text stringlengths 0 1.16k |
|---|
question: ['How many water buffalos are in the image?'], responses:['7'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.0994e-06, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many penguins are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)] |
[['7', '8', '11', '5', '9', '10', '6', '12']] |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 1.2339e-08, 1.5558e-09, 6.8979e-09, 2.3839e-10, 5.7236e-10, |
3.8477e-11, 6.8125e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2339e-08, 1.5558e-09, 6.8979e-09, 2.3839e-10, 5.7236e-10, |
3.8477e-11, 6.8125e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
question: ['How many penguins are in the image?'], responses:['2'] |
tensor([1.0000e+00, 8.5922e-10, 3.3093e-10, 3.1853e-10, 2.6931e-10, 1.0630e-08, |
2.2863e-08, 2.4216e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.5922e-10, 3.3093e-10, 3.1853e-10, 2.6931e-10, 1.0630e-08, |
2.2863e-08, 2.4216e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.5558e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.5558e-09, device='cuda:2', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many rodents are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)] |
[['2', '3', '4', '1', '5', '8', '7', '29']] |
torch.Size([5, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
question: ['How many rodents are in the image?'], responses:['2'] |
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)] |
[['2', '3', '4', '1', '5', '8', '7', '29']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
question: ['How many bottles are in the image?'], responses:['four'] |
[('7 eleven', 0.12650899275575006), ('4', 0.125210025275264), ('first', 0.12483048280083887), ('3', 0.12473532336671392), ('5', 0.1247268629491862), ('dark', 0.12470563072493092), ('forward', 0.12466964370422237), ('bag', 0.12461303842309367)] |
[['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([9.6923e-01, 4.0387e-04, 6.8055e-05, 2.3691e-04, 7.3412e-04, 4.8237e-05, |
2.9277e-02, 1.5269e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
7 ************* |
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.6923e-01, 4.0387e-04, 6.8055e-05, 2.3691e-04, 7.3412e-04, 4.8237e-05, |
2.9277e-02, 1.5269e-06], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 1.5870e-07, 9.9880e-09, 8.9022e-08, 4.5277e-10, 1.3905e-09, |
1.4743e-09, 1.1094e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.5870e-07, 9.9880e-09, 8.9022e-08, 4.5277e-10, 1.3905e-09, |
1.4743e-09, 1.1094e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.6214e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many rodents are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
question: ['How many rodents are in the image?'], responses:['2'] |
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)] |
[['2', '3', '4', '1', '5', '8', '7', '29']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([1.0000e+00, 9.6256e-08, 5.9641e-09, 1.1979e-07, 2.0892e-10, 4.2696e-10, |
6.1306e-10, 1.7499e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 9.6256e-08, 5.9641e-09, 1.1979e-07, 2.0892e-10, 4.2696e-10, |
6.1306e-10, 1.7499e-10], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.2344e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 5.1817e-09, 1.8988e-09, 2.9356e-08, 7.5074e-11, 1.3304e-09, |
4.0195e-10, 6.3102e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.1817e-09, 1.8988e-09, 2.9356e-08, 7.5074e-11, 1.3304e-09, |
4.0195e-10, 6.3102e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.8875e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([9.9710e-15, 9.9992e-01, 3.1218e-07, 1.2350e-06, 7.6594e-05, 4.5073e-06, |
2.0061e-06, 1.5834e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([9.9710e-15, 9.9992e-01, 3.1218e-07, 1.2350e-06, 7.6594e-05, 4.5073e-06, |
2.0061e-06, 1.5834e-07], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.2350e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.0333e-06, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-24 10:17:26,242] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.37 | optimizer_step: 0.33 |
[2024-10-24 10:17:26,243] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5795.07 | backward_microstep: 8245.42 | backward_inner_microstep: 5488.54 | backward_allreduce_microstep: 2756.78 | step_microstep: 7.80 |
[2024-10-24 10:17:26,243] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5795.07 | backward: 8245.41 | backward_inner: 5488.57 | backward_allreduce: 2756.76 | step: 7.82 |
97%|ββββββββββ| 4712/4844 [19:36:10<31:21, 14.25s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.