def get_basic_sparsity_config(model_size=4,
input_sample_size=(1, 1, 4, 4),
sparsity_init=0.02,
sparsity_target=0.5,
sparsity_steps=2,
sparsity_training_steps=3):
config = Config()
config.update({
"model": "basic_sparse_conv",
"model_size": model_size,
"input_info": {
"sample_size": input_sample_size,
},
"compression": {
"algorithm": "rb_sparsity",
"params": {
"schedule": "polynomial",
"sparsity_init": sparsity_init,
"sparsity_target": sparsity_target,
"sparsity_steps": sparsity_steps,
"sparsity_training_steps": sparsity_training_steps
},
"layers": {
"conv": {
"sparsify": True
},
}
}
})
return config
def get_empty_config(model_size=4, input_sample_size=(1, 1, 4, 4)):
config = Config()
config.update({
"model": "basic_sparse_conv",
"model_size": model_size,
"input_sample_size": input_sample_size,
})
return config
def get_basic_magnitude_sparsity_config(input_sample_size=(1, 1, 4, 4)):
config = Config()
config.update({
"model": "basic_sparse_conv",
"input_sample_size": input_sample_size,
"compression":
{
"algorithm": "magnitude_sparsity",
"params": {}
}
})
return config
def get_basic_sparsity_plus_quantization_config(input_sample_size=(1, 1, 4,
4)):
config = Config()
config.update({
"input_sample_size":
input_sample_size,
"compression": [{
"algorithm": "rb_sparsity",
}, {
"algorithm": "quantization",
}]
})
return config
def get_squeezenet_quantization_config(model_size=32):
config = Config()
config.update({
"model": "squeezenet1_1_custom",
"model_size": model_size,
"input_sample_size": (3, 3, model_size, model_size),
"compression": {
"algorithm": "quantization",
"initializer": {
"num_init_steps": 0
}
}
})
return config
def get_basic_quantization_config(model_size=4):
config = Config()
config.update({
"model": "basic_quant_conv",
"model_size": model_size,
"input_sample_size": (1, 1, model_size, model_size),
"compression": {
"algorithm": "quantization",
"initializer": {
"num_init_steps": 0
},
"params": {}
}
})
return config
def get_basic_pruning_config(input_sample_size=(1, 1, 4, 4)):
config = Config()
config.update({
"model": "pruning_conv_model",
"input_info":
{
"sample_size": input_sample_size,
},
"compression":
{
"params": {
}
}
})
return config
def test_get_default_weight_decay(algo, ref_weight_decay):
config = Config()
config.update({"compression": {"algorithm": algo}})
assert ref_weight_decay == get_default_weight_decay(config)