NVIDIA Feature Map Explorer | NVIDIA Developer

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border-bottom: 0px solid #ddd; border-radius: 0px 0px 5px 5px; padding: 0px; } .nav-tabs { margin-bottom: 0; } /*VIDEO CONTAINER*/ .video-container { position:relative; padding-bottom:56.25%; padding-top:30px; height:0; overflow:hidden; } .video-container iframe, .video-container object, .video-container embed { position:absolute; top:0; left:0; width:100%; height:100%; } </style> <br> <center> <h1><font color="#76b900">Feature Map Explorer</font></h1> </center> <br> <hr> <br> <div class="row"> <div class="col-md-2"></div> <div class="col-md-8"> <center> <p align="justify" class="lead">Feature Map Explorer (FME) enables visualization of 4-dimensional image-based feature map data using a range of views, from low-level channel visualizations to detailed numerical information about each channel slice. Think of this as a way to peer into the DNN processing “black box” to find intimate information about what the model is learning, where the model is failing to use resources efficiently, and what is changing as a model is learning during training to better process data handed to it.</p> <a class="btn btn-success btn" href="https://developer.nvidia.com/gameworksdownload#?dn=nvidia-feature-map-explorer-2022-1" target="_blank">Download Now</a> </center> </div> <div class="col-md-2"></div> </div> <br> <br> <center> <a target="_blank" href="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/gamedev/FME_Intro.png"><img class="img-responsive" width="80%" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/gamedev/FME_Intro.png"></a> </center> <br> <hr> <br> <center> <h2><font color="#76B900">FME 2022.1 Updates</font></h2> </center> <br> <div class="row"> <div class="col-md-2"></div> <div class="col-md-8"> <ul> <li>We improved the performance of loading tensor data from numpy files.</li> <li>We added an option to display channel values of literal 0 as blue to help distinguish from low-magnitude values that are non-zero, where this distinction is especially useful after ReLU activation or when comparing two tensors.</li> <li>We added an option to display FP16 safe regions that help users to spot tensor values that are susceptible to precision loss when converted to FP16 format.</li> <li>We added a right-click context menu for the main viewing region.</li> <li>We added a <b>Percent 0</b> column to the tensor stats view.</li> <li>We added an option to display channel numbers in the channel views.</li> </ul> </div> <div class="col-md-2"></div> </div> <br> <hr> <br> <div class="row"> <div class="col-md-3"> <center> <h3><font color="#76B900">Visualize Feature Maps at Multiple Levels</font></h3> <p>A range of analysis data is generated, including low-level channel visualizations and detailed numerical information about the full feature map tensor and each channel slice. </p> </center> </div> <div class="col-md-3"> <center> <h3><font color="#76B900">Check for Opportunities to Improve Speed</font></h3> <p>FME makes it straightforward to know when you can drop to lower precision numerical formats. </p> </center> </div> <div class="col-md-3"> <center> <h3><font color="#76B900">Understand Training Progressions</font></h3> <p>Comparison of feature maps from one epoch to another is made easy and flexible.</p> </center> </div> <div class="col-md-3"> <center> <h3><font color="#76B900">Compatible with Any Training Network</font></h3> <p>Works with feature map data (stored in standard numpy .npy files) that is available from any training network. </p> </center> </div> </div> <br> <hr> <br> <div class="row"> <div class="col-md-5"> <br><br><br> <h3><font color="#76B900">Dive Into Each Channel</font></h3> <p align="justify">Deep learning models consist of many layers of processing, arranged as a computation graph, where the output from one layer serves as input to subsequent layers. Deep learning developers can visualize and examine each channel to see what has been learned or what is missing in a feature.</p> </div> <div class="col-md-7"> <center> <img class="img-responsive" width="80%" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/gamedev/FME_Channel.png"> </center> </div> </div> <br> <hr> <br> <div class="row"> <div class="col-md-7"> <br> <center> <img class="img-responsive" width="80%" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/gamedev/FME_Compare.png"> </center> </div> <div class="col-md-5"> <br><br><br> <h3><font color="#76B900">Compare Feature Maps</font></h3> <p align="justify">By providing a fast way to visually inspect the processing taking place across the layers of a DL model, the DL developer is efficiently informed about where the model is performing well and where it is falling short. This, in turn, can be used to help guide changes to the model or other training-time parameters in order to improve overall quality.</p> </div> </div> <br> <hr> <br> <div class="row"> <div class="col-md-5"> <br><br><br> <h3><font color="#76B900">Get Better Inference Results</font></h3> <p align="justify">Understanding what can be found inside the feature maps of a network model provides a lot of information about the efficiency (and potential deficiencies) of that model, while also providing clues about how the model (or related training parameters) can be optimized to get better inference results. </p> </div> <div class="col-md-7"> <center> <img class="img-responsive" width="80%" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/gamedev/FME_Inference.png"> </center> </div> </div> <br> <hr> <br> <center> <h2><font color="#76B900">How it Works</font></h2> </center> <br> <center> <a target="_blank" href="https://developer.nvidia.com/gtc/2020/video/s21313"><img class="img-responsive" width="50%" src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/Featured_Map_Explorer/FME_GTC_thumb.png"> <p class="half-line"> </p> <small>GTC Talk: Interactive Deep Learning: Using the GPU for Visual Insight into Training and Inference [S21313]</small></a> </center> <br> <hr> <br> <center> <h2><font color="#76B900">Developer Benefits</font></h2> </center> <br> <div class="row"> <div class="col-md-2"></div> <div class="col-md-8"> <ul> <li>Visualize feature maps at multiple levels</li> <li>Explore rich set of data processing options to help highlight points of interest from a feature map</li> <li>Easy and flexible comparisons of feature maps to help understand training progressions</li> <li>Find opportunities to improve speed by dropping to lower precision numerical formats</li> </ul> </div> <div class="col-md-2"></div> </div> <br> <hr> <br> <center> <h2><font color="#76B900">FAQ</font></h2> </center> <br> <div class="panel-group" id="accordion"> <div class="panel panel-noborder"> <div class="panel-heading"> <a data-parent="#accordion" data-toggle="collapse" href="#collapseOne">Q: What does this application do?</a> </div> <div class="panel-collapse collapse in" id="collapseOne"> <div class="panel-body"> <p> A: Feature Map Explorer provides a way to use the power of the GPU to visualize feature map data in a fluid, interactive fashion, from a range of summary views to lower-level channel visualizations to providing detailed numerical information about each channel slice. Think of this as a way to peer into the DL processing “black box” to find intimate information about what the model is learning, where the model is failing to use resources efficiently, and what is changing as a model is learning to better process data handed to it.</p> </div> </div> </div> <div class="panel panel-noborder"> <div class="panel-heading"> <a data-parent="#accordion" data-toggle="collapse" href="#collapseOneplus">Q: Why is this useful?</a> </div> <div class="panel-collapse collapse" id="collapseOneplus"> <div class="panel-body"> <p> A: By providing a fast way to visually inspect the processing taking place across the layers of a DL model, the DL developer is efficiently informed about where the model is performing well and where it is falling short. This, in turn, can be used to help guide changes to the model or other training-time parameters in order to improve overall quality. In this manner, Feature Map Explorer is similar to a traditional debugger, where the ability to inspect runtime state is a valuable tool in any developer’s toolbox.</p> </div> </div> </div> <div class="panel panel-noborder"> <div class="panel-heading"> <a data-parent="#accordion" data-toggle="collapse" href="#collapseTwo">Q: Is this a new concept?</a> </div> <div class="panel-collapse collapse" id="collapseTwo"> <div class="panel-body"> <p> A: Exploring feature maps is not a new concept, but Feature Map Explorer takes what used to be a slow, tedious process and makes it fast and efficient by leveraging GPU processing to visualize what is contained in the large feature map tensors.</p> </div> </div> </div> <br><br> <div style="background-color:#666" class="clear_fix"> <br> <center> <p><font size="+2" color="white">Ready to try FME?</font></p> <br> <a class="btn btn-success btn" href="https://developer.nvidia.com/gameworksdownload#?dn=nvidia-feature-map-explorer-2022-1" target="_blank">Download Now</a> <br><br> </center> </div> </div> </body> </section> <aside id="right" class="" role="complementary"> </aside>  </div> </div> <div class="separator"></div> </div> <div class="white-background"> <div class="separator"></div> <div class="container"> <div class="row"> <div id="pre_footer_left" class="col-xs-12 col-sm-12 col-lg-6"> </div> <div id="pre_footer_right" class="col-xs-12 col-sm-12 col-lg-6"> </div> </div> <div class="separator"></div> </div> </div> </div> <div id='footer' class='mt-auto'></div> <script src="https://dirms4qsy6412.cloudfront.net/assets/feed-aggregator/feed-aggregator-7f147443abc2d1300a239c29e4ba3ca0d0d2eb0dc66b608765e2b3be50e18e10.js"></script> <script src="https://dirms4qsy6412.cloudfront.net/assets/devzone3/old/dist/dz3-old-bundle-f911ee7c2f8b7dd419510ab33721b4c4fbd7b17dfbdac32b2586ccd2c3480c2e.js"></script> <script> const template = document.createElement('template'); 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URL: ${url}`); } return {}; } class HorizontalChartElement extends HTMLElement { constructor() { const horizontalCharTemplate = document.getElementById('chart-template'); super(); this._shadowRoot = this.attachShadow({ 'mode': 'open' }); this._shadowRoot.appendChild(horizontalCharTemplate.content.cloneNode(true)); } connectedCallback() { const url = this.getAttribute('source'); const element = this._shadowRoot; document.addEventListener("DOMContentLoaded", function () { loadFileSource(url).then(data => { const { chartTitle: title, chartSubtitle: subTitle, chartFootnote: footNote, chartId: id, isGrouped: isGrouped, legendData, barPadding, xAxisLabel, bars, ticks } = data; element.querySelector('.chart-title').innerHTML = title; // Subtitle if (subTitle) { element.querySelector('.chart-subtitle').innerHTML = subTitle; } else { element.querySelector('.chart-subtitle').remove(); } // Chart const svgElement = element.querySelector('.chart-container svg'); svgElement.id = id; const dataAttributes = [ ['isGrouped', isGrouped ? 'true' : 'false', ''], ['chartLegend', JSON.stringify(legendData), ''], ['xAxisLabel', xAxisLabel, ''], ['barPadding', barPadding, 5], ['chartBars', bars, ''], ['chartTicks', ticks, ''], ]; dataAttributes.forEach(dataAttribute => { if (dataAttribute[0] === 'chartBars' && dataAttribute[1]) { dataAttribute[1] = JSON.stringify(dataAttribute[1]); } if (dataAttribute[0] === 'chartTicks' && dataAttribute[1]) { dataAttribute[1] = JSON.stringify(dataAttribute[1]); } svgElement.dataset[dataAttribute[0]] = dataAttribute[1] ? dataAttribute[1] : dataAttribute[2]; }); // Caption if (footNote) { element.querySelector('.chart-footnote').innerHTML = footNote; } else { element.querySelector('.chart-footnote').remove(); } // Init chart const chartContainer = element.querySelector('.chart-container'); setTimeout(function () { if (jQuery(chartContainer).is(':visible')) { chartInit(chartContainer); } }, 300); }); }) } } window.customElements.define('horizontal-chart', HorizontalChartElement); </script> <script src="https://dirms4qsy6412.cloudfront.net/assets/tablefilter/tablefilter-e8295498b8f34d53fbb98264abcc88fb72c57915ee6a13b15361d5df0127ebbb.js"></script> <template id="nv-filtered-table-template"> <div class="nvidia-filtered-table-wrapper"></div> </template> <script> const buildTableIdentity = (function (str) { let counter = 0; return function (str) {counter += 1; return `${str}-${counter}`} })(); function slugify(str) { return String(str) .normalize('NFKD') // split accented characters into their base characters and diacritical marks .replace(/[\u0300-\u036f]/g, '') // remove all the accents, which happen to be all in the \u03xx UNICODE block. .trim() // trim leading or trailing whitespace .toLowerCase() // convert to lowercase .replace(/[^a-z0-9 -]/g, '') // remove non-alphanumeric characters .replace(/\s+/g, '-') // replace spaces with hyphens .replace(/-+/g, '-'); // remove consecutive hyphens } /** * Returns URL params. * * @returns {any|{[p: string]: string}} */ function getUrlParams() { try { return Object.fromEntries(new URLSearchParams(location.search)); } catch (e) { // Fallback for IE11. let search = location.search.substring(1) .replace(/&/g, '","') .replace(/=/g, '":"'); return JSON.parse('{"' + search + '"}', function (key, value) { return key === "" ? value : decodeURIComponent(value); }); } } /** * Query string extension for TableFilter. * * @see http://www.tablefilter.com/extension-run-time.html * * @param {Object} tf * TableFilter instance. * @constructor */ function QueryStringExtension(tf) { this.tf = tf; } QueryStringExtension.prototype.init = function () { let tf = this.tf; tf.emitter.on(['after-filtering'], this.afterFiltering.bind(this)); }; QueryStringExtension.prototype.afterFiltering = function (tf, terms) { const config = tf.cfg; // Get query string. let existingParams = getUrlParams(); // Remove values related to current table. $.each(terms, function (index, element) { let key = config.qs_filters[index]; delete existingParams[config.table_identity + "_" + key]; }); $.each(terms, function (index, element) { if (element.length) { existingParams[config.table_identity + "_" + config.qs_filters[index]] = element; } }); const paramsKeys = Object.keys(existingParams).sort(); let sortedParams = {}; $.each(paramsKeys, function (i, e) { sortedParams[e] = existingParams[e]; }); const params = $.param(sortedParams); const url = window.location.pathname + '?' + decodeURI(params); window.history.replaceState({}, '', url); }; function addQueryStringExtension(tf) { let queryStringExtension = new QueryStringExtension(tf); try { tf.registerExtension(queryStringExtension, 'queryStringExtension'); queryStringExtension.init(); } catch (e) { console.log('Failed to init URL params handler.'); } } /** * Filtered table web component. */ class NvFilteredTableWebComponent extends HTMLElement { constructor() { super(); const elementTemplate = document.getElementById('nv-filtered-table-template'); this._shadowRoot = this.attachShadow({'mode': 'open'}); this._shadowRoot.appendChild(elementTemplate.content.cloneNode(true)); } async loadSourceFile(url) { try { const response = await fetch(url); return response.json(); } catch (e) { console.warn(`Failed to load table data. URL: ${url}`); } return {}; } fetchComponentData = (url, el, hash) => this.loadSourceFile(url).then((data) => this.instantiateComponent(data, el, hash)); getColumnTypes(config) { return Array.from(config).map((col) => { const {columnType} = col; return columnType ? columnType : 'string'; }); } addFilterWidgets(config, headerData) { headerData.forEach((col, idx) => { const {filterWidget} = col; if (filterWidget) { config[`col_${idx}`] = filterWidget; } }); return config; } instantiateComponent(data, element, hash) { const {tableIndex: headerData, tableItems: rowsData, tableIdentity} = data; let filterConfig = { base_path: '/', sticky_headers: true, alternate_rows: true, mark_active_columns: { highlight_column: true, }, loader: true, highlight_keywords: true, no_results_message: true, toolbar: false, help_instructions: false, paging: { results_per_page: ['Records: ', [25, 50, 100]] }, auto_filter: true, responsive: true, grid_layout: { width: '100%', tbl_cont_css_class: 'nvidia-filtered-table' }, clear_filter_text: 'All', col_types: this.getColumnTypes(headerData), extensions: [ {name: ''} ], flt_css_class: 'filter-control', on_filters_loaded: (e) => { const {cfg} = e; if (!cfg) { return; } const {defaultValues} = cfg; if(!Array.isArray(defaultValues)) { return; } if (defaultValues.length === 0) { return; } defaultValues.forEach((defaultValue) => { const {index, value} = defaultValue; let headerCell = document.querySelector('.header-row th[data-idx="' + index + '"]'); if (!headerCell) { return; } const filterIndex = headerCell.dataset['fidx']; e.setFilterValue(filterIndex, value); }); e.filter(); }, qs_filters: headerData.map((item) => { return slugify(item?.columnName); }), }; filterConfig = this.addFilterWidgets(filterConfig, headerData); const id = tableIdentity ? tableIdentity : buildTableIdentity('t'); filterConfig.table_identity = id; const defaultValues = headerData .filter(item => item?.defaultValue !== undefined) .map(item => { return {index: item['column'], value: item['defaultValue']}; }); const filtersMap = headerData.map(item => { const {columnName, column} = item; return { index: column, key: `${id}_${slugify(columnName)}`} }); const sp = new URLSearchParams(location.search); filtersMap.forEach(filter => { if (!sp.has(filter.key)) { return; } defaultValues.push({index: filter.index, value: sp.get(filter.key)}); }); const detail = { hash, headerData, rowsData, defaultValues, filterConfig: { table_identity: id, config: filterConfig, predefined_selects: JSON.stringify([]), labels_width: [], }, }; const event = new CustomEvent('nv.filterTable.build', {detail}); document.dispatchEvent(event); } connectedCallback() { const url = this.getAttribute('source'); const hash = this.getAttribute('data-hash'); const element = this._shadowRoot; document.addEventListener('DOMContentLoaded', () => this.fetchComponentData(url, element, hash)); } } // Register web component. window.customElements.define('nv-filtered-table', NvFilteredTableWebComponent); /** * Builds table header. * * @param {Array} data * @param {HTMLElement} parentElement */ const buildHeader = (data, parentElement) => { data.forEach((item, idx) => { const {column, columnName} = item; const cell = document.createElement('th') cell.dataset['idx'] = column; cell.dataset['fidx'] = idx; cell.innerHTML = columnName; const {labelWidth, columnWidth} = item; if (labelWidth) { cell.dataset['label_width'] = labelWidth; } if (columnWidth && Number.isInteger(columnWidth) && columnWidth > 0) { cell.width = columnWidth + '%'; } else if (columnWidth && (columnWidth.endsWith('em') || columnWidth.endsWith('px') || columnWidth.endsWith('%'))) { cell.width = columnWidth; } parentElement.appendChild(cell); }); } /** * Builds table rows. * * @param {Object} headerData * @param {Array} data * @param {HTMLElement} parentElement */ const buildRows = (headerData, data, parentElement) => { data.forEach(partner => { const {name, logo, URL: partnerUrl, cameras} = partner; const logoCellContent = ` <a href="${partnerUrl}"> <img src="${logo}" alt="${name}" title="${name}" loading="lazy"> </a> <span class="element-invisible">${name}</span> `; cameras.forEach(camera => { const row = document.createElement('tr'); const logoCell = document.createElement('td'); logoCell.innerHTML = logoCellContent; row.appendChild(logoCell); for (const [key, value] of Object.entries(headerData)) { if (!camera[value.column]) { continue; } const cell = document.createElement('td'); let cameraValue = camera[value.column]; let cellHTML = ''; if (typeof cameraValue === 'object') { const {type, product_url, product_img, td_text} = cameraValue let img = ''; if(product_img !== "—") { img = `<img src="${product_img}" alt="${td_text}" title="${td_text}">`; } let html = ` <div class="product-link"> <a href="${product_url}" target="_blank"> ${img} <div class="product-title">${td_text}</div> </a> </div>` cellHTML = html.trim(); } else if (typeof cameraValue === 'string') { cellHTML = cameraValue } cell.innerHTML = cellHTML; row.appendChild(cell); } parentElement.appendChild(row); }) }); } const initTable = (table, filtersConfig, defaultValues) => { filtersConfig.config.defaultValues = defaultValues; const nvidiaFilteredTable = new TableFilter(table, filtersConfig.config); nvidiaFilteredTable.emitter.on(['before-loading-extensions'], addQueryStringExtension); nvidiaFilteredTable.init(); }; document.addEventListener('nv.filterTable.build', (event) => { const {detail} = event; const {headerData, rowsData, filterConfig, hash, defaultValues} = detail; const component = document.querySelector('nv-filtered-table[data-hash="' + hash + '"]'); const element = document.createElement('div'); element.classList.add('nvidia-filtered-table-wrapper'); element.innerHTML = `<div class="table-responsive"><table class="table table-hover table-striped sticky-enabled"><thead></thead><tbody></tbody></table></div>`; component.after(element); const table = element.querySelector('table'); const tableHeader = element.querySelector('table thead'); const tableHeaderRow = document.createElement('tr'); tableHeaderRow.classList.add('header-row'); tableHeader.appendChild(tableHeaderRow); buildHeader(headerData, tableHeaderRow); const tableBody = element.querySelector('table tbody'); buildRows(headerData, rowsData, tableBody); initTable(table, filterConfig, defaultValues); 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