Create ocnet_w18_cityscapes.yaml

configs/ocnet_w18_cityscapes.yaml

+EVAL_CROP_SIZE: (2048, 1024) # (width, height), for unpadding rangescaling and stepscaling
+TRAIN_CROP_SIZE: (1024, 512) # (width, height), for unpadding rangescaling and stepscaling
+AUG:
+#    AUG_METHOD: "unpadding" # choice unpadding rangescaling and stepscaling
+    AUG_METHOD: "stepscaling" # choice unpadding rangescaling and stepscaling
+    FIX_RESIZE_SIZE: (1024, 512) # (width, height), for unpadding
+    INF_RESIZE_VALUE: 500  # for rangescaling
+    MAX_RESIZE_VALUE: 600  # for rangescaling
+    MIN_RESIZE_VALUE: 400  # for rangescaling
+    MAX_SCALE_FACTOR: 2.0  # for stepscaling
+    MIN_SCALE_FACTOR: 0.5  # for stepscaling
+    SCALE_STEP_SIZE: 0.25  # for stepscaling
+    MIRROR: True
+BATCH_SIZE: 4
+#BATCH_SIZE: 4
+DATASET:
+    DATA_DIR: "/home/ssd1/wenzongzheng/wenzongzheng/cityscapes/"
+    IMAGE_TYPE: "rgb"  # choice rgb or rgba
+    NUM_CLASSES: 19
+    TEST_FILE_LIST: "/home/ssd1/wenzongzheng/wenzongzheng/cityscapes/val.list"
+    TRAIN_FILE_LIST: "/home/ssd1/wenzongzheng/wenzongzheng/cityscapes/train.list"
+    VAL_FILE_LIST: "/home/ssd1/wenzongzheng/wenzongzheng/cityscapes/val.list"
+    VIS_FILE_LIST: "/home/ssd1/wenzongzheng/wenzongzheng/cityscapes/val.list"
+    IGNORE_INDEX: 255
+    SEPARATOR: " "
+FREEZE:
+    MODEL_FILENAME: "model"
+    PARAMS_FILENAME: "params"
+MODEL:
+    MODEL_NAME: "ocnet"
+    DEFAULT_NORM_TYPE: "bn"
+    HRNET:
+        STAGE2:
+            NUM_CHANNELS: [18, 36]
+        STAGE3:
+            NUM_CHANNELS: [18, 36, 72]
+        STAGE4:
+            NUM_CHANNELS: [18, 36, 72, 144]
+    OCR:
+        OCR_MID_CHANNELS: 512
+        OCR_KEY_CHANNELS: 256
+    MULTI_LOSS_WEIGHT: [1.0, 1.0]
+TRAIN:
+    PRETRAINED_MODEL_DIR: u"./pretrained_model/ocnet_w18_cityscape/best_model"
+    MODEL_SAVE_DIR: "output/ocnet_w18_bn_cityscapes"
+    SNAPSHOT_EPOCH: 1
+    SYNC_BATCH_NORM: True
+TEST:
+    TEST_MODEL: "output/ocnet_w18_bn_cityscapes/first"
+SOLVER:
+    LR: 0.01
+    LR_POLICY: "poly"
+    OPTIMIZER: "sgd"
+    NUM_EPOCHS: 500
+#    LOSS: ["lovasz_softmax_loss","softmax_loss"]
+#    LOSS_WEIGHT:
+#        LOVASZ_SOFTMAX_LOSS: 0.2
+#        SOFTMAX_LOSS: 0.8