ssd-rn34 example change dataset from raw to tfrecord
@@ -25,7 +25,7 @@ quantization: # optional. tuning constrai
dataloader: # optional. if not specified, user need construct a q_dataloader in code for neural_compressor.Quantization.
batch_size: 1
dataset:
- COCORaw:
+ COCORecord:
root: /path/to/calibration/dataset # NOTE: modify to coco2017 validation raw image folder
transform:
Rescale: {}
@@ -41,11 +41,7 @@ quantization: # optional. tuning constrai
weight:
algorithm: minmax
granularity: per_channel
- op_wise: { # optional. tuning constraints on op-wise for advance user to reduce tuning space.
- 'ssd1200/multibox_head/cls_0/Conv2D': {
- 'activation': {'dtype': ['fp32']},
- },
- }
+
evaluation: # optional. used to config evaluation process.
accuracy: # optional. required if user doesn't provide eval_func in neural_compressor.Quantization.
metric:
@@ -55,7 +51,7 @@ evaluation: # optional. used to config
dataloader: # optional. if not specified, user need construct a q_dataloader in code for neural_compressor.Quantization.
batch_size: 1
dataset:
- COCORaw:
+ COCORecord:
root: /path/to/evaluation/dataset/ # NOTE: modify to coco2017 validation raw image datafolder
transform:
Rescale: {}
@@ -74,7 +70,7 @@ evaluation: # optional. used to config
dataloader:
batch_size: 1
dataset:
- COCORaw:
+ COCORecord:
root: /path/to/evaluation/dataset/
transform:
Rescale: {}
@@ -86,7 +82,7 @@ evaluation: # optional. used to config
tuning:
accuracy_criterion:
- relative: 0.01 # optional. default value is relative, other value is absolute. this example allows relative accuracy loss: 1%.
+ absolute: 0.01 # optional. default value is relative, other value is absolute. this example allows relative accuracy loss: 1%.
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.
