update configs to latest

fcakyon · Mar 19, 2022 · 9477519 · 9477519
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diff --git a/mmdet_configs/visdrone_fcos/fcos_crop_480_960_cls_60.py b/mmdet_configs/visdrone_fcos/fcos_crop_480_960_cls_60.py
@@ -0,0 +1,115 @@
+_base_ = ["../fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py"]
+
+TAGS = ["fcos", "crop=480_960", "24epochs", "num_cls=60", "repeat=5"]
+EXP_NAME = "fcos_crop_480_960_cls_60"
+DATA_ROOT = "data/visdrone2019/"
+BATCH_MULTIPLIER = 16
+LR_MULTIPLIER = 1
+EVAL_INTERVAL = 3
+NUM_CLASSES = 10
+CLASSES = ("pedestrian", "people", "bicycle", "car", "van", "truck", "tricycle", "awning-tricycle", "bus", "motor")
+
+# model settings
+model = dict(
+    bbox_head=dict(
+        num_classes=NUM_CLASSES,
+    ),
+)
+
+# dataset settings
+img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type="LoadImageFromFile"),
+    dict(type="LoadAnnotations", with_bbox=True),
+    dict(
+        type="AutoAugment",
+        policies=[
+            [
+                dict(type="RandomCrop", crop_type="absolute_range", crop_size=(480, 960), allow_negative_crop=True),
+                dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
+            ],
+            [
+                dict(type="RandomCrop", crop_type="absolute_range", crop_size=(480, 960), allow_negative_crop=True),
+                dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
+            ],
+            [
+                dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
+            ],
+        ],
+    ),
+    dict(type="RandomFlip", flip_ratio=0.5),
+    dict(type="Normalize", **img_norm_cfg),
+    dict(type="Pad", size_divisor=32),
+    dict(type="DefaultFormatBundle"),
+    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
+]
+test_pipeline = [
+    dict(type="LoadImageFromFile"),
+    dict(
+        type="MultiScaleFlipAug",
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type="Resize", keep_ratio=True),
+            dict(type="RandomFlip"),
+            dict(type="Normalize", **img_norm_cfg),
+            dict(type="Pad", size_divisor=32),
+            dict(type="ImageToTensor", keys=["img"]),
+            dict(type="Collect", keys=["img"]),
+        ],
+    ),
+]
+
+data = dict(
+    samples_per_gpu=2 * BATCH_MULTIPLIER,
+    workers_per_gpu=2,
+    train=dict(
+        type="RepeatDataset",
+        times=5,
+        dataset=dict(
+            type="CocoDataset",
+            classes=CLASSES,
+            ann_file=DATA_ROOT + "coco/train.json",
+            img_prefix=DATA_ROOT + "VisDrone2019-DET-train/",
+            pipeline=train_pipeline,
+        ),
+    ),
+    val=dict(
+        classes=CLASSES,
+        ann_file=DATA_ROOT + "sliced/val_640_0.json",
+        img_prefix=DATA_ROOT + "sliced/val_images_640_0/",
+        pipeline=test_pipeline,
+    ),
+    test=dict(
+        classes=CLASSES,
+        ann_file=DATA_ROOT + "sliced/val_640_0.json",
+        img_prefix=DATA_ROOT + "sliced/val_images_640_0/",
+        pipeline=test_pipeline,
+    ),
+)
+
+# optimizer
+# default 8 gpu
+# /8 for 1 gpu
+optimizer = dict(
+    lr=0.01 / 8 * BATCH_MULTIPLIER * LR_MULTIPLIER, paramwise_cfg=dict(bias_lr_mult=2.0, bias_decay_mult=0.0)
+)
+
+checkpoint_config = dict(interval=1, max_keep_ckpts=1, save_optimizer=False)
+evaluation = dict(interval=EVAL_INTERVAL, metric="bbox", save_best="auto")
+
+# learning policy
+lr_config = dict(policy="step", warmup="constant", warmup_iters=500, warmup_ratio=1.0 / 3, step=[16, 22])
+runner = dict(type="EpochBasedRunner", max_epochs=24)
+
+# logger settings
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type="TextLoggerHook"),
+        dict(type="TensorboardLoggerHook", reset_flag=False),
+    ],
+)
+
+load_from = "https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth"
+work_dir = f"runs/visdrone/{EXP_NAME}/"
diff --git a/...s/xview_fcos/fcos_crop_300_9999_cls_60.py → ...configs/visdrone_fcos/fcos_full_cls_60.py b/...s/xview_fcos/fcos_crop_300_9999_cls_60.py → ...configs/visdrone_fcos/fcos_full_cls_60.py
@@ -1,91 +1,26 @@
 _base_ = ["../fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py"]
 
-TAGS = ["fcos", "crop=300_9999", "24epochs", "num_cls=60"]
-EXP_NAME = "fcos_crop_300_9999_cls_60"
-DATA_ROOT = "data/xview/"
+TAGS = ["fcos", "crop=False", "24epochs", "num_cls=60", "repeat=5"]
+EXP_NAME = "fcos_full_cls_60"
+DATA_ROOT = "data/visdrone2019/"
 BATCH_MULTIPLIER = 16
 LR_MULTIPLIER = 1
-MAX_DET_PER_IMAGE = 100
 EVAL_INTERVAL = 3
-NUM_CLASSES = 60
-CLASSES = (
-    "Fixed-wing Aircraft",
-    "Small Aircraft",
-    "Cargo Plane",
-    "Helicopter",
-    "Passenger Vehicle",
-    "Small Car",
-    "Bus",
-    "Pickup Truck",
-    "Utility Truck",
-    "Truck",
-    "Cargo Truck",
-    "Truck w/Box",
-    "Truck Tractor",
-    "Trailer",
-    "Truck w/Flatbed",
-    "Truck w/Liquid",
-    "Crane Truck",
-    "Railway Vehicle",
-    "Passenger Car",
-    "Cargo Car",
-    "Flat Car",
-    "Tank car",
-    "Locomotive",
-    "Maritime Vessel",
-    "Motorboat",
-    "Sailboat",
-    "Tugboat",
-    "Barge",
-    "Fishing Vessel",
-    "Ferry",
-    "Yacht",
-    "Container Ship",
-    "Oil Tanker",
-    "Engineering Vehicle",
-    "Tower crane",
-    "Container Crane",
-    "Reach Stacker",
-    "Straddle Carrier",
-    "Mobile Crane",
-    "Dump Truck",
-    "Haul Truck",
-    "Scraper/Tractor",
-    "Front loader/Bulldozer",
-    "Excavator",
-    "Cement Mixer",
-    "Ground Grader",
-    "Hut/Tent",
-    "Shed",
-    "Building",
-    "Aircraft Hangar",
-    "Damaged Building",
-    "Facility",
-    "Construction Site",
-    "Vehicle Lot",
-    "Helipad",
-    "Storage Tank",
-    "Shipping container lot",
-    "Shipping Container",
-    "Pylon",
-    "Tower",
-)
+NUM_CLASSES = 10
+CLASSES = ("pedestrian", "people", "bicycle", "car", "van", "truck", "tricycle", "awning-tricycle", "bus", "motor")
 
 # model settings
 model = dict(
     bbox_head=dict(
         num_classes=NUM_CLASSES,
     ),
-    # testing settings
-    test_cfg=dict(max_per_img=MAX_DET_PER_IMAGE),
 )
 
 # dataset settings
 img_norm_cfg = dict(mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
 train_pipeline = [
     dict(type="LoadImageFromFile"),
     dict(type="LoadAnnotations", with_bbox=True),
-    dict(type="RandomCrop", crop_type="absolute_range", crop_size=(300, 9999), allow_negative_crop=True),
     dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
     dict(type="RandomFlip", flip_ratio=0.5),
     dict(type="Normalize", **img_norm_cfg),
@@ -115,25 +50,25 @@
     workers_per_gpu=2,
     train=dict(
         type="RepeatDataset",
-        times=30,
+        times=5,
         dataset=dict(
             type="CocoDataset",
             classes=CLASSES,
             ann_file=DATA_ROOT + "coco/train.json",
-            img_prefix=DATA_ROOT + "train_images/",
+            img_prefix=DATA_ROOT + "VisDrone2019-DET-train/",
             pipeline=train_pipeline,
         ),
     ),
     val=dict(
         classes=CLASSES,
-        ann_file=DATA_ROOT + "sliced/val_400_0.json",
-        img_prefix=DATA_ROOT + "sliced/val_images_400_0/",
+        ann_file=DATA_ROOT + "sliced/val_640_0.json",
+        img_prefix=DATA_ROOT + "sliced/val_images_640_0/",
         pipeline=test_pipeline,
     ),
     test=dict(
         classes=CLASSES,
-        ann_file=DATA_ROOT + "sliced/val_400_0.json",
-        img_prefix=DATA_ROOT + "sliced/val_images_400_0/",
+        ann_file=DATA_ROOT + "sliced/val_640_0.json",
+        img_prefix=DATA_ROOT + "sliced/val_images_640_0/",
         pipeline=test_pipeline,
     ),
 )
@@ -158,19 +93,8 @@
     hooks=[
         dict(type="TextLoggerHook"),
         dict(type="TensorboardLoggerHook", reset_flag=False),
-        dict(
-            type="WandbLoggerHook",
-            init_kwargs=dict(
-                project="xview",
-                entity="fca",
-                name=EXP_NAME,
-                tags=TAGS,
-            ),
-            log_artifact=True,
-            out_suffix=(".py"),
-        ),
     ],
 )
 
 load_from = "https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth"
-work_dir = f"runs/xview/{EXP_NAME}/"
+work_dir = f"runs/visdrone/{EXP_NAME}/"
diff --git a/mmdet_configs/visdrone_tood/tood_crop_480_960_cls_60.py b/mmdet_configs/visdrone_tood/tood_crop_480_960_cls_60.py
@@ -0,0 +1,113 @@
+_base_ = ["../tood/tood_r50_fpn_1x_coco.py"]
+
+TAGS = ["tood", "crop=480_960", "24epochs", "num_cls=60", "repeat=5"]
+EXP_NAME = "tood_crop_480_960_cls_60"
+DATA_ROOT = "data/visdrone2019/"
+BATCH_MULTIPLIER = 8
+LR_MULTIPLIER = 1
+EVAL_INTERVAL = 3
+NUM_CLASSES = 10
+CLASSES = ("pedestrian", "people", "bicycle", "car", "van", "truck", "tricycle", "awning-tricycle", "bus", "motor")
+
+# model settings
+model = dict(
+    bbox_head=dict(
+        num_classes=NUM_CLASSES,
+    ),
+)
+
+# dataset settings
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type="LoadImageFromFile"),
+    dict(type="LoadAnnotations", with_bbox=True),
+    dict(
+        type="AutoAugment",
+        policies=[
+            [
+                dict(type="RandomCrop", crop_type="absolute_range", crop_size=(480, 960), allow_negative_crop=True),
+                dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
+            ],
+            [
+                dict(type="RandomCrop", crop_type="absolute_range", crop_size=(480, 960), allow_negative_crop=True),
+                dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
+            ],
+            [
+                dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
+            ],
+        ],
+    ),
+    dict(type="RandomFlip", flip_ratio=0.5),
+    dict(type="Normalize", **img_norm_cfg),
+    dict(type="Pad", size_divisor=32),
+    dict(type="DefaultFormatBundle"),
+    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
+]
+test_pipeline = [
+    dict(type="LoadImageFromFile"),
+    dict(
+        type="MultiScaleFlipAug",
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type="Resize", keep_ratio=True),
+            dict(type="RandomFlip"),
+            dict(type="Normalize", **img_norm_cfg),
+            dict(type="Pad", size_divisor=32),
+            dict(type="ImageToTensor", keys=["img"]),
+            dict(type="Collect", keys=["img"]),
+        ],
+    ),
+]
+
+data = dict(
+    samples_per_gpu=2 * BATCH_MULTIPLIER,
+    workers_per_gpu=2,
+    train=dict(
+        type="RepeatDataset",
+        times=5,
+        dataset=dict(
+            type="CocoDataset",
+            classes=CLASSES,
+            ann_file=DATA_ROOT + "coco/train.json",
+            img_prefix=DATA_ROOT + "VisDrone2019-DET-train/",
+            pipeline=train_pipeline,
+        ),
+    ),
+    val=dict(
+        classes=CLASSES,
+        ann_file=DATA_ROOT + "sliced/val_640_0.json",
+        img_prefix=DATA_ROOT + "sliced/val_images_640_0/",
+        pipeline=test_pipeline,
+    ),
+    test=dict(
+        classes=CLASSES,
+        ann_file=DATA_ROOT + "sliced/val_640_0.json",
+        img_prefix=DATA_ROOT + "sliced/val_images_640_0/",
+        pipeline=test_pipeline,
+    ),
+)
+
+# optimizer
+# default 8 gpu
+# /8 for 1 gpu
+optimizer = dict(lr=0.01 / 8 * BATCH_MULTIPLIER * LR_MULTIPLIER, momentum=0.9, weight_decay=0.0001)
+
+checkpoint_config = dict(interval=1, max_keep_ckpts=1, save_optimizer=False)
+evaluation = dict(interval=EVAL_INTERVAL, metric="bbox", save_best="auto")
+
+# learning policy
+lr_config = dict(policy="step", warmup="linear", warmup_iters=500, warmup_ratio=0.001, step=[16, 22])
+runner = dict(type="EpochBasedRunner", max_epochs=24)
+
+# logger settings
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type="TextLoggerHook"),
+        dict(type="TensorboardLoggerHook", reset_flag=False),
+    ],
+)
+
+load_from = "https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_1x_coco/tood_r50_fpn_1x_coco_20211210_103425-20e20746.pth"
+work_dir = f"runs/visdrone/{EXP_NAME}/"