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Home Page: https://unify.ai
License: Other
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sajadsaeedi prashant118 jeromeku nikolatesla-lj mzshan genghuiqu ntjohnson1 ashok-arjun abdelrhman-hosny djl11 soyabulislamlincoln dhruvsondhi stevenkolawole vincentwei2021 amanda-barbara overfittingstudyroom rohan-cod yatinsd ishticode nini0la kaingwade kvignesh1420 ishangarg9 abhishek9934 nudurupatisurya kanishk7559 iyk02 nnitiwe-dev jeffasante tanaymeh mrtooolddriver cpjacksparrow7 minhtanngo ahmedshoaib hussain0908 anster01 akash027 amit828as asifejazz eslam-zaher fongmunhong mohammedjassem mudassarhaque apar-mistry aliriaz1129 anubhavagr abdulrafay98 kbots-tech joteq7 kencox94 akaashkasi reginaofori conradp3 avdhootmukhedkar kushaldev75 arijit1000 vivek-balu shroedingercat taltrums albertotamajo ferox98 rdhmuhammad ravitejapuvvula nk1719 evabanegacom ebrarsahin gdtdavies emirsoyturk kashvi10shah cher-b juned202 adh1s yashodeepdeshmukh mariomihaly zcoriarty estandres billelbenoudjit zeyadhessuin alnamkani djendara berat-ademi sidr97 flociaglia adarshsaji rkantamneni navaneeth-sharma suri199507 joseph-adikwu simonetgordon rkuipers npradhan108 betty1999kamanthe datamonarch badershafiq joshuapriley zpvk ikust98 nilayaishwarya pratham1443 jakekonradivy's Issues
Add Miscellaneous Operations to PyTorch Frontend
Add miscellaneous operations to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- #12482
- #12557
- #12559
- #13643
- #22289
- #21978
- #21977
- #12566
- bucketize
- #4128
- #26797
- #12137
- #23508
- #20908
- #15021
- #4993
- #16082
- #22165
- #3982
- #3156
- #4137
- diag_embed
- #22094
- #3930
- #11198
- einsum
- #4562
- #2713
- #2714
- flipud
- #21838
- #5503
- #7896
- #23527
- histogram
- #11227
- #4937
- #4814
- #13664
- #4709
- #4818
- #2897
- #12593
- #9611
- #4078
- #4560
- #4070
- #4142
- #3818
- #22252
- #5562
- view_as_real
- #18986
- #26924
- #27105
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/miscellaneous\_ops.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_miscellaneous\_ops.py
test
Update Array Operators to Conform to Array API Standard
Update Array Operators to Conform to Array API Standard:
- __matmul__
Add Random Functions to Ivy Frontend
Add Random Functions to Ivy frontend:
- random_uniform
- random_normal
- multinomial
- randint
- seed
- shuffle
Add Reduction Functions to Ivy Frontend
Add Reduction Functions to Ivy frontend:
- reduce_sum
- reduce_prod
- reduce_mean
- reduce_var
- reduce_std
- reduce_min
- reduce_max
- einsum
Add Ivy Container Instance Methods
Add Ivy Container Instance Methods:
- update_config
- inplace_update
- set_framework
- all_true
- all_false
- reduce_sum
- reduce_prod
- reduce_mean
- reduce_var
- reduce_std
- reduce_min
- reduce_max
- minimum
- maximum
- clip
- clip_vector_norm
- einsum
- vector_norm
- matrix_norm
- flip
- shuffle
- slice_via_key
- as_ones
- as_zeros
- as_bools
- as_random_uniform
- to_native
- to_ivy
- expand_dims
- dev_clone
- dev_dist
- to_multi_dev
- unstack
- split
- gather
- gather_nd
- repeat
- swapaxes
- reshape
- einops_rearrange
- einops_reduce
- einops_repeat
- to_dev
- stop_gradients
- as_variables
- as_arrays
- num_arrays
- size_ordered_arrays
- to_numpy
- from_numpy
- arrays_as_lists
- to_disk_as_hdf5
- to_disk_as_pickled
- to_jsonable
- to_disk_as_json
- to_list
- to_raw
- to_dict
- to_iterator
- to_iterator_values
- to_iterator_keys
- to_flat_list
- from_flat_list
- has_key
- has_key_chain
- find_sub_container
- contains_sub_container
- assert_contains_sub_container
- find_sub_structure
- contains_sub_structure
- assert_contains_sub_structure
- has_nans
- at_keys
- at_key_chain
- at_key_chains
- all_key_chains
- key_chains_containing
- set_at_keys
- set_at_key_chain
- overwrite_at_key_chain
- set_at_key_chains
- overwrite_at_key_chains
- prune_keys
- prune_key_chain
- prune_key_chains
- format_key_chains
- sort_by_key
- prune_empty
- prune_key_from_key_chains
- prune_keys_from_key_chains
- restructure_key_chains
- restructure
- flatten_key_chains
- copy
- deep_copy
- map
- map_conts
- dtype
- with_entries_as_lists
- reshape_like
- create_if_absent
- if_exists
- try_kc
- cutoff_at_depth
- cutoff_at_height
- slice_keys
- with_print_limit
- remove_print_limit
- with_key_length_limit
- remove_key_length_limit
- with_print_indent
- with_print_line_spacing
- with_default_key_color
- with_ivy_backend
- set_ivy_backend
- show
- show_sub_container
Add Pointwise ops to PyTorch Frontend
Add Pointwise ops to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- #2538
- #6526
- #2535
- #2740
- #2730
- #2878
- #7137
- #6730
- #6731
- #10808
- #2782
- #2993
- #2995
- #6525
- #3014
- arctan
- #3058
- #3061
- #3135
- #7157
- #4072
- #3198
- #4073
- #4074
- #4075
- #4076
- #4246
- #4247
- #4248
- #11025
- #9290
- #2467
- #7285
- deg2rad
- #6528
- #6527
- #19215
- #14110
- #19281
- #19341
- #2673
- #7318
- #7903
- fake_quantize_per_channel_affine
- fake_quantize_per_tensor_affine
- #14094
- #14096
- #6510
- #14033
- #14032
- frac
- #14169
- #21758
- #13562
- #21874
- #10683
- #21008
- #7300
- #3204
- #14092
- #3183
- #7316
- #7905
- #4199
- #4200
- #4201
- #4202
- #13895
- #9930
- #7907
- #21030
- igammac
- #5355
- #5354
- #22167
- #14040
- #2933
- #3200
- #11120
- #26923
- #8529
- #6891
- quantized_batch_norm
- quantized_max_pool1d
- quantized_max_pool2d
- #8527
- #13099
- #14034
- #6708
- #14003
- #7685
- #10109
- #3374
- #13952
- #10712
- #2533
- #9292
- #2735
- #3243
- #3033
- #14002
- #2653
- #7754
- #1643
- #6529
- #3167
- #9210
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/pointwise\_ops.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_pointwise\_ops.pyivy/functional/frontends/torch/\_\_init\_\_.pyivy/functional/frontends/torch/non\_linear\_activation\_functions.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_non\_linear\_activation\_functions.pyivy\_tests/test\_ivy/test\_stateful/test\_activations.py
Add Activation Functions to Ivy Frontend
Add Activation Functions to Ivy frontend:
- relu
- leaky_relu
- gelu
- tanh
- sigmoid
- softmax
- softplus
Update Arithmetic Operators to Conform to Array API Standard
Update Arithmetic Operators to Conform to Array API Standard:
- __pos__
- __neg__
- __add__
- __sub__
- __mul__
- __truediv__
- __floordiv__
- __mod__
- __pow__
Add Ivy Container Static Methods
Add Ivy Container Static Methods:
- list_join
- list_stack
- unify
- concat
- stack
- combine
- diff
- structural_diff
- multi_map
- common_key_chains
- identical
- assert_identical
- identical_structure
- assert_identical_structure
- identical_configs
- identical_array_shapes
- from_disk_as_hdf5
- from_disk_as_pickled
- from_disk_as_json
- h5_file_size
- shuffle_h5_file
- reduce
- flatten_key_chain
- trim_key
Add BLAS and LAPACK Operations to PyTorch Frontend
Add BLAS and LAPACK Operations to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- #3783
- #3589
- #3787
- #3788
- #3789
- #3780
- #20856
- #3590
- #25782
- #25882
- #11917
- geqrf
- #3592
- #9901
- #3593
- #3594
- #3790
- #3595
- lu
- lu_solve
- #26225
- #3596
- #3755
- #3754
- #17884
- #3597
- #3785
- orgqr
- ormqr
- #3598
- #3599
- #3686
- #3688
- svd_lowrank
- pca_lowrank
- symeig
- lobpcg
- #23707
- #11898
- #23747
- #26083
- #3756
Note: If the function to be implemented has identical behavior to another PyTorch function, you should simply keep an alias in the blas\_and\_lapack\_ops.py file rather than creating a duplicate implementation.
For example:
torch.det is defined as an alias of torch.linalg.det in the official docs, and so it is defined as shown below
https://github.com/unifyai/ivy/blob/7c28666a4ff161117e7b9e4104f08be3bd7cad26/ivy/functional/frontends/torch/blas\_and\_lapack\_ops.py#L93
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/blas\_and\_lapack\_ops.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_blas\_and\_lapack\_ops.py
Add Nest Functions to Ivy Frontend
Add Nest Functions to Ivy frontend:
- index_nest
- set_nest_at_index
- map_nest_at_index
- multi_index_nest
- set_nest_at_indices
- map_nest_at_indices
- nested_indices_where
- all_nested_indices
- map
- nested_map
- copy_nest
Add Spectral Ops to PyTorch Frontend
Add Spectral Ops to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
Add Loss Functions to Ivy Frontend
Add Loss Functions to Ivy frontend:
- cross_entropy
- binary_cross_entropy
- sparse_cross_entropy
Add Linear Algebra Functions to Ivy Frontend
Add Linear Algebra Functions to Ivy frontend:
- svd
- vector_norm
- matrix_norm
- inv
- pinv
- vector_to_skew_symmetric_matrix
Add Norm Functions to Ivy Frontend
Add Norm Functions to Ivy frontend:
- layer_norm
Add Pooling Layers
Would be better to be able to add pooling in the layers, like MaxPool2D.
ivy.torch.use not exist
I have worked around ivy for some time and found the idea of integrating multiple frameworks very intersting and useful. I have noticed the following error on my machine. The following code does not work properly:
import ivy
import torch
import numpy as np
with ivy.numpy.use:
x = np.array([0.])
y = ivy.cos(x)
with ivy.torch.use:
x = torch.tensor([0.])
y = ivy.cos(x)
The above code emits the error: module 'ivy' has no attribute 'numpy'. This error is only resolved when I set the framework to numpy. Moreover, it seems that if I set the framework to torch, there is still no variable named use in ivy.torch. I have looked at the implementation and found this problem a bit weird, since the use variable seems to be declared in each module.
Thank you for viewing this request.
Add Math Functions to Ivy Frontend
Add Math Functions to Ivy frontend:
- sin
- cos
- tan
- asin
- acos
- atan
- atan2
- sinh
- cosh
- tanh
- asinh
- acosh
- atanh
- log
- exp
- erf
Add Gradient Functions + Classes to Ivy Frontend
Add Gradient Functions + Classes to Ivy frontend:
- GradientTracking
Gradient Mode
- with_grads
- set_with_grads
- unset_with_grads
Variables
- variable
- is_variable
- variable_data
- inplace_update
- inplace_decrement
- inplace_increment
- stop_gradient
AutoGrad
- execute_with_gradients
Optimizer Steps
- adam_step
Optimizer Updates
- optimizer_update
- gradient_descent_update
- lars_update
- adam_update
- lamb_update
Add Multi-Device Functions + Classes to Ivy Frontend
Add Multi-Device Functions + Classes to Ivy frontend:
Multi-Device
- MultiDev
- MultiDevItem
- MultiDevIter
- MultiDevNest
Device Distribution
- DevDistItem
- DevDistIter
- DevDistNest
- dev_dist_array
- dev_dist
- dev_dist_iter
- dev_dist_nest
Device Cloning
- DevClonedItem
- DevClonedIter
- DevClonedNest
- dev_clone_array
- dev_clone
- dev_clone_iter
- dev_clone_nest
Device Unification
- dev_unify_array
- dev_unify
- dev_unify_iter
- dev_unify_nest
Device Mappers
- DevMapper
- DevMapperMultiProc
Device Manager
- DevManager
Profiler
- Profiler
Add Tensor functions to PyTorch Frontend
Add Image Functions to Ivy Frontend
Add Image Functions to Ivy frontend:
- stack_images
- bilinear_resample
- gradient_image
- float_img_to_uint8_img
- uint8_img_to_float_img
- random_crop
Add Creation Ops to PyTorch Frontend
Add creation ops to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- #5748
- #22443
- #13084
- #5744
- #10128
- from_numpy
- #13647
- #14107
- #5743
- #6283
- #3915
- #3455
- #5532
- #5533
- #5534
- #5535
- #5536
- #4301
- #5745
- #22935
- #5746
- #5747
- quantize_per_tensor
- quantize_per_channel
- dequantize
- #16974
- #22752
- #9803
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/creation\_ops.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_creation\_ops.py
Add Utilities to PyTorch Frontend
Add Sparse functions to PyTorch Frontend
Add Sparse functions to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- embedding
- embedding_bag
- #3684
Add Compilation Functions to Ivy Frontend
Add Compilation Functions to Ivy frontend:
- compile
Add Locally disabling gradient computation to PyTorch Frontend
Add Locally disabling gradient computation to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- no_grad
- enable_grad
- set_grad_enabled
- is_grad_enabled
- inference_mode
- is_inference_mode_enabled
Add Comparison ops to PyTorch Frontend
Add Comparison ops to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
Add Ivy MultiDevContainer Instance Methods
Add Ivy MultiDevContainer Instance Methods:
- at_dev
- at_devs
Add Dropout functions to PyTorch Frontend
Add Dropout functions to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/nn/functional/dropout\_functions.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_dropout\_functions.py
Add Loss functions to PyTorch Frontend
Add Loss functions to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- #3037
- #6993
- #10484
- #10295
- #2751
- #24636
- #12017
- hinge_embedding_loss
- #9183
- #6965
- #6443
- #10198
- #19764
- multilabel_soft_margin_loss
- #20121
- #7140
- #7065
- #5546
- #7592
- #10609
- #12176
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/loss\_functions.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_loss\_functions.pyivy/functional/frontends/torch/nn/functional/loss\_functions.py
Add Functions to core API
Add new Ivy functions:
general
- is_floating_point
- is_nonzero
- set_default_dtype
- get_default_dtype
- numel
- full
- full_like
random
- bernoulli
- poisson
- randperm
math
- copysign
- deg2rad
- rad2deg
Add Device Functions to Ivy Frontend
Add Device Functions to Ivy frontend:
Device Queries
Array Printing:
- get_all_arrays_on_dev
- num_arrays_on_dev
- print_all_arrays_on_dev
Retireval:
- dev
- dev_str
Conversion:
- dev_to_str
- str_to_dev
Memory:
- clear_mem_on_dev
- total_mem_on_dev
- used_mem_on_dev
- percent_used_mem_on_dev
Utilization:
- dev_util
Availability:
- gpu_is_available
- num_cpu_cores
- num_gpus
- tpu_is_available
Default Device
- default_device
- set_default_device
- unset_default_device
Device Allocation
- to_dev
Function Splitting
- split_factor
- set_split_factor
- split_func_call
Add Logic Functions to Ivy Frontend
Add Logic Functions to Ivy frontend:
- logical_and
- logical_or
- logical_not
Add Indexing, Slicing, Joining, Mutating Ops to PyTorch Frontend
Add indexing, slicing, joining, mutating ops to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- #10097
- #7859
- cat
- #2621
- #16542
- #3622
- #10146
- #23385
- #7068
- #6861
- #10167
- #6793
- #13557
- #9854
- #22971
- #6878
- #14528
- #6860
- #12517
- #14565
- #3137
- #2792
- #2636
- row_stack
- #14388
- #26245
- #23719
- #26519
- #28290
- #26245_add
- #26245_reduce
- #10139
- #3194
- #2631
- #2704
- #2705
- #10474
- #14249
- take_along_dim
- #11157
- #18291
- #2703
- unbind
- #17721
- #10275
- #6772
- where
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/indexing\_slicing\_joining\_mutating\_ops.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_indexing\_slicing\_joining\_mutating\_ops.pyivy/array/experimental/manipulation.pyivy/container/experimental/manipulation.pyivy/functional/backends/jax/experimental/manipulation.pyivy/functional/backends/numpy/experimental/manipulation.pyivy/functional/backends/tensorflow/experimental/manipulation.pyivy/functional/backends/torch/experimental/manipulation.pyivy/functional/ivy/experimental/manipulation.py
Add Linear functions to PyTorch Frontend
Add Linear functions to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
Add General Functions to Ivy Frontend
Add General Functions to Ivy frontend:
- get_referrers_recursive
- array
- is_array
- copy_array
- array_equal
- arrays_equal
- equal
- to_numpy
- to_scalar
- to_list
- shape
- get_num_dims
- minimum
- maximum
- clip
- clip_vector_norm
- clip_matrix_norm
- round
- floormod
- floor
- ceil
- abs
- argmax
- argmin
- argsort
- cast
- arange
- linspace
- logspace
- concatenate
- flip
- stack
- unstack
- split
- repeat
- tile
- constant_pad
- zero_pad
- fourier_encode
- swapaxes
- transpose
- expand_dims
- where
- indices_where
- isnan
- value_is_nan
- has_nans
- reshape
- broadcast_to
- squeeze
- zeros
- zeros_like
- ones
- ones_like
- one_hot
- cross
- matmul
- cumsum
- cumprod
- identity
- meshgrid
- scatter_flat
- scatter_nd
- gather
- gather_nd
- linear_resample
- exists
- default
- try_else_none
- arg_names
- match_kwargs
- dtype
- dtype_to_str
- dtype_str
- cache_fn
- current_framework_str
- einops_rearrange
- einops_reduce
- einops_repeat
- get_min_denominator
- set_min_denominator
- stable_divide
- get_min_base
- set_min_base
- stable_pow
- multiprocessing
- set_queue_timeout
- queue_timeout
- tmp_dir
- set_tmp_dir
- get_all_arrays_in_memory
- num_arrays_in_memory
- print_all_arrays_in_memory
- container_types
Add Random Sampling to PyTorch Frontend
Add random sampling to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
Add Ivy Container Built-in Methods
Add Ivy Container Built-in Methods:
- repr
- dir
- getattr
- setattr
- getitem
- setitem
- contains
- pos
- neg
- pow
- rpow
- add
- radd
- sub
- rsub
- mul
- rmul
- truediv
- rtruediv
- floordiv
- rfloordiv
- abs
- lt
- le
- eq
- ne
- gt
- ge
- and
- rand
- or
- ror
- invert
- xor
- rxor
- getstate
- setstate
Add Non-linear activation functions to PyTorch Frontend
Add Non-linear activation functions to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
Add Convolution functions to PyTorch Frontend
Add Vision functions to PyTorch Frontend
Add Vision functions to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/vision\_functions.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_vision\_functions.pyivy/functional/frontends/torch/nn/functional/vision\_functions.py
Add Layer Functions to Ivy Frontend
Add Layer Functions to Ivy frontend:
Linear
- linear
Dropout
- dropout
Attention
- scaled_dot_product_attention
- multi_head_attention
Convolutions
- conv1d
- conv1d_transpose
- conv2d
- conv2d_transpose
- depthwise_conv2d
- conv3d
- conv3d_transpose
LSTM
- lstm_update
"Quick Start" issue
Avoid other changes when changing frameworks except ivy.set_framework(). In the example, Quick Start, if I want to run it in tensorflow, except changing 'torch' to 'tensorflow' for ivy.set_framework(), I have to change the shape of x_in from [3] to [1, 3].
Add Distance functions to PyTorch Frontend
Add Distance functions to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
Add Reduction ops to PyTorch Frontend
Add Reduction ops to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- #3922
- #3923
- #3924
- #3925
- #11546
- #4012
- #4013
- #6285
- #9414
- #4046
- #12475
- #4514
- #14023
- #13211
- #23397
- #23360
- #10836
- nansum
- #4515
- #10292
- #20890
- #4513
- std_mean
- #5900
- #14264
- unique_consecutive
- #4512
- #8942
- #11051
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/reduction\_ops.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_reduction\_ops.py
Add Meta Functions to Ivy Frontend
Add Meta Functions to Ivy frontend:
- fomaml_step
- reptile_step
- maml_step
Add Pooling functions to PyTorch Frontend
Add Pooling functions to PyTorch frontend:
_
Please keep in mind that the proper way to link an issue to this list is to comment "- [ ] #issue_number" while the issue's title only includes the name of the function you've chosen.
_
- #12448
- #21786
- avg_pool3d
- #6013
- #6014
- #22024
- #22435
- max_unpool2d
- #26339
- #13645
- #13785
- #22238
- #21496
- #26132
- adaptive_avg_pool1d
- adaptive_avg_pool2d
- #22670
- fractional_max_pool2d
- #27141
The main file paths where these functions are likely to be added are:
ivy/functional/frontends/torch/nn/functional/pooling\_functions.pyivy\_tests/test\_ivy/test\_frontends/test\_torch/test\_pooling\_functions.py
Add Ivy Container Properties
Add Ivy Container Properties:
- _ivy
- shape
- shapes
- dev_str
- ivy
- config
- max_depth
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