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TypeError: 'int' object is not subscriptable #23

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M-Amrollahi opened this issue Nov 27, 2022 · 8 comments
Open

TypeError: 'int' object is not subscriptable #23

M-Amrollahi opened this issue Nov 27, 2022 · 8 comments

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@M-Amrollahi
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I have just run the colab and install the requirements. How can I resolve this?

TypeError                                 Traceback (most recent call last)
[<ipython-input-16-75c695310352>](https://localhost:8080/#) in <module>
     20 
     21 # Restore checkpoint.
---> 22 model = model_lib.Model(config)
     23 checkpoint = tf.train.Checkpoint(
     24     model=model, global_step=tf.Variable(0, dtype=tf.int64))

[/content/pix2seq/models/ar_model.py](https://localhost:8080/#) in __init__(self, config, **kwargs)
     50     else:
     51       self.encoder = ResNetTransformer(
---> 52           config.image_size[0], config.image_size[1], config.resnet_variant,
     53           config.resnet_depth, config.resnet_width_multiplier,
     54           config.resnet_sk_ratio, config.num_encoder_layers, config.dim_att,

TypeError: 'int' object is not subscriptable
@hesamira
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Hi,
I have also faced this error.
Please, help us.
Thanks.

@chentingpc
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Looks like the image size should be a tuple instead of int, eg (320, 320) instead of 320. can you try to modify that and see if that works?

@M-Amrollahi
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Author

That solves the issue.

But I do not see any bbox on the result. The image and result are the same and there is no bbox or label on the image.

@saxenasaurabh
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Updating the image_size seems to work for me. This is what I tried:

del config.model.image_size
del config.task.image_size
del config.tasks[0]["image_size"]

config.model.image_size = (640, 640)
config.task.image_size = (640, 640)
config.tasks[0]["image_size"] = (640, 640)

@shamitb
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shamitb commented Mar 18, 2023

Still doesnt work for me tried to make the image_size a tuple:
config.model.image_size = (640, 640)

@shamitb
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shamitb commented Mar 19, 2023

With above changes I get this error:


TypeError Traceback (most recent call last)
in
28
29 # Restore checkpoint.
---> 30 model = model_lib.Model(config)
31 checkpoint = tf.train.Checkpoint(
32 model=model, global_step=tf.Variable(0, dtype=tf.int64))

10 frames
/usr/local/lib/python3.9/dist-packages/keras/utils/generic_utils.py in validate_kwargs(kwargs, allowed_kwargs, error_message)
513 for kwarg in kwargs:
514 if kwarg not in allowed_kwargs:
--> 515 raise TypeError(error_message, kwarg)
516
517

TypeError: ('Keyword argument not understood:', 'synchronized')

@saxenasaurabh
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@shamitb could you please paste the full stacktrace?

@bruce2233
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same error

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6 participants