Datasets:
TMarcus
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InternVL

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tensor([9.9921e-01, 7.3425e-04, 5.5291e-05, 2.7101e-10, 1.0217e-08, 1.2255e-07,
1.0076e-08, 1.4606e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9921e-01, 7.3425e-04, 5.5291e-05, 2.7101e-10, 1.0217e-08, 1.2255e-07,
1.0076e-08, 1.4606e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.5094e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:11:20,458] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.40 | optimizer_gradients: 0.32 | optimizer_step: 0.31
[2024-10-24 10:11:20,458] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 2521.37 | backward_microstep: 15239.56 | backward_inner_microstep: 2416.18 | backward_allreduce_microstep: 12823.30 | step_microstep: 7.65
[2024-10-24 10:11:20,458] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 2521.37 | backward: 15239.55 | backward_inner: 2416.20 | backward_allreduce: 12823.29 | step: 7.66
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4687/4844 [19:30:04<40:23, 15.44s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Does the image contain a chandelier?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many visible sails are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many striped pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many animals are standing on a rocky area?')
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 chandelier?'], responses:['yes']
question: ['How many visible sails are in the image?'], responses:['2']
question: ['How many striped pillows are in the image?'], responses:['2']
[('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']]
question: ['How many animals are standing on a rocky area?'], responses:['50']
[('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']]
[('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']]
[('50', 0.12746329354121594), ('51', 0.12494443111915052), ('60', 0.12471995183640609), ('55', 0.12470016949940634), ('54', 0.12460076157014638), ('52', 0.12454269500997545), ('44', 0.12453681395238846), ('48', 0.1244918834713108)]
[['50', '51', '60', '55', '54', '52', '44', '48']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.0000e+00, 6.3172e-09, 1.0691e-10, 8.8679e-08, 4.0334e-10, 5.9980e-10,
1.5797e-10, 4.6717e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.3172e-09, 1.0691e-10, 8.8679e-08, 4.0334e-10, 5.9980e-10,
1.5797e-10, 4.6717e-08], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 3.4663e-07, 1.1366e-06, 1.5535e-06, 3.1924e-09, 4.0126e-08,
2.1478e-08, 4.8571e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.4663e-07, 1.1366e-06, 1.5535e-06, 3.1924e-09, 4.0126e-08,
2.1478e-08, 4.8571e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.1366e-06, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many cheetahs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0691e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1910e-07, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.5312e-01, 3.9298e-03, 2.0137e-02, 9.8303e-03, 8.3372e-04, 1.5296e-03,
3.6545e-03, 6.9647e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
50 *************
['50', '51', '60', '55', '54', '52', '44', '48'] tensor([9.5312e-01, 3.9298e-03, 2.0137e-02, 9.8303e-03, 8.3372e-04, 1.5296e-03,
3.6545e-03, 6.9647e-03], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([9.8139e-01, 1.7977e-02, 6.1506e-04, 6.4188e-06, 1.0575e-05, 1.2784e-08,
1.1155e-06, 2.3624e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.8139e-01, 1.7977e-02, 6.1506e-04, 6.4188e-06, 1.0575e-05, 1.2784e-08,
1.1155e-06, 2.3624e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='How many pink cases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Are the containers empty?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([1, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.4188e-06, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many monkeys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')