Quantizing Models with Accuracy Control¶
The Accuracy-aware Quantization algorithm allows to perform quantization while maintaining accuracy within a pre-defined range. Note that it should be used only if the Default Quantization introduces a significant accuracy degradation. The reason for it not being the primary choice is its potential for performance degradation, due to some layers getting reverted to the original precision.
To proceed with this article, make sure you have read how to use Default Quantization.
Note
The Accuracy-aware Quantization algorithm’s behavior is different for the GNA target_device
. In this case it searches for the best configuration and selects between INT8 and INT16 precisions for weights of each layer. The algorithm works for the performance
preset only. It is not useful for the accuracy
preset, since the whole model is already in INT16 precision.
A script for Accuracy-aware Quantization includes four steps:
Prepare data and dataset interface.
Define accuracy metric.
Select quantization parameters.
Define and run quantization process.
Prepare data and dataset interface¶
This step is the same as in Default Quantization. The only difference is that __getitem__()
should return (data, annotation)
or (data, annotation, metadata)
. The annotation
is required and its format should correspond to the expectations of the Metric
class. The metadata
is an optional field that can be used to store additional information required for post-processing.
Define accuracy metric¶
To control accuracy during optimization, the openvino.tools.pot.Metric
interface should be implemented. Each implementation should override the following properties and methods:
Properties
value
- returns the accuracy metric value for the last model output in a format ofDict[str, numpy.array]
.avg_value
- returns the average accuracy metric over collected model results in a format ofDict[str, numpy.array]
.higher_better
if a higher value of the metric corresponds to better performance, returnsTrue
, otherwise,False
. The default implementation returnsTrue
.
Methods
update(output, annotation)
- calculates and updates the accuracy metric value, using the last model output and annotation. The model output and annotation should be passed in this method. It should also contain the model-specific post-processing in case the model returns the raw output.reset()
- resets collected accuracy metric.get_attributes()
- returns a dictionary of metric attributes:{metric_name: {attribute_name: value}}
Required attributes:
direction
- (higher-better
orhigher-worse
) a string parameter defining whether metric value should be increased in accuracy-aware algorithms.type
- a string representation of a metric type. For example, “accuracy” or “mean_iou”.
Below is an example of the accuracy top-1 metric implementation with POT API:
from openvino.tools.pot import Metric
class Accuracy(Metric):
# Required methods
def __init__(self, top_k=1):
super().__init__()
self._top_k = top_k
self._name = 'accuracy@top{}'.format(self._top_k)
self._matches = [] # container of the results
@property
def value(self):
""" Returns accuracy metric value for all model outputs. """
return {self._name: self._matches[-1]}
@property
def avg_value(self):
""" Returns accuracy metric value for all model outputs. """
return {self._name: np.ravel(self._matches).mean()}
def update(self, output, target):
""" Updates prediction matches.
:param output: model output
:param target: annotations
"""
if len(output) > 1:
raise Exception('The accuracy metric cannot be calculated '
'for a model with multiple outputs')
if isinstance(target, dict):
target = list(target.values())
predictions = np.argsort(output[0], axis=1)[:, -self._top_k:]
match = [float(t in predictions[i]) for i, t in enumerate(target)]
self._matches.append(match)
def reset(self):
""" Resets collected matches """
self._matches = []
def get_attributes(self):
"""
Returns a dictionary of metric attributes {metric_name: {attribute_name: value}}.
Required attributes: 'direction': 'higher-better' or 'higher-worse'
'type': metric type
"""
return {self._name: {'direction': 'higher-better',
'type': 'accuracy'}}
An instance of the Metric
implementation should be passed to IEEngine
object responsible for model inference.
metric = Accuracy()
engine = IEEngine(config=engine_config, data_loader=data_loader, metric=metric)
Select quantization parameters¶
Accuracy-aware Quantization uses the Default Quantization algorithm at the initialization step in such an order that all its parameters are also valid and can be specified. The only parameter required exclusively by Accuracy-aware Quantization is:
"maximal_drop"
- the maximum accuracy drop which has to be achieved after the quantization. The default value is0.01
(1%).
Run quantization¶
The example code below shows a basic quantization workflow with accuracy control. UserDataLoader()
is a placeholder for the implementation of DataLoader
.
from openvino.tools.pot import IEEngine
from openvino.tools.pot import load_model, save_model
from openvino.tools.pot import compress_model_weights
from openvino.tools.pot import create_pipeline
# Model config specifies the model name and paths to model .xml and .bin file
model_config = Dict(
{
"model_name": "model",
"model": path_to_xml,
"weights": path_to_bin,
}
)
# Engine config
engine_config = Dict({"device": "CPU"})
algorithms = [
{
"name": "AccuracyAwareQuantization",
"params": {
"target_device": "ANY",
"stat_subset_size": 300,
'maximal_drop': 0.02
},
}
]
# Step 1: Implement and create user's data loader.
data_loader = UserDataLoader()
# Step 2: Implement and create user's data loader.
metric = Accuracy()
# Step 3: Load the model.
model = load_model(model_config=model_config)
# Step 4: Initialize the engine for metric calculation and statistics collection.
engine = IEEngine(config=engine_config, data_loader=data_loader, metric=metric)
# Step 5: Create a pipeline of compression algorithms and run it.
pipeline = create_pipeline(algorithms, engine)
compressed_model = pipeline.run(model=model)
# Step 6 (Optional): Compress model weights to quantized precision
# in order to reduce the size of the final .bin file.
compress_model_weights(compressed_model)
# Step 7: Save the compressed model to the desired path.
# Set save_path to the directory where the model should be saved.
compressed_model_paths = save_model(
model=compressed_model,
save_path="optimized_model",
model_name="optimized_model",
)
# Step 8 (Optional): Evaluate the compressed model. Print the results.
metric_results = pipeline.evaluate(compressed_model)
It is worth noting that now the evaluate
method that can compute accuracy on demand is also available in the Pipeline
object.
In case when Accuracy-aware Quantization does not allow achieving the desired accuracy-performance trade-off, it is recommended to try Quantization-aware Training from NNCF.