utils.py

import os
import torch
import torch.nn as nn
from torch.nn.utils import fuse_conv_bn_eval

class CrossEntropyLabelSmooth(nn.Module):
    """Cross entropy loss with label smoothing regularizer.
    Reference:
    Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
    Equation: y = (1 - epsilon) * y + epsilon / K.
    Args:
        epsilon (float): weight.
    """
    def __init__(self, epsilon=0.1, use_gpu=True):
        super(CrossEntropyLabelSmooth, self).__init__()
        self.epsilon = epsilon
        self.use_gpu = use_gpu
        self.logsoftmax = nn.LogSoftmax(dim=1)

    def forward(self, inputs, targets):
        """
        Args:
            inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
            targets: ground truth labels with shape (num_classes)
        """
        _, num_classes = inputs.shape
        log_probs = self.logsoftmax(inputs)
        targets = torch.zeros(log_probs.size()).scatter_(1, targets.unsqueeze(1).data.cpu(), 1)
        if self.use_gpu: targets = targets.cuda()
        targets = (1 - self.epsilon) * targets + self.epsilon / num_classes
        loss = (- targets * log_probs).mean(0).sum()
        return loss

def fuse_all_conv_bn(model):
    stack = []
    for name, module in model.named_children():
        if list(module.named_children()):
            fuse_all_conv_bn(module)
            
        if isinstance(module, nn.BatchNorm2d):
            if not stack:
                continue
            if isinstance(stack[-1][1], nn.Conv2d):
                setattr(model, stack[-1][0], fuse_conv_bn_eval(stack[-1][1], module))
                setattr(model, name, nn.Identity())
        else:
            stack.append((name, module))
    return model

def save_network(network, dirname, epoch_label, local_rank=-1):
    if isinstance(epoch_label, int):
        save_filename = 'net_%03d.pth'% epoch_label
    else:
        save_filename = 'net_%s.pth'% epoch_label
    save_path = os.path.join('./model',dirname,save_filename)

    if local_rank>-1:
        if local_rank == 0: # save the main process model
            torch.save(network.state_dict(), save_path)
            network.cuda(local_rank)
    else:
        torch.save(network.state_dict(), save_path)
        network.cuda()


def load_state_dict_mute(self, state_dict: 'OrderedDict[str, Tensor]',
                        strict: bool = True):
        r"""Copies parameters and buffers from :attr:`state_dict` into
        this module and its descendants. If :attr:`strict` is ``True``, then
        the keys of :attr:`state_dict` must exactly match the keys returned
        by this module's :meth:`~torch.nn.Module.state_dict` function.

        Args:
            state_dict (dict): a dict containing parameters and
                persistent buffers.
            strict (bool, optional): whether to strictly enforce that the keys
                in :attr:`state_dict` match the keys returned by this module's
                :meth:`~torch.nn.Module.state_dict` function. Default: ``True``

        Returns:
            ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
                * **missing_keys** is a list of str containing the missing keys
                * **unexpected_keys** is a list of str containing the unexpected keys
        """
        missing_keys: List[str] = []
        unexpected_keys: List[str] = []
        error_msgs: List[str] = []

        # copy state_dict so _load_from_state_dict can modify it
        metadata = getattr(state_dict, '_metadata', None)
        state_dict = state_dict.copy()
        if metadata is not None:
            # mypy isn't aware that "_metadata" exists in state_dict
            state_dict._metadata = metadata  # type: ignore[attr-defined]

        def load(module, prefix=''):
            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
            module._load_from_state_dict(
                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
            for name, child in module._modules.items():
                if child is not None:
                    load(child, prefix + name + '.')

        load(self)
        del load

        if strict:
            if len(unexpected_keys) > 0:
                error_msgs.insert(
                    0, 'Unexpected key(s) in state_dict: {}. '.format(
                        ', '.join('"{}"'.format(k) for k in unexpected_keys)))
            if len(missing_keys) > 0:
                error_msgs.insert(
                    0, 'Missing key(s) in state_dict: {}. '.format(
                        ', '.join('"{}"'.format(k) for k in missing_keys)))