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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: "PPTSM_v2" #Mandatory, The name of backbone.
pretrained: "data/PPLCNetV2_base_ssld_pretrained.pdparams" #Optional, pretrained model path.
num_seg: 16
class_num: 400
head:
name: "MoViNetHead" #Mandatory, indicate the type of head, associate to the 'paddlevideo/modeling/heads'
DATASET: #DATASET field
batch_size: 16 #Mandatory, bacth size
num_workers: 4 #Mandatory, the number of subprocess on each GPU.
train:
format: "FrameDataset" #Mandatory, indicate the type of dataset, associate to the 'paddlevidel/loader/dateset'
data_prefix: "data/k400/rawframes" #Mandatory, train data root path
file_path: "data/k400/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: "data/k400/rawframes" #Mandatory, valid data root path
file_path: "data/k400/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: "data/k400/rawframes" #Mandatory, valid data root path
file_path: "data/k400/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: 16
seg_len: 1
valid_mode: False
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: 16
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: #Mandatory, indicate the pipeline to deal with the validing data. associate to the 'paddlevideo/loader/pipelines/'
decode:
name: "FrameDecoder"
sample:
name: "Sampler"
num_seg: 16
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]
OPTIMIZER: #OPTIMIZER field
name: 'Momentum'
momentum: 0.9
learning_rate:
iter_step: True
name: 'CustomWarmupCosineDecay'
max_epoch: 120
warmup_epochs: 10
warmup_start_lr: 0.005
cosine_base_lr: 0.01
weight_decay:
name: 'L2'
value: 1e-4
use_nesterov: True
MIX:
name: "Mixup"
alpha: 0.2
METRIC:
name: 'CenterCropMetric'
INFERENCE:
name: 'ppTSM_Inference_helper'
num_seg: 16
target_size: 224
model_name: "ppTSMv2"
log_interval: 10 #Optional, the interal of logger, default:10
epochs: 120 #Mandatory, total epoch
log_level: "INFO" #Optional, the logger level. default: "INFO"
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