Python v0.3.0.dev+adb6fa6

Overview

dragon
- arange
- assign
- broadcast_to
- cast
- channel_normalize
- channel_shuffle
- concat
- constant
- copy
- create_function
- device
- EagerTensor
- eager_mode
- eager_scope
- expand_dims
- eye
- eye_like
- fill
- flatten
- function
- get_workspace
- gradients
- GradientTape
- graph_mode
- index_select
- load_library
- masked_assign
- masked_select
- name_scope
- nonzero
- ones
- ones_like
- one_hot
- pad
- python_plugin
- repeat
- reset_workspace
- reshape
- shape
- slice
- split
- squeeze
- stack
- stop_gradient
- Tensor
- TensorSpec
- tile
- transpose
- where
- Workspace
- zeros
- zeros_like
dragon.autograph
- set_execution
- set_optimization
- set_scheduler
- set_verbosity
dragon.bitwise
- bitwise_and
- bitwise_or
- bitwise_xor
- invert
dragon.cuda
- current_device
- enable_cudnn
- enable_cudnn_benchmark
- get_device_capability
- is_available
- set_default_device
- set_device
- synchronize
dragon.distributed
- all_reduce
- broadcast
- init
- is_initialized
- get_backend
- get_group
- get_rank
- get_world_size
- new_group
dragon.dlpack
- from_dlpack
- to_dlpack
dragon.io
- DataReader
- KPLRecordDataset
- KPLRecordWriter
- TFRecordExample
- TFRecordWriter
dragon.logging
- debug
- error
- fatal
- info
- set_directory
- set_verbosity
- warning
dragon.losses
- ctc_loss
- l1_loss
- l2_loss
- nll_loss
- sigmoid_cross_entropy
- sigmoid_focal_loss
- smooth_l1_loss
- softmax_cross_entropy
- sparse_softmax_cross_entropy
dragon.math
- abs
- add
- affine
- argmax
- argmin
- axpby
- ceil
- clip
- cos
- cumsum
- div
- dot
- equal
- exp
- floor
- greater
- greater_equal
- is_inf
- is_nan
- less
- less_equal
- log
- lp_normalize
- matmul
- max
- maximum
- mean
- min
- minimum
- moments
- mul
- negative
- not_equal
- pow
- reciprocal
- round
- rsqrt
- sigmoid
- sign
- sin
- sqrt
- square
- sub
- sum
- tanh
dragon.metrics
- accuracy
dragon.nn
- batch_norm
- bias_add
- conv2d
- conv2d_transpose
- depthwise_conv2d
- depth_to_space
- dropout
- drop_block2d
- drop_path
- elu
- fully_connected
- group_norm
- GRU
- instance_norm
- layer_norm
- leaky_relu
- local_response_norm
- log_softmax
- LSTM
- pool2d
- prelu
- relu
- relu6
- RNN
- selu
- softmax
- space_to_depth
- sync_batch_norm
dragon.optimizers
- Adam
- Nesterov
- Optimizer
- RMSprop
- SGD
dragon.random
- glorot_normal
- glorot_uniform
- multinomial
- normal
- normal_like
- set_seed
- truncated_normal
- uniform
- uniform_like
dragon.workspace
- feed_tensor
- fetch_tensor
- has_tensor
- load
- reset_tensor
- run_operator
- save
dragon.vision
- DataIterator
- resize
- roi_align
- roi_pool
vm.caffe
- AdamSolver
- NesterovSolver
- Net
- RMSPropSolver
- SGDSolver
vm.caffe.layers
- Accuracy
- ArgMax
- BatchNorm
- Cast
- Concat
- Convolution
- Crop
- Data
- Deconvolution
- DepthwiseConv2d
- Dropout
- Eltwise
- ELU
- EuclideanLoss
- Flatten
- FusedBatchNorm
- FusedGroupNorm
- GroupNorm
- InnerProduct
- Input
- LRN
- Normalize
- Permute
- Pooling
- Power
- PReLU
- Python
- Reduction
- ReLU
- Reshape
- ROIAlign
- ROIPooling
- Scale
- SELU
- Sigmoid
- SigmoidCrossEntropyLoss
- SmoothL1Loss
- Softmax
- SoftmaxWithLoss
- StopGradient
- TanH
- Tile
vm.dali
- device
- get_device_type
- get_distributed_info
- Iterator
- Pipeline
vm.dali.ops
- BbFlip
- BBoxPaste
- BrightnessContrast
- Cast
- CoinFlip
- CropMirrorNormalize
- ExternalSource
- Hsv
- ImageDecoder
- ImageDecoderRandomCrop
- Pad
- Paste
- RandomBBoxCrop
- Reshape
- Resize
- Slice
- KPLRecordReader
- TFRecordReader
- Uniform
vm.onnx
- Shell
vm.tensorflow
- broadcast_to
- cast
- clip_by_value
- concat
- constant
- device
- expand_dims
- eye
- fill
- function
- gather
- gradients
- GradientTape
- identity
- name_scope
- ones
- ones_like
- one_hot
- pad
- range
- reshape
- shape
- slice
- split
- squeeze
- TensorSpec
- transpose
- zeros
- zeros_like
vm.tensorflow.bitwise
- bitwise_and
- bitwise_or
- bitwise_xor
- invert
vm.tensorflow.dtypes
- as_dtype
- DType
vm.tensorflow.initializers
vm.tensorflow.keras
- Input
- Sequential
- activations
  - elu
  - exponential
  - linear
  - relu
  - selu
  - sigmoid
  - softmax
  - tanh
- initializers
- layers
  - Add
  - AveragePooling2D
  - BatchNormalization
  - Concatenate
  - Conv2D
  - Conv2DTranspose
  - Dense
  - DepthwiseConv2D
  - Dropout
  - ELU
  - Flatten
  - GlobalAveragePooling2D
  - GlobalMaxPool2D
  - Layer
  - LeakyReLU
  - Maximum
  - MaxPool2D
  - Minimum
  - Multiply
  - Permute
  - ReLU
  - Reshape
  - SELU
  - Softmax
  - Subtract
- losses
- optimizers
  - Adam
  - Optimizer
  - RMSprop
  - SGD
- regularizers
  - l1
  - L1L2
  - l1_l2
  - l2
  - Regularizer
vm.tensorflow.linalg
- matmul
vm.tensorflow.losses
vm.tensorflow.math
- abs
- add
- add_n
- argmax
- argmin
- ceil
- cos
- cumsum
- divide
- equal
- exp
- floor
- greater
- greater_equal
- is_inf
- is_nan
- l2_normalize
- less
- less_equal
- log
- multiply
- negative
- not_equal
- pow
- reciprocal
- reduce_max
- reduce_mean
- reduce_min
- reduce_sum
- round
- rsqrt
- sigmoid
- sign
- sin
- sqrt
- square
- subtract
- tanh
vm.tensorflow.nn
- avg_pool
- avg_pool2d
- batch_normalization
- conv2d
- conv2d_transpose
- convolution
- conv_transpose
- depthwise_conv2d
- depth_to_space
- dropout
- elu
- leaky_relu
- local_response_normalization
- log_softmax
- max_pool
- max_pool2d
- moments
- relu
- relu6
- selu
- softmax
- space_to_depth
vm.tensorflow.optimizers
vm.tensorflow.random
- normal
- truncated_normal
- uniform
vm.tensorlayer.initializers
- Constant
- GlorotNormal
- GlorotUniform
- Initializer
- Ones
- RandomNormal
- RandomUniform
- TruncatedNormal
- Zeros
vm.tensorlayer.layers
- BatchNorm
- Concat
- Conv2d
- Dense
- Elementwise
- Flatten
- GlobalMaxPool2d
- GlobalMeanPool2d
- MaxPool2d
- MeanPool2d
- Input
- Layer
- LayerList
- Relu
- Reshape
- Transpose
vm.tensorlayer.models
- Model
vm.tensorrt
- Binding
- Engine
- ONNXBackendRep
vm.tensorrt.backend
- prepare
- run_model
- run_node
- supports_device
vm.torch
- abs
- add
- arange
- argmax
- argmin
- axpby
- bitwise_not
- bitwise_xor
- cat
- ceil
- channel_normalize
- channel_shuffle
- chunk
- clamp
- cos
- cumsum
- device
- div
- empty
- enable_grad
- eq
- exp
- expand
- eye
- floor
- from_numpy
- ge
- gt
- index_select
- isinf
- isnan
- le
- log
- logsumexp
- lt
- masked_select
- max
- maximum
- mean
- min
- minimum
- mm
- mul
- multinomial
- narrow
- ne
- no_grad
- nonzero
- normal
- ones
- ones_like
- one_hot
- permute
- pow
- rand
- randn
- reciprocal
- repeat
- reshape
- round
- rsqrt
- set_grad_enabled
- sign
- sin
- Size
- split
- sqrt
- squeeze
- stack
- sub
- sum
- Tensor
- tensor
- topk
- topk_acc
- uniform
- unsqueeze
- where
- zeros_like
- zeros
vm.torch.distributed
- all_reduce
- broadcast
vm.torch.jit
- trace
vm.torch.nn
- Affine
- AvgPool2d
- BatchNorm1d
- BatchNorm2d
- BatchNorm3d
- BCEWithLogitsLoss
- ConstantPad1d
- ConstantPad2d
- ConstantPad3d
- Conv2d
- ConvTranspose2d
- CrossEntropyLoss
- CTCLoss
- DepthwiseConv2d
- DropBlock2d
- Dropout
- DropPath
- ELU
- GroupNorm
- GRU
- GumbelSoftmax
- L1Loss
- LeakyReLU
- Linear
- LocalResponseNorm
- LogSoftmax
- LSTM
- LSTMCell
- MaxPool2d
- Module
- MSELoss
- NLLLoss
- Parameter
- PReLU
- ReflectionPad1d
- ReflectionPad2d
- ReflectionPad3d
- ReLU
- ReLU6
- ReplicationPad1d
- ReplicationPad2d
- ReplicationPad3d
- SCEWithLogitsLoss
- RNN
- SELU
- Sigmoid
- SigmoidFocalLoss
- SmoothL1Loss
- Softmax
- Tanh
- SyncBatchNorm
- Upsample
- UpsamplingBilinear2d
- UpsamplingNearest2d
- ZeroPad2d
vm.torch.nn.functional
- affine
- avg_pool2d
- batch_norm
- binary_cross_entropy_with_logits
- conv2d
- conv_transpose2d
- cross_entropy
- ctc_loss
- depthwise_conv2d
- drop_block2d
- drop_path
- dropout
- elu
- group_norm
- l1_loss
- leaky_relu
- linear
- local_response_norm
- log_softmax
- interpolate
- max_pool2d
- mse_loss
- nll_loss
- normalize
- pad
- prelu
- relu
- relu6
- selu
- sigmoid
- sigmoid_focal_loss
- smooth_l1_loss
- softmax
- softmax_cross_entropy_with_logits
- sync_batch_norm
- tanh
- upsample
- upsample_bilinear
- upsample_nearest
vm.torch.onnx
- export
vm.torch.optim
- Adam
- Optimizer
- RMSprop
- SGD
vm.torch.vision.ops
- roi_align
- roi_pool
vm.torch.utils.dlpack
- from_dlpack
- to_dlpack

eager_mode¶

dragon.eager_mode()[source]¶: Context-manager set the eager execution mode.

eager_mode