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说明文档
任务: image-classification
后端: sagemaker-training
后端参数: {'instance_type': 'ml.m5.2xlarge', 'supported_instructions': 'avx512'}
评估样本数量: 全部数据集
固定参数:
- 数据集: [{'path': 'beans', 'eval_split': 'validation', 'data_keys': {'primary': 'image'}, 'ref_keys': ['labels'], 'name': None, 'calibration_split': 'train'}]
- name_or_path:
nateraw/vit-base-beans - from_transformers:
True - node_exclusion:
[] - 校准:
- 方法:
percentile - num_calibration_samples:
128 - calibration_histogram_percentile:
99.999
- 方法:
基准测试参数:
- 框架:
onnxruntime,pytorch - 量化方法:
dynamic,static - 待量化算子:
['Add', 'MatMul'],['Add'] - 逐通道:
False,True - 框架参数:
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4},{} - 缩减范围:
True,False - 应用量化:
True,False
评估
非时间指标
| 框架 | 量化方法 | 待量化算子 | 逐通道 | 框架参数 | 缩减范围 | 应用量化 | 准确率 | |
|---|---|---|---|---|---|---|---|---|
onnxruntime |
None |
None |
None |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
None |
False |
| | 0.977 |
onnxruntime |
dynamic |
['Add', 'MatMul'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 0.977 |
onnxruntime |
dynamic |
['Add', 'MatMul'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 0.977 |
onnxruntime |
dynamic |
['Add', 'MatMul'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 0.977 |
onnxruntime |
dynamic |
['Add', 'MatMul'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 0.977 |
onnxruntime |
dynamic |
['Add'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 0.977 |
onnxruntime |
dynamic |
['Add'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 0.977 |
onnxruntime |
dynamic |
['Add'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 0.977 |
onnxruntime |
dynamic |
['Add'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 0.977 |
onnxruntime |
static |
['Add', 'MatMul'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 0.421 |
onnxruntime |
static |
['Add', 'MatMul'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 0.421 |
onnxruntime |
static |
['Add', 'MatMul'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 0.316 |
onnxruntime |
static |
['Add', 'MatMul'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 0.451 |
onnxruntime |
static |
['Add'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 0.361 |
onnxruntime |
static |
['Add'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 0.361 |
onnxruntime |
static |
['Add'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 0.361 |
onnxruntime |
static |
['Add'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 0.361 |
pytorch |
None |
None |
None |
{} |
None |
None |
| | 0.977 |
时间指标
时间基准测试每个配置运行 15 秒。
以下是批大小 = 1，输入长度 = 224 的时间指标。
| 框架 | 量化方法 | 待量化算子 | 逐通道 | 框架参数 | 缩减范围 | 应用量化 | 平均延迟 (ms) | 吞吐量 (/s) | ||
|---|---|---|---|---|---|---|---|---|---|---|
onnxruntime |
None |
None |
None |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
None |
False |
| | 130.41 | | | 7.73 |
onnxruntime |
dynamic |
['Add', 'MatMul'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 102.44 | | | 9.80 |
onnxruntime |
dynamic |
['Add', 'MatMul'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 101.57 | | | 9.87 |
onnxruntime |
dynamic |
['Add', 'MatMul'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 102.37 | | | 9.80 |
onnxruntime |
dynamic |
['Add', 'MatMul'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 102.36 | | | 9.80 |
onnxruntime |
dynamic |
['Add'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 130.67 | | | 7.67 |
onnxruntime |
dynamic |
['Add'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 131.29 | | | 7.67 |
onnxruntime |
dynamic |
['Add'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 132.65 | | | 7.60 |
onnxruntime |
dynamic |
['Add'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 131.03 | | | 7.67 |
onnxruntime |
static |
['Add', 'MatMul'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 127.99 | | | 7.87 |
onnxruntime |
static |
['Add', 'MatMul'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 128.27 | | | 7.80 |
onnxruntime |
static |
['Add', 'MatMul'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 131.10 | | | 7.67 |
onnxruntime |
static |
['Add', 'MatMul'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 130.29 | | | 7.73 |
onnxruntime |
static |
['Add'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 164.55 | | | 6.13 |
onnxruntime |
static |
['Add'] |
False |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 168.61 | | | 5.93 |
onnxruntime |
static |
['Add'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
False |
True |
| | 164.52 | | | 6.13 |
onnxruntime |
static |
['Add'] |
True |
{'opset': 13, 'optimization_level': 1, 'intra_op_num_threads': 4} |
True |
True |
| | 165.31 | | | 6.07 |
pytorch |
None |
None |
None |
{} |
None |
None |
| | 149.23 | | | 6.73 |
fxmarty/20220911-h13m59s08_vit_beans_quantization
作者 fxmarty
image-classification
↓ 0
♥ 0
创建时间: 2022-09-11 14:42:53+00:00
更新时间: 2022-09-11 14:44:14+00:00
在 Hugging Face 上查看文件 (105)
.gitattributes
20220911-h14m03s04_0/augmented_model.onnx
ONNX
20220911-h14m03s04_0/calibration_histograms.npy
20220911-h14m03s04_0/model.onnx
ONNX
20220911-h14m03s04_0/ort_config.json
20220911-h14m03s04_0/quantized_model.onnx
ONNX
20220911-h14m03s04_0/results.json
20220911-h14m04s10_1/model.onnx
ONNX
20220911-h14m04s10_1/ort_config.json
20220911-h14m04s10_1/quantized_model.onnx
ONNX
20220911-h14m04s10_1/results.json
20220911-h14m05s03_2/model.onnx
ONNX
20220911-h14m05s03_2/ort_config.json
20220911-h14m05s03_2/quantized_model.onnx
ONNX
20220911-h14m05s03_2/results.json
20220911-h14m09s58_3/augmented_model.onnx
ONNX
20220911-h14m09s58_3/calibration_histograms.npy
20220911-h14m09s58_3/model.onnx
ONNX
20220911-h14m09s58_3/ort_config.json
20220911-h14m09s58_3/quantized_model.onnx
ONNX
20220911-h14m09s58_3/results.json
20220911-h14m14s51_4/augmented_model.onnx
ONNX
20220911-h14m14s51_4/calibration_histograms.npy
20220911-h14m14s51_4/model.onnx
ONNX
20220911-h14m14s51_4/ort_config.json
20220911-h14m14s51_4/quantized_model.onnx
ONNX
20220911-h14m14s51_4/results.json
20220911-h14m18s36_5/augmented_model.onnx
ONNX
20220911-h14m18s36_5/calibration_histograms.npy
20220911-h14m18s36_5/model.onnx
ONNX
20220911-h14m18s36_5/ort_config.json
20220911-h14m18s36_5/quantized_model.onnx
ONNX
20220911-h14m18s36_5/results.json
20220911-h14m19s29_6/model.onnx
ONNX
20220911-h14m19s29_6/ort_config.json
20220911-h14m19s29_6/quantized_model.onnx
ONNX
20220911-h14m19s29_6/results.json
20220911-h14m20s17_7/model.onnx
ONNX
20220911-h14m20s17_7/ort_config.json
20220911-h14m20s17_7/quantized_model.onnx
ONNX
20220911-h14m20s17_7/results.json
20220911-h14m25s21_8/augmented_model.onnx
ONNX
20220911-h14m25s21_8/calibration_histograms.npy
20220911-h14m25s21_8/model.onnx
ONNX
20220911-h14m25s21_8/ort_config.json
20220911-h14m25s21_8/quantized_model.onnx
ONNX
20220911-h14m25s21_8/results.json
20220911-h14m29s08_9/augmented_model.onnx
ONNX
20220911-h14m29s08_9/calibration_histograms.npy
20220911-h14m29s08_9/model.onnx
ONNX
20220911-h14m29s08_9/ort_config.json
20220911-h14m29s08_9/quantized_model.onnx
ONNX
20220911-h14m29s08_9/results.json
20220911-h14m29s56_10/model.onnx
ONNX
20220911-h14m29s56_10/ort_config.json
20220911-h14m29s56_10/quantized_model.onnx
ONNX
20220911-h14m29s56_10/results.json
20220911-h14m31s00_11/model.onnx
ONNX
20220911-h14m31s00_11/ort_config.json
20220911-h14m31s00_11/quantized_model.onnx
ONNX
20220911-h14m31s00_11/results.json
20220911-h14m34s46_12/augmented_model.onnx
ONNX
20220911-h14m34s46_12/calibration_histograms.npy
20220911-h14m34s46_12/model.onnx
ONNX
20220911-h14m34s46_12/ort_config.json
20220911-h14m34s46_12/quantized_model.onnx
ONNX
20220911-h14m34s46_12/results.json
20220911-h14m35s34_13/model.onnx
ONNX
20220911-h14m35s34_13/ort_config.json
20220911-h14m35s34_13/quantized_model.onnx
ONNX
20220911-h14m35s34_13/results.json
20220911-h14m36s21_14/model.onnx
ONNX
20220911-h14m36s21_14/ort_config.json
20220911-h14m36s21_14/quantized_model.onnx
ONNX
20220911-h14m36s21_14/results.json
20220911-h14m41s28_15/augmented_model.onnx
ONNX
20220911-h14m41s28_15/calibration_histograms.npy
20220911-h14m41s28_15/model.onnx
ONNX
20220911-h14m41s28_15/ort_config.json
20220911-h14m41s28_15/quantized_model.onnx
ONNX
20220911-h14m41s28_15/results.json
20220911-h14m42s13_16/model.onnx
ONNX
20220911-h14m42s13_16/results.json
20220911-h14m42s53_17/results.json
README.md
runs.json
tensorboard/1662907378.8460534/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.1
tensorboard/1662907378.8475587/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.2
tensorboard/1662907378.848857/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.3
tensorboard/1662907378.8500683/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.4
tensorboard/1662907378.8512115/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.5
tensorboard/1662907378.8523538/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.6
tensorboard/1662907378.8535051/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.7
tensorboard/1662907378.8546307/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.8
tensorboard/1662907378.855868/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.9
tensorboard/1662907378.857226/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.10
tensorboard/1662907378.8584979/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.11
tensorboard/1662907378.8597944/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.12
tensorboard/1662907378.8610377/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.13
tensorboard/1662907378.8623042/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.14
tensorboard/1662907378.8636096/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.15
tensorboard/1662907378.8649743/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.16
tensorboard/1662907378.8668153/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.17
tensorboard/1662907378.868103/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.18
tensorboard/events.out.tfevents.1662907378.ip-10-0-138-55.ec2.internal.1.0