update synthia pipeline

configs/_base_/datasets/uda_synthiaSeq_CSSeq.py

@@ -1,16 +1,25 @@
 # dataset settings
+FRAME_OFFSET = -1
 dataset_type = 'SynthiaSeqDataset'
 synthia_data_root = '/srv/share4/datasets/SynthiaSeq/SYNTHIA-SEQS-04-DAWN'
 cs_data_root = '/coc/testnvme/datasets/VideoDA/cityscapes-seq'
-cs_train_flow_dir = '/srv/share4/datasets/cityscapes-seq_Flow/flow/forward/train'
 
 synthia_train_flow_dir = '/srv/share4/datasets/SynthiaSeq_Flow/frame_dist_1/forward/train/RGB/Stereo_Left/Omni_F'
-cs_val_flow_dir = '/srv/share4/datasets/cityscapes-seq_Flow/flow/forward/val'
+
+#backward flow 
+cs_train_flow_dir = "/coc/testnvme/datasets/VideoDA/cityscapes-seq_Flow/flow_test_bed/frame_dist_1/backward/train"
+cs_val_flow_dir = "/coc/testnvme/datasets/VideoDA/cityscapes-seq_Flow/flow_test_bed/frame_dist_1/backward/val"
+
+#forward flow
+cs_train_flow_dir = "/coc/testnvme/datasets/VideoDA/cityscapes-seq_Flow/flow/forward/train"
+cs_val_flow_dir = "/coc/testnvme/datasets/VideoDA/cityscapes-seq_Flow/flow/forward/val"
 
 img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 
 crop_size = (1024, 1024)
+ignore_index = [5, 3, 16, 12, 201, 255] #need to edit
+
 
 synthia_train_pipeline = {
     "im_load_pipeline": [
@@ -21,10 +30,10 @@
         dict(type='LoadImageFromFile'),
     ],
     "load_flow_pipeline": [
-        dict(type='LoadFlowFromFileStub'),
+        dict(type='LoadFlowFromFile'),
     ],
     "shared_pipeline": [
-        dict(type='Resize', img_scale=(2560, 1520)), 
+        dict(type='Resize', img_scale=(2560, 1520)),
         dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
         dict(type='RandomFlip', prob=0.5),
     ],
@@ -43,18 +52,6 @@
     ]
 }
 
-# cityscapes_train_pipeline = [
-#     dict(type='LoadImageFromFile'),
-#     dict(type='LoadAnnotations'),
-#     dict(type='Resize', img_scale=(2048, 1024)),
-#     dict(type='RandomCrop', crop_size=crop_size),
-#     dict(type='RandomFlip', prob=0.5),
-#     # dict(type='PhotoMetricDistortion'),  # is applied later in dacs.py
-#     dict(type='Normalize', **img_norm_cfg),
-#     dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-#     dict(type='DefaultFormatBundle'),
-#     dict(type='Collect', keys=['img', 'gt_semantic_seg']),
-# ]
 
 cityscapes_train_pipeline = {
     "im_load_pipeline": [
@@ -87,24 +84,6 @@
     ]
 }
 
-# test_pipeline = [
-#     dict(type='LoadImageFromFile'),
-#     dict(
-#         type='MultiScaleFlipAug',
-#         img_scale=(2048, 1024),
-#         # MultiScaleFlipAug is disabled by not providing img_ratios and
-#         # setting flip=False
-#         # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
-#         flip=False,
-#         transforms=[
-#             dict(type='Resize', keep_ratio=True),
-#             dict(type='RandomFlip'),
-#             dict(type='Normalize', **img_norm_cfg),
-#             dict(type='ImageToTensor', keys=['img']),
-#             dict(type='Collect', keys=['img']),
-#         ])
-# ]
-
 test_pipeline = {
     "im_load_pipeline": [
         dict(type='LoadImageFromFile'),
@@ -136,10 +115,7 @@
     ]
 }
 
-
 data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=4,
     train=dict(
         type='UDADataset',
         source=dict(
@@ -159,8 +135,9 @@
             ann_dir='gtFine/train',
             split='splits/train.txt',
             pipeline=cityscapes_train_pipeline,
-            frame_offset=1,
+            frame_offset=FRAME_OFFSET,
             flow_dir=cs_train_flow_dir,
+            ignore_index=ignore_index
         )
     ),
     val=dict(
@@ -170,8 +147,9 @@
         ann_dir='gtFine/val',
         split='splits/val.txt',
         pipeline=test_pipeline,
-        frame_offset=1,
-        flow_dir=cs_val_flow_dir
+        frame_offset=FRAME_OFFSET,
+        flow_dir=cs_val_flow_dir,
+        ignore_index=ignore_index
     ),
     test=dict(
         type='CityscapesSeqDataset',
@@ -180,7 +158,8 @@
         ann_dir='gtFine/val',
         split='splits/val.txt',
         pipeline=test_pipeline,
-        frame_offset=1,
-        flow_dir=cs_val_flow_dir
+        frame_offset=FRAME_OFFSET,
+        flow_dir=cs_val_flow_dir,
+        ignore_index=ignore_index
     )
 )

configs/mic/synthiaSeqHR2csHR_mic_hrda.py

@@ -65,7 +65,7 @@
         target=dict(crop_pseudo_margins=[30, 240, 30, 30]),
     ),
     # Use one separate thread/worker for data loading.
-    workers_per_gpu=2,
+    workers_per_gpu=3,
     # Batch size
     samples_per_gpu=2,
 )
@@ -83,29 +83,57 @@
     mask_lambda=1,
     # Use random patch masking with a patch size of 64x64
     # and a mask ratio of 0.7
-    l_warp_lambda=0,
-    l_mix_lambda=1.0,
+    l_warp_lambda=1.0,
+    l_mix_lambda=0.0,
+    consis_filter=False,
+    consis_confidence_filter=False,
+    consis_confidence_thresh=0,
+    consis_confidence_per_class_thresh=False,
+    consis_filter_rare_class=False,
+    pl_fill=False,
+    bottom_pl_fill=False,
     source_only2=False,
+    oracle_mask=False,
+    warp_cutmix=False,
+    stub_training=False,
+    l_warp_begin=1500,
     mask_generator=dict(
         type='block', mask_ratio=0.7, mask_block_size=64, _delete_=True),
     debug_mode=False,
+    class_mask_warp=None,
+    class_mask_cutmix=None,
+    exclusive_warp_cutmix=False,
+    modality="rgb",
+    modality_dropout_weights=None,
+    oracle_mask_add_noise=False,
+    oracle_mask_remove_pix=False,
+    oracle_mask_noise_percent=0.0,
+    TPS_warp_pl_confidence=False,
+    TPS_warp_pl_confidence_thresh=0.0,
 )
 # Optimizer Hyperparameters
 optimizer_config = None
-optimizer = dict(
-    lr=6e-05,
-    paramwise_cfg=dict(
-        custom_keys=dict(
-            head=dict(lr_mult=10.0),
-            pos_block=dict(decay_mult=0.0),
-            norm=dict(decay_mult=0.0))))
+# optimizer = dict(
+#     lr=6e-05,
+#     paramwise_cfg=dict(
+#         custom_keys=dict(
+#             head=dict(lr_mult=10.0),
+#             pos_block=dict(decay_mult=0.0),
+#             norm=dict(decay_mult=0.0))))
 n_gpus = None
 launcher = "slurm" #"slurm"
 gpu_model = 'A40'
 runner = dict(type='IterBasedRunner', max_iters=40000)
 # Logging Configuration
-checkpoint_config = dict(by_epoch=False, interval=4000, max_keep_ckpts=8)
-evaluation = dict(interval=2000, metric='mIoU', metrics=["mIoU", "pred_pred", "gt_pred", "M5", "mIoU_gt_pred"])
+checkpoint_config = dict(by_epoch=False, interval=4000, max_keep_ckpts=3)
+evaluation = dict(interval=4000, eval_settings={
+    "metrics": ["mIoU", "pred_pred", "gt_pred", "M5", "M5Fixed", "mIoU_gt_pred", "consis_confidence_filter"],
+    "sub_metrics": ["mask_count"],
+    "pixelwise accuracy": True,
+    "confusion matrix": True,
+    "return_logits": True,
+    "consis_confidence_thresh": 0.95
+})
 # Meta Information for Result Analysis
 name = 'synthiaSeqHR2csHR_mic_hrda_s2_corrected'
 exp = 'basic'

configs/mic/viperHR2csHR_mic_hrda.py

@@ -129,8 +129,8 @@
 gpu_model = 'A40'
 runner = dict(type='IterBasedRunner', max_iters=15000)
 # Logging Configuration
-checkpoint_config = dict(by_epoch=False, interval=3000, max_keep_ckpts=2)
-evaluation = dict(interval=3000, eval_settings={
+checkpoint_config = dict(by_epoch=False, interval=4000, max_keep_ckpts=3)
+evaluation = dict(interval=4000, eval_settings={
     "metrics": ["mIoU", "pred_pred", "gt_pred", "M5", "M5Fixed", "mIoU_gt_pred", "consis_confidence_filter"],
     "sub_metrics": ["mask_count"],
     "pixelwise accuracy": True,

mmseg/datasets/SynthiaSeq.py

@@ -16,7 +16,7 @@ class SynthiaSeqDataset(SeqUtils, SynthiaDataset):
     """Synthia Seq dataset with options for loading flow and neightboring frames.
     """
 
-    def __init__(self, split, img_suffix='.png', seg_map_suffix='_labelTrainIds_updated.png', frame_offset=1, flow_dir=None, **kwargs):
+    def __init__(self, split, img_suffix='.png', seg_map_suffix='_labelTrainIds_updated.png', frame_offset=1, flow_dir=None, data_type="rgb", **kwargs):
         SynthiaDataset.__init__(
             self, #must explicitly pass self
             split=split,
@@ -28,13 +28,13 @@ def __init__(self, split, img_suffix='.png', seg_map_suffix='_labelTrainIds_upda
 
         self.flow_dir = flow_dir
         self.fut_images = self.load_annotations_seq(self.img_dir, self.img_suffix, self.ann_dir, self.seg_map_suffix, self.split, frame_offset=-1)    #forward flow
-        # self.fut_images = self.load_annotations_seq(self.img_dir, self.img_suffix, self.ann_dir, self.seg_map_suffix, self.split, frame_offset=1)   #backward flow
         self.img_infos = self.load_annotations_seq(self.img_dir, self.img_suffix, self.ann_dir, self.seg_map_suffix, self.split, frame_offset=0)
         self.flows = None if self.flow_dir == None else self.load_annotations_seq(self.img_dir, ".png", self.ann_dir, self.seg_map_suffix, self.split, frame_offset=0)
 
+        self.data_type = data_type
+
 
         self.unpack_list = "train" in split
 
 
-        self.palette_to_id = [(k, i) for i, k in enumerate(self.PALETTE)]
 

tools/exps/tps/synthia/tps_hrda_synthia.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+#SBATCH --job-name=$1
+#SBATCH --output=$1.out
+#SBATCH --error=$1.err
+#SBATCH --gres=gpu:$2
+#SBATCH --ntasks=$2
+#SBATCH --ntasks-per-node=$2
+#SBATCH --cpus-per-task=15
+#SBATCH --constraint="a40"
+#SBATCH --partition=short
+#SBATCH --requeue
+#SBATCH --open-mode=append
+#SBATCH --exclude="ig-88,perseverance,cheetah,claptrap"
+
+export PYTHONUNBUFFERED=TRUE
+export MASTER_PORT=$P
+source ~/.bashrc
+conda activate openmmlab
+cd /coc/scratch/vvijaykumar6/mmseg
+
+set -x
+
+#change begin to 1500
+srun -u python -u ./tools/train.py configs/mic/synthiaSeqHR2csHR_mic_hrda.py --launcher="slurm" --l-warp-lambda=1 --l-mix-lambda=0 --l-warp-begin=0 --bottom-pl-fill True --no-masking True --TPS-warp-pl-confidence True --TPS-warp-pl-confidence-thresh 0.0 --lr 6e-5 --total-iters=40000 --seed 604 --deterministic --work-dir="./work_dirs/synthia_cs/tps_exp/$1$T" --nowandb True
+    #    --wandbid $1$T