Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many pink cases are in the image?'], responses:['1']
question: ['Are the containers empty?'], responses:['yes']
[('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']]
[('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
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many monkeys 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 5.8995e-10, 1.3384e-10, 2.9119e-10, 2.8780e-10, 1.1306e-08,
7.6581e-09, 1.7992e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 5.8995e-10, 1.3384e-10, 2.9119e-10, 2.8780e-10, 1.1306e-08,
7.6581e-09, 1.7992e-10], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 7.0264e-09, 1.4594e-06, 1.0248e-08, 1.2437e-09, 1.3308e-09,
1.1118e-10, 1.6830e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.0264e-09, 1.4594e-06, 1.0248e-08, 1.2437e-09, 1.3308e-09,
1.1118e-10, 1.6830e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.0447e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.4594e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.8852e-08, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many cheetahs 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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
tensor([1.0000e+00, 2.6205e-10, 8.3755e-11, 3.7239e-10, 1.7315e-10, 1.6441e-08,
3.4789e-09, 2.5029e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.6205e-10, 8.3755e-11, 3.7239e-10, 1.7315e-10, 1.6441e-08,
3.4789e-09, 2.5029e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.4789e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
tensor([1.0000e+00, 4.2203e-10, 4.0186e-11, 4.8283e-11, 3.5879e-11, 4.7910e-09,
1.3027e-08, 2.0679e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.2203e-10, 4.0186e-11, 4.8283e-11, 3.5879e-11, 4.7910e-09,
1.3027e-08, 2.0679e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.8385e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:11:30,349] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-24 10:11:30,349] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5052.20 | backward_microstep: 4814.21 | backward_inner_microstep: 4808.43 | backward_allreduce_microstep: 5.67 | step_microstep: 7.50
[2024-10-24 10:11:30,349] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5052.21 | backward: 4814.20 | backward_inner: 4808.46 | backward_allreduce: 5.64 | step: 7.51
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4688/4844 [19:30:14<35:48, 13.77s/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 EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there at least one person standing outside the store?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many basins are set in the counter?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the laptop facing forward?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a white bowl holding the food in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there a white bowl holding the food in the image?'], 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([5, 3, 448, 448]) knan debug pixel values shape
question: ['How many basins are set in the counter?'], responses:['δΈ€δΈͺ']
[('monday', 0.12549230061942876), ('leopard', 0.12538459168488658), ('kia', 0.12528000119427152), ('halloween', 0.12500937705608223), ('tigers', 0.12497983900108975), ('no', 0.12478765121697721), ('spring', 0.12453769928322637), ('awake', 0.12452853994403748)]
[['monday', 'leopard', 'kia', 'halloween', 'tigers', 'no', 'spring', 'awake']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is there at least one person standing outside the store?'], responses:['yes']
question: ['Is the laptop facing forward?'], 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']]
[('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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 4.9575e-09, 5.7564e-11, 2.3926e-08, 4.0427e-10, 1.7456e-10,