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)
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
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