_sigmoid = torch.sigmoid(x)
+    x_tanh_sp = F.softplus(x).tanh()
+    return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp))
+
+
+class MishJitAutoFn(torch.autograd.Function):
+    """ Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
+    A memory efficient, jit scripted variant of Mish
+    """
+    @staticmethod
+    def forward(ctx, x):
+        ctx.save_for_backward(x)
+        return mish_jit_fwd(x)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        x = ctx.saved_tensors[0]
+        return mish_jit_bwd(x, grad_output)
+
+
+def mish_me(x, inplace=False):
+    return MishJitAutoFn.apply(x)
+
+
+class MishMe(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(MishMe, self).__init__()
+
+    def forward(self, x):
+        return MishJitAutoFn.apply(x)
+
+
+@torch.jit.script
+def hard_sigmoid_jit_fwd(x, inplace: bool = False):
+    return (x + 3).clamp(min=0, max=6).div(6.)
+
+
+@torch.jit.script
+def hard_sigmoid_jit_bwd(x, grad_output):
+    m = torch.ones_like(x) * ((x >= -3.) & (x <= 3.)) / 6.
+    return grad_output * m
+
+
+class HardSigmoidJitAutoFn(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x):
+        ctx.save_for_backward(x)
+        return hard_sigmoid_jit_fwd(x)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        x = ctx.saved_tensors[0]
+        return hard_sigmoid_jit_bwd(x, grad_output)
+
+
+def hard_sigmoid_me(x, inplace: bool = False):
+    return HardSigmoidJitAutoFn.apply(x)
+
+
+class HardSigmoidMe(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(HardSigmoidMe, self).__init__()
+
+    def forward(self, x):
+        return HardSigmoidJitAutoFn.apply(x)
+
+
+@torch.jit.script
+def hard_swish_jit_fwd(x):
+    return x * (x + 3).clamp(min=0, max=6).div(6.)
+
+
+@torch.jit.script
+def hard_swish_jit_bwd(x, grad_output):
+    m = torch.ones_like(x) * (x >= 3.)
+    m = torch.where((x >= -3.) & (x <= 3.),  x / 3. + .5, m)
+    return grad_output * m
+
+
+class HardSwishJitAutoFn(torch.autograd.Function):
+    """A memory efficient, jit-scripted HardSwish activation"""
+    @staticmethod
+    def forward(ctx, x):
+        ctx.save_for_backward(x)
+        return hard_swish_jit_fwd(x)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        x = ctx.saved_tensors[0]
+        return hard_swish_jit_bwd(x, grad_output)
+
+
+def hard_swish_me(x, inplace=False):
+    return HardSwishJitAutoFn.apply(x)
+
+
+class HardSwishMe(nn.Module):
+    def __init__(self, inplace: bool = False):
+        super(HardSwishMe, self).__init__()
+
+    def forward(self, x):
+        return HardSwishJitAutoFn.apply(x)

RAVE-main/annotator/normalbae/models/submodules/efficientnet_repo/onnx_export.py

@@ -0,0 +1,120 @@
+""" ONNX export script
+
+Export PyTorch models as ONNX graphs.
+
+This export script originally started as an adaptation of code snippets found at
+https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html
+
+The default parameters work with PyTorch 1.6 and ONNX 1.7 and produce an optimal ONNX graph
+for hosting in the ONNX runtime (see onnx_validate.py). To export an ONNX model compatible
+with caffe2 (see caffe2_benchmark.py and caffe2_validate.py), the --keep-init and --aten-fallback
+flags are currently required.
+
+Older versions of PyTorch/ONNX (tested PyTorch 1.4, ONNX 1.5) do not need extra flags for
+caffe2 compatibility, but they produce a model that isn't as fast running on ONNX runtime.
+
+Most new release of PyTorch and ONNX cause some sort of breakage in the export / usage of ONNX models.
+Please do your research and search ONNX and PyTorch issue tracker before asking me. Thanks.
+
+Copyright 2020 Ross Wightman
+"""
+import argparse
+import torch
+import numpy as np
+
+import onnx
+import geffnet
+
+parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
+parser.add_argument('output', metavar='ONNX_FILE',
+                    help='output model filename')
+parser.add_argument('--model', '-m', metavar='MODEL', default='mobilenetv3_large_100',
+                    help='model architecture (default: mobilenetv3_large_100)')
+parser.add_argument('--opset', type=int, default=10,
+                    help='ONNX opset to use (default: 10)')
+parser.add_argument('--keep-init', action='store_true', default=False,
+                    help='Keep initializers as input. Needed for Caffe2 compatible export in newer PyTorch/ONNX.')
+parser.add_argument('--aten-fallback', action='store_true', default=False,
+                    help='Fallback to ATEN ops. Helps fix AdaptiveAvgPool issue with Caffe2 in newer PyTorch/ONNX.')
+parser.add_argument('--dynamic-size', action='store_true', default=False,
+                    help='Export model width dynamic width/height. Not recommended for "tf" models with SAME padding.')
+parser.add_argument('-b', '--batch-size', default=1, type=int,
+                    metavar='N', help='mini-batch size (default: 1)')
+parser.add_argument('--img-size', default=None, type=int,
+                    metavar='N', help='Input image dimension, uses model default if empty')
+parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
+                    help='Override mean pixel value of dataset')
+parser.add_argument('--std', type=float,  nargs='+', default=None, metavar='STD',
+                    help='Override std deviation of of dataset')
+parser.add_argument('--num-classes', type=int, default=1000,
+                    help='Number classes in dataset')
+parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
+                    help='path to checkpoint (default: none)')
+
+
+def main():
+    args = parser.parse_args()
+
+    args.pretrained = True
+    if args.checkpoint:
+        args.pretrained = False
+
+    print("==> Creating PyTorch {} model".format(args.model))
+    # NOTE exportable=True flag disables autofn/jit scripted activations and uses Conv2dSameExport layers
+    # for models using SAME padding
+    model = geffnet.create_model(
+        args.model,
+        num_classes=args.num_classes,
+        in_chans=3,
+        pretrained=args.pretrained,
+        checkpoint_path=args.checkpoint,
+        exportable=True)
+
+    model.eval()
+
+    example_input = torch.randn((args.batch_size, 3, args.img_size or 224, args.img_size or 224), requires_grad=True)
+
+    # Run model once before export trace, sets padding for models with Conv2dSameExport. This means
+    # that the padding for models with Conv2dSameExport (most models with tf_ prefix) is fixed for
+    # the input img_size specified in this script.
+    # Opset >= 11 should allow for dynamic padding, however I cannot get it to work due to
+    # issues in the tracing of the dynamic padding or errors attempting to export the model after jit
+    # scripting it (an approach that should work). Perhaps in a future PyTorch or ONNX versions...
+    model(example_input)
+
+    print("==> Exporting model to ONNX format at '{}'".format(args.output))
+    input_names = ["input0"]
+    output_names = ["output0"]
+    dynamic_axes = {'input0': {0: 'batch'}, 'output0': {0: 'batch'}}
+    if args.dynamic_size:
+        dynamic_axes['input0'][2] = 'height'
+        dynamic_axes['input0'][3] = 'width'
+    if args.aten_fallback:
+        export_type = torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK
+    else:
+        export_type = torch.onnx.OperatorExportTypes.ONNX
+
+    torch_out = torch.onnx._export(
+        model, example_input, args.output, export_params=True, verbose=True, input_names=input_names,
+        output_names=output_names, keep_initializers_as_inputs=args.keep_init, dynamic_axes=dynamic_axes,
+        opset_version=args.opset, operator_export_type=export_type)
+
+    print("==> Loading and checking exported model from '{}'".format(args.output))
+    onnx_model = onnx.load(args.output)
+    onnx.checker.check_model(onnx_model)  # assuming throw on error
+    print("==> Passed")
+
+    if args.keep_init and args.aten_fallback:
+        import caffe2.python.onnx.backend as onnx_caffe2
+        # Caffe2 loading only works properly in newer PyTorch/ONNX combos when
+        # keep_initializers_as_inputs and aten_fallback are set to True.
+        print("==> Loading model into Caffe2 backend and comparing forward pass.".format(args.output))
+        caffe2_backend = onnx_caffe2.prepare(onnx_model)
+        B = {onnx_model.graph.input[0].name: x.data.numpy()}
+        c2_out = caffe2_backend.run(B)[0]
+        np.testing.assert_almost_equal(torch_out.data.numpy(), c2_out, decimal=5)
+        print("==> Passed")
+
+
+if __name__ == '__main__':
+    main()

RAVE-main/annotator/normalbae/models/submodules/efficientnet_repo/onnx_optimize.py

@@ -0,0 +1,84 @@
+""" ONNX optimization script
+
+Run ONNX models through the optimizer to prune unneeded nodes, fuse batchnorm layers into conv, etc.
+
+NOTE: This isn't working consistently in recent PyTorch/ONNX combos (ie PyTorch 1.6 and ONNX 1.7),
+it seems time to switch to using the onnxruntime online optimizer (can also be saved for offline).
+
+Copyright 2020 Ross Wightman
+"""
+import argparse
+import warnings
+
+import onnx
+from onnx import optimizer
+
+
+parser = argparse.ArgumentParser(description="Optimize ONNX model")
+
+parser.add_argument("model", help="The ONNX model")
+parser.add_argument("--output", required=True, help="The optimized model output filename")
+
+
+def traverse_graph(graph, prefix=''):
+    content = []
+    indent = prefix + '  '
+    graphs = []
+    num_nodes = 0
+    for node in graph.node:
+        pn, gs = onnx.helper.printable_node(node, indent, subgraphs=True)
+        assert isinstance(gs, list)
+        content.append(pn)
+        graphs.extend(gs)
+        num_nodes += 1
+    for g in graphs:
+        g_count, g_str = traverse_graph(g)
+        content.append('\n' + g_str)
+        num_nodes += g_count
+    return num_nodes, '\n'.join(content)
+
+
+def main():
+    args = parser.parse_args()
+    onnx_model = onnx.load(args.model)
+    num_original_nodes, original_graph_str = traverse_graph(onnx_model.graph)
+
+    # Optimizer passes to perform
+    passes = [
+        #'eliminate_deadend',
+        'eliminate_identity',
+        'eliminate_nop_dropout',
+        'eliminate_nop_pad',
+        'eliminate_nop_transpose',
+        'eliminate_unused_initializer',
+        'extract_constant_to_initializer',
+        'fuse_add_bias_into_conv',
+        'fuse_bn_into_conv',
+        'fuse_consecutive_concats',
+        'fuse_consecutive_reduce_unsqueeze',
+        'fuse_consecutive_squeezes',
+        'fuse_consecutive_transposes',
+        #'fuse_matmul_add_bias_into_gemm',
+        'fuse_pad_into_conv',
+        #'fuse_transpose_into_gemm',
+        #'lift_lexical_references',
+    ]
+
+    # Apply the optimization on the original serialized model
+    # WARNING I've had issues with optimizer in recent versions of PyTorch / ONNX causing
+    # 'duplicate definition of name' errors, see: https://github.com/onnx/onnx/issues/2401
+    # It may be better to rely on onnxruntime optimizations, see onnx_validate.py script.
+    warnings.warn("I've had issues with optimizer in recent versions of PyTorch / ONNX."
+                  "Try onnxruntime optimization if this doesn't work.")
+    optimized_model = optimizer.optimize(onnx_model, passes)
+
+    num_optimized_nodes, optimzied_graph_str = traverse_graph(optimized_model.graph)
+    print('==> The model after optimization:\n{}\n'.format(optimzied_graph_str))
+    print('==> The optimized model has {} nodes, the original had {}.'.format(num_optimized_nodes, num_original_nodes))
+
+    # Save the ONNX model
+    onnx.save(optimized_model, args.output)
+
+
+if __name__ == "__main__":
+    main()

RAVE-main/annotator/normalbae/models/submodules/encoder.py

@@ -0,0 +1,34 @@
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Encoder(nn.Module):
+    def __init__(self):
+        super(Encoder, self).__init__()
+
+        basemodel_name = 'tf_efficientnet_b5_ap'
+        print('Loading base model ()...'.format(basemodel_name), end='')
+        repo_path = os.path.join(os.path.dirname(__file__), 'efficientnet_repo')
+        basemodel = torch.hub.load(repo_path, basemodel_name, pretrained=False, source='local')
+        print('Done.')
+
+        # Remove last layer
+        print('Removing last two layers (global_pool & classifier).')
+        basemodel.global_pool = nn.Identity()
+        basemodel.classifier = nn.Identity()
+
+        self.original_model = basemodel
+
+    def forward(self, x):
+        features = [x]
+        for k, v in self.original_model._modules.items():
+            if (k == 'blocks'):
+                for ki, vi in v._modules.items():
+                    features.append(vi(features[-1]))
+            else:
+                features.append(v(features[-1]))
+        return features
+
+

RAVE-main/annotator/normalbae/models/submodules/submodules.py

@@ -0,0 +1,140 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+########################################################################################################################
+
+
+# Upsample + BatchNorm
+class UpSampleBN(nn.Module):
+    def __init__(self, skip_input, output_features):
+        super(UpSampleBN, self).__init__()
+
+        self._net = nn.Sequential(nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1),
+                                  nn.BatchNorm2d(output_features),
+                                  nn.LeakyReLU(),
+                                  nn.Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1),
+                                  nn.BatchNorm2d(output_features),
+                                  nn.LeakyReLU())
+
+    def forward(self, x, concat_with):
+        up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True)
+        f = torch.cat([up_x, concat_with], dim=1)
+        return self._net(f)
+
+
+# Upsample + GroupNorm + Weight Standardization
+class UpSampleGN(nn.Module):
+    def __init__(self, skip_input, output_features):
+        super(UpSampleGN, self).__init__()
+
+        self._net = nn.Sequential(Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1),
+                                  nn.GroupNorm(8, output_features),
+                                  nn.LeakyReLU(),
+                                  Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1),
+                                  nn.GroupNorm(8, output_features),
+                                  nn.LeakyReLU())
+
+    def forward(self, x, concat_with):
+        up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True)
+        f = torch.cat([up_x, concat_with], dim=1)
+        return self._net(f)
+
+
+# Conv2d with weight standardization
+class Conv2d(nn.Conv2d):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1, bias=True):
+        super(Conv2d, self).__init__(in_channels, out_channels, kernel_size, stride,
+                 padding, dilation, groups, bias)
+
+    def forward(self, x):
+        weight = self.weight
+        weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2,
+                                  keepdim=True).mean(dim=3, keepdim=True)
+        weight = weight - weight_mean
+        std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
+        weight = weight / std.expand_as(weight)
+        return F.conv2d(x, weight, self.bias, self.stride,
+                        self.padding, self.dilation, self.groups)
+
+
+# normalize
+def norm_normalize(norm_out):
+    min_kappa = 0.01
+    norm_x, norm_y, norm_z, kappa = torch.split(norm_out, 1, dim=1)
+    norm = torch.sqrt(norm_x ** 2.0 + norm_y ** 2.0 + norm_z ** 2.0) + 1e-10
+    kappa = F.elu(kappa) + 1.0 + min_kappa
+    final_out = torch.cat([norm_x / norm, norm_y / norm, norm_z / norm, kappa], dim=1)
+    return final_out
+
+
+# uncertainty-guided sampling (only used during training)
+@torch.no_grad()
+def sample_points(init_normal, gt_norm_mask, sampling_ratio, beta):
+    device = init_normal.device
+    B, _, H, W = init_normal.shape
+    N = int(sampling_ratio * H * W)
+    beta = beta
+
+    # uncertainty map
+    uncertainty_map = -1 * init_normal[:, 3, :, :]  # B, H, W
+
+    # gt_invalid_mask (B, H, W)
+    if gt_norm_mask is not None:
+        gt_invalid_mask = F.interpolate(gt_norm_mask.float(), size=[H, W], mode='nearest')
+        gt_invalid_mask = gt_invalid_mask[:, 0, :, :] < 0.5
+        uncertainty_map[gt_invalid_mask] = -1e4
+
+    # (B, H*W)
+    _, idx = uncertainty_map.view(B, -1).sort(1, descending=True)
+
+    # importance sampling
+    if int(beta * N) > 0:
+        importance = idx[:, :int(beta * N)]    # B, beta*N
+
+        # remaining
+        remaining = idx[:, int(beta * N):]     # B, H*W - beta*N
+
+        # coverage
+        num_coverage = N - int(beta * N)
+
+        if num_coverage <= 0:
+            samples = importance
+        else:
+            coverage_list = []
+            for i in range(B):
+                idx_c = torch.randperm(remaining.size()[1])    # shuffles "H*W - beta*N"
+                coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1))     # 1, N-beta*N
+            coverage = torch.cat(coverage_list, dim=0)                                      # B, N-beta*N
+            samples = torch.cat((importance, coverage), dim=1)                              # B, N
+
+    else:
+        # remaining
+        remaining = idx[:, :]  # B, H*W
+
+        # coverage
+        num_coverage = N
+
+        coverage_list = []
+        for i in range(B):
+            idx_c = torch.randperm(remaining.size()[1])  # shuffles "H*W - beta*N"
+            coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1))  # 1, N-beta*N
+        coverage = torch.cat(coverage_list, dim=0)  # B, N-beta*N
+        samples = coverage
+
+    # point coordinates
+    rows_int = samples // W         # 0 for first row, H-1 for last row
+    rows_float = rows_int / float(H-1)         # 0 to 1.0
+    rows_float = (rows_float * 2.0) - 1.0       # -1.0 to 1.0
+
+    cols_int = samples % W          # 0 for first column, W-1 for last column
+    cols_float = cols_int / float(W-1)         # 0 to 1.0
+    cols_float = (cols_float * 2.0) - 1.0       # -1.0 to 1.0
+
+    point_coords = torch.zeros(B, 1, N, 2)
+    point_coords[:, 0, :, 0] = cols_float             # x coord
+    point_coords[:, 0, :, 1] = rows_float             # y coord
+    point_coords = point_coords.to(device)
+    return point_coords, rows_int, cols_int

RAVE-main/annotator/oneformer/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Caroline Chan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

RAVE-main/annotator/oneformer/__init__.py

@@ -0,0 +1,45 @@
+import os
+from modules import devices
+from annotator.annotator_path import models_path
+from .api import make_detectron2_model, semantic_run
+
+
+class OneformerDetector:
+    model_dir = os.path.join(models_path, "oneformer")
+    configs = {
+        "coco": {
+            "name": "150_16_swin_l_oneformer_coco_100ep.pth",
+            "config": 'configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml'
+        },
+        "ade20k": {
+            "name": "250_16_swin_l_oneformer_ade20k_160k.pth",
+            "config": 'configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml'
+        }
+    }
+
+    def __init__(self, config):
+        self.model = None
+        self.metadata = None
+        self.config = config
+        self.device = devices.get_device_for("controlnet")
+
+    def load_model(self):
+        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/" + self.config["name"]
+        modelpath = os.path.join(self.model_dir, self.config["name"])
+        if not os.path.exists(modelpath):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=self.model_dir)
+        config = os.path.join(os.path.dirname(__file__), self.config["config"])
+        model, self.metadata = make_detectron2_model(config, modelpath)
+        self.model = model
+
+    def unload_model(self):
+        if self.model is not None:
+            self.model.model.cpu()
+
+    def __call__(self, img):
+        if self.model is None:
+            self.load_model()
+            
+        self.model.model.to(self.device)
+        return semantic_run(img, self.model, self.metadata)

RAVE-main/annotator/oneformer/api.py

@@ -0,0 +1,39 @@
+import os
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+import torch
+
+from annotator.oneformer.detectron2.config import get_cfg
+from annotator.oneformer.detectron2.projects.deeplab import add_deeplab_config
+from annotator.oneformer.detectron2.data import MetadataCatalog
+
+from annotator.oneformer.oneformer import (
+    add_oneformer_config,
+    add_common_config,
+    add_swin_config,
+    add_dinat_config,
+)
+
+from annotator.oneformer.oneformer.demo.defaults import DefaultPredictor
+from annotator.oneformer.oneformer.demo.visualizer import Visualizer, ColorMode
+
+
+def make_detectron2_model(config_path, ckpt_path):
+    cfg = get_cfg()
+    add_deeplab_config(cfg)
+    add_common_config(cfg)
+    add_swin_config(cfg)
+    add_oneformer_config(cfg)
+    add_dinat_config(cfg)
+    cfg.merge_from_file(config_path)
+    cfg.MODEL.WEIGHTS = ckpt_path
+    cfg.freeze()
+    metadata = MetadataCatalog.get(cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else "__unused")
+    return DefaultPredictor(cfg), metadata
+
+
+def semantic_run(img, predictor, metadata):
+    predictions = predictor(img[:, :, ::-1], "semantic")  # Predictor of OneFormer must use BGR image !!!
+    visualizer_map = Visualizer(img, is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE)
+    out_map = visualizer_map.draw_sem_seg(predictions["sem_seg"].argmax(dim=0).cpu(), alpha=1, is_text=False).get_image()
+    return out_map

RAVE-main/annotator/oneformer/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml

@@ -0,0 +1,68 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("ade20k_panoptic_train",)
+  TEST_PANOPTIC: ("ade20k_panoptic_val",)
+  TEST_INSTANCE: ("ade20k_instance_val",)
+  TEST_SEMANTIC: ("ade20k_sem_seg_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  MAX_ITER: 160000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 0
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  LR_SCHEDULER_NAME: "WarmupPolyLR"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 512) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 512
+  MAX_SIZE_TRAIN: 2048
+  MAX_SIZE_TEST: 2048
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (512, 512)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 512  # used in dataset mapper
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "oneformer_unified"
+  MAX_SEQ_LEN: 77
+  TASK_SEQ_LEN: 77
+  TASK_PROB: 
+    SEMANTIC: 0.33
+    INSTANCE: 0.66
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [256, 384, 512, 640, 768, 896]
+    MAX_SIZE: 3584
+    FLIP: True
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2

RAVE-main/annotator/oneformer/configs/ade20k/oneformer_R50_bs16_160k.yaml

@@ -0,0 +1,58 @@
+_BASE_: Base-ADE20K-UnifiedSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "OneFormer"
+  SEM_SEG_HEAD:
+    NAME: "OneFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 150
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  ONE_FORMER:
+    TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    CONTRASTIVE_WEIGHT: 0.5
+    CONTRASTIVE_TEMPERATURE: 0.07
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 150
+    USE_TASK_NORM: True
+    NHEADS: 8
+    DROPOUT: 0.1
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    CLASS_DEC_LAYERS: 2
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+  TEXT_ENCODER:
+    WIDTH: 256
+    CONTEXT_LENGTH: 77
+    NUM_LAYERS: 6
+    VOCAB_SIZE: 49408
+    PROJ_NUM_LAYERS: 2
+    N_CTX: 16
+  TEST:
+    SEMANTIC_ON: True
+    INSTANCE_ON: True
+    PANOPTIC_ON: True
+    OVERLAP_THRESHOLD: 0.8
+    OBJECT_MASK_THRESHOLD: 0.8
+    TASK: "panoptic"
+TEST:
+  DETECTIONS_PER_IMAGE: 150

RAVE-main/annotator/oneformer/configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml

@@ -0,0 +1,40 @@
+_BASE_: oneformer_R50_bs16_160k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  ONE_FORMER:
+    NUM_OBJECT_QUERIES: 250
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 640
+  MAX_SIZE_TRAIN: 2560
+  MAX_SIZE_TEST: 2560
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (640, 640)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 640  # used in dataset mapper
+  FORMAT: "RGB"
+TEST:
+  DETECTIONS_PER_IMAGE: 250
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+    MAX_SIZE: 4480
+    FLIP: True

RAVE-main/annotator/oneformer/configs/coco/Base-COCO-UnifiedSegmentation.yaml

@@ -0,0 +1,54 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("coco_2017_train_panoptic_with_sem_seg",)
+  TEST_PANOPTIC: ("coco_2017_val_panoptic_with_sem_seg",)  # to evaluate instance and semantic performance as well
+  TEST_INSTANCE: ("coco_2017_val",)
+  TEST_SEMANTIC: ("coco_2017_val_panoptic_with_sem_seg",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  STEPS: (327778, 355092)
+  MAX_ITER: 368750
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 10
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  IMAGE_SIZE: 1024
+  MIN_SCALE: 0.1
+  MAX_SCALE: 2.0
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "coco_unified_lsj"
+  MAX_SEQ_LEN: 77
+  TASK_SEQ_LEN: 77
+  TASK_PROB: 
+    SEMANTIC: 0.33
+    INSTANCE: 0.66
+TEST:
+  EVAL_PERIOD: 5000
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2

RAVE-main/annotator/oneformer/configs/coco/oneformer_R50_bs16_50ep.yaml

@@ -0,0 +1,59 @@
+_BASE_: Base-COCO-UnifiedSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "OneFormer"
+  SEM_SEG_HEAD:
+    NAME: "OneFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 133
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  ONE_FORMER:
+    TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    CONTRASTIVE_WEIGHT: 0.5
+    CONTRASTIVE_TEMPERATURE: 0.07
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 150
+    USE_TASK_NORM: True
+    NHEADS: 8
+    DROPOUT: 0.1
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    CLASS_DEC_LAYERS: 2
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+  TEXT_ENCODER:
+    WIDTH: 256
+    CONTEXT_LENGTH: 77
+    NUM_LAYERS: 6
+    VOCAB_SIZE: 49408
+    PROJ_NUM_LAYERS: 2
+    N_CTX: 16
+  TEST:
+    SEMANTIC_ON: True
+    INSTANCE_ON: True
+    PANOPTIC_ON: True
+    DETECTION_ON: False
+    OVERLAP_THRESHOLD: 0.8
+    OBJECT_MASK_THRESHOLD: 0.8
+    TASK: "panoptic"
+TEST:
+  DETECTIONS_PER_IMAGE: 150

RAVE-main/annotator/oneformer/configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml

@@ -0,0 +1,25 @@
+_BASE_: oneformer_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  ONE_FORMER:
+    NUM_OBJECT_QUERIES: 150
+SOLVER:
+  STEPS: (655556, 735184)
+  MAX_ITER: 737500
+  AMP:
+    ENABLED: False
+TEST:
+  DETECTIONS_PER_IMAGE: 150

RAVE-main/annotator/oneformer/oneformer/__init__.py

@@ -0,0 +1,9 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from . import data  # register all new datasets
+from . import modeling
+
+# config
+from .config import *
+
+# models
+from .oneformer_model import OneFormer

RAVE-main/annotator/oneformer/oneformer/config.py

@@ -0,0 +1,239 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+from annotator.oneformer.detectron2.config import CfgNode as CN
+
+__all__ = ["add_common_config", "add_oneformer_config", "add_swin_config", 
+            "add_dinat_config", "add_beit_adapter_config", "add_convnext_config"]
+
+def add_common_config(cfg):
+    """
+    Add config for common configuration
+    """
+    # data config
+    # select the dataset mapper
+    cfg.INPUT.DATASET_MAPPER_NAME = "oneformer_unified"
+    # Color augmentation
+    cfg.INPUT.COLOR_AUG_SSD = False
+    # We retry random cropping until no single category in semantic segmentation GT occupies more
+    # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
+    cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
+    # Pad image and segmentation GT in dataset mapper.
+    cfg.INPUT.SIZE_DIVISIBILITY = -1
+
+    cfg.INPUT.TASK_SEQ_LEN = 77
+    cfg.INPUT.MAX_SEQ_LEN = 77
+
+    cfg.INPUT.TASK_PROB = CN()
+    cfg.INPUT.TASK_PROB.SEMANTIC = 0.33
+    cfg.INPUT.TASK_PROB.INSTANCE = 0.66
+
+    # test dataset
+    cfg.DATASETS.TEST_PANOPTIC = ("",)
+    cfg.DATASETS.TEST_INSTANCE = ("",)
+    cfg.DATASETS.TEST_SEMANTIC = ("",)
+
+    # solver config
+    # weight decay on embedding
+    cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0
+    # optimizer
+    cfg.SOLVER.OPTIMIZER = "ADAMW"
+    cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1
+
+    # wandb
+    cfg.WANDB = CN()
+    cfg.WANDB.PROJECT = "unified_dense_recognition"
+    cfg.WANDB.NAME = None
+
+    cfg.MODEL.IS_TRAIN = False
+    cfg.MODEL.IS_DEMO = True
+
+    # text encoder config
+    cfg.MODEL.TEXT_ENCODER = CN()
+
+    cfg.MODEL.TEXT_ENCODER.WIDTH = 256
+    cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77
+    cfg.MODEL.TEXT_ENCODER.NUM_LAYERS = 12
+    cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE = 49408
+    cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS = 2
+    cfg.MODEL.TEXT_ENCODER.N_CTX = 16
+
+    # mask_former inference config
+    cfg.MODEL.TEST = CN()
+    cfg.MODEL.TEST.SEMANTIC_ON = True
+    cfg.MODEL.TEST.INSTANCE_ON = False
+    cfg.MODEL.TEST.PANOPTIC_ON = False
+    cfg.MODEL.TEST.DETECTION_ON = False
+    cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD = 0.0
+    cfg.MODEL.TEST.OVERLAP_THRESHOLD = 0.0
+    cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
+    cfg.MODEL.TEST.TASK = "panoptic"
+
+    # TEST AUG Slide
+    cfg.TEST.AUG.IS_SLIDE = False
+    cfg.TEST.AUG.CROP_SIZE = (640, 640)
+    cfg.TEST.AUG.STRIDE = (426, 426)
+    cfg.TEST.AUG.SCALE = (2048, 640)
+    cfg.TEST.AUG.SETR_MULTI_SCALE = True
+    cfg.TEST.AUG.KEEP_RATIO = True
+    cfg.TEST.AUG.SIZE_DIVISOR = 32
+
+    # pixel decoder config
+    cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
+    # adding transformer in pixel decoder
+    cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
+    # pixel decoder
+    cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
+    cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256
+    cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256
+
+    # LSJ aug
+    cfg.INPUT.IMAGE_SIZE = 1024
+    cfg.INPUT.MIN_SCALE = 0.1
+    cfg.INPUT.MAX_SCALE = 2.0
+
+    # MSDeformAttn encoder configs
+    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"]
+    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4
+    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8
+
+def add_oneformer_config(cfg):
+    """
+    Add config for ONE_FORMER.
+    """
+
+    # mask_former model config
+    cfg.MODEL.ONE_FORMER = CN()
+
+    # loss
+    cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True
+    cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1
+    cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0
+    cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0
+    cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0
+    cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5
+    cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07
+
+    # transformer config
+    cfg.MODEL.ONE_FORMER.NHEADS = 8
+    cfg.MODEL.ONE_FORMER.DROPOUT = 0.1
+    cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD = 2048
+    cfg.MODEL.ONE_FORMER.ENC_LAYERS = 0
+    cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS = 2
+    cfg.MODEL.ONE_FORMER.DEC_LAYERS = 6
+    cfg.MODEL.ONE_FORMER.PRE_NORM = False
+
+    cfg.MODEL.ONE_FORMER.HIDDEN_DIM = 256
+    cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES = 120
+    cfg.MODEL.ONE_FORMER.NUM_OBJECT_CTX = 16
+    cfg.MODEL.ONE_FORMER.USE_TASK_NORM = True
+
+    cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE = "res5"
+    cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ = False
+
+    # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
+    # you can use this config to override
+    cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY = 32
+
+    # transformer module
+    cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME = "ContrastiveMultiScaleMaskedTransformerDecoder"
+
+    # point loss configs
+    # Number of points sampled during training for a mask point head.
+    cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS = 112 * 112
+    # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the
+    # original paper.
+    cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO = 3.0
+    # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in
+    # the original paper.
+    cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75
+
+def add_swin_config(cfg):
+    """
+    Add config forSWIN Backbone.
+    """
+    
+    # swin transformer backbone
+    cfg.MODEL.SWIN = CN()
+    cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224
+    cfg.MODEL.SWIN.PATCH_SIZE = 4
+    cfg.MODEL.SWIN.EMBED_DIM = 96
+    cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
+    cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
+    cfg.MODEL.SWIN.WINDOW_SIZE = 7
+    cfg.MODEL.SWIN.MLP_RATIO = 4.0
+    cfg.MODEL.SWIN.QKV_BIAS = True
+    cfg.MODEL.SWIN.QK_SCALE = None
+    cfg.MODEL.SWIN.DROP_RATE = 0.0
+    cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0
+    cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3
+    cfg.MODEL.SWIN.APE = False
+    cfg.MODEL.SWIN.PATCH_NORM = True
+    cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
+    cfg.MODEL.SWIN.USE_CHECKPOINT = False
+    ## Semask additions
+    cfg.MODEL.SWIN.SEM_WINDOW_SIZE = 7
+    cfg.MODEL.SWIN.NUM_SEM_BLOCKS = 1
+
+def add_dinat_config(cfg):
+    """
+    Add config for NAT Backbone.
+    """
+
+    # DINAT transformer backbone
+    cfg.MODEL.DiNAT = CN()
+    cfg.MODEL.DiNAT.DEPTHS = [3, 4, 18, 5]
+    cfg.MODEL.DiNAT.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
+    cfg.MODEL.DiNAT.EMBED_DIM = 64
+    cfg.MODEL.DiNAT.MLP_RATIO = 3.0
+    cfg.MODEL.DiNAT.NUM_HEADS = [2, 4, 8, 16]
+    cfg.MODEL.DiNAT.DROP_PATH_RATE = 0.2
+    cfg.MODEL.DiNAT.KERNEL_SIZE = 7
+    cfg.MODEL.DiNAT.DILATIONS = [[1, 16, 1], [1, 4, 1, 8], [1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]
+    cfg.MODEL.DiNAT.OUT_INDICES = (0, 1, 2, 3)
+    cfg.MODEL.DiNAT.QKV_BIAS = True
+    cfg.MODEL.DiNAT.QK_SCALE = None
+    cfg.MODEL.DiNAT.DROP_RATE = 0
+    cfg.MODEL.DiNAT.ATTN_DROP_RATE = 0.
+    cfg.MODEL.DiNAT.IN_PATCH_SIZE = 4
+
+def add_convnext_config(cfg):
+    """
+    Add config for ConvNeXt Backbone.
+    """
+    
+    # swin transformer backbone
+    cfg.MODEL.CONVNEXT = CN()
+    cfg.MODEL.CONVNEXT.IN_CHANNELS = 3
+    cfg.MODEL.CONVNEXT.DEPTHS = [3, 3, 27, 3]
+    cfg.MODEL.CONVNEXT.DIMS = [192, 384, 768, 1536]
+    cfg.MODEL.CONVNEXT.DROP_PATH_RATE = 0.4
+    cfg.MODEL.CONVNEXT.LSIT = 1.0
+    cfg.MODEL.CONVNEXT.OUT_INDICES = [0, 1, 2, 3]
+    cfg.MODEL.CONVNEXT.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
+
+def add_beit_adapter_config(cfg):
+    """
+    Add config for BEiT Adapter Backbone.
+    """
+
+    # beit adapter backbone
+    cfg.MODEL.BEiTAdapter = CN()
+    cfg.MODEL.BEiTAdapter.IMG_SIZE = 640
+    cfg.MODEL.BEiTAdapter.PATCH_SIZE = 16
+    cfg.MODEL.BEiTAdapter.EMBED_DIM = 1024
+    cfg.MODEL.BEiTAdapter.DEPTH = 24
+    cfg.MODEL.BEiTAdapter.NUM_HEADS = 16
+    cfg.MODEL.BEiTAdapter.MLP_RATIO = 4
+    cfg.MODEL.BEiTAdapter.QKV_BIAS = True
+    cfg.MODEL.BEiTAdapter.USE_ABS_POS_EMB = False
+    cfg.MODEL.BEiTAdapter.USE_REL_POS_BIAS = True
+    cfg.MODEL.BEiTAdapter.INIT_VALUES = 1e-6
+    cfg.MODEL.BEiTAdapter.DROP_PATH_RATE = 0.3
+    cfg.MODEL.BEiTAdapter.CONV_INPLANE = 64
+    cfg.MODEL.BEiTAdapter.N_POINTS = 4
+    cfg.MODEL.BEiTAdapter.DEFORM_NUM_HEADS = 16
+    cfg.MODEL.BEiTAdapter.CFFN_RATIO = 0.25
+    cfg.MODEL.BEiTAdapter.DEFORM_RATIO = 0.5
+    cfg.MODEL.BEiTAdapter.WITH_CP = True
+    cfg.MODEL.BEiTAdapter.INTERACTION_INDEXES=[[0, 5], [6, 11], [12, 17], [18, 23]]
+    cfg.MODEL.BEiTAdapter.OUT_FEATURES = ["res2", "res3", "res4", "res5"]

RAVE-main/annotator/oneformer/oneformer/data/__init__.py

@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from . import datasets

RAVE-main/annotator/oneformer/oneformer/data/build.py

@@ -0,0 +1,117 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from typing import Any, Callable, Dict, List, Optional, Union
+import torch.utils.data as torchdata
+
+from annotator.oneformer.detectron2.config import configurable
+
+
+from annotator.oneformer.detectron2.data.common import DatasetFromList, MapDataset
+from annotator.oneformer.detectron2.data.dataset_mapper import DatasetMapper
+from annotator.oneformer.detectron2.data.samplers import (
+    InferenceSampler,
+)
+from annotator.oneformer.detectron2.data.build import (
+    get_detection_dataset_dicts,
+    trivial_batch_collator
+)
+"""
+This file contains the default logic to build a dataloader for training or testing.
+"""
+
+__all__ = [
+    "build_detection_test_loader",
+]
+
+
+def _test_loader_from_config(cfg, dataset_name, mapper=None):
+    """
+    Uses the given `dataset_name` argument (instead of the names in cfg), because the
+    standard practice is to evaluate each test set individually (not combining them).
+    """
+    if isinstance(dataset_name, str):
+        dataset_name = [dataset_name]
+
+    dataset = get_detection_dataset_dicts(
+        dataset_name,
+        filter_empty=False,
+        proposal_files=[
+            cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name
+        ]
+        if cfg.MODEL.LOAD_PROPOSALS
+        else None,
+    )
+    if mapper is None:
+        mapper = DatasetMapper(cfg, False)
+    return {
+        "dataset": dataset,
+        "mapper": mapper,
+        "num_workers": cfg.DATALOADER.NUM_WORKERS,
+        "sampler": InferenceSampler(len(dataset))
+        if not isinstance(dataset, torchdata.IterableDataset)
+        else None,
+    }
+
+
+@configurable(from_config=_test_loader_from_config)
+def build_detection_test_loader(
+    dataset: Union[List[Any], torchdata.Dataset],
+    *,
+    mapper: Callable[[Dict[str, Any]], Any],
+    sampler: Optional[torchdata.Sampler] = None,
+    batch_size: int = 1,
+    num_workers: int = 0,
+    collate_fn: Optional[Callable[[List[Any]], Any]] = None,
+) -> torchdata.DataLoader:
+    """
+    Similar to `build_detection_train_loader`, with default batch size = 1,
+    and sampler = :class:`InferenceSampler`. This sampler coordinates all workers
+    to produce the exact set of all samples.
+
+    Args:
+        dataset: a list of dataset dicts,
+            or a pytorch dataset (either map-style or iterable). They can be obtained
+            by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
+        mapper: a callable which takes a sample (dict) from dataset
+           and returns the format to be consumed by the model.
+           When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.
+        sampler: a sampler that produces
+            indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,
+            which splits the dataset across all workers. Sampler must be None
+            if `dataset` is iterable.
+        batch_size: the batch size of the data loader to be created.
+            Default to 1 image per worker since this is the standard when reporting
+            inference time in papers.
+        num_workers: number of parallel data loading workers
+        collate_fn: same as the argument of `torch.utils.data.DataLoader`.
+            Defaults to do no collation and return a list of data.
+
+    Returns:
+        DataLoader: a torch DataLoader, that loads the given detection
+        dataset, with test-time transformation and batching.
+
+    Examples:
+    ::
+        data_loader = build_detection_test_loader(
+            DatasetRegistry.get("my_test"),
+            mapper=DatasetMapper(...))
+
+        # or, instantiate with a CfgNode:
+        data_loader = build_detection_test_loader(cfg, "my_test")
+    """
+    if isinstance(dataset, list):
+        dataset = DatasetFromList(dataset, copy=False)
+    if mapper is not None:
+        dataset = MapDataset(dataset, mapper)
+    if isinstance(dataset, torchdata.IterableDataset):
+        assert sampler is None, "sampler must be None if dataset is IterableDataset"
+    else:
+        if sampler is None:
+            sampler = InferenceSampler(len(dataset))
+    return torchdata.DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        drop_last=False,
+        num_workers=num_workers,
+        collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
+    )

RAVE-main/annotator/oneformer/oneformer/data/dataset_mappers/__init__.py

@@ -0,0 +1 @@
+

RAVE-main/annotator/oneformer/oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py

@@ -0,0 +1,341 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+
+import numpy as np
+import torch
+
+from annotator.oneformer.detectron2.data import MetadataCatalog
+from annotator.oneformer.detectron2.config import configurable
+from annotator.oneformer.detectron2.data import detection_utils as utils
+from annotator.oneformer.detectron2.data import transforms as T
+from annotator.oneformer.detectron2.structures import BitMasks, Instances
+from annotator.oneformer.oneformer.utils.box_ops import masks_to_boxes
+from annotator.oneformer.oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+
+__all__ = ["COCOUnifiedNewBaselineDatasetMapper"]
+
+
+def build_transform_gen(cfg, is_train):
+    """
+    Create a list of default :class:`Augmentation` from config.
+    Now it includes resizing and flipping.
+    Returns:
+        list[Augmentation]
+    """
+    assert is_train, "Only support training augmentation"
+    image_size = cfg.INPUT.IMAGE_SIZE
+    min_scale = cfg.INPUT.MIN_SCALE
+    max_scale = cfg.INPUT.MAX_SCALE
+
+    augmentation = []
+
+    if cfg.INPUT.RANDOM_FLIP != "none":
+        augmentation.append(
+            T.RandomFlip(
+                horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal",
+                vertical=cfg.INPUT.RANDOM_FLIP == "vertical",
+            )
+        )
+
+    augmentation.extend([
+        T.ResizeScale(
+            min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size
+        ),
+        T.FixedSizeCrop(crop_size=(image_size, image_size)),
+    ])
+
+    return augmentation
+
+
+# This is specifically designed for the COCO dataset.
+class COCOUnifiedNewBaselineDatasetMapper:
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by OneFormer.
+
+    This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies geometric transforms to the image and annotation
+    3. Find and applies suitable cropping to the image and annotation
+    4. Prepare image and annotation to Tensors
+    """
+
+    @configurable
+    def __init__(
+        self,
+        is_train=True,
+        *,
+        num_queries,
+        tfm_gens,
+        meta,
+        image_format,
+        max_seq_len,
+        task_seq_len,
+        semantic_prob,
+        instance_prob,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            is_train: for training or inference
+            augmentations: a list of augmentations or deterministic transforms to apply
+            crop_gen: crop augmentation
+            tfm_gens: data augmentation
+            image_format: an image format supported by :func:`detection_utils.read_image`.
+        """
+        self.tfm_gens = tfm_gens
+        logging.getLogger(__name__).info(
+            "[COCOUnifiedNewBaselineDatasetMapper] Full TransformGens used in training: {}".format(
+                str(self.tfm_gens)
+            )
+        )
+
+        self.img_format = image_format
+        self.is_train = is_train
+        self.meta = meta
+        self.ignore_label = self.meta.ignore_label
+        self.num_queries = num_queries
+
+        self.things = []
+        for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():
+            self.things.append(v)
+        self.class_names = self.meta.stuff_classes
+        self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
+        self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+        self.semantic_prob = semantic_prob
+        self.instance_prob = instance_prob
+
+    @classmethod
+    def from_config(cls, cfg, is_train=True):
+        # Build augmentation
+        tfm_gens = build_transform_gen(cfg, is_train)
+        dataset_names = cfg.DATASETS.TRAIN
+        meta = MetadataCatalog.get(dataset_names[0])
+
+        ret = {
+            "is_train": is_train,
+            "meta": meta,
+            "tfm_gens": tfm_gens,
+            "image_format": cfg.INPUT.FORMAT,
+            "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,
+            "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+            "max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
+            "semantic_prob": cfg.INPUT.TASK_PROB.SEMANTIC,
+            "instance_prob": cfg.INPUT.TASK_PROB.INSTANCE,
+        }
+        return ret
+    
+    def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+        instances = Instances(image_shape)
+        
+        classes = []
+        texts = ["a semantic photo"] * self.num_queries
+        masks = []
+        label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+        for segment_info in segments_info:
+            class_id = segment_info["category_id"]
+            if not segment_info["iscrowd"]:
+                mask = pan_seg_gt == segment_info["id"]
+                if not np.all(mask == False):
+                    if class_id not in classes:
+                        cls_name = self.class_names[class_id]
+                        classes.append(class_id)
+                        masks.append(mask)
+                        num_class_obj[cls_name] += 1
+                    else:
+                        idx = classes.index(class_id)
+                        masks[idx] += mask
+                        masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)
+                    label[mask] = class_id
+
+        num = 0
+        for i, cls_name in enumerate(self.class_names):
+            if num_class_obj[cls_name] > 0:
+                for _ in range(num_class_obj[cls_name]):
+                    if num >= len(texts):
+                        break
+                    texts[num] = f"a photo with a {cls_name}"
+                    num += 1
+                    
+        classes = np.array(classes)
+        instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+        if len(masks) == 0:
+            # Some image does not have annotation (all ignored)
+            instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+            instances.gt_bboxes = torch.zeros((0, 4))
+        else:
+            masks = BitMasks(
+                torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+            )
+            instances.gt_masks = masks.tensor
+            # Placeholder bounding boxes for stuff regions. Note that these are not used during training.
+            instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])
+        return instances, texts, label
+    
+    def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+        instances = Instances(image_shape)
+
+        classes = []
+        texts = ["an instance photo"] * self.num_queries
+        masks = []
+        label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+        for segment_info in segments_info:
+            class_id = segment_info["category_id"]
+            if class_id in self.things:
+                if not segment_info["iscrowd"]:
+                    mask = pan_seg_gt == segment_info["id"]
+                    if not np.all(mask == False):
+                        cls_name = self.class_names[class_id]
+                        classes.append(class_id)
+                        masks.append(mask)
+                        num_class_obj[cls_name] += 1
+                        label[mask] = class_id
+        
+        num = 0
+        for i, cls_name in enumerate(self.class_names):
+            if num_class_obj[cls_name] > 0:
+                for _ in range(num_class_obj[cls_name]):
+                    if num >= len(texts):
+                        break
+                    texts[num] = f"a photo with a {cls_name}"
+                    num += 1
+
+        classes = np.array(classes)
+        instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+        if len(masks) == 0:
+            # Some image does not have annotation (all ignored)
+            instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+            instances.gt_bboxes = torch.zeros((0, 4))
+        else:
+            masks = BitMasks(
+                torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+            )
+            instances.gt_masks = masks.tensor
+            instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
+        return instances, texts, label
+    
+    def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+        instances = Instances(image_shape)
+
+        classes = []
+        texts = ["a panoptic photo"] * self.num_queries
+        masks = []
+        label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+        for segment_info in segments_info:
+            class_id = segment_info["category_id"]
+            if not segment_info["iscrowd"]:
+                mask = pan_seg_gt == segment_info["id"]
+                if not np.all(mask == False):
+                    cls_name = self.class_names[class_id]
+                    classes.append(class_id)
+                    masks.append(mask)
+                    num_class_obj[cls_name] += 1
+                    label[mask] = class_id
+        
+        num = 0
+        for i, cls_name in enumerate(self.class_names):
+            if num_class_obj[cls_name] > 0:
+                for _ in range(num_class_obj[cls_name]):
+                    if num >= len(texts):
+                        break
+                    texts[num] = f"a photo with a {cls_name}"
+                    num += 1
+
+        classes = np.array(classes)
+        instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+        if len(masks) == 0:
+            # Some image does not have annotation (all ignored)
+            instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+            instances.gt_bboxes = torch.zeros((0, 4))
+        else:
+            masks = BitMasks(
+                torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+            )
+            instances.gt_masks = masks.tensor
+            instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
+            for i in range(instances.gt_classes.shape[0]):
+                # Placeholder bounding boxes for stuff regions. Note that these are not used during training.
+                if instances.gt_classes[i].item() not in self.things:
+                    instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])
+        return instances, texts, label
+
+    def __call__(self, dataset_dict):
+        """
+        Args:
+            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+        Returns:
+            dict: a format that builtin models in detectron2 accept
+        """
+        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
+        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+        utils.check_image_size(dataset_dict, image)
+        
+        image, transforms = T.apply_transform_gens(self.tfm_gens, image)
+        image_shape = image.shape[:2]  # h, w
+
+        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+        # Therefore it's important to use torch.Tensor.
+        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+
+        if not self.is_train:
+            # USER: Modify this if you want to keep them for some reason.
+            dataset_dict.pop("annotations", None)
+            return dataset_dict
+
+        # semantic segmentation
+        if "sem_seg_file_name" in dataset_dict:
+            # PyTorch transformation not implemented for uint16, so converting it to double first
+            sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
+            sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)
+        else:
+            sem_seg_gt = None
+        
+        if "pan_seg_file_name" in dataset_dict:
+            pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
+            segments_info = dataset_dict["segments_info"]
+
+            # apply the same transformation to panoptic segmentation
+            pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
+
+            from panopticapi.utils import rgb2id
+            pan_seg_gt = rgb2id(pan_seg_gt)
+
+        prob_task = np.random.uniform(0,1.)
+
+        num_class_obj = {}
+
+        for name in self.class_names:
+            num_class_obj[name] = 0
+
+        if prob_task < self.semantic_prob:
+            task = "The task is semantic"
+            instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+        elif prob_task < self.instance_prob:
+            task = "The task is instance"
+            instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+        else:
+            task = "The task is panoptic"
+            instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+
+
+        dataset_dict["sem_seg"] = torch.from_numpy(sem_seg).long()
+        dataset_dict["instances"] = instances
+        dataset_dict["orig_shape"] = image_shape
+        dataset_dict["task"] = task
+        dataset_dict["text"] = text
+        dataset_dict["thing_ids"] = self.things
+
+        return dataset_dict

RAVE-main/annotator/oneformer/oneformer/data/dataset_mappers/dataset_mapper.py

@@ -0,0 +1,203 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/dataset_mapper.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+import numpy as np
+from typing import List, Optional, Union
+import torch
+
+from annotator.oneformer.detectron2.config import configurable
+
+from annotator.oneformer.detectron2.data import detection_utils as utils
+from annotator.oneformer.detectron2.data import transforms as T
+from annotator.oneformer.oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+
+__all__ = ["DatasetMapper"]
+
+
+class DatasetMapper:
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by the model.
+
+    This is the default callable to be used to map your dataset dict into training data.
+    You may need to follow it to implement your own one for customized logic,
+    such as a different way to read or transform images.
+    See :doc:`/tutorials/data_loading` for details.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies cropping/geometric transforms to the image and annotations
+    3. Prepare data and annotations to Tensor and :class:`Instances`
+    """
+
+    @configurable
+    def __init__(
+        self,
+        is_train: bool,
+        *,
+        augmentations: List[Union[T.Augmentation, T.Transform]],
+        image_format: str,
+        task_seq_len: int,
+        task: str = "panoptic",
+        use_instance_mask: bool = False,
+        use_keypoint: bool = False,
+        instance_mask_format: str = "polygon",
+        keypoint_hflip_indices: Optional[np.ndarray] = None,
+        precomputed_proposal_topk: Optional[int] = None,
+        recompute_boxes: bool = False,
+    ):
+        """
+        NOTE: this interface is experimental.
+
+        Args:
+            is_train: whether it's used in training or inference
+            augmentations: a list of augmentations or deterministic transforms to apply
+            image_format: an image format supported by :func:`detection_utils.read_image`.
+            use_instance_mask: whether to process instance segmentation annotations, if available
+            use_keypoint: whether to process keypoint annotations if available
+            instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation
+                masks into this format.
+            keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices`
+            precomputed_proposal_topk: if given, will load pre-computed
+                proposals from dataset_dict and keep the top k proposals for each image.
+            recompute_boxes: whether to overwrite bounding box annotations
+                by computing tight bounding boxes from instance mask annotations.
+        """
+        if recompute_boxes:
+            assert use_instance_mask, "recompute_boxes requires instance masks"
+        # fmt: off
+        self.is_train               = is_train
+        self.augmentations          = T.AugmentationList(augmentations)
+        self.image_format           = image_format
+        self.use_instance_mask      = use_instance_mask
+        self.instance_mask_format   = instance_mask_format
+        self.use_keypoint           = use_keypoint
+        self.keypoint_hflip_indices = keypoint_hflip_indices
+        self.proposal_topk          = precomputed_proposal_topk
+        self.recompute_boxes        = recompute_boxes
+        self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+        self.task = task
+        assert self.task in ["panoptic", "semantic", "instance"]
+
+        # fmt: on
+        logger = logging.getLogger(__name__)
+        mode = "training" if is_train else "inference"
+        logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}")
+
+    @classmethod
+    def from_config(cls, cfg, is_train: bool = True):
+        augs = utils.build_augmentation(cfg, is_train)
+        if cfg.INPUT.CROP.ENABLED and is_train:
+            augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))
+            recompute_boxes = cfg.MODEL.MASK_ON
+        else:
+            recompute_boxes = False
+
+        ret = {
+            "is_train": is_train,
+            "augmentations": augs,
+            "image_format": cfg.INPUT.FORMAT,
+            "use_instance_mask": cfg.MODEL.MASK_ON,
+            "instance_mask_format": cfg.INPUT.MASK_FORMAT,
+            "use_keypoint": cfg.MODEL.KEYPOINT_ON,
+            "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+            "recompute_boxes": recompute_boxes,
+            "task": cfg.MODEL.TEST.TASK,
+        }
+
+        if cfg.MODEL.KEYPOINT_ON:
+            ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)
+
+        if cfg.MODEL.LOAD_PROPOSALS:
+            ret["precomputed_proposal_topk"] = (
+                cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN
+                if is_train
+                else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST
+            )
+        return ret
+
+    def _transform_annotations(self, dataset_dict, transforms, image_shape):
+        # USER: Modify this if you want to keep them for some reason.
+        for anno in dataset_dict["annotations"]:
+            if not self.use_instance_mask:
+                anno.pop("segmentation", None)
+            if not self.use_keypoint:
+                anno.pop("keypoints", None)
+
+        # USER: Implement additional transformations if you have other types of data
+        annos = [
+            utils.transform_instance_annotations(
+                obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices
+            )
+            for obj in dataset_dict.pop("annotations")
+            if obj.get("iscrowd", 0) == 0
+        ]
+        instances = utils.annotations_to_instances(
+            annos, image_shape, mask_format=self.instance_mask_format
+        )
+
+        # After transforms such as cropping are applied, the bounding box may no longer
+        # tightly bound the object. As an example, imagine a triangle object
+        # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
+        # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
+        # the intersection of original bounding box and the cropping box.
+        if self.recompute_boxes:
+            instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
+        dataset_dict["instances"] = utils.filter_empty_instances(instances)
+
+    def __call__(self, dataset_dict):
+        """
+        Args:
+            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+        Returns:
+            dict: a format that builtin models in detectron2 accept
+        """
+        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
+        # USER: Write your own image loading if it's not from a file
+        image = utils.read_image(dataset_dict["file_name"], format=self.image_format)
+        utils.check_image_size(dataset_dict, image)
+        
+        task = f"The task is {self.task}"
+        dataset_dict["task"] = task
+
+        # USER: Remove if you don't do semantic/panoptic segmentation.
+        if "sem_seg_file_name" in dataset_dict:
+            sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2)
+        else:
+            sem_seg_gt = None
+
+        aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
+        transforms = self.augmentations(aug_input)
+        image, sem_seg_gt = aug_input.image, aug_input.sem_seg
+
+        image_shape = image.shape[:2]  # h, w
+        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+        # Therefore it's important to use torch.Tensor.
+        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+        if sem_seg_gt is not None:
+            dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
+
+        # USER: Remove if you don't use pre-computed proposals.
+        # Most users would not need this feature.
+        if self.proposal_topk is not None:
+            utils.transform_proposals(
+                dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk
+            )
+
+        if not self.is_train:
+            # USER: Modify this if you want to keep them for some reason.
+            dataset_dict.pop("annotations", None)
+            dataset_dict.pop("sem_seg_file_name", None)
+            return dataset_dict
+
+        if "annotations" in dataset_dict:
+            self._transform_annotations(dataset_dict, transforms, image_shape)
+
+        return dataset_dict

RAVE-main/annotator/oneformer/oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py

@@ -0,0 +1,375 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+import os
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+from annotator.oneformer.detectron2.config import configurable
+from annotator.oneformer.detectron2.data import detection_utils as utils
+from annotator.oneformer.detectron2.data import transforms as T
+from annotator.oneformer.detectron2.structures import BitMasks, Instances
+from annotator.oneformer.detectron2.data import MetadataCatalog
+from annotator.oneformer.detectron2.projects.point_rend import ColorAugSSDTransform
+from annotator.oneformer.oneformer.utils.box_ops import masks_to_boxes
+from annotator.oneformer.oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+
+__all__ = ["OneFormerUnifiedDatasetMapper"]
+
+
+class OneFormerUnifiedDatasetMapper:
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by OneFormer for universal segmentation.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies geometric transforms to the image and annotation
+    3. Find and applies suitable cropping to the image and annotation
+    4. Prepare image and annotation to Tensors
+    """
+
+    @configurable
+    def __init__(
+        self,
+        is_train=True,
+        *,
+        name,
+        num_queries,
+        meta,
+        augmentations,
+        image_format,
+        ignore_label,
+        size_divisibility,
+        task_seq_len,
+        max_seq_len,
+        semantic_prob,
+        instance_prob,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            is_train: for training or inference
+            augmentations: a list of augmentations or deterministic transforms to apply
+            image_format: an image format supported by :func:`detection_utils.read_image`.
+            ignore_label: the label that is ignored to evaluation
+            size_divisibility: pad image size to be divisible by this value
+        """
+        self.is_train = is_train
+        self.meta = meta
+        self.name = name
+        self.tfm_gens = augmentations
+        self.img_format = image_format
+        self.ignore_label = ignore_label
+        self.size_divisibility = size_divisibility
+        self.num_queries = num_queries
+
+        logger = logging.getLogger(__name__)
+        mode = "training" if is_train else "inference"
+        logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}")
+    
+        self.things = []
+        for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():
+            self.things.append(v)
+        self.class_names = self.meta.stuff_classes
+        self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
+        self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+        self.semantic_prob = semantic_prob
+        self.instance_prob = instance_prob
+    
+    @classmethod
+    def from_config(cls, cfg, is_train=True):
+        # Build augmentation
+        augs = [
+            T.ResizeShortestEdge(
+                cfg.INPUT.MIN_SIZE_TRAIN,
+                cfg.INPUT.MAX_SIZE_TRAIN,
+                cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,
+            )
+        ]
+        if cfg.INPUT.CROP.ENABLED:
+            augs.append(
+                T.RandomCrop_CategoryAreaConstraint(
+                    cfg.INPUT.CROP.TYPE,
+                    cfg.INPUT.CROP.SIZE,
+                    cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,
+                    cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
+                )
+            )
+        if cfg.INPUT.COLOR_AUG_SSD:
+            augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
+        augs.append(T.RandomFlip())
+
+        # Assume always applies to the training set.
+        dataset_names = cfg.DATASETS.TRAIN
+        meta = MetadataCatalog.get(dataset_names[0])
+        ignore_label = meta.ignore_label
+
+        ret = {
+            "is_train": is_train,
+            "meta": meta,
+            "name": dataset_names[0],
+            "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,
+            "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+            "max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
+            "augmentations": augs,
+            "image_format": cfg.INPUT.FORMAT,
+            "ignore_label": ignore_label,
+            "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY,
+            "semantic_prob": cfg.INPUT.TASK_PROB.SEMANTIC,
+            "instance_prob": cfg.INPUT.TASK_PROB.INSTANCE,
+        }
+        return ret
+
+    def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+        pan_seg_gt = pan_seg_gt.numpy()
+        instances = Instances(image_shape)
+        
+        classes = []
+        texts = ["a semantic photo"] * self.num_queries
+        masks = []
+        label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+        for segment_info in segments_info:
+            class_id = segment_info["category_id"]
+            if not segment_info["iscrowd"]:
+                mask = pan_seg_gt == segment_info["id"]
+                if not np.all(mask == False):
+                    if class_id not in classes:
+                        cls_name = self.class_names[class_id]
+                        classes.append(class_id)
+                        masks.append(mask)
+                        num_class_obj[cls_name] += 1
+                    else:
+                        idx = classes.index(class_id)
+                        masks[idx] += mask
+                        masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)
+                    label[mask] = class_id
+        
+        num = 0
+        for i, cls_name in enumerate(self.class_names):
+            if num_class_obj[cls_name] > 0:
+                for _ in range(num_class_obj[cls_name]):
+                    if num >= len(texts):
+                        break
+                    texts[num] = f"a photo with a {cls_name}"
+                    num += 1
+                    
+        classes = np.array(classes)
+        instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+        if len(masks) == 0:
+            # Some image does not have annotation (all ignored)
+            instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+            instances.gt_bboxes = torch.zeros((0, 4))
+        else:
+            masks = BitMasks(
+                torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+            )
+            instances.gt_masks = masks.tensor
+            # Placeholder bounding boxes for stuff regions. Note that these are not used during training.
+            instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])
+        return instances, texts, label
+    
+    def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+        pan_seg_gt = pan_seg_gt.numpy()
+        instances = Instances(image_shape)
+        
+        classes = []
+        texts = ["an instance photo"] * self.num_queries
+        masks = []
+        label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+        for segment_info in segments_info:
+            class_id = segment_info["category_id"]
+            if class_id in self.things:
+                if not segment_info["iscrowd"]:
+                    mask = pan_seg_gt == segment_info["id"]
+                    if not np.all(mask == False):
+                        cls_name = self.class_names[class_id]
+                        classes.append(class_id)
+                        masks.append(mask)
+                        num_class_obj[cls_name] += 1
+                        label[mask] = class_id
+        
+        num = 0
+        for i, cls_name in enumerate(self.class_names):
+            if num_class_obj[cls_name] > 0:
+                for _ in range(num_class_obj[cls_name]):
+                    if num >= len(texts):
+                        break
+                    texts[num] = f"a photo with a {cls_name}"
+                    num += 1
+                    
+        classes = np.array(classes)
+        instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+        if len(masks) == 0:
+            # Some image does not have annotation (all ignored)
+            instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+            instances.gt_bboxes = torch.zeros((0, 4))
+        else:
+            masks = BitMasks(
+                torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+            )
+            instances.gt_masks = masks.tensor
+            instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
+        return instances, texts, label
+    
+    def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+        pan_seg_gt = pan_seg_gt.numpy()
+        instances = Instances(image_shape)
+        
+        classes = []
+        texts = ["a panoptic photo"] * self.num_queries
+        masks = []
+        label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+        for segment_info in segments_info:
+            class_id = segment_info["category_id"]
+            if not segment_info["iscrowd"]:
+                mask = pan_seg_gt == segment_info["id"]
+                if not np.all(mask == False):
+                    cls_name = self.class_names[class_id]
+                    classes.append(class_id)
+                    masks.append(mask)
+                    num_class_obj[cls_name] += 1
+                    label[mask] = class_id
+        
+        num = 0
+        for i, cls_name in enumerate(self.class_names):
+            if num_class_obj[cls_name] > 0:
+                for _ in range(num_class_obj[cls_name]):
+                    if num >= len(texts):
+                        break
+                    texts[num] = f"a photo with a {cls_name}"
+                    num += 1
+                    
+        classes = np.array(classes)
+        instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+        if len(masks) == 0:
+            # Some image does not have annotation (all ignored)
+            instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+            instances.gt_bboxes = torch.zeros((0, 4))
+        else:
+            masks = BitMasks(
+                torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+            )
+            instances.gt_masks = masks.tensor
+            instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
+            for i in range(instances.gt_classes.shape[0]):
+                # Placeholder bounding boxes for stuff regions. Note that these are not used during training.
+                if instances.gt_classes[i].item() not in self.things:
+                    instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])
+        return instances, texts, label
+    
+    def __call__(self, dataset_dict):
+        """
+        Args:
+            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+        Returns:
+            dict: a format that builtin models in detectron2 accept
+        """
+        assert self.is_train, "OneFormerUnifiedDatasetMapper should only be used for training!"
+
+        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
+        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+        utils.check_image_size(dataset_dict, image)
+
+        # semantic segmentation
+        if "sem_seg_file_name" in dataset_dict:
+            # PyTorch transformation not implemented for uint16, so converting it to double first
+            sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
+        else:
+            sem_seg_gt = None
+
+        # panoptic segmentation
+        if "pan_seg_file_name" in dataset_dict:
+            pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
+            segments_info = dataset_dict["segments_info"]
+        else:
+            pan_seg_gt = None
+            segments_info = None
+
+        if pan_seg_gt is None:
+            raise ValueError(
+                "Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.".format(
+                    dataset_dict["file_name"]
+                )
+            )
+
+        aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
+        aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
+        image = aug_input.image
+        if sem_seg_gt is not None:
+            sem_seg_gt = aug_input.sem_seg
+
+        # apply the same transformation to panoptic segmentation
+        pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
+
+        from panopticapi.utils import rgb2id
+
+        pan_seg_gt = rgb2id(pan_seg_gt)
+
+        # Pad image and segmentation label here!
+        image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+        if sem_seg_gt is not None:
+            sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
+        pan_seg_gt = torch.as_tensor(pan_seg_gt.astype("long"))
+
+        if self.size_divisibility > 0:
+            image_size = (image.shape[-2], image.shape[-1])
+            padding_size = [
+                0,
+                self.size_divisibility - image_size[1],
+                0,
+                self.size_divisibility - image_size[0],
+            ]
+            image = F.pad(image, padding_size, value=128).contiguous()
+            if sem_seg_gt is not None:
+                sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()
+            pan_seg_gt = F.pad(
+                pan_seg_gt, padding_size, value=0
+            ).contiguous()  # 0 is the VOID panoptic label
+
+        image_shape = (image.shape[-2], image.shape[-1])  # h, w
+
+        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+        # Therefore it's important to use torch.Tensor.
+        dataset_dict["image"] = image
+
+        if "annotations" in dataset_dict:
+            raise ValueError("Pemantic segmentation dataset should not have 'annotations'.")
+
+        prob_task = np.random.uniform(0,1.)
+
+        num_class_obj = {}
+
+        for name in self.class_names:
+            num_class_obj[name] = 0
+
+        if prob_task < self.semantic_prob:
+            task = "The task is semantic"
+            instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+        elif prob_task < self.instance_prob:
+            task = "The task is instance"
+            instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+        else:
+            task = "The task is panoptic"
+            instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+
+        dataset_dict["sem_seg"] = torch.from_numpy(sem_seg).long()
+        dataset_dict["instances"] = instances
+        dataset_dict["orig_shape"] = image_shape
+        dataset_dict["task"] = task
+        dataset_dict["text"] = text
+        dataset_dict["thing_ids"] = self.things
+        
+        return dataset_dict

RAVE-main/annotator/oneformer/oneformer/data/datasets/__init__.py

@@ -0,0 +1,7 @@
+from . import (
+    register_ade20k_panoptic,
+    register_cityscapes_panoptic,
+    register_coco_panoptic_annos_semseg,
+    register_ade20k_instance,
+    register_coco_panoptic2instance,
+)

RAVE-main/annotator/oneformer/oneformer/data/datasets/register_ade20k_instance.py

@@ -0,0 +1,56 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_instance.py
+# ------------------------------------------------------------------------------
+
+import json
+import logging
+import numpy as np
+import os
+from PIL import Image
+
+from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
+from annotator.oneformer.detectron2.data.datasets.coco import load_coco_json, register_coco_instances
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+ADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}]
+
+
+_PREDEFINED_SPLITS = {
+    # point annotations without masks
+    "ade20k_instance_train": (
+        "ADEChallengeData2016/images/training",
+        "ADEChallengeData2016/ade20k_instance_train.json",
+    ),
+    "ade20k_instance_val": (
+        "ADEChallengeData2016/images/validation",
+        "ADEChallengeData2016/ade20k_instance_val.json",
+    ),
+}
+
+
+def _get_ade_instances_meta():
+    thing_ids = [k["id"] for k in ADE_CATEGORIES]
+    assert len(thing_ids) == 100, len(thing_ids)
+    # Mapping from the incontiguous ADE category id to an id in [0, 99]
+    thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}
+    thing_classes = [k["name"] for k in ADE_CATEGORIES]
+    ret = {
+        "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
+        "thing_classes": thing_classes,
+    }
+    return ret
+
+
+def register_all_ade20k_instance(root):
+    for key, (image_root, json_file) in _PREDEFINED_SPLITS.items():
+        # Assume pre-defined datasets live in `./datasets`.
+        register_coco_instances(
+            key,
+            _get_ade_instances_meta(),
+            os.path.join(root, json_file) if "://" not in json_file else json_file,
+            os.path.join(root, image_root),
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_ade20k_instance(_root)

RAVE-main/annotator/oneformer/oneformer/data/datasets/register_ade20k_panoptic.py

@@ -0,0 +1,394 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_panoptic.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import json
+import os
+
+from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+ADE20K_150_CATEGORIES = [
+    {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "wall"},
+    {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "building"},
+    {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "sky"},
+    {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "floor"},
+    {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "tree"},
+    {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ceiling"},
+    {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "road, route"},
+    {"color": [204, 5, 255], "id": 7, "isthing": 1, "name": "bed"},
+    {"color": [230, 230, 230], "id": 8, "isthing": 1, "name": "window "},
+    {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "grass"},
+    {"color": [224, 5, 255], "id": 10, "isthing": 1, "name": "cabinet"},
+    {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "sidewalk, pavement"},
+    {"color": [150, 5, 61], "id": 12, "isthing": 1, "name": "person"},
+    {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "earth, ground"},
+    {"color": [8, 255, 51], "id": 14, "isthing": 1, "name": "door"},
+    {"color": [255, 6, 82], "id": 15, "isthing": 1, "name": "table"},
+    {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "mountain, mount"},
+    {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "plant"},
+    {"color": [255, 51, 7], "id": 18, "isthing": 1, "name": "curtain"},
+    {"color": [204, 70, 3], "id": 19, "isthing": 1, "name": "chair"},
+    {"color": [0, 102, 200], "id": 20, "isthing": 1, "name": "car"},
+    {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "water"},
+    {"color": [255, 6, 51], "id": 22, "isthing": 1, "name": "painting, picture"},
+    {"color": [11, 102, 255], "id": 23, "isthing": 1, "name": "sofa"},
+    {"color": [255, 7, 71], "id": 24, "isthing": 1, "name": "shelf"},
+    {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "house"},
+    {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "sea"},
+    {"color": [220, 220, 220], "id": 27, "isthing": 1, "name": "mirror"},
+    {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "rug"},
+    {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "field"},
+    {"color": [8, 255, 214], "id": 30, "isthing": 1, "name": "armchair"},
+    {"color": [7, 255, 224], "id": 31, "isthing": 1, "name": "seat"},
+    {"color": [255, 184, 6], "id": 32, "isthing": 1, "name": "fence"},
+    {"color": [10, 255, 71], "id": 33, "isthing": 1, "name": "desk"},
+    {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "rock, stone"},
+    {"color": [7, 255, 255], "id": 35, "isthing": 1, "name": "wardrobe, closet, press"},
+    {"color": [224, 255, 8], "id": 36, "isthing": 1, "name": "lamp"},
+    {"color": [102, 8, 255], "id": 37, "isthing": 1, "name": "tub"},
+    {"color": [255, 61, 6], "id": 38, "isthing": 1, "name": "rail"},
+    {"color": [255, 194, 7], "id": 39, "isthing": 1, "name": "cushion"},
+    {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "base, pedestal, stand"},
+    {"color": [0, 255, 20], "id": 41, "isthing": 1, "name": "box"},
+    {"color": [255, 8, 41], "id": 42, "isthing": 1, "name": "column, pillar"},
+    {"color": [255, 5, 153], "id": 43, "isthing": 1, "name": "signboard, sign"},
+    {
+        "color": [6, 51, 255],
+        "id": 44,
+        "isthing": 1,
+        "name": "chest of drawers, chest, bureau, dresser",
+    },
+    {"color": [235, 12, 255], "id": 45, "isthing": 1, "name": "counter"},
+    {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "sand"},
+    {"color": [0, 163, 255], "id": 47, "isthing": 1, "name": "sink"},
+    {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "skyscraper"},
+    {"color": [250, 10, 15], "id": 49, "isthing": 1, "name": "fireplace"},
+    {"color": [20, 255, 0], "id": 50, "isthing": 1, "name": "refrigerator, icebox"},
+    {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "grandstand, covered stand"},
+    {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "path"},
+    {"color": [255, 224, 0], "id": 53, "isthing": 1, "name": "stairs"},
+    {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "runway"},
+    {"color": [0, 0, 255], "id": 55, "isthing": 1, "name": "case, display case, showcase, vitrine"},
+    {
+        "color": [255, 71, 0],
+        "id": 56,
+        "isthing": 1,
+        "name": "pool table, billiard table, snooker table",
+    },
+    {"color": [0, 235, 255], "id": 57, "isthing": 1, "name": "pillow"},
+    {"color": [0, 173, 255], "id": 58, "isthing": 1, "name": "screen door, screen"},
+    {"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "stairway, staircase"},
+    {"color": [11, 200, 200], "id": 60, "isthing": 0, "name": "river"},
+    {"color": [255, 82, 0], "id": 61, "isthing": 0, "name": "bridge, span"},
+    {"color": [0, 255, 245], "id": 62, "isthing": 1, "name": "bookcase"},
+    {"color": [0, 61, 255], "id": 63, "isthing": 0, "name": "blind, screen"},
+    {"color": [0, 255, 112], "id": 64, "isthing": 1, "name": "coffee table"},
+    {
+        "color": [0, 255, 133],
+        "id": 65,
+        "isthing": 1,
+        "name": "toilet, can, commode, crapper, pot, potty, stool, throne",
+    },
+    {"color": [255, 0, 0], "id": 66, "isthing": 1, "name": "flower"},
+    {"color": [255, 163, 0], "id": 67, "isthing": 1, "name": "book"},
+    {"color": [255, 102, 0], "id": 68, "isthing": 0, "name": "hill"},
+    {"color": [194, 255, 0], "id": 69, "isthing": 1, "name": "bench"},
+    {"color": [0, 143, 255], "id": 70, "isthing": 1, "name": "countertop"},
+    {"color": [51, 255, 0], "id": 71, "isthing": 1, "name": "stove"},
+    {"color": [0, 82, 255], "id": 72, "isthing": 1, "name": "palm, palm tree"},
+    {"color": [0, 255, 41], "id": 73, "isthing": 1, "name": "kitchen island"},
+    {"color": [0, 255, 173], "id": 74, "isthing": 1, "name": "computer"},
+    {"color": [10, 0, 255], "id": 75, "isthing": 1, "name": "swivel chair"},
+    {"color": [173, 255, 0], "id": 76, "isthing": 1, "name": "boat"},
+    {"color": [0, 255, 153], "id": 77, "isthing": 0, "name": "bar"},
+    {"color": [255, 92, 0], "id": 78, "isthing": 1, "name": "arcade machine"},
+    {"color": [255, 0, 255], "id": 79, "isthing": 0, "name": "hovel, hut, hutch, shack, shanty"},
+    {"color": [255, 0, 245], "id": 80, "isthing": 1, "name": "bus"},
+    {"color": [255, 0, 102], "id": 81, "isthing": 1, "name": "towel"},
+    {"color": [255, 173, 0], "id": 82, "isthing": 1, "name": "light"},
+    {"color": [255, 0, 20], "id": 83, "isthing": 1, "name": "truck"},
+    {"color": [255, 184, 184], "id": 84, "isthing": 0, "name": "tower"},
+    {"color": [0, 31, 255], "id": 85, "isthing": 1, "name": "chandelier"},
+    {"color": [0, 255, 61], "id": 86, "isthing": 1, "name": "awning, sunshade, sunblind"},
+    {"color": [0, 71, 255], "id": 87, "isthing": 1, "name": "street lamp"},
+    {"color": [255, 0, 204], "id": 88, "isthing": 1, "name": "booth"},
+    {"color": [0, 255, 194], "id": 89, "isthing": 1, "name": "tv"},
+    {"color": [0, 255, 82], "id": 90, "isthing": 1, "name": "plane"},
+    {"color": [0, 10, 255], "id": 91, "isthing": 0, "name": "dirt track"},
+    {"color": [0, 112, 255], "id": 92, "isthing": 1, "name": "clothes"},
+    {"color": [51, 0, 255], "id": 93, "isthing": 1, "name": "pole"},
+    {"color": [0, 194, 255], "id": 94, "isthing": 0, "name": "land, ground, soil"},
+    {
+        "color": [0, 122, 255],
+        "id": 95,
+        "isthing": 1,
+        "name": "bannister, banister, balustrade, balusters, handrail",
+    },
+    {
+        "color": [0, 255, 163],
+        "id": 96,
+        "isthing": 0,
+        "name": "escalator, moving staircase, moving stairway",
+    },
+    {
+        "color": [255, 153, 0],
+        "id": 97,
+        "isthing": 1,
+        "name": "ottoman, pouf, pouffe, puff, hassock",
+    },
+    {"color": [0, 255, 10], "id": 98, "isthing": 1, "name": "bottle"},
+    {"color": [255, 112, 0], "id": 99, "isthing": 0, "name": "buffet, counter, sideboard"},
+    {
+        "color": [143, 255, 0],
+        "id": 100,
+        "isthing": 0,
+        "name": "poster, posting, placard, notice, bill, card",
+    },
+    {"color": [82, 0, 255], "id": 101, "isthing": 0, "name": "stage"},
+    {"color": [163, 255, 0], "id": 102, "isthing": 1, "name": "van"},
+    {"color": [255, 235, 0], "id": 103, "isthing": 1, "name": "ship"},
+    {"color": [8, 184, 170], "id": 104, "isthing": 1, "name": "fountain"},
+    {
+        "color": [133, 0, 255],
+        "id": 105,
+        "isthing": 0,
+        "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter",
+    },
+    {"color": [0, 255, 92], "id": 106, "isthing": 0, "name": "canopy"},
+    {
+        "color": [184, 0, 255],
+        "id": 107,
+        "isthing": 1,
+        "name": "washer, automatic washer, washing machine",
+    },
+    {"color": [255, 0, 31], "id": 108, "isthing": 1, "name": "plaything, toy"},
+    {"color": [0, 184, 255], "id": 109, "isthing": 0, "name": "pool"},
+    {"color": [0, 214, 255], "id": 110, "isthing": 1, "name": "stool"},
+    {"color": [255, 0, 112], "id": 111, "isthing": 1, "name": "barrel, cask"},
+    {"color": [92, 255, 0], "id": 112, "isthing": 1, "name": "basket, handbasket"},
+    {"color": [0, 224, 255], "id": 113, "isthing": 0, "name": "falls"},
+    {"color": [112, 224, 255], "id": 114, "isthing": 0, "name": "tent"},
+    {"color": [70, 184, 160], "id": 115, "isthing": 1, "name": "bag"},
+    {"color": [163, 0, 255], "id": 116, "isthing": 1, "name": "minibike, motorbike"},
+    {"color": [153, 0, 255], "id": 117, "isthing": 0, "name": "cradle"},
+    {"color": [71, 255, 0], "id": 118, "isthing": 1, "name": "oven"},
+    {"color": [255, 0, 163], "id": 119, "isthing": 1, "name": "ball"},
+    {"color": [255, 204, 0], "id": 120, "isthing": 1, "name": "food, solid food"},
+    {"color": [255, 0, 143], "id": 121, "isthing": 1, "name": "step, stair"},
+    {"color": [0, 255, 235], "id": 122, "isthing": 0, "name": "tank, storage tank"},
+    {"color": [133, 255, 0], "id": 123, "isthing": 1, "name": "trade name"},
+    {"color": [255, 0, 235], "id": 124, "isthing": 1, "name": "microwave"},
+    {"color": [245, 0, 255], "id": 125, "isthing": 1, "name": "pot"},
+    {"color": [255, 0, 122], "id": 126, "isthing": 1, "name": "animal"},
+    {"color": [255, 245, 0], "id": 127, "isthing": 1, "name": "bicycle"},
+    {"color": [10, 190, 212], "id": 128, "isthing": 0, "name": "lake"},
+    {"color": [214, 255, 0], "id": 129, "isthing": 1, "name": "dishwasher"},
+    {"color": [0, 204, 255], "id": 130, "isthing": 1, "name": "screen"},
+    {"color": [20, 0, 255], "id": 131, "isthing": 0, "name": "blanket, cover"},
+    {"color": [255, 255, 0], "id": 132, "isthing": 1, "name": "sculpture"},
+    {"color": [0, 153, 255], "id": 133, "isthing": 1, "name": "hood, exhaust hood"},
+    {"color": [0, 41, 255], "id": 134, "isthing": 1, "name": "sconce"},
+    {"color": [0, 255, 204], "id": 135, "isthing": 1, "name": "vase"},
+    {"color": [41, 0, 255], "id": 136, "isthing": 1, "name": "traffic light"},
+    {"color": [41, 255, 0], "id": 137, "isthing": 1, "name": "tray"},
+    {"color": [173, 0, 255], "id": 138, "isthing": 1, "name": "trash can"},
+    {"color": [0, 245, 255], "id": 139, "isthing": 1, "name": "fan"},
+    {"color": [71, 0, 255], "id": 140, "isthing": 0, "name": "pier"},
+    {"color": [122, 0, 255], "id": 141, "isthing": 0, "name": "crt screen"},
+    {"color": [0, 255, 184], "id": 142, "isthing": 1, "name": "plate"},
+    {"color": [0, 92, 255], "id": 143, "isthing": 1, "name": "monitor"},
+    {"color": [184, 255, 0], "id": 144, "isthing": 1, "name": "bulletin board"},
+    {"color": [0, 133, 255], "id": 145, "isthing": 0, "name": "shower"},
+    {"color": [255, 214, 0], "id": 146, "isthing": 1, "name": "radiator"},
+    {"color": [25, 194, 194], "id": 147, "isthing": 1, "name": "glass, drinking glass"},
+    {"color": [102, 255, 0], "id": 148, "isthing": 1, "name": "clock"},
+    {"color": [92, 0, 255], "id": 149, "isthing": 1, "name": "flag"},
+]
+
+ADE20k_COLORS = [k["color"] for k in ADE20K_150_CATEGORIES]
+
+MetadataCatalog.get("ade20k_sem_seg_train").set(
+    stuff_colors=ADE20k_COLORS[:],
+)
+
+MetadataCatalog.get("ade20k_sem_seg_val").set(
+    stuff_colors=ADE20k_COLORS[:],
+)
+
+
+def load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
+    """
+    Args:
+        image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
+        gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
+        json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
+    Returns:
+        list[dict]: a list of dicts in Detectron2 standard format. (See
+        `Using Custom Datasets </tutorials/datasets.html>`_ )
+    """
+
+    def _convert_category_id(segment_info, meta):
+        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = True
+        else:
+            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = False
+        return segment_info
+
+    with PathManager.open(json_file) as f:
+        json_info = json.load(f)
+
+    ret = []
+    for ann in json_info["annotations"]:
+        image_id = ann["image_id"]
+        # TODO: currently we assume image and label has the same filename but
+        # different extension, and images have extension ".jpg" for COCO. Need
+        # to make image extension a user-provided argument if we extend this
+        # function to support other COCO-like datasets.
+        image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
+        label_file = os.path.join(gt_dir, ann["file_name"])
+        sem_label_file = os.path.join(semseg_dir, ann["file_name"])
+        segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
+        ret.append(
+            {
+                "file_name": image_file,
+                "image_id": image_id,
+                "pan_seg_file_name": label_file,
+                "sem_seg_file_name": sem_label_file,
+                "segments_info": segments_info,
+            }
+        )
+    assert len(ret), f"No images found in {image_dir}!"
+    assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
+    assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
+    assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
+    return ret
+
+
+def register_ade20k_panoptic(
+    name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None,
+):
+    """
+    Register a "standard" version of ADE20k panoptic segmentation dataset named `name`.
+    The dictionaries in this registered dataset follows detectron2's standard format.
+    Hence it's called "standard".
+    Args:
+        name (str): the name that identifies a dataset,
+            e.g. "ade20k_panoptic_train"
+        metadata (dict): extra metadata associated with this dataset.
+        image_root (str): directory which contains all the images
+        panoptic_root (str): directory which contains panoptic annotation images in COCO format
+        panoptic_json (str): path to the json panoptic annotation file in COCO format
+        sem_seg_root (none): not used, to be consistent with
+            `register_coco_panoptic_separated`.
+        instances_json (str): path to the json instance annotation file
+    """
+    panoptic_name = name
+    DatasetCatalog.register(
+        panoptic_name,
+        lambda: load_ade20k_panoptic_json(
+            panoptic_json, image_root, panoptic_root, semantic_root, metadata
+        ),
+    )
+    MetadataCatalog.get(panoptic_name).set(
+        panoptic_root=panoptic_root,
+        image_root=image_root,
+        panoptic_json=panoptic_json,
+        json_file=instances_json,
+        evaluator_type="ade20k_panoptic_seg",
+        ignore_label=255,
+        label_divisor=1000,
+        **metadata,
+    )
+
+
+_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {
+    "ade20k_panoptic_train": (
+        "ADEChallengeData2016/images/training",
+        "ADEChallengeData2016/ade20k_panoptic_train",
+        "ADEChallengeData2016/ade20k_panoptic_train.json",
+        "ADEChallengeData2016/annotations_detectron2/training",
+        "ADEChallengeData2016/ade20k_instance_train.json",
+    ),
+    "ade20k_panoptic_val": (
+        "ADEChallengeData2016/images/validation",
+        "ADEChallengeData2016/ade20k_panoptic_val",
+        "ADEChallengeData2016/ade20k_panoptic_val.json",
+        "ADEChallengeData2016/annotations_detectron2/validation",
+        "ADEChallengeData2016/ade20k_instance_val.json",
+    ),
+}
+
+
+def get_metadata():
+    meta = {}
+    # The following metadata maps contiguous id from [0, #thing categories +
+    # #stuff categories) to their names and colors. We have to replica of the
+    # same name and color under "thing_*" and "stuff_*" because the current
+    # visualization function in D2 handles thing and class classes differently
+    # due to some heuristic used in Panoptic FPN. We keep the same naming to
+    # enable reusing existing visualization functions.
+    thing_classes = [k["name"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1]
+    thing_colors = [k["color"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1]
+    stuff_classes = [k["name"] for k in ADE20K_150_CATEGORIES]
+    stuff_colors = [k["color"] for k in ADE20K_150_CATEGORIES]
+
+    meta["thing_classes"] = thing_classes
+    meta["thing_colors"] = thing_colors
+    meta["stuff_classes"] = stuff_classes
+    meta["stuff_colors"] = stuff_colors
+
+    # Convert category id for training:
+    #   category id: like semantic segmentation, it is the class id for each
+    #   pixel. Since there are some classes not used in evaluation, the category
+    #   id is not always contiguous and thus we have two set of category ids:
+    #       - original category id: category id in the original dataset, mainly
+    #           used for evaluation.
+    #       - contiguous category id: [0, #classes), in order to train the linear
+    #           softmax classifier.
+    thing_dataset_id_to_contiguous_id = {}
+    stuff_dataset_id_to_contiguous_id = {}
+
+    for i, cat in enumerate(ADE20K_150_CATEGORIES):
+        if cat["isthing"]:
+            thing_dataset_id_to_contiguous_id[cat["id"]] = i
+        # else:
+        #     stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+        # in order to use sem_seg evaluator
+        stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+    meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+    meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+    return meta
+
+
+def register_all_ade20k_panoptic(root):
+    metadata = get_metadata()
+    for (
+        prefix,
+        (image_root, panoptic_root, panoptic_json, semantic_root, instance_json),
+    ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():
+        # The "standard" version of COCO panoptic segmentation dataset,
+        # e.g. used by Panoptic-DeepLab
+        register_ade20k_panoptic(
+            prefix,
+            metadata,
+            os.path.join(root, image_root),
+            os.path.join(root, panoptic_root),
+            os.path.join(root, semantic_root),
+            os.path.join(root, panoptic_json),
+            os.path.join(root, instance_json),
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_ade20k_panoptic(_root)

RAVE-main/annotator/oneformer/oneformer/data/datasets/register_cityscapes_panoptic.py

@@ -0,0 +1,199 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/cityscapes_panoptic.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import json
+import logging
+import os
+
+from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
+from annotator.oneformer.detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+"""
+This file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog.
+"""
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_cityscapes_panoptic_files(image_dir, gt_dir, json_info):
+    files = []
+    # scan through the directory
+    cities = PathManager.ls(image_dir)
+    logger.info(f"{len(cities)} cities found in '{image_dir}'.")
+    image_dict = {}
+    for city in cities:
+        city_img_dir = os.path.join(image_dir, city)
+        for basename in PathManager.ls(city_img_dir):
+            image_file = os.path.join(city_img_dir, basename)
+
+            suffix = "_leftImg8bit.png"
+            assert basename.endswith(suffix), basename
+            basename = os.path.basename(basename)[: -len(suffix)]
+
+            image_dict[basename] = image_file
+
+    for ann in json_info["annotations"]:
+        image_file = image_dict.get(ann["image_id"], None)
+        assert image_file is not None, "No image {} found for annotation {}".format(
+            ann["image_id"], ann["file_name"]
+        )
+        label_file = os.path.join(gt_dir, ann["file_name"])
+        segments_info = ann["segments_info"]
+        files.append((image_file, label_file, segments_info))
+
+    assert len(files), "No images found in {}".format(image_dir)
+    assert PathManager.isfile(files[0][0]), files[0][0]
+    assert PathManager.isfile(files[0][1]), files[0][1]
+    return files
+
+
+def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):
+    """
+    Args:
+        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
+        gt_dir (str): path to the raw annotations. e.g.,
+            "~/cityscapes/gtFine/cityscapes_panoptic_train".
+        gt_json (str): path to the json file. e.g.,
+            "~/cityscapes/gtFine/cityscapes_panoptic_train.json".
+        meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id"
+            and "stuff_dataset_id_to_contiguous_id" to map category ids to
+            contiguous ids for training.
+
+    Returns:
+        list[dict]: a list of dicts in Detectron2 standard format. (See
+        `Using Custom Datasets </tutorials/datasets.html>`_ )
+    """
+
+    def _convert_category_id(segment_info, meta):
+        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+        else:
+            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+        return segment_info
+
+    assert os.path.exists(
+        gt_json
+    ), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files."  # noqa
+
+    
+    with open(gt_json) as f:
+        json_info = json.load(f)
+    
+    files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)
+    ret = []
+    for image_file, label_file, segments_info in files:
+        sem_label_file = (
+            image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png"
+        )
+        segments_info = [_convert_category_id(x, meta) for x in segments_info]
+        ret.append(
+            {
+                "file_name": image_file,
+                "image_id": "_".join(
+                    os.path.splitext(os.path.basename(image_file))[0].split("_")[:3]
+                ),
+                "sem_seg_file_name": sem_label_file,
+                "pan_seg_file_name": label_file,
+                "segments_info": segments_info,
+            }
+        )
+    assert len(ret), f"No images found in {image_dir}!"
+    assert PathManager.isfile(
+        ret[0]["sem_seg_file_name"]
+    ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py"  # noqa
+    assert PathManager.isfile(
+        ret[0]["pan_seg_file_name"]
+    ), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py"  # noqa
+    return ret
+
+
+_RAW_CITYSCAPES_PANOPTIC_SPLITS = {
+    "cityscapes_fine_panoptic_train": (
+        "cityscapes/leftImg8bit/train",
+        "cityscapes/gtFine/cityscapes_panoptic_train",
+        "cityscapes/gtFine/cityscapes_panoptic_train.json",
+    ),
+    "cityscapes_fine_panoptic_val": (
+        "cityscapes/leftImg8bit/val",
+        "cityscapes/gtFine/cityscapes_panoptic_val",
+        "cityscapes/gtFine/cityscapes_panoptic_val.json",
+    ),
+    # "cityscapes_fine_panoptic_test": not supported yet
+}
+
+
+def register_all_cityscapes_panoptic(root):
+    meta = {}
+    # The following metadata maps contiguous id from [0, #thing categories +
+    # #stuff categories) to their names and colors. We have to replica of the
+    # same name and color under "thing_*" and "stuff_*" because the current
+    # visualization function in D2 handles thing and class classes differently
+    # due to some heuristic used in Panoptic FPN. We keep the same naming to
+    # enable reusing existing visualization functions.
+    thing_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
+    thing_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
+    stuff_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
+    stuff_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
+
+    meta["thing_classes"] = thing_classes
+    meta["thing_colors"] = thing_colors
+    meta["stuff_classes"] = stuff_classes
+    meta["stuff_colors"] = stuff_colors
+
+    # There are three types of ids in cityscapes panoptic segmentation:
+    # (1) category id: like semantic segmentation, it is the class id for each
+    #   pixel. Since there are some classes not used in evaluation, the category
+    #   id is not always contiguous and thus we have two set of category ids:
+    #       - original category id: category id in the original dataset, mainly
+    #           used for evaluation.
+    #       - contiguous category id: [0, #classes), in order to train the classifier
+    # (2) instance id: this id is used to differentiate different instances from
+    #   the same category. For "stuff" classes, the instance id is always 0; for
+    #   "thing" classes, the instance id starts from 1 and 0 is reserved for
+    #   ignored instances (e.g. crowd annotation).
+    # (3) panoptic id: this is the compact id that encode both category and
+    #   instance id by: category_id * 1000 + instance_id.
+    thing_dataset_id_to_contiguous_id = {}
+    stuff_dataset_id_to_contiguous_id = {}
+
+    for k in CITYSCAPES_CATEGORIES:
+        if k["isthing"] == 1:
+            thing_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
+        else:
+            stuff_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
+
+    meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+    meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+    for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items():
+        image_dir = os.path.join(root, image_dir)
+        gt_dir = os.path.join(root, gt_dir)
+        gt_json = os.path.join(root, gt_json)
+
+        if key in DatasetCatalog.list():
+            DatasetCatalog.remove(key)
+
+        DatasetCatalog.register(
+            key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta)
+        )
+        MetadataCatalog.get(key).set(
+            panoptic_root=gt_dir,
+            image_root=image_dir,
+            panoptic_json=gt_json,
+            gt_dir=gt_dir.replace("cityscapes_panoptic_", ""),
+            evaluator_type="cityscapes_panoptic_seg",
+            ignore_label=255,
+            label_divisor=1000,
+            **meta,
+        )
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_cityscapes_panoptic(_root)

RAVE-main/annotator/oneformer/oneformer/data/datasets/register_coco_panoptic2instance.py

@@ -0,0 +1,44 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/builtin.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+
+"""
+This file registers pre-defined datasets at hard-coded paths, and their metadata.
+
+We hard-code metadata for common datasets. This will enable:
+1. Consistency check when loading the datasets
+2. Use models on these standard datasets directly and run demos,
+   without having to download the dataset annotations
+
+We hard-code some paths to the dataset that's assumed to
+exist in "./datasets/".
+
+Users SHOULD NOT use this file to create new dataset / metadata for new dataset.
+To add new dataset, refer to the tutorial "docs/DATASETS.md".
+"""
+
+import os
+from annotator.oneformer.detectron2.data.datasets.builtin_meta import  _get_builtin_metadata
+from annotator.oneformer.detectron2.data.datasets.coco import register_coco_instances
+
+
+_PREDEFINED_SPLITS_COCO = {
+    "coco_2017_val_panoptic2instance": ("coco/val2017", "coco/annotations/panoptic2instances_val2017.json"),
+}
+
+
+def register_panoptic2instances_coco(root):
+    for key, (image_root, json_file) in _PREDEFINED_SPLITS_COCO.items():
+        # Assume pre-defined datasets live in `./datasets`.
+        register_coco_instances(
+            key,
+            _get_builtin_metadata("coco"),
+            os.path.join(root, json_file) if "://" not in json_file else json_file,
+            os.path.join(root, image_root),
+        )
+
+
+_root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets"))
+register_panoptic2instances_coco(_root)

RAVE-main/annotator/oneformer/oneformer/data/datasets/register_coco_panoptic_annos_semseg.py

@@ -0,0 +1,367 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import json
+import os
+
+from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
+from annotator.oneformer.detectron2.data.datasets import load_sem_seg
+from annotator.oneformer.detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+import contextlib
+import logging
+import io
+from fvcore.common.timer import Timer
+import annotator.oneformer.pycocotools.mask as mask_util
+from annotator.oneformer.detectron2.structures import BoxMode
+
+
+logger = logging.getLogger(__name__)
+
+
+_PREDEFINED_SPLITS_COCO_PANOPTIC = {
+    "coco_2017_train_panoptic": (
+        # This is the original panoptic annotation directory
+        "coco/panoptic_train2017",
+        "coco/annotations/panoptic_train2017.json",
+        # This directory contains semantic annotations that are
+        # converted from panoptic annotations.
+        # It is used by PanopticFPN.
+        # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py
+        # to create these directories.
+        "coco/panoptic_semseg_train2017",
+    ),
+    "coco_2017_val_panoptic": (
+        "coco/panoptic_val2017",
+        "coco/annotations/panoptic_val2017.json",
+        "coco/panoptic_semseg_val2017",
+    ),
+}
+
+def load_coco_instance_json(json_file, image_root, dataset_name=None):
+    from annotator.oneformer.pycocotools.coco import COCO
+
+    timer = Timer()
+    json_file = PathManager.get_local_path(json_file)
+    with contextlib.redirect_stdout(io.StringIO()):
+        coco_api = COCO(json_file)
+    if timer.seconds() > 1:
+        logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
+
+    id_map = None
+    if dataset_name is not None:
+        meta = MetadataCatalog.get(dataset_name)
+        cat_ids = sorted(coco_api.getCatIds())
+        cats = coco_api.loadCats(cat_ids)
+        # The categories in a custom json file may not be sorted.
+        thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])]
+        meta.thing_classes = thing_classes
+
+        # In COCO, certain category ids are artificially removed,
+        # and by convention they are always ignored.
+        # We deal with COCO's id issue and translate
+        # the category ids to contiguous ids in [0, 80).
+
+        # It works by looking at the "categories" field in the json, therefore
+        # if users' own json also have incontiguous ids, we'll
+        # apply this mapping as well but print a warning.
+        if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):
+            if "coco" not in dataset_name:
+                logger.warning(
+                    """
+Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
+"""
+                )
+        id_map = {v: i for i, v in enumerate(cat_ids)}
+        meta.thing_dataset_id_to_contiguous_id = id_map
+
+    # sort indices for reproducible results
+    img_ids = sorted(coco_api.imgs.keys())
+    # imgs is a list of dicts, each looks something like:
+    # {'license': 4,
+    #  'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
+    #  'file_name': 'COCO_val2014_000000001268.jpg',
+    #  'height': 427,
+    #  'width': 640,
+    #  'date_captured': '2013-11-17 05:57:24',
+    #  'id': 1268}
+    imgs = coco_api.loadImgs(img_ids)
+    # anns is a list[list[dict]], where each dict is an annotation
+    # record for an object. The inner list enumerates the objects in an image
+    # and the outer list enumerates over images. Example of anns[0]:
+    # [{'segmentation': [[192.81,
+    #     247.09,
+    #     ...
+    #     219.03,
+    #     249.06]],
+    #   'area': 1035.749,
+    #   'iscrowd': 0,
+    #   'image_id': 1268,
+    #   'bbox': [192.81, 224.8, 74.73, 33.43],
+    #   'category_id': 16,
+    #   'id': 42986},
+    #  ...]
+    anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]
+    total_num_valid_anns = sum([len(x) for x in anns])
+    total_num_anns = len(coco_api.anns)
+    if total_num_valid_anns < total_num_anns:
+        logger.warning(
+            f"{json_file} contains {total_num_anns} annotations, but only "
+            f"{total_num_valid_anns} of them match to images in the file."
+        )
+
+    if "minival" not in json_file:
+        # The popular valminusminival & minival annotations for COCO2014 contain this bug.
+        # However the ratio of buggy annotations there is tiny and does not affect accuracy.
+        # Therefore we explicitly white-list them.
+        ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
+        assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format(
+            json_file
+        )
+
+    imgs_anns = list(zip(imgs, anns))
+    logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file))
+
+    dataset_dicts = {}
+
+    ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"]
+
+    num_instances_without_valid_segmentation = 0
+
+    for (img_dict, anno_dict_list) in imgs_anns:
+        record = {}
+        record["file_name"] = os.path.join(image_root, img_dict["file_name"])
+        record["height"] = img_dict["height"]
+        record["width"] = img_dict["width"]
+        image_id = record["image_id"] = img_dict["id"]
+
+        objs = []
+        for anno in anno_dict_list:
+            # Check that the image_id in this annotation is the same as
+            # the image_id we're looking at.
+            # This fails only when the data parsing logic or the annotation file is buggy.
+
+            # The original COCO valminusminival2014 & minival2014 annotation files
+            # actually contains bugs that, together with certain ways of using COCO API,
+            # can trigger this assertion.
+            assert anno["image_id"] == image_id
+
+            assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.'
+
+            obj = {key: anno[key] for key in ann_keys if key in anno}
+            if "bbox" in obj and len(obj["bbox"]) == 0:
+                raise ValueError(
+                    f"One annotation of image {image_id} contains empty 'bbox' value! "
+                    "This json does not have valid COCO format."
+                )
+
+            segm = anno.get("segmentation", None)
+            if segm:  # either list[list[float]] or dict(RLE)
+                if isinstance(segm, dict):
+                    if isinstance(segm["counts"], list):
+                        # convert to compressed RLE
+                        segm = mask_util.frPyObjects(segm, *segm["size"])
+                else:
+                    # filter out invalid polygons (< 3 points)
+                    segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
+                    if len(segm) == 0:
+                        num_instances_without_valid_segmentation += 1
+                        continue  # ignore this instance
+                obj["segmentation"] = segm
+
+            keypts = anno.get("keypoints", None)
+            if keypts:  # list[int]
+                for idx, v in enumerate(keypts):
+                    if idx % 3 != 2:
+                        # COCO's segmentation coordinates are floating points in [0, H or W],
+                        # but keypoint coordinates are integers in [0, H-1 or W-1]
+                        # Therefore we assume the coordinates are "pixel indices" and
+                        # add 0.5 to convert to floating point coordinates.
+                        keypts[idx] = v + 0.5
+                obj["keypoints"] = keypts
+
+            obj["bbox_mode"] = BoxMode.XYWH_ABS
+            if id_map:
+                annotation_category_id = obj["category_id"]
+                try:
+                    obj["category_id"] = id_map[annotation_category_id]
+                except KeyError as e:
+                    raise KeyError(
+                        f"Encountered category_id={annotation_category_id} "
+                        "but this id does not exist in 'categories' of the json file."
+                    ) from e
+            objs.append(obj)
+        record["annotations"] = objs
+        dataset_dicts[image_id] = record
+
+    if num_instances_without_valid_segmentation > 0:
+        logger.warning(
+            "Filtered out {} instances without valid segmentation. ".format(
+                num_instances_without_valid_segmentation
+            )
+            + "There might be issues in your dataset generation process.  Please "
+            "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully"
+        )
+    return dataset_dicts
+
+def get_metadata():
+    meta = {}
+    # The following metadata maps contiguous id from [0, #thing categories +
+    # #stuff categories) to their names and colors. We have to replica of the
+    # same name and color under "thing_*" and "stuff_*" because the current
+    # visualization function in D2 handles thing and class classes differently
+    # due to some heuristic used in Panoptic FPN. We keep the same naming to
+    # enable reusing existing visualization functions.
+    thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]
+    thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]
+    stuff_classes = [k["name"] for k in COCO_CATEGORIES]
+    stuff_colors = [k["color"] for k in COCO_CATEGORIES]
+
+    meta["thing_classes"] = thing_classes
+    meta["thing_colors"] = thing_colors
+    meta["stuff_classes"] = stuff_classes
+    meta["stuff_colors"] = stuff_colors
+
+    # Convert category id for training:
+    #   category id: like semantic segmentation, it is the class id for each
+    #   pixel. Since there are some classes not used in evaluation, the category
+    #   id is not always contiguous and thus we have two set of category ids:
+    #       - original category id: category id in the original dataset, mainly
+    #           used for evaluation.
+    #       - contiguous category id: [0, #classes), in order to train the linear
+    #           softmax classifier.
+    thing_dataset_id_to_contiguous_id = {}
+    stuff_dataset_id_to_contiguous_id = {}
+
+    for i, cat in enumerate(COCO_CATEGORIES):
+        if cat["isthing"]:
+            thing_dataset_id_to_contiguous_id[cat["id"]] = i
+        # else:
+        #     stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+        # in order to use sem_seg evaluator
+        stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+    meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+    meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+    return meta
+
+
+def load_coco_panoptic_json(json_file, instances_json, instances_name, image_dir, gt_dir, semseg_dir, meta):
+    """
+    Args:
+        image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
+        gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
+        json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
+    Returns:
+        list[dict]: a list of dicts in Detectron2 standard format. (See
+        `Using Custom Datasets </tutorials/datasets.html>`_ )
+    """
+
+    def _convert_category_id(segment_info, meta):
+        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = True
+        else:
+            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = False
+        return segment_info
+
+    with PathManager.open(json_file) as f:
+        json_info = json.load(f)
+    
+    instance_data_dicts = load_coco_instance_json(instances_json, image_dir.replace("panoptic_", ""), instances_name)
+    
+    ret = []
+    for ann in json_info["annotations"]:
+        image_id = int(ann["image_id"])
+        # TODO: currently we assume image and label has the same filename but
+        # different extension, and images have extension ".jpg" for COCO. Need
+        # to make image extension a user-provided argument if we extend this
+        # function to support other COCO-like datasets.
+        image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
+        label_file = os.path.join(gt_dir, ann["file_name"])
+        sem_label_file = os.path.join(semseg_dir, ann["file_name"])
+        segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
+        ret.append(
+            {
+                "file_name": image_file,
+                "image_id": image_id,
+                "pan_seg_file_name": label_file,
+                "sem_seg_file_name": sem_label_file,
+                "segments_info": segments_info,
+                "annotations": instance_data_dicts[image_id]["annotations"],
+            }
+        )
+    assert len(ret), f"No images found in {image_dir}!"
+    assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
+    assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
+    assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
+    return ret
+
+
+def register_coco_panoptic_annos_sem_seg(
+    name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json, instances_name,
+):
+    panoptic_name = name
+    delattr(MetadataCatalog.get(panoptic_name), "thing_classes")
+    delattr(MetadataCatalog.get(panoptic_name), "thing_colors")
+    MetadataCatalog.get(panoptic_name).set(
+        thing_classes=metadata["thing_classes"],
+        thing_colors=metadata["thing_colors"],
+        # thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"],
+    )
+
+    # the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg"
+    semantic_name = name + "_with_sem_seg"
+    DatasetCatalog.register(
+        semantic_name,
+        lambda: load_coco_panoptic_json(panoptic_json, instances_json, instances_name, image_root, panoptic_root, sem_seg_root, metadata),
+    )
+    MetadataCatalog.get(semantic_name).set(
+        sem_seg_root=sem_seg_root,
+        panoptic_root=panoptic_root,
+        image_root=image_root,
+        panoptic_json=panoptic_json,
+        json_file=instances_json,
+        evaluator_type="coco_panoptic_seg",
+        ignore_label=255,
+        label_divisor=1000,
+        **metadata,
+    )
+
+
+def register_all_coco_panoptic_annos_sem_seg(root):
+    for (
+        prefix,
+        (panoptic_root, panoptic_json, semantic_root),
+    ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():
+
+        prefix_instances = prefix[: -len("_panoptic")]
+        instances_meta = MetadataCatalog.get(prefix_instances)
+        image_root, instances_json = instances_meta.image_root, instances_meta.json_file
+
+        if 'val' in instances_json:
+            instances_json = instances_json.replace('instances_', 'panoptic2instances_')
+
+        register_coco_panoptic_annos_sem_seg(
+            prefix,
+            get_metadata(),
+            image_root,
+            os.path.join(root, panoptic_root),
+            os.path.join(root, panoptic_json),
+            os.path.join(root, semantic_root),
+            instances_json,
+            prefix_instances,
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_coco_panoptic_annos_sem_seg(_root)

RAVE-main/annotator/oneformer/oneformer/data/tokenizer.py

@@ -0,0 +1,192 @@
+# -------------------------------------------------------------------------
+# MIT License
+#
+# Copyright (c) 2021 OpenAI
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+# Modified by Jiarui Xu
+# -------------------------------------------------------------------------
+
+import gzip
+import html
+import os
+from functools import lru_cache
+
+import ftfy
+import regex as re
+import torch
+
+
+@lru_cache()
+def default_bpe():
+    return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bpe_simple_vocab_16e6.txt.gz')
+
+@lru_cache()
+def bytes_to_unicode():
+    """Returns list of utf-8 byte and a corresponding list of unicode strings.
+
+    The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
+    if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent
+    coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables
+    between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = list(range(ord('!'), ord('~') + 1)) + list(range(ord('¡'), ord('¬') + 1)) + list(range(ord('®'), ord('ÿ') + 1))
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """Return set of symbol pairs in a word.
+
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r'\s+', ' ', text)
+    text = text.strip()
+    return text
+
+class Tokenize:
+
+    def __init__(self, tokenizer, max_seq_len=77, truncate=True):
+        self.tokenizer = tokenizer
+        self.max_seq_len = max_seq_len
+        self.truncate = truncate
+
+    def __call__(self, texts):
+        expanded_dim = False
+        if isinstance(texts, str):
+            texts = [texts]
+            expanded_dim = True
+
+        sot_token = self.tokenizer.encoder['<|startoftext|>']
+        eot_token = self.tokenizer.encoder['<|endoftext|>']
+        all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]
+        result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long)
+
+        for i, tokens in enumerate(all_tokens):
+            if len(tokens) > self.max_seq_len:
+                if self.truncate:
+                    tokens = tokens[:self.max_seq_len]
+                    tokens[-1] = eot_token
+                else:
+                    raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}')
+            result[i, :len(tokens)] = torch.tensor(tokens)
+
+        if expanded_dim:
+            return result[0]
+
+        return result
+
+
+class SimpleTokenizer(object):
+
+    def __init__(self, bpe_path: str = default_bpe()):
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        merges = gzip.open(bpe_path).read().decode('utf-8').split('\n')
+        merges = merges[1:49152 - 256 - 2 + 1]
+        merges = [tuple(merge.split()) for merge in merges]
+        vocab = list(bytes_to_unicode().values())
+        vocab = vocab + [v + '</w>' for v in vocab]
+        for merge in merges:
+            vocab.append(''.join(merge))
+        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
+        self.encoder = dict(zip(vocab, range(len(vocab))))
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
+        self.pat = re.compile(
+            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
+            re.IGNORECASE)
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token[:-1]) + (token[-1] + '</w>', )
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token + '</w>'
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except:  # noqa: E722
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = ' '.join(word)
+        self.cache[token] = word
+        return word
+
+    def encode(self, text):
+        bpe_tokens = []
+        text = whitespace_clean(basic_clean(text)).lower()
+        for token in re.findall(self.pat, text):
+            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
+            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
+        return bpe_tokens
+
+    def decode(self, tokens):
+        text = ''.join([self.decoder[token] for token in tokens])
+        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace').replace('</w>', ' ')
+        return text

RAVE-main/annotator/oneformer/oneformer/demo/colormap.py

@@ -0,0 +1,170 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+"""
+An awesome colormap for really neat visualizations.
+Copied from Detectron, and removed gray colors.
+"""
+
+import numpy as np
+import random
+random.seed(0)
+
+__all__ = ["colormap", "random_color", "random_colors"]
+
+# fmt: off
+# RGB:
+# _COLORS = np.array(
+#     [
+#         0.000, 0.447, 0.741,
+#         0.850, 0.325, 0.098,
+#         0.929, 0.694, 0.125,
+#         0.494, 0.184, 0.556,
+#         0.466, 0.674, 0.188,
+#         0.301, 0.745, 0.933,
+#         0.635, 0.078, 0.184,
+#         0.300, 0.300, 0.300,
+#         0.600, 0.600, 0.600,
+#         1.000, 0.000, 0.000,
+#         1.000, 0.500, 0.000,
+#         0.749, 0.749, 0.000,
+#         0.000, 1.000, 0.000,
+#         0.000, 0.000, 1.000,
+#         0.667, 0.000, 1.000,
+#         0.333, 0.333, 0.000,
+#         0.333, 0.667, 0.000,
+#         0.333, 1.000, 0.000,
+#         0.667, 0.333, 0.000,
+#         0.667, 0.667, 0.000,
+#         0.667, 1.000, 0.000,
+#         1.000, 0.333, 0.000,
+#         1.000, 0.667, 0.000,
+#         1.000, 1.000, 0.000,
+#         0.000, 0.333, 0.500,
+#         0.000, 0.667, 0.500,
+#         0.000, 1.000, 0.500,
+#         0.333, 0.000, 0.500,
+#         0.333, 0.333, 0.500,
+#         0.333, 0.667, 0.500,
+#         0.333, 1.000, 0.500,
+#         0.667, 0.000, 0.500,
+#         0.667, 0.333, 0.500,
+#         0.667, 0.667, 0.500,
+#         0.667, 1.000, 0.500,
+#         1.000, 0.000, 0.500,
+#         1.000, 0.333, 0.500,
+#         1.000, 0.667, 0.500,
+#         1.000, 1.000, 0.500,
+#         0.000, 0.333, 1.000,
+#         0.000, 0.667, 1.000,
+#         0.000, 1.000, 1.000,
+#         0.333, 0.000, 1.000,
+#         0.333, 0.333, 1.000,
+#         0.333, 0.667, 1.000,
+#         0.333, 1.000, 1.000,
+#         0.667, 0.000, 1.000,
+#         0.667, 0.333, 1.000,
+#         0.667, 0.667, 1.000,
+#         0.667, 1.000, 1.000,
+#         1.000, 0.000, 1.000,
+#         1.000, 0.333, 1.000,
+#         1.000, 0.667, 1.000,
+#         0.333, 0.000, 0.000,
+#         0.500, 0.000, 0.000,
+#         0.667, 0.000, 0.000,
+#         0.833, 0.000, 0.000,
+#         1.000, 0.000, 0.000,
+#         0.000, 0.167, 0.000,
+#         0.000, 0.333, 0.000,
+#         0.000, 0.500, 0.000,
+#         0.000, 0.667, 0.000,
+#         0.000, 0.833, 0.000,
+#         0.000, 1.000, 0.000,
+#         0.000, 0.000, 0.167,
+#         0.000, 0.000, 0.333,
+#         0.000, 0.000, 0.500,
+#         0.000, 0.000, 0.667,
+#         0.000, 0.000, 0.833,
+#         0.000, 0.000, 1.000,
+#         0.000, 0.000, 0.000,
+#         0.143, 0.143, 0.143,
+#         0.857, 0.857, 0.857,
+#         1.000, 1.000, 1.000
+#     ]
+# ).astype(np.float32).reshape(-1, 3)
+# fmt: on
+
+_COLORS = []
+
+
+def gen_color():
+    color = tuple(np.round(np.random.choice(range(256), size=3)/255, 3))
+    if color not in _COLORS and np.mean(color) != 0.0:
+        _COLORS.append(color)
+    else:
+        gen_color()
+
+
+for _ in range(300):
+    gen_color()
+
+
+def colormap(rgb=False, maximum=255):
+    """
+    Args:
+        rgb (bool): whether to return RGB colors or BGR colors.
+        maximum (int): either 255 or 1
+    Returns:
+        ndarray: a float32 array of Nx3 colors, in range [0, 255] or [0, 1]
+    """
+    assert maximum in [255, 1], maximum
+    c = _COLORS * maximum
+    if not rgb:
+        c = c[:, ::-1]
+    return c
+
+
+def random_color(rgb=False, maximum=255):
+    """
+    Args:
+        rgb (bool): whether to return RGB colors or BGR colors.
+        maximum (int): either 255 or 1
+    Returns:
+        ndarray: a vector of 3 numbers
+    """
+    idx = np.random.randint(0, len(_COLORS))
+    ret = _COLORS[idx] * maximum
+    if not rgb:
+        ret = ret[::-1]
+    return ret
+
+
+def random_colors(N, rgb=False, maximum=255):
+    """
+    Args:
+        N (int): number of unique colors needed
+        rgb (bool): whether to return RGB colors or BGR colors.
+        maximum (int): either 255 or 1
+    Returns:
+        ndarray: a list of random_color
+    """
+    indices = random.sample(range(len(_COLORS)), N)
+    ret = [_COLORS[i] * maximum for i in indices]
+    if not rgb:
+        ret = [x[::-1] for x in ret]
+    return ret
+
+
+if __name__ == "__main__":
+    import cv2
+
+    size = 100
+    H, W = 10, 10
+    canvas = np.random.rand(H * size, W * size, 3).astype("float32")
+    for h in range(H):
+        for w in range(W):
+            idx = h * W + w
+            if idx >= len(_COLORS):
+                break
+            canvas[h * size : (h + 1) * size, w * size : (w + 1) * size] = _COLORS[idx]
+    cv2.imshow("a", canvas)
+    cv2.waitKey(0)