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fix
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@NormanTUD
NormanTUD committed Feb 22, 2024
1 parent d366177 commit 9b63d59
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Showing 2 changed files with 89 additions and 57 deletions.
9 changes: 9 additions & 0 deletions debug.js
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Original file line number Diff line number Diff line change
Expand Up @@ -974,6 +974,15 @@ async function debug_unusual_function_inputs () {
"run_neural_network",
"repair_output_shape",
"gui_in_training",
"_predict_error",
"_run_predict_and_show",
"_predict_image",
"scaleNestedArray",
"_predict_result",
"predict_demo",
"set_item_natural_width",
"_get_tensor_img",
"load_file",
"write_error_and_reset",
"draw_images_in_grid",
"extractCategoryFromURL",
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137 changes: 80 additions & 57 deletions predict.js
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Original file line number Diff line number Diff line change
Expand Up @@ -97,54 +97,61 @@ async function get_label_data () {
}

function load_file (event) {
var files = event.target.files;
try {
var files = event.target.files;

var $output = $("#uploaded_file_predictions");

var $output = $("#uploaded_file_predictions");
var uploaded_file_pred =
`<span class='single_pred'>\n` +
`<img width='${width}' height='${height}' src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" alt="Image" class="uploaded_file_img"\n>` +
`<br>` +
`<span class="uploaded_file_prediction"></span>` +
`</span>\n`;

var uploaded_file_pred =
`<span class='single_pred'>\n` +
`<img width='${width}' height='${height}' src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" alt="Image" class="uploaded_file_img"\n>` +
`<br>` +
`<span class="uploaded_file_prediction"></span>` +
`</span>\n`;
var repeated_string = "";
for (var i = 0; i < files.length; i++) {
repeated_string += uploaded_file_pred;
}

var repeated_string = "";
for (var i = 0; i < files.length; i++) {
repeated_string += uploaded_file_pred;
}
$output.html(repeated_string);

$output.html(repeated_string);
for (var i = 0; i < files.length; i++) {
$($(".single_pred")[i]).removeAttr("src");

for (var i = 0; i < files.length; i++) {
$($(".single_pred")[i]).removeAttr("src");
var img_elem = $($(".uploaded_file_img")[i])[0];

var img_elem = $($(".uploaded_file_img")[i])[0];
var async_func;

var async_func;
eval(`async_func = async function() {
var _img_elem = $($(".uploaded_file_img")[${i}])[0];
URL.revokeObjectURL(_img_elem.src);
eval(`async_func = async function() {
var _img_elem = $($(".uploaded_file_img")[${i}])[0];
URL.revokeObjectURL(_img_elem.src);
var _result = await predict(_img_elem);
var _result = await predict(_img_elem);
var $set_this = $($(".uploaded_file_prediction")[${i}]);
var $set_this = $($(".uploaded_file_prediction")[${i}]);
assert($set_this.length, \`.uploaded_file_prediction[${i}] not found!\`);
assert($set_this.length, \`.uploaded_file_prediction[${i}] not found!\`);
//console.log("_img_elem:", _img_elem, "i:", ${i}, "$set_this:", $set_this, "_result:", _result, "_result md5:", await md5(_result));
//console.log("_img_elem:", _img_elem, "i:", ${i}, "$set_this:", $set_this, "_result:", _result, "_result md5:", await md5(_result));
$set_this.html(_result).show();
$set_this.html(_result).show();
$(".only_show_when_predicting_image_file").show();
}`);

$(".only_show_when_predicting_image_file").show();
}`);
img_elem.src = URL.createObjectURL(files[i]);
img_elem.onload = async_func;
}

img_elem.src = URL.createObjectURL(files[i]);
img_elem.onload = async_func;
}
$output.show();
} catch (e) {
if(Object.keys(e).includes("message")) {
e = e.message;
}

$output.show();
assert(false, e);
}
}

function _predict_error (e) {
Expand Down Expand Up @@ -343,11 +350,19 @@ async function predict_demo (item, nr, tried_again = 0) {
};

async function _run_predict_and_show (tensor_img, nr) {
if(tensor_img.isDisposedInternal) {
dbg("[_run_predict_and_show] Tensor was disposed internally", tensor_img);
console.trace();
return;
try {
if(tensor_img.isDisposedInternal) {
dbg("[_run_predict_and_show] Tensor was disposed internally", tensor_img);
console.trace();
return;

}
} catch (e) {
if(Object.keys(e).includes("message")) {
e = e.message;
}

assert(false, e);
}

if(!tensor_shape_matches_model(tensor_img)) {
Expand Down Expand Up @@ -418,37 +433,45 @@ async function _predict_result(predictions_tensor, nr, _dispose = 1) {
}

async function _predict_image (predictions_tensor, desc) {
var predictions_tensor_transposed = tf_transpose(predictions_tensor, [3, 1, 2, 0]);
var predictions = array_sync(predictions_tensor_transposed);
try {
var predictions_tensor_transposed = tf_transpose(predictions_tensor, [3, 1, 2, 0]);
var predictions = array_sync(predictions_tensor_transposed);

var pxsz = 1;
var pxsz = 1;

var largest = Math.max(predictions_tensor_transposed.shape[1], predictions_tensor_transposed.shape[2]);
assert(typeof(largest) == "number", "_predict_image: largest is not a number");
var largest = Math.max(predictions_tensor_transposed.shape[1], predictions_tensor_transposed.shape[2]);
assert(typeof(largest) == "number", "_predict_image: largest is not a number");

var max_height_width = Math.min(100, Math.floor(window.innerWidth / 5));
assert(typeof(max_height_width) == "number", "_predict_image: max_height_width is not a number");
var max_height_width = Math.min(100, Math.floor(window.innerWidth / 5));
assert(typeof(max_height_width) == "number", "_predict_image: max_height_width is not a number");

while ((pxsz * largest) < max_height_width) {
pxsz += 1;
}
while ((pxsz * largest) < max_height_width) {
pxsz += 1;
}

scaleNestedArray(predictions);
scaleNestedArray(predictions);

for (var i = 0; i < predictions.length; i++) {
var canvas = $("<canvas/>", {class: "layer_image"}).prop({
width: pxsz * predictions_tensor.shape[2],
height: pxsz * predictions_tensor.shape[1],
});
for (var i = 0; i < predictions.length; i++) {
var canvas = $("<canvas/>", {class: "layer_image"}).prop({
width: pxsz * predictions_tensor.shape[2],
height: pxsz * predictions_tensor.shape[1],
});

desc.append(canvas);
desc.append(canvas);

//draw_grid (canvas, pixel_size, colors, denormalize, black_and_white, onclick, multiply_by, data_hash, _class="") {
//log("predictions[i]:", predictions[i]);
var res = draw_grid(canvas, pxsz, predictions[i], 1, 1);
}
//draw_grid (canvas, pixel_size, colors, denormalize, black_and_white, onclick, multiply_by, data_hash, _class="") {
//log("predictions[i]:", predictions[i]);
var res = draw_grid(canvas, pxsz, predictions[i], 1, 1);
}

await dispose(predictions_tensor_transposed);
await dispose(predictions_tensor_transposed);
} catch (e) {
if(Object.keys(e).includes("message")) {
e = e.message;
}

assert(false, e);
}
}

function scaleNestedArray(arr) {
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