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InternVL

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1.5806e-02, 2.6134e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0158, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9842, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([6.2174e-01, 3.1263e-01, 2.9077e-02, 2.2645e-02, 1.0049e-02, 1.6700e-03,
2.1063e-03, 8.9404e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.2174e-01, 3.1263e-01, 2.9077e-02, 2.2645e-02, 1.0049e-02, 1.6700e-03,
2.1063e-03, 8.9404e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.6217, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3783, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-23 14:56:08,254] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.27 | optimizer_step: 0.32
[2024-10-23 14:56:08,254] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5045.61 | backward_microstep: 8942.37 | backward_inner_microstep: 4820.47 | backward_allreduce_microstep: 4121.81 | step_microstep: 7.63
[2024-10-23 14:56:08,254] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5045.63 | backward: 8942.36 | backward_inner: 4820.48 | backward_allreduce: 4121.79 | step: 7.64
1%| | 58/4844 [14:52<19:19:21, 14.53s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT 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='How many stacks of towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is there any dog lying down in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many slices of lemon are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are the eyes of the dog half open?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are the eyes of the dog half open?'], 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
question: ['Is there any dog lying down in the image?'], responses:['no']
question: ['How many slices of lemon are in the image?'], responses:['2']
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
[('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']]
[('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']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
tensor([5.3971e-01, 4.5941e-01, 2.0543e-05, 1.1215e-04, 5.5238e-05, 3.7461e-04,
2.7238e-04, 4.2017e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.3971e-01, 4.5941e-01, 2.0543e-05, 1.1215e-04, 5.5238e-05, 3.7461e-04,
2.7238e-04, 4.2017e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4594, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5397, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many golf balls are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['How many stacks of towels are in the image?'], responses:['6']
torch.Size([7, 3, 448, 448])
[('6', 0.12794147189263105), ('8', 0.12539492259598553), ('12', 0.12539359088927945), ('5', 0.12471292164321114), ('4', 0.12443617393590153), ('1', 0.12417386497855347), ('11', 0.12398049124372558), ('3', 0.12396656282071232)]
[['6', '8', '12', '5', '4', '1', '11', '3']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['How many golf balls 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([7, 3, 448, 448]) knan debug pixel values shape
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
tensor([9.7686e-01, 2.2974e-02, 9.8388e-06, 1.3109e-05, 1.1039e-05, 7.7214e-05,
4.2069e-05, 1.0413e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.7686e-01, 2.2974e-02, 9.8388e-06, 1.3109e-05, 1.1039e-05, 7.7214e-05,
4.2069e-05, 1.0413e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0230, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9769, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0002, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image lying down?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([1, 3, 448, 448])
tensor([0.2282, 0.2199, 0.2379, 0.0682, 0.1225, 0.0630, 0.0593, 0.0010],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.2282, 0.2199, 0.2379, 0.0682, 0.1225, 0.0630, 0.0593, 0.0010],
device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9318, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0682, 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 in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)