utils.py

import os
import os.path
import torch
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
from torch.nn import init
import torchvision


def get_data_loader(dataset, batch_size, cuda=False, collate_fn=None):
    collate_fn = collate_fn or default_collate
    dl =  DataLoader(dataset, batch_size=batch_size,
        shuffle=True, drop_last=True, collate_fn=collate_fn,
        **({'num_workers': 0, 'pin_memory': True} if cuda else {}))
    return dl

def save_checkpoint(model, model_dir, iteration):
    path = os.path.join(model_dir, model.name)

    # save the checkpoint.
    if not os.path.exists(model_dir):
        os.makedirs(model_dir)
    torch.save({
        'state': model.state_dict(),
        'iteration': iteration,
    }, path)

    # notify that we successfully saved the checkpoint.
    print('=> saved the model {name} to {path}'.format(
        name=model.name, path=path
    ))

    return iteration


def load_checkpoint(model, model_dir):
    path = os.path.join(model_dir, model.name)

    # load the checkpoint.
    checkpoint = torch.load(path)
    print('=> loaded checkpoint of {name} from {path}'.format(
        name=model.name, path=path
    ))

    # load parameters and return the checkpoint's epoch and precision.
    model.load_state_dict(checkpoint['state'])
    iteration = checkpoint['iteration']
    return iteration


def test_model(model, sample_size, path):
    os.makedirs(os.path.dirname(path), exist_ok=True)
    torchvision.utils.save_image(
        model.sample_image(sample_size).data,
        path + '.jpg'
    )
    print('=> generated sample images at "{}".'.format(path))


def xavier_initialize(model):
    modules = [
        m for n, m in model.named_modules() if
        'conv' in n or 'fc' in n
    ]

    parameters = [
        p for
        m in modules for
        p in m.parameters()
    ]

    for p in parameters:
        if p.dim() >= 2:
            init.xavier_normal_(p)
        else:
            init.constant_(p, 0)


def gaussian_intiailize(model, std=.01):
    modules = [
        m for n, m in model.named_modules() if
        'conv' in n or 'fc' in n
    ]

    parameters = [
        p for
        m in modules for
        p in m.parameters()
    ]

    for p in parameters:
        if p.dim() >= 2:
            init.normal_(p, std=std)
        else:
            init.constant_(p, 0)