add coco dataloader

Author: buptlihang

config.py

@@ -3,7 +3,7 @@
 
 from deepvac import AttrDict, new
 
-from data.dataloader import DBTrainDataset, DBTestDataset
+from data.dataloader import DBTrainDataset, DBTrainCocoDataset, DBTestDataset
 from modules.model_db import Resnet18DB, Mobilenetv3LargeDB
 from modules.loss import DBLoss
 
@@ -27,10 +27,10 @@
 #config.core.DBNetTrain.tensorboard_ip = None
 
 ## -------------------- script and quantize ------------------
-config.cast.TraceCast = AttrDict()
-config.cast.TraceCast.model_dir = "./script.pt"
-# config.cast.TraceCast.static_quantize_dir = "./script.sq"     # unsupported op nn.ConvTranspose2d for now
-config.cast.TraceCast.dynamic_quantize_dir = "./quantize.sq"
+config.cast.ScriptCast = AttrDict()
+config.cast.ScriptCast.model_dir = "./script.pt"
+# config.cast.ScriptCast.static_quantize_dir = "./script.sq"     # unsupported op nn.ConvTranspose2d for now
+# config.cast.ScriptCast.dynamic_quantize_dir = "./quantize.sq"
 
 ## -------------------- net and criterion ------------------
 config.arch = "resnet18"
@@ -49,17 +49,16 @@
 config.core.DBNetTrain.scheduler = optim.lr_scheduler.LambdaLR(config.core.DBNetTrain.optimizer, lr_lambda=lambda_lr)
 
 ## -------------------- loader ------------------
-config.sample_path = 'your train image dir'
-config.label_path = 'your train labels dir'
-config.is_transform = True
+config.sample_path = 'your train images dir'
+config.label_path = 'your train coco json path'
 config.img_size = 640
-config.datasets.DBTrainDataset = AttrDict()
-config.datasets.DBTrainDataset.shrink_ratio = 0.4
-config.datasets.DBTrainDataset.thresh_min = 0.3
-config.datasets.DBTrainDataset.thresh_max = 0.7
+config.datasets.DBTrainCocoDataset = AttrDict()
+config.datasets.DBTrainCocoDataset.shrink_ratio = 0.4
+config.datasets.DBTrainCocoDataset.thresh_min = 0.3
+config.datasets.DBTrainCocoDataset.thresh_max = 0.7
 config.core.DBNetTrain.batch_size = 8
 config.core.DBNetTrain.num_workers = 4
-config.core.DBNetTrain.train_dataset = DBTrainDataset(config, config.sample_path, config.label_path, config.is_transform, config.img_size)
+config.core.DBNetTrain.train_dataset = DBTrainCocoDataset(config, config.sample_path, config.label_path, config.img_size)
 config.core.DBNetTrain.train_loader = torch.utils.data.DataLoader(
     dataset = config.core.DBNetTrain.train_dataset,
     batch_size = config.core.DBNetTrain.batch_size,
@@ -70,11 +69,10 @@
 )
 
 ## -------------------- val ------------------
-config.sample_path = 'your val image dir'
-config.label_path = 'your val labels dir'
-config.is_transform = True
+config.sample_path = 'your val images dir'
+config.label_path = 'your val coco json path'
 config.img_size = 640
-config.core.DBNetTrain.val_dataset = DBTrainDataset(config, config.sample_path, config.label_path, config.is_transform, config.img_size)
+config.core.DBNetTrain.val_dataset = DBTrainCocoDataset(config, config.sample_path, config.label_path, config.img_size)
 config.core.DBNetTrain.val_loader = torch.utils.data.DataLoader(
     dataset = config.core.DBNetTrain.val_dataset,
     batch_size = 1,
@@ -85,10 +83,10 @@
 
 ## -------------------- test ------------------
 config.core.DBNetTest = config.core.DBNetTrain.clone()
-config.core.DBNetTest.model_path = 'your test model dir / pretrained weights'
+config.core.DBNetTest.model_path = 'output/disable_git/model__2021-06-30-04-55__acc_0__epoch_34__step_182__lr_0.00042280517.pth'
 # config.core.DBNetTest.jit_model_path = 'your torchscript model path'
 config.core.DBNetTest.is_output_polygon = True
-config.sample_path = 'your test image dir'
+config.sample_path = 'your test images path'
 config.core.DBNetTest.test_dataset = DBTestDataset(config, config.sample_path, long_size = 1280)
 config.core.DBNetTest.test_loader = torch.utils.data.DataLoader(
     dataset = config.core.DBNetTest.test_dataset,