integrated dino in codebase

DINO/datasets/__init__.py

@@ -2,7 +2,8 @@
 import torch.utils.data
 import torchvision
 
-from .coco import build as build_coco
+# from .coco import build as build_coco
+from DINO.datasets.coco import build as build_coco
 
 
 def get_coco_api_from_dataset(dataset):

DINO/datasets/coco.py

@@ -19,9 +19,9 @@
 import torchvision
 from pycocotools import mask as coco_mask
 
-from datasets.data_util import preparing_dataset
-import datasets.transforms as T
-from util.box_ops import box_cxcywh_to_xyxy, box_iou
+from DINO.datasets.data_util import preparing_dataset
+import DINO.datasets.transforms as T
+from DINO.util.box_ops import box_cxcywh_to_xyxy, box_iou
 
 __all__ = ['build']
 
@@ -390,15 +390,34 @@ def __getitem__(self, idx):
         image_id = self.ids[idx]
         target = {'image_id': image_id, 'annotations': target}
         img, target = self.prepare(img, target)
+        # print()
+        # print()
+        # print(type(img))
+        # print()
+        # print()
 
         if self._transforms is not None:
             img, target = self._transforms(img, target)
 
+        # print()
+        # print()
+        # print('type', type(img))
+        # print()
+        # print()
+        # qq
+
         # convert to needed format
         if self.aux_target_hacks is not None:
             for hack_runner in self.aux_target_hacks:
                 target, img = hack_runner(target, img=img)
 
+        # print()
+        # print()
+        # print(type(img))
+        # print()
+        # print()
+        # qqq
+
         return img, target
 
 
@@ -434,6 +453,7 @@ def __call__(self, image, target):
         anno = [obj for obj in anno if 'iscrowd' not in obj or obj['iscrowd'] == 0]
 
         boxes = [obj["bbox"] for obj in anno]
+
         # guard against no boxes via resizing
         boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)
         boxes[:, 2:] += boxes[:, :2]

DINO/datasets/coco_eval.py

@@ -16,7 +16,7 @@
 from pycocotools.coco import COCO
 import pycocotools.mask as mask_util
 
-from util.misc import all_gather
+from DINO.util.misc import all_gather
 
 
 class CocoEvaluator(object):

DINO/datasets/data_util.py

@@ -6,7 +6,7 @@
 
 import torch
 
-from util.slconfig import SLConfig
+from DINO.util.slconfig import SLConfig
 
 class Error(OSError):
     pass

DINO/datasets/panoptic_eval.py

@@ -2,7 +2,7 @@
 import json
 import os
 
-import util.misc as utils
+import DINO.util.misc as utils
 
 try:
     from panopticapi.evaluation import pq_compute

DINO/datasets/transforms.py

@@ -9,8 +9,8 @@
 import torchvision.transforms as T
 import torchvision.transforms.functional as F
 
-from util.box_ops import box_xyxy_to_cxcywh
-from util.misc import interpolate
+from DINO.util.box_ops import box_xyxy_to_cxcywh
+from DINO.util.misc import interpolate
 
 
 def crop(image, target, region):

DINO/engine.py

@@ -8,13 +8,13 @@
 import sys
 from typing import Iterable
 
-from util.utils import slprint, to_device
+from DINO.util.utils import slprint, to_device
 
 import torch
 
-import util.misc as utils
-from datasets.coco_eval import CocoEvaluator
-from datasets.panoptic_eval import PanopticEvaluator
+import DINO.util.misc as utils
+from DINO.datasets.coco_eval import CocoEvaluator
+from DINO.datasets.panoptic_eval import PanopticEvaluator
 
 
 def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,

DINO/main.py

@@ -10,25 +10,25 @@
 from pathlib import Path
 import os, sys
 from typing import Optional
-from util.get_param_dicts import get_param_dict
+from DINO.util.get_param_dicts import get_param_dict
 
 
 
 
-from util.logger import setup_logger
+from DINO.util.logger import setup_logger
 
 import numpy as np
 import torch
 from torch.utils.data import DataLoader, DistributedSampler
 import torch.distributed as dist
 
-import datasets
-import util.misc as utils
-from datasets import build_dataset, get_coco_api_from_dataset
-from engine import evaluate, train_one_epoch, test
-import models
-from util.slconfig import DictAction, SLConfig
-from util.utils import ModelEma, BestMetricHolder
+import DINO.datasets
+import DINO.util.misc as utils
+from DINO.datasets import build_dataset, get_coco_api_from_dataset
+from DINO.engine import evaluate, train_one_epoch, test
+import DINO.models
+from DINO.util.slconfig import DictAction, SLConfig
+from DINO.util.utils import ModelEma, BestMetricHolder
 
 
 def get_args_parser():
@@ -86,7 +86,7 @@ def get_args_parser():
 
 def build_model_main(args):
     # we use register to maintain models from catdet6 on.
-    from models.registry import MODULE_BUILD_FUNCS
+    from DINO.models.registry import MODULE_BUILD_FUNCS
     assert args.modelname in MODULE_BUILD_FUNCS._module_dict
     build_func = MODULE_BUILD_FUNCS.get(args.modelname)
     model, criterion, postprocessors = build_func(args)

DINO/models/dino/backbone.py

@@ -25,7 +25,7 @@
 from typing import Dict, List
 
 
-from util.misc import NestedTensor, clean_state_dict, is_main_process
+from DINO.util.misc import NestedTensor, clean_state_dict, is_main_process
 
 from .position_encoding import build_position_encoding
 from .convnext import build_convnext

DINO/models/dino/convnext.py

@@ -12,7 +12,7 @@
 import torch.nn.functional as F
 from timm.models.layers import trunc_normal_, DropPath
 
-from util.misc import NestedTensor
+from DINO.util.misc import NestedTensor
 # from timm.models.registry import register_model
 
 class Block(nn.Module):

DINO/models/dino/deformable_transformer.py

@@ -15,7 +15,7 @@
 import copy
 from typing import Optional
 
-from util.misc import inverse_sigmoid
+from DINO.util.misc import inverse_sigmoid
 
 import torch
 from torch import nn, Tensor

DINO/models/dino/dino.py

@@ -21,8 +21,8 @@
 from torch import nn
 from torchvision.ops.boxes import nms
 
-from util import box_ops
-from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
+from DINO.util import box_ops
+from DINO.util.misc import (NestedTensor, nested_tensor_from_tensor_list,
                        accuracy, get_world_size, interpolate,
                        is_dist_avail_and_initialized, inverse_sigmoid)
 

DINO/models/dino/dn_components.py

@@ -9,11 +9,11 @@
 
 
 import torch
-from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
+from DINO.util.misc import (NestedTensor, nested_tensor_from_tensor_list,
                        accuracy, get_world_size, interpolate,
                        is_dist_avail_and_initialized, inverse_sigmoid)
 # from .DABDETR import sigmoid_focal_loss
-from util import box_ops
+from DINO.util import box_ops
 import torch.nn.functional as F
 
 

DINO/models/dino/matcher.py

@@ -19,7 +19,7 @@
 from scipy.optimize import linear_sum_assignment
 from torch import nn
 
-from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
+from DINO.util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
 
 
 class HungarianMatcher(nn.Module):

DINO/models/dino/position_encoding.py

@@ -18,7 +18,7 @@
 import torch
 from torch import nn
 
-from util.misc import NestedTensor
+from DINO.util.misc import NestedTensor
 
 
 class PositionEmbeddingSine(nn.Module):

DINO/models/dino/segmentation.py

@@ -24,8 +24,8 @@
 from torch import Tensor
 from PIL import Image
 
-import util.box_ops as box_ops
-from util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
+import DINO.util.box_ops as box_ops
+from DINO.util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
 
 try:
     from panopticapi.utils import id2rgb, rgb2id

DINO/models/dino/swin_transformer.py

@@ -12,7 +12,7 @@
 import torch.utils.checkpoint as checkpoint
 import numpy as np
 from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from util.misc import NestedTensor
+from DINO.util.misc import NestedTensor
 
 
 class Mlp(nn.Module):

DINO/util/utils.py

@@ -202,7 +202,7 @@ def inverse_sigmoid(x, eps=1e-5):
     return torch.log(x1/x2)
 
 import argparse
-from util.slconfig import SLConfig
+from DINO.util.slconfig import SLConfig
 def get_raw_dict(args):
     """
     return the dicf contained in args.