Datasets:
TMarcus
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InternVL

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ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0276, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9724, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-23 14:44:59,213] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.28 | optimizer_step: 0.32
[2024-10-23 14:44:59,214] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3125.82 | backward_microstep: 10806.71 | backward_inner_microstep: 3017.57 | backward_allreduce_microstep: 7789.06 | step_microstep: 7.42
[2024-10-23 14:44:59,214] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3125.83 | backward: 10806.70 | backward_inner: 3017.60 | backward_allreduce: 7789.05 | step: 7.43
0%| | 14/4844 [03:43<20:09:02, 15.02s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image show the back end of a bus?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the toe of the shoe pointed to the left?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many bottles have blue lids in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many water bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many water bottles 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
question: ['Does the image show the back end of a bus?'], 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['Is the toe of the shoe pointed to the left?'], 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']]
tensor([8.0546e-01, 4.5335e-02, 2.8366e-02, 1.1465e-02, 1.5192e-02, 8.9198e-03,
8.4751e-02, 5.0909e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.0546e-01, 4.5335e-02, 2.8366e-02, 1.1465e-02, 1.5192e-02, 8.9198e-03,
8.4751e-02, 5.0909e-04], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8055, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1945, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
ANSWER0=VQA(image=RIGHT,question='Are the dogs sitting on grass?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
tensor([5.4136e-01, 2.3070e-02, 4.3208e-01, 1.6333e-03, 1.6224e-04, 7.8696e-04,
1.2708e-04, 7.7990e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.4136e-01, 2.3070e-02, 4.3208e-01, 1.6333e-03, 1.6224e-04, 7.8696e-04,
1.2708e-04, 7.7990e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5414, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4321, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0266, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many parrots are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
question: ['How many bottles have blue lids in the image?'], responses:['2']
torch.Size([7, 3, 448, 448])
[('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: 5, images per sample: 5.0, dynamic token length: 1351
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
tensor([5.4483e-01, 2.4892e-02, 4.2432e-01, 6.6370e-04, 2.7473e-04, 4.2539e-03,
1.2191e-04, 6.5137e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.4483e-01, 2.4892e-02, 4.2432e-01, 6.6370e-04, 2.7473e-04, 4.2539e-03,
1.2191e-04, 6.5137e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5448, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.4243, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0309, device='cuda:0', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Do the mittens have hands in them?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many parrots are in the image?'], responses:['2']
question: ['Are the dogs sitting on grass?'], responses:['no']
[('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']]
[('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
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['Do the mittens have hands in them?'], 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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([9.2139e-01, 2.6138e-02, 5.6520e-03, 4.3089e-02, 2.0153e-03, 8.1261e-04,