/
accel_34_cityscapes_end2end_ohem.yaml
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/
accel_34_cityscapes_end2end_ohem.yaml
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---
MXNET_VERSION: "mxnet"
output_path: "./output/dff_deeplab/cityscapes"
symbol: accel_34
gpus: '0'
SCALES:
- 1024
- 2048
default:
frequent: 100
kvstore: device
network:
pretrained: "./model/rfcn_dff_flownet_vid"
pretrained_base: "./model/pretrained/deeplab-101"
pretrained_ec: "./model/pretrained/deeplab-34"
pretrained_epoch: 0
PIXEL_MEANS:
- 103.06
- 115.90
- 123.15
IMAGE_STRIDE: 0
FIXED_PARAMS:
- conv1
- bn_conv1
- res2
- bn2
- gamma
- beta
# res-101
- res3
- bn3
- res4
- bn4
- res5
- bn5
# task
- fc6
- score
- upsampling
# res-34
- "34_"
# flow
- ReLU
- flow_conv1
- conv2
- conv3
- conv4
- conv5
- conv6
- Convolution
- deconv
- crop_deconv
- upsample_flow
- crop_upsampled
- Concat
FIXED_PARAMS_SHARED:
- conv1
- bn_conv1
- res2
- bn2
- res3
- bn3
- res4
- bn4
- gamma
- beta
dataset:
NUM_CLASSES: 19
dataset: CityScape
dataset_path: "./data/cityscapes"
image_set: leftImg8bit_train
root_path: "./data"
test_image_set: leftImg8bit_val
TRAIN:
warmup: true
warmup_lr: 0.00005
# typically we will use 4000 warmup step for single GPU
warmup_step: 1000
begin_epoch: 0
end_epoch: 53
lr: 0.0005
lr_step: '53'
model_prefix: "dff_deeplab_vid"
arg_prefix: "34_"
# whether flip image
FLIP: false
# size of images for each device
BATCH_IMAGES: 1
KEY_INTERVAL: 5
# e2e changes behavior of anchor loader and metric
END2END: true
# wheter crop image during training
ENABLE_CROP: True
# scale of cropped image during training
CROP_HEIGHT: 768
CROP_WIDTH: 1024
# whether resume training
RESUME: false
# whether shuffle image
SHUFFLE: true
TEST:
# size of images for each device
BATCH_IMAGES: 1
test_epoch: 53