Overview

dragon
- DeviceSpec
- GradientTape
- Tensor
- Workspace
- argsort
- assign
- boolean_mask
- broadcast_to
- cast
- concat
- constant
- device
- eager_mode
- expand_dims
- eye
- eye_like
- fill
- flatten
- function
- gather
- gather_elements
- get_num_threads
- get_workspace
- graph_mode
- identity
- linspace
- load_library
- name_scope
- nonzero
- ones
- ones_like
- one_hot
- pad
- python_plugin
- range
- repeat
- reset_workspace
- reshape
- reverse
- roll
- scatter_add
- scatter_elements
- set_num_threads
- shape
- slice
- sort
- split
- squeeze
- stack
- stop_gradient
- tile
- transpose
- tril
- triu
- unique
- unstack
- variable_scope
- where
- zeros
- zeros_like
dragon.autograph
- set_optimization
- set_scheduler
- set_verbosity
dragon.bitwise
- bitwise_and
- bitwise_or
- bitwise_xor
- invert
dragon.cuda
- Stream
- current_device
- get_device_capability
- get_device_name
- is_available
- memory_allocated
- set_cublas_flags
- set_cudnn_flags
- set_default_device
- set_device
- synchronize
dragon.distributed
- all_reduce
- broadcast
- is_initialized
- is_mpi_available
- is_nccl_available
- get_backend
- get_group
- get_rank
- get_world_size
- new_group
dragon.dlpack
- from_dlpack
- to_dlpack
dragon.logging
- debug
- error
- fatal
- get_verbosity
- info
- log
- set_directory
- set_verbosity
- warning
dragon.losses
- ctc_loss
- l1_loss
- l2_loss
- nll_loss
- sigmoid_cross_entropy_loss
- sigmoid_focal_loss
- smooth_l1_loss
- softmax_cross_entropy_loss
dragon.math
- abs
- add
- affine
- argmax
- argmin
- atan2
- ceil
- clip
- cos
- cumsum
- div
- equal
- exp
- floor
- gemm
- greater
- greater_equal
- is_finite
- is_inf
- is_nan
- less
- less_equal
- log
- logical_and
- logical_not
- logical_or
- logical_xor
- matmul
- max
- maximum
- mean
- min
- minimum
- mul
- negative
- norm
- not_equal
- pow
- reciprocal
- round
- rsqrt
- sigmoid
- sign
- sin
- sqrt
- square
- sub
- sum
- tanh
- top_k
- var
dragon.metrics
- accuracy
dragon.mps
- current_device
- get_device_family
- is_available
- set_default_device
- set_device
- synchronize
dragon.nn
- GRU
- LSTM
- RNN
- batch_norm
- bias_add
- channel_norm
- channel_shuffle
- conv
- conv_transpose
- conv1d
- conv1d_transpose
- conv2d
- conv2d_transpose
- conv3d
- conv3d_transpose
- depthwise_conv2d
- depth_to_space
- dropout
- drop_block
- drop_path
- elu
- gelu
- group_norm
- hardsigmoid
- hardswish
- instance_norm
- layer_norm
- leaky_relu
- local_response_norm
- log_softmax
- lp_norm
- moments
- pool
- pool1d
- pool2d
- pool3d
- prelu
- relu
- relu6
- selu
- silu
- softmax
- space_to_depth
- sync_batch_norm
dragon.onnx
- BackendRep
- prepare_backend
- export
- record
- run_model
- supports_device
dragon.optimizers
- Adam
- AdamW
- Optimizer
- RMSprop
- SGD
dragon.random
- glorot_normal
- glorot_uniform
- multinomial
- normal
- normal_like
- permutation
- set_seed
- truncated_normal
- uniform
- uniform_like
dragon.sysconfig
- get_build_info
- get_include
- get_lib
dragon.vision
- extract_patches
- resize
- roi_align
- roi_pool
vm.dali
- Iterator
- Pipeline
- device
- get_device_type
- get_distributed_info
vm.dali.ops
- BbFlip
- BBoxPaste
- Brightness
- BrightnessContrast
- Cast
- CoinFlip
- ColorSpaceConversion
- ColorTwist
- Contrast
- CropMirrorNormalize
- Erase
- ExternalSource
- GaussianBlur
- Hsv
- ImageDecoder
- ImageDecoderRandomCrop
- Normalize
- Pad
- Paste
- RandomBBoxCrop
- RandomResizedCrop
- Reshape
- Resize
- Rotate
- Slice
- TFRecordReader
- Uniform
- WarpAffine
vm.tensorflow
- GradientTape
- Module
- TensorShape
- TensorSpec
- argsort
- broadcast_to
- cast
- clip_by_value
- concat
- constant
- convert_to_tensor
- device
- expand_dims
- eye
- fill
- function
- gather
- identity
- linspace
- name_scope
- ones
- ones_like
- one_hot
- pad
- range
- reshape
- reverse
- roll
- shape
- slice
- sort
- split
- squeeze
- tile
- transpose
- unique
- unique_with_counts
- unstack
- zeros
- zeros_like
vm.tensorflow.bitwise
- bitwise_and
- bitwise_or
- bitwise_xor
- invert
vm.tensorflow.dtypes
- DType
- as_dtype
vm.tensorflow.keras
- Input
- Sequential
- activations
  - elu
  - exponential
  - get
  - hard_sigmoid
  - linear
  - relu
  - selu
  - sigmoid
  - softmax
  - swish
  - tanh
- initializers
  - Constant
  - GlorotNormal
  - GlorotUniform
  - Initializer
  - Ones
  - RandomNormal
  - RandomUniform
  - TruncatedNormal
  - VarianceScaling
  - Zeros
  - get
- layers
  - Activation
  - Add
  - AveragePooling1D
  - AveragePooling2D
  - AveragePooling3D
  - BatchNormalization
  - Concatenate
  - Conv1D
  - Conv1DTranspose
  - Conv2D
  - Conv2DTranspose
  - Conv3D
  - Conv3DTranspose
  - Dense
  - DepthwiseConv2D
  - Dropout
  - ELU
  - Flatten
  - GlobalAveragePooling1D
  - GlobalAveragePooling2D
  - GlobalAveragePooling3D
  - GlobalMaxPool1D
  - GlobalMaxPool2D
  - GlobalMaxPool3D
  - Layer
  - LayerNormalization
  - LeakyReLU
  - Maximum
  - MaxPool1D
  - MaxPool2D
  - MaxPool3D
  - Minimum
  - Multiply
  - Permute
  - ReLU
  - Reshape
  - SELU
  - Softmax
  - Subtract
  - UpSampling1D
  - UpSampling2D
  - UpSampling3D
  - ZeroPadding1D
  - ZeroPadding2D
  - ZeroPadding3D
- losses
- optimizers
  - Adam
  - Optimizer
  - RMSprop
  - SGD
- regularizers
  - L1
  - L1L2
  - L2
  - Regularizer
  - get
  - l1_l2
vm.tensorflow.linalg
- matmul
vm.tensorflow.math
- abs
- add
- add_n
- argmax
- argmin
- atan2
- ceil
- cos
- cumsum
- divide
- equal
- exp
- floor
- greater
- greater_equal
- is_finite
- is_inf
- is_nan
- l2_normalize
- less
- less_equal
- log
- multiply
- negative
- not_equal
- pow
- reciprocal
- reduce_max
- reduce_mean
- reduce_min
- reduce_sum
- reduce_variance
- round
- rsqrt
- sigmoid
- sign
- sin
- sqrt
- square
- subtract
- tanh
- top_k
vm.tensorflow.nn
- avg_pool
- avg_pool1d
- avg_pool2d
- avg_pool3d
- bias_add
- conv1d
- conv1d_transpose
- conv2d
- conv2d_transpose
- conv3d
- conv3d_transpose
- convolution
- conv_transpose
- depthwise_conv2d
- depth_to_space
- dropout
- elu
- fused_batch_norm
- gelu
- l2_loss
- leaky_relu
- local_response_normalization
- log_softmax
- max_pool
- max_pool1d
- max_pool2d
- max_pool3d
- moments
- relu
- relu6
- selu
- sigmoid_cross_entropy_with_logits
- silu
- softmax
- softmax_cross_entropy_with_logits
- space_to_depth
- sparse_softmax_cross_entropy_with_logits
vm.tensorflow.random
- normal
- truncated_normal
- uniform
vm.tensorrt
- Binding
- Engine
vm.tensorrt.onnx
- BackendRep
- prepare_backend
- run_model
- run_node
- supports_device
vm.torch
- Size
- Tensor
- abs
- add
- addmm
- arange
- argmax
- argmin
- argsort
- atan2
- baddbmm
- bitwise_and
- bitwise_not
- bitwise_or
- bitwise_xor
- bmm
- cat
- ceil
- chunk
- clamp
- cos
- cumsum
- device
- div
- dtype
- empty
- enable_grad
- eq
- exp
- eye
- flatten
- flip
- fliplr
- flipud
- floor
- from_numpy
- full
- full_like
- gather
- ge
- gt
- index_select
- isfinite
- isinf
- isnan
- le
- linspace
- log
- logical_and
- logical_not
- logical_or
- logical_xor
- logsumexp
- lt
- masked_select
- matmul
- max
- maximum
- mean
- min
- minimum
- mm
- mul
- multinomial
- narrow
- ne
- neg
- no_grad
- nonzero
- norm
- ones
- ones_like
- permute
- pow
- rand
- randn
- randperm
- reciprocal
- reshape
- roll
- round
- rsqrt
- scatter
- scatter_add
- set_grad_enabled
- sign
- sin
- sort
- split
- sqrt
- square
- squeeze
- stack
- sub
- sum
- tensor
- tile
- topk
- transpose
- tril
- triu
- unbind
- unique
- unsqueeze
- where
- var
- var_mean
- zeros_like
- zeros
vm.torch.autograd
- backward
vm.torch.backends
- cuda
- cudnn
- mps
- openmp
vm.torch.cuda
- current_device
- get_device_capability
- get_device_name
- is_available
- set_device
- synchronize
vm.torch.distributed
- all_reduce
- broadcast
vm.torch.jit
- trace
vm.torch.nn
- AdaptiveAvgPool1d
- AdaptiveAvgPool2d
- AdaptiveAvgPool3d
- AdaptiveMaxPool1d
- AdaptiveMaxPool2d
- AdaptiveMaxPool3d
- Affine
- AvgPool1d
- AvgPool2d
- AvgPool3d
- BatchNorm1d
- BatchNorm2d
- BatchNorm3d
- BCEWithLogitsLoss
- ChannelShuffle
- ConstantPad1d
- ConstantPad2d
- ConstantPad3d
- Conv1d
- Conv2d
- Conv3d
- ConvTranspose1d
- ConvTranspose2d
- ConvTranspose3d
- CosineSimilarity
- CrossEntropyLoss
- CTCLoss
- DepthwiseConv2d
- DropBlock2d
- Dropout
- DropPath
- ELU
- Flatten
- GELU
- GroupNorm
- GRU
- GumbelSoftmax
- Hardsigmoid
- Hardswish
- Identity
- KLDivLoss
- L1Loss
- LayerNorm
- LeakyReLU
- Linear
- LocalResponseNorm
- LogSoftmax
- LSTM
- LSTMCell
- MaxPool1d
- MaxPool2d
- MaxPool3d
- Module
- ModuleList
- MSELoss
- MultiheadAttention
- NLLLoss
- Parameter
- PixelShuffle
- PixelUnshuffle
- PReLU
- ReflectionPad1d
- ReflectionPad2d
- ReflectionPad3d
- ReLU
- ReLU6
- ReplicationPad1d
- ReplicationPad2d
- ReplicationPad3d
- RNN
- SELU
- Sequential
- Sigmoid
- SigmoidFocalLoss
- SiLU
- SmoothL1Loss
- Softmax
- Tanh
- TransformerDecoder
- TransformerDecoderLayer
- TransformerEncoder
- TransformerEncoderLayer
- SyncBatchNorm
- Unfold
- Upsample
- UpsamplingBilinear2d
- UpsamplingNearest2d
- ZeroPad2d
vm.torch.nn.functional
- adaptive_avg_pool1d
- adaptive_avg_pool2d
- adaptive_avg_pool3d
- adaptive_max_pool1d
- adaptive_max_pool2d
- adaptive_max_pool3d
- affine
- avg_pool1d
- avg_pool2d
- avg_pool3d
- batch_norm
- binary_cross_entropy_with_logits
- channel_norm
- channel_shuffle
- conv1d
- conv2d
- conv3d
- conv_transpose1d
- conv_transpose2d
- conv_transpose3d
- cosine_similarity
- cross_entropy
- ctc_loss
- depthwise_conv2d
- drop_block2d
- drop_path
- dropout
- elu
- gelu
- group_norm
- hardsigmoid
- hardswish
- kl_div
- l1_loss
- leaky_relu
- linear
- layer_norm
- local_response_norm
- log_softmax
- interpolate
- max_pool1d
- max_pool2d
- max_pool3d
- mse_loss
- multi_head_attention_forward
- nll_loss
- normalize
- one_hot
- pad
- pixel_shuffle
- pixel_unshuffle
- prelu
- relu
- relu6
- selu
- sigmoid
- sigmoid_focal_loss
- silu
- smooth_l1_loss
- softmax
- sync_batch_norm
- tanh
- unfold
- upsample
- upsample_bilinear
- upsample_nearest
vm.torch.nn.init
- calculate_gain
- constant_
- dirac_
- eye_
- xavier_normal_
- kaiming_uniform_
- normal_
- ones_
- trunc_normal_
- uniform_
- kaiming_normal_
- xavier_uniform_
- zeros_
vm.torch.onnx
- export
vm.torch.optim
- Adam
- AdamW
- LARS
- Optimizer
- RMSprop
- SGD
vm.torch.utils.checkpoint
- checkpoint
- checkpoint_sequential
- no_checkpoint
vm.torch.utils.dlpack
- from_dlpack
- to_dlpack
vm.torchvision.ops
- roi_align
- roi_pool

vm.tensorflow.linalg¶

Functions¶

matmul(…) : Compute the matrix multiplication.