Source code for catalyst.utils.torch

from typing import Dict, Iterable, List, Union
import collections
import os
import re

import numpy as np

import torch
from torch import nn, Tensor
import torch.backends
from torch.backends import cudnn

from catalyst.settings import IS_XLA_AVAILABLE
from catalyst.typing import Device, Model, Optimizer
from catalyst.utils.dict import merge_dicts

[docs]def get_optimizable_params(model_or_params): """Returns all the parameters that requires gradients.""" params: Iterable[torch.Tensor] = model_or_params if isinstance(model_or_params, nn.Module): params = model_or_params.parameters() master_params = [p for p in params if p.requires_grad] return master_params
[docs]def get_optimizer_momentum(optimizer: Optimizer) -> float: """Get momentum of current optimizer. Args: optimizer: PyTorch optimizer Returns: float: momentum at first param group """ betas = optimizer.param_groups[0].get("betas", None) momentum = optimizer.param_groups[0].get("momentum", None) return betas[0] if betas is not None else momentum
[docs]def set_optimizer_momentum(optimizer: Optimizer, value: float, index: int = 0): """Set momentum of ``index`` 'th param group of optimizer to ``value``. Args: optimizer: PyTorch optimizer value: new value of momentum index (int, optional): integer index of optimizer's param groups, default is 0 """ betas = optimizer.param_groups[0].get("betas", None) momentum = optimizer.param_groups[0].get("momentum", None) if betas is not None: _, beta = betas optimizer.param_groups[index]["betas"] = (value, beta) elif momentum is not None: optimizer.param_groups[index]["momentum"] = value
[docs]def get_device() -> torch.device: """Simple returning the best available device (TPU > GPU > CPU).""" is_available_gpu = torch.cuda.is_available() device = "cpu" if IS_XLA_AVAILABLE: import torch_xla.core.xla_model as xm device = xm.xla_device() elif is_available_gpu: device = "cuda" return torch.device(device)
[docs]def get_available_gpus(): """Array of available GPU ids. Examples: >>> os.environ["CUDA_VISIBLE_DEVICES"] = "0,2" >>> get_available_gpus() [0, 2] >>> os.environ["CUDA_VISIBLE_DEVICES"] = "0,-1,1" >>> get_available_gpus() [0] >>> os.environ["CUDA_VISIBLE_DEVICES"] = "" >>> get_available_gpus() [] >>> os.environ["CUDA_VISIBLE_DEVICES"] = "-1" >>> get_available_gpus() [] Returns: iterable: available GPU ids """ if "CUDA_VISIBLE_DEVICES" in os.environ: result = os.environ["CUDA_VISIBLE_DEVICES"].split(",") result = [id_ for id_ in result if id_ != ""] # invisible GPUs # if -1 in result: index = result.index(-1) result = result[:index] elif torch.cuda.is_available(): result = list(range(torch.cuda.device_count())) else: result = [] return result
[docs]def get_activation_fn(activation: str = None): """Returns the activation function from ``torch.nn`` by its name.""" if activation is None or activation.lower() == "none": activation_fn = lambda x: x # noqa: E731 else: activation_fn = torch.nn.__dict__[activation]() return activation_fn
[docs]def any2device(value, device: Device): """ Move tensor, list of tensors, list of list of tensors, dict of tensors, tuple of tensors to target device. Args: value: Object to be moved device: target device ids Returns: Same structure as value, but all tensors and np.arrays moved to device """ if isinstance(value, dict): return {k: any2device(v, device) for k, v in value.items()} elif isinstance(value, (tuple, list)): return [any2device(v, device) for v in value] elif torch.is_tensor(value): return, non_blocking=True) elif ( isinstance(value, (np.ndarray, np.void)) and value.dtype.fields is not None ): return { k: any2device(value[k], device) for k in value.dtype.fields.keys() } elif isinstance(value, np.ndarray): return torch.Tensor(value).to(device) return value
[docs]def prepare_cudnn(deterministic: bool = None, benchmark: bool = None) -> None: """ Prepares CuDNN benchmark and sets CuDNN to be deterministic/non-deterministic mode Args: deterministic: deterministic mode if running in CuDNN backend. benchmark: If ``True`` use CuDNN heuristics to figure out which algorithm will be most performant for your model architecture and input. Setting it to ``False`` may slow down your training. """ if torch.cuda.is_available(): # CuDNN reproducibility # if deterministic is None: deterministic = ( os.environ.get("CUDNN_DETERMINISTIC", "True") == "True" ) cudnn.deterministic = deterministic # if benchmark is None: benchmark = os.environ.get("CUDNN_BENCHMARK", "True") == "True" cudnn.benchmark = benchmark
[docs]def process_model_params( model: Model, layerwise_params: Dict[str, dict] = None, no_bias_weight_decay: bool = True, lr_scaling: float = 1.0, ) -> List[Union[torch.nn.Parameter, dict]]: """Gains model parameters for ``torch.optim.Optimizer``. Args: model: Model to process layerwise_params: Order-sensitive dict where each key is regex pattern and values are layer-wise options for layers matching with a pattern no_bias_weight_decay: If true, removes weight_decay for all ``bias`` parameters in the model lr_scaling: layer-wise learning rate scaling, if 1.0, learning rates will not be scaled Returns: iterable: parameters for an optimizer Example:: >>> model = catalyst.contrib.models.segmentation.ResnetUnet() >>> layerwise_params = collections.OrderedDict([ >>> ("conv1.*", dict(lr=0.001, weight_decay=0.0003)), >>> ("conv.*", dict(lr=0.002)) >>> ]) >>> params = process_model_params(model, layerwise_params) >>> optimizer = torch.optim.Adam(params, lr=0.0003) """ params = list(model.named_parameters()) layerwise_params = layerwise_params or collections.OrderedDict() model_params = [] for name, parameters in params: options = {} for pattern, pattern_options in layerwise_params.items(): if re.match(pattern, name) is not None: # all new LR rules write on top of the old ones options = merge_dicts(options, pattern_options) # no bias decay from if no_bias_weight_decay and name.endswith("bias"): options["weight_decay"] = 0.0 # lr linear scaling from if "lr" in options: options["lr"] *= lr_scaling model_params.append({"params": parameters, **options}) return model_params
[docs]def get_requires_grad(model: Model): """Gets the ``requires_grad`` value for all model parameters. Example:: >>> model = SimpleModel() >>> requires_grad = get_requires_grad(model) Args: model: model Returns: requires_grad (Dict[str, bool]): value """ requires_grad = {} for name, param in model.named_parameters(): requires_grad[name] = param.requires_grad return requires_grad
[docs]def set_requires_grad( model: Model, requires_grad: Union[bool, Dict[str, bool]] ): """Sets the ``requires_grad`` value for all model parameters. Example:: >>> model = SimpleModel() >>> set_requires_grad(model, requires_grad=True) >>> # or >>> model = SimpleModel() >>> set_requires_grad(model, requires_grad={""}) Args: model: model requires_grad (Union[bool, Dict[str, bool]]): value """ if isinstance(requires_grad, dict): for name, param in model.named_parameters(): assert ( name in requires_grad ), f"Parameter `{name}` does not exist in requires_grad" param.requires_grad = requires_grad[name] else: requires_grad = bool(requires_grad) for param in model.parameters(): param.requires_grad = requires_grad
[docs]def get_network_output(net: Model, *input_shapes_args, **input_shapes_kwargs): """# noqa: D202 For each input shape returns an output tensor Examples: >>> net = nn.Linear(10, 5) >>> utils.get_network_output(net, (1, 10)) tensor([[[-0.2665, 0.5792, 0.9757, -0.5782, 0.1530]]]) Args: net: the model *input_shapes_args: variable length argument list of shapes **input_shapes_kwargs: key-value arguemnts of shapes Returns: tensor with network output """ def _rand_sample( input_shape, ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]: if isinstance(input_shape, dict): input_t = { key: torch.Tensor(torch.randn((1,) + key_input_shape)) for key, key_input_shape in input_shape.items() } else: input_t = torch.Tensor(torch.randn((1,) + input_shape)) return input_t input_args = [ _rand_sample(input_shape) for input_shape in input_shapes_args ] input_kwargs = { key: _rand_sample(input_shape) for key, input_shape in input_shapes_kwargs.items() } output_t = net(*input_args, **input_kwargs) return output_t
[docs]def detach(tensor: torch.Tensor) -> np.ndarray: """Detach a pytorch tensor from graph and convert it to numpy array Args: tensor: PyTorch tensor Returns: numpy ndarray """ return tensor.cpu().detach().numpy()
[docs]def trim_tensors(tensors): """ Trim padding off of a batch of tensors to the smallest possible length. Should be used with ``. Adapted from `Dynamic minibatch trimming to improve BERT training speed`_. Args: tensors: list of tensors to trim. Returns: List[torch.tensor]: list of trimmed tensors. .. _`Dynamic minibatch trimming to improve BERT training speed`: """ max_len = torch.max(torch.sum((tensors[0] != 0), 1)) if max_len > 2: tensors = [tsr[:, :max_len] for tsr in tensors] return tensors
[docs]def normalize(samples: Tensor) -> Tensor: """ Args: samples: tensor with shape of [n_samples, features_dim] Returns: normalized tensor with the same shape """ norms = torch.norm(samples, p=2, dim=1).unsqueeze(1) samples = samples / (norms + torch.finfo(torch.float32).eps) return samples
__all__ = [ "get_optimizable_params", "get_optimizer_momentum", "set_optimizer_momentum", "get_device", "get_available_gpus", "get_activation_fn", "any2device", "prepare_cudnn", "process_model_params", "get_requires_grad", "set_requires_grad", "get_network_output", "detach", "trim_tensors", "normalize", ]