Merge pull request #90 from ModelTC/docs-dev

[Feature]: update support matrix, fix docs
ModelTC · May 10, 2022 · 4336493 · 4336493
2 parents 95947bd + f857ba5
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diff --git a/docs/source/get_started/support_matrix.rst b/docs/source/get_started/support_matrix.rst
@@ -26,4 +26,6 @@ Algorithm
 4. `PACT <https://arxiv.org/pdf/1805.06085.pdf>`_
 5. `APoT <https://arxiv.org/pdf/1909.13144v2.pdf>`_
 6. `AdaRound <https://arxiv.org/pdf/2004.10568.pdf>`_
-7. `QIL <https://arxiv.org/pdf/1808.05779.pdf>`_
+7. `QIL <https://arxiv.org/pdf/1808.05779.pdf>`_
+8. `BRECQ  <https://arxiv.org/pdf/2102.05426.pdf>`_
+9. `QDrop <https://arxiv.org/pdf/2203.05740.pdf>`_
diff --git a/docs/source/user_guide/PTQ/advanced.rst b/docs/source/user_guide/PTQ/advanced.rst
@@ -23,15 +23,15 @@ MQBench provides a simple API for advanced PTQ, learn our step-by-step instructi
 
     # configuration
     ptq_reconstruction_config = {
-        'pattern': 'block',                   #? 'layer' for Adaround or 'block' for BRECQ
+        'pattern': 'block',                   #? 'layer' for Adaround or 'block' for BRECQ and QDROP
         'scale_lr': 4.0e-5,                   #? learning rate for learning step size of activation
         'warm_up': 0.2,                       #? 0.2 * max_count iters without regularization to floor or ceil
         'weight': 0.01,                       #? loss weight for regularization item
         'max_count': 20000,                   #? optimization iteration
         'b_range': [20,2],                    #? beta decaying range
         'keep_gpu': True,                     #? calibration data restore in gpu or cpu
         'round_mode': 'learned_hard_sigmoid', #? ways to reconstruct the weight, currently only support learned_hard_sigmoid
-        'prob': 0.5,                          #? dropping probability of QDROP
+        'prob': 1.0,                          #? dropping probability of QDROP, 1.0 for Adaround and BRECQ
     }
 
     # backend options

diff --git a/docs/source/user_guide/QAT/detection.rst b/docs/source/user_guide/QAT/detection.rst
@@ -20,7 +20,7 @@ Getting Started
     # Prepare your float pretrained model.
     cd eod/scripts
     # Follow the prompts to set config in train_quant.sh.
-    sh train_quant.sh
+    sh train_qat.sh
 
 
 **We have several examples of qat config in EOD repository:**