skindeep_infer.py

from fastai.vision.image import Image
from fastai.vision import load_learner
import PIL.Image
import torchvision.transforms as T
from pathlib import Path





from torch.nn import Module


class FeatureLoss(Module):
    def __init__(self, m_feat, layer_ids, layer_wgts):
        super().__init__()
        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):
        out_feat = self.make_features(target, clone=True)
        in_feat = self.make_features(input)
        self.feat_losses = [base_loss(input, target)]
        self.feat_losses += [base_loss(f_in, f_out)*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))*w**2 * 5e3
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.metrics = dict(zip(self.metric_names, self.feat_losses))
        return sum(self.feat_losses)

    def __del__(self): self.hooks.remove()


path = Path(".")
learn = load_learner(path, 'SkinDeep2.pkl')



def process_image(filename):
    img = PIL.Image.open("inked.jpg").convert("RGB")
    img_t = T.ToTensor()(img)
    img_fast = Image(img_t)

    _, img_hr, _ = learn.predict(img_fast)
    Image(img_hr).save('out.png')