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    variables (Container) – Variables to be optimized during the meta step

  1. inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  2. inner_learning_rate (float) – The learning rate of the inner loop.

    3. -

  3. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fab4e8012d0>) – The function used for the inner loop optimization. +

  4. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f663464d2d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  5. inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    6. @@ -1476,7 +1476,7 @@

    Meta#

    variables (Container) – Variables to be optimized during the meta step

  6. inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  7. inner_learning_rate (float) – The learning rate of the inner loop.

    8. -

  8. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fab4e8012d0>) – The function used for the inner loop optimization. +

  9. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f663464d2d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  10. inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    11. @@ -1553,7 +1553,7 @@

    Meta#

    variables (Container) – Variables to be optimized.

  11. inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  12. inner_learning_rate (float) – The learning rate of the inner loop.

    13. -

  13. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fab4e8012d0>) – The function used for the inner loop optimization. It takes the learnable +

  14. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f663464d2d0>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    15. diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html index b77ae2aa6..0b3622722 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html @@ -1425,7 +1425,7 @@

    fomaml_stepContainer) – Variables to be optimized during the meta step

  15. inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  16. inner_learning_rate (float) – The learning rate of the inner loop.

    17. -

  17. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fab4e8012d0>) – The function used for the inner loop optimization. +

  18. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f663464d2d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  19. inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    20. diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html index 96be417cc..dbfdbb8c2 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html @@ -1425,7 +1425,7 @@

    maml_stepContainer) – Variables to be optimized during the meta step

  20. inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  21. inner_learning_rate (float) – The learning rate of the inner loop.

    22. -

  22. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fab4e8012d0>) – The function used for the inner loop optimization. +

  23. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f663464d2d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  24. inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    25. diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html index b7df9db47..fde3da6ad 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html @@ -1422,7 +1422,7 @@

    reptile_stepContainer) – Variables to be optimized.

  25. inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  26. inner_learning_rate (float) – The learning rate of the inner loop.

    27. -

  27. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7fab4e8012d0>) – The function used for the inner loop optimization. It takes the learnable +

  28. inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f663464d2d0>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    29. diff --git a/docs/helpers/ivy_tests.test_ivy.helpers.globals.html b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html index 380ff5082..3837c9773 100644 --- a/docs/helpers/ivy_tests.test_ivy.helpers.globals.html +++ b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html @@ -1411,7 +1411,7 @@

    Should not be used inside any of the test functions.

    -ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7fab427f9fb0>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f6628411fb0>#
    diff --git a/docs/stateful/ivy.stateful.layers.html b/docs/stateful/ivy.stateful.layers.html index 43b5fcb46..84330cf22 100644 --- a/docs/stateful/ivy.stateful.layers.html +++ b/docs/stateful/ivy.stateful.layers.html @@ -1538,8 +1538,8 @@

  29. strides – The stride of the sliding window for each dimension of input.

  30. padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    31. -

  31. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fab4e411d80>) – Initializer for the weights. Default is GlorotUniform.

    32. -

  32. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fab4e411d20>) – Initializer for the bias. Default is Zeros.

    33. +

  33. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f663425db40>) – Initializer for the weights. Default is GlorotUniform.

    34. +

  34. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f663425dae0>) – Initializer for the bias. Default is Zeros.

  35. with_bias (default: True) – Whether or not to include a bias term, default is True.

  36. data_format (default: 'NWC') – NWC” or “NCW”. Defaults to “NWC”.

  37. dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    38. @@ -1576,8 +1576,8 @@

  38. strides – The stride of the sliding window for each dimension of input.

  39. padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    40. -

  40. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fab4e411cc0>) – Initializer for the weights. Default is GlorotUniform.

    41. -

  41. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fab4e411c60>) – Initializer for the bias. Default is Zeros.

    42. +

  42. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f663425da80>) – Initializer for the weights. Default is GlorotUniform.

    43. +

  43. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f663425da20>) – Initializer for the bias. Default is Zeros.

  44. with_bias (default: True) – Whether or not to include a bias term, default is True.

  45. output_shape (default: None) – Shape of the output (Default value = None)

  46. data_format (default: 'NWC') – NWC” or “NCW”. Defaults to “NWC”.

    47. @@ -1615,8 +1615,8 @@

  47. strides – The stride of the sliding window for each dimension of input.

  48. padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    49. -

  49. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fab4e411c00>) – Initializer for the weights. Default is GlorotUniform.

    50. -

  50. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fab4e411ba0>) – Initializer for the bias. Default is Zeros.

    51. +

  51. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f663425d9c0>) – Initializer for the weights. Default is GlorotUniform.

    52. +

  52. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f663425d960>) – Initializer for the bias. Default is Zeros.

  53. with_bias (default: True) – Whether or not to include a bias term, default is True.

  54. data_format (default: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.

  55. dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    56. @@ -1653,8 +1653,8 @@

  56. strides – The stride of the sliding window for each dimension of input.

  57. padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    58. -

  58. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fab4e411b40>) – Initializer for the weights. Default is GlorotUniform.

    59. -

  59. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fab4e411ae0>) – Initializer for the bias. Default is Zeros.

    60. +

  60. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f663425d900>) – Initializer for the weights. Default is GlorotUniform.

    61. +

  61. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f663425d8a0>) – Initializer for the bias. Default is Zeros.

  62. with_bias (default: True) – Whether or not to include a bias term, default is True.

  63. output_shape (default: None) – Shape of the output (Default value = None)

  64. data_format (default: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.

    65. @@ -1692,8 +1692,8 @@

  65. strides – The stride of the sliding window for each dimension of input.

  66. padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    67. -

  67. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fab4e4119c0>) – Initializer for the weights. Default is GlorotUniform.

    68. -

  68. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fab4e411960>) – Initializer for the bias. Default is Zeros.

    69. +

  69. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f663425d780>) – Initializer for the weights. Default is GlorotUniform.

    70. +

  70. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f663425d720>) – Initializer for the bias. Default is Zeros.

  71. with_bias (default: True) – Whether or not to include a bias term, default is True.

  72. data_format (default: 'NDHWC') – NDHWC” or “NCDHW”. Defaults to “NDHWC”.

  73. dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    74. @@ -1730,8 +1730,8 @@

  74. strides – The stride of the sliding window for each dimension of input.

  75. padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    76. -

  76. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fab4e411900>) – Initializer for the weights. Default is GlorotUniform.

    77. -

  77. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fab4e4118a0>) – Initializer for the bias. Default is Zeros.

    78. +

  78. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f663425d6c0>) – Initializer for the weights. Default is GlorotUniform.

    79. +

  79. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f663425d660>) – Initializer for the bias. Default is Zeros.

  80. with_bias (default: True) – Whether or not to include a bias term, default is True.

  81. output_shape (default: None) – Shape of the output (Default value = None)

  82. data_format (default: 'NDHWC') – NDHWC” or “NCDHW”. Defaults to “NDHWC”.

    83. @@ -1794,8 +1794,8 @@

  83. strides – The stride of the sliding window for each dimension of input.

  84. padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    85. -

  85. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fab4e411a80>) – Initializer for the weights. Default is GlorotUniform.

    86. -

  86. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fab4e411a20>) – Initializer for the bias. Default is Zeros.

    87. +

  87. weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f663425d840>) – Initializer for the weights. Default is GlorotUniform.

    88. +

  88. bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f663425d7e0>) – Initializer for the bias. Default is Zeros.

  89. with_bias (default: True) – Whether or not to include a bias term, default is True.

  90. data_format (default: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.

  91. dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    92. @@ -1842,11 +1842,11 @@
    -class ivy.stateful.layers.Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, /, *, weight_initializer=<ivy.stateful.initializers.GlorotUniform object>, device=None, v=None, dtype=None)[source]#
    +class ivy.stateful.layers.Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, /, *, weight_initializer=<ivy.stateful.initializers.GlorotUniform object>, device=None, v=None, dtype=None)[source]#

    Bases: Module

    -__init__(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, /, *, weight_initializer=<ivy.stateful.initializers.GlorotUniform object>, device=None, v=None, dtype=None)[source]#
    +__init__(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, /, *, weight_initializer=<ivy.stateful.initializers.GlorotUniform object>, device=None, v=None, dtype=None)[source]#

    Class for embedding indices into a dense representation. The Embedding layer is a simple lookup table for dense vectors. It’s typically used to store word embeddings and query them using indices.

    @@ -1897,12 +1897,13 @@
    -class ivy.stateful.layers.IDct(*args, **kwargs)[source]#
    +class ivy.stateful.layers.IDct(*args, **kwargs)[source]#

    Bases: Module

    __init__(*, type=2, n=None, axis=-1, norm=None, device=None, dtype=None)[source]#
    -

    Class for applying the Discrete Cosine Transform over mini-batch of inputs.

    +

    Class for applying the Discrete Cosine Transform over mini-batch of +inputs.

    Parameters:
    -class ivy.stateful.layers.IFFT(*args, **kwargs)[source]#
    +class ivy.stateful.layers.IFFT(*args, **kwargs)[source]#

    Bases: Module

    -__init__(dim, /, *, norm='backward', n=None, out=None, device=None, dtype=None)[source]#
    +__init__(dim, /, *, norm='backward', n=None, out=None, device=None, dtype=None)[source]#

    Class for applying IFFT to input.

    Parameters:
    @@ -1955,11 +1956,11 @@
    -class ivy.stateful.layers.Identity(*args, **kwargs)[source]#
    +class ivy.stateful.layers.Identity(*args, **kwargs)[source]#

    Bases: Module

    -__init__()[source]#
    +__init__()[source]#

    Identity layer. The layer is argument insensitive and returns the input argument as output when called.

    It’s typically used as a placeholder when no operation is to be @@ -1981,7 +1982,7 @@

    • input_channels – Number of input channels for the layer

    • output_channels – Number of output channels for the layer

      • -

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fab4e411840>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f663425d600>) – Initializer for the weights. Default is GlorotUniform.

    • num_layers (default: 1) – Number of lstm cells in the lstm layer, default is 1.

    • return_sequence (default: True) – Whether or not to return the entire output sequence, or just the latest timestep. @@ -2040,8 +2041,8 @@

      • input_channels – Number of input channels for the layer.

      • output_channels – Number of output channels for the layer.

        • -

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7fab4e411e40>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7fab4e411de0>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f663425dc00>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f663425dba0>) – Initializer for the bias. Default is Zeros.

      • with_bias (default: True) – Whether or not to include a bias term, default is True.

      • device (default: None) – device on which to create the layer’s variables ‘cuda:0’, ‘cuda:1’, ‘cpu’ etc. Default is cpu.

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851, 852, 853, 854, 856, 857, 858, 859, 861, 863, 864, 865, 867, 872, 882, 883, 885, 886, 888, 889], "avail": [0, 2, 4, 5, 8, 9, 12, 13, 20, 21, 22, 37, 38, 40, 42, 43, 58, 69, 92, 207, 213, 215, 216, 227, 557, 642, 645, 648, 699, 788, 822, 824, 831, 832, 839, 840, 841, 842, 844, 845, 853, 856, 859, 867, 868, 871, 875, 876, 877, 887, 888], "cpu": [0, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 37, 38, 39, 40, 42, 56, 57, 58, 60, 61, 64, 66, 68, 77, 87, 89, 91, 100, 137, 143, 146, 148, 149, 152, 153, 154, 160, 204, 205, 207, 208, 209, 210, 215, 218, 220, 222, 225, 226, 228, 230, 387, 393, 449, 519, 520, 522, 523, 640, 642, 654, 749, 750, 751, 752, 784, 802, 803, 804, 805, 806, 807, 808, 822, 824, 828, 831, 832, 838, 841, 842, 846, 853, 856, 867, 880, 882, 885, 887], "gpu": [0, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 56, 58, 60, 61, 207, 209, 210, 213, 216, 218, 220, 222, 223, 226, 228, 230, 642, 822, 824, 831, 832, 840, 842, 863, 868, 880, 882, 885, 886, 887], "tpu": [0, 56, 205, 211, 220, 222, 227, 642, 822, 842, 882, 885], "explicitli": [0, 648, 684, 685, 700, 784, 803, 804, 805, 828, 835, 836, 837, 839, 841, 844, 845, 846, 849, 850, 851, 852, 854, 856, 861, 867, 876, 882], "hardwar": [0, 4, 5, 56, 113, 117, 831, 859, 872, 878, 880, 881, 882, 883, 884, 885, 886, 887, 888], "mai": [0, 1, 8, 9, 66, 67, 68, 73, 79, 80, 89, 90, 96, 103, 113, 114, 137, 144, 155, 225, 251, 252, 258, 263, 271, 279, 280, 284, 285, 287, 302, 346, 347, 383, 415, 555, 591, 640, 642, 643, 645, 648, 656, 657, 658, 696, 705, 760, 761, 762, 763, 764, 767, 771, 772, 773, 775, 787, 818, 829, 830, 831, 832, 835, 839, 840, 841, 845, 846, 849, 850, 851, 853, 854, 856, 859, 862, 863, 865, 873, 889], "vari": [0, 68, 79, 108, 109, 302, 415, 556, 643, 645, 648, 656, 695, 761, 762, 763, 818, 839, 843, 853, 856, 863], "known": [0, 68, 91, 295, 387, 459, 461, 643, 802, 835, 840, 841, 853, 856], "advanc": [0, 31, 54, 831, 833, 881], "set_soft_device_mod": [0, 4, 5, 25, 29, 229, 642, 842], "section": [0, 1, 2, 8, 9, 10, 11, 22, 23, 24, 25, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 47, 48, 49, 62, 68, 79, 91, 123, 386, 389, 420, 431, 481, 490, 510, 656, 760, 761, 762, 763, 824, 825, 828, 829, 830, 831, 832, 834, 835, 836, 837, 838, 839, 840, 841, 842, 844, 845, 846, 847, 848, 849, 850, 852, 853, 854, 855, 856, 857, 859, 860, 864, 865, 877, 878, 885, 888], "binari": [0, 8, 9, 25, 37, 38, 40, 68, 69, 72, 74, 91, 95, 97, 241, 244, 246, 281, 301, 386, 388, 432, 467, 470, 643, 647, 649, 670, 674, 707], "logist": [0, 25], "gblinear": [0, 25], "booster": [0, 25], "linear": [0, 4, 5, 20, 21, 22, 29, 41, 42, 43, 54, 55, 56, 58, 61, 68, 69, 72, 84, 91, 92, 95, 121, 123, 125, 126, 129, 306, 310, 314, 316, 317, 318, 322, 364, 378, 383, 386, 389, 398, 422, 457, 495, 543, 560, 583, 637, 645, 647, 652, 674, 697, 736, 787, 789, 790, 802, 803, 824, 839, 844, 849, 850, 852, 853, 856, 859, 861, 864, 865, 866, 876, 880, 881, 882, 885], "estim": [0, 68, 91, 360, 383, 398, 533, 822], "rate": [0, 68, 70, 91, 93, 386, 393, 428, 523, 627, 630, 632, 633, 634, 646, 647, 651, 672, 726, 727, 728, 807, 840], "fine": [0, 27, 29, 42, 43, 831, 832, 841, 843, 853, 863, 866, 888], "tune": [0, 27, 29, 42, 43, 887, 888], "regular": [0, 57, 91, 387, 398, 449, 454, 537, 831, 853, 882], "term": [0, 8, 9, 22, 68, 91, 323, 330, 333, 380, 388, 467, 468, 647, 672, 673, 803, 818, 824, 832, 839, 861, 869, 871, 882], "reg_lambda": [0, 25], "reg_alpha": [0, 25], "overfit": [0, 647, 670], "compil": [0, 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 37, 38, 40, 42, 43, 46, 59, 61, 302, 643, 795, 831, 853, 857, 861, 867, 869, 876, 878, 881, 882, 883, 886, 889], "param": [0, 18, 19, 23, 24, 25, 42, 56, 57, 58, 60, 85, 91, 92, 114, 546, 563, 564, 645, 809, 824, 866, 876], "n_estim": [0, 25], "100": [0, 5, 8, 9, 10, 11, 14, 15, 18, 19, 20, 21, 23, 24, 25, 54, 56, 58, 64, 67, 68, 87, 90, 91, 92, 95, 112, 149, 158, 245, 285, 298, 339, 362, 371, 380, 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533, 534, 535, 536, 545, 548, 549, 551, 552, 556, 557, 558, 559, 560, 563, 564, 567, 569, 571, 572, 573, 575, 576, 579, 587, 588, 602, 603, 604, 606, 608, 610, 611, 624, 630, 635, 652, 661, 662, 663, 664, 670, 671, 677, 678, 679, 684, 685, 686, 687, 688, 689, 691, 693, 695, 696, 702, 707, 708, 709, 710, 714, 717, 718, 719, 720, 721, 724, 725, 742, 749, 750, 751, 752, 754, 757, 760, 761, 762, 763, 764, 768, 769, 772, 774, 775, 777, 778, 779, 788, 817, 838, 849, 856], "colab": [2, 6, 7, 22, 23, 24, 25, 27, 29, 33, 34, 35, 36, 37, 38, 39, 40, 43, 56, 58, 60, 61], "manual": [2, 8, 9, 10, 11, 22, 23, 24, 25, 27, 29, 33, 34, 35, 36, 37, 38, 39, 40, 43, 652, 729, 739, 740, 830, 831, 832, 841, 847, 856, 865, 868], "mind": [2, 27, 29, 33, 39, 42, 46, 830, 831, 836, 839, 856, 868, 876], "click": [2, 4, 5, 58, 830, 831, 832, 840, 844, 846, 847, 862], "runtim": [2, 4, 5, 6, 7, 12, 13, 18, 19, 20, 21, 22, 23, 24, 35, 42, 45, 56, 57, 834, 849, 856, 859, 882], "restart": [2, 4, 5, 6, 7, 12, 13, 20, 21, 22, 56, 57, 831, 846], "git": [2, 4, 5, 6, 7, 12, 13, 20, 21, 42, 56, 57, 58, 59, 824, 826, 829, 831, 832, 835, 838, 840, 846, 847, 856, 868], "clone": [2, 4, 5, 12, 13, 20, 21, 42, 56, 58, 59, 824, 826, 832, 846, 868], "http": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23, 24, 29, 37, 38, 39, 40, 42, 43, 56, 57, 58, 59, 60, 61, 67, 68, 90, 91, 93, 158, 166, 254, 264, 265, 280, 339, 346, 347, 380, 383, 386, 389, 398, 430, 503, 533, 626, 627, 640, 641, 643, 646, 648, 650, 658, 696, 697, 725, 775, 824, 826, 831, 832, 835, 838, 840, 841, 844, 846, 868, 876], "github": [2, 4, 5, 6, 7, 12, 13, 18, 19, 20, 21, 23, 24, 42, 56, 57, 58, 59, 60, 824, 826, 827, 829, 832, 833, 835, 838, 840, 841, 843, 844, 846, 847, 855, 856, 868, 871, 890], "com": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 23, 24, 29, 42, 56, 57, 58, 59, 60, 824, 826, 831, 832, 835, 838, 840, 841, 846, 868], "unifyai": [2, 4, 5, 12, 13, 20, 21, 42, 56, 57, 58, 59, 60, 824, 826, 831, 832, 838, 846, 868], "model": [2, 3, 4, 5, 14, 15, 25, 26, 31, 32, 33, 59, 61, 251, 284, 388, 464, 643, 800, 804, 805, 822, 864, 865, 869, 875, 876, 880, 881, 882, 883, 884, 885, 886, 888, 889], "depth": [2, 4, 5, 8, 9, 12, 13, 20, 21, 57, 64, 68, 72, 87, 91, 95, 152, 386, 389, 422, 482, 556, 568, 640, 645, 647, 665, 666, 832, 840, 864, 865, 866, 868], "repositori": [2, 4, 5, 12, 13, 20, 21, 826, 830, 831, 832, 834, 835, 838, 846, 855, 873], "cd": [2, 4, 5, 12, 13, 20, 21, 42, 59, 824, 826, 831, 832, 846, 868], "resnet": [3, 8, 9, 23, 24, 31, 42, 875, 876], "imag": [3, 4, 5, 8, 9, 10, 11, 18, 19, 23, 24, 27, 31, 39, 42, 43, 56, 57, 58, 59, 60, 61, 68, 72, 90, 91, 95, 113, 231, 232, 233, 234, 237, 240, 249, 252, 254, 256, 265, 266, 267, 272, 274, 287, 294, 295, 297, 298, 302, 386, 405, 406, 422, 423, 424, 426, 556, 643, 645, 647, 660, 661, 662, 663, 664, 667, 668, 669, 803, 824, 831, 846, 859, 861, 862, 864, 866, 868, 875, 876, 882], "classif": [3, 4, 5, 20, 21, 25, 31, 56, 882], "acceler": [3, 31, 841, 853, 880, 884, 885, 886, 887], "convert": [3, 12, 13, 14, 15, 18, 19, 23, 24, 25, 27, 29, 31, 32, 34, 36, 39, 40, 42, 43, 44, 46, 48, 56, 59, 61, 63, 64, 67, 85, 86, 87, 90, 108, 138, 139, 151, 161, 162, 204, 205, 206, 207, 218, 226, 230, 250, 290, 389, 394, 473, 474, 475, 524, 589, 607, 609, 610, 611, 613, 640, 641, 642, 643, 645, 648, 652, 706, 730, 741, 742, 784, 812, 817, 830, 836, 837, 850, 851, 853, 856, 858, 861, 867, 869, 873, 876, 880, 881, 888], "faster": [3, 4, 5, 14, 15, 18, 19, 23, 24, 25, 31, 42, 43, 59, 61, 68, 73, 91, 96, 387, 460, 648, 698, 826, 829, 838, 869, 884, 887], "infer": [3, 8, 9, 10, 11, 14, 15, 18, 19, 22, 23, 24, 25, 31, 35, 45, 47, 48, 57, 59, 61, 64, 68, 69, 72, 75, 87, 91, 92, 95, 98, 137, 139, 142, 146, 147, 151, 154, 160, 169, 170, 171, 172, 173, 323, 324, 386, 389, 393, 422, 507, 521, 567, 601, 602, 640, 641, 645, 647, 650, 670, 717, 812, 813, 834, 837, 841, 842, 856, 861, 866, 876, 880, 881, 884, 886], "mmpretrain": [3, 31], "segment": [3, 31, 68, 91, 341, 342, 343, 380, 838, 843], "unet": [3, 31], "alexnet": [3, 31], "written": [3, 4, 5, 6, 7, 8, 9, 22, 31, 33, 42, 43, 56, 69, 389, 484, 831, 835, 836, 844, 847, 848, 852, 853, 857, 861, 863, 866, 867, 871, 876, 880, 882, 886, 888, 889], "xgboost": [3, 31], "paddlepaddl": [3, 31, 346, 347, 383, 831], "dinov2": [3, 10, 11, 31], "project": [3, 20, 21, 23, 24, 31, 36, 37, 38, 39, 40, 42, 43, 46, 109, 647, 674, 803, 824, 826, 827, 830, 831, 832, 833, 836, 837, 838, 856, 865, 867, 871, 872, 873, 876, 878, 880, 882, 885, 889, 890], "convnext": [3, 8, 9, 18, 19, 22, 31], "finetun": [3, 31, 56], "video": [4, 12, 18, 20, 23, 27, 29, 33, 34, 35, 36, 37, 38, 39, 40, 43, 824, 825, 830, 831, 832, 835, 836, 837, 839, 840, 841, 842, 843, 844, 845, 847, 848, 849, 850, 851, 852, 853, 854, 856, 857, 859, 868, 880], "tutori": [4, 8, 9, 10, 11, 12, 18, 20, 22, 23, 27, 29, 33, 34, 35, 36, 37, 38, 39, 40, 43, 824, 832, 853, 868], "three": [4, 5, 6, 7, 31, 37, 47, 48, 58, 68, 150, 323, 380, 389, 475, 640, 831, 832, 839, 840, 841, 843, 853, 856, 859, 860, 861, 883, 888], "major": [4, 5, 6, 7, 655, 758, 841, 842, 854, 856, 867, 872, 879, 882], "ml": [4, 5, 6, 7, 8, 9, 22, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 56, 58, 61, 825, 829, 853, 860, 861, 862, 864, 865, 866, 870, 872, 873, 876, 878, 879, 880, 881, 882, 885, 887, 889], "framework": [4, 5, 6, 7, 10, 11, 14, 15, 27, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47, 49, 56, 58, 60, 63, 69, 181, 203, 213, 216, 227, 554, 570, 574, 606, 609, 641, 642, 645, 652, 731, 782, 784, 788, 795, 800, 807, 812, 813, 827, 828, 830, 831, 834, 835, 836, 837, 838, 840, 841, 842, 843, 845, 846, 848, 849, 850, 852, 853, 856, 857, 859, 860, 861, 863, 866, 867, 868, 869, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 883, 886], "sinc": [4, 5, 12, 13, 20, 21, 22, 39, 40, 42, 43, 56, 58, 68, 91, 109, 383, 826, 831, 832, 835, 836, 837, 838, 839, 840, 841, 842, 845, 852, 853, 867, 872, 882, 888], "automat": [4, 5, 12, 13, 14, 15, 20, 21, 22, 40, 42, 43, 48, 830, 831, 832, 834, 837, 838, 840, 841, 847, 849, 852, 856, 859, 860, 862, 865, 866, 868, 869, 873, 882, 885, 889], "sure": [4, 5, 12, 13, 18, 19, 20, 21, 22, 23, 24, 25, 42, 56, 827, 830, 831, 832, 835, 840, 845, 846, 853, 854, 856, 859, 868], "enabl": [4, 5, 6, 7, 8, 9, 12, 13, 18, 19, 20, 21, 22, 23, 24, 25, 37, 38, 40, 57, 68, 73, 85, 96, 114, 386, 388, 409, 467, 591, 645, 648, 691, 805, 822, 824, 831, 832, 833, 836, 839, 841, 849, 850, 851, 852, 853, 856, 857, 860, 862, 864, 866, 867, 869, 872, 875, 880, 881, 882, 883, 884, 885, 888, 889], "dm": [4, 5, 6, 7, 12, 13, 18, 19, 23, 24, 42, 43, 54, 56], "haiku": [4, 5, 6, 7, 12, 13, 18, 19, 23, 24, 40, 42, 43, 54, 56, 60, 800, 824, 866, 873, 876, 882], "exit": [4, 12, 20, 22, 42, 43, 842], "download": [4, 5, 8, 9, 10, 11, 20, 21, 22, 27, 29, 42, 43, 57, 58, 61, 826, 831, 838, 856, 875, 876], "imagenet": [4, 5, 8, 9, 22, 29, 57, 59, 824], "class": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 22, 25, 27, 29, 33, 42, 43, 54, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 145, 154, 160, 176, 179, 192, 194, 195, 254, 291, 349, 371, 383, 397, 398, 406, 407, 440, 539, 540, 547, 556, 560, 573, 583, 606, 640, 641, 642, 643, 645, 647, 648, 649, 652, 653, 668, 673, 677, 683, 693, 697, 698, 700, 707, 723, 730, 741, 748, 763, 770, 774, 775, 784, 785, 792, 793, 794, 795, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 811, 812, 815, 817, 822, 824, 830, 837, 838, 839, 841, 842, 843, 844, 848, 850, 851, 854, 855, 856, 859, 861, 862, 864, 865, 866, 869, 875, 876, 880, 882, 883, 889], "wget": [4, 5, 8, 9, 12, 13, 20, 21, 56, 57, 60, 831], "raw": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 23, 24, 39, 42, 43, 56, 59, 60, 85, 824, 844, 876, 883], "githubusercont": [4, 5, 8, 9, 12, 13, 20, 21, 56, 60], "hub": [4, 5, 8, 9, 12, 13, 20, 21, 56, 59, 61], "master": [4, 5, 12, 13, 20, 21, 34, 35, 36, 44, 45, 46, 47, 48, 49, 56, 58, 59, 60, 827, 840, 882, 890], "imagenet_class": [4, 5, 20, 21], "categori": [4, 5, 8, 9, 20, 21, 830, 835, 836, 839, 841, 845, 853, 857, 860], "strip": [4, 5, 20, 21, 35, 45, 872], "readlin": [4, 5, 20, 21, 57], "cat": [4, 5, 10, 11, 20, 21, 57, 854, 859, 861, 866, 875, 876], "jpg": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 23, 24, 39, 42, 43, 58, 59, 824, 876], "filenam": [4, 5, 12, 13, 20, 21, 22, 42, 43, 56, 58, 61, 69, 805, 811, 864], "import": [4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 22, 23, 24, 27, 29, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 56, 57, 59, 60, 61, 68, 79, 83, 87, 91, 106, 205, 206, 210, 222, 318, 398, 533, 568, 584, 642, 645, 651, 656, 727, 728, 763, 795, 812, 813, 824, 829, 830, 831, 832, 833, 835, 836, 837, 838, 839, 841, 842, 843, 844, 847, 850, 851, 852, 853, 854, 855, 856, 857, 861, 863, 864, 866, 867, 868, 872, 875, 876, 877, 878, 880, 882, 885, 886, 888], "devic": [4, 5, 8, 9, 10, 11, 12, 14, 15, 18, 19, 20, 21, 22, 23, 24, 57, 58, 61, 64, 68, 77, 85, 87, 91, 100, 113, 116, 117, 118, 137, 138, 139, 141, 142, 143, 146, 147, 148, 149, 151, 152, 153, 154, 156, 157, 158, 159, 160, 204, 205, 206, 207, 208, 209, 210, 211, 212, 217, 218, 219, 220, 222, 223, 224, 225, 226, 228, 230, 323, 324, 339, 340, 380, 393, 483, 519, 520, 522, 523, 547, 561, 562, 640, 645, 654, 749, 750, 751, 752, 782, 784, 785, 800, 802, 803, 804, 805, 806, 807, 808, 809, 822, 832, 834, 837, 841, 845, 849, 850, 854, 856, 857, 859, 861, 866, 867, 868, 869, 872, 881, 882, 884, 885, 886, 887], "torchvis": [4, 5, 8, 9, 18, 19, 20, 21, 22, 56, 873], "transform": [4, 5, 6, 7, 8, 9, 10, 11, 18, 19, 20, 21, 22, 23, 24, 39, 42, 43, 56, 57, 59, 68, 72, 91, 95, 386, 387, 408, 409, 414, 415, 418, 419, 420, 430, 431, 434, 451, 647, 671, 787, 790, 803, 824, 850, 856, 866, 869, 875, 876, 880, 882, 883, 884], "pil": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 23, 24, 39, 42, 43, 57, 58, 59, 824, 876], "time": [4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 22, 23, 24, 40, 42, 43, 48, 56, 58, 59, 60, 68, 70, 73, 79, 91, 93, 102, 108, 109, 145, 352, 383, 386, 387, 389, 398, 415, 420, 432, 434, 455, 462, 495, 501, 533, 627, 632, 640, 646, 647, 648, 650, 651, 655, 656, 670, 673, 688, 723, 726, 727, 728, 755, 756, 760, 761, 803, 804, 805, 822, 830, 831, 832, 835, 837, 839, 840, 841, 843, 846, 848, 849, 850, 852, 853, 856, 857, 861, 864, 866, 867, 868, 871, 872, 873, 875, 876, 880, 882, 883, 886, 887, 888], "filterwarn": [4, 5, 6, 7, 22], "ignor": [4, 5, 6, 7, 22, 55, 63, 64, 68, 85, 91, 150, 386, 387, 389, 398, 410, 411, 412, 441, 449, 457, 497, 498, 502, 541, 640, 647, 652, 674, 740, 741, 807, 831, 838, 840, 843, 856, 867, 888], "compos": [4, 5, 8, 9, 10, 11, 18, 19, 20, 21, 22, 42, 43, 56, 68, 91, 386, 400, 401, 402, 403, 831, 839, 853, 856, 875, 877, 882, 889], "resiz": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 56, 57, 68, 91, 386, 422, 859], "centercrop": [4, 5, 20, 21, 22], "224": [4, 5, 8, 9, 10, 11, 20, 21, 22, 27, 29, 42, 43, 56, 57, 59, 824, 876], "totensor": [4, 5, 8, 9, 10, 11, 18, 19, 20, 21, 22, 56], "485": [4, 5, 20, 21, 22, 56], "456": [4, 5, 20, 21, 22, 56, 856], "406": [4, 5, 20, 21, 22, 56, 68, 91, 408, 551, 645], "229": [4, 5, 20, 21, 22, 56, 290, 643], "225": [4, 5, 20, 21, 22, 56, 58, 245, 643], "torch_img": [4, 5, 12, 13, 20, 21], "unsqueez": [4, 5, 12, 13, 18, 19, 20, 21], "img": [4, 5, 12, 13, 20, 21, 39, 42, 43, 56, 57, 58, 60, 824, 864, 876], "ipython": [4, 5, 12, 13, 20, 21, 37, 38, 39, 40, 42, 43, 61], "displai": [4, 5, 12, 13, 20, 21, 22, 39, 42, 43, 56, 57, 58, 60, 61, 831, 838, 840, 845, 856, 864], "end": [4, 5, 12, 13, 22, 56, 57, 68, 91, 137, 239, 295, 364, 383, 386, 388, 389, 434, 463, 485, 495, 497, 498, 640, 643, 818, 831, 832, 837, 840, 846, 852, 857, 859, 860, 867, 880, 885], "set_default_devic": [4, 6, 8, 9, 12, 13, 18, 20, 22, 23, 228, 642, 842], "ivy_model": [4, 5, 6, 7, 12, 13, 20, 21, 59], "ivy_alexnet": [4, 5], "quick": [4, 5, 31, 43, 832, 834, 854, 865], "trace_graph": [4, 5, 6, 7, 12, 13, 20, 21, 35, 36, 37, 38, 42, 43, 45, 46, 47, 48, 49, 50, 59, 805, 824, 861, 866, 874], "moment": [4, 5, 68, 70, 91, 93, 387, 444, 626, 627, 632, 646, 807, 822, 830, 837, 867, 875, 876], "cost": [4, 5, 70, 93, 626, 627, 630, 632, 633, 634, 646, 651, 726, 727, 728, 818, 841, 859, 880], "arg": [4, 5, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 27, 29, 37, 38, 40, 42, 43, 47, 48, 49, 60, 63, 85, 107, 117, 133, 214, 224, 612, 639, 640, 642, 645, 782, 784, 799, 800, 803, 804, 805, 809, 812, 817, 822, 824, 836, 841, 842, 845, 851, 852, 853, 859, 861, 865, 875, 876, 877], "asarrai": [4, 5, 6, 7, 12, 13, 18, 19, 20, 21, 57, 64, 68, 69, 80, 87, 91, 92, 103, 138, 396, 525, 526, 556, 567, 571, 572, 602, 603, 604, 640, 645, 647, 656, 657, 661, 761, 765, 845, 850, 853, 854], "cuda": [4, 5, 6, 8, 9, 10, 11, 12, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 33, 42, 57, 58, 61, 64, 68, 77, 87, 91, 100, 148, 149, 152, 204, 205, 206, 222, 393, 519, 520, 522, 523, 640, 642, 648, 654, 699, 749, 750, 751, 752, 802, 803, 804, 805, 806, 807, 808, 822, 861, 867, 869, 887], "output": [4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 20, 21, 22, 33, 39, 40, 42, 43, 55, 56, 57, 59, 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 113, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 152, 153, 154, 155, 156, 157, 159, 160, 163, 165, 190, 224, 225, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 328, 329, 333, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 375, 376, 377, 378, 380, 383, 385, 386, 387, 388, 389, 392, 393, 394, 396, 398, 399, 400, 401, 402, 403, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 418, 419, 420, 422, 423, 424, 425, 428, 430, 431, 432, 434, 435, 437, 438, 439, 441, 443, 446, 447, 449, 452, 453, 454, 455, 457, 458, 461, 463, 464, 465, 466, 467, 468, 469, 470, 471, 478, 479, 480, 483, 485, 486, 487, 488, 489, 492, 493, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 507, 508, 509, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 526, 531, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 550, 551, 552, 556, 557, 558, 560, 564, 573, 580, 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"torch_class": [4, 5, 20, 21], "torch_logit": [4, 5, 20, 21], "tensor": [4, 5, 6, 7, 8, 9, 14, 15, 18, 19, 20, 21, 22, 23, 24, 27, 29, 33, 34, 37, 38, 40, 42, 43, 44, 48, 54, 56, 64, 67, 68, 69, 72, 73, 74, 75, 77, 81, 85, 87, 90, 91, 92, 95, 96, 97, 98, 100, 104, 107, 140, 148, 149, 152, 158, 174, 190, 282, 283, 313, 330, 334, 335, 336, 337, 338, 339, 348, 371, 378, 380, 383, 386, 387, 388, 389, 398, 399, 405, 406, 409, 413, 422, 423, 424, 425, 432, 434, 436, 443, 444, 445, 446, 449, 451, 453, 455, 456, 459, 461, 462, 463, 465, 468, 469, 485, 488, 493, 496, 497, 498, 499, 502, 507, 508, 539, 544, 587, 588, 640, 641, 643, 645, 647, 648, 649, 650, 654, 658, 670, 673, 674, 689, 700, 707, 717, 719, 749, 772, 803, 812, 818, 822, 824, 836, 837, 841, 842, 846, 848, 849, 852, 853, 854, 856, 857, 859, 861, 863, 864, 866, 867, 869, 871, 875, 876, 877, 879, 880, 883, 885, 886, 889], "6477": [4, 5], "2950": [4, 5], "0453": [4, 5], "grad_fn": [4, 5, 20, 21, 40, 54, 629, 636, 646, 864], 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852, 853, 854, 855, 856, 859, 863, 864, 865, 867, 871, 875, 876, 877, 882, 887], "ident": [4, 5, 8, 9, 14, 15, 25, 40, 57, 59, 73, 85, 143, 212, 566, 592, 640, 642, 645, 648, 652, 686, 690, 742, 803, 824, 839, 849, 850, 853, 854, 857, 859, 863, 864, 867, 869, 871, 873], "had": [4, 5, 839, 840, 852, 857, 861, 882, 883], "postprocess": [4, 5], "routin": [4, 5, 840, 852, 853, 859, 867, 882], "feed": [4, 5, 224, 642, 875, 882, 883], "carefulli": [4, 5, 289, 643, 802, 853, 880, 885], "rewrit": [4, 5], "easili": [4, 5, 39, 42, 43, 54, 831, 836, 840, 846, 853, 856, 859, 864, 865, 866, 867, 872, 882, 888, 889], "quickest": [4, 5], "particular": [4, 5, 42, 43, 279, 643, 788, 831, 832, 835, 837, 840, 841, 843, 850, 852, 853, 856, 857, 878, 882, 888], "again": [4, 5, 12, 13, 36, 37, 45, 46, 47, 48, 648, 696, 832, 836, 837, 838, 839, 843, 845, 847, 852, 853, 856, 857, 859, 864, 866, 867, 872, 873, 887, 888], "speed": [4, 5, 18, 19, 23, 24, 25, 42, 43, 56, 61, 69, 92, 580, 645, 856, 871, 885], "repeat": [4, 5, 6, 7, 36, 46, 68, 69, 75, 91, 92, 98, 386, 389, 398, 415, 420, 484, 533, 558, 645, 650, 651, 723, 727, 728, 817, 832, 836, 837, 843, 844, 852, 856], "previou": [4, 5, 25, 35, 36, 37, 39, 45, 46, 47, 49, 70, 91, 93, 198, 199, 200, 201, 202, 375, 385, 386, 432, 613, 615, 616, 617, 618, 620, 621, 623, 627, 632, 641, 645, 646, 802, 821, 831, 832, 835, 837, 840, 842, 848, 853, 856, 859, 866, 867, 885], "trace": [4, 5, 6, 7, 8, 9, 12, 13, 18, 19, 20, 21, 22, 23, 24, 31, 32, 36, 39, 42, 45, 47, 48, 60, 69, 73, 85, 92, 96, 575, 576, 579, 590, 599, 614, 622, 645, 648, 784, 795, 805, 807, 822, 824, 835, 839, 841, 853, 858, 859, 861, 866, 867, 874, 875, 876, 883, 888], "026875037000081647": [4, 5], "overrid": [4, 5, 12, 13, 48, 57, 64, 68, 87, 91, 152, 398, 533, 640, 836, 838], "prealloc": [4, 5, 12, 13], "temporari": [4, 5, 12, 13, 600, 623, 645, 818, 841, 858], "fix": [4, 5, 12, 13, 58, 68, 91, 108, 109, 383, 386, 387, 432, 462, 647, 674, 824, 828, 831, 832, 835, 841, 847, 856, 857], "until": [4, 5, 12, 13, 818, 832, 852, 861, 867, 872, 875, 889], "o": [4, 5, 12, 13, 22, 55, 56, 57, 58, 60, 583, 645, 647, 674, 824, 831, 834, 840, 861, 868], "environ": [4, 5, 12, 13, 23, 24, 37, 38, 39, 40, 57, 60, 824, 825, 832, 868, 882, 884], "xla_python_client_alloc": [4, 5, 12, 13], "platform": [4, 5, 8, 9, 12, 13, 22, 25, 37, 38, 40, 826, 829, 831, 838, 880, 884, 886], "jit": [4, 5, 18, 19, 23, 24, 42, 45, 861, 867, 875, 882], "img_jax": [4, 5, 12, 13], "device_put": [4, 5, 18, 19], "warm": [4, 5], "_": [4, 5, 14, 15, 16, 17, 18, 19, 23, 24, 25, 42, 55, 56, 67, 68, 85, 90, 91, 93, 109, 166, 254, 256, 264, 265, 280, 346, 347, 383, 386, 389, 398, 430, 459, 462, 503, 533, 556, 626, 627, 641, 643, 645, 646, 648, 650, 652, 658, 696, 697, 699, 725, 736, 775, 832, 840, 841, 844, 852, 856, 864], "0022192720000475674": [4, 5], "64773613": [4, 5], "29496723": [4, 5], "exact": [4, 5, 68, 84, 85, 121, 386, 388, 422, 427, 467, 468, 656, 760, 762, 789, 799, 831, 832, 835, 843, 861], "note": [4, 5, 8, 9, 12, 13, 22, 25, 38, 42, 43, 48, 57, 58, 59, 68, 69, 73, 75, 79, 91, 96, 98, 108, 145, 158, 190, 258, 293, 294, 301, 339, 340, 360, 380, 383, 386, 387, 389, 409, 440, 445, 455, 456, 462, 485, 503, 641, 643, 647, 648, 650, 656, 658, 674, 683, 684, 695, 696, 698, 717, 721, 761, 763, 772, 803, 818, 822, 828, 830, 831, 832, 836, 841, 843, 844, 847, 852, 853, 854, 856, 857, 859], "were": [4, 5, 12, 13, 59, 85, 88, 179, 183, 184, 258, 643, 647, 674, 830, 831, 832, 841, 845, 847, 851, 852, 854, 856, 857, 859, 861, 875, 882, 883, 888], "function": [4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 22, 25, 27, 29, 31, 32, 34, 35, 36, 37, 38, 39, 40, 44, 45, 46, 47, 48, 49, 50, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 113, 114, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 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21, 22, 25, 56, 57, 58, 59, 68, 74, 91, 97, 388, 464, 467, 470, 649, 707, 708, 709, 824, 841], "down": [4, 5, 35, 45, 59, 68, 91, 386, 389, 422, 487, 824, 831, 856, 869, 882, 888], "itself": [4, 5, 10, 11, 37, 47, 67, 108, 285, 546, 612, 643, 645, 652, 741, 818, 828, 831, 832, 835, 838, 839, 840, 841, 842, 845, 846, 847, 852, 853, 865, 867, 871, 875, 881, 882, 883, 888], "version": [4, 5, 8, 9, 14, 15, 25, 39, 40, 45, 56, 57, 58, 61, 62, 68, 91, 108, 121, 302, 351, 353, 383, 398, 538, 543, 625, 643, 645, 648, 684, 685, 784, 812, 813, 824, 831, 832, 838, 840, 841, 844, 852, 854, 861, 871, 872, 873, 876, 888, 889], "004749261999904775": [4, 5], "7245": [4, 5], "1394": [4, 5], "0587": [4, 5], "promis": [4, 5, 10, 11, 872], "sourc": [4, 5, 10, 11, 14, 15, 16, 17, 20, 21, 29, 34, 35, 36, 37, 38, 39, 40, 42, 43, 48, 49, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 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"mlp_encod": [27, 42, 43, 824, 876], "create_model": [27, 42, 43, 824, 876], "mixer_b16_224": [27, 42, 43, 824, 876], "nois": [27, 29, 42, 43, 824, 875, 876], "randn": [27, 29, 42, 43, 389, 507, 824, 876], "tf_mlp_encod": [27, 42, 43], "output_torch": [27, 29], "output_tf": [27, 29], "output_dens": [27, 42, 43, 824], "dens": [27, 40, 42, 43, 327, 380, 803, 824], "unit": [27, 42, 43, 68, 84, 91, 108, 109, 121, 123, 124, 125, 126, 127, 128, 129, 306, 307, 310, 314, 316, 317, 320, 321, 322, 378, 515, 516, 637, 824, 831, 835, 841, 853, 854, 856, 867, 883, 886], "mention": [27, 29, 42, 43, 48, 830, 831, 832, 836, 843, 848, 849, 852, 853, 856, 859, 872, 877, 882], "fulli": [27, 29, 31, 32, 35, 40, 42, 43, 56, 68, 91, 398, 540, 803, 824, 836, 841, 848, 851, 859, 861, 862, 863, 864, 865, 866, 867, 873, 877, 880, 881, 882, 888, 889], "ground": [27, 29, 388, 464, 782, 784, 795, 828, 846, 853, 856, 871], "ret": [27, 29, 42, 43, 62, 63, 64, 65, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 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"deepmind": [28, 873], "perceiverio": [28, 873], "backbon": [28, 56, 824, 861, 864], "TO": [28, 30, 41], "efficientnet": 29, "eff_encod": [29, 824], "efficientnet_v2": [29, 824], "efficientnetv2b0": [29, 824], "storag": [29, 56, 57, 864, 872], "googleapi": [29, 56, 57], "efficientnetv2": 29, "b0_notop": 29, "h5": [29, 85], "24274472": 29, "0u": 29, "torch_eff_encod": [29, 824], "modes_to_trac": 29, "1280": [29, 556, 645, 824], "welcom": [31, 57, 824, 825, 831, 832, 833, 855], "varieti": [31, 835, 840, 841, 842, 856, 858, 878, 880, 884, 885, 888, 889], "organ": [31, 836, 839, 849, 853, 855, 857, 869, 872], "main": [31, 43, 64, 68, 73, 91, 96, 143, 156, 157, 158, 324, 339, 340, 380, 387, 389, 438, 484, 640, 648, 681, 682, 702, 824, 827, 830, 831, 832, 833, 835, 838, 839, 846, 850, 852, 880, 882, 883, 888], "exactli": [31, 35, 45, 54, 55, 59, 301, 643, 830, 839, 840, 841, 842, 843, 845, 856, 859, 871, 873], "rush": [31, 873], "jump": [31, 854], "straight": [31, 824, 840, 853, 856, 863], "quickstart": [31, 824], "introduct": [31, 33, 40, 42, 43, 882], "point": [31, 40, 65, 67, 68, 73, 77, 79, 81, 88, 90, 91, 96, 100, 104, 137, 138, 139, 141, 143, 146, 153, 154, 159, 163, 176, 180, 184, 191, 231, 232, 233, 234, 236, 237, 238, 239, 240, 247, 248, 249, 251, 252, 254, 256, 257, 258, 264, 265, 266, 267, 272, 273, 274, 275, 276, 284, 286, 287, 289, 291, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 323, 324, 326, 346, 347, 364, 365, 368, 370, 380, 383, 386, 387, 388, 393, 398, 401, 410, 411, 412, 430, 440, 460, 464, 519, 520, 521, 522, 523, 533, 534, 535, 543, 638, 640, 641, 643, 648, 654, 655, 656, 657, 658, 678, 680, 683, 684, 685, 687, 689, 690, 691, 694, 695, 696, 697, 698, 699, 700, 702, 705, 751, 752, 758, 760, 761, 762, 763, 766, 768, 769, 771, 772, 773, 774, 775, 776, 777, 812, 813, 822, 828, 830, 831, 832, 835, 836, 838, 840, 841, 843, 844, 846, 848, 852, 853, 856, 857, 859, 861, 863, 864, 873, 875, 888], "showcas": [31, 824], "real": [31, 39, 67, 68, 81, 90, 91, 104, 113, 123, 126, 129, 153, 154, 231, 232, 233, 234, 236, 237, 238, 239, 240, 249, 251, 252, 254, 256, 258, 262, 263, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 281, 284, 286, 287, 289, 293, 294, 295, 297, 298, 299, 300, 301, 302, 304, 305, 346, 347, 353, 354, 355, 365, 383, 386, 387, 409, 430, 431, 440, 441, 637, 640, 643, 648, 655, 658, 683, 684, 685, 689, 696, 698, 699, 702, 705, 758, 771, 773, 774, 775, 776, 839, 884], "world": [31, 39, 832, 884], "beginn": [31, 825, 882], "got": [31, 54, 845], "cover": [31, 42, 68, 91, 386, 423, 424, 425, 830, 835, 836, 838, 841, 843, 844, 849, 850, 856, 859, 860], "familiar": [31, 32, 33, 830, 831], "concept": [31, 32, 33], "turn": [31, 32, 35, 45, 72, 95, 108, 109, 410, 411, 412, 647, 670, 803, 831, 838, 839, 842, 843, 853, 856, 873], "unus": [31, 32, 35, 843, 852], "part": [31, 32, 35, 64, 67, 68, 90, 91, 96, 113, 123, 126, 129, 156, 157, 158, 264, 268, 291, 339, 340, 366, 380, 383, 386, 387, 389, 398, 430, 441, 495, 543, 637, 640, 643, 648, 684, 685, 784, 830, 831, 832, 833, 835, 838, 841, 847, 849, 852, 853, 856, 857, 859, 861, 862, 866, 867, 875, 876, 877, 880, 882, 887, 888, 889], "lazi": [31, 32, 35, 38, 45, 48, 49, 60], "kornia": [31, 32, 39, 42, 43, 56, 60, 824, 876], "roundup": 33, "indep": [33, 42], "proof": [33, 42], "delv": [33, 43, 824], "theori": [33, 826, 838], "esenti": [33, 42], "abstract": [33, 42, 43, 802, 807, 824, 839, 841, 852, 853, 856, 859, 865, 871, 880, 882, 884, 885, 889], "quirk": [33, 42], "perk": [33, 42, 824, 836, 839], "under": [33, 42, 43, 68, 388, 467, 468, 817, 824, 830, 831, 834, 835, 842, 843, 844, 847, 853, 854, 856, 859, 860, 861, 864, 866, 867, 875, 876, 882, 885, 889], "hood": [33, 42, 43, 824, 834, 842, 843, 847, 853, 856, 859, 860, 861, 864, 866, 875, 876, 889], "appropi": 33, "string": [33, 42, 43, 58, 68, 69, 72, 85, 91, 95, 161, 162, 174, 181, 203, 204, 205, 206, 207, 209, 218, 225, 226, 230, 386, 387, 389, 429, 433, 441, 495, 506, 535, 554, 641, 642, 645, 647, 648, 660, 661, 662, 663, 665, 667, 669, 685, 782, 784, 788, 817, 818, 837, 838, 840, 841, 842, 845, 853, 861, 864], "simplest": [33, 831, 843, 856, 859], "interact": [33, 42, 57, 60, 830, 881, 882, 887], "submodul": [33, 42, 56, 58, 113, 114, 637, 638, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 799, 800, 802, 803, 805, 806, 807, 808, 830, 831, 832, 835, 838, 840, 842, 846, 849, 850, 856, 860, 861, 865, 869], "likewis": [33, 38, 42, 49, 832, 839, 841, 844, 848, 849, 853, 859, 864, 875, 876, 888], "nativearrai": [33, 42, 43, 63, 64, 65, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 79, 81, 84, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 113, 117, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 133, 134, 136, 138, 139, 140, 142, 147, 148, 149, 150, 151, 152, 154, 156, 157, 160, 163, 164, 165, 166, 169, 170, 171, 172, 173, 174, 176, 179, 182, 183, 184, 186, 188, 190, 191, 197, 207, 208, 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466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 478, 479, 480, 481, 483, 484, 485, 486, 487, 489, 490, 492, 493, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 507, 508, 509, 510, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 533, 534, 535, 536, 537, 545, 548, 549, 551, 552, 556, 557, 558, 560, 563, 564, 565, 566, 567, 569, 571, 572, 573, 576, 579, 580, 582, 587, 588, 589, 592, 601, 602, 603, 604, 605, 606, 608, 610, 611, 613, 624, 626, 627, 628, 630, 632, 633, 634, 635, 637, 639, 640, 641, 642, 643, 645, 646, 647, 648, 649, 650, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 673, 674, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 729, 730, 731, 732, 736, 737, 738, 741, 746, 747, 748, 749, 750, 751, 752, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 808, 836, 839, 843, 845, 848, 849, 850, 852, 853, 857, 858, 861, 863, 869], "alia": [33, 42, 346, 347, 383, 638, 830, 853, 874, 877], "lastli": [33, 42, 836], "subclass": [33, 42, 43, 850, 853, 859, 876], "dict": [33, 42, 43, 56, 60, 63, 69, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 114, 121, 122, 123, 124, 125, 126, 127, 128, 129, 134, 136, 145, 147, 152, 154, 160, 164, 166, 177, 178, 179, 183, 184, 191, 207, 210, 211, 225, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 256, 257, 258, 262, 263, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 278, 279, 280, 281, 282, 283, 284, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 304, 305, 306, 307, 308, 309, 310, 313, 314, 315, 316, 317, 318, 320, 321, 322, 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842, 852], "normalize_via_oper": 48, "determin": [48, 67, 68, 73, 75, 79, 82, 85, 90, 91, 92, 96, 103, 105, 108, 111, 113, 114, 143, 166, 168, 175, 181, 182, 183, 184, 186, 187, 188, 203, 213, 215, 216, 227, 232, 233, 234, 236, 237, 238, 239, 240, 241, 243, 244, 245, 246, 248, 249, 251, 254, 256, 258, 264, 265, 266, 267, 268, 272, 273, 274, 275, 276, 281, 284, 289, 293, 296, 297, 298, 299, 300, 301, 302, 305, 315, 319, 365, 370, 378, 383, 386, 387, 388, 389, 398, 422, 430, 441, 463, 464, 503, 507, 533, 545, 548, 569, 570, 574, 575, 576, 577, 578, 579, 606, 624, 640, 641, 642, 643, 645, 648, 650, 651, 656, 659, 678, 679, 680, 682, 686, 687, 688, 690, 691, 693, 694, 696, 697, 702, 704, 705, 711, 726, 727, 728, 760, 761, 762, 763, 764, 778, 779, 789, 795, 802, 806, 839, 841, 842, 844, 849, 853, 856, 858, 859, 871], "think": [48, 830, 832, 840, 843, 859, 883], "uniqu": [48, 58, 68, 69, 79, 91, 92, 102, 386, 387, 389, 434, 457, 494, 495, 509, 580, 645, 651, 652, 656, 726, 727, 728, 731, 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520, 521, 522, 523, 524, 525, 526, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 548, 549, 551, 552, 555, 556, 557, 558, 559, 560, 563, 564, 567, 569, 571, 572, 573, 575, 576, 577, 579, 580, 582, 587, 588, 602, 603, 604, 605, 606, 608, 610, 611, 624, 626, 627, 630, 632, 633, 634, 635, 661, 662, 663, 664, 665, 666, 669, 670, 671, 673, 677, 678, 679, 681, 682, 683, 684, 685, 686, 687, 688, 689, 691, 694, 695, 696, 698, 702, 703, 705, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 748, 749, 750, 751, 752, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 777, 778, 779, 836, 843, 844, 859], "docstr": [62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 113, 121, 122, 123, 124, 125, 126, 127, 128, 129, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 159, 160, 164, 165, 166, 176, 179, 183, 184, 191, 208, 225, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 315, 316, 317, 318, 320, 321, 322, 323, 324, 325, 326, 328, 329, 330, 333, 340, 342, 343, 344, 345, 346, 347, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 383, 386, 389, 398, 405, 406, 407, 408, 410, 411, 412, 414, 418, 419, 420, 423, 424, 425, 429, 430, 433, 434, 435, 436, 437, 438, 440, 441, 442, 443, 444, 445, 447, 451, 452, 453, 454, 455, 456, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 479, 480, 481, 482, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 518, 520, 521, 522, 523, 524, 525, 526, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 548, 549, 551, 552, 555, 556, 557, 558, 559, 560, 563, 564, 567, 569, 571, 572, 573, 575, 576, 577, 579, 580, 582, 587, 588, 602, 603, 604, 605, 606, 608, 610, 611, 624, 625, 626, 627, 630, 632, 633, 634, 635, 640, 641, 643, 645, 648, 650, 655, 656, 657, 658, 659, 661, 662, 663, 664, 665, 666, 669, 670, 671, 673, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 704, 705, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 748, 749, 750, 751, 752, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 829, 830, 834, 838, 847, 848, 849, 850, 853, 855, 857], "liter": [62, 67, 68, 73, 84, 90, 91, 96, 121, 122, 123, 124, 125, 126, 127, 128, 129, 302, 306, 311, 312, 314, 378, 386, 387, 389, 392, 408, 418, 422, 430, 445, 451, 456, 459, 462, 495, 517, 637, 643, 648, 657, 689, 705, 766, 799, 859], "magnitud": [62, 67, 68, 84, 90, 91, 121, 122, 123, 124, 125, 126, 127, 128, 129, 231, 234, 251, 258, 284, 302, 306, 311, 312, 314, 378, 637, 643, 648, 698, 699, 799, 841], "handle_complex_input": [62, 67, 68, 84, 90, 91, 121, 122, 123, 124, 125, 126, 127, 128, 129, 302, 306, 311, 312, 314, 378, 637, 643, 799, 850], "element": [62, 64, 67, 68, 69, 72, 73, 75, 77, 78, 79, 81, 84, 85, 87, 88, 90, 91, 92, 95, 96, 98, 100, 101, 102, 104, 109, 113, 114, 117, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 137, 140, 146, 147, 156, 157, 158, 174, 176, 179, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 312, 314, 316, 317, 318, 320, 321, 322, 339, 340, 341, 342, 343, 345, 346, 347, 348, 349, 353, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 378, 380, 383, 386, 387, 388, 389, 398, 399, 410, 411, 412, 415, 420, 423, 424, 425, 429, 431, 432, 433, 439, 440, 441, 463, 473, 474, 475, 485, 486, 487, 489, 492, 502, 503, 505, 507, 509, 531, 532, 534, 535, 536, 537, 538, 539, 541, 542, 544, 548, 551, 552, 563, 564, 580, 582, 602, 603, 604, 606, 610, 611, 637, 640, 643, 645, 647, 648, 650, 652, 654, 655, 656, 657, 658, 659, 670, 679, 681, 683, 684, 688, 693, 695, 696, 698, 702, 710, 713, 714, 715, 716, 717, 718, 719, 720, 729, 732, 738, 749, 757, 758, 759, 760, 761, 762, 763, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 782, 784, 787, 789, 803, 818, 844, 854, 856, 859, 861, 886], "138": [62, 121, 637], "165": [62, 121, 637, 647, 671], "hardswish": [62, 68, 84, 91, 309, 378, 637, 799], "leaky_relu": [62, 84, 91, 306, 637, 788], "alpha": [62, 67, 68, 84, 90, 91, 118, 123, 234, 300, 306, 307, 315, 319, 325, 378, 380, 387, 392, 393, 441, 517, 520, 521, 522, 637, 643, 799, 848, 853, 854], "slope": [62, 68, 84, 91, 123, 306, 307, 313, 315, 319, 378, 637, 799], "leaki": [62, 84, 123, 637, 799], "log_softmax": [62, 84, 637, 799], "0719": [62, 84, 124], "mish": [62, 84, 637, 799], "30340147": [62, 125, 637], "86509842": [62, 84, 125, 637], "269": [62, 127], "881": [62, 67, 90, 127, 237, 250, 290, 643], "422": [62, 128, 637], "155": [62, 95, 128, 637, 647, 671], "softplu": [62, 84, 637, 799, 859], "beta": [62, 68, 76, 84, 91, 99, 129, 315, 319, 325, 328, 329, 378, 380, 387, 388, 392, 393, 441, 469, 517, 521, 522, 637, 653, 748, 799, 824, 859], "threshold": [62, 67, 68, 84, 90, 91, 129, 282, 283, 322, 348, 378, 383, 388, 389, 464, 469, 502, 637, 643, 799, 859], "union": [62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 217, 218, 219, 220, 222, 223, 224, 225, 226, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 378, 380, 383, 384, 386, 387, 388, 389, 392, 393, 394, 396, 398, 400, 401, 402, 403, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 418, 419, 420, 422, 423, 424, 425, 426, 428, 429, 430, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 451, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 478, 479, 480, 481, 483, 484, 485, 486, 487, 488, 489, 490, 492, 493, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 507, 508, 509, 510, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 545, 548, 549, 551, 552, 556, 557, 558, 559, 560, 563, 564, 565, 566, 567, 569, 571, 572, 573, 575, 576, 579, 580, 582, 583, 587, 588, 592, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 624, 625, 626, 627, 628, 629, 630, 632, 633, 634, 635, 637, 639, 640, 641, 642, 643, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 673, 674, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 736, 737, 738, 740, 741, 746, 747, 748, 749, 750, 751, 752, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 784, 787, 802, 807, 808, 836, 839, 841, 842, 843, 845, 848, 849, 852, 857, 859, 861, 866, 875, 876, 877], "3461": [62, 84, 129, 637], "6491": [62, 84, 129, 637], "_array_to_new_backend": 63, "_to_ivi": 63, "_to_n": 63, "to_ignor": [63, 83, 106, 652, 740, 741], "_to_new_backend": 63, "args_to_ivi": 63, "include_deriv": [63, 86, 652, 730, 741, 784], "nest": [63, 85, 86, 114, 117, 254, 578, 608, 625, 628, 643, 645, 646, 651, 726, 727, 729, 730, 731, 732, 733, 734, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 807, 836, 838, 839, 849, 851, 857, 864, 865, 867, 869, 882], "unchang": [63, 67, 386, 389, 431, 485, 647, 670], "deriv": [63, 64, 68, 70, 86, 87, 91, 93, 142, 147, 154, 160, 324, 328, 353, 380, 383, 626, 627, 630, 631, 632, 633, 634, 640, 646, 651, 652, 728, 730, 741, 805, 807, 808, 841, 842, 863, 865], "word": [63, 137, 389, 488, 640, 654, 752, 800, 803, 839, 852, 853, 869], "args_to_n": [63, 852], "cont_inplac": 63, "decid": [63, 85, 652, 740, 741, 830, 831, 841, 859], "args_to_new_backend": 63, "shallow": [63, 652, 736, 737, 741, 746, 747], "nativevari": 63, "mutabl": [63, 652, 730, 736, 737, 741, 746, 747, 837], "to_ivi": [63, 86, 652, 742, 852], "leaf": [63, 85, 92, 104, 114, 559, 652, 739, 740, 742, 769, 839, 849, 864], "travers": [63, 86, 652, 733, 741, 839, 841, 845, 861], "lowest": [63, 68, 77, 86, 91, 100, 398, 536, 652, 654, 741, 750, 818, 849, 867, 869, 879, 883, 887], "search": [63, 68, 86, 91, 755, 756, 795, 829, 831, 839, 843, 846, 856, 857, 871], "to_new_backend": 63, "_arraywithcr": [64, 113], "boolean": [64, 65, 67, 68, 69, 75, 78, 81, 85, 87, 88, 90, 91, 92, 98, 101, 104, 113, 114, 134, 136, 138, 139, 140, 146, 163, 179, 181, 183, 184, 187, 203, 213, 221, 227, 241, 242, 243, 244, 245, 246, 278, 279, 280, 281, 346, 347, 362, 383, 387, 389, 445, 456, 462, 473, 474, 475, 481, 483, 485, 486, 487, 490, 494, 501, 503, 510, 545, 548, 559, 566, 569, 570, 574, 575, 576, 577, 578, 579, 580, 589, 592, 595, 596, 598, 599, 624, 639, 640, 641, 642, 643, 645, 647, 650, 651, 652, 655, 658, 674, 713, 714, 715, 717, 719, 720, 722, 724, 726, 727, 739, 757, 758, 759, 771, 773, 787, 788, 789, 790, 795, 806, 839, 841, 849, 853, 856, 859], "never": [64, 68, 75, 87, 91, 98, 139, 389, 473, 474, 475, 481, 483, 485, 486, 487, 490, 494, 501, 510, 566, 645, 650, 713, 714, 715, 717, 719, 720, 722, 724, 832, 841, 852, 853, 856], "buffer": [64, 87, 91, 98, 139, 145, 473, 474, 481, 483, 485, 486, 487, 494, 510, 640, 713, 714, 715, 717, 719, 720, 722, 724, 804, 805, 852, 867], "nativedtyp": [64, 65, 68, 72, 73, 77, 78, 81, 87, 91, 96, 100, 101, 104, 137, 138, 139, 141, 142, 143, 145, 146, 147, 148, 149, 151, 152, 153, 154, 159, 160, 162, 163, 168, 169, 170, 171, 172, 173, 174, 175, 180, 181, 185, 187, 189, 193, 203, 323, 324, 325, 326, 327, 328, 329, 344, 351, 367, 380, 383, 393, 398, 519, 520, 521, 522, 523, 533, 534, 535, 536, 539, 542, 640, 641, 647, 648, 654, 655, 657, 658, 670, 689, 705, 750, 751, 752, 755, 756, 766, 768, 769, 772, 774, 776, 802, 841, 842, 848, 857, 861], "datatyp": [64, 68, 85, 87, 91, 139, 147, 151, 168, 189, 193, 386, 434, 640, 641, 782, 857, 875], "nativedevic": [64, 66, 68, 77, 87, 89, 91, 100, 137, 138, 139, 141, 142, 143, 146, 147, 148, 149, 151, 152, 153, 154, 158, 159, 160, 205, 206, 207, 208, 209, 212, 217, 218, 219, 220, 222, 223, 224, 225, 226, 230, 323, 324, 339, 380, 393, 519, 520, 522, 523, 640, 642, 654, 749, 750, 751, 752, 802, 807, 808, 841, 842, 845, 848, 857], "39999998": [64, 138, 139, 640, 656, 761], "5999999": [64, 68, 91, 95, 138, 139, 308, 378, 387, 436, 640, 647, 670, 677], "0999999": [64, 81, 138, 139, 308, 318, 321, 364, 378, 383, 640, 772], "10000038": [64, 138, 139, 640], "90786433e": [64, 138, 139, 640], "310": [64, 138, 139, 640], "copy_arrai": [64, 87, 640], "to_ivy_arrai": [64, 87, 140, 640], "empty_lik": [64, 68, 87, 91, 275, 387, 439, 640, 643], "uniniti": [64, 141, 142, 640, 847], "from_dlpack": 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"sequenc": [64, 68, 72, 73, 75, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 114, 121, 122, 123, 124, 125, 126, 127, 128, 129, 143, 145, 147, 149, 152, 154, 160, 164, 166, 179, 183, 184, 191, 225, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 256, 257, 258, 262, 263, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 278, 279, 280, 281, 282, 283, 284, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 314, 315, 316, 317, 318, 320, 321, 322, 324, 327, 334, 335, 336, 337, 338, 345, 346, 347, 348, 349, 351, 353, 361, 362, 368, 370, 372, 373, 374, 376, 377, 380, 383, 384, 385, 386, 387, 389, 393, 398, 399, 401, 402, 403, 410, 411, 412, 414, 415, 419, 420, 422, 429, 430, 431, 432, 433, 436, 444, 445, 446, 448, 454, 455, 456, 459, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 473, 474, 475, 479, 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"unset_soft_device_mode": [[229, "unset-soft-device-mode"]], "is_uint_dtype": [[188, "is-uint-dtype"]], "promote_types_of_inputs": [[190, "promote-types-of-inputs"]], "set_default_int_dtype": [[195, "set-default-int-dtype"]], "set_default_uint_dtype": [[196, "set-default-uint-dtype"]], "unset_default_dtype": [[199, "unset-default-dtype"]], "unset_default_device": [[228, "unset-default-device"]], "as_native_dev": [[205, "as-native-dev"]], "set_split_factor": [[222, "set-split-factor"]], "add": [[234, "add"]], "unset_default_float_dtype": [[200, "unset-default-float-dtype"]], "Examples and Demos": [[3, "examples-and-demos"], [31, "examples-and-demos"]], "Using Ivy ResNet": [[20, "Using-Ivy-ResNet"], [21, "Using-Ivy-ResNet"]], "Installation": [[20, "Installation"], [21, "Installation"], [22, "Installation"], [5, "Installation"], [4, "Installation"]], "Imports": [[20, "Imports"], [25, "Imports"], [12, "Imports"], [21, "Imports"], [13, "Imports"]], "Data Preparation": [[20, "Data-Preparation"], [12, "Data-Preparation"], [21, "Data-Preparation"], [6, "Data-Preparation"], [5, "Data-Preparation"], [4, "Data-Preparation"], [13, "Data-Preparation"], [7, "Data-Preparation"]], "Prepare the set of labels": [[20, "Prepare-the-set-of-labels"], [21, "Prepare-the-set-of-labels"]], "Load the image example \ud83d\uddbc\ufe0f": [[20, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [21, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [13, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[20, "Visualise-image"], [12, "Visualise-image"], [21, "Visualise-image"], [13, "Visualise-image"]], "Model Inference ResNet34": [[20, "Model-Inference-ResNet34"], [21, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[20, "Initializing-Native-Torch-ResNet34"], [21, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[20, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"], [21, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[20, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [20, "id1"], [21, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [21, "id1"]], "Model Inference ResNet50": [[20, "Model-Inference-ResNet50"], [21, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[20, "Initializing-Native-Torch-ResNet50"], [21, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[20, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"], [21, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "Accelerating PyTorch models with JAX": [[24, "Accelerating-PyTorch-models-with-JAX"], [23, "Accelerating-PyTorch-models-with-JAX"]], "Transpiling a PyTorch model to build on top": [[27, "Transpiling-a-PyTorch-model-to-build-on-top"]], "How To Convert Models from PyTorch to PaddlePaddle": [[11, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"], [10, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[11, "About-the-Model"], [10, "About-the-Model"]], "Transpiling the Model": [[11, "Transpiling-the-Model"], [10, "Transpiling-the-Model"]], "Comparing the results": [[11, "Comparing-the-results"], [9, "Comparing-the-results"], [22, "Comparing-the-results"], [8, "Comparing-the-results"], [10, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[11, "Fine-tuning-the-transpiled-model"], [9, "Fine-tuning-the-transpiled-model"], [22, "Fine-tuning-the-transpiled-model"], [8, "Fine-tuning-the-transpiled-model"], [10, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[11, "Conclusion"], [9, "Conclusion"], [22, "Conclusion"], [8, "Conclusion"], [10, "Conclusion"]], "Accelerating XGBoost with JAX": [[25, "Accelerating-XGBoost-with-JAX"]], "Tests": [[25, "Tests"]], "Loading the Data": [[25, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[25, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[25, "JAX-backend"]], "Tensorflow backend": [[25, "Tensorflow-backend"]], "PyTorch backend": [[25, "PyTorch-backend"]], "More exhaustive example": [[25, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[25, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[25, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[25, "Comparison-of-Metrics"]], "Write Ivy code": [[33, "Write-Ivy-code"]], "Contents": [[33, "Contents"]], "Installing Ivy": [[33, "Installing-Ivy"]], "Importing Ivy": [[33, "Importing-Ivy"], [0, "Importing-Ivy"]], "Ivy Backend Handler": [[33, "Ivy-Backend-Handler"], [42, "Ivy-Backend-Handler"]], "Data Structures": [[33, "Data-Structures"], [42, "Data-Structures"]], "Ivy Functional API": [[33, "Ivy-Functional-API"], [42, "Ivy-Functional-API"]], "Transpile any library": [[39, "Transpile-any-library"]], "Image Segmentation with Ivy UNet": [[12, "Image-Segmentation-with-Ivy-UNet"], [13, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[12, "Custom-Preprocessing"], [13, "Custom-Preprocessing"]], "Model Inference": [[12, "Model-Inference"], [13, "Model-Inference"]], "Initializing Native Torch UNet": [[12, "Initializing-Native-Torch-UNet"], [13, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"], [13, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[12, "Custom-masking-function"], [13, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[12, "Use-the-model-to-segment-your-images-\ud83d\ude80"], [13, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[12, "TensorFlow-backend"], [13, "TensorFlow-backend"]], "JAX": [[12, "JAX"], [13, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[12, "Appendix:-the-Ivy-native-implementation-of-UNet"], [13, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "# Ivy Bert Demo": [[6, "#-Ivy-Bert-Demo"], [7, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[6, "Install-the-dependecies"], [7, "Install-the-dependecies"]], "Import the modules": [[6, "Import-the-modules"], [7, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[6, "Ivy-inference-with-Sequence-Classification"], [7, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[6, "Ivy-model-inference-with-tensorflow"], [7, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[6, "Ivy-model-inference-with-Jax"], [7, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[6, "Ivy-model-inference-with-torch"], [7, "Ivy-model-inference-with-torch"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Transpile code": [[36, "Transpile-code"]], "0.1: Compile": [[45, "0.1:-Compile"]], "Write a model using Ivy": [[41, "Write-a-model-using-Ivy"]], "Guides": [[26, "guides"], [31, "guides"]], "Transpiling a haiku model to build on top": [[28, "Transpiling-a-haiku-model-to-build-on-top"]], "Accelerating MMPreTrain models with JAX": [[18, "Accelerating-MMPreTrain-models-with-JAX"], [19, "Accelerating-MMPreTrain-models-with-JAX"]], "Using TensorFlow Models in your PyTorch Projects": [[9, "Using-TensorFlow-Models-in-your-PyTorch-Projects"], [8, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[9, "Framework-Incompatibility"], [22, "Framework-Incompatibility"], [8, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[9, "Transpiling-a-TensorFlow-model-to-PyTorch"], [8, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[9, "About-the-transpiled-model"], [22, "About-the-transpiled-model"], [8, "About-the-transpiled-model"]], "Setting-up the source model": [[9, "Setting-up-the-source-model"], [22, "Setting-up-the-source-model"], [8, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[9, "Converting-the-model-from-TensorFlow-to-PyTorch"], [22, "Converting-the-model-from-TensorFlow-to-PyTorch"], [8, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Transpile any model": [[40, "Transpile-any-model"]], "Round up": [[40, "Round-up"]], "How to use decorators": [[38, "How-to-use-decorators"]], "Unify": [[38, "Unify"], [37, "Unify"], [49, "Unify"], [48, "Unify"], [47, "Unify"]], "Trace": [[38, "Trace"], [37, "Trace"]], "Transpile": [[38, "Transpile"], [37, "Transpile"], [49, "Transpile"], [48, "Transpile"], [47, "Transpile"]], "Tutorials And Examples": [[31, "tutorials-and-examples"]], "Learn the basics": [[31, "learn-the-basics"], [32, "learn-the-basics"]], "Transpiling a Tensorflow model to build on top": [[29, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Training PyTorch ResNet in your TensorFlow Projects": [[22, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Transpiling a PyTorch model to TensorFlow": [[22, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "Load the Data": [[22, "Load-the-Data"]], "Visualize a few images": [[22, "Visualize-a-few-images"]], "Load the pre-trained model": [[22, "Load-the-pre-trained-model"]], "Unify code": [[34, "Unify-code"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "Ivy AlexNet demo": [[5, "Ivy-AlexNet-demo"], [4, "Ivy-AlexNet-demo"]], "Ivy AlexNet inference in Torch": [[5, "Ivy-AlexNet-inference-in-Torch"], [4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[5, "TensorFlow-inference"], [4, "TensorFlow-inference"]], "JAX inference": [[5, "JAX-inference"], [4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[5, "Appendix-(Ivy-code-for-AlexNet-implementation)"], [4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "0.0: Unify": [[44, "0.0:-Unify"]], "Developing a convolutional network using Ivy": [[30, "Developing-a-convolutional-network-using-Ivy"]], "Trace code": [[35, "Trace-code"]], "Quickstart": [[43, "Quickstart"]], "Get familiar with Ivy": [[43, "Get-familiar-with-Ivy"]], "Functional API": [[43, "Functional-API"]], "Stateful API": [[43, "Stateful-API"]], "Tracing code": [[43, "Tracing-code"]], "Any function": [[43, "Any-function"], [42, "Any-function"]], "Any library": [[43, "Any-library"], [42, "Any-library"]], "Any model": [[43, "Any-model"], [42, "Any-model"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "ODSC Ivy Demo": [[42, "ODSC-Ivy-Demo"]], "Graph Tracer": [[42, "Graph-Tracer"]], "Lazy vs Eager": [[37, "Lazy-vs-Eager"]], "0.2: Transpile": [[46, "0.2:-Transpile"]], "1.3: Dynamic vs Static": [[50, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[50, "Dynamic"]], "Static": [[50, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[50, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Deepmind PerceiverIO on GPU": [[57, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[57, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[57, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[57, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[57, "Run-the-demo..."]], "\u2026with torch backend": [[57, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[57, "....with-tensorflow-backend"]], "\u2026with jax backend": [[57, "...with-jax-backend"]], "\u2026with numpy backend": [[57, "...with-numpy-backend"]], "1.2: As a Decorator": [[49, "1.2:-As-a-Decorator"]], "Compile": [[49, "Compile"], [48, "Compile"], [47, "Compile"]], "Conversions": [[63, "module-ivy.data_classes.array.conversions"], [86, "module-ivy.data_classes.container.conversions"]], "HuggingFace Tensorflow DeiT": [[59, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[59, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Basic Operations with Ivy": [[54, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[54, "Installs-\ud83d\udcbe"], [55, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[54, "Imports-\ud83d\udec3"], [55, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[54, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[54, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[54, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[54, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[54, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[54, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[54, "Set-Backend-Framework"]], "Define Model": [[54, "Define-Model"], [55, "Define-Model"]], "Create Model": [[54, "Create-Model"]], "Create Optimizer": [[54, "Create-Optimizer"]], "Input and Target": [[54, "Input-and-Target"]], "Loss Function": [[54, "Loss-Function"]], "Training Loop": [[54, "Training-Loop"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[56, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[56, "Table-of-Contents"]], "Defining the model": [[56, "Defining-the-model"]], "Model construction": [[56, "Model-construction"]], "Some helper functions": [[56, "Some-helper-functions"]], "Transpiling the model": [[56, "Transpiling-the-model"]], "PyTorch pipeline": [[56, "PyTorch-pipeline"]], "Dataset download": [[56, "Dataset-download"]], "DataLoader": [[56, "DataLoader"]], "Training": [[56, "Training"]], "1.1: Framework Selection": [[48, "1.1:-Framework-Selection"]], "End-to-End Training Pipeline in Ivy": [[58, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[58, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[58, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[58, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[58, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[58, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[58, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[58, "Plotting-the-training-metrics"]], "Save the trained Model": [[58, "Save-the-trained-Model"]], "3.1: Stable Diffusion": [[53, "3.1:-Stable-Diffusion"]], "1.0: Lazy vs Eager": [[47, "1.0:-Lazy-vs-Eager"]], "Compilation of a Basic Function": [[55, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[55, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[55, "Function-compilation-\ud83d\udee0"]], "Set backend": [[55, "Set-backend"]], "Sample input": [[55, "Sample-input"]], "Define function to compile": [[55, "Define-function-to-compile"]], "Compile the function": [[55, "Compile-the-function"]], "Check results": [[55, "Check-results"], [55, "id1"]], "Compiling simple neural network \ud83e\udde0": [[55, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[55, "Create-model"]], "Define input": [[55, "Define-input"]], "Compile network": [[55, "Compile-network"]], "2.0: Kornia": [[51, "2.0:-Kornia"]], "Ivy as a Transpiler Introduction": [[60, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[60, "To-use-the-transpiler:"]], "Transpiler Interface": [[60, "Transpiler-Interface"]], "Telemetry": [[60, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[60, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[60, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[60, "3.-Transpile-Models-\ud83c\udf10"]], "3.0: Perceiver": [[52, "3.0:-Perceiver"]], "Resnet 18": [[61, "Resnet-18"]]}, "indexentries": {"_arraywithactivations (class in ivy.data_classes.array.activations)": [[62, "ivy.data_classes.array.activations._ArrayWithActivations"]], "_abc_impl (ivy.data_classes.array.activations._arraywithactivations attribute)": [[62, "ivy.data_classes.array.activations._ArrayWithActivations._abc_impl"]], "gelu() (ivy.data_classes.array.activations._arraywithactivations method)": [[62, "ivy.data_classes.array.activations._ArrayWithActivations.gelu"]], "hardswish() (ivy.data_classes.array.activations._arraywithactivations method)": [[62, "ivy.data_classes.array.activations._ArrayWithActivations.hardswish"]], "ivy.data_classes.array.activations": [[62, "module-ivy.data_classes.array.activations"]], "leaky_relu() (ivy.data_classes.array.activations._arraywithactivations method)": [[62, 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716, 830, 853, 861, 878, 879, 882], "magic": [0, 840], "durat": 0, "70": [0, 25, 54, 56, 68, 91, 92, 386, 408, 418, 564, 588, 648, 658, 693, 770, 872], "m": [0, 5, 18, 19, 20, 21, 22, 23, 24, 25, 42, 55, 57, 59, 61, 64, 68, 73, 77, 90, 91, 96, 100, 113, 150, 156, 157, 158, 278, 339, 340, 380, 386, 387, 388, 389, 393, 409, 440, 445, 446, 448, 449, 464, 475, 486, 487, 501, 519, 520, 521, 522, 523, 640, 648, 652, 654, 678, 680, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 702, 737, 750, 751, 752, 824, 831, 832, 834, 840, 861], "per": [0, 18, 19, 23, 24, 25, 35, 56, 58, 68, 72, 91, 95, 330, 380, 386, 387, 389, 405, 406, 407, 423, 424, 425, 426, 455, 502, 647, 661, 663, 664, 665, 666, 669, 674, 803, 832, 840, 850, 853, 864], "loop": [0, 8, 9, 10, 11, 18, 19, 22, 23, 24, 25, 35, 50, 83, 91, 106, 133, 136, 386, 432, 639, 651, 726, 727, 728, 837, 867, 875], "dev": [0, 4, 5, 18, 19, 20, 21, 23, 24, 25, 35, 56, 58, 61, 66, 85, 89, 212, 219, 642, 824, 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648, 686, 693, 817, 818, 830, 831, 832, 837, 839, 841, 842, 848, 852, 853, 859, 867, 868, 882, 884, 889], "instal": [2, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 23, 24, 25, 27, 29, 34, 35, 36, 37, 38, 39, 40, 42, 43, 56, 58, 59, 60, 61, 826, 831, 832, 837, 838, 846, 847], "skip": [2, 6, 7, 22, 58, 68, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 121, 122, 123, 124, 125, 126, 127, 128, 129, 145, 147, 152, 154, 160, 164, 166, 191, 225, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 256, 257, 258, 262, 263, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 278, 279, 280, 281, 282, 283, 284, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 304, 305, 306, 307, 308, 309, 310, 314, 315, 316, 317, 318, 320, 321, 322, 324, 345, 346, 347, 348, 349, 351, 353, 361, 362, 368, 370, 372, 373, 374, 387, 389, 410, 411, 412, 430, 446, 448, 455, 463, 464, 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12, 13, 18, 19, 20, 21, 22, 23, 24, 35, 42, 45, 56, 57, 834, 849, 856, 859, 882], "restart": [2, 4, 5, 6, 7, 12, 13, 20, 21, 22, 56, 57, 831, 846], "git": [2, 4, 5, 6, 7, 12, 13, 20, 21, 42, 56, 57, 58, 59, 824, 826, 829, 831, 832, 835, 838, 840, 846, 847, 856, 868], "clone": [2, 4, 5, 12, 13, 20, 21, 42, 56, 58, 59, 824, 826, 832, 846, 868], "http": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23, 24, 29, 37, 38, 39, 40, 42, 43, 56, 57, 58, 59, 60, 61, 67, 68, 90, 91, 93, 158, 166, 254, 264, 265, 280, 339, 346, 347, 380, 383, 386, 389, 398, 430, 503, 533, 626, 627, 640, 641, 643, 646, 648, 650, 658, 696, 697, 725, 775, 824, 826, 831, 832, 835, 838, 840, 841, 844, 846, 868, 876], "github": [2, 4, 5, 6, 7, 12, 13, 18, 19, 20, 21, 23, 24, 42, 56, 57, 58, 59, 60, 824, 826, 827, 829, 832, 833, 835, 838, 840, 841, 843, 844, 846, 847, 855, 856, 868, 871, 890], "com": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 23, 24, 29, 42, 56, 57, 58, 59, 60, 824, 826, 831, 832, 835, 838, 840, 841, 846, 868], "unifyai": [2, 4, 5, 12, 13, 20, 21, 42, 56, 57, 58, 59, 60, 824, 826, 831, 832, 838, 846, 868], "model": [2, 3, 4, 5, 14, 15, 25, 26, 31, 32, 33, 59, 61, 251, 284, 388, 464, 643, 800, 804, 805, 822, 864, 865, 869, 875, 876, 880, 881, 882, 883, 884, 885, 886, 888, 889], "depth": [2, 4, 5, 8, 9, 12, 13, 20, 21, 57, 64, 68, 72, 87, 91, 95, 152, 386, 389, 422, 482, 556, 568, 640, 645, 647, 665, 666, 832, 840, 864, 865, 866, 868], "repositori": [2, 4, 5, 12, 13, 20, 21, 826, 830, 831, 832, 834, 835, 838, 846, 855, 873], "cd": [2, 4, 5, 12, 13, 20, 21, 42, 59, 824, 826, 831, 832, 846, 868], "resnet": [3, 8, 9, 23, 24, 31, 42, 875, 876], "imag": [3, 4, 5, 8, 9, 10, 11, 18, 19, 23, 24, 27, 31, 39, 42, 43, 56, 57, 58, 59, 60, 61, 68, 72, 90, 91, 95, 113, 231, 232, 233, 234, 237, 240, 249, 252, 254, 256, 265, 266, 267, 272, 274, 287, 294, 295, 297, 298, 302, 386, 405, 406, 422, 423, 424, 426, 556, 643, 645, 647, 660, 661, 662, 663, 664, 667, 668, 669, 803, 824, 831, 846, 859, 861, 862, 864, 866, 868, 875, 876, 882], "classif": [3, 4, 5, 20, 21, 25, 31, 56, 882], "acceler": [3, 31, 841, 853, 880, 884, 885, 886, 887], "convert": [3, 12, 13, 14, 15, 18, 19, 23, 24, 25, 27, 29, 31, 32, 34, 36, 39, 40, 42, 43, 44, 46, 48, 56, 59, 61, 63, 64, 67, 85, 86, 87, 90, 108, 138, 139, 151, 161, 162, 204, 205, 206, 207, 218, 226, 230, 250, 290, 389, 394, 473, 474, 475, 524, 589, 607, 609, 610, 611, 613, 640, 641, 642, 643, 645, 648, 652, 706, 730, 741, 742, 784, 812, 817, 830, 836, 837, 850, 851, 853, 856, 858, 861, 867, 869, 873, 876, 880, 881, 888], "faster": [3, 4, 5, 14, 15, 18, 19, 23, 24, 25, 31, 42, 43, 59, 61, 68, 73, 91, 96, 387, 460, 648, 698, 826, 829, 838, 869, 884, 887], "infer": [3, 8, 9, 10, 11, 14, 15, 18, 19, 22, 23, 24, 25, 31, 35, 45, 47, 48, 57, 59, 61, 64, 68, 69, 72, 75, 87, 91, 92, 95, 98, 137, 139, 142, 146, 147, 151, 154, 160, 169, 170, 171, 172, 173, 323, 324, 386, 389, 393, 422, 507, 521, 567, 601, 602, 640, 641, 645, 647, 650, 670, 717, 812, 813, 834, 837, 841, 842, 856, 861, 866, 876, 880, 881, 884, 886], "mmpretrain": [3, 31], "segment": [3, 31, 68, 91, 341, 342, 343, 380, 838, 843], "unet": [3, 31], "alexnet": [3, 31], "written": [3, 4, 5, 6, 7, 8, 9, 22, 31, 33, 42, 43, 56, 69, 389, 484, 831, 835, 836, 844, 847, 848, 852, 853, 857, 861, 863, 866, 867, 871, 876, 880, 882, 886, 888, 889], "xgboost": [3, 31], "paddlepaddl": [3, 31, 346, 347, 383, 831], "dinov2": [3, 10, 11, 31], "project": [3, 20, 21, 23, 24, 31, 36, 37, 38, 39, 40, 42, 43, 46, 109, 647, 674, 803, 824, 826, 827, 830, 831, 832, 833, 836, 837, 838, 856, 865, 867, 871, 872, 873, 876, 878, 880, 882, 885, 889, 890], "convnext": [3, 8, 9, 18, 19, 22, 31], "finetun": [3, 31, 56], "video": [4, 12, 18, 20, 23, 27, 29, 33, 34, 35, 36, 37, 38, 39, 40, 43, 824, 825, 830, 831, 832, 835, 836, 837, 839, 840, 841, 842, 843, 844, 845, 847, 848, 849, 850, 851, 852, 853, 854, 856, 857, 859, 868, 880], "tutori": [4, 8, 9, 10, 11, 12, 18, 20, 22, 23, 27, 29, 33, 34, 35, 36, 37, 38, 39, 40, 43, 824, 832, 853, 868], "three": [4, 5, 6, 7, 31, 37, 47, 48, 58, 68, 150, 323, 380, 389, 475, 640, 831, 832, 839, 840, 841, 843, 853, 856, 859, 860, 861, 883, 888], "major": [4, 5, 6, 7, 655, 758, 841, 842, 854, 856, 867, 872, 879, 882], "ml": [4, 5, 6, 7, 8, 9, 22, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 56, 58, 61, 825, 829, 853, 860, 861, 862, 864, 865, 866, 870, 872, 873, 876, 878, 879, 880, 881, 882, 885, 887, 889], "framework": [4, 5, 6, 7, 10, 11, 14, 15, 27, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47, 49, 56, 58, 60, 63, 69, 181, 203, 213, 216, 227, 554, 570, 574, 606, 609, 641, 642, 645, 652, 731, 782, 784, 788, 795, 800, 807, 812, 813, 827, 828, 830, 831, 834, 835, 836, 837, 838, 840, 841, 842, 843, 845, 846, 848, 849, 850, 852, 853, 856, 857, 859, 860, 861, 863, 866, 867, 868, 869, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 883, 886], "sinc": [4, 5, 12, 13, 20, 21, 22, 39, 40, 42, 43, 56, 58, 68, 91, 109, 383, 826, 831, 832, 835, 836, 837, 838, 839, 840, 841, 842, 845, 852, 853, 867, 872, 882, 888], "automat": [4, 5, 12, 13, 14, 15, 20, 21, 22, 40, 42, 43, 48, 830, 831, 832, 834, 837, 838, 840, 841, 847, 849, 852, 856, 859, 860, 862, 865, 866, 868, 869, 873, 882, 885, 889], "sure": [4, 5, 12, 13, 18, 19, 20, 21, 22, 23, 24, 25, 42, 56, 827, 830, 831, 832, 835, 840, 845, 846, 853, 854, 856, 859, 868], "enabl": [4, 5, 6, 7, 8, 9, 12, 13, 18, 19, 20, 21, 22, 23, 24, 25, 37, 38, 40, 57, 68, 73, 85, 96, 114, 386, 388, 409, 467, 591, 645, 648, 691, 805, 822, 824, 831, 832, 833, 836, 839, 841, 849, 850, 851, 852, 853, 856, 857, 860, 862, 864, 866, 867, 869, 872, 875, 880, 881, 882, 883, 884, 885, 888, 889], "dm": [4, 5, 6, 7, 12, 13, 18, 19, 23, 24, 42, 43, 54, 56], "haiku": [4, 5, 6, 7, 12, 13, 18, 19, 23, 24, 40, 42, 43, 54, 56, 60, 800, 824, 866, 873, 876, 882], "exit": [4, 12, 20, 22, 42, 43, 842], "download": [4, 5, 8, 9, 10, 11, 20, 21, 22, 27, 29, 42, 43, 57, 58, 61, 826, 831, 838, 856, 875, 876], "imagenet": [4, 5, 8, 9, 22, 29, 57, 59, 824], "class": [4, 5, 8, 9, 10, 11, 12, 13, 20, 21, 22, 25, 27, 29, 33, 42, 43, 54, 55, 56, 57, 58, 59, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 145, 154, 160, 176, 179, 192, 194, 195, 254, 291, 349, 371, 383, 397, 398, 406, 407, 440, 539, 540, 547, 556, 560, 573, 583, 606, 640, 641, 642, 643, 645, 647, 648, 649, 652, 653, 668, 673, 677, 683, 693, 697, 698, 700, 707, 723, 730, 741, 748, 763, 770, 774, 775, 784, 785, 792, 793, 794, 795, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 811, 812, 815, 817, 822, 824, 830, 837, 838, 839, 841, 842, 843, 844, 848, 850, 851, 854, 855, 856, 859, 861, 862, 864, 865, 866, 869, 875, 876, 880, 882, 883, 889], "wget": [4, 5, 8, 9, 12, 13, 20, 21, 56, 57, 60, 831], "raw": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 23, 24, 39, 42, 43, 56, 59, 60, 85, 824, 844, 876, 883], "githubusercont": [4, 5, 8, 9, 12, 13, 20, 21, 56, 60], "hub": [4, 5, 8, 9, 12, 13, 20, 21, 56, 59, 61], "master": [4, 5, 12, 13, 20, 21, 34, 35, 36, 44, 45, 46, 47, 48, 49, 56, 58, 59, 60, 827, 840, 882, 890], "imagenet_class": [4, 5, 20, 21], "categori": [4, 5, 8, 9, 20, 21, 830, 835, 836, 839, 841, 845, 853, 857, 860], "strip": [4, 5, 20, 21, 35, 45, 872], "readlin": [4, 5, 20, 21, 57], "cat": [4, 5, 10, 11, 20, 21, 57, 854, 859, 861, 866, 875, 876], "jpg": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 23, 24, 39, 42, 43, 58, 59, 824, 876], "filenam": [4, 5, 12, 13, 20, 21, 22, 42, 43, 56, 58, 61, 69, 805, 811, 864], "import": [4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 22, 23, 24, 27, 29, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 56, 57, 59, 60, 61, 68, 79, 83, 87, 91, 106, 205, 206, 210, 222, 318, 398, 533, 568, 584, 642, 645, 651, 656, 727, 728, 763, 795, 812, 813, 824, 829, 830, 831, 832, 833, 835, 836, 837, 838, 839, 841, 842, 843, 844, 847, 850, 851, 852, 853, 854, 855, 856, 857, 861, 863, 864, 866, 867, 868, 872, 875, 876, 877, 878, 880, 882, 885, 886, 888], "devic": [4, 5, 8, 9, 10, 11, 12, 14, 15, 18, 19, 20, 21, 22, 23, 24, 57, 58, 61, 64, 68, 77, 85, 87, 91, 100, 113, 116, 117, 118, 137, 138, 139, 141, 142, 143, 146, 147, 148, 149, 151, 152, 153, 154, 156, 157, 158, 159, 160, 204, 205, 206, 207, 208, 209, 210, 211, 212, 217, 218, 219, 220, 222, 223, 224, 225, 226, 228, 230, 323, 324, 339, 340, 380, 393, 483, 519, 520, 522, 523, 547, 561, 562, 640, 645, 654, 749, 750, 751, 752, 782, 784, 785, 800, 802, 803, 804, 805, 806, 807, 808, 809, 822, 832, 834, 837, 841, 845, 849, 850, 854, 856, 857, 859, 861, 866, 867, 868, 869, 872, 881, 882, 884, 885, 886, 887], "torchvis": [4, 5, 8, 9, 18, 19, 20, 21, 22, 56, 873], "transform": [4, 5, 6, 7, 8, 9, 10, 11, 18, 19, 20, 21, 22, 23, 24, 39, 42, 43, 56, 57, 59, 68, 72, 91, 95, 386, 387, 408, 409, 414, 415, 418, 419, 420, 430, 431, 434, 451, 647, 671, 787, 790, 803, 824, 850, 856, 866, 869, 875, 876, 880, 882, 883, 884], "pil": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 23, 24, 39, 42, 43, 57, 58, 59, 824, 876], "time": [4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 22, 23, 24, 40, 42, 43, 48, 56, 58, 59, 60, 68, 70, 73, 79, 91, 93, 102, 108, 109, 145, 352, 383, 386, 387, 389, 398, 415, 420, 432, 434, 455, 462, 495, 501, 533, 627, 632, 640, 646, 647, 648, 650, 651, 655, 656, 670, 673, 688, 723, 726, 727, 728, 755, 756, 760, 761, 803, 804, 805, 822, 830, 831, 832, 835, 837, 839, 840, 841, 843, 846, 848, 849, 850, 852, 853, 856, 857, 861, 864, 866, 867, 868, 871, 872, 873, 875, 876, 880, 882, 883, 886, 887, 888], "filterwarn": [4, 5, 6, 7, 22], "ignor": [4, 5, 6, 7, 22, 55, 63, 64, 68, 85, 91, 150, 386, 387, 389, 398, 410, 411, 412, 441, 449, 457, 497, 498, 502, 541, 640, 647, 652, 674, 740, 741, 807, 831, 838, 840, 843, 856, 867, 888], "compos": [4, 5, 8, 9, 10, 11, 18, 19, 20, 21, 22, 42, 43, 56, 68, 91, 386, 400, 401, 402, 403, 831, 839, 853, 856, 875, 877, 882, 889], "resiz": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 56, 57, 68, 91, 386, 422, 859], "centercrop": [4, 5, 20, 21, 22], "224": [4, 5, 8, 9, 10, 11, 20, 21, 22, 27, 29, 42, 43, 56, 57, 59, 824, 876], "totensor": [4, 5, 8, 9, 10, 11, 18, 19, 20, 21, 22, 56], "485": [4, 5, 20, 21, 22, 56], "456": [4, 5, 20, 21, 22, 56, 856], "406": [4, 5, 20, 21, 22, 56, 68, 91, 408, 551, 645], "229": [4, 5, 20, 21, 22, 56, 290, 643], "225": [4, 5, 20, 21, 22, 56, 58, 245, 643], "torch_img": [4, 5, 12, 13, 20, 21], "unsqueez": [4, 5, 12, 13, 18, 19, 20, 21], "img": [4, 5, 12, 13, 20, 21, 39, 42, 43, 56, 57, 58, 60, 824, 864, 876], "ipython": [4, 5, 12, 13, 20, 21, 37, 38, 39, 40, 42, 43, 61], "displai": [4, 5, 12, 13, 20, 21, 22, 39, 42, 43, 56, 57, 58, 60, 61, 831, 838, 840, 845, 856, 864], "end": [4, 5, 12, 13, 22, 56, 57, 68, 91, 137, 239, 295, 364, 383, 386, 388, 389, 434, 463, 485, 495, 497, 498, 640, 643, 818, 831, 832, 837, 840, 846, 852, 857, 859, 860, 867, 880, 885], "set_default_devic": [4, 6, 8, 9, 12, 13, 18, 20, 22, 23, 228, 642, 842], "ivy_model": [4, 5, 6, 7, 12, 13, 20, 21, 59], "ivy_alexnet": [4, 5], "quick": [4, 5, 31, 43, 832, 834, 854, 865], "trace_graph": [4, 5, 6, 7, 12, 13, 20, 21, 35, 36, 37, 38, 42, 43, 45, 46, 47, 48, 49, 50, 59, 805, 824, 861, 866, 874], "moment": [4, 5, 68, 70, 91, 93, 387, 444, 626, 627, 632, 646, 807, 822, 830, 837, 867, 875, 876], "cost": [4, 5, 70, 93, 626, 627, 630, 632, 633, 634, 646, 651, 726, 727, 728, 818, 841, 859, 880], "arg": [4, 5, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 27, 29, 37, 38, 40, 42, 43, 47, 48, 49, 60, 63, 85, 107, 117, 133, 214, 224, 612, 639, 640, 642, 645, 782, 784, 799, 800, 803, 804, 805, 809, 812, 817, 822, 824, 836, 841, 842, 845, 851, 852, 853, 859, 861, 865, 875, 876, 877], "asarrai": [4, 5, 6, 7, 12, 13, 18, 19, 20, 21, 57, 64, 68, 69, 80, 87, 91, 92, 103, 138, 396, 525, 526, 556, 567, 571, 572, 602, 603, 604, 640, 645, 647, 656, 657, 661, 761, 765, 845, 850, 853, 854], "cuda": [4, 5, 6, 8, 9, 10, 11, 12, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 33, 42, 57, 58, 61, 64, 68, 77, 87, 91, 100, 148, 149, 152, 204, 205, 206, 222, 393, 519, 520, 522, 523, 640, 642, 648, 654, 699, 749, 750, 751, 752, 802, 803, 804, 805, 806, 807, 808, 822, 861, 867, 869, 887], "output": [4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 20, 21, 22, 33, 39, 40, 42, 43, 55, 56, 57, 59, 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 113, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 152, 153, 154, 155, 156, 157, 159, 160, 163, 165, 190, 224, 225, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 328, 329, 333, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 375, 376, 377, 378, 380, 383, 385, 386, 387, 388, 389, 392, 393, 394, 396, 398, 399, 400, 401, 402, 403, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 418, 419, 420, 422, 423, 424, 425, 428, 430, 431, 432, 434, 435, 437, 438, 439, 441, 443, 446, 447, 449, 452, 453, 454, 455, 457, 458, 461, 463, 464, 465, 466, 467, 468, 469, 470, 471, 478, 479, 480, 483, 485, 486, 487, 488, 489, 492, 493, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 507, 508, 509, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 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84, 95, 388, 465, 637, 647, 674, 677, 799, 824], "pass": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 33, 40, 42, 43, 49, 55, 56, 58, 60, 61, 67, 68, 83, 85, 90, 91, 106, 114, 133, 134, 136, 168, 190, 205, 224, 239, 285, 386, 388, 389, 392, 393, 398, 432, 465, 485, 512, 514, 519, 539, 540, 573, 639, 641, 642, 643, 645, 651, 726, 727, 782, 784, 788, 795, 800, 804, 805, 807, 808, 812, 817, 822, 824, 828, 830, 832, 835, 836, 837, 839, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 856, 859, 867, 875, 876, 877, 880], "argsort": [4, 5, 20, 21, 80, 103, 657, 766, 853], "descend": [4, 5, 20, 21, 80, 103, 648, 657, 698, 699, 764, 767], "top": [4, 5, 20, 21, 26, 31, 40, 42, 43, 56, 57, 68, 75, 91, 330, 380, 388, 389, 463, 505, 556, 645, 711, 831, 832, 841, 846, 853, 855, 856, 859, 864, 865, 882, 886], "logit": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 20, 21, 22, 56, 57, 58, 59, 68, 74, 91, 97, 378, 393, 519, 522, 649, 707, 709, 799, 875], "gather": [4, 5, 20, 21, 56, 68, 69, 91, 92, 341, 342, 343, 380, 564, 566, 645, 889], "to_list": [4, 5, 20, 21, 69, 92, 645], "arrai": [4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20, 21, 22, 23, 24, 25, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 45, 47, 48, 49, 54, 55, 56, 57, 58, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 108, 109, 111, 114, 117, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 133, 134, 136, 137, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 163, 164, 165, 166, 169, 170, 171, 172, 173, 174, 176, 179, 180, 182, 183, 184, 186, 188, 189, 190, 191, 197, 207, 208, 212, 217, 219, 221, 224, 225, 229, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 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152, 324, 380, 386, 389, 404, 414, 415, 416, 419, 427, 485, 507, 640, 647, 660, 667, 668, 673, 789, 803, 841, 853, 854, 859], "torch_class": [4, 5, 20, 21], "torch_logit": [4, 5, 20, 21], "tensor": [4, 5, 6, 7, 8, 9, 14, 15, 18, 19, 20, 21, 22, 23, 24, 27, 29, 33, 34, 37, 38, 40, 42, 43, 44, 48, 54, 56, 64, 67, 68, 69, 72, 73, 74, 75, 77, 81, 85, 87, 90, 91, 92, 95, 96, 97, 98, 100, 104, 107, 140, 148, 149, 152, 158, 174, 190, 282, 283, 313, 330, 334, 335, 336, 337, 338, 339, 348, 371, 378, 380, 383, 386, 387, 388, 389, 398, 399, 405, 406, 409, 413, 422, 423, 424, 425, 432, 434, 436, 443, 444, 445, 446, 449, 451, 453, 455, 456, 459, 461, 462, 463, 465, 468, 469, 485, 488, 493, 496, 497, 498, 499, 502, 507, 508, 539, 544, 587, 588, 640, 641, 643, 645, 647, 648, 649, 650, 654, 658, 670, 673, 674, 689, 700, 707, 717, 719, 749, 772, 803, 812, 818, 822, 824, 836, 837, 841, 842, 846, 848, 849, 852, 853, 854, 856, 857, 859, 861, 863, 864, 866, 867, 869, 871, 875, 876, 877, 879, 880, 883, 885, 886, 889], "6477": [4, 5], "2950": [4, 5], "0453": [4, 5], "grad_fn": [4, 5, 20, 21, 40, 54, 629, 636, 646, 864], "takebackward0": [4, 5, 20, 21], "great": [4, 5, 10, 11, 12, 13, 832, 856, 861, 863, 872, 873, 888], "simpl": [4, 5, 10, 11, 27, 31, 32, 34, 37, 39, 40, 41, 42, 43, 44, 45, 47, 48, 54, 56, 58, 61, 68, 91, 398, 533, 789, 803, 818, 824, 830, 831, 832, 836, 838, 839, 841, 842, 843, 844, 849, 852, 853, 856, 857, 859, 863, 865, 866, 867, 869, 871, 875, 876, 881, 882, 883, 884], "let": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 22, 23, 24, 25, 27, 29, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 45, 47, 48, 49, 54, 56, 57, 59, 61, 69, 81, 92, 231, 232, 233, 234, 237, 240, 249, 252, 254, 256, 265, 266, 267, 272, 274, 287, 295, 297, 298, 302, 563, 564, 643, 645, 648, 658, 702, 772, 774, 775, 776, 777, 824, 830, 833, 836, 838, 839, 840, 841, 842, 843, 844, 845, 846, 853, 854, 856, 857, 858, 859, 861, 863, 864, 865, 866, 873, 875, 876, 889], "ll": [4, 5, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 22, 23, 24, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 57, 824, 825, 827, 828, 830, 831, 832, 833, 838, 843, 846, 847, 851, 852, 864, 868, 873, 875, 876], "try": [4, 5, 8, 9, 10, 11, 22, 34, 44, 54, 57, 61, 85, 612, 645, 802, 812, 824, 830, 831, 832, 835, 836, 839, 840, 841, 845, 847, 852, 854, 861, 863, 867, 870, 872, 873, 877], "tf": [4, 5, 8, 9, 12, 13, 14, 15, 16, 17, 22, 23, 24, 27, 29, 34, 37, 38, 40, 42, 43, 44, 45, 47, 49, 54, 59, 60, 800, 824, 836, 841, 842, 848, 852, 853, 856, 857, 859, 861, 866, 867, 869, 875, 876, 877, 882], "onc": [4, 5, 8, 9, 12, 13, 42, 43, 54, 56, 73, 77, 96, 100, 224, 387, 440, 642, 648, 654, 683, 684, 685, 698, 749, 824, 830, 831, 832, 839, 840, 841, 842, 843, 846, 847, 852, 853, 856, 859, 861, 864, 867, 868, 873, 875], "set": [4, 5, 10, 11, 14, 15, 27, 29, 35, 42, 43, 45, 48, 56, 57, 58, 59, 60, 63, 68, 69, 72, 73, 78, 80, 81, 85, 91, 92, 95, 96, 101, 103, 104, 126, 129, 136, 156, 158, 192, 193, 194, 195, 196, 207, 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886, 888, 889], "st": [4, 5, 6, 7, 18, 19, 787, 835, 854, 856], "perf_count": [4, 5, 14, 15, 16, 17, 18, 19], "raw_logit": [4, 5], "latenc": [4, 5, 18, 19], "nn": [4, 5, 8, 9, 10, 11, 12, 13, 16, 17, 29, 40, 42, 43, 56, 60, 150, 640, 824, 849, 854, 859, 866, 876, 883], "direct": [4, 5, 68, 91, 352, 359, 363, 368, 372, 383, 386, 389, 420, 431, 486, 487, 501, 657, 767, 830, 836, 838, 853, 859, 865, 866, 878, 882, 883, 886], "tolist": [4, 5], "652289830999962": [4, 5], "int32": [4, 5, 54, 56, 65, 68, 69, 77, 78, 81, 88, 91, 92, 100, 101, 143, 148, 152, 154, 160, 163, 166, 168, 170, 172, 174, 177, 179, 180, 184, 187, 191, 195, 199, 201, 219, 246, 282, 283, 394, 398, 524, 534, 535, 536, 564, 573, 610, 640, 641, 642, 643, 645, 654, 655, 658, 750, 751, 752, 756, 768, 769, 774, 776, 787, 788, 841, 853, 856, 861], "6477362": [4, 5], "29496726": [4, 5], "04526032": [4, 5], "As": [4, 5, 8, 9, 10, 11, 12, 13, 18, 19, 22, 23, 24, 25, 27, 29, 35, 39, 40, 42, 43, 45, 48, 54, 55, 79, 83, 106, 648, 656, 696, 760, 761, 762, 763, 828, 830, 831, 832, 833, 836, 838, 839, 840, 841, 842, 845, 846, 847, 848, 849, 852, 853, 854, 855, 856, 859, 863, 864, 865, 867, 871, 875, 876, 877, 882, 887], "ident": [4, 5, 8, 9, 14, 15, 25, 40, 57, 59, 73, 85, 143, 212, 566, 592, 640, 642, 645, 648, 652, 686, 690, 742, 803, 824, 839, 849, 850, 853, 854, 857, 859, 863, 864, 867, 869, 871, 873], "had": [4, 5, 839, 840, 852, 857, 861, 882, 883], "postprocess": [4, 5], "routin": [4, 5, 840, 852, 853, 859, 867, 882], "feed": [4, 5, 224, 642, 875, 882, 883], "carefulli": [4, 5, 289, 643, 802, 853, 880, 885], "rewrit": [4, 5], "easili": [4, 5, 39, 42, 43, 54, 831, 836, 840, 846, 853, 856, 859, 864, 865, 866, 867, 872, 882, 888, 889], "quickest": [4, 5], "particular": [4, 5, 42, 43, 279, 643, 788, 831, 832, 835, 837, 840, 841, 843, 850, 852, 853, 856, 857, 878, 882, 888], "again": [4, 5, 12, 13, 36, 37, 45, 46, 47, 48, 648, 696, 832, 836, 837, 838, 839, 843, 845, 847, 852, 853, 856, 857, 859, 864, 866, 867, 872, 873, 887, 888], "speed": [4, 5, 18, 19, 23, 24, 25, 42, 43, 56, 61, 69, 92, 580, 645, 856, 871, 885], "repeat": [4, 5, 6, 7, 36, 46, 68, 69, 75, 91, 92, 98, 386, 389, 398, 415, 420, 484, 533, 558, 645, 650, 651, 723, 727, 728, 817, 832, 836, 837, 843, 844, 852, 856], "previou": [4, 5, 25, 35, 36, 37, 39, 45, 46, 47, 49, 70, 91, 93, 198, 199, 200, 201, 202, 375, 385, 386, 432, 613, 615, 616, 617, 618, 620, 621, 623, 627, 632, 641, 645, 646, 802, 821, 831, 832, 835, 837, 840, 842, 848, 853, 856, 859, 866, 867, 885], "trace": [4, 5, 6, 7, 8, 9, 12, 13, 18, 19, 20, 21, 22, 23, 24, 31, 32, 36, 39, 42, 45, 47, 48, 60, 69, 73, 85, 92, 96, 575, 576, 579, 590, 599, 614, 622, 645, 648, 784, 795, 805, 807, 822, 824, 835, 839, 841, 853, 858, 859, 861, 866, 867, 874, 875, 876, 883, 888], "026875037000081647": [4, 5], "overrid": [4, 5, 12, 13, 48, 57, 64, 68, 87, 91, 152, 398, 533, 640, 836, 838], "prealloc": [4, 5, 12, 13], "temporari": [4, 5, 12, 13, 600, 623, 645, 818, 841, 858], "fix": [4, 5, 12, 13, 58, 68, 91, 108, 109, 383, 386, 387, 432, 462, 647, 674, 824, 828, 831, 832, 835, 841, 847, 856, 857], "until": [4, 5, 12, 13, 818, 832, 852, 861, 867, 872, 875, 889], "o": [4, 5, 12, 13, 22, 55, 56, 57, 58, 60, 583, 645, 647, 674, 824, 831, 834, 840, 861, 868], "environ": [4, 5, 12, 13, 23, 24, 37, 38, 39, 40, 57, 60, 824, 825, 832, 868, 882, 884], "xla_python_client_alloc": [4, 5, 12, 13], "platform": [4, 5, 8, 9, 12, 13, 22, 25, 37, 38, 40, 826, 829, 831, 838, 880, 884, 886], "jit": [4, 5, 18, 19, 23, 24, 42, 45, 861, 867, 875, 882], "img_jax": [4, 5, 12, 13], "device_put": [4, 5, 18, 19], "warm": [4, 5], "_": [4, 5, 14, 15, 16, 17, 18, 19, 23, 24, 25, 42, 55, 56, 67, 68, 85, 90, 91, 93, 109, 166, 254, 256, 264, 265, 280, 346, 347, 383, 386, 389, 398, 430, 459, 462, 503, 533, 556, 626, 627, 641, 643, 645, 646, 648, 650, 652, 658, 696, 697, 699, 725, 736, 775, 832, 840, 841, 844, 852, 856, 864], "0022192720000475674": [4, 5], "64773613": [4, 5], "29496723": [4, 5], "exact": [4, 5, 68, 84, 85, 121, 386, 388, 422, 427, 467, 468, 656, 760, 762, 789, 799, 831, 832, 835, 843, 861], "note": [4, 5, 8, 9, 12, 13, 22, 25, 38, 42, 43, 48, 57, 58, 59, 68, 69, 73, 75, 79, 91, 96, 98, 108, 145, 158, 190, 258, 293, 294, 301, 339, 340, 360, 380, 383, 386, 387, 389, 409, 440, 445, 455, 456, 462, 485, 503, 641, 643, 647, 648, 650, 656, 658, 674, 683, 684, 695, 696, 698, 717, 721, 761, 763, 772, 803, 818, 822, 828, 830, 831, 832, 836, 841, 843, 844, 847, 852, 853, 854, 856, 857, 859], "were": [4, 5, 12, 13, 59, 85, 88, 179, 183, 184, 258, 643, 647, 674, 830, 831, 832, 841, 845, 847, 851, 852, 854, 856, 857, 859, 861, 875, 882, 883, 888], "function": [4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 22, 25, 27, 29, 31, 32, 34, 35, 36, 37, 38, 39, 40, 44, 45, 46, 47, 48, 49, 50, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 113, 114, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 164, 165, 166, 176, 177, 178, 179, 182, 183, 184, 186, 190, 191, 208, 210, 211, 220, 224, 225, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 328, 329, 330, 333, 339, 340, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 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836, 837, 838, 845, 849, 852, 853, 855, 856, 861, 862, 864, 866, 867, 873, 875, 877, 882, 883, 885], "__init__": [4, 5, 12, 13, 27, 29, 42, 43, 54, 55, 56, 58, 85, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 785, 792, 793, 794, 799, 802, 803, 804, 805, 806, 807, 808, 811, 812, 815, 817, 819, 822, 824, 830, 836, 837, 841, 845, 853, 857, 861, 863, 864, 865, 866, 876], "self": [4, 5, 8, 9, 10, 11, 12, 13, 27, 29, 42, 43, 54, 55, 56, 58, 60, 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 113, 114, 117, 121, 122, 123, 124, 125, 126, 127, 128, 129, 139, 140, 142, 144, 145, 147, 148, 149, 150, 151, 152, 154, 156, 157, 158, 160, 163, 164, 165, 166, 174, 176, 179, 182, 183, 184, 186, 188, 191, 208, 225, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 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859, 871, 873], "rush": [31, 873], "jump": [31, 854], "straight": [31, 824, 840, 853, 856, 863], "quickstart": [31, 824], "introduct": [31, 33, 40, 42, 43, 882], "point": [31, 40, 65, 67, 68, 73, 77, 79, 81, 88, 90, 91, 96, 100, 104, 137, 138, 139, 141, 143, 146, 153, 154, 159, 163, 176, 180, 184, 191, 231, 232, 233, 234, 236, 237, 238, 239, 240, 247, 248, 249, 251, 252, 254, 256, 257, 258, 264, 265, 266, 267, 272, 273, 274, 275, 276, 284, 286, 287, 289, 291, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 323, 324, 326, 346, 347, 364, 365, 368, 370, 380, 383, 386, 387, 388, 393, 398, 401, 410, 411, 412, 430, 440, 460, 464, 519, 520, 521, 522, 523, 533, 534, 535, 543, 638, 640, 641, 643, 648, 654, 655, 656, 657, 658, 678, 680, 683, 684, 685, 687, 689, 690, 691, 694, 695, 696, 697, 698, 699, 700, 702, 705, 751, 752, 758, 760, 761, 762, 763, 766, 768, 769, 771, 772, 773, 774, 775, 776, 777, 812, 813, 822, 828, 830, 831, 832, 835, 836, 838, 840, 841, 843, 844, 846, 848, 852, 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178, 210, 211, 387, 459, 561, 562, 568, 641, 642, 645, 652, 729, 730, 733, 739, 740, 741, 782, 831, 835, 838, 839, 846, 849, 852, 865, 867], "fashion": [33, 789, 856, 876], "native_arrai": [33, 42, 43, 64, 65, 67, 87, 89, 90, 91, 92, 96, 103, 121, 124, 147, 150, 152, 154, 160, 163, 164, 165, 166, 174, 179, 186, 208, 217, 225, 241, 245, 250, 251, 252, 254, 258, 262, 270, 271, 279, 284, 287, 290, 293, 298, 346, 347, 374, 383, 388, 389, 469, 495, 501, 505, 545, 548, 575, 576, 579, 610, 637, 640, 641, 642, 643, 645, 647, 648, 649, 650, 654, 655, 658, 659, 661, 662, 669, 677, 680, 684, 685, 690, 691, 695, 699, 700, 702, 705, 707, 709, 710, 717, 749, 758, 767, 773, 776, 778, 784, 794, 812, 828, 846, 854, 856], "data_class": [33, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 118, 406, 407, 556, 560, 698, 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756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 829, 830, 834, 838, 847, 848, 849, 850, 853, 855, 857], "liter": [62, 67, 68, 73, 84, 90, 91, 96, 121, 122, 123, 124, 125, 126, 127, 128, 129, 302, 306, 311, 312, 314, 378, 386, 387, 389, 392, 408, 418, 422, 430, 445, 451, 456, 459, 462, 495, 517, 637, 643, 648, 657, 689, 705, 766, 799, 859], "magnitud": [62, 67, 68, 84, 90, 91, 121, 122, 123, 124, 125, 126, 127, 128, 129, 231, 234, 251, 258, 284, 302, 306, 311, 312, 314, 378, 637, 643, 648, 698, 699, 799, 841], "handle_complex_input": [62, 67, 68, 84, 90, 91, 121, 122, 123, 124, 125, 126, 127, 128, 129, 302, 306, 311, 312, 314, 378, 637, 643, 799, 850], "element": [62, 64, 67, 68, 69, 72, 73, 75, 77, 78, 79, 81, 84, 85, 87, 88, 90, 91, 92, 95, 96, 98, 100, 101, 102, 104, 109, 113, 114, 117, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 137, 140, 146, 147, 156, 157, 158, 174, 176, 179, 231, 232, 233, 234, 235, 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329, 378, 380, 387, 388, 392, 393, 441, 469, 517, 521, 522, 637, 653, 748, 799, 824, 859], "threshold": [62, 67, 68, 84, 90, 91, 129, 282, 283, 322, 348, 378, 383, 388, 389, 464, 469, 502, 637, 643, 799, 859], "union": [62, 63, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 133, 134, 136, 137, 138, 139, 140, 141, 142, 143, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 217, 218, 219, 220, 222, 223, 224, 225, 226, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 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752, 800, 803, 839, 852, 853, 869], "args_to_n": [63, 852], "cont_inplac": 63, "decid": [63, 85, 652, 740, 741, 830, 831, 841, 859], "args_to_new_backend": 63, "shallow": [63, 652, 736, 737, 741, 746, 747], "nativevari": 63, "mutabl": [63, 652, 730, 736, 737, 741, 746, 747, 837], "to_ivi": [63, 86, 652, 742, 852], "leaf": [63, 85, 92, 104, 114, 559, 652, 739, 740, 742, 769, 839, 849, 864], "travers": [63, 86, 652, 733, 741, 839, 841, 845, 861], "lowest": [63, 68, 77, 86, 91, 100, 398, 536, 652, 654, 741, 750, 818, 849, 867, 869, 879, 883, 887], "search": [63, 68, 86, 91, 755, 756, 795, 829, 831, 839, 843, 846, 856, 857, 871], "to_new_backend": 63, "_arraywithcr": [64, 113], "boolean": [64, 65, 67, 68, 69, 75, 78, 81, 85, 87, 88, 90, 91, 92, 98, 101, 104, 113, 114, 134, 136, 138, 139, 140, 146, 163, 179, 181, 183, 184, 187, 203, 213, 221, 227, 241, 242, 243, 244, 245, 246, 278, 279, 280, 281, 346, 347, 362, 383, 387, 389, 445, 456, 462, 473, 474, 475, 481, 483, 485, 486, 487, 490, 494, 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640, 643], "uniniti": [64, 141, 142, 640, 847], "from_dlpack": [64, 87, 640], "full_lik": [64, 87, 640, 857], "fill_valu": [64, 68, 78, 87, 91, 101, 146, 147, 263, 271, 389, 393, 503, 523, 640, 643, 655, 758, 841, 854, 857], "scalar": [64, 67, 68, 69, 73, 84, 87, 90, 91, 92, 96, 108, 123, 147, 152, 234, 255, 300, 306, 349, 350, 352, 357, 360, 362, 364, 369, 383, 386, 387, 388, 389, 434, 441, 463, 473, 474, 475, 484, 489, 611, 624, 640, 643, 645, 648, 705, 841, 851, 853, 867, 882], "fill": [64, 67, 68, 77, 78, 85, 87, 90, 91, 100, 101, 141, 146, 147, 149, 152, 153, 154, 159, 160, 285, 324, 380, 387, 389, 393, 445, 451, 456, 462, 484, 503, 504, 520, 522, 523, 640, 643, 654, 655, 750, 758, 802, 830, 854], "000123": [64, 147, 640], "stop": [64, 68, 70, 87, 91, 93, 137, 148, 149, 224, 387, 456, 462, 589, 627, 630, 632, 633, 634, 635, 640, 642, 645, 646, 651, 652, 726, 727, 728, 740, 807, 822, 848, 851, 859, 861, 867, 882], "num": [64, 87, 148, 149, 640, 787, 832, 848, 861], "endpoint": [64, 87, 148, 149, 640, 802, 848], "logspac": [64, 87, 640, 861], "sequenc": [64, 68, 72, 73, 75, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 100, 101, 102, 103, 104, 105, 114, 121, 122, 123, 124, 125, 126, 127, 128, 129, 143, 145, 147, 149, 152, 154, 160, 164, 166, 179, 183, 184, 191, 225, 231, 232, 233, 234, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 256, 257, 258, 262, 263, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 278, 279, 280, 281, 282, 283, 284, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 314, 315, 316, 317, 318, 320, 321, 322, 324, 327, 334, 335, 336, 337, 338, 345, 346, 347, 348, 349, 351, 353, 361, 362, 368, 370, 372, 373, 374, 376, 377, 380, 383, 384, 385, 386, 387, 389, 393, 398, 399, 401, 402, 403, 410, 411, 412, 414, 415, 419, 420, 422, 429, 430, 431, 432, 433, 436, 444, 445, 446, 448, 454, 455, 456, 459, 462, 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Model": [[10, "About-the-Model"], [11, "About-the-Model"]], "Transpiling the Model": [[10, "Transpiling-the-Model"], [11, "Transpiling-the-Model"]], "Comparing the results": [[10, "Comparing-the-results"], [22, "Comparing-the-results"], [9, "Comparing-the-results"], [11, "Comparing-the-results"], [8, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[10, "Fine-tuning-the-transpiled-model"], [22, "Fine-tuning-the-transpiled-model"], [9, "Fine-tuning-the-transpiled-model"], [11, "Fine-tuning-the-transpiled-model"], [8, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[10, "Conclusion"], [22, "Conclusion"], [9, "Conclusion"], [11, "Conclusion"], [8, "Conclusion"]], "Accelerating MMPreTrain models with JAX": [[18, "Accelerating-MMPreTrain-models-with-JAX"], [19, "Accelerating-MMPreTrain-models-with-JAX"]], "# Ivy Bert Demo": [[6, "#-Ivy-Bert-Demo"], [7, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[6, "Install-the-dependecies"], [7, "Install-the-dependecies"]], "Import the modules": [[6, "Import-the-modules"], [7, "Import-the-modules"]], "Data Preparation": [[6, "Data-Preparation"], [5, "Data-Preparation"], [7, "Data-Preparation"], [13, "Data-Preparation"], [20, "Data-Preparation"], [4, "Data-Preparation"], [21, "Data-Preparation"], [12, "Data-Preparation"]], "Ivy inference with Sequence Classification": [[6, "Ivy-inference-with-Sequence-Classification"], [7, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[6, "Ivy-model-inference-with-tensorflow"], [7, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[6, "Ivy-model-inference-with-Jax"], [7, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[6, "Ivy-model-inference-with-torch"], [7, "Ivy-model-inference-with-torch"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "Transpiling a PyTorch model to build on top": [[27, "Transpiling-a-PyTorch-model-to-build-on-top"]], "0.0: Unify": [[44, "0.0:-Unify"]], "Guides": [[26, "guides"], [31, "guides"]], "Lazy vs Eager": [[37, "Lazy-vs-Eager"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Training PyTorch ResNet in your TensorFlow Projects": [[22, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Framework Incompatibility": [[22, "Framework-Incompatibility"], [9, "Framework-Incompatibility"], [8, "Framework-Incompatibility"]], "Transpiling a PyTorch model to TensorFlow": [[22, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "About the transpiled model": [[22, "About-the-transpiled-model"], [9, "About-the-transpiled-model"], [8, "About-the-transpiled-model"]], "Installation": [[22, "Installation"], [5, "Installation"], [20, "Installation"], [4, "Installation"], [21, "Installation"]], "Setting-up the source model": [[22, "Setting-up-the-source-model"], [9, "Setting-up-the-source-model"], [8, "Setting-up-the-source-model"]], "Load the Data": [[22, "Load-the-Data"]], "Visualize a few images": [[22, "Visualize-a-few-images"]], "Load the pre-trained model": [[22, "Load-the-pre-trained-model"]], "Converting the model from TensorFlow to PyTorch": [[22, "Converting-the-model-from-TensorFlow-to-PyTorch"], [9, "Converting-the-model-from-TensorFlow-to-PyTorch"], [8, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Accelerating XGBoost with JAX": [[25, "Accelerating-XGBoost-with-JAX"]], "Imports": [[25, "Imports"], [13, "Imports"], [20, "Imports"], [21, "Imports"], [12, "Imports"]], "Tests": [[25, "Tests"]], "Loading the Data": [[25, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[25, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[25, "JAX-backend"]], "Tensorflow backend": [[25, "Tensorflow-backend"]], "PyTorch backend": [[25, "PyTorch-backend"]], "More exhaustive example": [[25, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[25, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[25, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[25, "Comparison-of-Metrics"]], "Accelerating PyTorch models with JAX": [[23, "Accelerating-PyTorch-models-with-JAX"], [24, "Accelerating-PyTorch-models-with-JAX"]], "Trace code": [[35, "Trace-code"]], "ODSC Ivy Demo": [[42, "ODSC-Ivy-Demo"]], "Ivy Backend Handler": [[42, "Ivy-Backend-Handler"], [33, "Ivy-Backend-Handler"]], "Data Structures": [[42, "Data-Structures"], [33, "Data-Structures"]], "Ivy Functional API": [[42, "Ivy-Functional-API"], [33, "Ivy-Functional-API"]], "Graph Tracer": [[42, "Graph-Tracer"]], "Any function": [[42, "Any-function"], [43, "Any-function"]], "Any library": [[42, "Any-library"], [43, "Any-library"]], "Any model": [[42, "Any-model"], [43, "Any-model"]], "Tutorials And Examples": [[31, "tutorials-and-examples"]], "Examples and Demos": [[31, "examples-and-demos"], [3, "examples-and-demos"]], "Ivy AlexNet demo": [[5, "Ivy-AlexNet-demo"], [4, "Ivy-AlexNet-demo"]], "Ivy AlexNet inference in Torch": [[5, "Ivy-AlexNet-inference-in-Torch"], [4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[5, "TensorFlow-inference"], [4, "TensorFlow-inference"]], "JAX inference": [[5, "JAX-inference"], [4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[5, "Appendix-(Ivy-code-for-AlexNet-implementation)"], [4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "Transpiling a haiku model to build on top": [[28, "Transpiling-a-haiku-model-to-build-on-top"]], "Transpile any model": [[40, "Transpile-any-model"]], "Round up": [[40, "Round-up"]], "Using TensorFlow Models in your PyTorch Projects": [[9, "Using-TensorFlow-Models-in-your-PyTorch-Projects"], [8, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Transpiling a TensorFlow model to PyTorch": [[9, "Transpiling-a-TensorFlow-model-to-PyTorch"], [8, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "Image Segmentation with Ivy UNet": [[13, "Image-Segmentation-with-Ivy-UNet"], [12, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[13, "Custom-Preprocessing"], [12, "Custom-Preprocessing"]], "Load the image example \ud83d\uddbc\ufe0f": [[13, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [20, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [21, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [12, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[13, "Visualise-image"], [20, "Visualise-image"], [21, "Visualise-image"], [12, "Visualise-image"]], "Model Inference": [[13, "Model-Inference"], [12, "Model-Inference"]], "Initializing Native Torch UNet": [[13, "Initializing-Native-Torch-UNet"], [12, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[13, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"], [12, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[13, "Custom-masking-function"], [12, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[13, "Use-the-model-to-segment-your-images-\ud83d\ude80"], [12, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[13, "TensorFlow-backend"], [12, "TensorFlow-backend"]], "JAX": [[13, "JAX"], [12, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[13, "Appendix:-the-Ivy-native-implementation-of-UNet"], [12, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Transpile code": [[36, "Transpile-code"]], "Quickstart": [[43, "Quickstart"]], "Get familiar with Ivy": [[43, "Get-familiar-with-Ivy"]], "Functional API": [[43, "Functional-API"]], "Stateful API": [[43, "Stateful-API"]], "Tracing code": [[43, "Tracing-code"]], "0.1: Compile": [[45, "0.1:-Compile"]], "Using Ivy ResNet": [[20, "Using-Ivy-ResNet"], [21, "Using-Ivy-ResNet"]], "Prepare the set of labels": [[20, "Prepare-the-set-of-labels"], [21, "Prepare-the-set-of-labels"]], "Model Inference ResNet34": [[20, "Model-Inference-ResNet34"], [21, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[20, "Initializing-Native-Torch-ResNet34"], [21, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[20, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"], [21, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[20, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [20, "id1"], [21, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [21, "id1"]], "Model Inference ResNet50": [[20, "Model-Inference-ResNet50"], [21, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[20, "Initializing-Native-Torch-ResNet50"], [21, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[20, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"], [21, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "Write a model using Ivy": [[41, "Write-a-model-using-Ivy"]], "Transpile any library": [[39, "Transpile-any-library"]], "0.2: Transpile": [[46, "0.2:-Transpile"]], "Transpiling a Tensorflow model to build on top": [[29, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Unify code": [[34, "Unify-code"]], "Developing a convolutional network using Ivy": [[30, "Developing-a-convolutional-network-using-Ivy"]], "Write Ivy code": [[33, "Write-Ivy-code"]], "Contents": [[33, "Contents"]], "Installing Ivy": [[33, "Installing-Ivy"]], "Importing Ivy": [[33, "Importing-Ivy"], [0, "Importing-Ivy"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "2.0: Kornia": [[51, "2.0:-Kornia"]], "1.3: Dynamic vs Static": [[50, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[50, "Dynamic"]], "Static": [[50, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[50, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[56, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[56, "Table-of-Contents"]], "Defining the model": [[56, "Defining-the-model"]], "Model construction": [[56, "Model-construction"]], "Some helper functions": [[56, "Some-helper-functions"]], "Transpiling the model": [[56, "Transpiling-the-model"]], "PyTorch pipeline": [[56, "PyTorch-pipeline"]], "Dataset download": [[56, "Dataset-download"]], "DataLoader": [[56, "DataLoader"]], "Training": [[56, "Training"]], "Conversions": [[86, "module-ivy.data_classes.container.conversions"], [63, "module-ivy.data_classes.array.conversions"]], "1.1: Framework Selection": [[48, "1.1:-Framework-Selection"]], "Compile": [[48, "Compile"], [49, "Compile"], [47, "Compile"]], "Ivy as a Transpiler Introduction": [[60, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[60, "To-use-the-transpiler:"]], "Transpiler Interface": [[60, "Transpiler-Interface"]], "Telemetry": [[60, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[60, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[60, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[60, "3.-Transpile-Models-\ud83c\udf10"]], "1.2: As a Decorator": [[49, "1.2:-As-a-Decorator"]], "Basic Operations with Ivy": [[54, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[54, "Installs-\ud83d\udcbe"], [55, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[54, "Imports-\ud83d\udec3"], [55, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[54, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[54, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[54, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[54, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[54, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[54, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[54, "Set-Backend-Framework"]], "Define Model": [[54, "Define-Model"], [55, "Define-Model"]], "Create Model": [[54, "Create-Model"]], "Create Optimizer": [[54, "Create-Optimizer"]], "Input and Target": [[54, "Input-and-Target"]], "Loss Function": [[54, "Loss-Function"]], "Training Loop": [[54, "Training-Loop"]], "End-to-End Training Pipeline in Ivy": [[58, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[58, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[58, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[58, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[58, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[58, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[58, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[58, "Plotting-the-training-metrics"]], "Save the trained Model": [[58, "Save-the-trained-Model"]], "3.0: Perceiver": [[52, "3.0:-Perceiver"]], "Resnet 18": [[61, "Resnet-18"]], "Compilation of a Basic Function": [[55, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[55, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[55, "Function-compilation-\ud83d\udee0"]], "Set backend": [[55, "Set-backend"]], "Sample input": [[55, "Sample-input"]], "Define function to compile": [[55, "Define-function-to-compile"]], "Compile the function": [[55, "Compile-the-function"]], "Check results": [[55, "Check-results"], [55, "id1"]], "Compiling simple neural network \ud83e\udde0": [[55, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[55, "Create-model"]], "Define input": [[55, "Define-input"]], "Compile network": [[55, "Compile-network"]], "Deepmind PerceiverIO on GPU": [[57, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[57, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[57, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[57, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[57, "Run-the-demo..."]], "\u2026with torch backend": [[57, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[57, "....with-tensorflow-backend"]], "\u2026with jax backend": [[57, "...with-jax-backend"]], "\u2026with numpy backend": [[57, "...with-numpy-backend"]], "1.0: Lazy vs Eager": [[47, "1.0:-Lazy-vs-Eager"]], "3.1: Stable Diffusion": [[53, "3.1:-Stable-Diffusion"]], "HuggingFace Tensorflow DeiT": [[59, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[59, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]]}, "indexentries": {"_arraywithactivations (class in ivy.data_classes.array.activations)": [[62, "ivy.data_classes.array.activations._ArrayWithActivations"]], 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