From d414c2431012f49d79572f22d72a51e921d004b0 Mon Sep 17 00:00:00 2001
From: neurolabusc <rorden@sc.edu>
Date: Sun, 28 Apr 2024 18:30:15 -0400
Subject: [PATCH] await tf.memory() promise

---
 brainchop.js | 24 +++---------------------
 1 file changed, 3 insertions(+), 21 deletions(-)

diff --git a/brainchop.js b/brainchop.js
index 8a47d17..3d90056 100644
--- a/brainchop.js
+++ b/brainchop.js
@@ -1248,7 +1248,7 @@ class SequentialConvLayer {
       const seqTimer = window.setInterval(async function () {
         tf.engine().startScope() // Start TensorFlow.js scope
         console.log('=======================')
-        const memoryInfo0 = tf.memory()
+        const memoryInfo0 = await tf.memory()
         console.log(`| Number of Tensors: ${memoryInfo0.numTensors}`)
         console.log(`| Number of Data Buffers: ${memoryInfo0.numDataBuffers}`)
         console.log('Channel : ', chIdx)
@@ -1273,20 +1273,12 @@ class SequentialConvLayer {
           return [newoutC, newoutB]
         })
 
-        // -- await showMemStatus(chIdx, self.outChannels);
-
-        const memoryInfo1 = tf.memory()
-        console.log(`| Number of Tensors: ${memoryInfo1.numTensors}`)
-        console.log(`| Number of Data Buffers: ${memoryInfo1.numDataBuffers}`)
         console.log('=======================')
-
-        // Log memory usage
-
-        const memoryInfo = tf.memory()
+        const memoryInfo = await tf.memory()
         self.callbackUI(`Iteration ${chIdx}`, chIdx / self.outChannels)
         console.log(`Number of Tensors: ${memoryInfo.numTensors}`)
         console.log(`Number of Data Buffers: ${memoryInfo.numDataBuffers}`)
-        console.log(`Bytes In Use: ${memoryInfo.numBytes}`)
+        
         console.log(`Megabytes In Use: ${(memoryInfo.numBytes / 1048576).toFixed(3)} MB`)
         if (memoryInfo.unreliable) {
           console.log(`Unreliable: ${memoryInfo.unreliable}`)
@@ -1643,16 +1635,6 @@ async function inferenceFullVolumeSeqCovLayerPhase2(
             3
           ) // important for memory use
         }
-
-        // // Log memory usage
-        // const memoryInfo = tf.memory();
-        // console.log(`Iteration ${i}:`);
-        // console.log(`Number of Tensors: ${memoryInfo.numTensors}`);
-        // console.log(`Number of Data Buffers: ${memoryInfo.numDataBuffers}`);
-        // console.log(`Bytes In Use: ${memoryInfo.numBytes}`);
-        // console.log(`Megabytes In Use: ${(memoryInfo.numBytes / 1048576).toFixed(3)} MB`);
-        // console.log(`Unreliable: ${memoryInfo.unreliable}`);
-
         tf.dispose(curTensor[i - 1])
       } catch (err) {
         // ? original code provided special dialog for shaders if( err.message === "Failed to compile fragment shader.") {