Streamline images in AIMET docs (#3727)

* Streamlining images in docs Signed-off-by: Priyanka Dangi <[email protected]>
quic · Jan 14, 2025 · 57c38ea · 57c38ea
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diff --git a/Docs/featureguide/autoquant.rst b/Docs/featureguide/autoquant.rst
@@ -26,8 +26,8 @@ Workflow
 
 The workflow looks like this:
 
-    .. image:: ../../images/auto_quant_v2_flowchart.png
-
+.. image:: ../images/auto_quant_1.png
+   :height: 450
 Before entering the optimization workflow, AutoQuant prepares by:
 
 1. Checking the validity of the model and converting the model into an AIMET quantization-friendly format (`Prepare Model`).

diff --git a/Docs/featureguide/mixed precision/amp.rst b/Docs/featureguide/mixed precision/amp.rst
@@ -27,16 +27,16 @@ allowable accuracy drop, is passed to the API.
 The function changes the QuantSim Sim model in place with different quantizers having different
 bit-widths. This QuantSim model can be either exported or evaluated to get a quantization accuracy.
 
-.. image:: ../../images/work_flow_amp.png
+.. image:: ../../images/automatic_mixed_precision_1.png
     :width: 900px
 
 Mixed Precision Algorithm
 =========================
 
 The algorithm involves 4 phases:
 
-    .. image:: ../../images/stages.png
-        :width: 150px
+.. image:: ../../images/automatic_mixed_precision_2.png
+    :width: 700px
 
 1) Find layer groups
 --------------------
@@ -45,8 +45,8 @@ The algorithm involves 4 phases:
     This helps in reducing search space over which the mixed precision algorithm operates.
     It also ensures that we search only over the valid bit-width settings for parameters and activations.
 
-    .. image:: ../../images/quantizer_groups.png
-        :width: 900px
+.. image:: ../../images/automatic_mixed_precision_3.png
+    :width: 900px
 
 2) Perform sensitivity analysis (Phase 1)
 -----------------------------------------

diff --git a/Docs/images/auto_quant_1.png b/Docs/images/auto_quant_1.png
diff --git a/Docs/images/automatic_mixed_precision_1.png b/Docs/images/automatic_mixed_precision_1.png
diff --git a/Docs/images/automatic_mixed_precision_2.png b/Docs/images/automatic_mixed_precision_2.png
diff --git a/Docs/images/automatic_mixed_precision_3.png b/Docs/images/automatic_mixed_precision_3.png
diff --git a/Docs/images/debugging_guidelines_1.png b/Docs/images/debugging_guidelines_1.png
diff --git a/Docs/images/quantization_workflow_1.png b/Docs/images/quantization_workflow_1.png
diff --git a/Docs/images/quantization_workflow_2.png b/Docs/images/quantization_workflow_2.png
diff --git a/Docs/images/quantization_workflow_3.png b/Docs/images/quantization_workflow_3.png
diff --git a/Docs/images/quantization_workflow_4.png b/Docs/images/quantization_workflow_4.png
diff --git a/Docs/images/quantization_workflow_5.png b/Docs/images/quantization_workflow_5.png
diff --git a/Docs/userguide/debugging_guidelines.rst b/Docs/userguide/debugging_guidelines.rst
@@ -15,11 +15,11 @@ Debugging workflow
 
 The steps are shown as a flow chart in the following figure and are described in more detail below:
 
-.. image:: ../images/quantization_debugging_flow_chart.png
-   :height: 800
-   :width: 700
+.. image:: ../images/debugging_guidelines_1.png
+   :height: 500
 
-1. FP32 confidence check
+
+1. FP32 confidence checks
 ------------------------
 
 First, ensure that the floating-point and quantized model behave similarly in the forward pass,

diff --git a/Docs/userguide/quantization_workflow.rst b/Docs/userguide/quantization_workflow.rst
@@ -24,10 +24,10 @@ without requiring actual quantized hardware.
 
 A quantization simulation workflow is illustrated here:
 
-.. image:: ../images/quant_use_case_1.PNG
-
-2. Post-training quantization
------------------------------
+.. image:: ../images/quantization_workflow_1.png
+  
+2. Post-training quantization (PTQ):
+------------------------------------
 
 Post-training quantization (PTQ) techniques make a model more quantization-friendly without requiring model retraining
 or fine-tuning. PTQ is recommended as a go-to tool in a quantization workflow because:
@@ -37,7 +37,7 @@ or fine-tuning. PTQ is recommended as a go-to tool in a quantization workflow be
 
 The PTQ workflow is illustrated here:
 
-.. image:: ../images/quant_use_case_3.PNG
+.. image:: ../images/quantization_workflow_2.png
 
 3. Quantization-aware training
 ------------------------------
@@ -55,7 +55,7 @@ but it can provide better accuracy, especially at lower bit-widths.
 
 A typical QAT workflow is illustrated here:
 
-.. image:: ../images/quant_use_case_2.PNG
+.. image:: ../images/quantization_workflow_3.png
 
 Supported precisions for on-target inference
 ============================================
@@ -108,7 +108,7 @@ lowering the precision.
 The figure below illustrates the recommended quantization workflow and the steps required
 to deploy the quantized model on the target device.
 
-.. figure:: ../images/overall_quantization_workflow.png
+.. figure:: ../images/quantization_workflow_4.png
 
    Recommended quantization workflow
 
@@ -144,7 +144,7 @@ If the off-target quantized accuracy metric is not meeting expectations, you can
 techniques to improve the quantized accuracy for the desired precision. The decision between
 PTQ and QAT should be based on the quantized accuracy and runtime needs.
 
-.. image:: ../images/quantization_workflow.png
+.. image:: ../images/quantization_workflow_5.png
 
 Once the off-target quantized accuracy metric is satisfactory, proceed to :ref:`evaluate the
 on-target metrics<opt-guide-on-target-inference>` at this precision. If the on-target metrics