from fastai2.vision.all import *

path = untar_data(URLs.PETS)
path_hr = path/'images'
path_lr = path/'small-96'
path_mr = path/'small-256'

items = get_image_files(path_hr)

def resize_one(fn, path, size):
    dest = path/fn.relative_to(path_hr)
    dest.parent.mkdir(parents=True, exist_ok=True)
    img = Image.open(fn)
    targ_sz = resize_to(img, size, use_min=True)
    img = img.resize(targ_sz, resample=Image.BILINEAR).convert('RGB')
    img.save(dest, quality=60)

# create smaller image sets the first time this nb is run
sets = [(path_lr, 96), (path_mr, 256)]
for p,size in sets:
    if not p.exists(): 
        print(f"resizing to {size} into {p}")
        parallel(partial(resize_one, path=p, size=size), items)

bs,size=32,128
arch = resnet34

def get_dls(bs,size):
    dblock = DataBlock(blocks=(ImageBlock, ImageBlock),
                   get_items=get_image_files,
                   get_y=lambda x: path_hr/x.name,
                   splitter=RandomSplitter(),
                   item_tfms=Resize(size),
                   batch_tfms=[*aug_transforms(max_zoom=2.), Normalize.from_stats(*imagenet_stats)])
    dls = dblock.dataloaders(path_lr, bs=bs, path=path, item_tfms=Resize(size))         
    dls.c = 3
    return dls

dls = get_dls(bs,size)

t = tensor(dls.valid_ds[0][1]).float().permute(2,0,1)/255.
t = torch.stack([t,t])

def gram_matrix(x):
    n,c,h,w = x.size()
    x = x.view(n, c, -1)
    return (x @ x.transpose(1,2))/(c*h*w)

base_loss = F.l1_loss

class FeatureLoss(Module):
    def __init__(self, m_feat, layer_ids, layer_wgts):
        self.m_feat = m_feat
        self.loss_features = [self.m_feat[i] for i in layer_ids]
        self.hooks = hook_outputs(self.loss_features, detach=False)
        self.wgts = layer_wgts
        self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))
              ] + [f'gram_{i}' for i in range(len(layer_ids))]

    def make_features(self, x, clone=False):
        self.m_feat(x)
        return [(o.clone() if clone else o) for o in self.hooks.stored]
    
    def forward(self, input, target, reduction='mean'):
        out_feat = self.make_features(target, clone=True)
        in_feat = self.make_features(input)
        self.feat_losses = [base_loss(input,target,reduction=reduction)]
        self.feat_losses += [base_loss(f_in, f_out,reduction=reduction)*w
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out),reduction=reduction)*w**2 * 5e3
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        if reduction=='none': 
            self.feat_losses = [f.mean(dim=[1,2,3]) for f in self.feat_losses[:4]] + [f.mean(dim=[1,2]) for f in self.feat_losses[4:]]
        for n,l in zip(self.metric_names, self.feat_losses): setattr(self, n, l)
        return sum(self.feat_losses)
    
    def __del__(self): self.hooks.remove()

feat_loss = FeatureLoss(vgg_m, blocks[2:5], [5,15,2])

learn = unet_learner(dls, arch, loss_func=feat_loss, metrics=LossMetrics(feat_loss.metric_names),
                     config=unet_config(blur=True, norm_type=NormType.Weight))

lr = 1e-3
wd = 1e-3

def do_fit(save_name, lrs=slice(lr), pct_start=0.9):
    learn.fit_one_cycle(10, lrs, pct_start=pct_start, wd=wd)
    learn.save(save_name)
    learn.show_results(ds_idx=1, max_n=2, figsize=(15,11))

learn.unfreeze()

dls = get_dls(12,size*2)

learn.dls = dls
learn.freeze()

learn.load('1b');

learn.unfreeze()

learn = unet_learner(dls, arch, loss_func=feat_loss, metrics=LossMetrics(feat_loss.metric_names),
                     config=unet_config(blur=True, norm_type=NormType.Weight))

dblock = DataBlock(blocks=(ImageBlock, ImageBlock),
                   get_items=get_image_files,
                   get_y=lambda x: path_mr/x.name,
                   splitter=RandomSplitter(),
                   item_tfms=Resize(size),
                   batch_tfms=[*aug_transforms(), Normalize.from_stats(*imagenet_stats)])
dbunch_mr = dblock.dataloaders(path_mr, bs=1, val_bs=1, path=path)         
dbunch_mr.c = 3

learn.load('2b');

learn.dls = dbunch_mr