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avoid random lib usage (openvinotoolkit#2344)
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17 files changed

+527
-86
lines changed

17 files changed

+527
-86
lines changed

‎notebooks/ct-segmentation-quantize/ct-segmentation-quantize-nncf.ipynb

+458-52
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‎notebooks/explainable-ai-3-map-interpretation/explainable-ai-3-map-interpretation.ipynb

+38-3
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,7 @@
11
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -62,6 +63,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -93,6 +95,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -107,7 +110,6 @@
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"outputs": [],
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"source": [
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"import os\n",
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"import random\n",
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"import zipfile\n",
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"from pathlib import Path\n",
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"\n",
@@ -131,6 +133,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -139,6 +142,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -211,12 +215,13 @@
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"print(f\"Number of images to get explanations: {len(img_files)}\")\n",
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"\n",
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"# Get a fewer subset for fast execution\n",
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"random.seed(42)\n",
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"img_files = random.sample(img_files, 1)\n",
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"np.random.seed(42)\n",
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"img_files = np.random.choice(img_files, 1)\n",
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"print(f\"Run explanations on fewer number of images: {len(img_files)}\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -257,6 +262,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -265,6 +271,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -302,6 +309,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -322,6 +330,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -330,6 +339,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -377,6 +387,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -385,6 +396,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -393,6 +405,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -432,6 +445,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -440,6 +454,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -490,6 +505,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -498,6 +514,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -527,6 +544,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -554,6 +572,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -562,6 +581,7 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -668,6 +688,7 @@
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"source": [
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]
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},
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"source": [
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"source": [
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"source": [
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]
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"attachments": {},
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"cell_type": "markdown",
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"source": [
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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]
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
@@ -967,13 +997,15 @@
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"`True positive low confidence` basically means that key features are not well available or are transformed. From the saliency maps, we see that the model is paying attention to the whole object, trying to make a decision mostly based on high-level features."
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]
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},
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"attachments": {},
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]
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},
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"attachments": {},
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"source": [
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]
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"attachments": {},
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"cell_type": "markdown",
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [

‎notebooks/florence2/gradio_helper.py

+9-4
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,5 @@
11
import io
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import copy
3-
import random
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import requests
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from pathlib import Path
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@@ -68,8 +67,13 @@ def draw_polygons(image, prediction, fill_mask=False):
6867
draw = ImageDraw.Draw(image)
6968
scale = 1
7069
for polygons, label in zip(prediction["polygons"], prediction["labels"]):
71-
color = random.choice(colormap)
72-
fill_color = random.choice(colormap) if fill_mask else None
70+
color_id = np.random.choice(len(colormap))
71+
color = colormap[color_id]
72+
if fill_mask:
73+
fill_color_id = np.random.choice(len(colormap))
74+
fill_color = colormap[fill_color_id]
75+
else:
76+
fill_color = None
7377
for _polygon in polygons:
7478
_polygon = np.array(_polygon).reshape(-1, 2)
7579
if len(_polygon) < 3:
@@ -96,7 +100,8 @@ def draw_ocr_bboxes(image, prediction):
96100
draw = ImageDraw.Draw(image)
97101
bboxes, labels = prediction["quad_boxes"], prediction["labels"]
98102
for box, label in zip(bboxes, labels):
99-
color = random.choice(colormap)
103+
color_id = np.random.choice(len(colormap))
104+
color = colormap[color_id]
100105
new_box = (np.array(box) * scale).tolist()
101106
draw.polygon(new_box, width=3, outline=color)
102107
draw.text((new_box[0] + 8, new_box[1] + 2), "{}".format(label), align="right", fill=color)

‎notebooks/flux.1-image-generation/gradio_helper.py

+1-2
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,5 @@
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import gradio as gr
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import numpy as np
3-
import random
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import torch
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MAX_SEED = np.iinfo(np.int32).max
@@ -23,7 +22,7 @@
2322
def make_demo(ov_pipe):
2423
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, guidance_scale=0, progress=gr.Progress(track_tqdm=True)):
2524
if randomize_seed:
26-
seed = random.randint(0, MAX_SEED)
25+
seed = np.random.randint(0, MAX_SEED)
2726
generator = torch.Generator().manual_seed(seed)
2827
image = ov_pipe(
2928
prompt=prompt, width=width, height=height, num_inference_steps=num_inference_steps, generator=generator, guidance_scale=guidance_scale

‎notebooks/grounded-segment-anything/grounded-segment-anything.ipynb

+3-4
Original file line numberDiff line numberDiff line change
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"outputs": [],
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"source": [
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"def draw_mask(mask, draw, random_color=False):\n",
969-
" import random\n",
970969
"\n",
971970
" if random_color:\n",
972971
" color = (\n",
973-
" random.randint(0, 255),\n",
974-
" random.randint(0, 255),\n",
975-
" random.randint(0, 255),\n",
972+
" np.random.randint(0, 255),\n",
973+
" np.random.randint(0, 255),\n",
974+
" np.random.randint(0, 255),\n",
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" 153,\n",
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" )\n",
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" else:\n",

‎notebooks/instant-id/gradio_helper.py

+1-2
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from pathlib import Path
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from typing import Callable
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import gradio as gr
4-
import random
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import numpy as np
65
from diffusers.utils import load_image
76
from style_template import styles
@@ -32,7 +31,7 @@
3231

3332
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
3433
if randomize_seed:
35-
seed = random.randint(0, MAX_SEED)
34+
seed = np.random.randint(0, MAX_SEED)
3635
return seed
3736

3837

‎notebooks/llava-next-multimodal-chatbot/llava-next-multimodal-chatbot.ipynb

+1-1
Original file line numberDiff line numberDiff line change
@@ -986,7 +986,7 @@
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" batch_size = input_ids.shape[0]\n",
987987
" if not self.stateful:\n",
988988
" for input_name in self.key_value_input_names:\n",
989-
" model_inputs = self.modeget_anyres_image_grid_shapel.input(input_name)\n",
989+
" model_inputs = self.model.input(input_name)\n",
990990
" shape = model_inputs.get_partial_shape()\n",
991991
" shape[0] = batch_size\n",
992992
" if shape[2].is_dynamic:\n",

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