enable label balance on maskrcnn
@@ -6,7 +6,7 @@ model: # mandatory. lpot uses this
quantization: # optional. tuning constraints on model-wise for advance user to reduce tuning space.
calibration:
- sampling_size: 10, 50, 100, 200 # optional. default value is the size of whole dataset. used to set how many portions of calibration dataset is used. exclusive with iterations field.
+ sampling_size: 50 # optional. default value is the size of whole dataset. used to set how many portions of calibration dataset is used. exclusive with iterations field.
dataloader: # optional. if not specified, user need construct a q_dataloader in code for lpot.Quantization.
batch_size: 1
dataset:
@@ -23,6 +23,11 @@ quantization: # optional. tuning constrai
weight:
granularity: per_tensor
algorithm: minmax
+ op_wise: {
+ 'FirstStageFeatureExtractor/InceptionV2/InceptionV2/Conv2d_1a_7x7/separable_conv2d': {
+ 'activation': {'dtype': ['fp32']},
+ }
+ }
evaluation: # optional. used to config evaluation process.
accuracy: # optional. required if user doesn't provide eval_func in lpot.Quantization.
@@ -54,7 +59,7 @@ evaluation: # optional. used to config
ParseDecodeCoco:
tuning:
accuracy_criterion:
- relative: 0.02 # optional. default value is relative, other value is absolute. this example allows relative accuracy loss: 2%.
+ relative: 0.01 # optional. default value is relative, other value is absolute. this example allows relative accuracy loss: 2%.
exit_policy:
timeout: 0 # optional. tuning timeout (seconds). default value is 0 which means early stop. combine with max_trials field to decide when to exit.
random_seed: 9527 # optional. random seed for deterministic tuning.
