Datasets:
TMarcus
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InternVL

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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([9.8333e-01, 1.2401e-02, 1.6519e-06, 4.5004e-04, 3.3966e-04, 2.2800e-03,
6.5467e-04, 5.4644e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([9.8333e-01, 1.2401e-02, 1.6519e-06, 4.5004e-04, 3.3966e-04, 2.2800e-03,
6.5467e-04, 5.4644e-04], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([9.8568e-01, 6.2350e-03, 2.8012e-05, 2.7136e-05, 3.3080e-04, 5.5850e-06,
7.5308e-03, 1.6652e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
white *************
['white', 'black', 'purple', 'orange', 'maroon', 'color', 'brown', 'dark'] tensor([9.8568e-01, 6.2350e-03, 2.8012e-05, 2.7136e-05, 3.3080e-04, 5.5850e-06,
7.5308e-03, 1.6652e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are standing on a grassy area?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
question: ['How many dogs are standing on a grassy area?'], responses:['1']
tensor([1.0000e+00, 3.2766e-09, 8.3755e-11, 1.7045e-08, 3.9494e-10, 2.0408e-10,
5.6421e-11, 1.3925e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.2766e-09, 8.3755e-11, 1.7045e-08, 3.9494e-10, 2.0408e-10,
5.6421e-11, 1.3925e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(8.3755e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-8.3755e-11, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.9993e-01, 6.6052e-05, 1.9533e-07, 3.1788e-11, 2.2867e-11, 3.0914e-10,
8.3912e-11, 1.5459e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9993e-01, 6.6052e-05, 1.9533e-07, 3.1788e-11, 2.2867e-11, 3.0914e-10,
8.3912e-11, 1.5459e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=RIGHT,question='Are the animals eating grass?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.6052e-05, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:0', grad_fn=<DivBackward0>)}
[('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']]
ANSWER0=VQA(image=LEFT,question='How many folders are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([1, 3, 448, 448])
question: ['How many folders 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([1.0000e+00, 1.4222e-09, 6.1405e-10, 2.0252e-09, 6.4857e-10, 4.2037e-08,
1.2825e-08, 5.2929e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4222e-09, 6.1405e-10, 2.0252e-09, 6.4857e-10, 4.2037e-08,
1.2825e-08, 5.2929e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(6.0101e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['Are the animals eating grass?'], responses:['no']
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 5.7236e-10, 1.8395e-07, 1.0483e-11, 1.2339e-11, 5.8017e-09,
3.1571e-10, 2.7124e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.7236e-10, 1.8395e-07, 1.0483e-11, 1.2339e-11, 5.8017e-09,
3.1571e-10, 2.7124e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.7236e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 5.9391e-11, 6.4585e-12, 3.4912e-11, 2.7835e-11, 2.5107e-08,
1.1861e-08, 8.4998e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 5.9391e-11, 6.4585e-12, 3.4912e-11, 2.7835e-11, 2.5107e-08,
1.1861e-08, 8.4998e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.7182e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:53:21,637] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.31 | optimizer_step: 0.32
[2024-10-24 09:53:21,638] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3195.39 | backward_microstep: 8031.13 | backward_inner_microstep: 3016.72 | backward_allreduce_microstep: 5014.33 | step_microstep: 7.48
[2024-10-24 09:53:21,638] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3195.41 | backward: 8031.12 | backward_inner: 3016.74 | backward_allreduce: 5014.30 | step: 7.49
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4617/4844 [19:12:05<52:15, 13.81s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image have a solid black background?')
FINAL_ANSWER=RESULT(var=ANSWER0)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step