Resnet¶

class olympus.models.resnet.BasicBlock(inplanes, planes, stride=1, downsample=None)[source]¶

Bases: torch.nn.modules.module.Module

See :class`.ResNet` for license and references`

Methods

`add_module`	Adds a child module to the current module.
`apply`	Applies `fn` recursively to every submodule (as returned by `.children()`) as well as self.
`bfloat16`()	Casts all floating point parameters and buffers to `bfloat16` datatype.
`buffers`(recurse)	Returns an iterator over module buffers.
`children`	Returns an iterator over immediate children modules.
`cpu`()	Moves all model parameters and buffers to the CPU.
`cuda`(device, torch.device, NoneType] = None)	Moves all model parameters and buffers to the GPU.
`double`()	Casts all floating point parameters and buffers to `double` datatype.
`eval`()	Sets the module in evaluation mode.
`extra_repr`()	Set the extra representation of the module
`float`()	Casts all floating point parameters and buffers to `float` datatype.
`forward`(x)	Defines the computation performed at every call.
`get_buffer`(target)	Returns the buffer given by `target` if it exists, otherwise throws an error.
`get_extra_state`()	Returns any extra state to include in the module’s state_dict.
`get_parameter`(target)	Returns the parameter given by `target` if it exists, otherwise throws an error.
`get_submodule`(target)	Returns the submodule given by `target` if it exists, otherwise throws an error.
`half`()	Casts all floating point parameters and buffers to `half` datatype.
`load_state_dict`(state_dict, Tensor], strict)	Copies parameters and buffers from `state_dict` into this module and its descendants.
`modules`	Returns an iterator over all modules in the network.
`named_buffers`(prefix, recurse)	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
`named_children`	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
`named_modules`	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
`named_parameters`(prefix, recurse)	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
`parameters`(recurse)	Returns an iterator over module parameters.
`register_backward_hook`	Registers a backward hook on the module.
`register_buffer`(name, tensor, NoneType], …)	Adds a buffer to the module.
`register_forward_hook`(hook, NoneType])	Registers a forward hook on the module.
`register_forward_pre_hook`(hook, NoneType])	Registers a forward pre-hook on the module.
`register_full_backward_hook`	Registers a backward hook on the module.
`register_parameter`(name, param, NoneType])	Adds a parameter to the module.
`requires_grad_`(requires_grad)	Change if autograd should record operations on parameters in this module.
`set_extra_state`(state)	This function is called from `load_state_dict()` to handle any extra state found within the `state_dict`.
`share_memory`()	See `torch.Tensor.share_memory_()`
`state_dict`([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
`to`(args, *kwargs)	Moves and/or casts the parameters and buffers.
`to_empty`(*, device, torch.device])	Moves the parameters and buffers to the specified device without copying storage.
`train`(mode)	Sets the module in training mode.
`type`(dst_type, str])	Casts all parameters and buffers to `dst_type`.
`xpu`(device, torch.device, NoneType] = None)	Moves all model parameters and buffers to the XPU.
`zero_grad`(set_to_none)	Sets gradients of all model parameters to zero.

__call__

expansion = 1¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class olympus.models.resnet.Bottleneck(inplanes, planes, stride=1, downsample=None)[source]¶

Bases: torch.nn.modules.module.Module

See :class`.ResNet` for license and references`

Methods

`add_module`	Adds a child module to the current module.
`apply`	Applies `fn` recursively to every submodule (as returned by `.children()`) as well as self.
`bfloat16`()	Casts all floating point parameters and buffers to `bfloat16` datatype.
`buffers`(recurse)	Returns an iterator over module buffers.
`children`	Returns an iterator over immediate children modules.
`cpu`()	Moves all model parameters and buffers to the CPU.
`cuda`(device, torch.device, NoneType] = None)	Moves all model parameters and buffers to the GPU.
`double`()	Casts all floating point parameters and buffers to `double` datatype.
`eval`()	Sets the module in evaluation mode.
`extra_repr`()	Set the extra representation of the module
`float`()	Casts all floating point parameters and buffers to `float` datatype.
`forward`(x)	Defines the computation performed at every call.
`get_buffer`(target)	Returns the buffer given by `target` if it exists, otherwise throws an error.
`get_extra_state`()	Returns any extra state to include in the module’s state_dict.
`get_parameter`(target)	Returns the parameter given by `target` if it exists, otherwise throws an error.
`get_submodule`(target)	Returns the submodule given by `target` if it exists, otherwise throws an error.
`half`()	Casts all floating point parameters and buffers to `half` datatype.
`load_state_dict`(state_dict, Tensor], strict)	Copies parameters and buffers from `state_dict` into this module and its descendants.
`modules`	Returns an iterator over all modules in the network.
`named_buffers`(prefix, recurse)	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
`named_children`	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
`named_modules`	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
`named_parameters`(prefix, recurse)	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
`parameters`(recurse)	Returns an iterator over module parameters.
`register_backward_hook`	Registers a backward hook on the module.
`register_buffer`(name, tensor, NoneType], …)	Adds a buffer to the module.
`register_forward_hook`(hook, NoneType])	Registers a forward hook on the module.
`register_forward_pre_hook`(hook, NoneType])	Registers a forward pre-hook on the module.
`register_full_backward_hook`	Registers a backward hook on the module.
`register_parameter`(name, param, NoneType])	Adds a parameter to the module.
`requires_grad_`(requires_grad)	Change if autograd should record operations on parameters in this module.
`set_extra_state`(state)	This function is called from `load_state_dict()` to handle any extra state found within the `state_dict`.
`share_memory`()	See `torch.Tensor.share_memory_()`
`state_dict`([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
`to`(args, *kwargs)	Moves and/or casts the parameters and buffers.
`to_empty`(*, device, torch.device])	Moves the parameters and buffers to the specified device without copying storage.
`train`(mode)	Sets the module in training mode.
`type`(dst_type, str])	Casts all parameters and buffers to `dst_type`.
`xpu`(device, torch.device, NoneType] = None)	Moves all model parameters and buffers to the XPU.
`zero_grad`(set_to_none)	Sets gradients of all model parameters to zero.

__call__

expansion = 4¶

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class olympus.models.resnet.ResNet(block, layers, input_size, conv, maxpool, avgpool, num_classes)[source]¶

Bases: torch.nn.modules.module.Module

A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or short-cuts to jump over some layers. Typical ResNet models are implemented with double- or triple- layer skips that contain nonlinearities (ReLU) and batch normalization in between. An additional weight matrix may be used to learn the skip weights; these models are known as HighwayNets. Models with several parallel skips are referred to as DenseNets. In the context of residual neural networks, a non-residual network may be described as a plain network. More on wikipedia.

Paper available on arxiv. Original source github.

Notes

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

References

[1]	Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. “Deep Residual Learning for Image Recognition”, Dec 2015

Attributes:	input_size: (1, 28, 28), (3, 32, 32), (3, 64, 64)

Methods

`add_module`	Adds a child module to the current module.
`apply`	Applies `fn` recursively to every submodule (as returned by `.children()`) as well as self.
`bfloat16`()	Casts all floating point parameters and buffers to `bfloat16` datatype.
`buffers`(recurse)	Returns an iterator over module buffers.
`children`	Returns an iterator over immediate children modules.
`cpu`()	Moves all model parameters and buffers to the CPU.
`cuda`(device, torch.device, NoneType] = None)	Moves all model parameters and buffers to the GPU.
`double`()	Casts all floating point parameters and buffers to `double` datatype.
`eval`()	Sets the module in evaluation mode.
`extra_repr`()	Set the extra representation of the module
`float`()	Casts all floating point parameters and buffers to `float` datatype.
`forward`(x)	Defines the computation performed at every call.
`get_buffer`(target)	Returns the buffer given by `target` if it exists, otherwise throws an error.
`get_extra_state`()	Returns any extra state to include in the module’s state_dict.
`get_parameter`(target)	Returns the parameter given by `target` if it exists, otherwise throws an error.
`get_submodule`(target)	Returns the submodule given by `target` if it exists, otherwise throws an error.
`half`()	Casts all floating point parameters and buffers to `half` datatype.
`load_state_dict`(state_dict, Tensor], strict)	Copies parameters and buffers from `state_dict` into this module and its descendants.
`modules`	Returns an iterator over all modules in the network.
`named_buffers`(prefix, recurse)	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
`named_children`	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
`named_modules`	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
`named_parameters`(prefix, recurse)	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
`parameters`(recurse)	Returns an iterator over module parameters.
`register_backward_hook`	Registers a backward hook on the module.
`register_buffer`(name, tensor, NoneType], …)	Adds a buffer to the module.
`register_forward_hook`(hook, NoneType])	Registers a forward hook on the module.
`register_forward_pre_hook`(hook, NoneType])	Registers a forward pre-hook on the module.
`register_full_backward_hook`	Registers a backward hook on the module.
`register_parameter`(name, param, NoneType])	Adds a parameter to the module.
`requires_grad_`(requires_grad)	Change if autograd should record operations on parameters in this module.
`set_extra_state`(state)	This function is called from `load_state_dict()` to handle any extra state found within the `state_dict`.
`share_memory`()	See `torch.Tensor.share_memory_()`
`state_dict`([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
`to`(args, *kwargs)	Moves and/or casts the parameters and buffers.
`to_empty`(*, device, torch.device])	Moves the parameters and buffers to the specified device without copying storage.
`train`(mode)	Sets the module in training mode.
`type`(dst_type, str])	Casts all parameters and buffers to `dst_type`.
`xpu`(device, torch.device, NoneType] = None)	Moves all model parameters and buffers to the XPU.
`zero_grad`(set_to_none)	Sets gradients of all model parameters to zero.

__call__

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

olympus.models.resnet.build(block, cfg, input_size, output_size)[source]¶

olympus.models.resnet.conv3x3(in_planes, out_planes, stride=1)[source]¶: 3x3 convolution with padding