commit

configs/tusimple.py

@@ -0,0 +1,93 @@
+net = dict(
+    type='RESANet',
+)
+
+backbone = dict(
+    type='ResNetWrapper',
+    resnet='resnet34',
+    pretrained=True,
+    replace_stride_with_dilation=[False, True, True],
+    out_conv=True,
+    fea_stride=8,
+)
+
+resa = dict(
+    type='RESA',
+    alpha=2.0,
+    iter=5,
+    input_channel=128,
+    conv_stride=9,
+)
+
+decoder = 'BUSD'        
+
+trainer = dict(
+    type='RESA'
+)
+
+evaluator = dict(
+    type='Tusimple',
+    thresh = 0.60
+)
+
+optimizer = dict(
+  type='sgd',
+  lr=0.020,
+  weight_decay=1e-4,
+  momentum=0.9
+)
+
+total_iter = 80000
+import math
+scheduler = dict(
+    type = 'LambdaLR',
+    lr_lambda = lambda _iter : math.pow(1 - _iter/total_iter, 0.9)
+)
+
+bg_weight = 0.4
+
+img_norm = dict(
+    mean=[103.939, 116.779, 123.68],
+    std=[1., 1., 1.]
+)
+
+img_height = 368
+img_width = 640
+cut_height = 160
+seg_label = "seg_label"
+
+dataset_path = '/data/workspace/myshixun/step4/dataset/tusimple'
+test_json_file = '/data/workspace/myshixun/step4/dataset/tusimple/test_label.json'
+
+dataset = dict(
+    train=dict(
+        type='TuSimple',
+        img_path=dataset_path,
+        data_list='train_val_gt.txt',
+    ),
+    val=dict(
+        type='TuSimple',
+        img_path=dataset_path,
+        data_list='predict_val.txt'
+    ),
+    test=dict(
+        type='TuSimple',
+        img_path=dataset_path,
+        data_list='test_gt.txt'
+    )
+)
+
+
+loss_type = 'cross_entropy'
+seg_loss_weight = 1.0
+
+
+batch_size = 4
+workers = 0
+num_classes = 6 + 1
+ignore_label = 255
+epochs = 2
+log_interval = 100
+eval_ep = 1
+save_ep = epochs
+log_note = ''