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architecture_config:
model: MlpMixer_ext_1
model_params:
input_n: 10
joints: 22
output_n: 25
n_txcnn_layers: 4
txc_kernel_size: 3
reduction: 8
hidden_dim: 64
input_gcn:
model_complexity:
- 32
- 32
- 32
- 32
interpretable:
- true
- true
- true
- true
- true
output_gcn:
model_complexity:
- 3
interpretable:
- true
clipping: 15
learning_config:
WarmUp: 100
normalize: false
dropout: 0.1
weight_decay: 1e-4
epochs: 50
lr: 0.01
# max_norm: 3
scheduler:
type: StepLR
params:
step_size: 3000
gamma: 0.8
loss:
weights: ""
type: "mpjpe"
augmentations:
random_scale:
x:
- 0.95
- 1.05
y:
- 0.90
- 1.10
z:
- 0.95
- 1.05
random_noise: ""
random_flip:
x: true
y: ""
z: true
random_rotation:
x:
- -5
- 5
y:
- -180
- 180
z:
- -5
- 5
random_translation:
x:
- -0.10
- 0.10
y:
- -0.10
- 0.10
z:
- -0.10
- 0.10
environment_config:
actions: all
evaluate_from: 0
is_norm: true
job: 16
sample_rate: 2
return_all_joints: true
save_grads: false
test_batch: 128
train_batch: 128
general_config:
data_dir: /ai-research/datasets/attention/ann_h3.6m/
experiment_name: STSGCN-tests
load_model_path: ''
log_path: /ai-research/notebooks/testing_repos/logdir/
model_name_rel_path: STSGCN-benchmark
save_all_intermediate_models: false
save_models: true
tensorboard:
num_mesh: 4
meta_config:
comment: Testing a new architecture based on STSGCN paper.
project: Attention
task: 3d keypoint prediction
version: 0.1.1
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