Review readme

src/brevitas_examples/imagenet_classification/ptq/README.md

@@ -42,11 +42,12 @@ To run the PTQ Benchmark suite on ImageNet simply make sure you have Brevitas in
 
 For example, to run the script on the GPU 0:
 ```bash
-brevitas_ptq_imagenet_benchmark --calib-dir /path/to/imagenet/calibration/folder --validation-dir /path/to/imagenet/validation/folder --gpu 0
+brevitas_ptq_imagenet_benchmark --calibration-dir /path/to/imagenet/calibration/folder --validation-dir /path/to/imagenet/validation/folder --gpu 0
 ```
-The script requires to specify the calibration folder (`--calib-dir`), from which the calibration samples will be taken (configurable with the `--calibration-samples` argument), and a validation folder (`--valid-dir`).
+The script requires to specify the calibration folder (`--calibration-dir`), from which the calibration samples will be taken (configurable with the `--calibration-samples` argument), and a validation folder (`--valid-dir`).
 
-After launching the script, a `RESULT.md` markdown file will be generated two tables correspoding to the two types of benchmarks flows.
+After launching the script, a `RESULT_TORCHVISION.md` markdown file will be generated with the results on the torchvision models,
+and a `RESULTS_IMGCLSMOB.md` with the results on manually quantized models starting from floating point weights.
 
 
 ## Evaluation flow
@@ -58,14 +59,16 @@ The quantization and export options to specify are the following:
 ```bash
  -h, --help show this help message and exit
  --calibration-dir CALIBRATION_DIR
- path to folder containing Imagenet calibration folder
+ Path to folder containing Imagenet calibration folder
  --validation-dir VALIDATION_DIR
- path to folder containing Imagenet validation folder
+ Path to folder containing Imagenet validation folder
  --workers WORKERS Number of data loading workers (default: 8)
  --batch-size-calibration BATCH_SIZE_CALIBRATION
  Minibatch size for calibration (default: 64)
  --batch-size-validation BATCH_SIZE_VALIDATION
  Minibatch size for validation (default: 256)
+ --export-dir EXPORT_DIR
+ Directory where to store the exported models
  --gpu GPU GPU id to use (default: None)
  --calibration-samples CALIBRATION_SAMPLES
  Calibration size (default: 1000)
@@ -74,8 +77,6 @@ The quantization and export options to specify are the following:
  densenet121 | densenet161 | densenet169 | densenet201
  | efficientnet_b0 | efficientnet_b1 | efficientnet_b2
  | efficientnet_b3 | efficientnet_b4 | efficientnet_b5
- | efficientnet_b6 | efficientnet_b7 |
- efficientnet_v2_l | efficientnet_v2_m |
  efficientnet_v2_s | googlenet | inception_v3 |
  list_models | maxvit_t | mnasnet0_5 | mnasnet0_75 |
  mnasnet1_0 | mnasnet1_3 | mobilenet_v2 |
@@ -87,7 +88,6 @@ The quantization and export options to specify are the following:
  regnet_y_400mf | regnet_y_800mf | regnet_y_8gf |
  resnet101 | resnet152 | resnet18 | resnet34 | resnet50
  | resnext101_32x8d | resnext101_64x4d |
- resnext50_32x4d | shufflenet_v2_x0_5 |
  shufflenet_v2_x1_0 | shufflenet_v2_x1_5 |
  shufflenet_v2_x2_0 | squeezenet1_0 | squeezenet1_1 |
  swin_b | swin_s | swin_t | swin_v2_b | swin_v2_s |
@@ -99,22 +99,20 @@ The quantization and export options to specify are the following:
  Backend to target for quantization (default: generic)
  --scale-factor-type {float32,po2}
  Type for scale factors (default: float32)
- --bit-width BIT_WIDTH
- Weights and activations bit width (default: 8)
+ --act-bit-width ACT_BIT_WIDTH
+ Activations bit width (default: 8)
+ --weight-bit-width WEIGHT_BIT_WIDTH
+ Weights bit width (default: 8)
  --bias-bit-width {int32,int16}
  Bias bit width (default: int32)
  --act-quant-type {symmetric,asymmetric}
  Activation quantization type (default: symmetric)
  --graph-eq-iterations GRAPH_EQ_ITERATIONS
  Numbers of iterations for graph equalization (default: 20)
  --act-quant-percentile ACT_QUANT_PERCENTILE
- Percentile to use for stats of activation quantization
- (default: 99.999)
- --export-path-onnx-qcdq EXPORT_PATH_ONNX_QCDQ
- If specified, path where to export the model in onnx qcdq format
- --export-path-torch-qcdq EXPORT_PATH_TORCH_QCDQ
- If specified, path where to export the model in torch
- qcdq format (default: none)
+ Percentile to use for stats of activation quantization (default: 99.999)
+ --export-onnx-qcdq If true, export the model in onnx qcdq format
+ --export-torch-qcdq If true, export the model in torch qcdq format
  --scaling-per-output-channel
  Enable Weight scaling per output channel (default: enabled)
  --no-scaling-per-output-channel
@@ -125,14 +123,12 @@ The quantization and export options to specify are the following:
  Enable Merge bias when performing graph equalization (default: enabled)
  --no-graph-eq-merge-bias
  Disable Merge bias when performing graph equalization (default: enabled)
+ --weight-narrow-range
+ Enable Narrow range for weight quantization (default: enabled)
+ --no-weight-narrow-range
+ Disable Narrow range for weight quantization (default: enabled)
 ```
 
-The script requires to specify the calibration folder (`--calib-dir`), from which the calibration samples will be taken (configurable with the `--calibration-samples` argument), and a validation folder (`--valid-dir`)
-
-For example, to run the script on the GPU 0:
-```bash
-brevitas_ptq_imagenet_evaluate --imagenet-dir /path/to/imagenet --gpu 0 --model-name resnet18 --scale-type po2 --act-quant-type asymmetric --act-quant-percentile 99.999 --export-path-qcdq ./quantized_model.onnx
-```
 
 [<sup>1 </sup>]: https://arxiv.org/abs/1906.04721
 [<sup>2 </sup>]: https://github.com/Xilinx/Vitis-AI/blob/50da04ddae396d10a1545823aca30b3abb24a276/src/vai_quantizer/vai_q_pytorch/nndct_shared/optimization/commander.py#L450