Datasets:
TMarcus
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InternVL

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question: ['How many animals 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']]
[('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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([9.9993e-01, 2.0037e-08, 7.4799e-05, 7.1288e-09, 4.2400e-11, 1.8569e-10,
1.1851e-09, 3.1704e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9993e-01, 2.0037e-08, 7.4799e-05, 7.1288e-09, 4.2400e-11, 1.8569e-10,
1.1851e-09, 3.1704e-09], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.7450e-09, 1.5230e-08, 1.3575e-08, 2.3604e-11, 5.9391e-11,
8.3543e-12, 1.7593e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.7450e-09, 1.5230e-08, 1.3575e-08, 2.3604e-11, 5.9391e-11,
8.3543e-12, 1.7593e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(7.4799e-05, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(6.4021e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.5230e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.5230e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many window shades are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is there land on the horizon of the image?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many window shades 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
tensor([1.0000e+00, 1.6537e-06, 1.2626e-08, 2.0176e-08, 3.0339e-10, 4.9028e-10,
9.4366e-10, 2.3116e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.6537e-06, 1.2626e-08, 2.0176e-08, 3.0339e-10, 4.9028e-10,
9.4366e-10, 2.3116e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.6885e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
tensor([6.9622e-06, 9.9999e-01, 1.2751e-07, 2.2414e-10, 3.7110e-08, 1.0989e-09,
2.2380e-07, 8.7457e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.9622e-06, 9.9999e-01, 1.2751e-07, 2.2414e-10, 3.7110e-08, 1.0989e-09,
2.2380e-07, 8.7457e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.9622e-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>)}
question: ['Is there land on the horizon of 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([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 7.5826e-10, 1.3436e-10, 1.9664e-10, 1.1857e-10, 8.2789e-09,
5.1817e-09, 2.3621e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 7.5826e-10, 1.3436e-10, 1.9664e-10, 1.1857e-10, 8.2789e-09,
5.1817e-09, 2.3621e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.4905e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.9029e-01, 2.2729e-08, 9.7082e-03, 8.3776e-08, 4.2756e-09, 2.7730e-10,
6.1337e-09, 2.5739e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9029e-01, 2.2729e-08, 9.7082e-03, 8.3776e-08, 4.2756e-09, 2.7730e-10,
6.1337e-09, 2.5739e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0097, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9903, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.5926e-07, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:21:21,439] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.24 | optimizer_step: 0.31
[2024-10-24 09:21:21,439] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5111.83 | backward_microstep: 8689.92 | backward_inner_microstep: 4961.96 | backward_allreduce_microstep: 3727.91 | step_microstep: 7.68
[2024-10-24 09:21:21,439] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5111.84 | backward: 8689.91 | backward_inner: 4961.97 | backward_allreduce: 3727.90 | step: 7.70
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4487/4844 [18:40:05<1:24:08, 14.14s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many pairs of free weights are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many skincare items are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
torch.Size([1, 3, 448, 448])
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Are the dogs outside in the grass?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many puppies are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)