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MODEL: #MODEL field
framework: "Recognizer2D" #Mandatory, indicate the type of network, associate to the 'paddlevideo/modeling/framework/' .
backbone: #Mandatory, indicate the type of backbone, associate to the 'paddlevideo/modeling/backbones/' .
name: "ResNetTweaksTSM" #Mandatory, The name of backbone.
pretrained: "data/ResNet101_vd_ssld_pretrained.pdparams" #Optional, pretrained model path.
depth: 101 #Optional, the depth of backbone architecture.
head:
name: "ppTSMHead" #Mandatory, indicate the type of head, associate to the 'paddlevideo/modeling/heads'
num_classes: 400 #Optional, the number of classes to be classified.
in_channels: 2048 #input channel of the extracted feature.
drop_ratio: 0.5 #the ratio of dropout
std: 0.01 #std value in params initialization
ls_eps: 0.1
DATASET: #DATASET field
batch_size: 16 #Mandatory, bacth size
num_workers: 4 #Mandatory, XXX the number of subprocess on each GPU.
test_batch_size: 1 #Mandatory, test bacth size
train:
format: "FrameDataset" #Mandatory, indicate the type of dataset, associate to the 'paddlevidel/loader/dateset'
data_prefix: "" #Mandatory, train data root path
file_path: "data/k400_frames/train.list" #Mandatory, train data index file path
suffix: 'img_{:05}.jpg'
valid:
format: "FrameDataset" #Mandatory, indicate the type of dataset, associate to the 'paddlevidel/loader/dateset'
data_prefix: "" #Mandatory, valid data root path
file_path: "data/k400_frames/val.list" #Mandatory, valid data index file path
suffix: 'img_{:05}.jpg'
test:
format: "FrameDataset" #Mandatory, indicate the type of dataset, associate to the 'paddlevidel/loader/dateset'
data_prefix: "" #Mandatory, valid data root path
file_path: "data/k400_frames/val.list" #Mandatory, valid data index file path
suffix: 'img_{:05}.jpg'
PIPELINE: #PIPELINE field
train: #Mandotary, indicate the pipeline to deal with the training data, associate to the 'paddlevideo/loader/pipelines/'
decode:
name: "FrameDecoder"
sample:
name: "Sampler"
num_seg: 8
seg_len: 1
valid_mode: False
dense_sample: True
transform: #Mandotary, image transfrom operator
- Scale:
short_size: 256
- MultiScaleCrop:
target_size: 256
- RandomCrop:
target_size: 224
- RandomFlip:
- Image2Array:
- Normalization:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
valid: #Mandatory, indicate the pipeline to deal with the validing data. associate to the 'paddlevideo/loader/pipelines/'
decode:
name: "FrameDecoder"
sample:
name: "Sampler"
num_seg: 8
seg_len: 1
valid_mode: True
transform:
- Scale:
short_size: 256
- CenterCrop:
target_size: 224
- Image2Array:
- Normalization:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
test:
decode:
name: "FrameDecoder"
sample:
name: "Sampler"
num_seg: 8
seg_len: 1
valid_mode: True
dense_sample: True
transform:
- Scale:
short_size: 256
- GroupFullResSample:
crop_size: 224
- Image2Array:
- Normalization:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
OPTIMIZER: #OPTIMIZER field
name: 'Momentum'
momentum: 0.9
learning_rate:
iter_step: True
name: 'CustomWarmupCosineDecay'
max_epoch: 100
warmup_epochs: 10
warmup_start_lr: 0.01
cosine_base_lr: 0.02
weight_decay:
name: 'L2'
value: 1e-4
use_nesterov: True
MIX:
name: "Mixup"
alpha: 0.2
PRECISEBN:
preciseBN_interval: 5 # epoch interval to do preciseBN, default 1.
num_iters_preciseBN: 200 # how many batches used to do preciseBN, default 200.
METRIC:
name: 'CenterCropMetric'
INFERENCE:
name: 'ppTSM_Inference_helper'
num_seg: 8
target_size: 224
model_name: "ppTSM"
log_interval: 10 #Optional, the interal of logger, default:10
epochs: 100 #Mandatory, total epoch
log_level: "INFO" #Optional, the logger level. default: "INFO"
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