[Frontend][Tensorflow] Support explicit_paddings for TF 2.x

[trevor-m]

• Ignore "_cloned" attr for squeeze.
• Support explicit padding for conv2d, depthwise conv2d, conv2d transpose and max_pool2d.
• Support explicit padding for conv3d also, but TF API doesnt allow you to create a conv3d op with explicit padding so there is no unit test.

[FrozenGene]

I worry about we can not ignore it simply. According to doc: https://www.tensorflow.org/api_docs/python/tf/nn/conv2d

Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top,pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0],[pad_top, pad_bottom], [pad_left, pad_right]].

If explicit_padding is not None, we should apply its values. However, @trevor-m you could make a double check.

[trevor-m]

I worry about we can not ignore it simply. According to doc: https://www.tensorflow.org/api_docs/python/tf/nn/conv2d

Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top,pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0],[pad_top, pad_bottom], [pad_left, pad_right]].

If explicit_padding is not None, we should apply its values. However, @trevor-m you could make a double check.

Thanks for the review! That is true, I will update this PR to properly support explicit padding.

[FrozenGene]

I worry about we can not ignore it simply. According to doc: https://www.tensorflow.org/api_docs/python/tf/nn/conv2d

Either the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top,pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0],[pad_top, pad_bottom], [pad_left, pad_right]].

If explicit_padding is not None, we should apply its values. However, @trevor-m you could make a double check.

Thanks for the review! That is true, I will update this PR to properly support explicit padding.

Thanks @trevor-m Our convolution op ignore explicit_padding attribute simply is wrong too (Note: be careful of conv2d_transpose: https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose, its explicit padding is not the same as conv2d), not only just pooling op. You could correct it too.Thanks.

[trevor-m]

@FrozenGene I've updated the PR with explicit padding support. PTAL

I noticed that while many ops now have the "explicit_paddings" attribute, the ability to actually use explicit padding in the TF python API has only exposed it for a few ops.

[FrozenGene]

Thanks @trevor-m Merged.