diff --git a/akida1/model_zoo/plant_village/plant_village_benchmark.py b/akida1/model_zoo/plant_village/plant_village_benchmark.py index 0dab8af..702e557 100644 --- a/akida1/model_zoo/plant_village/plant_village_benchmark.py +++ b/akida1/model_zoo/plant_village/plant_village_benchmark.py @@ -13,6 +13,7 @@ import argparse import json import pathlib +import sys import time import numpy as np @@ -48,7 +49,9 @@ # Device # ------------------------------------------------------------------------- device = get_akida_device(target_version=ak_model.ip_version) - assert device is not None, 'No compatible Akida hardware device found.' + if device is None: + sys.exit('No compatible Akida hardware device found. Skipping benchmarking') + # TODO: Add a check to get clock frequency specific to device CLOCK_FREQUENCY = 400e6 # 400 MHz for AKD1500 diff --git a/akida1/model_zoo/plant_village/plant_village_eval.py b/akida1/model_zoo/plant_village/plant_village_eval.py index b12e9b8..85a3735 100644 --- a/akida1/model_zoo/plant_village/plant_village_eval.py +++ b/akida1/model_zoo/plant_village/plant_village_eval.py @@ -10,6 +10,7 @@ import json import pathlib import numpy as np +import tensorflow as tf from tqdm import tqdm import akida @@ -19,6 +20,8 @@ from plant_village_data import get_data from brainchip_utils.hardware_utils import get_akida_device +tf.config.experimental.enable_op_determinism() + # --------------------------------------------------------------------------- # Evaluation on Akida # --------------------------------------------------------------------------- diff --git a/akida1/model_zoo/plant_village/plant_village_train.py b/akida1/model_zoo/plant_village/plant_village_train.py index a2252be..3aa242c 100644 --- a/akida1/model_zoo/plant_village/plant_village_train.py +++ b/akida1/model_zoo/plant_village/plant_village_train.py @@ -46,10 +46,6 @@ def train_plant_village(model, train_ds, val_ds, epochs, learning_rate, regulari # --------------------------------------------------------------------------- # Model # --------------------------------------------------------------------------- - model.compile(optimizer=Adam(learning_rate=learning_rate), - loss=SparseCategoricalCrossentropy(from_logits=True), - metrics=['accuracy']) - if regularization is not None: print('Adding Activity Regularization to ReLU layers') regularizer = regularizers.L1L2(regularization, regularization) @@ -57,6 +53,11 @@ def train_plant_village(model, train_ds, val_ds, epochs, learning_rate, regulari if isinstance(layer, ReLU): layer.activity_regularizer = regularizer + model.compile(optimizer=Adam(learning_rate=learning_rate), + loss=SparseCategoricalCrossentropy(from_logits=True), + metrics=['accuracy']) + + # --------------------------------------------------------------------------- # Training # --------------------------------------------------------------------------- diff --git a/akida1/model_zoo/vww/README.md b/akida1/model_zoo/vww/README.md index 675e99a..d2eb35f 100644 --- a/akida1/model_zoo/vww/README.md +++ b/akida1/model_zoo/vww/README.md @@ -2,22 +2,7 @@ # Visual Wake Words (VWW) - -## Dataset - -![Sample VWW images (MS-COCO 2014, 96×96)](docs/vww_sample_mosaic.png) - -Visual Wake Words is a binary image classification benchmark, specifically -designed to target edge deployment on resource-constrained devices. It is -derived from the MS-COCO 2014 dataset. Each image is labelled **person** -or **non-person** based on whether a person occupies at least 2% of the frame. -Images are resized to **96 × 96 RGB**. The dataset contains approximately -115k training images and 8k validation images. - -Reference: Chowdhery et al., *Visual Wake Words Dataset* (2019), -[arXiv:1906.05721](https://arxiv.org/abs/1906.05721). - -## Model +## Model Card @@ -31,9 +16,9 @@ Reference: Chowdhery et al., *Visual Wake Words Dataset* (2019), - - - + + + @@ -104,39 +89,24 @@ sparsity is particularly high take very little processing time. Per-layer latency breakdown -## Pipeline +## Requirements -Training follows a three-stage quantization pipeline, followed -by conversion to Akida format: +For environment requirements and setup, see the [Requirements](../../../README.md#requirements) +section of the top-level README. -| Stage | Description | -|---|---| -| Full-precision | Float32 training from scratch, 50 epochs | -| Post-training quantization | `cnn2snn quantize` reduces to 4-bit weights and activations (8-bit input) | -| Quantization-aware tuning | 2 epochs fine-tuning of the quantized model to recover accuracy | -| Conversion to Akida | Automated conversion to Akida model format | +## Dataset -## Requirements -This example generated and tested under -``` -tensorflow[and-cuda]==2.19.1 -tf_keras==2.19.0 -akida==2.19.1 -quantizeml==1.2.3 -cnn2snn==2.19.1 -akida_models==1.14.0 - -ipykernel -pooch -``` +![Sample VWW images (MS-COCO 2014, 96×96)](docs/vww_sample_mosaic.png) -The hardware benchmarking (`vww_benchmark.py` and step 9 in the notebook) and results plotting steps -additionally require the `brainchip_utils` package from this repository. Install it into -your environment from the repo root: +Visual Wake Words is a binary image classification benchmark, specifically +designed to target edge deployment on resource-constrained devices. It is +derived from the MS-COCO 2014 dataset. Each image is labelled **person** +or **non-person** based on whether a person occupies at least 2% of the frame. +Images are resized to **96 × 96 RGB**. The dataset contains approximately +115k training images and 8k validation images. -```bash -pip install -v -e . -``` +Reference: Chowdhery et al., *Visual Wake Words Dataset* (2019), +[arXiv:1906.05721](https://arxiv.org/abs/1906.05721). ## Dataset setup @@ -159,17 +129,44 @@ ln -s /path/to/your/data/vw_coco2014_96 ./data/vw_coco2014_96 This way the scripts work out of the box without any extra arguments. +## Pipeline + +Training follows a three-stage quantization pipeline, followed +by conversion to Akida format: + +| Stage | Description | +|---|---| +| Full-precision | Float32 training from scratch, 20 epochs | +| Post-training quantization | `cnn2snn quantize` reduces to 4-bit weights and activations (8-bit input) | +| Quantization-aware tuning | 5 epochs fine-tuning of the quantized model to recover accuracy | +| Conversion to Akida | Automated conversion to Akida model format | + +## Reference Models + +Pretrained models are made available here, within the `pretrained_models/` +folder. However, those are handled using the `git-lfs` package (git large +file storage). For those to be downloaded with the repo, you will need to +set up `git-lfs`. For further instructions, see the +[Trained models](../../../README.md#trained-models) section of the top-level README. + ## Usage ### Notebook -[vww_notebook.ipynb](vww_notebook.ipynb) walks through the complete training -pipeline end-to-end. It is written to expose and explain the Akida-specific +Two notebooks are provided that walk through a) preparation of a trained Akida-compatible model and +b) evaluation and benchmarking of that model on Akida. + +[vww_notebook_training.ipynb](vww_notebook_training.ipynb) walks through the +complete training pipeline end-to-end. It is written to expose and explain the Akida-specific aspects of the workflow: how the model is constructed for Akida compatibility, what the quantization constraints mean in practice, and what the conversion step does. Start here if you want to understand *why* the pipeline is structured the way it is. +[vww_notebook_benchmark.ipynb](vww_notebook_benchmark.ipynb) walks through +evaluation of model accuracy on Akida and, if a hardware device is available, covers benchmarking +of model latency and power. + ### Script For straightforward reproduction of the training and evaluation results, run @@ -182,99 +179,7 @@ bash vww_train.sh [DATADIR] The optional `DATADIR` argument overrides the default dataset location (`./data/vw_coco2014_96`). -The script executes the following steps in order. This will take from -1-2 hours to run if a modern GPU is available, almost all of which is -the 50 epochs of initial training. If you want to save time and focus on -the Akida-evaluation steps only, you can skip directly to step 5b, -which downloads a pretrained, quantized model directly from the BrainChip -servers: - -**1. Build the model** -```bash -python vww_model.py -s models/akidanet_vww_untrained.h5 -``` -Instantiates AkidaNet (alpha = 0.25, 96 × 96 input) and saves the untrained -weights. - -**2. Float training** -```bash -python vww_train.py -l models/akidanet_vww_untrained.h5 \ - -s models/akidanet_vww.h5 \ - -e 50 -lr 1e-3 -``` -Trains from scratch for 50 epochs in full precision (Float32). - -**3. Float evaluation** -```bash -python vww_eval.py -l models/akidanet_vww.h5 -``` -Reports validation accuracy of the float model. - -**4. Post-training quantization** -```bash -cnn2snn quantize -m models/akidanet_vww.h5 -i 8 -w 4 -a 4 -``` -Quantizes the model to 8-bit inputs, 4-bit weights, and 4-bit activations, -producing `akidanet_vww_iq8_wq4_aq4.h5`. - -**5. Quantization-aware tuning (QAT)** -```bash -python vww_train.py -l models/akidanet_vww_iq8_wq4_aq4.h5 \ - -s models/akidanet_vww_qat.h5 \ - -lr 1e-4 -e 2 -``` -Fine-tunes the quantized model for 2 epochs at a lower learning rate to -recover accuracy lost during quantization. - -**5b. ALTERNATIVE SHORTCUT: Download pretrained quantized model** -```bash -wget -N https://data.brainchip.com/models/AkidaV1/akidanet/akidanet_vww_iq8_wq4_aq4.h5 -mv akidanet_vww_iq8_wq4_aq4.h5 ./models/akidanet_vww_qat.h5 -``` -To save time by skipping the training steps and focussing on the Akida-specific -evaluation steps, you can download the pretrained, quantized model directly from -BrainChip's servers. - -**6. Quantized evaluation** -```bash -python vww_eval.py -l models/akidanet_vww_qat.h5 -``` -Reports validation accuracy of the quantized model. - -**7. Conversion to Akida format** -```bash -cnn2snn convert -m models/akidanet_vww_qat.h5 -``` -Converts the quantized Keras model to the Akida `.fbz` format ready for -on-chip deployment. - -**8. Akida evaluation** -```bash -python vww_eval.py -l models/akidanet_vww_qat.fbz -``` -Reports validation accuracy running inference through the Akida model, -confirming parity with the quantized Keras result. This evaluation step -is done using an Akida 1 hardware device if one is connected. Otherwise -it will fall back to using the Akida software backend, that is, you can -check the final accuracy of an Akida model without the need for access -to a hardware device. - -**9. Akida benchmark** -```bash -python vww_benchmark.py -l models/akidanet_vww_qat.fbz -``` -This will only run if an Akida 1 hardware device is connected. It benchmarks -the inference latency across a large number of samples (but at batch-size=1) -and if power measurement is available on the machine, records total and -dynamic power consumption, and energy per inference. These benchmarks are run -in both `Minimal` and `AllNps` mapping modes. - -Following this, a second set of benchmarks are run (in `Minimal` mapping mode) -which break down the latency per layer The activation sparsity per layer is -also recorded. - -Benchmark results are recorded in two plots, `benchmark_results_full.png` and -`benchmark_results_layers.png`. +That will take about 1 hour to run if a modern GPU is available. ## Contributing and Maintenance @@ -288,10 +193,10 @@ and Akida model versions, plus the hardware benchmark, including the `--save-metrics` argument, and then regenerate the README from the template using `update_readme.py`: ```bash -python vww_eval.py -l models/akidanet_vww.h5 --save-metrics -python vww_eval.py -l models/akidanet_vww_qat.h5 --save-metrics -python vww_eval.py -l models/akidanet_vww_qat.fbz --save-metrics -python vww_benchmark.py -l models/akidanet_vww_qat.fbz --save-metrics +python vww_eval.py -l pretrained_models/akidanet_vww.h5 --save-metrics +python vww_eval.py -l pretrained_models/akidanet_vww_qat.h5 --save-metrics +python vww_eval.py -l pretrained_models/akidanet_vww_qat.fbz --save-metrics +python vww_benchmark.py -l pretrained_models/akidanet_vww_qat.fbz --save-metrics python update_readme.py ``` Then commit the changed files (template, metrics and updated README). diff --git a/akida1/model_zoo/vww/docs/README.md.template b/akida1/model_zoo/vww/docs/README.md.template index 6276b34..2514af3 100644 --- a/akida1/model_zoo/vww/docs/README.md.template +++ b/akida1/model_zoo/vww/docs/README.md.template @@ -2,22 +2,7 @@ # Visual Wake Words (VWW) - -## Dataset - -![Sample VWW images (MS-COCO 2014, 96×96)](docs/vww_sample_mosaic.png) - -Visual Wake Words is a binary image classification benchmark, specifically -designed to target edge deployment on resource-constrained devices. It is -derived from the MS-COCO 2014 dataset. Each image is labelled **person** -or **non-person** based on whether a person occupies at least 2% of the frame. -Images are resized to **96 × 96 RGB**. The dataset contains approximately -115k training images and 8k validation images. - -Reference: Chowdhery et al., *Visual Wake Words Dataset* (2019), -[arXiv:1906.05721](https://arxiv.org/abs/1906.05721). - -## Model +## Model Card
87.01%84.65%84.68%89.33%88.54%88.42% 68.29% 226,906
@@ -104,39 +89,24 @@ sparsity is particularly high take very little processing time. Per-layer latency breakdown -## Pipeline +## Requirements -Training follows a three-stage quantization pipeline, followed -by conversion to Akida format: +For environment requirements and setup, see the [Requirements](../../../README.md#requirements) +section of the top-level README. -| Stage | Description | -|---|---| -| Full-precision | Float32 training from scratch, 50 epochs | -| Post-training quantization | `cnn2snn quantize` reduces to 4-bit weights and activations (8-bit input) | -| Quantization-aware tuning | 2 epochs fine-tuning of the quantized model to recover accuracy | -| Conversion to Akida | Automated conversion to Akida model format | +## Dataset -## Requirements -This example generated and tested under -``` -tensorflow[and-cuda]==2.19.1 -tf_keras==2.19.0 -akida==2.19.1 -quantizeml==1.2.3 -cnn2snn==2.19.1 -akida_models==1.14.0 - -ipykernel -pooch -``` +![Sample VWW images (MS-COCO 2014, 96×96)](docs/vww_sample_mosaic.png) -The hardware benchmarking (`vww_benchmark.py` and step 9 in the notebook) and results plotting steps -additionally require the `brainchip_utils` package from this repository. Install it into -your environment from the repo root: +Visual Wake Words is a binary image classification benchmark, specifically +designed to target edge deployment on resource-constrained devices. It is +derived from the MS-COCO 2014 dataset. Each image is labelled **person** +or **non-person** based on whether a person occupies at least 2% of the frame. +Images are resized to **96 × 96 RGB**. The dataset contains approximately +115k training images and 8k validation images. -```bash -pip install -v -e . -``` +Reference: Chowdhery et al., *Visual Wake Words Dataset* (2019), +[arXiv:1906.05721](https://arxiv.org/abs/1906.05721). ## Dataset setup @@ -159,17 +129,44 @@ ln -s /path/to/your/data/vw_coco2014_96 ./data/vw_coco2014_96 This way the scripts work out of the box without any extra arguments. +## Pipeline + +Training follows a three-stage quantization pipeline, followed +by conversion to Akida format: + +| Stage | Description | +|---|---| +| Full-precision | Float32 training from scratch, 20 epochs | +| Post-training quantization | `cnn2snn quantize` reduces to 4-bit weights and activations (8-bit input) | +| Quantization-aware tuning | 5 epochs fine-tuning of the quantized model to recover accuracy | +| Conversion to Akida | Automated conversion to Akida model format | + +## Reference Models + +Pretrained models are made available here, within the `pretrained_models/` +folder. However, those are handled using the `git-lfs` package (git large +file storage). For those to be downloaded with the repo, you will need to +set up `git-lfs`. For further instructions, see the +[Trained models](../../../README.md#trained-models) section of the top-level README. + ## Usage ### Notebook -[vww_notebook.ipynb](vww_notebook.ipynb) walks through the complete training -pipeline end-to-end. It is written to expose and explain the Akida-specific +Two notebooks are provided that walk through a) preparation of a trained Akida-compatible model and +b) evaluation and benchmarking of that model on Akida. + +[vww_notebook_training.ipynb](vww_notebook_training.ipynb) walks through the +complete training pipeline end-to-end. It is written to expose and explain the Akida-specific aspects of the workflow: how the model is constructed for Akida compatibility, what the quantization constraints mean in practice, and what the conversion step does. Start here if you want to understand *why* the pipeline is structured the way it is. +[vww_notebook_benchmark.ipynb](vww_notebook_benchmark.ipynb) walks through +evaluation of model accuracy on Akida and, if a hardware device is available, covers benchmarking +of model latency and power. + ### Script For straightforward reproduction of the training and evaluation results, run @@ -182,99 +179,7 @@ bash vww_train.sh [DATADIR] The optional `DATADIR` argument overrides the default dataset location (`./data/vw_coco2014_96`). -The script executes the following steps in order. This will take from -1-2 hours to run if a modern GPU is available, almost all of which is -the 50 epochs of initial training. If you want to save time and focus on -the Akida-evaluation steps only, you can skip directly to step 5b, -which downloads a pretrained, quantized model directly from the BrainChip -servers: - -**1. Build the model** -```bash -python vww_model.py -s models/akidanet_vww_untrained.h5 -``` -Instantiates AkidaNet (alpha = 0.25, 96 × 96 input) and saves the untrained -weights. - -**2. Float training** -```bash -python vww_train.py -l models/akidanet_vww_untrained.h5 \ - -s models/akidanet_vww.h5 \ - -e 50 -lr 1e-3 -``` -Trains from scratch for 50 epochs in full precision (Float32). - -**3. Float evaluation** -```bash -python vww_eval.py -l models/akidanet_vww.h5 -``` -Reports validation accuracy of the float model. - -**4. Post-training quantization** -```bash -cnn2snn quantize -m models/akidanet_vww.h5 -i 8 -w 4 -a 4 -``` -Quantizes the model to 8-bit inputs, 4-bit weights, and 4-bit activations, -producing `akidanet_vww_iq8_wq4_aq4.h5`. - -**5. Quantization-aware tuning (QAT)** -```bash -python vww_train.py -l models/akidanet_vww_iq8_wq4_aq4.h5 \ - -s models/akidanet_vww_qat.h5 \ - -lr 1e-4 -e 2 -``` -Fine-tunes the quantized model for 2 epochs at a lower learning rate to -recover accuracy lost during quantization. - -**5b. ALTERNATIVE SHORTCUT: Download pretrained quantized model** -```bash -wget -N https://data.brainchip.com/models/AkidaV1/akidanet/akidanet_vww_iq8_wq4_aq4.h5 -mv akidanet_vww_iq8_wq4_aq4.h5 ./models/akidanet_vww_qat.h5 -``` -To save time by skipping the training steps and focussing on the Akida-specific -evaluation steps, you can download the pretrained, quantized model directly from -BrainChip's servers. - -**6. Quantized evaluation** -```bash -python vww_eval.py -l models/akidanet_vww_qat.h5 -``` -Reports validation accuracy of the quantized model. - -**7. Conversion to Akida format** -```bash -cnn2snn convert -m models/akidanet_vww_qat.h5 -``` -Converts the quantized Keras model to the Akida `.fbz` format ready for -on-chip deployment. - -**8. Akida evaluation** -```bash -python vww_eval.py -l models/akidanet_vww_qat.fbz -``` -Reports validation accuracy running inference through the Akida model, -confirming parity with the quantized Keras result. This evaluation step -is done using an Akida 1 hardware device if one is connected. Otherwise -it will fall back to using the Akida software backend, that is, you can -check the final accuracy of an Akida model without the need for access -to a hardware device. - -**9. Akida benchmark** -```bash -python vww_benchmark.py -l models/akidanet_vww_qat.fbz -``` -This will only run if an Akida 1 hardware device is connected. It benchmarks -the inference latency across a large number of samples (but at batch-size=1) -and if power measurement is available on the machine, records total and -dynamic power consumption, and energy per inference. These benchmarks are run -in both `Minimal` and `AllNps` mapping modes. - -Following this, a second set of benchmarks are run (in `Minimal` mapping mode) -which break down the latency per layer The activation sparsity per layer is -also recorded. - -Benchmark results are recorded in two plots, `benchmark_results_full.png` and -`benchmark_results_layers.png`. +That will take about 1 hour to run if a modern GPU is available. ## Contributing and Maintenance @@ -288,10 +193,10 @@ and Akida model versions, plus the hardware benchmark, including the `--save-metrics` argument, and then regenerate the README from the template using `update_readme.py`: ```bash -python vww_eval.py -l models/akidanet_vww.h5 --save-metrics -python vww_eval.py -l models/akidanet_vww_qat.h5 --save-metrics -python vww_eval.py -l models/akidanet_vww_qat.fbz --save-metrics -python vww_benchmark.py -l models/akidanet_vww_qat.fbz --save-metrics +python vww_eval.py -l pretrained_models/akidanet_vww.h5 --save-metrics +python vww_eval.py -l pretrained_models/akidanet_vww_qat.h5 --save-metrics +python vww_eval.py -l pretrained_models/akidanet_vww_qat.fbz --save-metrics +python vww_benchmark.py -l pretrained_models/akidanet_vww_qat.fbz --save-metrics python update_readme.py ``` Then commit the changed files (template, metrics and updated README). diff --git a/akida1/model_zoo/vww/docs/metrics.json b/akida1/model_zoo/vww/docs/metrics.json index 5fc953f..9e8caf9 100644 --- a/akida1/model_zoo/vww/docs/metrics.json +++ b/akida1/model_zoo/vww/docs/metrics.json @@ -1,7 +1,7 @@ { - "float_acc": "87.01%", - "qat_acc": "84.65%", - "akida_acc": "84.68%", + "float_acc": "89.33%", + "qat_acc": "88.54%", + "akida_acc": "88.42%", "sparsity": "68.29%", "params": "226,906", "minimal_cycles": "1743040", diff --git a/akida1/model_zoo/vww/models/.gitignore b/akida1/model_zoo/vww/models/.gitignore new file mode 100644 index 0000000..647613e --- /dev/null +++ b/akida1/model_zoo/vww/models/.gitignore @@ -0,0 +1,4 @@ +# Git to Ignore everything in this directory +* +# Except this .gitignore file +!.gitignore \ No newline at end of file diff --git a/akida1/model_zoo/vww/pretrained_models/akidanet_vww.h5 b/akida1/model_zoo/vww/pretrained_models/akidanet_vww.h5 new file mode 100644 index 0000000..573f693 --- /dev/null +++ b/akida1/model_zoo/vww/pretrained_models/akidanet_vww.h5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5e802b7fc98d5a991cd37e070bcd5c45e986b4dd3c6d71acf42ca743b9e09bf3 +size 1070008 diff --git a/akida1/model_zoo/vww/pretrained_models/akidanet_vww_qat.fbz b/akida1/model_zoo/vww/pretrained_models/akidanet_vww_qat.fbz new file mode 100644 index 0000000..79f1d8e --- /dev/null +++ b/akida1/model_zoo/vww/pretrained_models/akidanet_vww_qat.fbz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:90531d024b3359f836651748ff867aeaf0019aa0dd7067b9849fb5a3f5381b22 +size 285739 diff --git a/akida1/model_zoo/vww/pretrained_models/akidanet_vww_qat.h5 b/akida1/model_zoo/vww/pretrained_models/akidanet_vww_qat.h5 new file mode 100644 index 0000000..d4956d9 --- /dev/null +++ b/akida1/model_zoo/vww/pretrained_models/akidanet_vww_qat.h5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15b15fde287d83780bbcd5b025db7b6d5510e61a3ebe4bfbd86e02ee09021c8c +size 981848 diff --git a/akida1/model_zoo/vww/vww_benchmark.py b/akida1/model_zoo/vww/vww_benchmark.py index 094d633..f990973 100644 --- a/akida1/model_zoo/vww/vww_benchmark.py +++ b/akida1/model_zoo/vww/vww_benchmark.py @@ -4,15 +4,16 @@ VWW per-layer hardware benchmark. Runs a per-layer timing benchmark on an Akida VWW model, prints a summary -table, saves results as CSV, and generates a summary plot. +table, and generates a summary plot. Example ------- - python vww_benchmark.py -l akidanet_vww.fbz + python vww_benchmark.py -l pretrained_models/akidanet_vww.fbz """ import argparse import json import pathlib +import sys import time import numpy as np @@ -48,7 +49,9 @@ # Device # ------------------------------------------------------------------------- device = get_akida_device(target_version=ak_model.ip_version) - assert device is not None, 'No compatible Akida hardware device found.' + if device is None: + sys.exit('No compatible Akida hardware device found. Skipping benchmarking') + # TODO: Add a check to get clock frequency specific to device CLOCK_FREQUENCY = 400e6 # 400 MHz for AKD1500 diff --git a/akida1/model_zoo/vww/vww_data.py b/akida1/model_zoo/vww/vww_data.py index c189538..b001c84 100644 --- a/akida1/model_zoo/vww/vww_data.py +++ b/akida1/model_zoo/vww/vww_data.py @@ -1,14 +1,14 @@ #!/usr/bin/env python # Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License -import os import numpy as np import tensorflow as tf from tf_keras.preprocessing.image import ImageDataGenerator +from tf_keras.utils import set_random_seed # Define the base directory for the VWW dataset -def get_data(data_path, input_shape, batch_size, dtype=tf.uint8): +def get_data(data_path, input_shape, batch_size, seed=42): """ Loads VWW data. Args: @@ -20,10 +20,7 @@ def get_data(data_path, input_shape, batch_size, dtype=tf.uint8): Returns: tf_keras.data.Dataset, tf_keras.data.Dataset: training dataset, validation dataset """ - - def cast_data(image, label): - image = tf.cast(image, dtype) - return image, label + set_random_seed(seed) # Set aside .1 split for validation validation_split = 0.1 @@ -44,7 +41,8 @@ def cast_data(image, label): batch_size=batch_size, subset = 'training', color_mode='rgb', - class_mode = 'sparse') + class_mode = 'sparse', + shuffle=True) val_datagen = ImageDataGenerator( validation_split = validation_split) @@ -55,7 +53,8 @@ def cast_data(image, label): batch_size=batch_size, subset = 'validation', color_mode='rgb', - class_mode = 'sparse') + class_mode = 'sparse', + shuffle=False) return train_generator, val_generator diff --git a/akida1/model_zoo/vww/vww_eval.py b/akida1/model_zoo/vww/vww_eval.py index b66425c..156be45 100644 --- a/akida1/model_zoo/vww/vww_eval.py +++ b/akida1/model_zoo/vww/vww_eval.py @@ -10,6 +10,9 @@ import json import pathlib import numpy as np +import tensorflow as tf + +from tqdm import tqdm import akida @@ -18,6 +21,8 @@ from vww_data import get_data from brainchip_utils.hardware_utils import get_akida_device +tf.config.experimental.enable_op_determinism() + # --------------------------------------------------------------------------- # Evaluation on Akida # --------------------------------------------------------------------------- @@ -32,14 +37,18 @@ def evaluate_akida_model(akida_model, val_dataset): labels_all = None logits_all = None - # Akida can't directly digest the tensorflow dataset, we need to - # manually iterate over the dataset to deliver inputs as numpy arrays - for batch, label_batch in val_dataset: + # Akida can't directly digest the tensorflow dataset, we need to + # manually iterate over the dataset to deliver inputs as numpy arrays. + # val_dataset is a Keras DirectoryIterator, which cycles indefinitely, + # so we must limit iteration to a single epoch (len(val_dataset) batches). + num_batches = len(val_dataset) + for _ in tqdm(range(num_batches), desc="Evaluating on Akida"): + batch, label_batch = next(val_dataset) if not isinstance(batch, np.ndarray): batch = batch.numpy() # Inference on Akida - logits_batch = akida_model.predict(batch) + logits_batch = akida_model.predict(batch.astype(np.uint8)) logits_batch = logits_batch.squeeze(axis=(1, 2)) # (B, 1, 1, C) -> (B, C) if labels_all is None: diff --git a/akida1/model_zoo/vww/vww_model.py b/akida1/model_zoo/vww/vww_model.py index 0fc676c..da6a54d 100644 --- a/akida1/model_zoo/vww/vww_model.py +++ b/akida1/model_zoo/vww/vww_model.py @@ -35,7 +35,6 @@ def build_vww_model(seed = 42): classes = 2 with set_akida_version(AkidaVersion.v1): base_model = akidanet_imagenet_pretrained( - alpha=0.25, quantized=False ) @@ -64,10 +63,8 @@ def build_vww_model(seed = 42): parser.add_argument('--seed', type=int, default=42, help='Random seed for reproducibility') args = parser.parse_args() - - set_random_seed(args.seed) - model = build_vww_model() + model = build_vww_model(seed=args.seed) model.summary() model.save(args.savepath, include_optimizer=False) print(f'Model saved to {args.savepath}') \ No newline at end of file diff --git a/akida1/model_zoo/vww/vww_notebook.ipynb b/akida1/model_zoo/vww/vww_notebook.ipynb deleted file mode 100644 index 0388888..0000000 --- a/akida1/model_zoo/vww/vww_notebook.ipynb +++ /dev/null @@ -1,1349 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1be5d78d", - "metadata": {}, - "source": [ - "\"BrainChip\n", - "\n", - "# Visual Wake Words on Akida 1\n", - "\n", - "

\n", - "Run Time: ~1 hour with training included / ~2 minutes with training skipped\n", - "

\n", - "\n", - "This notebook steps through the full pipeline for a **Visual Wake Words (VWW)**\n", - "binary classifier on **Akida 1 (AKD1500)**: training a tf_keras model,\n", - "quantization and conversion to Akida format, and evaluation of the resulting\n", - "Akida model. For background on the VWW task, dataset, and model performance\n", - "benchmarks, see the [README](README.md).\n", - "\n", - "The focus is on the **Akida-specific** aspects of the pipeline. The full data\n", - "preprocessing and training code is available in the accompanying Python files —\n", - "it is standard tf_keras code and is not described further in this notebook.\n", - "\n", - "By default, model training is run to ensure reproducibility. However, you can\n", - "cut the running time of the notebook to under a minute if desired by \n", - "skipping the training runs and downloading pretrained float and quantized models\n", - "instead: simply set the relevant `RUN_FLOAT_TRAINING` and `RUN_QAT_TRAINING`\n", - "variables in the first code cell to `False`." - ] - }, - { - "cell_type": "markdown", - "id": "0902d32c", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "The default dataset path is `./data/vw_coco2014_96`. See the [README](README.md)\n", - "for download instructions and symlink setup. Update `DATA_PATH` below if needed." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "9fd73f67", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pooch\n", - "\n", - "import numpy as np\n", - "from tf_keras.utils import set_random_seed" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e90609b2", - "metadata": {}, - "outputs": [], - "source": [ - "DATA_PATH = './data/vw_coco2014_96'\n", - "MODELS_DIR = './models'\n", - "os.makedirs(MODELS_DIR, exist_ok=True)\n", - "\n", - "RUN_FLOAT_TRAINING = True\n", - "RUN_QAT_TRAINING = True\n", - "\n", - "SEED = 42\n", - "\n", - "set_random_seed(SEED)" - ] - }, - { - "cell_type": "markdown", - "id": "bfdbcd30", - "metadata": {}, - "source": [ - "## Dataset\n", - "\n", - "`get_data` returns a training and a validation `tf.data.Dataset`. The full\n", - "preprocessing and augmentation code is in [vww_data.py](vww_data.py) —\n", - "standard tf_keras pipeline code, not described further here." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1c645ac1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 98658 images belonging to 2 classes.\n", - "Found 10961 images belonging to 2 classes.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1781174085.998956 9402 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22428 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:04:00.0, compute capability: 8.6\n" - ] - } - ], - "source": [ - "from vww_data import get_data\n", - "\n", - "BATCH_SIZE = 32\n", - "INPUT_SHAPE = (96, 96, 3)\n", - "\n", - "train_ds, val_ds = get_data(DATA_PATH, INPUT_SHAPE, BATCH_SIZE)" - ] - }, - { - "cell_type": "markdown", - "id": "5e9e5fd5", - "metadata": {}, - "source": [ - "## Model\n", - "\n", - "The backbone is **AkidaNet** — a MobileNet V1 variant whose layer structure\n", - "maps efficiently onto Akida hardware — with width multiplier `alpha=0.25`.\n", - "The narrow variant keeps parameter count low while retaining sufficient\n", - "capacity for the binary VWW task.\n", - "\n", - "Key design choices:\n", - "\n", - "- **Input resolution 96×96**, down from the 224×224 used for the ImageNet\n", - " base model.\n", - "- **2-class output head** (person / non-person), returning raw logits — no\n", - " softmax. The loss function handles the softmax implicitly via\n", - " `from_logits=True`.\n", - "- **`input_scaling=(255, 0)`** embeds a linear mapping from uint8 [0, 255]\n", - " to float [0, 1] as the first layer. This keeps the data pipeline\n", - " hardware-friendly — inputs never need to be normalised outside the model.\n", - "- **`AkidaVersion.v1` context** constrains the layer configuration to what\n", - " the AKD1500 hardware supports." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b4eb1598", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[0;93m2026-06-11 12:34:46.480962330 [W:onnxruntime:Default, device_discovery.cc:164 DiscoverDevicesForPlatform] GPU device discovery failed: device_discovery.cc:89 ReadFileContents Failed to open file: \"/sys/class/drm/card0/device/vendor\"\u001b[m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"akidanet_0.25_96_2\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " input (InputLayer) [(None, 96, 96, 3)] 0 \n", - " \n", - " rescaling (Rescaling) (None, 96, 96, 3) 0 \n", - " \n", - " conv_0 (Conv2D) (None, 48, 48, 8) 216 \n", - " \n", - " conv_0/BN (BatchNormalizat (None, 48, 48, 8) 32 \n", - " ion) \n", - " \n", - " conv_0/relu (ReLU) (None, 48, 48, 8) 0 \n", - " \n", - " conv_1 (Conv2D) (None, 48, 48, 16) 1152 \n", - " \n", - " conv_1/BN (BatchNormalizat (None, 48, 48, 16) 64 \n", - " ion) \n", - " \n", - " conv_1/relu (ReLU) (None, 48, 48, 16) 0 \n", - " \n", - " conv_2 (Conv2D) (None, 24, 24, 32) 4608 \n", - " \n", - " conv_2/BN (BatchNormalizat (None, 24, 24, 32) 128 \n", - " ion) \n", - " \n", - " conv_2/relu (ReLU) (None, 24, 24, 32) 0 \n", - " \n", - " conv_3 (Conv2D) (None, 24, 24, 32) 9216 \n", - " \n", - " conv_3/BN (BatchNormalizat (None, 24, 24, 32) 128 \n", - " ion) \n", - " \n", - " conv_3/relu (ReLU) (None, 24, 24, 32) 0 \n", - " \n", - " separable_4 (SeparableConv (None, 12, 12, 64) 2336 \n", - " 2D) \n", - " \n", - " separable_4/BN (BatchNorma (None, 12, 12, 64) 256 \n", - " lization) \n", - " \n", - " separable_4/relu (ReLU) (None, 12, 12, 64) 0 \n", - " \n", - " separable_5 (SeparableConv (None, 12, 12, 64) 4672 \n", - " 2D) \n", - " \n", - " separable_5/BN (BatchNorma (None, 12, 12, 64) 256 \n", - " lization) \n", - " \n", - " separable_5/relu (ReLU) (None, 12, 12, 64) 0 \n", - " \n", - " separable_6 (SeparableConv (None, 6, 6, 128) 8768 \n", - " 2D) \n", - " \n", - " separable_6/BN (BatchNorma (None, 6, 6, 128) 512 \n", - " lization) \n", - " \n", - " separable_6/relu (ReLU) (None, 6, 6, 128) 0 \n", - " \n", - " separable_7 (SeparableConv (None, 6, 6, 128) 17536 \n", - " 2D) \n", - " \n", - " separable_7/BN (BatchNorma (None, 6, 6, 128) 512 \n", - " lization) \n", - " \n", - " separable_7/relu (ReLU) (None, 6, 6, 128) 0 \n", - " \n", - " separable_8 (SeparableConv (None, 6, 6, 128) 17536 \n", - " 2D) \n", - " \n", - " separable_8/BN (BatchNorma (None, 6, 6, 128) 512 \n", - " lization) \n", - " \n", - " separable_8/relu (ReLU) (None, 6, 6, 128) 0 \n", - " \n", - " separable_9 (SeparableConv (None, 6, 6, 128) 17536 \n", - " 2D) \n", - " \n", - " separable_9/BN (BatchNorma (None, 6, 6, 128) 512 \n", - " lization) \n", - " \n", - " separable_9/relu (ReLU) (None, 6, 6, 128) 0 \n", - " \n", - " separable_10 (SeparableCon (None, 6, 6, 128) 17536 \n", - " v2D) \n", - " \n", - " separable_10/BN (BatchNorm (None, 6, 6, 128) 512 \n", - " alization) \n", - " \n", - " separable_10/relu (ReLU) (None, 6, 6, 128) 0 \n", - " \n", - " separable_11 (SeparableCon (None, 6, 6, 128) 17536 \n", - " v2D) \n", - " \n", - " separable_11/BN (BatchNorm (None, 6, 6, 128) 512 \n", - " alization) \n", - " \n", - " separable_11/relu (ReLU) (None, 6, 6, 128) 0 \n", - " \n", - " separable_12 (SeparableCon (None, 3, 3, 256) 33920 \n", - " v2D) \n", - " \n", - " separable_12/BN (BatchNorm (None, 3, 3, 256) 1024 \n", - " alization) \n", - " \n", - " separable_12/relu (ReLU) (None, 3, 3, 256) 0 \n", - " \n", - " separable_13 (SeparableCon (None, 3, 3, 256) 67840 \n", - " v2D) \n", - " \n", - " separable_13/global_avg (G (None, 256) 0 \n", - " lobalAveragePooling2D) \n", - " \n", - " separable_13/BN (BatchNorm (None, 256) 1024 \n", - " alization) \n", - " \n", - " separable_13/relu (ReLU) (None, 256) 0 \n", - " \n", - " dropout (Dropout) (None, 256) 0 \n", - " \n", - " classifier (Dense) (None, 2) 514 \n", - " \n", - "=================================================================\n", - "Total params: 226906 (886.35 KB)\n", - "Trainable params: 223914 (874.66 KB)\n", - "Non-trainable params: 2992 (11.69 KB)\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "from akida_models import akidanet_imagenet\n", - "from cnn2snn import set_akida_version, AkidaVersion\n", - "\n", - "with set_akida_version(AkidaVersion.v1):\n", - " model = akidanet_imagenet(\n", - " input_shape=INPUT_SHAPE,\n", - " classes=2,\n", - " alpha=0.25,\n", - " include_top=True,\n", - " input_scaling=(255, 0),\n", - " )\n", - "\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "647666ff", - "metadata": {}, - "source": [ - "## Float Training\n", - "\n", - "The model is trained for 50 epochs using Adam with a step-decay learning rate\n", - "schedule. The full training code is in [vww_train.py](vww_train.py) —\n", - "standard tf_keras code, not described further here.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e909bccd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best model saved as /tmp/tmpa3ymm38x/best_model.h5.\n", - "Epoch 1/50\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I0000 00:00:1781174088.196029 9591 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3084/3084 [==============================] - 124s 40ms/step - loss: 0.6550 - accuracy: 0.6703 - val_loss: 0.6279 - val_accuracy: 0.6935 - lr: 0.0010\n", - "Epoch 2/50\n", - " 1/3084 [..............................] - ETA: 2:29 - loss: 0.5571 - accuracy: 0.7812" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dmclelland/miniconda3/envs/ak2191/lib/python3.10/site-packages/tf_keras/src/engine/training.py:3098: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native TF-Keras format, e.g. `model.save('my_model.keras')`.\n", - " saving_api.save_model(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3084/3084 [==============================] - 119s 39ms/step - loss: 0.5641 - accuracy: 0.7374 - val_loss: 0.5885 - val_accuracy: 0.7148 - lr: 0.0010\n", - "Epoch 3/50\n", - "3084/3084 [==============================] - 117s 38ms/step - loss: 0.5128 - accuracy: 0.7654 - val_loss: 0.5012 - val_accuracy: 0.7754 - lr: 0.0010\n", - "Epoch 4/50\n", - "3084/3084 [==============================] - 122s 39ms/step - loss: 0.4860 - accuracy: 0.7824 - val_loss: 0.4756 - val_accuracy: 0.7904 - lr: 0.0010\n", - "Epoch 5/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.4709 - accuracy: 0.7893 - val_loss: 0.4961 - val_accuracy: 0.7813 - lr: 0.0010\n", - "Epoch 6/50\n", - "3084/3084 [==============================] - 120s 39ms/step - loss: 0.4571 - accuracy: 0.7978 - val_loss: 0.4587 - val_accuracy: 0.7914 - lr: 0.0010\n", - "Epoch 7/50\n", - "3084/3084 [==============================] - 118s 38ms/step - loss: 0.4502 - accuracy: 0.8016 - val_loss: 0.4619 - val_accuracy: 0.7932 - lr: 0.0010\n", - "Epoch 8/50\n", - "3084/3084 [==============================] - 122s 39ms/step - loss: 0.4442 - accuracy: 0.8059 - val_loss: 0.4389 - val_accuracy: 0.8038 - lr: 0.0010\n", - "Epoch 9/50\n", - "3084/3084 [==============================] - 116s 38ms/step - loss: 0.4372 - accuracy: 0.8105 - val_loss: 0.4487 - val_accuracy: 0.8042 - lr: 0.0010\n", - "Epoch 10/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.4320 - accuracy: 0.8135 - val_loss: 0.4164 - val_accuracy: 0.8229 - lr: 0.0010\n", - "Epoch 11/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.4281 - accuracy: 0.8159 - val_loss: 0.4430 - val_accuracy: 0.8097 - lr: 0.0010\n", - "Epoch 12/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.4243 - accuracy: 0.8196 - val_loss: 0.4316 - val_accuracy: 0.8098 - lr: 0.0010\n", - "Epoch 13/50\n", - "3084/3084 [==============================] - 122s 39ms/step - loss: 0.4197 - accuracy: 0.8213 - val_loss: 0.4161 - val_accuracy: 0.8211 - lr: 0.0010\n", - "Epoch 14/50\n", - "3084/3084 [==============================] - 120s 39ms/step - loss: 0.4184 - accuracy: 0.8232 - val_loss: 0.4183 - val_accuracy: 0.8224 - lr: 0.0010\n", - "Epoch 15/50\n", - "3084/3084 [==============================] - 118s 38ms/step - loss: 0.4132 - accuracy: 0.8264 - val_loss: 0.4308 - val_accuracy: 0.8133 - lr: 0.0010\n", - "Epoch 16/50\n", - "3084/3084 [==============================] - 123s 40ms/step - loss: 0.4119 - accuracy: 0.8272 - val_loss: 0.4097 - val_accuracy: 0.8260 - lr: 0.0010\n", - "Epoch 17/50\n", - "3084/3084 [==============================] - 124s 40ms/step - loss: 0.4067 - accuracy: 0.8306 - val_loss: 0.4424 - val_accuracy: 0.8085 - lr: 0.0010\n", - "Epoch 18/50\n", - "3084/3084 [==============================] - 120s 39ms/step - loss: 0.4050 - accuracy: 0.8314 - val_loss: 0.4181 - val_accuracy: 0.8177 - lr: 0.0010\n", - "Epoch 19/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.4036 - accuracy: 0.8318 - val_loss: 0.4260 - val_accuracy: 0.8181 - lr: 0.0010\n", - "Epoch 20/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.4019 - accuracy: 0.8344 - val_loss: 0.3959 - val_accuracy: 0.8351 - lr: 0.0010\n", - "Epoch 21/50\n", - "3084/3084 [==============================] - 119s 39ms/step - loss: 0.3833 - accuracy: 0.8444 - val_loss: 0.4082 - val_accuracy: 0.8264 - lr: 5.0000e-04\n", - "Epoch 22/50\n", - "3084/3084 [==============================] - 120s 39ms/step - loss: 0.3750 - accuracy: 0.8488 - val_loss: 0.3783 - val_accuracy: 0.8405 - lr: 5.0000e-04\n", - "Epoch 23/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.3687 - accuracy: 0.8511 - val_loss: 0.3909 - val_accuracy: 0.8405 - lr: 5.0000e-04\n", - "Epoch 24/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.3695 - accuracy: 0.8507 - val_loss: 0.3737 - val_accuracy: 0.8467 - lr: 5.0000e-04\n", - "Epoch 25/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3671 - accuracy: 0.8516 - val_loss: 0.4555 - val_accuracy: 0.7966 - lr: 5.0000e-04\n", - "Epoch 26/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3652 - accuracy: 0.8521 - val_loss: 0.4046 - val_accuracy: 0.8288 - lr: 5.0000e-04\n", - "Epoch 27/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.3613 - accuracy: 0.8559 - val_loss: 0.3768 - val_accuracy: 0.8434 - lr: 5.0000e-04\n", - "Epoch 28/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3610 - accuracy: 0.8543 - val_loss: 0.3696 - val_accuracy: 0.8494 - lr: 5.0000e-04\n", - "Epoch 29/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3595 - accuracy: 0.8553 - val_loss: 0.3631 - val_accuracy: 0.8545 - lr: 5.0000e-04\n", - "Epoch 30/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.3581 - accuracy: 0.8548 - val_loss: 0.3652 - val_accuracy: 0.8503 - lr: 5.0000e-04\n", - "Epoch 31/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3563 - accuracy: 0.8573 - val_loss: 0.3704 - val_accuracy: 0.8488 - lr: 5.0000e-04\n", - "Epoch 32/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.3532 - accuracy: 0.8578 - val_loss: 0.3708 - val_accuracy: 0.8465 - lr: 5.0000e-04\n", - "Epoch 33/50\n", - "3084/3084 [==============================] - 115s 37ms/step - loss: 0.3526 - accuracy: 0.8593 - val_loss: 0.3733 - val_accuracy: 0.8449 - lr: 5.0000e-04\n", - "Epoch 34/50\n", - "3084/3084 [==============================] - 120s 39ms/step - loss: 0.3501 - accuracy: 0.8599 - val_loss: 0.3641 - val_accuracy: 0.8517 - lr: 5.0000e-04\n", - "Epoch 35/50\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.3503 - accuracy: 0.8596 - val_loss: 0.3664 - val_accuracy: 0.8484 - lr: 5.0000e-04\n", - "Epoch 36/50\n", - "3084/3084 [==============================] - 123s 40ms/step - loss: 0.3480 - accuracy: 0.8611 - val_loss: 0.3605 - val_accuracy: 0.8561 - lr: 5.0000e-04\n", - "Epoch 37/50\n", - "3084/3084 [==============================] - 119s 39ms/step - loss: 0.3509 - accuracy: 0.8584 - val_loss: 0.4007 - val_accuracy: 0.8371 - lr: 5.0000e-04\n", - "Epoch 38/50\n", - "3084/3084 [==============================] - 120s 39ms/step - loss: 0.3465 - accuracy: 0.8624 - val_loss: 0.3649 - val_accuracy: 0.8539 - lr: 5.0000e-04\n", - "Epoch 39/50\n", - "3084/3084 [==============================] - 118s 38ms/step - loss: 0.3510 - accuracy: 0.8595 - val_loss: 0.3488 - val_accuracy: 0.8610 - lr: 5.0000e-04\n", - "Epoch 40/50\n", - "3084/3084 [==============================] - 120s 39ms/step - loss: 0.3472 - accuracy: 0.8611 - val_loss: 0.3588 - val_accuracy: 0.8517 - lr: 5.0000e-04\n", - "Epoch 41/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3337 - accuracy: 0.8690 - val_loss: 0.3578 - val_accuracy: 0.8522 - lr: 2.5000e-04\n", - "Epoch 42/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3313 - accuracy: 0.8691 - val_loss: 0.3417 - val_accuracy: 0.8668 - lr: 2.5000e-04\n", - "Epoch 43/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3283 - accuracy: 0.8707 - val_loss: 0.3604 - val_accuracy: 0.8572 - lr: 2.5000e-04\n", - "Epoch 44/50\n", - "3084/3084 [==============================] - 123s 40ms/step - loss: 0.3259 - accuracy: 0.8715 - val_loss: 0.3386 - val_accuracy: 0.8701 - lr: 2.5000e-04\n", - "Epoch 45/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3251 - accuracy: 0.8723 - val_loss: 0.3441 - val_accuracy: 0.8645 - lr: 2.5000e-04\n", - "Epoch 46/50\n", - "3084/3084 [==============================] - 122s 39ms/step - loss: 0.3240 - accuracy: 0.8713 - val_loss: 0.3595 - val_accuracy: 0.8559 - lr: 2.5000e-04\n", - "Epoch 47/50\n", - "3084/3084 [==============================] - 116s 38ms/step - loss: 0.3212 - accuracy: 0.8737 - val_loss: 0.3377 - val_accuracy: 0.8628 - lr: 2.5000e-04\n", - "Epoch 48/50\n", - "3084/3084 [==============================] - 121s 39ms/step - loss: 0.3219 - accuracy: 0.8732 - val_loss: 0.3416 - val_accuracy: 0.8630 - lr: 2.5000e-04\n", - "Epoch 49/50\n", - "3084/3084 [==============================] - 119s 39ms/step - loss: 0.3197 - accuracy: 0.8739 - val_loss: 0.3436 - val_accuracy: 0.8624 - lr: 2.5000e-04\n", - "Epoch 50/50\n", - "3084/3084 [==============================] - 119s 39ms/step - loss: 0.3197 - accuracy: 0.8742 - val_loss: 0.3410 - val_accuracy: 0.8659 - lr: 2.5000e-04\n", - "Float model saved to ./models/akidanet_vww.h5\n" - ] - } - ], - "source": [ - "from vww_train import train_vww\n", - "\n", - "\n", - "if RUN_FLOAT_TRAINING:\n", - " LEARNING_RATE = 1e-3\n", - " EPOCHS = 50\n", - "\n", - " train_vww(model, train_ds, val_ds,\n", - " EPOCHS,\n", - " LEARNING_RATE,\n", - " )\n", - "\n", - " float_model_path = os.path.join(MODELS_DIR, 'akidanet_vww.h5')\n", - " model.save(float_model_path, include_optimizer=False)\n", - " print(f'Float model saved to {float_model_path}')\n", - "else:\n", - " from tf_keras.models import load_model\n", - " print('Training skipped. Retreiving model from BrainChip server...')\n", - " model_url = 'https://data.brainchip.com/models/AkidaV1/akidanet/akidanet_vww.h5'\n", - " model_path = pooch.retrieve(\n", - " url=model_url,\n", - " known_hash='00e03f13226cd622ad92bdb3402c4b4399a69875f2dde6ccadfb235ad6994d78',\n", - " path=\"./models\",\n", - " fname='pretrained_' + os.path.basename(model_url),\n", - " )\n", - " model = load_model(model_path)" - ] - }, - { - "cell_type": "markdown", - "id": "5fe28a05", - "metadata": {}, - "source": [ - "### Evaluate float model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9316bac8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Float validation accuracy: 0.8701\n" - ] - } - ], - "source": [ - "model.compile(metrics=['accuracy'])\n", - "_, float_accuracy = model.evaluate(val_ds, verbose=0)\n", - "print(f'Float validation accuracy: {float_accuracy:.4f}')" - ] - }, - { - "cell_type": "markdown", - "id": "72693c80", - "metadata": {}, - "source": [ - "## Quantization\n", - "\n", - "Akida 1 operates with integer weights and activations. We use `cnn2snn` to\n", - "quantize the float model to 4 bits for both weights and activations (8-bit\n", - "weights are enabled for the first layer only, which is also unusual in\n", - "receiving uint8 inputs):\n", - "\n", - "Post-training quantization maps the float parameters to their nearest\n", - "representable integer values. Some accuracy is typically lost in this step,\n", - "which the subsequent QAT pass recovers.\n", - "\n", - "Note: the quantized model can be saved using the standard method. However,\n", - "for later reloading, because of the custom quantized layers in the model\n", - "we have to use the `load_quantized_model` function from cnn2snn (a wrapper\n", - "around the standard tf_keras loading function)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9f1430d0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n", - "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n" - ] - } - ], - "source": [ - "from cnn2snn import quantize, load_quantized_model\n", - "\n", - "quantized_model = quantize(\n", - " model,\n", - " input_weight_quantization=8,\n", - " weight_quantization=4,\n", - " activ_quantization=4,\n", - ")\n", - "ptq_model_path = 'akidanet_vww_ptq.h5'\n", - "quantized_model.save(ptq_model_path, include_optimizer=False)\n", - "\n", - "del quantized_model\n", - "\n", - "quantized_model = load_quantized_model(ptq_model_path)" - ] - }, - { - "cell_type": "markdown", - "id": "76a4ccbb", - "metadata": {}, - "source": [ - "### Quantization-Aware Training (QAT)\n", - "\n", - "Two epochs of fine-tuning at a constant learning rate of 1×10⁻⁴ are\n", - "sufficient to recover most of the accuracy lost during quantization. It is\n", - "typical to find that a lower learning rate (e.g. /10) is required during this \n", - "phase than during the initial training.\n", - "\n", - "Note that, although Quatization Aware Training can sound intimidating,\n", - "the model quantized via `cnn2snn` can simply be reinserted into the \n", - "same training function that was used for the initial float training." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f0d0707d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best model saved as /tmp/tmpsm787w7k/best_model.h5.\n", - "Epoch 1/2\n", - "3084/3084 [==============================] - 120s 38ms/step - loss: 0.3950 - accuracy: 0.8599 - val_loss: 0.4255 - val_accuracy: 0.8465 - lr: 1.0000e-04\n", - "Epoch 2/2\n", - "3084/3084 [==============================] - 122s 40ms/step - loss: 0.3851 - accuracy: 0.8640 - val_loss: 0.4549 - val_accuracy: 0.8332 - lr: 1.0000e-04\n", - "QAT model saved to ./models/akidanet_vww_qat.h5\n" - ] - } - ], - "source": [ - "\n", - "if RUN_QAT_TRAINING:\n", - " QAT_LEARNING_RATE = 1e-4\n", - " QAT_EPOCHS = 2\n", - "\n", - " train_vww(quantized_model, train_ds, val_ds,\n", - " QAT_EPOCHS,\n", - " QAT_LEARNING_RATE,\n", - " )\n", - "\n", - " qat_model_path = os.path.join(MODELS_DIR, 'akidanet_vww_qat.h5')\n", - " quantized_model.save(qat_model_path, include_optimizer=False)\n", - " print(f'QAT model saved to {qat_model_path}')\n", - "else:\n", - " print('QAT skipped. Retreiving model from BrainChip server...')\n", - " q_model_url = 'https://data.brainchip.com/models/AkidaV1/akidanet/akidanet_vww_iq8_wq4_aq4.h5'\n", - " q_model_path = pooch.retrieve(\n", - " url=q_model_url,\n", - " known_hash='cd130d90ed736447b6244dc1228e708b9dab20af0d2bf57b9a49df4362467ea8',\n", - " path=\"./models\",\n", - " fname='pretrained_'+ os.path.basename(q_model_url),\n", - " )\n", - " quantized_model = load_quantized_model(q_model_path)" - ] - }, - { - "cell_type": "markdown", - "id": "7c370145", - "metadata": {}, - "source": [ - "### Evaluate quantized model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "78ae04ed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "QAT validation accuracy: 0.8465\n" - ] - } - ], - "source": [ - "quantized_model.compile(metrics=['accuracy'])\n", - "_, qat_accuracy = quantized_model.evaluate(val_ds, verbose=0)\n", - "print(f'QAT validation accuracy: {qat_accuracy:.4f}')" - ] - }, - { - "cell_type": "markdown", - "id": "58fcef07", - "metadata": {}, - "source": [ - "## Conversion to Akida Format\n", - "\n", - "`cnn2snn.convert` compiles the quantized Keras model into an Akida `.fbz`\n", - "model that can be loaded and executed directly on AKD1500 hardware.\n", - "The converter verifies hardware compatibility and maps each layer to its\n", - "corresponding Akida primitive." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "536ef853", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Akida model saved to ./models/akidanet_vww_qat.fbz\n", - " Model Summary \n", - "______________________________________________\n", - "Input shape Output shape Sequences Layers\n", - "==============================================\n", - "[96, 96, 3] [1, 1, 2] 1 15 \n", - "______________________________________________\n", - "\n", - "__________________________________________________________\n", - "Layer (type) Output shape Kernel shape \n", - "\n", - "============= SW/conv_0-classifier (Software) ============\n", - "\n", - "conv_0 (InputConv.) [48, 48, 8] (3, 3, 3, 8) \n", - "__________________________________________________________\n", - "conv_1 (Conv.) [48, 48, 16] (3, 3, 8, 16) \n", - "__________________________________________________________\n", - "conv_2 (Conv.) [24, 24, 32] (3, 3, 16, 32) \n", - "__________________________________________________________\n", - "conv_3 (Conv.) [24, 24, 32] (3, 3, 32, 32) \n", - "__________________________________________________________\n", - "separable_4 (Sep.Conv.) [12, 12, 64] (3, 3, 32, 1) \n", - "__________________________________________________________\n", - " (1, 1, 32, 64) \n", - "__________________________________________________________\n", - "separable_5 (Sep.Conv.) [12, 12, 64] (3, 3, 64, 1) \n", - "__________________________________________________________\n", - " (1, 1, 64, 64) \n", - "__________________________________________________________\n", - "separable_6 (Sep.Conv.) [6, 6, 128] (3, 3, 64, 1) \n", - "__________________________________________________________\n", - " (1, 1, 64, 128) \n", - "__________________________________________________________\n", - "separable_7 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", - "__________________________________________________________\n", - " (1, 1, 128, 128)\n", - "__________________________________________________________\n", - "separable_8 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", - "__________________________________________________________\n", - " (1, 1, 128, 128)\n", - "__________________________________________________________\n", - "separable_9 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", - "__________________________________________________________\n", - " (1, 1, 128, 128)\n", - "__________________________________________________________\n", - "separable_10 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", - "__________________________________________________________\n", - " (1, 1, 128, 128)\n", - "__________________________________________________________\n", - "separable_11 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", - "__________________________________________________________\n", - " (1, 1, 128, 128)\n", - "__________________________________________________________\n", - "separable_12 (Sep.Conv.) [3, 3, 256] (3, 3, 128, 1) \n", - "__________________________________________________________\n", - " (1, 1, 128, 256)\n", - "__________________________________________________________\n", - "separable_13 (Sep.Conv.) [1, 1, 256] (3, 3, 256, 1) \n", - "__________________________________________________________\n", - " (1, 1, 256, 256)\n", - "__________________________________________________________\n", - "classifier (Fully.) [1, 1, 2] (1, 1, 256, 2) \n", - "__________________________________________________________\n" - ] - } - ], - "source": [ - "from cnn2snn import convert\n", - "\n", - "akida_model = convert(quantized_model)\n", - "\n", - "akida_model_path = os.path.join(MODELS_DIR, 'akidanet_vww_qat.fbz')\n", - "akida_model.save(akida_model_path)\n", - "print(f'Akida model saved to {akida_model_path}')\n", - "akida_model.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "655c4739", - "metadata": {}, - "source": [ - "## Evaluation of Akida Model\n", - "\n", - "We now run evaluation through the Akida model, to check that accuracy is \n", - "comparable to that obtained from the quantized tf_keras model. If an Akida 1\n", - "hardware device is connected, it will be used for inference; if not, the\n", - "code will fall back to using the software backend: this delivers a\n", - "bit-accurate simulation of the results that will be obtained when running\n", - "the model on hardware. Let's run that check before going any further\n", - "\n", - "### Check for a connected Akida hardware device\n", - "\n", - "We can use the `akida.devices()` function to detect connected hardware devices.\n", - "That returns a list - if it's empty, there were no hardware devices. Otherwise, \n", - "typically we'd only have a single Akida device connected on a given machine, \n", - "and we can just select the first (and only) device returned." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "bd9a1aa4", - "metadata": {}, - "outputs": [], - "source": [ - "import akida\n", - "devices = akida.devices()\n", - "if len(devices)>0:\n", - " # Hardware is available\n", - " device = devices[0]\n", - "else:\n", - " # Hardware is not available\n", - " device = None" - ] - }, - { - "cell_type": "markdown", - "id": "62abc26c", - "metadata": {}, - "source": [ - "In the present case, we want to be a bit more careful and ensure that the\n", - "device is the right version for the model we want to test (here, Akida IP\n", - "version 1). We'll import a local function to do that - check out the details \n", - "if interested" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "696a0034", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Target Akida device found\n" - ] - } - ], - "source": [ - "from brainchip_utils.hardware_utils import get_akida_device\n", - "\n", - "# Load the Akida model\n", - "akida_model = akida.Model(akida_model_path)\n", - "# Look for a matching hardware device\n", - "device = get_akida_device(target_version = akida_model.ip_version)\n", - "if device is not None:\n", - " akida_model.map(device, mode=akida.MapMode.Minimal, hw_only=True)" - ] - }, - { - "cell_type": "markdown", - "id": "d8f31c3d", - "metadata": {}, - "source": [ - "### Run Evaluation on Akida\n", - "\n", - "The Akida runtime cannot consume `tf.data.Dataset` objects directly, rather\n", - "it expects a 4D numpy array (n, h, w, c) in uint8 format. So we\n", - "iterate over validation batches manually.\n", - "\n", - "The model output tensor has shape `(B, 1, 1, C)` which is squeezed to \n", - "`(B, C)` before taking the class argmax." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "20ffb4cd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Akida accuracy: 0.8452\n" - ] - } - ], - "source": [ - "\n", - "labels_all = []\n", - "logits_all = []\n", - "for batch, label_batch in val_ds:\n", - " if not isinstance(batch, np.ndarray):\n", - " batch = batch.numpy()\n", - "\n", - " logits_batch = akida_model.predict(batch, batch_size=1)\n", - "\n", - " logits_batch = logits_batch.squeeze(axis=(1, 2))\n", - " labels_all.append(label_batch)\n", - " logits_all.append(logits_batch)\n", - "\n", - "labels_all = np.concatenate(labels_all)\n", - "logits_all = np.concatenate(logits_all)\n", - "preds = np.argmax(logits_all, axis=1)\n", - "\n", - "akida_accuracy = float(np.mean(preds == np.array(labels_all)))\n", - "print(f'Akida accuracy: {akida_accuracy:.4f}')" - ] - }, - { - "cell_type": "markdown", - "id": "abdad1f1", - "metadata": {}, - "source": [ - "### Activation Sparsity\n", - "\n", - "Akida hardware skips computation for zero-valued activations, so activation\n", - "sparsity directly reduces both energy consumption and inference latency.\n", - "Below we measure per-layer sparsity on a 1024-sample calibration batch drawn\n", - "from the training set." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "50ce55a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 98658 images belonging to 2 classes.\n", - "\n", - "Layer Sparsity\n", - "-------------------------\n", - "conv_0 33.92%\n", - "conv_1 59.18%\n", - "conv_2 66.11%\n", - "conv_3 64.05%\n", - "separable_4 59.69%\n", - "separable_5 72.51%\n", - "separable_6 74.60%\n", - "separable_7 84.21%\n", - "separable_8 92.35%\n", - "separable_9 95.27%\n", - "separable_10 94.21%\n", - "separable_11 96.86%\n", - "separable_12 73.39%\n", - "separable_13 58.04%\n", - "classifier 0.00%\n", - "-------------------------\n", - "Mean 68.29%\n" - ] - } - ], - "source": [ - "from tf_keras.preprocessing.image import ImageDataGenerator\n", - "from akida_models.sparsity import compute_sparsity\n", - "\n", - "def get_samples(data_path, input_shape, num_samples=1024):\n", - " generator = ImageDataGenerator().flow_from_directory(\n", - " os.path.join(data_path, 'train'),\n", - " target_size=input_shape[:2],\n", - " batch_size=num_samples,\n", - " shuffle=False,\n", - " )\n", - " images, _ = next(generator)\n", - " return images[:num_samples].astype(np.uint8)\n", - "\n", - "samples = get_samples(DATA_PATH, INPUT_SHAPE)\n", - "sparsity_dict = compute_sparsity(akida_model, samples=samples)\n", - "\n", - "col_w = max(len(k) for k in sparsity_dict) + 2\n", - "print(f\"\\n{'Layer':<{col_w}} {'Sparsity':>10}\")\n", - "print(\"-\" * (col_w + 11))\n", - "for layer, sparsity in sparsity_dict.items():\n", - " print(f\"{layer:<{col_w}} {sparsity:>9.2%}\")\n", - "print(\"-\" * (col_w + 11))\n", - "print(f\"{'Mean':<{col_w}} {np.mean(list(sparsity_dict.values())):>9.2%}\")" - ] - }, - { - "cell_type": "markdown", - "id": "bc67b63b", - "metadata": {}, - "source": [ - "## Hardware Benchmarking\n", - "\n", - "**These cells require a physical AKD1500 device to be connected.** If `device is\n", - "None` (reported in the evaluation section above), skip ahead to the Summary.\n", - "\n", - "Akida is an event-driven architecture: computations scale with the number of\n", - "non-zero activations, not with tensor size. That means benchmark results are\n", - "*input-dependent* — random or synthetic data would give artificially fast or\n", - "slow timings. The `samples` array loaded above (real images from the training\n", - "split) is therefore the correct input to use here." - ] - }, - { - "cell_type": "markdown", - "id": "a422ad44", - "metadata": {}, - "source": [ - "### Simple Benchmark\n", - "\n", - "The simplest way to time an Akida model is to call `forward` in a loop and\n", - "read back two clocks after each inference:\n", - "\n", - "- **System clock** (`time.perf_counter_ns`) — wall time including Python and\n", - " USB/PCIe transfer overhead.\n", - "- **On-chip clock** (`akida_model.metrics['inference_clk']`) — raw clock cycles\n", - " counted by the AKD1500 itself. Dividing by the 400 MHz core frequency gives\n", - " the pure compute time.\n", - "\n", - "The two numbers should agree closely; a large divergence would indicate a\n", - "transfer or driver bottleneck." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "4a68cff6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean inference time (system clock): 4.447 ms\n", - "Mean on-chip time (chip clock cycles): 4.358 ms\n" - ] - } - ], - "source": [ - "import time\n", - "\n", - "CLOCK_FREQUENCY = 400e6 # 400 MHz for AKD1500\n", - "\n", - "if device is not None:\n", - " akida_model.map(device, mode=akida.MapMode.Minimal, hw_only=True)\n", - "\n", - " inf_clks = []\n", - " inf_times = []\n", - " for i in range(len(samples)):\n", - " start_t = time.perf_counter_ns()\n", - " akida_model.forward(samples[i:i+1])\n", - " inf_times.append(time.perf_counter_ns() - start_t)\n", - " inf_clks.append(akida_model.metrics['inference_clk'])\n", - "\n", - " mean_inf_clk = np.mean(inf_clks) / CLOCK_FREQUENCY * 1e3 # cycles → ms\n", - " mean_inf_time = np.mean(inf_times) * 1e-6 # ns → ms\n", - " print(f'Mean inference time (system clock): {mean_inf_time:.3f} ms')\n", - " print(f'Mean on-chip time (chip clock cycles): {mean_inf_clk:.3f} ms')" - ] - }, - { - "cell_type": "markdown", - "id": "a0d7f360", - "metadata": {}, - "source": [ - "### Full Model Benchmark\n", - "\n", - "The loop above is clear, but it misses two things: power consumption and a\n", - "comparison between mapping modes. `full_model_benchmark` from\n", - "[brainchip_utils/hardware_utils.py](../../../brainchip_utils/hardware_utils.py)\n", - "runs the same timed loop while also coordinating optional INA219 power\n", - "measurement in a separate process. It sweeps both `MapMode.Minimal` (fewest\n", - "NPs, lowest power) and `MapMode.AllNps` (all NPs, maximum parallelism) so the\n", - "trade-off is visible. The multiprocessing and power-meter wiring are\n", - "non-trivial and not of interest to most users — consult the source if needed." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "3e33f2ea", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running full-model benchmark (MapMode=Minimal, 10 repeat(s))...\n", - "Checking I2C power measurement hardware...\n", - " I2C INA sensor detected — power measurement enabled.\n", - " Supply voltage: 0.805 V\n", - " Power measurement active.\n", - " Repeat 1/10: recording floor (1.0 s)...\n", - " Repeat 1/10 done.\n", - " Repeat 2/10: recording floor (1.0 s)...\n", - " Repeat 2/10 done.\n", - " Repeat 3/10: recording floor (1.0 s)...\n", - " Repeat 3/10 done.\n", - " Repeat 4/10: recording floor (1.0 s)...\n", - " Repeat 4/10 done.\n", - " Repeat 5/10: recording floor (1.0 s)...\n", - " Repeat 5/10 done.\n", - " Repeat 6/10: recording floor (1.0 s)...\n", - " Repeat 6/10 done.\n", - " Repeat 7/10: recording floor (1.0 s)...\n", - " Repeat 7/10 done.\n", - " Repeat 8/10: recording floor (1.0 s)...\n", - " Repeat 8/10 done.\n", - " Repeat 9/10: recording floor (1.0 s)...\n", - " Repeat 9/10 done.\n", - " Repeat 10/10: recording floor (1.0 s)...\n", - " Power process: 3994 ok, 0 errors\n", - " Repeat 10/10 done.\n", - "\n", - " Mean inference time: 4.443 ms (σ=0.108 ms)\n", - " Mean on-chip time: 4.357 ms (1742898 clocks)\n", - " Total inferences run: 10240\n", - " Avg dynamic power: 26.4 mW\n", - " Avg dynamic energy: 0.117 mJ/inf\n", - " Mapping: 17 NP(s), 1 pass(es), 1 sequence(s)\n", - "Running full-model benchmark (MapMode=AllNps, 10 repeat(s))...\n", - "Checking I2C power measurement hardware...\n", - " I2C INA sensor detected — power measurement enabled.\n", - " Supply voltage: 0.805 V\n", - " Power measurement active.\n", - " Repeat 1/10: recording floor (1.0 s)...\n", - " Repeat 1/10 done.\n", - " Repeat 2/10: recording floor (1.0 s)...\n", - " Repeat 2/10 done.\n", - " Repeat 3/10: recording floor (1.0 s)...\n", - " Repeat 3/10 done.\n", - " Repeat 4/10: recording floor (1.0 s)...\n", - " Repeat 4/10 done.\n", - " Repeat 5/10: recording floor (1.0 s)...\n", - " Repeat 5/10 done.\n", - " Repeat 6/10: recording floor (1.0 s)...\n", - " Repeat 6/10 done.\n", - " Repeat 7/10: recording floor (1.0 s)...\n", - " Repeat 7/10 done.\n", - " Repeat 8/10: recording floor (1.0 s)...\n", - " Repeat 8/10 done.\n", - " Repeat 9/10: recording floor (1.0 s)...\n", - " Repeat 9/10 done.\n", - " Repeat 10/10: recording floor (1.0 s)...\n", - " Power process: 3345 ok, 0 errors\n", - " Repeat 10/10 done.\n", - "\n", - " Mean inference time: 3.400 ms (σ=0.091 ms)\n", - " Mean on-chip time: 3.314 ms (1325794 clocks)\n", - " Total inferences run: 10240\n", - " Avg dynamic power: 34.2 mW\n", - " Avg dynamic energy: 0.116 mJ/inf\n", - " Mapping: 29 NP(s), 1 pass(es), 1 sequence(s)\n" - ] - } - ], - "source": [ - "from brainchip_utils.hardware_utils import full_model_benchmark, get_mapping_stats\n", - "from brainchip_utils.plot_utils import plot_full_model_results\n", - "\n", - "if device is not None:\n", - " map_modes = ['Minimal', 'AllNps']\n", - " POWER_REPEATS = 10\n", - " full_results = {}\n", - " for mm in map_modes:\n", - " map_mode = getattr(akida.MapMode, mm)\n", - " print(f'Running full-model benchmark (MapMode={mm}, {POWER_REPEATS} repeat(s))...')\n", - " full_results[mm] = full_model_benchmark(\n", - " akida_model, device, samples, map_mode=map_mode, repeats=POWER_REPEATS)\n", - "\n", - " akida_model.map(device, mode=map_mode)\n", - " num_nps, num_passes, num_sequences = get_mapping_stats(akida_model)\n", - " full_results[mm]['num_nps'] = num_nps\n", - " full_results[mm]['num_passes'] = num_passes\n", - " print(f' Mapping: {num_nps} NP(s), {num_passes} pass(es), {num_sequences} sequence(s)')\n", - " if num_sequences > 1:\n", - " print('WARNING: model not completely mapped to hardware')" - ] - }, - { - "cell_type": "markdown", - "id": "a06b41fe", - "metadata": {}, - "source": [ - "The plot below shows one column per map mode: a power trace (if a power meter\n", - "was connected) and the hardware mapping layout." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "ae93a65d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if device is not None:\n", - " plot_full_model_results(full_results, akida_model, device,\n", - " model_name=akida_model_path,\n", - " savepath='benchmark_results_full.png')" - ] - }, - { - "cell_type": "markdown", - "id": "b9164faf", - "metadata": {}, - "source": [ - "### Per-Layer Benchmark\n", - "\n", - "Full-model timing tells us the total cost but not where time is spent.\n", - "`per_layer_benchmark` from\n", - "[brainchip_utils/hardware_utils.py](../../../brainchip_utils/hardware_utils.py)\n", - "reconstructs latency layer by layer by running cumulative sub-models and\n", - "differencing the results.\n", - "\n", - "Because Akida processes events (non-zero activations), a layer's cost is\n", - "proportional to its *input* sparsity: a layer receiving 90% sparse inputs has\n", - "far fewer events to process than one receiving 10% sparse inputs. The per-layer\n", - "timing and the sparsity values computed above are therefore naturally correlated —\n", - "low-sparsity layers are typically the latency bottlenecks." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e2238a7b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running per-layer benchmark (1024 samples)...\n", - "\n", - "Layer Latency (ms) Clocks\n", - "----------------------------------\n", - "conv_0 0.1860\n", - "conv_1 0.2064\n", - "conv_2 0.4963\n", - "conv_3 0.3973\n", - "separable_4 0.7378\n", - "separable_5 0.4666\n", - "separable_6 0.6151\n", - "separable_7 0.3132\n", - "separable_8 0.1952\n", - "separable_9 0.0980\n", - "separable_10 0.0600\n", - "separable_11 0.0708\n", - "separable_12 0.1039\n", - "separable_13 0.3921\n", - "classifier 0.0202\n", - "----------------------------------\n", - "Total 4.3590\n" - ] - } - ], - "source": [ - "from brainchip_utils.hardware_utils import per_layer_benchmark\n", - "from brainchip_utils.plot_utils import plot_per_layer_results\n", - "\n", - "if device is not None:\n", - " # Map without hw_only so akida_model.sequences is populated for the plot\n", - " akida_model.map(device, mode=akida.MapMode.Minimal)\n", - "\n", - " print(f'Running per-layer benchmark ({len(samples)} samples)...')\n", - " per_layer_results = per_layer_benchmark(akida_model, device, samples)" - ] - }, - { - "cell_type": "markdown", - "id": "2f0103d9", - "metadata": {}, - "source": [ - "The plot stacks three panels: per-layer latency, input sparsity per layer, and\n", - "the hardware mapping. The inverse relationship between sparsity and latency is\n", - "the direct signature of the event-driven compute model: dense activations\n", - "generate more events, and more events mean more work for the hardware." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "687786ee", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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USUePHtUzzzyjdu3aKX/+/KpZs6aef/55BQcHa/Xq1b4eOV3KkyePBg8erNWrV6tBgwb67rvv9MMPP6hdu3YKCgrSs88+q1tvvdX9vpvvL5dWoEABtWrVSitXrtS4cePcj8/kTzh26dJF99xzj0aNGqWEhASOhLuM5COrk79nN2rUSIMGDVLx4sXVsGFDbdq0SYGBgTpx4oR+/vln9zInuJBZRESE6tWr57E9S5Yscrlcyps3r/7880+P6/7880/3pypwgZkpMDDQvdyLmenIkSPaunWrPv/8c73wwguqW7eu+vbtqyeffFIffvihYmNjeU5f5OKTxY0ePVpPP/202rdvr2+++UZHjhxRqVKlNH/+fMXHx6tLly5avXo173cyIN4NwHFVq1bVkCFDtHfvXg0dOtS9ZERwcLCkCx/DCg0NdX98H/8u+WOR2bNn1yOPPOJRCI8ePVqPPPKIjh49ypv7K0j+Bh8REeFeJ8rMNGPGDNWtW1cbN2708YQZQ8uWLXX77bd7bNu6davy5MmjChUq+GiqjCG5UKtQoYIOHjyo0NBQffTRR3r++ef17LPP6rbbbtPUqVP18ssv+3jS9OniQnj8+PEaMmSI++zuZuZ+E1qlShWdPHlSMTExPps1vUouJ4OCgjR27FiVLFlSMTEx+vrrr3Xy5En395EcOXIoKChIWbNmdd8O/y75+d2lSxcVL15c/fr1U1xcHN+b/0VyNpGRkRo3bpwGDhyopKQkRUVFyeVyuQuOAgUKKDg4OMUvJ3FBYGCgWrVqpfHjx+vgwYNq3Lix6tatq0OHDmnGjBmSpKJFiyo2NpbS6P+tW7dOx44dc19Ofl186aWXVLx4cU2YMEETJkxQYmKismbN6s6tZs2aOnnypM6cOeOr0dOVf+Z4scDAQPfrYqNGjTRw4EB3Ibx69Wp98sknuvvuu7V79+60HDnduTjDi5czuFhyjkFBQe6lxMxMH3zwgerUqeP3GUqeOf7zPUtykT5z5kw1adLE4xe1FStWVEBAAGsH/0PyY7FVq1YaM2aMoqOjtXfvXj3wwAN6+eWXtX79epUqVUoLFy7U+fPn1b17dy1fvpxCOIPhHSquiwYNGmjixIk6ePCgXn31VY0ZM0aSdPz4cW3ZskXShR82cXWSv2mFhoaqU6dO6tOnj1auXKlevXpp0KBBeuyxx3w9YoaRNWtWuVwunT9/XnPmzNGjjz6q/v3768EHH/T1aBnGxUcJnj9/XitWrFBERISKFi3qw6nSv+Q3p/fcc48iIyPVunVrPfzww+rXr59Gjx6td955R2XLllXZsmV9PGn6dXEhfN999yk4ONi9VllAQIDOnTunn3/+WdWrV1dERISPp02fkgu4OnXq6OWXX1aFChU0cuRIzZ49W2fOnNGZM2e0cOFCnTlzRqVLl/bxtBlH8vM7X758aty4sdatW8cvGVMhe/bs+uyzzyRdKD7i4+O1ePFiZcmSRWXKlPHxdOlXjhw51K5dO0VHR2vp0qVatGiRNm7cqIoVK+rcuXP69ddfVblyZWXJksXvf1h/5plndPPNN+uzzz7TyZMnJV14XUxMTFSuXLn04YcfKn/+/Bo3bpwGDBighIQEBQUFSbqwxnXu3LmVkJDg95+YuFSO/3TxOt+NGzfWkCFDVLx4cdWrV09PPfWUOnXqpP/85z9pOXa6cjUZSv87yjoiIsL9vWbGjBl64okn1KZNG5UrVy5N5k2vrjbH5KPWpf+dtPmnn37SzTffrLCwML9/Tv/TuHHjtHbtWs2aNUvz5s3TqlWr9OSTT+q9997TL7/8osTERJUsWVILFizQH3/8ob59+1KqZzRpvCwF/MDF6+2sWrXKHnjgAQsNDbXSpUvbjTfeaJGRkTZ48GAfTpixnTt3zoYNG2Yul8vefPNN93bW6vl3yY/L77//3goVKmT9+/e34OBg9xqtZmR4rfbv328TJ060HDly2OjRo309ToaQmJhop0+ftnvvvdeyZ89ugwcP9ljb9uTJkz6cLmM7duyYTZ061bJnz27vvPOOr8fJEOLj423Dhg122223WUREhJUoUcJuvfVWCwsLsyFDhvh6vAwn+fvM0aNHLTw83B588EEfT5RxJH//nTlzpoWFhVn9+vVt5MiR9txzz1muXLls2LBhPp4w/bvUe5jDhw/btGnTLDw83N5//30fTJW+TJ8+3fLkyWMlS5a0HDly2DvvvGOxsbHu65PPk3D48GFr3769lShRwmrUqGFDhw61nj17Wvbs2W3MmDE+mj79uFKO/5T82njy5Enr3LkzP8PYtWWYnF/nzp3ttttus2nTpllQUJANHDjQvY8/Zmh27Y/FZCdOnLBp06ZZ7ty5bfLkyWkwafp17tw593lgLvbUU09Z8+bNLS4uzszMdu7cafny5bP27dvbmTNnzOx/P7fs3r3bduzYkXZDwxGUwbjuDh8+bKtXr7bnnnvORo4caXPnznVf56/fuLzx4Ycfmsvlstdff929jRyv3i+//GIul8tcLpfHLyXI8NosXrzY2rVrZ4ULF7bhw4e7t3Pyhauza9cumz17tsXExLi3XZwdOV6b77//3lq3bm25c+e21157zb2dHK/ehx9+aK+88ooNGTLEPv/8c/d2XhuvTWJioiUkJFjXrl1t7Nixvh4nw4mNjbUPP/zQbrjhBouMjLRbb73VJk6c6L6ex+PV27lzpz3zzDNWsGBBj1/u+Ovr4sGDB61Zs2ZWs2ZN27Jliz311FMWFBR02UI4NjbWZs6cac2aNbMyZcpYvXr17O2333bvR47/nuM/nT592oYMGWIul8vjfaM/PqdTm2Hfvn0tMDDQ44TDZv6ZoVnqc1y2bJk9//zzlitXLr9/bYyLi7OqVat6/HIm2X333We1a9c2swtlb86cOe3+++93H8Tyzjvv2Pz589N0XjiLxbdwTczMYx2eixcXv5y8efMqb968uvnmmz22JyUl+e1aeqnJMVnr1q0VHx+vDh06SPLfHFObYXh4uKpWraouXbqoW7dukvw3Qyn1OebKlUtVqlRRu3bt1Lx5c0n+neO1SEpKUokSJVS8eHGP7C/3d/y78+fP6/Dhw8qePbveeOMNv39tvFbJz/lLLZVDhtcuOa+hQ4cqMjJSUsrXWVxeWFiYHnzwQT344IP6888/lT17dneOPB6vTcmSJVWjRg3deeedatmypST/zrBAgQKqUqWKateurQoVKmjcuHGKj4/Xc889J0l6+OGHFRYWpixZsigxMVFhYWFq3bq1WrdurdOnTytLlizu85+Q45Vz/Kfs2bPr2LFj6t27t3r37i3Jf3O81gyTc8qTJ4+SkpI0ZswYPfPMMx7X+aPUPBbj4+P19ddfa+fOnRoxYoQ6deokyT9zPH36tM6ePavnn3/e/T3i2LFjypUrl8xMVatW1aZNm/Txxx+re/fuatiwoSZNmqTQ0FDt3r1bc+fOVZMmTdSoUaOr7jGQvrjMWBwF127+/PmqVq2aChcu7OtRMrRrzTEhIcHjBCr++I3rn1LzWDx48KAKFiwoiQyTpSbH+Ph49zp6lB3wpfPnz+v06dOURkh3eG28dpd6/pLjtblUXv78uvjPPJIvm5meeOIJTZs2TWPGjNFDDz2k8PBwSZ6/HE9KSpLL5XLfxl8fi6nJ8XKPO399PHrzWPz777+1du1a3X333ZL8N0PJuxxPnz6tmJgYFSpUSJJ/5njq1CkVKFBA9913n6ZNmyaXy6UePXooJiZGgwcPVrFixXTw4EHddNNNOnjwoO666y7NmTNHwcHBOnz4sPr27asVK1bom2++UfHixX395SCVODIY1+zgwYPq0aOH7r77bo0dO9bvXjydkpoc/3kmbX/PPrWPxfz580u68MbB3zOUUp9j1qxZ3X/nB6Nruw6evM0xa9asfl8E81h0htM5+mvu3uR4qeevP+boTYb/vM7f3+/8M4/kyy6XSxMnTpQk99GEHTt2lJnp66+/VoUKFVSpUiWP7PzxsZjM2xwv5q+Px9RmWKZMGVWrVo0i+P+lNsdy5cqpSpUqCg0NleSfr41JSUnq2rWrKlasqP/+97/uEj0kJESffvqpcubMqWeeeUYlS5bUV199pebNm2vv3r0aMWKEsmbNqlWrVumHH37QkiVLKIIzOMpgXLOCBQuqdu3aWr58uU6dOqXw8PArfkO6+E3r2bNnlS1btrQaN90iR++lNsPk68+dO+f3GUo8FlPr4gwWLFigVatWac+ePWrQoIFuvfVWlSlT5ppy9NfCzukcyZDHYmqRozPI0XtOZ4jLc7lcmjBhgqQL5VFcXJzOnDmjQYMGafLkySlKTFwaOXrvShlWq1bNva+/FZjX4ko5VqlSxWNff5OUlKT4+HgVKVJELpdLbdu2VcmSJfXGG28oMDBQkydPlpmpZ8+euuGGG7Ry5Ur16tVLX375peLj41WtWjWtXLlSFSpU8PWXAm95u+gw/EvySRX2799vuXPntr59+17xNhcvxj569Gjr06ePnT179rrNmBGQo/fI0Bnk6L0pU6ZYcHCwNWjQwKpWrWqVK1e2ypUr2w8//PCvt7s4x2nTptnw4cP98uQVycjRe2ToDHJ0Bjl6jwzTVseOHS95kmFcG3L0Hhk6gxw9JZ9ssE+fPpY7d26rVq2a5cuXzzZt2uTe54UXXrA8efJYjx49bOfOnWZmFh8fb+fOnbNz5865f3ZExkcZjKty/Phx998TExPt7Nmz9swzz9hNN93kfpG4lIvfeL711lvmcrk8zgrtb8jRe2ToDHJMnX/+ML1x40YrWrSojRo1yo4ePWpmZsuXLzeXy2W33XabnTlz5or3M3bsWHO5XDZjxozrN3g6Q47eI0NnkKMzyNF7ZOhbsbGx9sYbb5jL5bKxY8e6tyeXJ7g65Og9MnQGOf7PyZMnbceOHR7bcuXKZSEhIdajRw+PnwvNPAvhPXv2pOGkSEuUwbii2bNn2x133OF+EU1+Af3+++8tKCjI3nvvPTNL+Sb24svjxo2zgIAAmzx5chpNnf6Qo/fI0BnkmDonTpwwM7OEhAT3toULF1qhQoVs/fr17m133323lStXzjZs2GBmF96AXeyfOWbJksWduT8gR++RoTPI0Rnk6D0y9K34+HibPn26uVwuGzRokHu7P5ZG3iBH75GhM8jxfxISEqxZs2Z27733WlJSkp0/f94WLFhgpUuXtoYNG1quXLnsjTfesL///tvjdi+88IIVLFjQOnToYPv37/fR9LieKINxRbNmzbK7777bcuTIYbVr17YxY8ZYbGysmZk999xzVqJEiRRHEl6qNPL3N6Pk6D0ydAY5XrtRo0ZZWFiY7d2718wuvMk0Mxs/fryFhYW592vSpIlFRUXZL7/8YmZma9assT59+lhMTIyZkSM5eo8MnUGOziBH75Fh+jBlyhSbNGmS+7I/lkZOIEfvkaEzyPF/Vq9ebadOnTIzs0OHDpnZ/34J2b59e8uVK5cNHz7cjh075nG7bt26WenSpd23QeZCGQwPl1tP7MSJE/bzzz9bo0aNrESJEhYVFWVTp061MWPG2C233GJTpky55O3GjBljAQEB9t///vc6Tp3+kKP3yNAZ5OiMTz75xCpWrGhlypRx/8BuZrZ9+3bLkyePDRw40O655x6LioryOGKrb9++9vDDD9vhw4c97u/NN9+0rFmz+t0P6uToPTJ0Bjk6gxy9R4bpjz+XRk4iR++RoTP8Ncd//hw4cOBACwgIsK1bt3psb9euneXKlctGjBiRohD+66+/rvuc8A3KYFzS4sWLbcyYMTZs2DCPNWTi4+Nt1apV9tBDD1mRIkWsZs2a5nK5rFatWh4fbTMz++GHH6xIkSJ+tZ7oP5Gj98jQGeTovXnz5lnlypWtZMmStm/fPjMzO3LkiHXs2NEiIyMtIiLCtm/fbmZmZ8+etWnTpln+/Plt6tSpHvezePFic7lcHkcr+BNy9B4ZOoMcnUGO3iND7/zbyfGu9cR5/nyiPXL0Hhk6gxyd8c8SfObMmVajRg2Lioq6bCE8cuTIFEtGIHOiDEYKU6ZMsdDQUCtcuLCFhoZasWLFbMWKFe6PrSX78ssv7eWXX7aAgADr379/ivs5ceKE/fjjj2k0dfpDjt4jQ2eQo3cufhM5d+5c9w/sySdU2Lx5szVt2tSKFStmXbp0sffff9+6d+9uYWFhNmTIkBT3s3fvXluxYkXafhHpADl6jwydQY7OIEfvkaH3Ls5w/vz51q9fP3vwwQdtypQp9ttvv5nZlY8KvPg+/LU4IkfvkaEzyNEZFx/Uc/FJ4ObPn281atSwQoUKpSiEH374YfcJ9/z1aGp/QhkMDydOnLDmzZvbW2+9Zbt27bKffvrJ6tWrZ/ny5bOFCxde8kXhyJEj7r8nv9j6+4sHOXqPDJ1Bjs6bPXu2Va5c2UqUKGG7d+82swsf5x02bJjdcMMNVrx4cWvRooXHyfXILyVy9B4ZOoMcnUGO3iPD1JsyZYoFBwdbgwYNrGrVqla5cmWrXLmy/fDDD/96u4uLomnTptnw4cP9tjwyI0cnkKEzyDH1Li6Cn3vuOWvSpIl999137m3/Vgg//vjjtm3btjSbFb5DGQy3hQsXWrdu3ax27dq2adMm9/ZDhw5ZkyZNLG/evJctj8x4M5qMHL1Hhs4gx9S7+E3j33//badPn3afpMfMbM6cOVapUiWPH9jPnz9vZmanTp3yOOqaHC8gx9QhQ2eQozPI0Xtk6L1/FjsbN260okWL2qhRo+zo0aNmZrZ8+XJzuVx222232ZkzZ654P2PHjjWXy2UzZsy4foOnM+ToPTJ0Bjk65+IMWrdubSVLlrQJEybYjh07PPb76quv7MYbb7RChQpR/vopymCY2YX1x5o2bWr58+e3smXLurcn/1bpr7/+siZNmljBggVt/vz5fvvm80rI0Xtk6AxyTJ3kH7yTffTRR3brrbdaiRIlrEGDBh7rJSev8Vi8eHH3x6+S801+I+ZvRyIkI0fvkaEzyNEZ5Og9MnTGiRMnzMzzyLeFCxdaoUKFbP369e5td999t5UrV87jpHsXuzi/cePGWZYsWfzqpHvk6D0ydAY5Xh+DBw+2kiVL2po1a+zs2bNmZhYXF+fO2+xCzrVq1bLg4GD3uvTwH5TBcPvjjz+sbdu25nK5rGfPnu7tySXR4cOHrX79+pYlSxaPsx3DEzl6jwydQY7XZsKECRYYGGjLly83swtrKIeEhFjHjh2tR48e1qJFC3O5XNatWzf3UVqzZ8+26tWrW+HChW3Xrl2+HD/dIEfvkaEzyNEZ5Og9MnTGqFGjLCwszP2eJfko6fHjx1tYWJh7vyZNmlhUVJT98ssvZma2Zs0a69Onj/sI7H+WRgEBAX5VGpGj98jQGeR4/Tz00EN27733ui//+uuvdt9991mtWrWsU6dOdujQITO78GmUunXrutdjhv+gDPZD/3YkwYEDB6xVq1ZWqlQpGzx4sHt7cnl06NAhmzdv3nWfMSMgR++RoTPI0Rnbtm2z22+/3QoXLmxr1661gQMH2rPPPus+8uD48eP23//+14KCgqxfv37u282aNcuioqJsypQpPpo8fSFH75GhM8jRGeToPTJ0xieffGIVK1a0MmXKePwSe/v27ZYnTx4bOHCg3XPPPRYVFeVx9GDfvn3t4YcftsOHD3vc35tvvmlZs2b1u9KIHL1Hhs4gR2f88xOeZ8+etXvuucdq165tkydPtldeecVCQkLslltusUcffdRCQ0Ote/fu7v1PnTqV1iMjHaAM9jMXl0aLFy+2119/3Tp16mSTJ092/zZo3759du+991qZMmVs0KBB7v3/+SLjzx8rJ0fvkaEzyNFZO3bssDp16ljBggWtevXqNnr0aI/rz58/b0OHDrUsWbLY0qVL3dt37tyZxpOmb+ToPTJ0Bjk6gxy9R4bOSF5Co2TJkrZv3z4zu3Di244dO1pkZKRFRES4P+589uxZmzZtmuXPn9+mTp3qcT+LFy82l8tlkyZNSvOvIT0gR++RoTPI0TsXL6/xxRdf2B9//GFmZps2bbKoqCgrUKCAVa5c2UaMGOHer2PHjla3bl07d+5cms+L9IMy2E9NmTLFsmfPbtWrV7eiRYtazpw5rWTJku6zcyaXR+XLl7f+/fv7dth0jBy9R4bOIEfn/P777+6P7Hbt2jXFSSq2bdtm+fLlswkTJqS4rb+u4Xgp5Og9MnQGOTqDHL1Hhql38dc/d+5cd3mUvKby5s2brWnTplasWDHr0qWLvf/++9a9e3cLCwuzIUOGpLifvXv32ooVK9L2i0gHyNF7ZOgMcvTexUXwI488YlWqVLHevXvb6dOnzczs4MGDtmPHDo9fKh49etQaNWpkTzzxhMft4X8og/3Qhg0brECBAjZq1Cj766+/zMxs5syZdscdd1h4eLh7ofY//vjDmjdvbnnz5rV169b5cuR0iRy9R4bOIEfnbdu2zZo2bWoRERH2zTffeBw1ffjwYYuKirKhQ4f6cMKMgRy9R4bOIEdnkKP3yNAZs2fPtsqVK1uJEiXcJ+fbvn27DRs2zG644QYrXry4tWjRwiZPnuy+DZ+ASokcvUeGziDH1Gvfvr0VLVrUFixYYAcOHDCzS/8CcevWrdahQwfLly+fbd26Na3HRDpDGeyH5s2bZ3ny5LGffvrJY/vy5cutRo0a1qBBAzt27JiZme3fv9++/vprX4yZ7pGj98jQGeSYOhe/SYqPj7fExESLi4szswtvLrdv32533nmn5cuXzz788EM7cuSIHTlyxP773/9acHCwffnll74aPV0hR++RoTPI0Rnk6D0ydMbFOf799992+vRp9wmjzC6c+KhSpUoe5VHyCfhOnTrlPhmVmX+XRuToPTJ0Bjk6b86cORYVFWXLli1zb4uJibH169fbli1b3NtefPFFq169upUpU8Z9Ij74N8rgTO7iF9zkjwskv9FM/q3RxWvFvPbaa5YzZ073ej0X8+cXXHL0Hhk6gxydcXGO8+bNs8cff9xq1qxp3bp1s9WrV5vZ/35gr1+/vgUEBFi5cuWsRYsWVqZMGXv99dd9NXq6Qo7eI0NnkKMzyNF7ZOi95BIo2UcffWS33nqrlShRwho0aGATJ050X5e83mjx4sXdHy9P/uhz8v8Lf11igxy9R4bOIMfr5+OPP7aoqCjbtm2bnT9/3r799lsrU6aMFShQwFwul73xxhtmZvbjjz9a//79bdeuXT6eGOkFZXAmdvGL5Jdffmn9+vWz77//3vbs2WO5c+e2Dh06uK9PPlph6dKlFhwcbD///HNaj5tukaP3yNAZ5Oi86dOnW2hoqHXu3Nl69eplN998s+XLl8+WLFliZhd+YN+2bZu1adPGXC6XjR8/3jZt2uS+vT8X6hcjR++RoTPI0Rnk6D0yTJ0JEyZYYGCgLV++3MwuvN8JCQmxjh07Wo8ePdxrLnfr1s19xODs2bOtevXqVrhwYYqO/0eO3iNDZ5Dj9fX1119bSEiItWjRwho1amTZsmWzhx56yD7//HN76aWXLDAw0H7//Xcz89/vK7g0ymA/MGXKFAsPD7fOnTvb8uXLLSEhwV5++WXLnTu3vfDCC+79EhISbOjQoVaqVCn3b+HwP+ToPTJ0Bjk6Y+nSpVa0aFEbOXKkmV04Y3uOHDksb968liNHDlu8eLGZXchxy5YtVqNGDY8zF3NUwgXk6D0ydAY5OoMcvUeGqbdt2za7/fbbrXDhwrZ27VobOHCgPfvss3by5EkzMzt+/Lj997//taCgIOvXr5/7drNmzbKoqCibMmWKjyZPX8jRe2ToDHJ0xr+d7O3dd9+1unXr2mOPPWbTp093b583b56VLl06xZHZgBllcKa3YsUKy5kzp40aNcq9ZqiZ2Z49e6xr166WPXt2a9Cggb388svWo0cPy5Ytm40YMcKHE6dP5Og9MnQGOabeP3+4fuutt6xr166WkJBg0dHRFh4ebl26dLGFCxdapUqVLCwszH1W4sTERDtx4oQvxk53yNF7ZOgMcnQGOXqPDJ21Y8cOq1OnjhUsWNCqV69uo0eP9rj+/PnzNnToUMuSJYstXbrUvX3nzp1pPGn6Ro7eI0NnkKN3Li6C//vf/9orr7xir7zyivuTJmYXlg9MPrLa7MIJSTt06GA333yzx8+MQDLK4Exu7NixVrFiRY+jApM/HnDo0CGbOnWq3XLLLVagQAGrU6eOvfPOO+79/PmohH8iR++RoTPI0Xtz5861M2fOWHR0tK1bt87OnDljt956qz3wwAPuN0vDhw83l8tlQUFB9tVXX3ncnhwvIEfvkaEzyNEZ5Og9MnTO77//7v74eNeuXe3MmTMe12/bts3y5ctnEyZMSHFbcvwfcvQeGTqDHFPn4qUd2rZta0WKFLHGjRtbuXLlrFKlSjZ06NAUt/n888+tQ4cOljNnTvv111/TclxkIFmETG3z5s06deqUihUrJkkyMwUEBEiS8ufPrwYNGujRRx/VmTNnlJSUpBw5ckiSkpKS3PuBHJ1Ahs4gR+98/vnnuu+++zRp0iQ9/vjjkqTffvtNe/bsUdeuXZUzZ05JUqlSpVS1alWFhYVp3759HvfhcrnSfO70hhy9R4bOIEdnkKP3yNBZpUuX1vDhw5WQkKBPPvlE9957rxo0aOB+L5MrVy4FBwfr+PHjKW5Ljv9Djt4jQ2eQY+ok5/P000/rxx9/1IwZM1S7dm0NHDhQAwcO1HvvvafExET169dPkvT111+rR48eKly4sJYvX67KlSv7cnykYzQDmVylSpUUGxurJUuWSLrwQmpmkqRdu3bprbfeUnR0tLJnz67Q0FBJnuUSLiBH75GhM8gx9Q4fPqz58+fr1VdfVevWrd3bjxw5opiYGMXHx7u3/fbbb6pTp44++eQTPfnkk74YN90iR++RoTPI0Rnk6D0yTL3k9zCSdP78eSUlJbnzKlOmjEaPHq0bbrhBDz/8sD755BMdPXpUR48e1RdffKHDhw9TdPw/cvQeGTqDHL1z9uxZbdiwQYmJie5tGzdu1O7du/XGG2+odu3aGjp0qAYPHqy3335bJUqU0KhRozRixAhJUtOmTfXxxx9rzpw5fp8lriDtD0ZGWvr9998tIiLC7rnnHtu8ebN7e1xcnE2YMMHKlStn69ev9+GEGQM5eo8MnUGOqfPZZ59Zo0aN7Oabb7bPP//czDw/cnbXXXdZnjx5rHv37tazZ0/Lnj27TZ482X29P3887WLk6D0ydAY5OoMcvUeGqXfx1z5v3jx7/PHHrWbNmtatWzdbvXq1mV34iPT27dutfv36FhAQYOXKlbMWLVpYmTJl7PXXX/fV6OkKOXqPDJ1Bjt5r06aNRURE2LJly9xrBR85csSGDx9usbGxNnfuXMudO7e9//77ZnZhPeaIiAgrXbq0x4nEgSuhDPYDCxcutJCQEKtdu7aNGTPGFixYYH379rXQ0FB74403fD1ehkGO3iNDZ5Djtfvmm2/M5XKZy+Wyd9991709Pj7ezC68MW3durUVLVrUKlSokOLEFriAHL1Hhs4gR2eQo/fI0HvTp0+30NBQ69y5s/Xq1ctuvvlmy5cvn/vkSImJibZt2zZr06aNuVwuGz9+vG3atMl9+4vX1PRn5Og9MnQGOabenj17rHr16la2bFlbunSp+3vJ2bNnzczsiSeesEaNGtmRI0fM7MLJ5SpVqmTFixe3mjVrurcDV0IZ7Cd++OEHq169ukVGRlpwcLBVrlzZ482oPx+VcC3I0Xtk6AxyvLLkDJLfUK5atcqyZs1q1apVszVr1rj3u/jMu0eOHLHDhw+7L/vzm9Fk5Og9MnQGOTqDHL1Hhs5ZunSpFS1a1EaOHGlmZjt37rQcOXJY3rx5LUeOHLZ48WIzu1B4bNmyxWrUqGFTp0513573OxeQo/fI0BnkmHrJ3xf2799vVatWtdKlS9vSpUvdRwifO3fO6tSpY/Xq1XPfZu/evXbvvffa0qVL7cCBAz6ZGxkTZbAfOXHihO3evduio6M9Xih4M3ptyNF7ZOgMckzpSm8glyxZYoGBgdakSROP5TSSf+t+LfeVmZGj98jQGeToDHL0Hhk6459f+1tvvWVdu3a1hIQEi46OtvDwcOvSpYstXLjQKlWqZGFhYbZixQozu/D+5sSJE74YO90hR++RoTPI0RnJha+Z2alTp2zfvn1Wo0YNK1OmjC1dutT9y8UpU6ZYQECADR482D777DPr2LGjlShRwv7++29fjY4MijLYz/nzm1EnkaP3yNAZ/pzjxV/7okWLrGfPnvbQQw/ZgAEDPPZbtGiR+wf2DRs2pPWY6R45eo8MnUGOziBH75Gh8+bOnWtnzpyx6OhoW7dunZ05c8ZuvfVWe+CBB+zYsWNmZjZ8+HBzuVwWFBRkX331lcft/fn9zsXI0Xtk6AxyTL2Li+BevXpZ9+7dbfny5bZv3z6rXLmyuxBOSEiwmJgY69evn7lcLsuTJ48VK1bMfvnlFx9Oj4yKMhgAkKlMmTLFcubMaU2bNrXOnTtbcHCwtWvXzvbu3et+o7lo0SILCQmx+vXr29q1a308cfpEjt4jQ2eQozPI0Xtk6Ix58+aZy+Wy9957z71t+/btVrhwYfvggw/c22bNmmXVqlWz2267zWM9ZlxAjt4jQ2eQozNat25txYsXt9GjR9v+/fvN7MKSEVWqVLEyZcrYsmXL3Ptu2bLF1q9fb4cOHfLVuMjgKIMBAJnGN998YwUKFLARI0aYmdlPP/1kISEh5nK5rEGDBrZv3z73D+zJJ/2ZN2+eL0dOl8jRe2ToDHJ0Bjl6jwyd8ddff1mXLl2sf//+dvz4cff2lStXWo4cOWzy5Mnuba+//rp1797dXYrgf8jRe2ToDHJ0xujRo61QoUK2cuVK9/JCyd9T9u/fb5UrV7bSpUvb999/f8nlh4BrRRkMAMgUzp07Zy+++KJ16NDBzMw2b95s4eHh9vjjj9usWbMsPDzcWrRoYXv37nWvq7xv3z5fjpwukaP3yNAZ5OgMcvQeGTrjs88+s0aNGtnNN99sn3/+uZl5fjT8rrvusjx58lj37t2tZ8+elj17do8iyZ8/Rn4xcvQeGTqDHJ3ToUMHa9y4cYqi9+KTytWsWdNy5szpXnMZ8AZlMAAg01izZo2tWrXKYmJirHLlytauXTs7efKkxcbG2oMPPmgul8uaNWtme/bsMbOUZ4THBeToPTJ0Bjk6gxy9R4beSz5i2uVyeXxEPLn8SExMtNatW1vRokWtQoUKNnr0aF+Nmq6Ro/fI0Bnk6IzExES78847rX79+u5t/yzKN23aZHv27LE6derY77//ntYjIhOiDAYAZDiXO5Lg7NmzZmb23XffWdGiRT1+cz569GirUqWKBQUF2cyZM9NkzvSOHL1Hhs4gR2eQo/fI0Fn/LMVXrVplWbNmtWrVqtmaNWvc+50/f9799yNHjtjhw4fdlynUydEJZOgMcrw+nn76aStcuLBHhsmWLl1qTz/9tP35558euQLeCBAAABmImcnlckmSfv31Vy1dulTff/+9zp07p5CQEEnSuXPndOTIEf3999+SpLi4OO3du1etWrXSn3/+qdatW/ts/vSCHL1Hhs4gR2eQo/fI0Blm5v57cp4BARd+7Kxdu7a++eYbRUdHa8CAAdqwYYMkKUuWLDp//rwkKU+ePMqbN6/7vpJv62/I0Xtk6AxyvP66dOmi48ePa9iwYfr111/d2//++29NnTpV69atU1BQkLJkyeLDKZGZ8EgCAGQoyW9C33//fb344os6ffq0smXLpnz58mn8+PG69dZbVa5cOZUtW1ZvvfWWtmzZouDgYE2bNk3jx49X7ty5JUlJSUl+/WaUHL1Hhs4gR2eQo/fI0HsXF+qLFy/WggULdPjwYZUuXVr9+/eXJNWtW1cLFy5UkyZNJElDhgzRjTfeqKxZs6a4v+T78jfk6D0ydAY5po1KlSpp5syZatWqlXbu3KlGjRopMjJSK1eu1Nq1a7V8+XL39xjAEWl9KDIAAKlx8Ud3N2zYYEWLFrUhQ4bYsmXL7KuvvrKGDRtaWFiYffDBB2Zm9sUXX9gtt9xikZGRFhUVZcOHD/fV6OkKOXqPDJ1Bjs4gR++RofOmTJliOXPmtKZNm1rnzp0tODjY2rVrZ3v37nXnvWjRIgsJCbH69evb2rVrfTxx+kSO3iNDZ5Bj2li/fr01btzYihYtamXLlrVmzZpZdHS0r8dCJkQZDADIUDZs2GDTpk2z5s2bp1h/rG3btpY7d273iRX27Nlju3btss2bN3vsB3J0Ahk6gxydQY7eI0NnfPPNN1agQAEbMWKEmZn99NNPFhISYi6Xyxo0aGD79u1zl0fJJ6CaN2+eL0dOl8jRe2ToDHJMW2fPnrUTJ05YTEyMe716wGmUwQCADOPgwYPmcrksMDDQGjRo4N6efDKFw4cPW7ly5ax9+/aXPAnQ5U4M5G/I0Xtk6AxydAY5eo8MnXHu3Dl78cUXrUOHDmZmtnnzZgsPD7fHH3/cZs2aZeHh4daiRQvbu3evuzzft2+fL0dOl8jRe2ToDHIEMif/XMwKAJAhFShQQAsXLlS+fPn0ww8/aMmSJTIz98kU8ubNqypVqmjv3r2XXJOMdcouIEfvkaEzyNEZ5Og9MnRGcHCw7r33Xj3++OOKjY3VAw88oKZNm2rMmDFq1KiRmjdvri+++EJPPfWU9u/fL0kqUqSIpAtrLeMCcvQeGTqDHIHMiTIYAJAu2UVnLr5Yo0aNNH36dAUGBmrkyJGKjo52X5eYmKjg4GAFBwfrzJkzl70Pf0KO3iNDZ5CjM8jRe2TojMtlUK1aNdWuXVvr169XbGysunXrphw5cigsLEw1atRQ5cqV9e233+rHH3+U9L8i3Z9Puncp5Hj1yNAZ5Aj4jyy+HgAAgH+yi85cvH79eh06dEhHjx5V06ZNFR4ergYNGmjGjBlq06aNnnjiCT3yyCOqWLGiNmzYoBkzZmjcuHHKnj27j78K3yNH75GhM8jRGeToPTJ0xsU5/vrrr/r7778VEBCgWrVqKSQkRJJ07tw5HTlyRH///bckKS4uTnv37lWrVq3UvXt35c6d22fzpxfk6D0ydAY5An7m+q9EAQBA6rz//vuWJ08eK1y4sIWHh1vRokVt7NixduDAATO7cJKKnDlzmsvlslq1alnLli3tnXfecd+eNRwvIEfvkaEzyNEZ5Og9MnTG5MmTLU+ePJYtWzbLlSuXlS9f3pYuXWrx8fG2Y8cOq1atmtWvX99ef/11GzVqlEVGRtonn3zivj0n3buAHL1Hhs4gR8A/UAYDANKlxYsXW1hYmL355pv266+/mpnZE088YSEhITZixAiLj483M7PvvvvOcuXKZU2bNrWNGze6b5980h9/R47eI0NnkKMzyNF7ZJh6F5fgGzZssKJFi9qQIUNs2bJl9tVXX1nDhg0tLCzMPvjgAzMz++KLL+yWW26xyMhIi4qKsuHDh/tq9HSFHL1Hhs4gR8A/UQYDANKl119/3e644w77888/3dtatGhhpUuXtk2bNpmZWUJCgpmZLViwwLJnz2733HOPbd++3Sfzplfk6D0ydAY5OoMcvUeG3tuwYYNNmzbNmjdvbocPH3ZvT0xMtLZt21ru3Lnt999/NzOzPXv22K5du2zz5s0e+4EcnUCGziBHwL9QBgMAfO5SH7dt3bq13XDDDe7LTZo0sSJFitgvv/xiZmarVq2yVatWuW/7zTffWI4cOey2226zgwcPps3g6Qw5eo8MnUGOziBH75Gh8w4ePGgul8sCAwOtQYMG7u3JR0wfPnzYypUrZ+3bt79k/iyxcQE5eo8MnUGOgP/h9I4AAJ+yi05YsWXLFm3evFmSdNNNNykuLk7bt2/X3XffrejoaH355ZeqVq2ajh07pnHjxmnZsmVKSEiQdOEs8B9++KHKly+vAgUK+Ozr8RVy9B4ZOoMcnUGO3iPD66NAgQJauHCh8uXLpx9++EFLliyRmSlLlgvnJs+bN6+qVKmivXv3uvO/2KW2+SNy9B4ZOoMcAT/kqxYaAICLjyT48MMPrWrVqtalSxc7duyYrV271kJCQixnzpxWrFgxi46ONjOz+Ph4mzp1qpUoUcLmzZt3Vfed2ZGj98jQGeToDHL0Hhk649++1kWLFllYWJjddddd7rWXzS4ssfHggw9avXr17PTp036V1+WQo/fI0BnkCMCMZSIAAOnAhx9+aNmyZbPBgwe7P6ZrZjZz5kwLDAy0unXr2nfffWcbNmywoUOHWmhoqA0bNsyHE6dP5Og9MnQGOTqDHL1Hhql3ceHz448/2pdffmlTpkyxw4cP27lz58zMbP78+RYaGmq33HKLjR8/3r7//nsbPXq0ZcmSxd59911fjZ6ukKP3yNAZ5AggGWUwAMCndu7caWXLlrVBgwa5z+BuZrZr1y47cOCArV692kqVKmWFCxe2yMhIu/HGG23MmDHu/ThhxQXk6D0ydAY5OoMcvUeGznj//fctT548VrhwYQsPD7eiRYva2LFj7cCBA2Z2YU3lnDlzmsvlslq1alnLli3tnXfecd+eowgvIEfvkaEzyBEAZTAAwKeio6OtUKFCtnDhQjMzO3LkiD3wwANWsmRJc7lcNmzYMDt69Kht27bNfv75Z9u/f7/7tvyg/j/k6D0ydAY5OoMcvUeG3lu8eLGFhYXZm2++6f7Y+BNPPGEhISE2YsQId8n+3XffWa5cuaxp06a2ceNG9+2TT0Dl78jRe2ToDHIEYGaWxddrFgMA/FtkZKRiY2M1ceJEzZw5U8uWLZPL5VK3bt10+vRp9enTRxUqVNA999zjcTszU0AA50FNRo7eI0NnkKMzyNF7ZOi9devW6cYbb9T999+vggULSpIOHTqkIkWK6K677lLWrFmVmJioevXq6aOPPlKrVq30yiuv6I033lDZsmXdJ6Dyd+ToPTJ0BjkCkCTe5QAAfKpw4cKaN2+eNm7cqP379+vuu+/Wb7/9pueff15t2rRR0aJFL/lDOWcu9kSO3iNDZ5CjM8jRe2R4bcwsxbaffvpJsbGx7tLorrvu0oYNGzRr1ixVrlxZP/zwg9auXSszU5MmTTR37lwtWbJEnTp10qFDh9L6S0gXyNF7ZOgMcgRwOfxaBwDgc/Xr19fGjRsVHBysrFmzSpLOnj2rH3/8US6XS3ny5PHxhBkDOXqPDJ1Bjs4gR++R4dUxM3cJvmXLFpmZKlWqpJtuuklbtmzR9u3b9dxzzyk6Olpffvnl/7V3xyBt7VEAh08QsUUXi4NgsSgUOumkCHaRoqBDt24Wt2Khg1KETkVwsF06dOjskM0lQ1fBVWNAoVOhChaHgjgpKRX9v+kFy1NfffdS67vfNyUXEm5+Uzi5OTf6+/vj4OAg3r9/H319fTEwMBDNzc0xNjYW5XI5Pn78GJ2dndf8qX4/HbPTMB86Apf6nTspAOBXrK+vpw8fPqTW1tb09u3b6z6dG0vH7DTMh4750DE7Df/p7M2gyuVy6uvrS8+ePUsHBwdpbW0t3bp1K7W3t6d79+6lT58+pZRS+vHjR1paWko9PT2pUqn80nv/3+mYnYb50BH4N4bBAPxRvnz5kh4+fJju37+f3r171zjuy+fV6JidhvnQMR86Zqfh5crlcrp9+3ZaWFhIm5ubjePLy8upqakpjYyMpJWVlVSr1dLi4mJqbW1Nb968ucYz/jPpmJ2G+dARuEgppXMWyQDANTk5OYlarRbHx8cxPDwcERGnp6du5nNFOmanYT50zIeO2Wl4se3t7RgfH4/Jycl49epVY6XGzs5OtLS0xO7ubkxOTsb379/j6Ogoent74+nTpzEzMxMROv5Nx+w0zIeOwGXsDAbgj9LU1BSDg4ON58ld3f8THbPTMB865kPH7DS8WL1ej8PDwxgcHIzm5ubY39+PFy9eRLVajZ2dnVhcXIy1tbXY39+Per0eHR0dcffu3YgwNDpLx+w0zIeOwGVcGQwAAFBge3t78eDBgxgdHY07d+7E6upqlEqlmJ6ejqOjo5ifn49KpRKPHz/+6XXpzE2q0DEPGuZDR+Ayfu4BAAAosK6urqhUKrG1tRVfv36NiYmJ+Pz5c7x8+TKePHkS3d3d514paGj0Mx2z0zAfOgKXsSYCAACg4B49ehRbW1vR0tLS2C9ar9ejWq1GqVSKjo6Oaz7Dm0HH7DTMh47ARQyDAQAAiLa2tsbjarUaGxsbMTc3F69fv46hoaFrPLObRcfsNMyHjsB57AwGAACgYXt7O6ampuLbt2/x/PnzmJ2djQj7RK9Kx+w0zIeOwFmGwQAAADScnJxErVaL4+PjGB4ejoiI09PTc3eMcjEds9MwHzoCZxkGAwAAcCFXD+ZDx+w0zIeOUGyGwQAAAAAABeA/AQAAAAAABWAYDAAAAABQAIbBAAAAAAAFYBgMAAAAAFAAhsEAAAAAAAVgGAwAAAAAUACGwQAAAAAABWAYDAAAAABQAIbBAAAAAAAFYBgMAAAAAFAAfwFWTJXl3xBH0gAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if device is not None:\n", - " plot_per_layer_results(per_layer_results, akida_model, sparsity_dict,\n", - " model_name=akida_model_path,\n", - " savepath='benchmark_results_layers.png')" - ] - }, - { - "cell_type": "markdown", - "id": "67c6a41e", - "metadata": {}, - "source": [ - "## Summary\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "15a8c528", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Float: 0.8694\n", - "QAT: 0.8424\n", - "Akida: 0.8452\n" - ] - } - ], - "source": [ - "print(f\"Float: {float_accuracy:.4f}\")\n", - "print(f\"QAT: {qat_accuracy:.4f}\")\n", - "print(f\"Akida: {akida_accuracy:.4f}\")" - ] - } - ], - "metadata": { - "jupytext": { - "formats": "ipynb,py:percent" - }, - "kernelspec": { - "display_name": "ak2191", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.20" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/akida1/model_zoo/vww/vww_notebook_benchmark.ipynb b/akida1/model_zoo/vww/vww_notebook_benchmark.ipynb new file mode 100644 index 0000000..bb34be5 --- /dev/null +++ b/akida1/model_zoo/vww/vww_notebook_benchmark.ipynb @@ -0,0 +1,678 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1be5d78d", + "metadata": {}, + "source": [ + "# Visual Wake Words\n", + "\n", + "

\n", + "Run Time: ~10 minutes\n", + "

\n", + "\n", + "This notebook walks through benchmarking a model on the **Visual Wake Words** dataset,\n", + "targeting Akida 1 hardware.\n", + "\n", + "For details on the dataset and preparation of the model, see the neighbouring README.md\n", + "and vww_notebook_training.ipynb.\n", + "\n", + "## Model\n", + "\n", + "A pretrained Akida model is included in this repo, at \n", + "`pretrained_models/akidanet_vww_qat.fbz`. As with all model files in the repo, \n", + "that is handled via `git-lfs` (for efficient large file storage). If you haven't set that \n", + "up yet, see the [Trained models](../../../README.md#trained-models) section of the \n", + "top-level README.\n", + "\n", + "If you have run through the training scripts or notebook, and prefer to benchmark the \n", + "Akida model generated through that, simply modify the model directory and filename in the\n", + "following cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9fd73f67", + "metadata": {}, + "outputs": [], + "source": [ + "import akida\n", + "import os\n", + "import numpy as np\n", + "\n", + "from tqdm import tqdm\n", + "\n", + "os.environ.setdefault('TF_CPP_MIN_LOG_LEVEL', '1')\n", + "\n", + "DATA_PATH = './data/vw_coco2014_96'\n", + "MODELS_DIR = './pretrained_models/'\n", + "MODEL_FILENAME = 'akidanet_vww_qat.fbz'\n", + "akida_model_path = os.path.join(MODELS_DIR, MODEL_FILENAME)\n", + "\n", + "SEED = 42" + ] + }, + { + "cell_type": "markdown", + "id": "ecb45684", + "metadata": {}, + "source": [ + "To load the model, we simply have to pass the path to the `.fbz` file to the `akida.Model()`\n", + "method:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "abe7c05a", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the Akida model\n", + "akida_model = akida.Model(akida_model_path)" + ] + }, + { + "cell_type": "markdown", + "id": "bfdbcd30", + "metadata": {}, + "source": [ + "## Dataset\n", + "\n", + "`get_data` returns a training and a validation `tf.data.Dataset`. The full\n", + "preprocessing and augmentation code is in [vww_data.py](vww_data.py) —\n", + "standard tf_keras pipeline code, not described further here.\n", + "\n", + "Images are resized from variable original sizes to **96 × 96 RGB** and delivered as pixel values \n", + "in the uint8 range (0–255)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1c645ac1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 98658 images belonging to 2 classes.\n", + "Found 10961 images belonging to 2 classes.\n" + ] + } + ], + "source": [ + "from vww_data import get_data\n", + "\n", + "BATCH_SIZE = 32\n", + "INPUT_SHAPE = (96, 96, 3)\n", + "\n", + "train_ds, val_ds = get_data(DATA_PATH, INPUT_SHAPE, BATCH_SIZE, seed=SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "655c4739", + "metadata": {}, + "source": [ + "## Evaluation of Akida Model\n", + "\n", + "We now run evaluation through the Akida model, to check that accuracy is \n", + "comparable to that obtained from the quantized tf_keras model. If an Akida 1\n", + "hardware device is connected, it will be used for inference; if not, the\n", + "code will fall back to using the software backend: this delivers a\n", + "bit-accurate simulation of the results that will be obtained when running\n", + "the model on hardware. Let's run that check before going any further\n", + "\n", + "### Check for a connected Akida hardware device\n", + "\n", + "We can use the `akida.devices()` function to detect connected hardware devices.\n", + "That returns a list - if it's empty, there were no hardware devices. Otherwise, \n", + "typically we'd only have a single Akida device connected on a given machine, \n", + "and we can just select the first (and only) device returned." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bd9a1aa4", + "metadata": {}, + "outputs": [], + "source": [ + "devices = akida.devices()\n", + "if len(devices)>0:\n", + " # Hardware is available\n", + " device = devices[0]\n", + "else:\n", + " # Hardware is not available\n", + " device = None" + ] + }, + { + "cell_type": "markdown", + "id": "62abc26c", + "metadata": {}, + "source": [ + "In the present case, we want to be a bit more careful and ensure that the\n", + "device is the right version for the model we want to test (here, Akida IP\n", + "version 1). We'll import a local function to do that - check out the details \n", + "if interested" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "696a0034", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Target Akida device found\n" + ] + } + ], + "source": [ + "from brainchip_utils.hardware_utils import get_akida_device\n", + "\n", + "# Look for a matching hardware device\n", + "device = get_akida_device(target_version = akida_model.ip_version)\n", + "if device is not None:\n", + " akida_model.map(device, mode=akida.MapMode.Minimal, hw_only=True)" + ] + }, + { + "cell_type": "markdown", + "id": "d8f31c3d", + "metadata": {}, + "source": [ + "### Run Evaluation on Akida\n", + "\n", + "The Akida runtime cannot consume `tf.data.Dataset` objects directly. Rather\n", + "it expects a 4D numpy array (n, h, w, c) in uint8 format, so we\n", + "iterate over test batches manually. Obviously that situation arises because\n", + "the dataset is acquired here in tensorflow format. In a more representative\n", + "deployment situation, images would be acquired and sent to Akida without\n", + "ever going via the tensorflow dataset format.\n", + "\n", + "The model output tensor has shape `(B, 1, 1, C)` which is squeezed to \n", + "`(B, C)` before taking the class argmax.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "20ffb4cd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Evaluating on Akida: 100%|██████████| 343/343 [00:18<00:00, 18.52it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Akida accuracy: 0.8842\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "labels_all = []\n", + "logits_all = []\n", + "num_batches = len(val_ds)\n", + "val_ds.reset() # Ensure a clean run through the full validation dataset\n", + "for _ in tqdm(range(num_batches), desc=\"Evaluating on Akida\"):\n", + " batch, label_batch = next(val_ds)\n", + " if not isinstance(batch, np.ndarray):\n", + " batch = batch.numpy()\n", + "\n", + " logits_batch = akida_model.predict(batch.astype(np.uint8))\n", + "\n", + " logits_batch = logits_batch.squeeze(axis=(1, 2))\n", + " labels_all.append(label_batch)\n", + " logits_all.append(logits_batch)\n", + "\n", + "labels_all = np.concatenate(labels_all)\n", + "logits_all = np.concatenate(logits_all)\n", + "preds = np.argmax(logits_all, axis=1)\n", + "\n", + "akida_accuracy = float(np.mean(preds == np.array(labels_all)))\n", + "print(f'Akida accuracy: {akida_accuracy:.4f}')" + ] + }, + { + "cell_type": "markdown", + "id": "abdad1f1", + "metadata": {}, + "source": [ + "### Activation Sparsity\n", + "\n", + "Akida hardware skips computation for zero-valued activations, so activation\n", + "sparsity directly reduces both energy consumption and inference latency.\n", + "Below we measure per-layer sparsity on a 1024-sample calibration batch drawn\n", + "from the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "50ce55a0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-07-15 23:33:23.065412: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 109619 images belonging to 2 classes.\n", + "\n", + "Layer Sparsity\n", + "-------------------------\n", + "conv_0 23.86%\n", + "conv_1 37.03%\n", + "conv_2 56.00%\n", + "conv_3 40.28%\n", + "separable_4 33.30%\n", + "separable_5 65.21%\n", + "separable_6 40.46%\n", + "separable_7 48.44%\n", + "separable_8 60.60%\n", + "separable_9 65.68%\n", + "separable_10 74.43%\n", + "separable_11 59.27%\n", + "separable_12 89.35%\n", + "separable_13 89.25%\n", + "predictions 0.50%\n", + "-------------------------\n", + "Mean 52.24%\n" + ] + } + ], + "source": [ + "from akida_models.sparsity import compute_sparsity\n", + "from brainchip_utils.plot_utils import pretty_print_sparsity\n", + "from vww_data import get_samples\n", + "\n", + "NUM_SAMPLES=1024\n", + "samples = get_samples(DATA_PATH, INPUT_SHAPE, num_samples=NUM_SAMPLES)\n", + "sparsity_dict = compute_sparsity(akida_model, samples=samples)\n", + "pretty_print_sparsity(sparsity_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "bc67b63b", + "metadata": {}, + "source": [ + "## Hardware Benchmarking\n", + "\n", + "**These cells require a physical AKD1500 device to be connected.** If `device is\n", + "None` (reported in the evaluation section above), skip ahead to the Summary.\n", + "\n", + "Akida is an event-driven architecture: computations scale with the number of\n", + "non-zero activations, not with tensor size. That means benchmark results are\n", + "*input-dependent* — random or synthetic data would give artificially fast or\n", + "slow timings. The `samples` array loaded above (real images from the training\n", + "split) is therefore the correct input to use here." + ] + }, + { + "cell_type": "markdown", + "id": "a422ad44", + "metadata": {}, + "source": [ + "### Simple Benchmark\n", + "\n", + "The simplest way to time an Akida model is to call `forward` in a loop and\n", + "read back two clocks after each inference:\n", + "\n", + "- **System clock** (`time.perf_counter_ns`) — wall time including Python and\n", + " USB/PCIe transfer overhead.\n", + "- **On-chip clock** (`akida_model.metrics['inference_clk']`) — raw clock cycles\n", + " counted by the AKD1500 itself. Dividing by the 400 MHz core frequency gives\n", + " the pure compute time.\n", + "\n", + "The two numbers should agree closely; a large divergence would indicate a\n", + "transfer or driver bottleneck." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4a68cff6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean inference time (system clock): 8.103 ms\n", + "Mean on-chip time (chip clock cycles): 8.023 ms\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "CLOCK_FREQUENCY = 400e6 # 400 MHz for AKD1500\n", + "\n", + "if device is not None:\n", + " akida_model.map(device, mode=akida.MapMode.Minimal, hw_only=True)\n", + "\n", + " # Run a priming frame, so that the model is loaded to the device\n", + " # and the subsequent benchmarking reflects inference time only\n", + " akida_model.forward(samples[:2])\n", + " \n", + " inf_clks = []\n", + " inf_times = []\n", + " for i in range(len(samples)):\n", + " start_t = time.perf_counter_ns()\n", + " akida_model.forward(samples[i:i+1])\n", + " inf_times.append(time.perf_counter_ns() - start_t)\n", + " inf_clks.append(akida_model.metrics['inference_clk'])\n", + "\n", + " mean_inf_clk = np.mean(inf_clks) / CLOCK_FREQUENCY * 1e3 # cycles → ms\n", + " mean_inf_time = np.mean(inf_times) * 1e-6 # ns → ms\n", + " print(f'Mean inference time (system clock): {mean_inf_time:.3f} ms')\n", + " print(f'Mean on-chip time (chip clock cycles): {mean_inf_clk:.3f} ms')" + ] + }, + { + "cell_type": "markdown", + "id": "a0d7f360", + "metadata": {}, + "source": [ + "### Full Model Benchmark\n", + "\n", + "The loop above is clear, but it misses two things: power consumption and a\n", + "comparison between mapping modes. `full_model_benchmark` from\n", + "[brainchip_utils/hardware_utils.py](../../../brainchip_utils/hardware_utils.py)\n", + "runs the same timed loop while also coordinating optional INA219 power\n", + "measurement in a separate process. It sweeps both `MapMode.Minimal` (fewest\n", + "NPs, lowest power) and `MapMode.AllNps` (all NPs, maximum parallelism) so the\n", + "trade-off is visible. The multiprocessing and power-meter wiring are\n", + "non-trivial and not of interest to most users — consult the source if needed." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3e33f2ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running full-model benchmark (MapMode=Minimal, 10 repeat(s))...\n", + "Checking I2C power measurement hardware...\n", + " I2C INA sensor detected — power measurement enabled.\n", + " Supply voltage: 0.805 V\n", + " Power measurement active.\n", + " Repeat 1/10: recording floor (1.0 s)...\n", + " Repeat 1/10 done.\n", + " Repeat 2/10: recording floor (1.0 s)...\n", + " Repeat 2/10 done.\n", + " Repeat 3/10: recording floor (1.0 s)...\n", + " Repeat 3/10 done.\n", + " Repeat 4/10: recording floor (1.0 s)...\n", + " Repeat 4/10 done.\n", + " Repeat 5/10: recording floor (1.0 s)...\n", + " Repeat 5/10 done.\n", + " Repeat 6/10: recording floor (1.0 s)...\n", + " Repeat 6/10 done.\n", + " Repeat 7/10: recording floor (1.0 s)...\n", + " Repeat 7/10 done.\n", + " Repeat 8/10: recording floor (1.0 s)...\n", + " Repeat 8/10 done.\n", + " Repeat 9/10: recording floor (1.0 s)...\n", + " Repeat 9/10 done.\n", + " Repeat 10/10: recording floor (1.0 s)...\n", + " Power process: 6340 ok, 0 errors\n", + " Repeat 10/10 done.\n", + "\n", + " Mean inference time: 8.107 ms (σ=0.129 ms)\n", + " Mean on-chip time: 8.026 ms (3210214 clocks)\n", + " Total inferences run: 10240\n", + " Avg dynamic power: 25.1 mW\n", + " Avg dynamic energy: 0.203 mJ/inf\n", + " Mapping: 17 NP(s), 1 pass(es), 1 sequence(s)\n", + "Running full-model benchmark (MapMode=AllNps, 10 repeat(s))...\n", + "Checking I2C power measurement hardware...\n", + " I2C INA sensor detected — power measurement enabled.\n", + " Supply voltage: 0.805 V\n", + " Power measurement active.\n", + " Repeat 1/10: recording floor (1.0 s)...\n", + " Repeat 1/10 done.\n", + " Repeat 2/10: recording floor (1.0 s)...\n", + " Repeat 2/10 done.\n", + " Repeat 3/10: recording floor (1.0 s)...\n", + " Repeat 3/10 done.\n", + " Repeat 4/10: recording floor (1.0 s)...\n", + " Repeat 4/10 done.\n", + " Repeat 5/10: recording floor (1.0 s)...\n", + " Repeat 5/10 done.\n", + " Repeat 6/10: recording floor (1.0 s)...\n", + " Repeat 6/10 done.\n", + " Repeat 7/10: recording floor (1.0 s)...\n", + " Repeat 7/10 done.\n", + " Repeat 8/10: recording floor (1.0 s)...\n", + " Repeat 8/10 done.\n", + " Repeat 9/10: recording floor (1.0 s)...\n", + " Repeat 9/10 done.\n", + " Repeat 10/10: recording floor (1.0 s)...\n", + " Power process: 4763 ok, 0 errors\n", + " Repeat 10/10 done.\n", + "\n", + " Mean inference time: 5.652 ms (σ=0.101 ms)\n", + " Mean on-chip time: 5.569 ms (2227619 clocks)\n", + " Total inferences run: 10240\n", + " Avg dynamic power: 36.0 mW\n", + " Avg dynamic energy: 0.204 mJ/inf\n", + " Mapping: 29 NP(s), 1 pass(es), 1 sequence(s)\n" + ] + } + ], + "source": [ + "from brainchip_utils.hardware_utils import full_model_benchmark, get_mapping_stats\n", + "from brainchip_utils.plot_utils import plot_full_model_results\n", + "\n", + "if device is not None:\n", + " map_modes = ['Minimal', 'AllNps']\n", + " POWER_REPEATS = 10\n", + " full_results = {}\n", + " for mm in map_modes:\n", + " map_mode = getattr(akida.MapMode, mm)\n", + " print(f'Running full-model benchmark (MapMode={mm}, {POWER_REPEATS} repeat(s))...')\n", + " full_results[mm] = full_model_benchmark(\n", + " akida_model, device, samples, map_mode=map_mode, repeats=POWER_REPEATS)\n", + "\n", + " akida_model.map(device, mode=map_mode)\n", + " num_nps, num_passes, num_sequences = get_mapping_stats(akida_model)\n", + " full_results[mm]['num_nps'] = num_nps\n", + " full_results[mm]['num_passes'] = num_passes\n", + " print(f' Mapping: {num_nps} NP(s), {num_passes} pass(es), {num_sequences} sequence(s)')\n", + " if num_sequences > 1:\n", + " print('WARNING: model not completely mapped to hardware')" + ] + }, + { + "cell_type": "markdown", + "id": "a06b41fe", + "metadata": {}, + "source": [ + "The plot below shows one column per map mode: a power trace (if a power meter\n", + "was connected) and the hardware mapping layout." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ae93a65d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if device is not None:\n", + " plot_full_model_results(full_results, akida_model, device,\n", + " model_name=akida_model_path)" + ] + }, + { + "cell_type": "markdown", + "id": "b9164faf", + "metadata": {}, + "source": [ + "### Per-Layer Benchmark\n", + "\n", + "Full-model timing tells us the total cost but not where time is spent.\n", + "`per_layer_benchmark` from\n", + "[brainchip_utils/hardware_utils.py](../../../brainchip_utils/hardware_utils.py)\n", + "reconstructs latency layer by layer by running cumulative sub-models and\n", + "differencing the results.\n", + "\n", + "Because Akida processes events (non-zero activations), a layer's cost is\n", + "proportional to its *input* sparsity: a layer receiving 90% sparse inputs has\n", + "far fewer events to process than one receiving 10% sparse inputs. The per-layer\n", + "timing and the sparsity values computed above are therefore naturally correlated —\n", + "low-sparsity layers are typically the latency bottlenecks." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e2238a7b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running per-layer benchmark (1024 samples)...\n", + "\n", + "Layer Latency (ms) Clocks\n", + "----------------------------------\n", + "conv_0 0.1789\n", + "conv_1 0.2294\n", + "conv_2 0.8048\n", + "conv_3 0.4924\n", + "separable_4 1.1952\n", + "separable_5 0.7267\n", + "separable_6 0.7690\n", + "separable_7 0.6932\n", + "separable_8 0.6028\n", + "separable_9 0.4766\n", + "separable_10 0.4221\n", + "separable_11 0.3233\n", + "separable_12 0.9419\n", + "separable_13 0.1571\n", + "predictions 0.0202\n", + "----------------------------------\n", + "Total 8.0337\n" + ] + } + ], + "source": [ + "from brainchip_utils.hardware_utils import per_layer_benchmark\n", + "from brainchip_utils.plot_utils import plot_per_layer_results\n", + "\n", + "if device is not None:\n", + " # Map without hw_only so akida_model.sequences is populated for the plot\n", + " akida_model.map(device, mode=akida.MapMode.Minimal)\n", + "\n", + " print(f'Running per-layer benchmark ({len(samples)} samples)...')\n", + " per_layer_results = per_layer_benchmark(akida_model, device, samples)" + ] + }, + { + "cell_type": "markdown", + "id": "2f0103d9", + "metadata": {}, + "source": [ + "The plot stacks three panels: per-layer latency, input sparsity per layer, and\n", + "the hardware mapping. The inverse relationship between sparsity and latency is\n", + "the direct signature of the event-driven compute model: dense activations\n", + "generate more events, and more events mean more work for the hardware." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "687786ee", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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S/7Z48WK5e/eutGzZ8glW67gmTpwou3fvlm+++Ubmzp0r7dq1k7p168r8+fMlU6ZMds9577335IcffpC1a9dK3rx5n3DFjun555+XIUOGSM2aNaVNmzYyb948+eqrr+T111+3e/zp06dlzpw50qJFCylcuPATrtbxxcTEyNChQ2Xz5s0yceJEqzmAFy9eLM2bN5f27dvL119/zVz+To4wGIBL+vvvv6VAgQJGlwEAAIAnZP78+dK0aVMxmUySmJhIIJwKsbGxsnLlSmnQoIGI3A/XUhoI4x8HDx4Ud3d3KV68uIhIigLh2NhYiYqKkpCQkCddrsP6+eefpVmzZiIikpiYmKJAGMk7cuSIhIeHy9WrV2XDhg3y/PPPW+0nEM44CIORagcPHpSSJUsaXYbTO3ToEB+a0ujKlSuiqhIYGGh0KU6N1zQAAHAlyQXCsbGxEh8fL1myZDG4QueQXCB89epV7th5BMkFwvRjyiUXCN+9e1fMZrP4+voaXKHj279/v9SsWVPy5s0r69evt/k7uHjxYtm+fbt8+OGHBlWI9OBmdAFwThcuXJDKlSvLzp07jS7FqU2ZMkWaNGkifCeTNi+//LJ88cUXRpfh1E6dOiUVK1aUAwcOGF0KAADAE+Hu7i7ffvuttG7dWrp27SpffvmlxMbGSuPGjWX8+PFGl+c08ubNK+vWrRMfHx+pWbOmHDp0SA4dOiRlypSRQ4cOGV2e02jVqpXMnj1bli9fLi+99JLExcXJhAkTpG7dulwvppC7u7t899130qJFC3nzzTfl66+/lrt370rjxo1l0qRJRpfnFEqXLi1r1qyR06dPS7169eT27dtW+xs1akQQnAEQBiNVQkJCpEiRIjJ69OhHOi8xMVE2bdr0mKpyPs8//7wcO3ZMlixZ8kjnnTp1Sk6dOvWYqnI+L7zwgnz55Zdy9+7dRzpv06ZNYjabH1NVziVv3rySP3/+R35NJyQkyObNmx9TVc5l3Lhxki9fPvHz85N69erJjh07UnzusWPH5Ny5c4+xOudw7do1adeunfj7+0toaKj07NlTbty4keLzN27cyGta7vdDlSpVxNfXV8qWLSvTpk1L8bnx8fGyZcuWx1idc1BVGTlypOTNm1eyZ88uDRs2lL1796b4/KNHj8qFCxceX4FO4urVq9KmTRvx9/eX3LlzS69eveTmzZspPn/Dhg0EICKyfv16eeaZZ8TX11fKlSsnM2fOTPG5sbGxsm3btsdYnfP7dyBcsWJFyZkzp/Tt29fo0pzKvwPhiIgIGT16NHdAPqIHA+GKFSvKmDFjZP78+dyO/wj+HQhXqlRJQkJCpE+fPkaX5nDi4uJk0KBBkjdvXgkMDJRXXnlFLl68aAmEDx8+bDcQRgZgzLp1yAhmzJihHh4eeubMmRQdn5CQoG3bttX69es/5sqcS926dbVatWopPv7kyZNaoEABnTlz5uMryslcunRJvby8Hroi9L/Nnj1bc+fOzUq8D/jyyy/Vy8tLL168mKLj4+PjtXnz5vrSSy895soc38CBA7Vs2bK6bNkyXbJkidaqVUs9PT3122+//c9zjx49qnnz5nX51bXv3LmjTz/9tHbp0kU3bdqkX375pYaEhGjBggX12LFj/3n+zJkzNU+ePHr27NknUK3j2rRpk+bKlUunTp2q69ev1549e6qbm5u2bdvWsiJ0cuLi4rRp06b68ssvP6FqHVefPn20UqVK+ttvv+miRYu0WrVqmilTphS9To8cOaJ58uTRefPmPYFKHVd0dLSWLFlSu3Xrpps2bdJJkyZpUFCQFilSRE+cOPGf53/11VeaL1++FL8nZVTr16/XXLly6TfffKPr1q3THj16qJubm3bo0EETEhIeeu69e/e0fv362rFjxydTrJO7ffu2BgYGatu2bf+zb5G8ZcuWqbu7u8t/rkmrDh06aMGCBblWSYPo6GjNmTOndujQQRMTE40ux+EkJCRo3bp1tXbt2jpnzhwdNmyYZs+eXXPnzq3Hjx9XVdV9+/Zpzpw59fnnn9d79+4ZXDHSE2EwUi02NlZDQ0O1b9++/3lsUhAcGRnJH5F/WblypYqI7t69+z+PTQqCJ02a9AQqcy6vvfaaFitWTM1m838emxQEHz58+AlU5jxiYmI0Z86cOmTIkP88NikIbtSokcbGxj6B6hzX0aNH1cvLS0+fPm1pO3funGbLlk1HjBjxn+fmzZtXp02b9rjLdHgff/yxzRdjU6dOVX9////8+5gUBB85cuQxVugcnn76aasvxsxms0ZERGjNmjU1JiYm2fOSguCmTZtqXFzckyjVYR08eFC9vb31woULlraTJ0+qr6+vjhkz5qHnJgXBfGGrOnz4cK1Vq5ZV2+TJkzUgIED37t370HOTguCki1FXVqJECZ06daplOzExUWvWrKkRERF69+7dZM9LCoJbtmxJsJkC9+7d08jISILgNDp48KCGhoYSBKfR+PHjCYLTKCYmRiMiIgiCH+Krr77SMmXKWA0WOH78uIaGhmqFChUs19X79u3TDz74wKgy8ZgQBiNFVq9erS+//LJu3rzZqv3jjz/W7Nmz6+3bt5M9lyD4HwMGDND33ntPz58/b9VepkwZbdeu3UPPJQi+7/Lly/rSSy/pt99+axVYHDx4UE0mky5duvSh5xME37d8+XJt1aqV/v7771btQ4YM0Vy5cj30AtNVg+AbN27okCFDbEZXfvnll5o7d27L9oULF7R48eKWILhLly52wzVXDYI3btyo3333nU179erVtU+fPpbtRYsWaXBwsO7evVtPnz6tw4YNs/t4rhoEf/bZZ3ry5EmrtosXL6qI6I4dO1T1fhD8yiuvaM2aNfXOnTs6YcIE3bVrl81juWoQHBUVpUOHDrW5SBw7dqwWKlTIsn3mzBktXLiwJQh+44037IZFrhoEr169Wn/66Seb9vDwcO3Xr59le/78+RocHKx79+7VEydOJHth6apB8CeffGL1paKq6tmzZ1VEdM+ePap6Pwju0KGDRkREaExMjH7xxRd2g3WC4Ef3119/abdu3eivNJo6dSpBcBolJibqa6+9RhCcRocPH9bu3bsTBOv9a47Ro0fbtDdu3Fi7d+9u075s2TIVEd22bduTKA8GIQxGiixZskSfffZZFRGtUqWKzps3TxMSEjQqKkp9fX31iy++sHseQfA/7t69qx9++KGGhIRopkyZtGPHjvrHH3+o6v0pNzw9PfXcuXN2zyUI/sdff/2l7du3V09PTw0NDdURI0ZoVFSUqt6fcqNmzZrJnksQ/I8FCxZo5cqVVUQ0PDxc58+fr4mJiZYpN7766iu757lqEKyqumXLFvX29tYWLVpYBcLTp09XEdETJ07YBMEnTpzQkiVL2jyWqwbBqqr9+/dXd3d3nTNnjlV7ZGSkPvPMM6pqHQSr3g9J7IXBrhoER0dHa6lSpTR//vxWgXBUVJSaTCYdM2aMTRBsNpu1QoUK+ueff1o9lqsGwaqq69atUy8vL23btq3VxeLkyZPVzc1Nz549axME//nnn1quXDmbx3LVIFhVtWfPnurh4WETCNeqVUufe+45VbUOglVVP/zwQ/3www9tHstVg+Bbt25piRIltGDBglaB8JUrV1REdPz48TZBcGJiopYpU8ZmGh2CYADAg6ZPn64mk8nmuqNNmzZatmxZmztrExMTNVOmTLpgwYInWCWeNMJgPJKtW7dq8+bN1d3dXfPnz69jx47VDh06aKFChWy+dSMIti82NlanT5+upUuXVhHRiIgIS/DRv39/m+MJgu07d+6c9u/fX/39/dXX11fffvttnTx5soqI3VEyc+bMIQi2Y+PGjdqkSRN1c3PTQoUK6fjx47VVq1b61FNP2XwwcOUgOMnq1attAuFLly6pr6+v1qhRwyoIjouL0/DwcP3yyy+tHuPYsWMuGwQn+d///mcTCH/zzTcqIvraa69ZBcEHDx7U4OBgmxEyrhoEJ7l8+bLdQDgiIkL9/f21VatWliBYVXXEiBFat25dq8dw5SA4ya+//moTCJ89e1YzZ86sERERVkHwvXv3tHLlyjpjxgyrx3DlIFj1/gj0Ll262ATCU6ZMURHR119/3SoI3rdvnwYHB9vM7+2qQXCSixcv2g2Ea9SooQEBAfryyy9bgmBV1Q8++EAbNGhg9RixsbEEwQAAGz/99JNeunTJ6rPKb7/9piKiQ4cOtTr2yJEjmilTJpu7mZGxEAYjWXfv3tU9e/ZYLiQfdOLECe3Ro4dmyZJF3dzcNFeuXDYLfCQkJOioUaNcPgi+cOGCHjx40O6+FStWaGRkpIqIurm52V205++//3bZC8wHHTx40Gr+xiS3b9/WL774QgsVKqRubm5qMpns3q76ww8/uHwQHBMTo3v27LE7b+jRo0e1W7du6uPjo25ubhocHKzXrl2zOiY+Pl5HjhzpskFwEnuB8Lfffqsmk0lLliypf/31l27ZskWfe+45bdmypc35R48etRkV64r+HQgnJiZqo0aNVES0V69eeubMGZ05c6YGBgbqjz/+aHP+999/77JBcBJ7gfDx48c1MDBQPTw89LffftO//vpLe/Toofnz57f5UB8XF6cjR4502SA4ib1A+KuvvlKTyaTlypXTY8eO6caNG/WZZ56xuxDXkSNH9Pvvv3/CVTsWe4FwQkKC1q9fX0VE33nnHT1z5oxOnz5dc+XKpT///LPNY8yaNctlg+Ak9gLhv/76S3PlyqWenp66atUqPXLkiL799ttasGBBm8/e9+7d05EjRxIEAwBsLFiwQE0mk1X427t3bxURbdOmjW7atEl/+eUXLV68uH766afGFYongjAYdq1bt04DAwNVRDRHjhy6cOFCu8ddv35dP/vsM923b98TrtA5DBo0SN3d3VVEtHLlysmucn/gwAF9//33XT44t+fy5cv63HPPWQLz3r172537KTExUX/++Wf99ttvDajS8a1evVpz5cqlIqIBAQG6ZMkSu8ddu3ZNR4wY4fLB+X+xFwgvW7ZMy5cvr56enpbRhMxT9nD/DoTj4uJ0yJAhGhISopkzZ9bq1avrpk2bDK7SsdkLhP/++29t2rSp+vj4aI4cOfTNN9/Uy5cvG1ypY7MXCC9cuFDLlCmjnp6eWqxYMZ0wYUKKFil1VfYC4bi4OB04cKDlNV2jRg3dsmWLwZU6NnuB8PHjx7VJkybq4+OjAQEB2rlzZ7169arBlToes9msI0eO1OLFi2uBAgX0jTfeSPaztz1//vnnQxfadBUXLlzQ9u3ba1hYmJYtW1bHjBljs17Cw6RkUWxXsHr1ag0PD9fQ0FB98cUXde3atSk+NyYmxmZaJ1eUmJioH3/8sRYrVkwLFiyoXbp0sTs4KDmHDx9+6DooruL27duWz4ETJ060CYTHjx9vyX4CAwNt7mpExkQYDBsnT57UoKAg/eGHH/TYsWParl07dXd31x9++MHo0pzKpEmTtFy5cvrHH3/ohg0btEiRIja3/uG/VatWTXv27KmnTp3SCRMmWOZbJmRLuRMnTmhQUJDOmzdPjx49qq1bt7Y7vyMejb1AGI/O3pQReDTJTRmBR2MvEMajSW7KCDya5KaMwMN16dJFn3nmGf3555/1q6++0mLFimmOHDlsFsC2Z9++fRoSEqKrV69+ApU6rmvXrmmhQoX0f//7ny5dulQHDhyomTNn1mrVqunNmzf/8/wxY8ZokSJF7N5Z6kp++eUXDQoK0i+//FJ/+uknbdSokZpMJstUYg8TExOjERER2qNHjydQqWPr1KmTPvfcc7pgwQL98ssvtXDhwporVy7dvn37f567d+9eDQ4O1nXr1j2BSh1LYmKi9u/fX0+fPq23b9/WatWq6fDhwy377QXCiYmJevnyZT7/uBDCYFg5duyYDhgwwOaNqlu3bgTCj+D48eNasGBBPXHihKWND/aP5tKlS7pq1SqtUqWK1UispUuXEgg/gqNHj+q7776rI0eOtLRxsf7o/vrrL50/f77u37/fqp1AOOUSExN1/fr1umTJEr106ZLVPgLhlIuKitIlS5bo2rVrraZ3IBB+NIcPH9b58+fbTONEIJxyCQkJunbtWl26dKleuXLF0s57zKO5evWqLl68WNetW2f1PsLnxkezefNm9ff3t5riav/+/erh4aFTpkx56LlJQfC8efMed5kOr3v37jZTXA0ZMkSDg4P1r7/+eui5Y8aM0cKFC+uZM2ceZ4kOLz4+XkNDQ60W34qPj9eSJUtqo0aNHjqNS1IQ3KFDB5d/D1q9erUGBgZafQmxc+dO9fDwsJm7/9+SguD58+c/7jIdUlRUlOX9Izw8XN944w2bO5vsBcJwLYTBsDh//rz6+Pion5+f/vrrrzb7CYRTZsqUKZotWzb19/e32ccH+5SrUaOG+vn5aefOnW32EQinzNmzZ9Xb21v9/Px05cqVVvu4WLfvxo0bOnXqVKu2d999V93c3NTb21tFRGvVqmU1TyOBsK2NGzfq77//btm+fPmyVqxYUT09PS0/Q4cOtfpgSiBsa8qUKRodHW3ZXr58uWbLlk19fHxURDQsLEzXrFlj2U8gbCsqKspmscYePXpYvabr1q1rFWYSCNtas2aN1a3f58+ft0yh4eHhoV5eXvrRRx9Z9vMeY9/kyZOtpiFYvHixZsmSxfKazp8/v27cuNGyn8+Nti5fvmx3UeX33ntPw8PDLdv/XoC5V69edh/PVYPg5cuXW71PJylYsKBOmDDBsj179mzLAsy///57svOju2IQbDabdcyYMVbv06qq27dvVxGxTOXy7wWYR44cqefOnbN5PFcNgi9cuKBfffWVTXvv3r21du3alu2jR49aFmA2m83au3dvu4/n6kFwkuPHj6unp6f6+Pgk+5mQQNi1EQbDypQpU9RkMmnr1q3t7u/WrZuGhoa6/K0/D3Pv3j2tV6+eiojduZaTPth36dLFgOqcx4EDBzQwMFBz586tUVFRNvuXLl2qmTNntgpCYCvpTb5du3Y2+5Iu1sPCwpgj7//9+OOPVh+Kpk2bpmXLltVTp05pYmKiLly4UHPmzKlPPfWU1UiF1atXa8uWLV1+cb0kzZo1Uz8/P8uFZp06dbRnz54aFxenN27c0P79+6vJZLL5IN+rVy/95ptvjCjZ4Zw/f15z5sypzz//vEZHR+vp06c1Z86c+ttvv6nq/ZGtVatW1UyZMlmFR5cvX9a6devq0aNHjSrdocyaNUtFRD/55BNVvT+FU6VKlfTs2bOakJCg8+bNU39/fy1durTevn3bct6vv/6qbdu25Que/1e/fn3NkSOHJRB+4YUXtG/fvhofH69RUVGWBWjee+89yzlms1m7deums2fPNqpsh3Lq1Cn19/fXmjVrakxMjB4/flxz5cpl+Ryzf/9+rVSpkmbOnNkqpLt48aLWrVvX5RfXS/Ldd9/ZDS8GDRqk2bJl05iYGJsgeNu2bVqrVi2bx3LVIFhVtXHjxlbv00kKFy6s7du3V1XrIFhVtWfPnjZfmKu6ZhCsqpb35aT36SQ7d+5UEdFffvnFJgiOjY3VwoUL21zbuGoQrKo6ffp0q/fpJO+8847mzJlTY2NjrYJg1fvrG9WvX9/msQiC//Hbb7/pq6+++p9fKE6cOFH79+//hKuDIyAMho2kQHjw4MF299v7JhPWkgLhbNmy2V0o5cqVKywWlwJJgXDVqlX11q1bNvsfZVEQV/awb33NZjOv6X95sL+effZZmy8c9u/frz4+Pnxweoi7d+9qZGSk+vn56bx58zQkJMTmtsiPP/5YTSaT7tmzx5gincC+ffssF5qDBw+2mT/w3r17WqlSJS1evLhBFTqH0aNHWy40y5UrZ/O+vGvXLs2cObMOGzbMoAod3507d7RGjRqaI0cOnTt3rubPn9/mltMhQ4aom5ubzdQb+MeuXbssgXD//v21b9++VvtjYmK0TJkyWrZsWYMqdA72PtckBXDt27e3CoJjYmK0dOnSNuGQKwfBqtbv0w8Gwkl3Q/Xr188qCN66davmzp1br1+/bvU4rhoEJ3nwfTopEE5ISND8+fPrU089pc2aNbMEwaqqffr00Q4dOlg9hisHwUkefJ9OsnnzZhURfeWVV6yC4Nu3b2vx4sV12bJlVo9BEPyPq1ev6qlTp1Q1+TtMrl69ajNtG1wLYbCLu3Llik6YMEGHDx+u69evt7T/VyAMa7t27dIPPvhAx4wZY/kj+1+BMKwlJCTojz/+qEOHDtU5c+ZYwvL/CoRh7dKlS/rFF1/o8OHDrRZM4TagR5PUX+7u7rpp0yab/X369NGnnnrKgMqcR9KFpru7u4aFhdnsN5vNmidPHn3//fcNqM55JF1ouru76zvvvGOzf+PGjSoieuTIEQOqcx5JF5ru7u66c+dOm/3dunXTcuXKGVCZ80gKhN3d3bVw4cI2+xMSEjQwMFA//fRTA6pzHkmBsLu7uw4aNMhm/8qVK1VELBfysM/e55p3331XRUQjIiL0xo0bun37dq1YsaLdu/HWrFnj8lOY2AuEb968qaVKlVIR0QkTJujNmzd1xowZmitXLsudKQ/6+OOPXTYITmIvEF6zZo1mypRJ/fz89M8//9QzZ87oW2+9pcWLF9cbN25YnX/t2jUdOnSoywbBSewFwt27d1cR0fr16+vNmzd169atWrZsWe3Zs6fN+StXrrSap9kVxcTEaKdOnbRQoUJWfxsfDISPHj2qFy5c0DJlyuj48eONKxaGIwx2YRs2bNCAgAAtW7as5smTR0VE27RpYwnhCIRTZtCgQern56eVK1dWb29v9fb21pkzZ6oqgXBKXb16VatUqaL58uXTp59+WkVEixUrZlmogkA4ZVavXq3+/v5arlw5DQ0NVRHRTp06WRaZIhB+NEn91bRpU5t9U6ZM0WLFihlQlXNJutAUEbvzzdeoUYPnYwokXWgGBQXZjOK4dOmSiohl9BaSl3ShaW8qrHHjxmmZMmWefFFOJikQFhFdtGiRzf5nn33WZhFi2EoKhPPkyWO14Jnq/ekkRET//vtvY4pzIvY+14wbN06Dg4Mt86p/8cUXNqPY8Q97gfC1a9e0Xbt26uXlpSaTScPDw+3OL4x/2AuEN23apBUrVlQRUV9fX33zzTftTn2Hf/w7EDabzTpq1CgNDAy0zKs+efJkg6t0XG3bttVmzZrp3bt3bfZdunRJy5Ytq5kzZ9Zs2bLpBx98YECFcCSEwS7qwoULmiNHDl2yZImq3v9DO2nSJPX09LS6dWXKlCkaHBzMLQTJmD59uhYtWlQvXLigqvcXn2ratKmaTCZdunSpqv4TCLdq1crIUh1aZGSkdunSxXIb+fbt2zVv3ryaL18+y+1oBw4c0KCgIG79Scbp06c1e/bsllEbiYmJOnbsWHV3d9c333zTctzEiRM1JCTEarEkJC/pQrNnz56W5+e9e/e0SpUqOmTIEIOrcw5JF5pZsmSxWpz0jz/+UF9fX/3jjz8MrM55JF1oVqpUSc+fP29pf//997Vs2bKEHSmUdKHZr18/yyisu3fvavny5fXjjz82uDrnkBQI+/n56apVqyztO3fuVF9fX/3zzz8NrM55JAXCVatWtfqcPWDAAK1cubKBlTmX5L7oZi2ElEtuyoiEhATWQngE9gJh1fv96+qjfh+FvRHCZrOZ1/R/OHjwoLq5udl8wfige/fu6Q8//KC7du16gpXBUREGu6iPPvpIa9asadM+btw4FRHdsWOHpe3BRZJgrXTp0jarn8bHx+vzzz9vdQvlvXv3+DCVjIMHD6qI2Iz4PXTokPr6+urAgQMtbTwXkzd48GCtV6+eTfunn36qIqL79u2ztNGP9m3evFkHDx6sX3/9tdWc3kkXmkWKFNFXX31VixYtqu3atWNhKTvi4uJ0xowZOmjQIF23bp2lPelC083NTSMiIrRdu3aaM2dO/e677wys1nGdOXNGP/30U/3kk0+s5ndLutD08/PTNm3aaK1atfSpp57SEydOGFit49q4caMOHjxYp02bZvUenHShWaxYMX311Ve1cOHC+uqrr9rMaw3V2NhYnT59ug4aNEg3bNhgaU8KhN3c3DQyMlLbtGmjOXPmdPnb7pNz+vRp/eSTT/TTTz+1Wu8gKRD29/fXtm3bavXq1bV06dLJLvTj6k6fPq0bN260+UKbO59Szmw26549e3THjh1WfxeTC4Rh361bt3TTpk2WuxiTJBcIw76TJ0/qpk2bbAJMe4Ew7jt27JhWqlTJ5rk3a9Ys9fPzszs44NSpU1b5DqBKGOyy3n77bX322Wdt2uPj4zUgIEDHjh1rQFXOJ2fOnDpu3Dib9l9//VVFRC9evGhAVc5l1apVajKZLKOrH9SxY0etXr26AVU5n9dff11r1Khh0x4bG6vZsmXTL7/80oCqnIPZbNY+ffpozpw5tUmTJhoQEKClS5e2umBPutAsU6aMbtu2zcBqHdeFCxe0QoUKWrp0aa1bt66aTCbt1q2b5UPpg1NGdO3alYULk/HLL7+ov7+/RkZGasmSJTVLliz6yy+/WPYnXWj6+PjorFmz+KLRDrPZrD179rS8pnPkyKFlypSxGlGddKFZsWJFLpCScf78eS1btqyWKVNG69SpoyaTSXv06GF5TT84ZUTPnj3tvo9DdfHixZbXdIkSJTRr1qy6fPlyy/6kQDhr1qw6d+5cy9ROruzq1atW89OazWbt0aOHmkwmFRH18PDQbt26Wf39IxC2tXbtWqu/e2fPntXy5curiKiIaFBQkP7444+W/QTC9s2dO9cqYFuwYIH6+flZ+jE8PFyPHz9u2U8gbOvSpUtWd5IkJiZqly5dLK9pT09P7dWrl9XfPwJh+65fv64VKlSwuet4zZo1KiJW/Zxk2rRp3NEIG4TBLmrOnDlqMpnsLgSQN29e/f777w2oyvk0bNhQCxYsaLOy7ubNmzVz5sx25+uBtWvXrqmXl5d27drVZl/37t21ZcuWBlTlfL755ht1c3PTtWvX2uwLDAx0+QUVkmM2m7VLly763HPPWUYajRkzRkVECxcubBMIN27cmNGDdly4cEGLFy+uQ4cOVbPZrPHx8RoeHq4ioq+++qpVIPziiy/qjBkzDK7YMS1atEiDg4Mtixb++eef6uXlpZkyZdLFixdbjtu3b5+WLVuWEcF2mM1mfeONN/SFF17Qq1evqqrqyJEjVUS0aNGiNoFwixYtmGLDjnPnzmnRokV1+PDhqnp/1P+zzz6rIqJvvvmmVSBcu3ZtPjcmY/78+RocHKxbt25V1ft3Q2XKlEm9vLysvuTZtWuXlitXjhHB/+/DDz9UT09Py2eXzz77TJ999lk9evSoXrt2TT/99FP19PTUBg0aWL1+J06cqO3ateM1rffDtqefftrydy8xMVErVaqk/fv315s3b+rhw4e1UaNGajKZ9Ntvv7Wcd/fuXa1fvz6j/P/fnj171NPT0/J3b//+/RoUFKS//vqr3rlzR5cuXaphYWEaHBxscydPlSpV+OL7/w0aNMjq797w4cP1hRde0BMnTujVq1f1ww8/VHd3d23WrJnVeaNHj9ZXX33ViJId2vXr1/XSpUvaqlUrywjhhIQELVOmjObOnVsPHDhgOfbs2bNaoEAB3b17t1HlwkERBruII0eOaLt27TQiIkKjo6M1ISFBn332Wc2ePbvVh9GJEydqgQIFmJMnGT/++KNGRERYRh3s379ffXx8tEqVKpbQ6M6dOxoZGWl3lVOo3r59WwcOHKiVK1e2jDr46KOPVER0wIABlqDt4MGDmiNHDt28ebOR5TqsQ4cOaevWrbVu3boaExOjcXFxWqFCBc2RI4euWLHCctznn3+uRYsWZfRgMoYNG6bh4eGWkRvfffed5s6dW1etWqW5cuWyCYS5wLQVHx+vpUqVsiwalZCQoM2bN9eGDRvq119/bRMI04f2bd++XYODgy0f1o8fP6558+bVqVOnaoMGDWwCYfrRvkGDBmm1atX09u3bqnr/tsk8efLoqlWrNCAgwCYQph9txcXFaYkSJXTkyJGqev813rRpU23SpIlOmTLFJhCmD+3bunWrBgcH6969e1VV9ejRo5onTx6dNm2avvjiizaBMP34D7PZrK+//rolEA4LC7OZi/qnn35SEbEs2gxb586d0yJFimjRokV13rx5WrZsWav9ZrNZX375Zc2aNavlyzPY+vnnny2B8Ntvv20zUvXcuXMaHBxsd8Fh3JeYmKjt27e3/N3LlSuXnjx50uqYOXPmJLvgsKs7evSozfMuaYRwaGioJRA+fvy45s+fX318fLRNmzb62muvaY4cOfSzzz4zomw4OMJgF7Br1y7NlSuXfv3111a3Xly9etUycqtUqVL69NNPa968efXQoUMGVuu4hg0bpqVLl7a5nXT16tWaI0cOzZQpkz733HMaEhKizZo1I3yzIyYmRsuXL6+vv/66zYfOfv36qclk0qCgIA0PD9esWbPq9OnTjSnUwW3dulVz5cqlM2fOtJq39uLFi/rMM8+oiOjTTz+tpUqV0oIFC9rMKYX7F0DHjx/Xc+fOWYLgjRs3ao4cOSwX7p988ollhDBTvth38eJFjY6OtrpI79mzp77wwguWeZfLli2rIqKvv/66UWU6vGPHjmlCQoIeO3ZMVVWjo6O1SJEiOmbMGFW9P0JYRDRTpky6bNkyAyt1XImJiXrixAk9c+aMJQhet26dBgQE6P79+1X1/mjDpBHCLIxr3/nz5zUmJsbqNf32229r9erVLZ9rSpUqpSKiXbp0MapMh/fv1/TNmze1UKFCOn78eFW9vyiuiKiXl5fdu/RgHQi7ubnZfR+uX7++NmjQwIDqnEdSIOzl5WV36rVr166pp6enzp4924DqnEdSIOzl5WV36rWpU6eqh4eH1ZoTsPZgICwieuPGDZtjatWqpc2bNzegOseWdFf3gAEDrNrtBcLXr1/XYcOGaWRkpLZv317Xr19vRMlwAoTBLqB8+fLJzgGcmJio8+fP14EDB+qUKVOY1ygZhw8fVl9fX5tvMJNcvXpVJ0yYoAMHDrSaBw7Whg8frjVq1Eh29MuePXt0+PDhOmLECMsFFGyVLFky2TmAExIS9KefftKBAwfq1KlT9c6dO0+4OufQt29frV+/vlVbhQoVrP5WfvPNN1qzZk3t0aMHX+7YcevWLS1VqpTOmjXL0vbXX3+pl5eX1a2SERER+vrrr+u0adOMKNPhrVu3ToODg63CyQ8++ECrVatm2b527Zp6e3tr37599ejRowZU6fh69Ohhc3tpmTJldMKECZbtKVOmaO3atfV///sf87Lacf36dS1evLjOmzfP0nbo0CHNnDmz1a3ONWrU0DfeeMPq1nL8Y8WKFRoaGmq1INLQoUO1du3alu1Lly5plixZ9J133rGaaxTWkgJhEbE73+V7772nERERBlTmXJICYXd3d6sFhZMULFhQv/nmGwMqcy5JgXCFChVs3kO2b9+uJpOJa+n/kBQIi4h+/PHHNvt79+6tDRs2NKAyx/fNN9+kOBAGUoIwOIM7deqUikiywRq3BKXMp59+qhUrVrS779q1a9zal0LlypXTUaNG2d3HczFljhw5oiJiNXXBg+jH/7Znzx4tUKCAzYgEDw8PS7gRFxenzzzzDCNlHmLAgAE2IwN/+ukn9fb2tvTtvn37NFeuXBoVFWVEiQ7PbDZr4cKFdc2aNVbtzZs315deesmy/f777zNS5iG2bdumRYoUsboIT0xMVDc3N/3uu+9U9f5imhUqVNC5c+caVabD69Wrl80UV3PnztUsWbLorVu3VPX+38/AwEC7I7pw/wvZ/Pnz20xx1aRJE3355Zct24MHD7ZZ/Af2JQXCbm5uVneM3b17V0uUKGEZbY2HSwqECxQoYDXyf8uWLerr68t81SmUFAi3adPGahRwr169tFatWgZW5jySAmEPDw/Le7Tq/akECxcurFOnTjWwOsdGIIz05CHI0BITE0VE5M8//5RChQpZ7YuKipL27dvLsmXLjCjNqSQmJsqJEyckPj5ePD09rfbNnTtXfH19pWPHjgZV5zwSExPlzz//tLuvVatWMn/+fMmaNesTrsq5PPiazp07t9W+CxcuSNeuXWXBggVGlObwVFV69uwpx44dk9atW4ufn5/V/pdeekl69Oghe/bskfXr10vhwoWlTZs2BlXruC5evChvvfWWHD58WObNm2e1r1q1auLt7S21atWS8PBwmTNnjkycOFH8/f0NqtZxLV68WGbPni2ZMmWSGjVqWO176aWXpE2bNtKuXTtJSEiQbdu2yZYtWwyq1HGpqnTr1k3+/vtvad++vWTJksWyz83NTZo0aSLdunWTHTt2yNq1a+Wpp56Sli1bGlixYzp79qx0795d9u/fL7/++qvVvurVq4unp6fUrFlTqlatKt99951MmTLF5u8nRH766SeZO3eu5MiRQ6pWrWq176WXXpKOHTuKl5eX3Lt3T3bu3MlrOhlbt26VLVu2SKlSpSQyMlJMJpN89dVXIiLyyiuvyJw5c6R8+fKyePFiqVq1qnTr1s3gih1PXFyczJ8/Xy5evCj169eXIkWKSGhoqKxbt06qV68uFStWlJdfflm8vLxk3rx5Mnv2bAkLCzO6bIdz9uxZWbBggWTNmlWaN28uWbJkkZdeeknmzp0rrVq1ku3bt0vjxo3lzz//lJMnT8pvv/1mdMkOafPmzbJt2zYpXbq01KlTR9zc3GTGjBkiItKmTRuZOXOmPP3007Jw4UKJiIiQ1157zdiCHVBsbKz0799fZsyYIaoqI0aMEBGRjz/+WEREsmfPLqtWrZLatWtL9erVZd++fRIQEGBkyXAGBofReAJKly6txYsX15s3b1q1Dx8+XPv06WNQVc5l//796ubmpr169bJqj4+P1zJlytjMIwz7Bg8erJ6enrpp0yar9t27d2uxYsUMqsq5mM1mLVq0qJYuXdrmVrT33ntPBw4caFBlji8xMVFfeeUVFRG7oyxjYmK0X79+GhERoaNHj9bExEQDqnR8Z86c0UKFCqmI2F1t/NChQ/ryyy9rkyZNmKfsIebNm6ceHh6aLVs2u9O5zJ49W+vUqaNdu3bVy5cvG1Ch40tISNA2bdqoiGi7du1s9t+5c0f79u2rEREROnbsWF7Tyfj77781X758KiK6dOlSm/0HDx7UFi1aaNOmTXXjxo0GVOgcZs+erW5ubhoQEGB33tCZM2dqnTp19O2339YrV64YUKFjS0hI0C5dumiOHDm0evXq6unpqY0aNbIsav3glBHVq1fn/SUZp06d0lKlSulTTz2lZcqUUU9PT508ebJlf9IIYRHRfv36MSI4GfPmzVM/Pz8NDw/XoKAgzZcvnx44cMCyP2mEcGBgoH777bfMFWxHQkKCvvHGG1av6aZNm+rdu3dV1XrKiIiICJvrQ/yjQYMG2qRJEz137pyeO3dOe/XqZVl8/UHXr1+3er0DD0MY7AK2b9+uPj4+WqRIEf3xxx91x44d2q9fP82dOzcfRh/B4MGDVUS0ZcuWumHDBl21apVWq1bN7gUo7Lt9+7aWK1dOvb299dNPP9Vdu3bp9OnTNSgoSBcvXmx0eU5j8+bNmjlzZn3qqaf0559/1u3bt2ufPn00b968ev36daPLc2gPBsIPzouJR5MUCOfJk0cvXLhgdDlO68cff1QPDw9t37490w2lUlIgbDKZeB9Jg6RAuECBAnz5kAZJgTALZj6apNdxRESE5XNMnz59VES0Zs2aNoFw+/btDazWcf39999aoEABHTlypKren0qjcOHCKiJWayKcO3dOn3rqKV24cKFRpTq0OXPmaO7cuS2DfTZu3Kgiorly5bIsSKp6PxAuVqwYfzPtSEhI0JYtW2pkZKRlWqH//e9/luD334HwG2+8YWS5Dm3Tpk2aOXNmm0FAn376qd1AGEgpwmAXsXPnTq1cubKKiJpMJo2MjGTBilSYOHGi5sqVS0VEs2TJov369WMRmkd0/fp1bd++vXp6eqqIaOHChbmAT4Vt27Zp+fLlVUTUzc1N69evr6dOnTK6LKeQFAhnypSJ514aJAXCTz31FIFwGiQFwl27diUQTqWkIClz5sws4poGSYFw6dKlGSyQBkmB8L/vJkPyOnfurHXr1rWMrvzyyy81f/78+sMPP6inp6dNIJyQkGBkuQ7pxo0bWqBAAZ00aZKqqt67d08jIyO1TZs2lmD9wUCY6xf7fvnlF82dO7cePnxYVe/fHRoSEqLTp0/XEiVK2ATC9KN9r732mtavX9/ymp4wYYIWLFhQ586dqx4eHjaBMHfuJG/WrFmaPXt2u31UvHhxAmGkGmGwi7l69arNdBF4NAkJCXr+/Hne/NPozp07evHiRaPLcHpXrlyxLOwDW1OmTNGnnnpK8+fPr0OHDtX4+HhVJRB+FDdu3NCuXbtqaGioVqxY0eo2cgLhlNu5c6fWrFlTg4ODtXnz5lZf3hAIp9ykSZO0ePHiWqBAAX3//fctr2kC4ZSLiorSN954Q0NDQ7Vy5cr622+/WfYRCKfctm3btFq1ahocHKytWrWyWtiVQDhlEhMT9erVq7p//35LaLR48WINCgrSo0ePqur9UClphHBSeARrV65cUbPZbDVtXbt27bRBgwaakJCg8fHxmj17dptAGNYuXbqkN27csCzAdenSJc2dO7fOmDFDVVWXLFliGSH84JQR+EdCQoJeu3ZN9+3bZ3lNL1iwQIODg/XEiROqqtqxY0fLCGGm1/hv+/fvVxHR77//3mZfixYttF69eoTBSBXCYABAhmM2m7Vbt25aokQJ/f7773XChAmaJUsWffHFFy0fPB8MhLds2WJwxY7p0qVLWqpUKW3durUuWbJE33zzTTWZTDphwgTLMUmBcMmSJTU2NtbAah3X0qVLNSAgQD/99FNdsGCBlilTRoODg/XgwYOWY5IC4UGDBhlYqeMym83auXNnLVWqlH7//fc6fvx49fX11QYNGliedw8Gwjt37jS4Ysd07tw5LV68uLZv316XLFmir7zyirq5uVmt3p4UCJctW9YStsPa/PnzNSAgQD///HOdP3++lixZUnPnzm21intSIPzhhx8aWKlj69y5s7755puW7cTERA0LC9O5c+da2oYMGaIvvviivvXWW4wItuPKlStauHBhXb16taVtw4YN6u/vbzV1WNGiRbV9+/ZMkZWMJUuWaIECBazCya5du2qrVq0s27t379Z8+fJp+/bt9cyZM0aU6fA6duyoPXr0sGwnJCRoaGio1RoT7733ntavX1+7devGiOAUatasmWbLlk3XrVtnabt48aIGBQVxtzdSjTAYAJChmM1mfeuttzQ8PNwyanr27NmaO3duzZYtm9WtqImJiTphwgRCTDsuXbqkJUqU0A8++EBV/5n/rWjRoioiNoHwgxfv+MfSpUs1KChIt2/frqqqx48f13z58mnhwoU1KCjIKhBeuHChZTQc/mE2m/WNN97QatWq6e3bt1X1/mJcuXPn1ixZsmj9+vWtAuHx48cTYtpx7tw5LVq0qH722Weqev/25qZNm2rRokXVZDLZBML2FojE/SA4ODhY9+zZo6qqR44c0Tx58mihQoU0NDTUKhD+6aef9OTJkwZV6thWrFihJUuWtEz/oHr/vUREdOvWrap6f2HXYsWK6apVq4wq0+G9+uqr+t5771m1jRw5UgsWLGgJ2jZu3Kh58uThrsZk3LlzR0NDQ22+RKxQoYL269fPst29e3ft3r37ky7PaSxevFjLlStnFaj//fffKiKWUeu3b9/WwoULW4Wa+Mfdu3e1e/fu6u/vr35+ftqqVSs9ceKE3rp1S1944QX18PDQ5s2ba+/evTU0NFQ/+ugjo0uGEyMMBgBkKEeOHNG6detaFlqYN2+eBgcH6+HDh3XOnDkqIlaBMOz74osvLEGw2WzWtm3bamRkpN67d0+bNm1qEwjDltls1vr161sugs6cOaP58uXTL7/8Ui9cuKD+/v42gTBsHTp0SOvVq2cJgufOnashISF65MgRnTlzpoqIVSAM+z777DPLwlKJiYnaokULbdSokcbFxemLL75oEwjDVmJiokZGRurevXtV9X7QkXQb+ZkzZzRbtmw2gTCsJSYmas+ePbV+/fo2q96bzWZ9+umntVChQvrBBx9o2bJlrUYO4x+nTp3SLl26aFBQkM3iwXv37lUPDw9t3LixDhgwQAMCAqymg8E/5s6dq506ddLIyEibfYMGDdLMmTNr3759tVWrVlqsWDGNiooyoErHlpCQoN27d9fIyEidPn261b7ExEQtWbKkFilSRD/44AN9+umntWvXrsYU6uDMZrO++OKL2rhxY920aZP++OOPWrp0ac2ePbtu2LBB4+PjdcqUKdqgQQNt1KgRo/yRZoTBAIAM6/jx45otWzbdtm2bqt4fCefl5aXe3t7cuvsIJkyYoCVLlrSEcT///LN6e3urm5sbQeYjeP75561GGbVo0UK9vb21XLlyzBWcQkePHtWsWbNaAvbY2Fj18PBQb29v/eSTTwyuznmMGjVKy5UrZxmVOWfOHPX29lYPDw9Gp6eQ2WzWypUr69ChQy1tDRs2VG9vb61SpYpxhTm427dv67PPPqsiYndanHPnzmmbNm20SpX/a+++w6I60//xv2cAkSioRFBsAUSqokhsWBJR0ViIZi0xsURN7KJiwcTYiMSSoCbGgiXGJMYeECwgKKjYDSpRVxNFRVSsNOkD7+8f/jg6C+66+9lfzsx6v/6b5zlc1/s615yZ4T7PuZ/WXLZsmXw2vsCJEydoaWlJABX2r42JiWGHDh3YrVs3aYX1T8yaNYsA2Lhx43JzxcXFnDt3Lt98802OGjVKCsEvkJWVRW9vbwKo8Ld1Wloa33//fbZu3ZrffvutXNMvsHPnTr7xxht6i1UOHTpEc3Nzvf06hPhv0ZAkhBBCiP9BM2bMwMWLFxEVFQUASE9PR6NGjZCYmAg3NzdUqlRJ5YTGwcnJCbNmzcLQoUMBAN999x1OnDiBKVOmwMvLS+V0xuHMmTNo1aoVnjx5AgsLCwBAu3btMHbsWLRq1QoNGzZUOaFxmDp1Kq5du4bw8HAAQFpaGjw8PHDkyBG4ubnBzMxM5YTGoV69eggNDcWAAQMAAF9//TUuX76M8ePHo1mzZuqGMxKJiYno3LkzsrKyYG5uDgBo0aIFpk+fjhYtWsDe3l7dgAYsOzsb3bp1w/nz53H8+HF4enqqHckoHTt2DN26dYOnpycOHjwov2n+Q59//jlCQkIQGhqKwMBAteMYpczMTHTu3Bl//PEHTp06BVdXV7UjGZ3Ro0cjLS0Nu3fvBvD0d2PPnj2xfv16+Pn5YezYsVi7dq3KKcX/Eq3aAYQQQoj/q9TUVMTGxiI1NbXc3Llz55Ceno6cnBwMGzYM48ePR9OmTeWfpn9AEqdPn8ahQ4fw5MmTcvOxsbEoKSlBcnIyQkJCMGHCBCkEVyAnJwfx8fFISkrCP95vLy0txf79+0ESy5YtQ0ZGBvr27SuF4Apcv34dsbGxSEtLKzeXlJSE+/fvIzs7G8OHD0dAQAA8PT2lEPwPSktLcerUKRw+fBi5ubnl5vfv34/S0lIkJSXh66+/lkLwC2RlZeHgwYM4d+5cubmioiLEx8eDJBYtWgSdTof33ntPCsH/gpWVFaKjo9G0aVP4+fnh0qVLakcySj4+PoiOjkZycjLee+89FBUVqR3JKM2fPx8zZ87E1KlTsW7dOrXjGKXq1asjLi4Ozs7O6NSpE/7880+1IxmFwsJCjBgxAg8fPoSFhQVOnTqFgoICvUJwjx49sHv3buTl5akdV/yvUXNZshBCCPF/FRQURK1WS41GQ41Gw/79+zMrK4skeefOHTo6OrJSpUqsXLkyBw0aJBtLVeDevXts1aoVAVCj0dDS0lKvH/DOnTtpbm5OKysrWlhY8Oeff1YxreGKjo5m9erVqdFoCIAeHh5MSkpS5gcPHkyNRkMrKyu6uLjIDtAvMGnSJOV61mg0/PDDD5UWJWlpabS3t6e5uTkrV67MoUOHUqfTqZzY8Ny5c4fNmzcnAAJgtWrVuGbNGmV+8+bNNDMzo5WVFatUqcJt27apmNZwRUZG0srKSrmmPT09mZycrMz3799f+cz08PDgzZs3VUxrmEpLS7lixQo2b96cLVu25I8//qjMZWVlsU2bNtI7/SU8evSIY8aMoaurK/39/fXeh0ePHqWlpaX0Tn8Jx48fZ9euXenm5sapU6cq+0uQ5MyZM6V3+ksoLS3l8uXL6eXlxdatW3PTpk3KXEZGBr29vaV3+ksaNWoUP/zwQ5aWlvLYsWMEwL59+7JWrVpKa4h79+6xfv36ysaaQvy3SDFYCCGE0QoLC6OXlxf//PNP5ufnc/369axSpQpbtWql9NzKycnhjh075EfUP9GxY0dOnDiRubm5TEtL44gRIwhArwfr1atXuWXLFqampqqY1HClpKTQxsaGe/bsoU6n49GjR+nh4UFLS0u9f9rj4+O5a9cu5ubmqpjWcH3zzTds2bIlU1JSmJuby9WrV9PCwoLt27dnUVERSTI7O5s7duxQeoELfaWlpfTx8eH06dOZn5/P1NRUDhkyhAC4bNky5bgrV65w69atTEtLUzGt4bpy5QptbW0ZExNDnU7HQ4cO0cXFhdWrV+ff//535bgDBw4wKiqK+fn5KqY1TDqdjkOGDGHTpk25fv16Tp8+nVqtllOmTFGOeb4gfOPGDRXTGq6bN2/SycmJgwYN4s8//8wuXbqwSpUqjI2NVY4pKwi/++676gU1cFu2bOHrr7/OBQsWcNWqVbSzs6OXlxcfPnyoHFNWEH6+wCme0el0/OCDD+jl5cX169dz6tSp1Gq1nD59unLM8wVh+X6pWG5uLk+dOkUbGxu934NBQUEEQH9/f6akpDA8PJz29vZcvHiximnF/yopBgshhDA6paWlPHToEJs2bVquyHvkyBGamZlx0aJFKqUzHnfv3mVERAQdHBxYUlKiNzdp0iSampry6tWrKqUzHseOHeOnn36qtzkc+bTI4eLiwjZt2qiUzHiUlpby8OHDdHFx4dmzZ/XmDhw4QBMTE37zzTfqhDMit27d4o4dO+jq6lpuk57Ro0fT3Nxcbui8hMTERE6ZMkVvcziSfPz4MR0dHdmxY0d1ghkRnU7HDz/8kF27dlU2Kly5ciXt7Oyo1Wo5efJk5disrCzOmzdPVvlX4ObNm3R0dFQ+/woKCtijRw/WrVuXFhYW5QrCssq/Yps3b2adOnX4+++/kyQvXrzIOnXqsFatWmzWrJleQTg0NFRW+VdAp9NxwIAB7NGjh3Lza/ny5co1PW3aNOXYjIwMBgcHl/ttKZ6aMmUKLSws6OrqWm5uzZo1dHFx4WuvvcaWLVsyIiJChYTiVSDFYCGEEEZn9+7d1Gg0NDU11Vt1WWb48OFs1aqVCsmMy6BBg2hmZkZ3d/dyc4WFhaxRowaXLl361wczIllZWbS2tqaZmRmDg4PLzUdGRhIA7969q0I647Fjxw5qtVqamJhU+GjpBx98wLfeeuuvD2Zk+vbtSzMzMzZv3rzcXF5eHqtWrcpVq1apkMx4PHr0iFZWVjQzM6twNda2bdsIgBkZGX99OCNy8OBBvvPOO8pTOuvWrWODBg1448YNzpw5kwD0CsKiYhMmTOC3335LkiwuLmavXr3Yr18/5ubm0tPTs1xBWJSXl5fHN998U2lF8scff9DOzo6bN2/m6dOnaWJiUq4gLMqLjY1lz549lVYkq1evpr29PW/evKmsaH2+ICxeLCcnh23btiUAHjx4UO044hUlxWAhhBBGac6cOQTATz75pNxcaGhohcUQoS8zM5MtW7YkAB44cKDcfIsWLeTRtJdw+PBhVq1alY6OjszOztabu3btGgHIasyXUPbP5IQJE8rNhYSE0MfHR4VUxuXRo0f08vIiACYmJpabb9KkiVJYEi924MABWlhY0MXFpVxLl4sXLxIA7927p1I641G2Ov23335jtWrVeOHCBZLk9evXqdVqqdVq5XH8f+H5Ff6zZ8+mj4+PUoz76quvaGZmRktLSz548ECtiEah7DwWFxfTw8ODX331lTLn7e1NMzMz9unTR614RqPsPJ46dUqvZc6ff/5JExMTarVabt26Vc2IRqOsIFyzZk1lxboQfyXtX7tdnRBCCPHfMXfuXMyZMwdr167FggULlPGioiJs2bIFffr0UTGdcahWrRr279+Pli1b4v3338fJkyeVufPnz+PSpUvo2bOnigmNQ/v27bF3717cv38ff/vb35CVlaXMbdy4EW+++Sbq16+vYkLjsHDhQgQFBWH58uVYsmSJMl5QUIBt27bJNf0SrK2tERcXBy8vL/Tt2xdJSUnK3OnTp5GSkoLu3burmNA4+Pr6Yvfu3UhNTUX//v2Rk5OjzG3cuBFt27aFra2tigmNg0ajAQAsWbIEAwcOhIeHBwDg8ePHcHBwQEJCAgYMGKBmRINXdg51Oh2WLFmC4OBgVKpUCcDT8xgQEIC9e/eiZs2aasY0eGXncdhy7EAAABq9SURBVPfu3Xj48CECAwMBACSRkZGBLVu2YNmyZSomNA5l5zE0NBSDBg2Cq6srgKfvRScnJyQkJKBv375qRjRYGRkZuH37tvK6atWqiI6OhouLC3x9fXHhwgUV04lXkanaAYQQQoiXER4ejvDwcFSqVAl9+vRBjx49MHfuXADAZ599hp07d6Jt27aIi4tDs2bNMGPGDHUDG6AnT57gu+++w9mzZ+Ho6IgRI0bAyckJ+/fvh5+fHzp06AB/f3/UqFED4eHhWLNmDdzc3NSObXAuXbqElStX4vHjx2jbti2GDRumFIS7d+8ONzc3vPvuu7h16xauXr2Kffv2qR3ZIG3btg1RUVGoXLky+vXrBz8/PyxcuBAAMGXKFGzduhWtW7dGTEwM2rRpo/zzLp7Jzs7G8uXLkZycDCcnJ3z88cdwcHBAXFwcOnfuDB8fH/Tu3RuWlpaIiIjAxo0b0bBhQ7VjG5zff/8dq1atQlZWFtq1a4dhw4YpBeGePXvCzc0N/v7+uH79Om7duiXX9Avcv38fd+/eRaNGjfDaa68p4wUFBUhJSUFBQQEKCwsREBCAcePGoX379iqmNVx//PEHSMLZ2VkpvpWUlKCoqAgnT55Ep06dcObMGaxduxbHjh1Do0aNVE5seAoKCnD58mXY2dmhVq1aeuNZWVm4fPky3N3dERwcDDs7O7z33nsqpjVc9+7dQ3p6OpydnWFhYaGMFxQU4Ny5cygsLER+fj4mTpwo1/QL5OfnY+TIkdi0aRNIomHDhli2bBl69uypFIS7desGX19fHDx4EI0bN1Y7snhVqLwyWQghhPiXAgMD6ejoyBkzZrB3797UarXs27evsilNWcuIli1b8tChQyqnNUyPHz9m48aN2b17d06fPp1NmjShubk5165dS1K/ZURAQIBsnvIC+/fvp7W1NceMGcPRo0fT2tqaTk5OyqOSZS0jrKysuHHjRmWTFaFv3LhxdHZ25owZM9irVy9qNBp+8MEHSn/RspYRbdu2rbDdgSAfPHhAV1dX9urVi9OmTaO7uzstLCy4ceNGkvotI6ZOncpbt26pnNgwRUVF0dramuPGjePIkSNZvXp1uri48M8//yT5rGVEjRo1+PPPPyuP6ItnSktLOXnyZJqYmBAAq1Spwjlz5iibR50+fZpWVlasWbMmLS0tOWrUqHIbHAoyLS1N+R4GQCcnJ8bFxSnzISEhBMAGDRrQ0tKS4eHh6oU1YBEREbS2tiYAajQa+vv7Mz09nSSZn5/Pli1b0tzcnHZ2dmzSpAlv376tcmLDU1JSwnHjxlGr1RIAq1atypCQEOW6PXHiBC0tLWljY8OqVaty3LhxKic2XIMHD6a/vz/Pnz/PI0eOsEuXLtRoNMp3NfmsZcT8+fNVTCpeNVIMFkIIYdD27dvHOnXq8PHjx8rYTz/9RAB6vQbnzJlDjUYjmyO9wNChQzls2DDltU6no7+/P62trZVeg2UF4dq1a/Py5ctqRTVY2dnZtLGxYUxMjDKWmprK6tWrs3///spYWUHY39+fRUVFakQ1aOHh4WzQoIFef+W1a9cSAHfu3KmMBQUFUavV8vvvv1cjpsEbMGAAx4wZo7wuLi5m165daWtrq2xuVlYQrlevHq9evapSUsP1+PFjWltbMz4+XhlLSUlh1apVOWTIEGWsrCDcr18/FhcXq5DUsH355Zds3bo1f//9d16/fp3Tpk2jRqPR+85JTU1lWFgYjxw5omJSw6XT6di8eXMGBQXx7t27PH78ONu1a0dTU1Pu3r1bOS4xMZFr1qzhjRs3VExruM6dO8fatWszMjKS9+7d448//khra2s2atRI2SCusLCQv/zyC7ds2aIsKhD6Zs+ezfbt2/PixYu8du0aJ02aRAB63zk3btxgWFiY3LB9gdzcXJ45c4a1atXSe5+VlJTw/fff52uvvaZ3I6LsZrgQfxUpBgshhDBoAwcO1Nsk7u7du3R1deWCBQtYUFDAiIgIZU4KwhUrLCykubm53iZxu3btYu3atZmUlMSjR48yLS2NpBSE/5mtW7fSxsZGeV1aWsphw4bR19eXubm53L59u7JqRgrCL9anTx+9TeJu3bpFJycnLl26lLm5uYyKilLmpCBcsSdPntDU1JRHjx5VxrZv3047OzsmJyfz0KFDvHv3LkkpCP8zGzduZP369ZXXOp2OH374Ibt27cqCggJu27ZNuaalIFxeSUkJL168yJo1a/L69et6c6tWrSIAvetZVCw1NZXbt29n69at9caLiorYuXNn2tjYlNvIUJSXnJzMESNGcOnSpXrjly9fppWVFUeMGKFOMCOi0+l44cIFWllZKd8hZZYuXUoAjI2NVSmdcRk2bBirVavGxo0bl5vLyclh1apVZUNXoSrZQE4IIYRBy8zMxM2bNwEA6enp6NixI4YOHYoZM2bgwIED2L59u3Ls3LlzMXv2bCxZsgT5+flqRTY4ZX0ay85jZGQkRo0ahb1798LLywurV6/G2bNnATzbVK5BgwZYt26dmrENTmZmJjIzM5GdnQ2SGDFiBG7evImoqCiYmJhgypQp0Ol0AJ5tKpeYmIgzZ86onNywPH9Np6WloWPHjhg3bhwmTZqE6OhoREREKMcuXLgQ06ZNw9dff42ioiKVEhue3Nxc6HQ65Tzu2LEDAQEBiImJQZMmTfDdd98pm9GUbSpnY2ODH374QcXUhiczMxMPHjxAfn4+SkpKMHToUDx8+BC7du2CTqfDtGnTUFpaCuDZpnJxcXE4f/68yskNw8qVK9GiRQs8evQINjY2enOjR4+Gj48PtmzZolI64zFw4EAMHTq03KaEZmZmWL9+PR4+fIiDBw+qlM44XL9+Hd7e3ti8ebNej2AAcHFxweeff46tW7eCpEoJjcPSpUvRsmVLFBQUoEaNGnpzkyZNgre3t1zTLyk4OBg1a9bEhQsXlN/YZapWrQpPT09kZmaqE04IQHoGCyGEMGxffvkltVott2/frqwIJp/2fWvcuDH3799f7m+ef/xcPOXp6UkXFxdu3bpVWRFMPu37Vq9evXJ9MHNycqSn4z+4ePEiNRoNp06dqrcimCSnTJnCsWPHlvubrKysvzqmwZs9ezZNTEz466+/KiuCyaePVLq4uFTY91uu6fJcXFzYuHFjbtq0SVkRTJKHDh2ivb09dTqd3vHZ2dlyTf+DpKQkAuBnn32mtyKYfNrXOjAwsNzfyDX9TFFREXv37k0A/Oabb8rNBwQE8N133/3rgxmZK1eu0M7OjhYWFuVWWJOkra0tN2/e/NcHMzJhYWHUaDTs1KlTuc+6gwcP0sTEpNznotBXUFDA7t27EwDDwsLKzY8cOZIDBgxQIZlxunXrFhs2bEhHR0empKQo47dv36aVlZXe0z1C/NWkGCyEEMKgJCcnc/HixcrjaTk5OXR2dlY2NiPJ+/fvs2vXrhw+fLiaUQ1WSUkJIyMjuWLFCr1HnE1NTanVapX+bocPH2adOnUYHR2tZlyDdf/+fa5du1avvcaYMWMIgK6urszNzWVxcTFDQkLYsGFDZmZmqpjWcJ09e5aLFi1SelNnZGTQwcGBADh9+nSSZHp6On19fSssqIunj+5GRERw9erVyti+ffuo1WppYmLCU6dOkXx6ndeuXVuvB654Jj09nWFhYTx8+LAyNmzYMAKgp6cnCwoKWFRUxDlz5tDFxYU5OTkqpjUOZQVhc3Nzvd62eXl5dHZ2rrCgJMorKwh7enrq9RFNTExklSpVyj2yLypWVhAOCAjQK/xOmDCB77zzjorJjEdZQdjCwkJvwcWTJ0/o4OCgt/GZ+NfKCsI1atRgUFAQFy9eTHt7ewYHB6sdTbzipBgshBDCYMyaNYv29vZctWoVb926pYzfunWLPj4+NDMzY9OmTWlpacmpU6dK38YKPHjwgO3atWP37t3566+/6m1IERERwZo1a9LGxoaurq6sU6eO9HN8gdjYWNatW5czZsxQCm3k0026AgICqNVq6ezszFq1arFjx45671fxTFBQEBs2bMg1a9boFTiuX7/OFi1a0NzcXLmmP/30U5aUlKiY1jClp6ezVatW9Pf3Z0REhN4q/m3bttHa2pq2trZ0cXFhvXr15ObOC+zdu5d16tThzJkzeebMGWW8qKiIY8aMoVarpYuLC21tbenn58c7d+6omNZwRUZGMiAggLNmzeLFixdJPisIazQa9uvXj/PmzaOHhweHDRsmq9ErkJ2dzWXLlnHs2LFcsWKFciOxrCBcs2ZNTp48mYGBgaxZsybDw8PVDWygLl26xE8//ZSTJ09mVFSU8l4rKwg3b96cc+fOZZ8+feju7q73HSSeCQ8P54QJEzhnzhxlr4iygrCJiQnff/99zps3j66urhw9erTKaY1TWUEYAAcOHKh3Q1IItUgxWAghhEFYt24dmzRpUm515fPFoXPnzjE2Npb379//q+MZjc6dOyurLcs8fw7z8vKYkJDAxMRE2djsBVJSUmhra8tjx47pjT9/HtPS0hgTEyOb7P0T3333Hb29vcutriw7j6WlpUxKSmJsbKyyy7sor3379pw9e7be2PPvxdzcXMbHx/PYsWNyg+wFrly5QhsbG70iMKl/HlNTUxkTE8M//vjjr45nNEaNGkVHR0d+/PHHbNasGU1MTDhv3jyS+i0jOnfuXGELJ/H05o6zszM7derEIUOG0MbGhrVr1+aRI0dIPisIazQaBgUF6T1aLp4JDw9n9erVOXjwYPbs2ZNmZmbs0qULHz9+TPJZQbhu3bpcu3Yt8/PzVU5seEpLS/nRRx+xUaNG/Pjjj9mkSROamppy4cKFJPVbRnTr1o0HDx5UObFxKysIu7m5yUp/YRCkGCyEEMIguLu7c+vWrcrrTZs2sVGjRqxUqVK5naFFxZKTk1m5cmVl5WBWVhYDAwNpZWXF+vXr89y5cyonNA6fffYZBw4cqLw+f/48/fz8aGpqyu7du0sR/SU5Ojpy165dyusffviBjo6OrFy5MleuXKliMuNx6tQpWllZKUXejIwMBgQE0NLSkg4ODsrKTPHPBQYGctiwYcrr3377jb6+vjQxMWHv3r2lj+hL2LhxI52dnfnkyRNl7NNPPyUAJiQkkHxWEK5SpUqFvb8F+c477+j1os7IyKCXlxft7e2V7+6ygnCLFi2YkZGhUlLDdefOHVarVo3Hjx9XxhITE2lqaqp3bssKwrNmzVIjpsELCwujh4eHXqE8MDCQGo2GJ06cIPmsIGxlZVXuBrn490lBWBgSrUr71gkhhBB6zM3NsWPHDuzZswedOnVCcHAw5s6di8DAQEyfPh35+flqRzR4lSpVQmFhITZs2ICwsDC4u7vj1q1b2LJlC+rWrYv58+erHdEomJub4/jx44iJicHEiRPx9ttvo0OHDti8eTPi4+MRGRmpdkSjYG5ujm3btiEqKgpvvfUWvvrqK4SEhGDs2LGYPHkySkpK1I5o8MzNzfHkyRP8+OOPWLlyJdzc3PDw4UNs3boV1tbWWLBggdoRjYK5uTkSExOxf/9+jBs3Dl26dIGfnx9+/vln7N27F9HR0WpHNHjr1q3D4MGDUaVKFQBAamoqtm7diuXLl8Pd3R3JyckwMzPDtm3b0KVLF3Tv3h2HDx9WObVhuXPnDvbt24cxY8YoYzExMbh37x727NmD06dPo6CgAM7OzkhISEBaWhr8/PyQmZmpXmgDtHnzZjg5OaF169YAgJKSEixfvhxdu3bFl19+ibi4OADAyJEjsXr1asyfPx+zZ89WM7JBWrduHT766CNUrlwZAJCSkoKdO3ciLCwM9evXx6VLl2Bubo5ff/0V7dq1Q7du3XD8+HGVUxu3evXqISEhAUVFRZgzZ47accSrTu1qtBBCCEGSJ06coIODA+vWrctFixYpK2TOnj3LatWqyWrMl/Tll1/SysqKrVu3ZmxsrDIeFBSktzJOvFh2dja7dOnCatWqcciQIXr9gO3t7RkTE6NiOuNx5MgRvvHGG6xfvz5DQ0OVa/j48eN8/fXXpT/wS5o7dy4tLS3Ztm1bZQUmSU6cOJFjxoxRMZnxePz4MTt27Mhq1apx+PDhSj/g0tJS2tnZySrWl+Dq6srx48eTJG/evElHR0cuX76cJLl+/XpOmzZNObZshXDLli2lZ/Bzzp07RwDKJq5btmxhnTp1lBX+Xbp04W+//aYcX7ZCODQ0VJW8hmrGjBmsX78+yaebaw4YMIA9evRgQUEB79+/T1dXV73jw8LC+Nprr/Hq1atqxDVY9vb2nDp1Kkny2rVrfOONN7hmzRqS5IoVK/RWVJetEG7fvr0qWf/X3L59m7m5uWrHEK84DUmqXZAWQgghKlJYWIh3330Xbdq0kTvo/wd///vf4evri+joaDRt2lTtOEZr2bJl+Omnn3D69GlotfJw1X8iPz8f3bt3R7du3RAUFKR2HKOVnJwMPz8/xMfHw83NTe04RmvhwoWIjIzE0aNHodFo1I5j0IYOHYpdu3Zh7969GDx4MCZPnozx48cjOzsbXl5e2LFjB7y8vJTji4uLkZ2djddff13F1IaloKAAtWvXRrt27TBo0CBMmTIFsbGxcHd3x/79+zF58mRcvHhR72/S09NRu3ZtlRIbpvDwcLz33nvYsGEDoqOj8eTJE+zcuRPm5uYYPnw43njjjXK/Ge/evQs7OzuVEhumAQMG4MCBA4iKisLAgQMxc+ZMfPLJJ8jIyECzZs2wb98+uLu7K8cXFhYiLy8PNWrUUDG1EOK/Ru1qtBBCCPGPCgoKuHv3bjZu3JhDhw6VFYT/ofT0dIaGhrJWrVp6/ZjFvycpKYmDBg2ih4cHb968qXYco5Sfn89du3bRzc2NI0eOlNWC/6E7d+5w0aJFrFWrFsPDw9WOY7TOnDnD/v37s2nTprx9+7bacQzS5cuX+f333ytP6Vy9epVWVlbUaDRcsGABSfLBgwfs2LEjp0yZomZUg6XT6XjgwAHu2bNHGVu+fDkBsGrVqrxw4QJJMiEhgbVr15aerC/w6NEjbt68WdncsaSkhO3bt6dGo2GHDh1YUFDAkpISzpkzp1wPXPHMpUuX+P333ytP6Vy6dIlVqlShRqNR9uZIT09nu3btOHPmTBWTCiH+CrKsRQghhMHZt28ffvnlF4SGhuKHH36QVZj/oZCQENy/fx/Hjx9H//791Y5jlG7fvo3g4GD4+Pjgt99+Q4MGDdSOZJQiIyOxfft2LF++HGFhYbIK8z9AEl988QWysrJw8uRJ9O7dW+1IRunmzZv44osv0KlTJ5w6dQp16tRRO5LBmTFjBt555x1kZGTg0aNHAICGDRsiOjoaderUQWhoKDp16oSGDRuiRYsWWLx4scqJDc+dO3fQoUMHfPXVV8jIyAD/v4dxx48fj5CQEBQWFmLAgAFo1aoVBg4ciA0bNqBNmzYqpzY8e/bsQZMmTXD69GlkZGQAALRaLSIiItCpUyccO3YMXbt2hYODAw4dOoS4uDilB654iiQCAwPRq1cvZGVlKefRzc0Ne/fuRe3atbFgwQJ06tQJzs7OePvtt/HFF1+onFoI8f83aRMhhBBCCCGEEAKLFy9GREQEYmJiYGlpWW4+Pz8fCQkJyMnJQdu2bVG3bl0VUhq2kpIStGnTBv7+/vj8888rPOb27dtITEyElZUVOnbsKAXMCiQnJ6NLly6Ijo7Wa0HyvFOnTuHq1atwcXGBt7f3X5zQOAQHB+PgwYPYs2ePsgHk8/Ly8hAfH4+8vDy0a9dO2mkI8YqQYrAQQgghhBBCvOJKSkpga2uLqKgo+Pj4AAA2b96MhQsXIjMzE2FhYejWrZvKKQ1fdHQ0Ro0ahRs3bkCj0SA7Oxvz58/Hpk2b0KhRI2zfvh02NjZqxzR4H330EWxtbZWV58nJyZg5cyZOnjyJESNGYMGCBSonNHxFRUWwsbFBfHw8mjdvDpL46aefsHjxYuTl5WHdunXw9fVVO6YQQgXy3K0QQgghhBBCvOK0Wi2KiooQExODQ4cOoXPnzpg3bx7Gjx8Pb29vjBkzRu2IRkGn0+Hhw4dISEjAhg0b4O7ujpSUFCxcuBDXrl3DkiVL1I5oFHQ6HU6ePImkpCQEBgbi7bffhre3NyZPnoyFCxfi/Pnzakc0eBqNBsXFxYiOjkZ8fDzefvttLFq0CJMmTYKHhwfGjRundkQhhEpM1Q4ghBBCCCGEEEJdGo0Gq1evxieffIIVK1Zg6tSpCAwMRKVKlWBnZ4fJkyerHdEo9OjRA7169YKvry+8vLywYcMGdOnSBQAQExMDU1P5F/xlzJ07F127dkXr1q3Rr18/JCcno169eiguLsYXX3wBExMTtSMaPDMzM6xYsQJjxoyBlZUVpk+fjokTJ8LMzAzVq1fHvHnz1I4ohFCJtIkQQgghhBBCCAEAKC4uhomJibJ5a0FBAXx9ffHBBx9g/PjxKqczHoWFhTA3N1denzt3Dl27dsWJEyfg4OCgYjLjUlRUhEqVKimvg4ODcfjwYcTFxamYyrgUFxfD1NRU2bw1Ly8Pb731FkaNGoWPP/5Y5XRCCDVImwghhBBCCCGEEACeribUarUoKSlBXFwc3nzzTXh6ekoh+N9UVgjOzMzEt99+i27dumHdunVSCP43lRWCr1y5gkGDBmHHjh345ZdfVE5lXMzMzKDRaKDT6RATEwNvb2/4+PhIIViIV5g8oyKEEEIIIYQQQs+3336Lw4cPY/HixejevbvacYxSUVERhg4dCnt7exw7dgyOjo5qRzJK58+fx/jx4/G3v/0N69ev11txLV7e119/jTNnzuCbb76Bn5+f2nGEECqSNhFCCCGEEEIIIYQQQgjxCpA2EUIIIYQQQgghhBBCCPEKkGKwEEIIIYQQQgghhBBCvAKkGCyEEEIIIYQQQgghhBCvACkGCyGEEEIIIYQQQgghxCtAisFCCCGEEEIIIYQQQgjxCpBisBBCCCGEEEIIIYQQQrwCpBgshBBCCCGEEEIIIYQQrwApBgshhBBCCCGEEEIIIcQrQIrBQgghhBBCCCGEEEII8QqQYrAQQgghhBBCCCGEEEK8AqQYLIQQQgghhBBCCCGEEK8AKQYLIYQQQgghhBBCCCHEK0CKwUIIIYQQQgghhBBCCPEK+H8fvql5+PBxHQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if device is not None:\n", + " plot_per_layer_results(per_layer_results, akida_model, sparsity_dict,\n", + " model_name=akida_model_path)" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "brainchip_devhub_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/akida1/model_zoo/vww/vww_notebook_training.ipynb b/akida1/model_zoo/vww/vww_notebook_training.ipynb new file mode 100644 index 0000000..1bc67ca --- /dev/null +++ b/akida1/model_zoo/vww/vww_notebook_training.ipynb @@ -0,0 +1,750 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1be5d78d", + "metadata": {}, + "source": [ + "\"BrainChip\n", + "\n", + "# Visual Wake Words on Akida 1\n", + "\n", + "

\n", + "Run Time: ~1 hour with training included / ~2 minutes with training skipped\n", + "

\n", + "\n", + "This notebook steps through the full pipeline for a **Visual Wake Words (VWW)**\n", + "binary classifier on **Akida 1 (AKD1500)**: training a tf_keras model,\n", + "quantization and conversion to Akida format, and evaluation of the resulting\n", + "Akida model. For background on the VWW task, dataset, and model performance\n", + "benchmarks, see the [README](README.md).\n", + "\n", + "The focus is on the **Akida-specific** aspects of the pipeline. The full data\n", + "preprocessing and training code is available in the accompanying Python files —\n", + "it is standard tf_keras code and is not described further in this notebook.\n", + "\n", + "By default, model training is run to ensure reproducibility. However, you can\n", + "cut the running time of the notebook to under a minute if desired by \n", + "skipping the training runs and downloading pretrained float and quantized models\n", + "instead: simply set the relevant `RUN_FLOAT_TRAINING` and `RUN_QAT_TRAINING`\n", + "variables in the first code cell to `False`." + ] + }, + { + "cell_type": "markdown", + "id": "0902d32c", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "The default dataset path is `./data/vw_coco2014_96`. See the [README](README.md)\n", + "for download instructions and symlink setup. Update `DATA_PATH` below if needed." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9fd73f67", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from tqdm import tqdm\n", + "\n", + "DATA_PATH = './data/vw_coco2014_96'\n", + "MODELS_DIR = './models'\n", + "os.makedirs(MODELS_DIR, exist_ok=True)\n", + "\n", + "RUN_FLOAT_TRAINING = True\n", + "RUN_QAT_TRAINING = True\n", + "\n", + "SEED = 42\n", + "\n", + "# Must be called before any TF ops to make GPU ops (conv backward passes,\n", + "# bilinear resize, etc.) deterministic. Has a small throughput cost.\n", + "tf.config.experimental.enable_op_determinism()" + ] + }, + { + "cell_type": "markdown", + "id": "bfdbcd30", + "metadata": {}, + "source": [ + "## Dataset\n", + "\n", + "`get_data` returns a training and a validation `tf.data.Dataset`. The full\n", + "preprocessing and augmentation code is in [vww_data.py](vww_data.py) —\n", + "standard tf_keras pipeline code, not described further here." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1c645ac1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 98658 images belonging to 2 classes.\n", + "Found 10961 images belonging to 2 classes.\n" + ] + } + ], + "source": [ + "from vww_data import get_data\n", + "\n", + "BATCH_SIZE = 32\n", + "INPUT_SHAPE = (96, 96, 3)\n", + "\n", + "train_ds, val_ds = get_data(DATA_PATH, INPUT_SHAPE, BATCH_SIZE, seed=SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "5e9e5fd5", + "metadata": {}, + "source": [ + "## Model\n", + "\n", + "The backbone is **AkidaNet** — a MobileNet V1 variant whose layer structure\n", + "maps efficiently onto Akida hardware — with width multiplier `alpha=0.25`.\n", + "The narrow variant keeps parameter count low while retaining sufficient\n", + "capacity for the binary VWW task.\n", + "\n", + "Key design choices:\n", + "\n", + "- **Input resolution 96×96**, down from the 224×224 used for the ImageNet\n", + " base model.\n", + "- **2-class output head** (person / non-person), returning raw logits — no\n", + " softmax. The loss function handles the softmax implicitly via\n", + " `from_logits=True`.\n", + "- **`input_scaling=(255, 0)`** embeds a linear mapping from uint8 [0, 255]\n", + " to float [0, 1] as the first layer. This keeps the data pipeline\n", + " hardware-friendly — inputs never need to be normalised outside the model.\n", + "- **`AkidaVersion.v1` context** constrains the layer configuration to what\n", + " the AKD1500 hardware supports." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b4eb1598", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1783890897.543076 201207 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22284 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:04:00.0, compute capability: 8.6\n", + "/home/dmclelland/miniconda3/envs/brainchip_devhub_env/lib/python3.12/site-packages/akida_models/model_io.py:147: UserWarning: Model akidanet_imagenet_224_alpha_25.h5 has been trained with akida_models 1.1.10 which is the last version supporting 1.0 models training. Continuing execution.\n", + " warnings.warn(f'Model {model_name_v1} has been trained with akida_models 1.1.10 which is '\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n" + ] + } + ], + "source": [ + "from vww_model import build_vww_model\n", + "model = build_vww_model(seed=SEED)\n" + ] + }, + { + "cell_type": "markdown", + "id": "647666ff", + "metadata": {}, + "source": [ + "## Float Training\n", + "\n", + "The model is trained for 50 epochs using Adam with a step-decay learning rate\n", + "schedule. The full training code is in [vww_train.py](vww_train.py) —\n", + "standard tf_keras code, not described further here.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e909bccd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 98658 images belonging to 2 classes.\n", + "Found 10961 images belonging to 2 classes.\n", + "Epoch 1/20\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1783890900.794973 201359 cuda_dnn.cc:529] Loaded cuDNN version 90300\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3084/3084 [==============================] - 127s 41ms/step - loss: 0.6449 - accuracy: 0.7737 - val_loss: 0.5579 - val_accuracy: 0.8083\n", + "Epoch 2/20\n", + "3084/3084 [==============================] - 125s 41ms/step - loss: 0.4864 - accuracy: 0.8346 - val_loss: 0.5962 - val_accuracy: 0.7758\n", + "Epoch 3/20\n", + "3084/3084 [==============================] - 126s 41ms/step - loss: 0.4724 - accuracy: 0.8388 - val_loss: 0.4870 - val_accuracy: 0.8257\n", + "Epoch 4/20\n", + "3084/3084 [==============================] - 128s 41ms/step - loss: 0.4510 - accuracy: 0.8463 - val_loss: 0.6158 - val_accuracy: 0.7837\n", + "Epoch 5/20\n", + "3084/3084 [==============================] - 125s 41ms/step - loss: 0.4345 - accuracy: 0.8505 - val_loss: 0.5322 - val_accuracy: 0.8033\n", + "Epoch 6/20\n", + "3084/3084 [==============================] - 126s 41ms/step - loss: 0.4174 - accuracy: 0.8564 - val_loss: 0.7843 - val_accuracy: 0.7158\n", + "Epoch 7/20\n", + "3084/3084 [==============================] - 125s 41ms/step - loss: 0.4134 - accuracy: 0.8564 - val_loss: 0.8596 - val_accuracy: 0.6848\n", + "Epoch 8/20\n", + "3084/3084 [==============================] - 125s 41ms/step - loss: 0.4066 - accuracy: 0.8596 - val_loss: 0.4366 - val_accuracy: 0.8436\n", + "Epoch 9/20\n", + "3084/3084 [==============================] - 126s 41ms/step - loss: 0.3839 - accuracy: 0.8705 - val_loss: 0.3912 - val_accuracy: 0.8673\n", + "Epoch 10/20\n", + "3084/3084 [==============================] - 129s 42ms/step - loss: 0.3739 - accuracy: 0.8735 - val_loss: 0.4229 - val_accuracy: 0.8549\n", + "Epoch 11/20\n", + "3084/3084 [==============================] - 126s 41ms/step - loss: 0.3618 - accuracy: 0.8769 - val_loss: 0.3585 - val_accuracy: 0.8779\n", + "Epoch 12/20\n", + "3084/3084 [==============================] - 126s 41ms/step - loss: 0.3518 - accuracy: 0.8822 - val_loss: 0.3610 - val_accuracy: 0.8759\n", + "Epoch 13/20\n", + "3084/3084 [==============================] - 127s 41ms/step - loss: 0.3380 - accuracy: 0.8878 - val_loss: 0.3892 - val_accuracy: 0.8637\n", + "Epoch 14/20\n", + "3084/3084 [==============================] - 126s 41ms/step - loss: 0.3272 - accuracy: 0.8914 - val_loss: 0.3799 - val_accuracy: 0.8695\n", + "Epoch 15/20\n", + "3084/3084 [==============================] - 128s 42ms/step - loss: 0.3153 - accuracy: 0.8965 - val_loss: 0.3890 - val_accuracy: 0.8649\n", + "Epoch 16/20\n", + "3084/3084 [==============================] - 127s 41ms/step - loss: 0.3044 - accuracy: 0.9007 - val_loss: 0.3478 - val_accuracy: 0.8835\n", + "Epoch 17/20\n", + "3084/3084 [==============================] - 126s 41ms/step - loss: 0.2959 - accuracy: 0.9047 - val_loss: 0.3411 - val_accuracy: 0.8871\n", + "Epoch 18/20\n", + "3084/3084 [==============================] - 127s 41ms/step - loss: 0.2895 - accuracy: 0.9082 - val_loss: 0.3311 - val_accuracy: 0.8905\n", + "Epoch 19/20\n", + "3084/3084 [==============================] - 124s 40ms/step - loss: 0.2840 - accuracy: 0.9091 - val_loss: 0.3321 - val_accuracy: 0.8927\n", + "Epoch 20/20\n", + "3084/3084 [==============================] - 125s 40ms/step - loss: 0.2807 - accuracy: 0.9116 - val_loss: 0.3298 - val_accuracy: 0.8933\n", + "Float model saved to ./models/akidanet_vww.h5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dmclelland/miniconda3/envs/brainchip_devhub_env/lib/python3.12/site-packages/tf_keras/src/engine/training.py:3098: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native TF-Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + } + ], + "source": [ + "from vww_train import train_vww\n", + "\n", + "\n", + "if RUN_FLOAT_TRAINING:\n", + " LEARNING_RATE = 1e-3\n", + " EPOCHS = 20\n", + " # Make sure the dataset seed is freshly set for reproducibility\n", + " train_ds, val_ds = get_data(DATA_PATH, INPUT_SHAPE, BATCH_SIZE, seed=SEED)\n", + "\n", + " train_vww(model, train_ds, val_ds,\n", + " EPOCHS,\n", + " LEARNING_RATE,\n", + " seed=SEED\n", + " )\n", + "\n", + " float_model_path = os.path.join(MODELS_DIR, 'akidanet_vww.h5')\n", + " model.save(float_model_path, include_optimizer=False)\n", + " print(f'Float model saved to {float_model_path}')\n", + "else:\n", + " from tf_keras.models import load_model\n", + " print('Training skipped. Using pretrained model...')\n", + " model_path = 'pretrained_models/akidanet_vww.h5'\n", + " model = load_model(model_path)" + ] + }, + { + "cell_type": "markdown", + "id": "5fe28a05", + "metadata": {}, + "source": [ + "### Evaluate float model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9316bac8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Float validation accuracy: 0.8933\n" + ] + } + ], + "source": [ + "model.compile(metrics=['accuracy'])\n", + "_, float_accuracy = model.evaluate(val_ds, verbose=0)\n", + "print(f'Float validation accuracy: {float_accuracy:.4f}')" + ] + }, + { + "cell_type": "markdown", + "id": "72693c80", + "metadata": {}, + "source": [ + "## Quantization\n", + "\n", + "Akida 1 operates with integer weights and activations. We use `cnn2snn` to\n", + "quantize the float model to 4 bits for both weights and activations (8-bit\n", + "weights are enabled for the first layer only, which is also unusual in\n", + "receiving uint8 inputs):\n", + "\n", + "Post-training quantization maps the float parameters to their nearest\n", + "representable integer values. Some accuracy is typically lost in this step,\n", + "which the subsequent QAT pass recovers.\n", + "\n", + "Note: the quantized model can be saved using the standard method. However,\n", + "for later reloading, because of the custom quantized layers in the model\n", + "we have to use the `load_quantized_model` function from cnn2snn (a wrapper\n", + "around the standard tf_keras loading function)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9f1430d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n", + "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n" + ] + } + ], + "source": [ + "from cnn2snn import quantize, load_quantized_model\n", + "\n", + "quantized_model = quantize(\n", + " model,\n", + " input_weight_quantization=8,\n", + " weight_quantization=4,\n", + " activ_quantization=4,\n", + ")\n", + "ptq_model_path = os.path.join(MODELS_DIR, 'akidanet_vww_ptq.h5')\n", + "quantized_model.save(ptq_model_path, include_optimizer=False)\n", + "\n", + "del quantized_model\n", + "\n", + "quantized_model = load_quantized_model(ptq_model_path)" + ] + }, + { + "cell_type": "markdown", + "id": "76a4ccbb", + "metadata": {}, + "source": [ + "### Quantization-Aware Training (QAT)\n", + "\n", + "A few epochs of fine-tuning sufficient to recover most of the accuracy lost \n", + "during quantization. It is typical to find that a lower learning rate (e.g. /10)\n", + "is required during this phase than during the initial training.\n", + "\n", + "Note that, although Quatization Aware Training can sound intimidating,\n", + "the model quantized via `cnn2snn` can simply be reinserted into the \n", + "same training function that was used for the initial float training." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f0d0707d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 98658 images belonging to 2 classes.\n", + "Found 10961 images belonging to 2 classes.\n", + "Epoch 1/5\n", + "3084/3084 [==============================] - 143s 45ms/step - loss: 0.3430 - accuracy: 0.8611 - val_loss: 0.3320 - val_accuracy: 0.8697\n", + "Epoch 2/5\n", + "3084/3084 [==============================] - 139s 45ms/step - loss: 0.3256 - accuracy: 0.8696 - val_loss: 0.3347 - val_accuracy: 0.8667\n", + "Epoch 3/5\n", + "3084/3084 [==============================] - 136s 44ms/step - loss: 0.3076 - accuracy: 0.8783 - val_loss: 0.3273 - val_accuracy: 0.8727\n", + "Epoch 4/5\n", + "3084/3084 [==============================] - 136s 44ms/step - loss: 0.2863 - accuracy: 0.8891 - val_loss: 0.3082 - val_accuracy: 0.8813\n", + "Epoch 5/5\n", + "3084/3084 [==============================] - 138s 45ms/step - loss: 0.2645 - accuracy: 0.8998 - val_loss: 0.3035 - val_accuracy: 0.8854\n", + "QAT model saved to ./models/akidanet_vww_qat.h5\n" + ] + } + ], + "source": [ + "\n", + "if RUN_QAT_TRAINING:\n", + " QAT_LEARNING_RATE = 1e-4\n", + " QAT_EPOCHS = 5\n", + " \n", + " # We re-fetch the dataset just for reproducibility vs the script version of the code\n", + " train_ds, val_ds = get_data(DATA_PATH, INPUT_SHAPE, BATCH_SIZE, seed=SEED)\n", + " \n", + " train_vww(quantized_model, train_ds, val_ds,\n", + " QAT_EPOCHS,\n", + " QAT_LEARNING_RATE,\n", + " )\n", + "\n", + " qat_model_path = os.path.join(MODELS_DIR, 'akidanet_vww_qat.h5')\n", + " quantized_model.save(qat_model_path, include_optimizer=False)\n", + " print(f'QAT model saved to {qat_model_path}')\n", + "else:\n", + " print('QAT skipped. Loading pretrained model...')\n", + " q_model_path = 'pretrained_models/akidanet_vww_qat.h5'\n", + " quantized_model = load_quantized_model(q_model_path)" + ] + }, + { + "cell_type": "markdown", + "id": "7c370145", + "metadata": {}, + "source": [ + "### Evaluate quantized model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "78ae04ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "QAT validation accuracy: 0.8854\n" + ] + } + ], + "source": [ + "quantized_model.compile(metrics=['accuracy'])\n", + "_, qat_accuracy = quantized_model.evaluate(val_ds, verbose=0)\n", + "print(f'QAT validation accuracy: {qat_accuracy:.4f}')" + ] + }, + { + "cell_type": "markdown", + "id": "58fcef07", + "metadata": {}, + "source": [ + "## Conversion to Akida Format\n", + "\n", + "`cnn2snn.convert` compiles the quantized Keras model into an Akida `.fbz`\n", + "model that can be loaded and executed directly on AKD1500 hardware.\n", + "The converter verifies hardware compatibility and maps each layer to its\n", + "corresponding Akida primitive." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "536ef853", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Akida model saved to ./models/akidanet_vww_qat.fbz\n", + " Model Summary \n", + "______________________________________________\n", + "Input shape Output shape Sequences Layers\n", + "==============================================\n", + "[96, 96, 3] [1, 1, 2] 1 15 \n", + "______________________________________________\n", + "\n", + "__________________________________________________________\n", + "Layer (type) Output shape Kernel shape \n", + "\n", + "============ SW/conv_0-predictions (Software) ============\n", + "\n", + "conv_0 (InputConv.) [48, 48, 8] (3, 3, 3, 8) \n", + "__________________________________________________________\n", + "conv_1 (Conv.) [48, 48, 16] (3, 3, 8, 16) \n", + "__________________________________________________________\n", + "conv_2 (Conv.) [24, 24, 32] (3, 3, 16, 32) \n", + "__________________________________________________________\n", + "conv_3 (Conv.) [24, 24, 32] (3, 3, 32, 32) \n", + "__________________________________________________________\n", + "separable_4 (Sep.Conv.) [12, 12, 64] (3, 3, 32, 1) \n", + "__________________________________________________________\n", + " (1, 1, 32, 64) \n", + "__________________________________________________________\n", + "separable_5 (Sep.Conv.) [12, 12, 64] (3, 3, 64, 1) \n", + "__________________________________________________________\n", + " (1, 1, 64, 64) \n", + "__________________________________________________________\n", + "separable_6 (Sep.Conv.) [6, 6, 128] (3, 3, 64, 1) \n", + "__________________________________________________________\n", + " (1, 1, 64, 128) \n", + "__________________________________________________________\n", + "separable_7 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", + "__________________________________________________________\n", + " (1, 1, 128, 128)\n", + "__________________________________________________________\n", + "separable_8 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", + "__________________________________________________________\n", + " (1, 1, 128, 128)\n", + "__________________________________________________________\n", + "separable_9 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", + "__________________________________________________________\n", + " (1, 1, 128, 128)\n", + "__________________________________________________________\n", + "separable_10 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", + "__________________________________________________________\n", + " (1, 1, 128, 128)\n", + "__________________________________________________________\n", + "separable_11 (Sep.Conv.) [6, 6, 128] (3, 3, 128, 1) \n", + "__________________________________________________________\n", + " (1, 1, 128, 128)\n", + "__________________________________________________________\n", + "separable_12 (Sep.Conv.) [3, 3, 256] (3, 3, 128, 1) \n", + "__________________________________________________________\n", + " (1, 1, 128, 256)\n", + "__________________________________________________________\n", + "separable_13 (Sep.Conv.) [1, 1, 256] (3, 3, 256, 1) \n", + "__________________________________________________________\n", + " (1, 1, 256, 256)\n", + "__________________________________________________________\n", + "predictions (Fully.) [1, 1, 2] (1, 1, 256, 2) \n", + "__________________________________________________________\n" + ] + } + ], + "source": [ + "from cnn2snn import convert\n", + "\n", + "akida_model = convert(quantized_model)\n", + "\n", + "akida_model_path = os.path.join(MODELS_DIR, 'akidanet_vww_qat.fbz')\n", + "akida_model.save(akida_model_path)\n", + "print(f'Akida model saved to {akida_model_path}')\n", + "akida_model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "655c4739", + "metadata": {}, + "source": [ + "## Evaluation of Akida Model\n", + "\n", + "We now run evaluation through the Akida model, to check that accuracy is \n", + "comparable to that obtained from the quantized tf_keras model. He, we deliberately\n", + "use the software backend (the default, since we do not check for and map to\n", + "a connected hardware device): this delivers a\n", + "bit-accurate simulation of the results that will be obtained when running\n", + "the model on hardware.\n", + "\n", + "In the accompanying [vww_notebook_benchmark.ipynb](plant_village_notebook_benchmark.ipynb)\n", + "the same evaluation is run using the hardware backend (if, of course, a hardware Akida\n", + "device is connected), allowing you to confirm that the results are identical." + ] + }, + { + "cell_type": "markdown", + "id": "d8f31c3d", + "metadata": {}, + "source": [ + "### Run Evaluation on Akida\n", + "\n", + "The Akida runtime cannot consume `tf.data.Dataset` objects directly, rather\n", + "it expects a 4D numpy array (n, h, w, c) in uint8 format. So we\n", + "iterate over validation batches manually.\n", + "\n", + "The model output tensor has shape `(B, 1, 1, C)` which is squeezed to \n", + "`(B, C)` before taking the class argmax." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "20ffb4cd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Evaluating on Akida: 100%|██████████| 343/343 [00:07<00:00, 42.91it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Akida accuracy: 0.8842\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "\n", + "labels_all = []\n", + "logits_all = []\n", + "num_batches = len(val_ds)\n", + "val_ds.reset() # Ensure a clean run through the full validation dataset\n", + "for _ in tqdm(range(num_batches), desc=\"Evaluating on Akida\"):\n", + " batch, label_batch = next(val_ds)\n", + " if not isinstance(batch, np.ndarray):\n", + " batch = batch.numpy()\n", + "\n", + " logits_batch = akida_model.predict(batch.astype(np.uint8))\n", + "\n", + " logits_batch = logits_batch.squeeze(axis=(1, 2))\n", + " labels_all.append(label_batch)\n", + " logits_all.append(logits_batch)\n", + "\n", + "labels_all = np.concatenate(labels_all)\n", + "logits_all = np.concatenate(logits_all)\n", + "preds = np.argmax(logits_all, axis=1)\n", + "\n", + "akida_accuracy = float(np.mean(preds == np.array(labels_all)))\n", + "print(f'Akida accuracy: {akida_accuracy:.4f}')" + ] + }, + { + "cell_type": "markdown", + "id": "abdad1f1", + "metadata": {}, + "source": [ + "### Activation Sparsity\n", + "\n", + "Akida hardware skips computation for zero-valued activations, so activation\n", + "sparsity directly reduces both energy consumption and inference latency.\n", + "Below we measure per-layer sparsity on a 1024-sample calibration batch drawn\n", + "from the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "50ce55a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 109619 images belonging to 2 classes.\n", + "\n", + "Layer Sparsity\n", + "-------------------------\n", + "conv_0 23.86%\n", + "conv_1 37.03%\n", + "conv_2 56.00%\n", + "conv_3 40.28%\n", + "separable_4 33.30%\n", + "separable_5 65.21%\n", + "separable_6 40.46%\n", + "separable_7 48.44%\n", + "separable_8 60.60%\n", + "separable_9 65.68%\n", + "separable_10 74.43%\n", + "separable_11 59.27%\n", + "separable_12 89.35%\n", + "separable_13 89.25%\n", + "predictions 0.50%\n", + "-------------------------\n", + "Mean 52.24%\n" + ] + } + ], + "source": [ + "from akida_models.sparsity import compute_sparsity\n", + "from brainchip_utils.plot_utils import pretty_print_sparsity\n", + "from vww_data import get_samples\n", + "\n", + "NUM_SAMPLES=1024\n", + "samples = get_samples(DATA_PATH, INPUT_SHAPE, num_samples=NUM_SAMPLES)\n", + "sparsity_dict = compute_sparsity(akida_model, samples=samples)\n", + "pretty_print_sparsity(sparsity_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "67c6a41e", + "metadata": {}, + "source": [ + "## Summary\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "15a8c528", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Float: 0.8933\n", + "QAT: 0.8854\n", + "Akida: 0.8842\n" + ] + } + ], + "source": [ + "print(f\"Float: {float_accuracy:.4f}\")\n", + "print(f\"QAT: {qat_accuracy:.4f}\")\n", + "print(f\"Akida: {akida_accuracy:.4f}\")" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "brainchip_devhub_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/akida1/model_zoo/vww/vww_train.py b/akida1/model_zoo/vww/vww_train.py index d32f040..7b52f0a 100644 --- a/akida1/model_zoo/vww/vww_train.py +++ b/akida1/model_zoo/vww/vww_train.py @@ -9,12 +9,11 @@ -l akidanet_vww_untrained.h5 -s akidanet_vww.h5 """ import argparse +import tensorflow as tf -from tf_keras import Model from tf_keras.losses import SparseCategoricalCrossentropy from tf_keras.optimizers.legacy import Adam from tf_keras.optimizers.schedules import CosineDecay -from tf_keras.callbacks import LearningRateScheduler from tf_keras import regularizers from tf_keras.layers import ReLU from tf_keras.utils import set_random_seed @@ -23,9 +22,12 @@ from vww_data import get_data +# Must be called before any TF ops to make GPU ops (conv backward passes, +# bilinear resize, etc.) deterministic. Has a small throughput cost. +tf.config.experimental.enable_op_determinism() - -def train_vww(model, train_ds, val_ds, epochs, learning_rate, regularization=None): +def train_vww(model, train_ds, val_ds, epochs, learning_rate, regularization=None, seed=42): + set_random_seed(seed) steps_per_epoch = len(train_ds) total_steps = steps_per_epoch * epochs @@ -40,10 +42,6 @@ def train_vww(model, train_ds, val_ds, epochs, learning_rate, regularization=Non # --------------------------------------------------------------------------- # Model # --------------------------------------------------------------------------- - model.compile(optimizer=Adam(learning_rate=lr_scheduler), - loss=SparseCategoricalCrossentropy(from_logits=True), - metrics=['accuracy']) - if regularization is not None: print('Adding Activity Regularization to ReLU layers') regularizer = regularizers.L1L2(regularization, regularization) @@ -51,7 +49,11 @@ def train_vww(model, train_ds, val_ds, epochs, learning_rate, regularization=Non if isinstance(layer, ReLU): layer.activity_regularizer = regularizer - + model.compile(optimizer=Adam(learning_rate=lr_scheduler), + loss=SparseCategoricalCrossentropy(from_logits=True), + metrics=['accuracy']) + + # --------------------------------------------------------------------------- # Training # --------------------------------------------------------------------------- @@ -82,8 +84,6 @@ def train_vww(model, train_ds, val_ds, epochs, learning_rate, regularization=Non help='Random seed for reproducibility') args = parser.parse_args() - set_random_seed(args.seed) - # --------------------------------------------------------------------------- # Model # --------------------------------------------------------------------------- @@ -92,14 +92,15 @@ def train_vww(model, train_ds, val_ds, epochs, learning_rate, regularization=Non # --------------------------------------------------------------------------- # Data loading # --------------------------------------------------------------------------- - train_ds, val_ds = get_data(args.data, model.input_shape[1:], args.batch_size) + train_ds, val_ds = get_data(args.data, model.input_shape[1:], args.batch_size, seed=args.seed) train_vww(model=model, train_ds=train_ds, val_ds=val_ds, epochs=args.epochs, learning_rate=args.learning_rate, - regularization=args.regularization) + regularization=args.regularization, + seed=args.seed) model.save(args.savemodel, include_optimizer=False) print(f'Model saved as {args.savemodel}.') diff --git a/akida1/model_zoo/vww/vww_train.sh b/akida1/model_zoo/vww/vww_train.sh index 62e65d9..870d796 100755 --- a/akida1/model_zoo/vww/vww_train.sh +++ b/akida1/model_zoo/vww/vww_train.sh @@ -3,12 +3,12 @@ DATA_ARG=${DATADIR:+-d "$DATADIR"} python vww_model.py -s models/akidanet_vww_untrained.h5 -python vww_train.py -l models/akidanet_vww_untrained.h5 -s models/akidanet_vww.h5 -e 50 -lr 1e-3 $DATA_ARG +python vww_train.py -l models/akidanet_vww_untrained.h5 -s models/akidanet_vww.h5 -e 20 -lr 1e-3 $DATA_ARG python vww_eval.py -l models/akidanet_vww.h5 $DATA_ARG # 4 bits quantization and tuning cnn2snn quantize -m models/akidanet_vww.h5 -i 8 -w 4 -a 4 -python vww_train.py -l models/akidanet_vww_iq8_wq4_aq4.h5 -s models/akidanet_vww_qat.h5 -lr 1e-4 -e 2 $DATA_ARG +python vww_train.py -l models/akidanet_vww_iq8_wq4_aq4.h5 -s models/akidanet_vww_qat.h5 -lr 1e-4 -e 5 $DATA_ARG python vww_eval.py -l models/akidanet_vww_qat.h5 $DATA_ARG diff --git a/pyproject.toml b/pyproject.toml index 445b926..ea99612 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,6 +10,7 @@ dependencies = [ "tensorflow[and-cuda]==2.19.1", "tensorflow-metadata<1.21", "akida_models==1.14.0", + "pyftdi", "ipykernel" ]