Exemple #1
0
def _get(img_dir, clf_ckpt_p):
    out_file = os.path.join(img_dir, OPTIMAL_QS_TXT)

    clf = load_classifier(clf_ckpt_p)

    t = timer.TimeAccumulator()
    opt = {}
    for i, p in enumerate(
            cached_listdir_imgs(img_dir, discard_shitty=False).ps):
        with t.execute():
            img = torch.from_numpy(np.array(Image.open(p))).to(
                pe.DEVICE, non_blocking=True).permute(2, 0, 1)
            assert img.shape[0] == 3
            img = img.unsqueeze(0)
            img = SymbolTensor(img.long(), L=256).to_norm().get()
            q = clf.get_q(img)
            opt[os.path.splitext(os.path.basename(p))[0]] = q
        if i > 0 and i % 10 == 0:
            print(i, t.mean_time_spent())
    with open(out_file, 'w', newline='') as csvfile:
        w = csv.writer(csvfile, quoting=csv.QUOTE_MINIMAL)
        w.writerow(['fn', 'q'])
        for filename, q in sorted(opt.items()):
            w.writerow([filename, q])
    print('Created', out_file)
Exemple #2
0
    def unpack(self, img_batch, fixed_first=None):
        raw = img_batch['raw'].to(pe.DEVICE,
                                  non_blocking=True)  # uint8 or int16
        q = img_batch['q'].to(pe.DEVICE).view(-1)  # 1d tensor of floats

        if fixed_first is not None:
            raw[0, ...] = fixed_first['raw']
            q[0, ...] = fixed_first['q']

        q = q - self.config_clf.first_class

        if self.padding_fac:
            raw = self.pad(raw)

        raw = SymbolTensor(raw.long(), L=256)
        return raw.to_norm(), q
Exemple #3
0
    def unpack_batch_pad(self, img_or_imgbatch):
        raw = img_or_imgbatch['raw'].to(pe.DEVICE,
                                        non_blocking=True)  # uint8 or int16
        q = img_or_imgbatch['q'].to(pe.DEVICE).view(-1)  # 1d tensor of floats

        if len(raw.shape) == 3:
            raw.unsqueeze_(0)

        if self.padding_fac:
            raw = self.pad(raw)

        q = q - self.config_clf.first_class

        assert len(raw.shape) == 4

        raw = SymbolTensor(raw.long(), L=256)
        return raw.to_norm(), q