def test_OmniglotBatchedSeq():
import cv2
root = './data/'
h = 240
w = 240
dataset = Omniglot(root=root, h=h, w=w)
#dataset = Omniglot(root=root,background=False,download=True)
print(len(dataset))
batch_size = 2
max_nbr_char = 5
idx_alphabet = list()
for i in range(batch_size):
idx_alphabet.append(i)
idx_sample = 0
keys = ('alphabet', 'character', 'sample', 'target', 'nbrCharacter')
seqs = list()
for s in range(batch_size):
seq, nbrCharacter4Task, nbrSample4Task = dataset.generateIterFewShotInputSequence(
alphabet_idx=idx_alphabet[s], max_nbr_char=max_nbr_char)
dseqs = {
'alphabet': [],
'character': [],
'sample': [],
'target': [],
'nbrCharacter': []
}
for el in seq:
for k in el.keys():
dseqs[k].append(el[k])
seqs.append(dseqs)
seqs = [{k: [seqs[b][k] for b in range(batch_size)]} for k in keys]
dseqs = dict()
for d in seqs:
dseqs.update(d)
# key x task_idx x seq_idx
for k in dseqs.keys():
dseqs[k] = zip(*dseqs[k])
print(seqs)
raise
batch = dataset.getBatchedSample(seqs)
def test_OmniglotSeq():
import cv2
root = './data/'
h = 240
w = 240
dataset = Omniglot(root=root, h=h, w=w)
#dataset = Omniglot(root=root,background=False,download=True)
print(len(dataset))
idx_alphabet = 0
idx_sample = 0
seq, nbrCharacter4Task, nbrSample4Task = dataset.generateIterFewShotInputSequence(
alphabet_idx=idx_alphabet)
sample = dataset.getSample(seq[idx_sample]['alphabet'],
seq[idx_sample]['character'],
seq[idx_sample]['sample'])
image_path = dataset.sampleSample4Character4Alphabet(
seq[idx_sample]['alphabet'], seq[idx_sample]['character'],
seq[idx_sample]['sample'])
changed = False
seqChanged = False
idx = 0
while True:
#sample = dataset[idx]
img = (sample['image'].numpy() * 255).transpose((1, 2, 0))
cv2.imshow('test', img)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
elif key == ord('n'):
idx += 1
idx_sample = (idx_sample + 1) % len(seq)
changed = True
elif key == ord('a'):
idx_alphabet += 1
idx_character = 0
idx_sample = 0
seqChanged = True
if changed:
changed = False
sample = dataset.getSample(seq[idx_sample]['alphabet'],
seq[idx_sample]['character'],
seq[idx_sample]['sample'])
image_path = dataset.sampleSample4Character4Alphabet(
seq[idx_sample]['alphabet'], seq[idx_sample]['character'],
seq[idx_sample]['sample'])
print(image_path, '/{}'.format(nbrCharacter4Task))
print('Sample {} / {}'.format(idx, nbrSample4Task))
print('Target :{}'.format(seq[idx_sample]['target']))
if seqChanged:
seqChanged = False
idx = 0
seq, nbrCharacter4Task, nbrSample4Task = dataset.generateFewShotLearningTask(
alphabet_idx=idx_alphabet)
sample = dataset.getSample(seq[idx_sample]['alphabet'],
seq[idx_sample]['character'],
seq[idx_sample]['sample'])
image_path = dataset.sampleSample4Character4Alphabet(
seq[idx_sample]['alphabet'], seq[idx_sample]['character'],
seq[idx_sample]['sample'])
print(image_path, '/{}'.format(nbrCharacter4Task))