Skip to content

Commit ae8406f

Browse files
eaidovaeaidova
authored
fix windows model loading issues (openvinotoolkit#2348)
1 parent c81e765 commit ae8406f

File tree

4 files changed

+20
-222
lines changed

4 files changed

+20
-222
lines changed

‎notebooks/3D-pose-estimation-webcam/3D-pose-estimation.ipynb

+2-2
Original file line numberDiff line numberDiff line change
@@ -165,8 +165,8 @@
165165
"model_path = Path(BASE_MODEL_NAME).with_suffix(\".pth\")\n",
166166
"onnx_path = Path(BASE_MODEL_NAME).with_suffix(\".onnx\")\n",
167167
"\n",
168-
"ir_model_path = f\"model/public/{model_name}/{precision}/{model_name}.xml\"\n",
169-
"model_weights_path = f\"model/public/{model_name}/{precision}/{model_name}.bin\"\n",
168+
"ir_model_path = Path(f\"model/public/{model_name}/{precision}/{model_name}.xml\")\n",
169+
"model_weights_path = Path(f\"model/public/{model_name}/{precision}/{model_name}.bin\")\n",
170170
"\n",
171171
"if not model_path.exists():\n",
172172
" download_command = f\"omz_downloader \" f\"--name {model_name} \" f\"--output_dir {base_model_dir}\"\n",

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

+1-199
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,6 @@
11
{
22
"cells": [
33
{
4-
"attachments": {},
54
"cell_type": "markdown",
65
"metadata": {},
76
"source": [
@@ -99,7 +98,6 @@
9998
]
10099
},
101100
{
102-
"attachments": {},
103101
"cell_type": "markdown",
104102
"metadata": {},
105103
"source": [
@@ -174,7 +172,6 @@
174172
]
175173
},
176174
{
177-
"attachments": {},
178175
"cell_type": "markdown",
179176
"metadata": {},
180177
"source": [
@@ -200,7 +197,6 @@
200197
]
201198
},
202199
{
203-
"attachments": {},
204200
"cell_type": "markdown",
205201
"metadata": {},
206202
"source": [
@@ -273,7 +269,6 @@
273269
]
274270
},
275271
{
276-
"attachments": {},
277272
"cell_type": "markdown",
278273
"metadata": {},
279274
"source": [
@@ -312,7 +307,6 @@
312307
]
313308
},
314309
{
315-
"attachments": {},
316310
"cell_type": "markdown",
317311
"metadata": {},
318312
"source": [
@@ -321,7 +315,6 @@
321315
]
322316
},
323317
{
324-
"attachments": {},
325318
"cell_type": "markdown",
326319
"metadata": {},
327320
"source": [
@@ -391,7 +384,6 @@
391384
]
392385
},
393386
{
394-
"attachments": {},
395387
"cell_type": "markdown",
396388
"metadata": {},
397389
"source": [
@@ -440,7 +432,6 @@
440432
]
441433
},
442434
{
443-
"attachments": {},
444435
"cell_type": "markdown",
445436
"metadata": {
446437
"tags": []
@@ -486,7 +477,6 @@
486477
]
487478
},
488479
{
489-
"attachments": {},
490480
"cell_type": "markdown",
491481
"metadata": {
492482
"tags": []
@@ -519,7 +509,6 @@
519509
]
520510
},
521511
{
522-
"attachments": {},
523512
"cell_type": "markdown",
524513
"metadata": {
525514
"jupyter": {
@@ -566,7 +555,6 @@
566555
]
567556
},
568557
{
569-
"attachments": {},
570558
"cell_type": "markdown",
571559
"metadata": {
572560
"execution": {
@@ -698,7 +686,6 @@
698686
]
699687
},
700688
{
701-
"attachments": {},
702689
"cell_type": "markdown",
703690
"metadata": {},
704691
"source": [
@@ -740,7 +727,6 @@
740727
]
741728
},
742729
{
743-
"attachments": {},
744730
"cell_type": "markdown",
745731
"metadata": {
746732
"jupyter": {
@@ -754,7 +740,6 @@
754740
]
755741
},
756742
{
757-
"attachments": {},
758743
"cell_type": "markdown",
759744
"metadata": {
760745
"tags": []
@@ -795,7 +780,6 @@
795780
]
796781
},
797782
{
798-
"attachments": {},
799783
"cell_type": "markdown",
800784
"metadata": {},
801785
"source": [
@@ -834,7 +818,6 @@
834818
]
835819
},
836820
{
837-
"attachments": {},
838821
"cell_type": "markdown",
839822
"metadata": {
840823
"tags": []
@@ -869,7 +852,6 @@
869852
]
870853
},
871854
{
872-
"attachments": {},
873855
"cell_type": "markdown",
874856
"metadata": {
875857
"tags": []
@@ -1093,7 +1075,6 @@
10931075
]
10941076
},
10951077
{
1096-
"attachments": {},
10971078
"cell_type": "markdown",
10981079
"metadata": {
10991080
"tags": []
@@ -1200,7 +1181,6 @@
12001181
]
12011182
},
12021183
{
1203-
"attachments": {},
12041184
"cell_type": "markdown",
12051185
"metadata": {
12061186
"tags": []
@@ -1217,7 +1197,6 @@
12171197
]
12181198
},
12191199
{
1220-
"attachments": {},
12211200
"cell_type": "markdown",
12221201
"metadata": {},
12231202
"source": [
@@ -1253,7 +1232,6 @@
12531232
]
12541233
},
12551234
{
1256-
"attachments": {},
12571235
"cell_type": "markdown",
12581236
"metadata": {
12591237
"tags": []
@@ -1306,7 +1284,6 @@
13061284
]
13071285
},
13081286
{
1309-
"attachments": {},
13101287
"cell_type": "markdown",
13111288
"metadata": {},
13121289
"source": [
@@ -1370,182 +1347,7 @@
13701347
},
13711348
"widgets": {
13721349
"application/vnd.jupyter.widget-state+json": {
1373-
"state": {
1374-
"00f134ea2cd44f698a65c6e19eec5887": {
1375-
"model_module": "@jupyter-widgets/base",
1376-
"model_module_version": "2.0.0",
1377-
"model_name": "LayoutModel",
1378-
"state": {}
1379-
},
1380-
"0efbcb2df8c34ac2baf17daf00032484": {
1381-
"model_module": "@jupyter-widgets/controls",
1382-
"model_module_version": "2.0.0",
1383-
"model_name": "HTMLModel",
1384-
"state": {
1385-
"layout": "IPY_MODEL_15258f9879ec4060bfebe5a7f175ddbc",
1386-
"style": "IPY_MODEL_2614315073bd42ae8a4bf07d918df4f9",
1387-
"value": "pretrained_model/unet_kits19_state_dict.pth: 100%"
1388-
}
1389-
},
1390-
"15258f9879ec4060bfebe5a7f175ddbc": {
1391-
"model_module": "@jupyter-widgets/base",
1392-
"model_module_version": "2.0.0",
1393-
"model_name": "LayoutModel",
1394-
"state": {}
1395-
},
1396-
"2614315073bd42ae8a4bf07d918df4f9": {
1397-
"model_module": "@jupyter-widgets/controls",
1398-
"model_module_version": "2.0.0",
1399-
"model_name": "HTMLStyleModel",
1400-
"state": {
1401-
"description_width": "",
1402-
"font_size": null,
1403-
"text_color": null
1404-
}
1405-
},
1406-
"2c7654bcbec94188a96bf9d786ecb327": {
1407-
"model_module": "@jupyter-widgets/controls",
1408-
"model_module_version": "2.0.0",
1409-
"model_name": "ProgressStyleModel",
1410-
"state": {
1411-
"description_width": ""
1412-
}
1413-
},
1414-
"35a1768113b147b1a23dfda69f31ee0f": {
1415-
"model_module": "@jupyter-widgets/output",
1416-
"model_module_version": "1.0.0",
1417-
"model_name": "OutputModel",
1418-
"state": {
1419-
"layout": "IPY_MODEL_00f134ea2cd44f698a65c6e19eec5887",
1420-
"outputs": [
1421-
{
1422-
"data": {
1423-
"text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Statistics collection <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #0068b5; text-decoration-color: #0068b5\">69/69</span> • <span style=\"color: #0068b5; text-decoration-color: #0068b5\">0:00:16</span> • <span style=\"color: #0068b5; text-decoration-color: #0068b5\">0:00:00</span>\n</pre>\n",
1424-
"text/plain": "Statistics collection \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[38;2;0;104;181m69/69\u001b[0m • \u001b[38;2;0;104;181m0:00:16\u001b[0m • \u001b[38;2;0;104;181m0:00:00\u001b[0m\n"
1425-
},
1426-
"metadata": {},
1427-
"output_type": "display_data"
1428-
}
1429-
]
1430-
}
1431-
},
1432-
"3f95879a28a24fab97cca44f431f0139": {
1433-
"model_module": "@jupyter-widgets/base",
1434-
"model_module_version": "2.0.0",
1435-
"model_name": "LayoutModel",
1436-
"state": {}
1437-
},
1438-
"4b18ce800dac4a2d9b8f027612a69b72": {
1439-
"model_module": "@jupyter-widgets/controls",
1440-
"model_module_version": "2.0.0",
1441-
"model_name": "HBoxModel",
1442-
"state": {
1443-
"children": [
1444-
"IPY_MODEL_0efbcb2df8c34ac2baf17daf00032484",
1445-
"IPY_MODEL_d67b4e32272142e4bdb3354d6a69fcf2",
1446-
"IPY_MODEL_fb42224f9f234377a01709d7abf517d4"
1447-
],
1448-
"layout": "IPY_MODEL_e1b9f1b7a4c642759c77b457d4b37554"
1449-
}
1450-
},
1451-
"51de3acdee29431fab0596ce98cb0073": {
1452-
"model_module": "@jupyter-widgets/base",
1453-
"model_module_version": "2.0.0",
1454-
"model_name": "LayoutModel",
1455-
"state": {}
1456-
},
1457-
"600061fc47e643128bacc52317a0792f": {
1458-
"model_module": "@jupyter-widgets/controls",
1459-
"model_module_version": "2.0.0",
1460-
"model_name": "HTMLStyleModel",
1461-
"state": {
1462-
"description_width": "",
1463-
"font_size": null,
1464-
"text_color": null
1465-
}
1466-
},
1467-
"813a5d43c3ec4ba2b22ddc7b4ddfae3a": {
1468-
"model_module": "@jupyter-widgets/controls",
1469-
"model_module_version": "2.0.0",
1470-
"model_name": "DropdownModel",
1471-
"state": {
1472-
"_options_labels": [
1473-
"CPU",
1474-
"AUTO"
1475-
],
1476-
"description": "Device:",
1477-
"index": 1,
1478-
"layout": "IPY_MODEL_51de3acdee29431fab0596ce98cb0073",
1479-
"style": "IPY_MODEL_e66a2a642d634b6cbe086b7121f2eb0c"
1480-
}
1481-
},
1482-
"85de3a24d92e4deea91e80af0d5d4b63": {
1483-
"model_module": "@jupyter-widgets/base",
1484-
"model_module_version": "2.0.0",
1485-
"model_name": "LayoutModel",
1486-
"state": {}
1487-
},
1488-
"980624b6233b45bbb3c4dad204ca515e": {
1489-
"model_module": "@jupyter-widgets/output",
1490-
"model_module_version": "1.0.0",
1491-
"model_name": "OutputModel",
1492-
"state": {
1493-
"layout": "IPY_MODEL_85de3a24d92e4deea91e80af0d5d4b63",
1494-
"outputs": [
1495-
{
1496-
"data": {
1497-
"text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Applying Fast Bias correction <span style=\"color: #729c1f; text-decoration-color: #729c1f\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span style=\"color: #800080; text-decoration-color: #800080\">100%</span> <span style=\"color: #0068b5; text-decoration-color: #0068b5\">23/23</span> • <span style=\"color: #0068b5; text-decoration-color: #0068b5\">0:00:00</span> • <span style=\"color: #0068b5; text-decoration-color: #0068b5\">0:00:00</span>\n</pre>\n",
1498-
"text/plain": "Applying Fast Bias correction \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100%\u001b[0m \u001b[38;2;0;104;181m23/23\u001b[0m • \u001b[38;2;0;104;181m0:00:00\u001b[0m • \u001b[38;2;0;104;181m0:00:00\u001b[0m\n"
1499-
},
1500-
"metadata": {},
1501-
"output_type": "display_data"
1502-
}
1503-
]
1504-
}
1505-
},
1506-
"add743fcdf0c466ca500dc343a733334": {
1507-
"model_module": "@jupyter-widgets/base",
1508-
"model_module_version": "2.0.0",
1509-
"model_name": "LayoutModel",
1510-
"state": {}
1511-
},
1512-
"d67b4e32272142e4bdb3354d6a69fcf2": {
1513-
"model_module": "@jupyter-widgets/controls",
1514-
"model_module_version": "2.0.0",
1515-
"model_name": "FloatProgressModel",
1516-
"state": {
1517-
"bar_style": "success",
1518-
"layout": "IPY_MODEL_3f95879a28a24fab97cca44f431f0139",
1519-
"max": 7946156,
1520-
"style": "IPY_MODEL_2c7654bcbec94188a96bf9d786ecb327",
1521-
"value": 7946156
1522-
}
1523-
},
1524-
"e1b9f1b7a4c642759c77b457d4b37554": {
1525-
"model_module": "@jupyter-widgets/base",
1526-
"model_module_version": "2.0.0",
1527-
"model_name": "LayoutModel",
1528-
"state": {}
1529-
},
1530-
"e66a2a642d634b6cbe086b7121f2eb0c": {
1531-
"model_module": "@jupyter-widgets/controls",
1532-
"model_module_version": "2.0.0",
1533-
"model_name": "DescriptionStyleModel",
1534-
"state": {
1535-
"description_width": ""
1536-
}
1537-
},
1538-
"fb42224f9f234377a01709d7abf517d4": {
1539-
"model_module": "@jupyter-widgets/controls",
1540-
"model_module_version": "2.0.0",
1541-
"model_name": "HTMLModel",
1542-
"state": {
1543-
"layout": "IPY_MODEL_add743fcdf0c466ca500dc343a733334",
1544-
"style": "IPY_MODEL_600061fc47e643128bacc52317a0792f",
1545-
"value": " 7.58M/7.58M [00:01&lt;00:00, 11.2MB/s]"
1546-
}
1547-
}
1548-
},
1350+
"state": {},
15491351
"version_major": 2,
15501352
"version_minor": 0
15511353
}

0 commit comments

Comments
 (0)