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InternVL

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torch.Size([3, 3, 448, 448])
question: ['How many cats 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
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([7.6820e-01, 5.5548e-02, 2.0435e-02, 7.0539e-03, 9.9579e-03, 5.1302e-03,
1.3326e-01, 4.1458e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([7.6820e-01, 5.5548e-02, 2.0435e-02, 7.0539e-03, 9.9579e-03, 5.1302e-03,
1.3326e-01, 4.1458e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.1333, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8667, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([6.7519e-01, 6.2774e-02, 8.2340e-03, 2.4839e-01, 3.5409e-03, 8.3931e-04,
9.5306e-04, 8.0260e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.7519e-01, 6.2774e-02, 8.2340e-03, 2.4839e-01, 3.5409e-03, 8.3931e-04,
9.5306e-04, 8.0260e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.6752, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.3248, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([0.8336, 0.0501, 0.0533, 0.0108, 0.0025, 0.0403, 0.0060, 0.0034],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.8336, 0.0501, 0.0533, 0.0108, 0.0025, 0.0403, 0.0060, 0.0034],
device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([7, 3, 448, 448])
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0936, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9064, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are standing on all fours?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many dogs are standing on all fours?'], 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
question: ['How many animals 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
tensor([8.8363e-01, 2.5862e-02, 1.0787e-02, 3.0887e-03, 5.5923e-03, 2.6267e-03,
6.8180e-02, 2.2934e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.8363e-01, 2.5862e-02, 1.0787e-02, 3.0887e-03, 5.5923e-03, 2.6267e-03,
6.8180e-02, 2.2934e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0682, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9318, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.6996e-01, 6.1397e-03, 2.1891e-03, 8.0480e-04, 1.2088e-03, 7.2847e-04,
1.8912e-02, 5.6045e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6996e-01, 6.1397e-03, 2.1891e-03, 8.0480e-04, 1.2088e-03, 7.2847e-04,
1.8912e-02, 5.6045e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0189, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9811, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-23 14:44:45,262] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.27 | optimizer_step: 0.31
[2024-10-23 14:44:45,262] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3829.24 | backward_microstep: 9877.97 | backward_inner_microstep: 3497.27 | backward_allreduce_microstep: 6380.62 | step_microstep: 7.59
[2024-10-23 14:44:45,262] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3829.26 | backward: 9877.95 | backward_inner: 3497.28 | backward_allreduce: 6380.61 | step: 7.60
0%| | 13/4844 [03:29<20:46:28, 15.48s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many sets of measuring utensils are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many mountain goats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are all of the sails on the boat red?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the vehicle driving in front of a house?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many sets of measuring utensils are in the image?'], responses:['8']
[('8', 0.12723446457289017), ('9', 0.12488291461145089), ('12', 0.12481394644705951), ('7', 0.12480302292408052), ('5', 0.12471410185987472), ('6', 0.12470198211184266), ('11', 0.12452966814724155), ('10', 0.12431989932555992)]
[['8', '9', '12', '7', '5', '6', '11', '10']]
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: 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: 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: 329