Datasets:
TMarcus
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InternVL

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ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([6.1538e-01, 2.2638e-01, 3.7276e-02, 1.0131e-01, 1.3595e-02, 2.5142e-03,
3.3315e-03, 2.0448e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.1538e-01, 2.2638e-01, 3.7276e-02, 1.0131e-01, 1.3595e-02, 2.5142e-03,
3.3315e-03, 2.0448e-04], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([7, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1013, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8987, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([8.3614e-01, 1.7923e-02, 2.5248e-02, 3.7569e-03, 7.6190e-04, 1.1316e-01,
2.2062e-03, 8.0709e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([8.3614e-01, 1.7923e-02, 2.5248e-02, 3.7569e-03, 7.6190e-04, 1.1316e-01,
2.2062e-03, 8.0709e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8616, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1384, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.7572e-01, 4.1137e-03, 2.0045e-03, 9.7424e-04, 1.3768e-03, 9.2960e-04,
1.4815e-02, 6.9514e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7572e-01, 4.1137e-03, 2.0045e-03, 9.7424e-04, 1.3768e-03, 9.2960e-04,
1.4815e-02, 6.9514e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0243, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9757, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many dogs 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
question: ['How many dogs are in the image?'], responses:['1']
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.3526e-01, 1.6092e-02, 4.7563e-03, 4.1092e-02, 1.4488e-03, 7.4644e-04,
5.4695e-04, 5.3163e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.3526e-01, 1.6092e-02, 4.7563e-03, 4.1092e-02, 1.4488e-03, 7.4644e-04,
5.4695e-04, 5.3163e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9589, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0411, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.7447e-01, 3.9819e-03, 1.8804e-03, 8.8324e-04, 1.2128e-03, 7.4590e-04,
1.6766e-02, 5.4781e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7447e-01, 3.9819e-03, 1.8804e-03, 8.8324e-04, 1.2128e-03, 7.4590e-04,
1.6766e-02, 5.4781e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0255, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9745, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-23 14:48:34,766] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-23 14:48:34,767] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5067.12 | backward_microstep: 12955.88 | backward_inner_microstep: 4824.90 | backward_allreduce_microstep: 8130.91 | step_microstep: 7.49
[2024-10-23 14:48:34,767] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5067.14 | backward: 12955.87 | backward_inner: 4824.91 | backward_allreduce: 8130.90 | step: 7.51
1%| | 29/4844 [07:18<20:53:29, 15.62s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many wild pigs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many dogs are standing in the snow?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image contain a black dispenser with a chrome top?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many cats are lying down?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Does the image contain a black dispenser with a chrome top?'], responses:['yes']
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 332
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
tensor([6.2532e-01, 1.9550e-02, 3.5109e-01, 1.9127e-03, 1.5335e-04, 5.0835e-04,
1.7082e-04, 1.2918e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.2532e-01, 1.9550e-02, 3.5109e-01, 1.9127e-03, 1.5335e-04, 5.0835e-04,
1.7082e-04, 1.2918e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6253, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.3511, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0236, device='cuda:0', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])