def test_findall():
pattern = "[aA]"
s = ["hello", "and héllo", "this was empty", ""]
nvstrs = nvstrings.to_device(s)
got = nvstrs.findall(pattern)[0]
expected = [None, "a", "a", None]
assert_eq(got, expected)
def test_ftos():
s = np.array([0, 103, -254848.5929, 8395794.248339, np.nan, np.inf],
dtype=np.float32)
got = nvstrings.ftos(s)
expected = nvstrings.to_device(
['0.0', '103.0', '-254848.5938', '8395794.0', 'NaN', 'Inf'])
assert_eq(got, expected)
def test_match(pattern):
s = ["hello", "and héllo", None, ""]
pstrs = pd.Series(s)
nvstrs = nvstrings.to_device(s)
got = nvstrs.match(pattern)
expected = pstrs.str.match(pattern).values
assert_eq(got, expected)
def test_count(pattern):
s = ["hello", "and héllo", "this was empty", ""]
pstrs = pd.Series(s)
nvstrs = nvstrings.to_device(s)
got = nvstrs.count(pattern)
expected = pstrs.str.count(pattern).values
assert_eq(got, expected)
def test_indexes_for_key():
strs = nvstrings.to_device(
["eee", "aaa", "eee", "ddd", "ccc", "ccc", "ccc", "eee", "aaa"])
cat = nvcategory.from_strings(strs)
got = cat.indexes_for_key("ccc")
expected = [4, 5, 6]
assert_eq(got, expected)
def test_decode_url():
s = nvstrings.to_device(urls2)
got = s.url_decode()
expected = []
for url in urls2:
expected.append(urllib.parse.unquote(url))
assert_eq(got, expected)
def test_from_offsets_with_bitmask():
values = np.array([97, 112, 112, 108, 101], dtype=np.int8)
offsets = np.array([0, 1, 2, 3, 4, 5], dtype=np.int32)
bitmask = np.array([29], dtype=np.int8)
s = nvstrings.from_offsets(values, offsets, 5, bitmask, 1)
expected = ['a', None, 'p', 'l', 'e']
assert_eq(s, expected)
def eval(self, train_xs, test_xs):
# print(train_xs.shape)
# print(test_xs.shape)
batch_size = 1000
utils.assert_eq(len(train_xs) % batch_size, 0)
utils.assert_eq(len(test_xs) % batch_size, 0)
train_fes = np.zeros(len(train_xs) // batch_size)
test_fes = np.zeros(len(test_xs) // batch_size)
x_shape = (batch_size, ) + train_xs.shape[1:]
x_node = Variable(torch.cuda.FloatTensor(*x_shape))
for i in xrange(len(train_fes)):
x_node.data.copy_(
torch.from_numpy(train_xs[i * batch_size:(i + 1) *
batch_size]))
train_fes[i] = self.net_f(x_node).data[0]
for i in xrange(len(test_fes)):
# print(test_xs.shape)
x_node.data.copy_(
torch.from_numpy(test_xs[i * batch_size:(i + 1) * batch_size]))
test_fes[i] = self.net_f(x_node).data[0]
mean_train_fes = train_fes.mean()
mean_test_fes = test_fes.mean()
log = 'Eval:\n'
log += '\tfree_energy on train: %s;\n' % mean_train_fes
log += '\tfree_energy on test: %s;\n' % mean_test_fes
log += '\tratio: %s' % np.exp(mean_train_fes - mean_test_fes)
print(log)
return log
def test_fillna(repl):
s = ["abcdefghij", "0123456789", "9876543210", None, "accénted", ""]
strs = nvstrings.to_device(s)
pstrs = pd.Series(s)
got = strs.fillna(repl)
expected = pstrs.fillna(repl)
assert_eq(got.to_host(), expected)
def tokenize(self, max_length=14):
"""Tokenizes the questions.
This will add q_token in each entry of the dataset.
-1 represent nil, and should be treated as padding_idx in embedding
"""
for entry in self.entries:
tokens = self.dictionary.tokenize(entry['question'], False)
tokens = tokens[:max_length]
if len(tokens) < max_length:
# Note here we pad in front of the sentence
padding = [self.dictionary.padding_idx
] * (max_length - len(tokens))
tokens = padding + tokens
utils.assert_eq(len(tokens), max_length)
entry['q_token'] = tokens
if entry['caption']:
tokens = self.dictionary.tokenize(entry['caption'], False)
tokens = tokens[:50]
if len(tokens) < 50:
# Note here we pad in front of the sentence
padding = [self.dictionary.padding_idx
] * (50 - len(tokens))
tokens = padding + tokens
utils.assert_eq(len(tokens), 50)
entry['c_token'] = tokens
else:
entry['c_token'] = [0] * 50
def test_get(index):
index = 0
s = ["abcdefghij", "0123456789", "9876543210", None, "accénted", ""]
strs = nvstrings.to_device(s)
got = strs.get(index)
expected = ['a', '0', '9', None, 'a', '']
assert_eq(got.to_host(), expected)
def test_replace(find, replace):
s = ["abcdefghij", "0123456789", "9876543210", None, "accénted", ""]
pstrs = pd.Series(s)
nvstrs = nvstrings.to_device(s)
got = nvstrs.replace(find, replace, regex=False)
expected = pstrs.str.replace(find, replace, regex=False)
assert_eq(got, expected)
def test_slice_replace(start, stop, repl):
s = ["abcdefghij", "0123456789", "9876543210", None, "accénted", ""]
strs = nvstrings.to_device(s)
pstrs = pd.Series(s)
got = strs.slice_replace(start, stop, repl)
expected = pstrs.str.slice_replace(start, stop, repl)
assert_eq(got.to_host(), expected)
def test_slice_from():
strs = nvstrings.to_device(
["hello world", "holy accéntéd", "batman", None, ""])
d_arr = rmm.to_device(np.asarray([2, 3, -1, -1, -1], dtype=np.int32))
got = strs.slice_from(starts=d_arr.device_ctypes_pointer.value)
expected = ['llo world', 'y accéntéd', '', None, '']
assert_eq(got, expected)
def test_cat():
strs = nvstrings.to_device(
["abc", "def", None, "", "jkl", "mno", "accént"]
)
got = strs.cat()
expected = ['abcdefjklmnoaccént']
assert_eq(got, expected)
# non-default separator
got = strs.cat(sep=':')
expected = ['abc:def::jkl:mno:accént']
assert_eq(got, expected)
# non default separator and na_rep
got = strs.cat(sep=':', na_rep='_')
expected = ['abc:def:_::jkl:mno:accént']
assert_eq(got, expected)
# non-null others, default separator, and na_rep
strs2 = nvstrings.to_device(["1", "2", "3", "4", "5", "é", None])
got = strs.cat(strs2, sep=":", na_rep="_")
expected = ['abc:1', 'def:2', '_:3', ':4', 'jkl:5', 'mno:é', 'accént:_']
assert_eq(got, expected)
# nvstrings others
strs2 = nvstrings.to_device(["1", "2", "3", None, "5", "é", ""])
got = strs.cat(strs2)
expected = ['abc1', 'def2', None, None, 'jkl5', 'mnoé', 'accént']
assert_eq(got, expected)
def test_rstrip():
s = [" hello ", " there ", " world ", None, " accénté ", ""]
strs = nvstrings.to_device(s)
pstrs = pd.Series(s)
got = strs.rstrip()
expected = pstrs.str.rstrip()
assert_eq(got.to_host(), expected)
def test_encode_url():
s = nvstrings.to_device(urls1)
got = s.url_encode()
expected = []
for url in urls1:
expected.append(urllib.parse.quote(url, safe="~"))
assert_eq(got, expected)
def test_values():
strs = nvstrings.to_device(
["eee", "aaa", "eee", "ddd", "ccc", "ccc", "ccc", "eee", "aaa"])
cat = nvcategory.from_strings(strs)
got = cat.values()
expected = [3, 0, 3, 2, 1, 1, 1, 3, 0]
assert_eq(got, expected)
def test_extract():
pattern = r"Flight:([A-Z]+)(\d+)"
s = [
"ALA-PEK Flight:HU7934",
"HKT-PEK Flight:CA822",
"FRA-PEK Flight:LA8769",
"FRA-PEK Flight:LH7332",
"",
None,
"Flight:ZZ",
]
nvstrs = nvstrings.to_device(s)
got = nvstrs.extract(pattern)
expected = np.array(
[
["HU", "7934"],
["CA", "822"],
["LA", "8769"],
["LH", "7332"],
[None, None],
[None, None],
[None, None],
]
)
assert_eq(got[0], expected[:, 0])
assert_eq(got[1], expected[:, 1])
def test_remove_unused_keys():
strs1 = nvstrings.to_device(["a", "b", "b", "f", "c", "f"])
strs2 = nvstrings.to_device(["b", "c", "e", "d"])
cat = nvcategory.from_strings(strs1)
cat1 = cat.set_keys(strs2)
cat1_unused_removed = cat1.remove_unused_keys()
assert_eq(cat1_unused_removed.keys(), ["b", "c"])
def test_dtos():
s = np.array([0, 103342.313, -25.4294, 839542223232.794248339],
dtype=np.float64)
got = nvstrings.dtos(s)
expected = nvstrings.to_device(
['0', '103342.313', '-25.4294', '8.395422232e+11'])
assert_eq(got, expected)
def tokenize(self, max_qu_length=14, max_cap_length=18):
"""Tokenizes the questions.
This will add q_token in each entry of the dataset.
-1 represent nil, and should be treated as padding_idx in embedding
"""
for entry in self.entries:
qtokens = self.question_dictionary.tokenize(
entry['question'], False)
qtokens = qtokens[:max_qu_length]
ctokens = [self.caption_dictionary.word2idx['<start>']]
ctokens.extend(
self.caption_dictionary.tokenize(entry['caption'], False))
ctokens.append(self.caption_dictionary.word2idx['<end>'])
ctokens = ctokens[:max_cap_length]
if len(qtokens) < max_qu_length:
# Note here we pad in front of the sentence
qpadding = [self.question_dictionary.padding_idx] \
* (max_qu_length - len(qtokens))
qtokens = qpadding + qtokens
utils.assert_eq(len(qtokens), max_qu_length)
entry['c_len'] = len(ctokens)
if len(ctokens) < max_cap_length:
# Note here we pad in front of the sentence
cpadding = [self.caption_dictionary.padding_idx] \
* (max_cap_length - len(ctokens))
ctokens = ctokens + cpadding
utils.assert_eq(len(ctokens), max_cap_length)
entry['q_token'] = qtokens
entry['c_token'] = ctokens
def test_extract_record():
pattern = r"Flight:([A-Z]+)(\d+)"
s = [
"ALA-PEK Flight:HU7934",
"HKT-PEK Flight:CA822",
"FRA-PEK Flight:LA8769",
"FRA-PEK Flight:LH7332",
"",
None,
"Flight:ZZ",
]
nvstrs = nvstrings.to_device(s)
got = nvstrs.extract_record(pattern)
expected = np.array(
[
["HU", "7934"],
["CA", "822"],
["LA", "8769"],
["LH", "7332"],
[None, None],
[None, None],
[None, None],
]
)
for i in range(len(got)):
assert_eq(got[i], expected[i, :])
def _load_dataset(dataroot, name, img_id2val, label2ans, args):
"""Load entries
img_id2val: dict {img_id -> val} val can be used to retrieve image or features_path
data_root: root path of dataset
name: 'train', 'val', 'test-dev2015', test2015'
"""
question_path = os.path.join(dataroot,
'questions/%s_questions.json' % (name))
print(f"Question Path : {question_path}")
if name == 'trainval':
combine_trainval(dataroot)
questions = json.load(open(question_path))
if 'questions' in questions:
questions = questions['questions']
questions = sorted(questions, key=lambda x: x['question_id'])
answer_not_found = 0
print(name)
# if 'test2015' not in name or 'test_dev' not in name:
if 'test_dev' not in name:
qn_id_to_ans = {}
answer_path = os.path.join(dataroot, 'features',
'%s_target.json' % name)
print(f"Answer Path : {answer_path}")
answers = json.load(open(answer_path, 'r'))
for answer in answers:
qn_id_to_ans[str(answer['question_id'])] = answer
entries = []
for question in questions:
if str(question['question_id']) in qn_id_to_ans:
answer = qn_id_to_ans[str(question['question_id'])].copy()
else:
answer_not_found += 1
answer = {
'question_id': question['question_id'],
'image_id': question['image_id'],
'scores': [],
'labels': []
}
try:
utils.assert_eq(question['question_id'], answer['question_id'])
utils.assert_eq(question['image_id'], answer['image_id'])
except AssertionError as e:
print(e)
img_id = question['image_id']
entries.append(
_create_entry(img_id2val[str(img_id)], question, answer))
else: # test2015
entries = []
for question in questions:
img_id = question['image_id']
entries.append(
_create_entry(img_id2val[str(img_id)], question, None))
print("answers not found {}".format(answer_not_found))
return entries
def test_match_strings():
s1 = ["hello", "here", None, "accéntéd", None, ""]
s2 = ["hello", "there", "world", "accéntéd", None, ""]
strs1 = nvstrings.to_device(s1)
strs2 = nvstrings.to_device(s2)
got = strs1.match_strings(strs2)
expected = [True, False, False, True, True, True]
assert_eq(got, expected)
def test_values():
narr = np.array([4, 1, 2, 3, 2, 1, 4, 1, 1])
cat = nvcategory.from_numbers(narr)
values = np.empty([cat.size()], dtype=np.int32)
cat.values(values)
got = values.tolist()
expected = [3, 0, 1, 2, 1, 0, 3, 0, 0]
assert_eq(got, expected)
def test_to_numbers():
narr = np.array([2, 1, 1.25, 1.5, 1, 1.25, 1, 1, 2])
cat = nvcategory.from_numbers(narr)
nbrs = np.empty([cat.size()], dtype=narr.dtype)
cat.to_numbers(nbrs)
got = nbrs.tolist()
expected = narr.tolist()
assert_eq(got, expected)
def test_order_length_alphabetical():
strs = nvstrings.to_device([
"abc", "defghi", None, "jkl", "mno", "pqr", "stu", "dog and cat",
"accénted", ""
])
sorted_strs = strs.order(3)
expected = [2, 9, 0, 3, 4, 5, 6, 1, 8, 7]
assert_eq(sorted_strs, expected)
def test_keys():
narr = np.array([2, 1, 1.25, 1.5, 1, 1.25, 1, 1, 2])
cat = nvcategory.from_numbers(narr)
keys = np.empty([cat.keys_size()], dtype=narr.dtype)
cat.keys(keys)
got = keys.tolist()
expected = [1.0, 1.25, 1.5, 2.0]
assert_eq(got, expected)
def test_from_offsets():
values = np.array([97, 112, 112, 108, 101], dtype=np.int8)
offsets = np.array([0, 1, 2, 3, 4, 5], dtype=np.int32)
cat = nvcategory.from_offsets(values, offsets, 5)
expected_keys = ['a', 'e', 'l', 'p']
expected_values = [0, 3, 3, 2, 1]
assert_eq(cat.keys(), expected_keys)
assert_eq(cat.values(), expected_values)
def __init__(self, state, action, reward, next_state, end):
utils.assert_eq(type(state), type(next_state))
self._state = (state * 255.0).astype(np.uint8)
self._next_state = (next_state * 255.0).astype(np.uint8)
self.action = action
self.reward = reward
self.end = end
def loss(self, states, actions, targets):
"""
params:
states: Variable [batch, channel, w, h]
actions: Variable [batch, num_actions] one hot encoding
targets: Variable [batch]
"""
utils.assert_eq(actions.size(1), self.num_actions)
qs = self.online_q_net(states)
preds = (qs * actions).sum(1)
err = nn.functional.smooth_l1_loss(preds, targets)
return err
def loss(self, states, actions, targets):
"""
params:
states: Variable [batch, channel, w, h]
actions: Variable [batch, num_actions] one hot encoding
targets: Variable [batch, num_atoms]
"""
utils.assert_eq(actions.size(1), self.num_actions)
actions = actions.unsqueeze(2)
probs = self.online_q_net(states) # [batch, num_actions, num_atoms]
probs = (probs * actions).sum(1) # [batch, num_atoms]
xent = -(targets * torch.log(probs.clamp(min=utils.EPS))).sum(1)
xent = xent.mean(0)
return xent
def tokenize(self, max_length=14):
"""Tokenizes the questions.
This will add q_token in each entry of the dataset.
-1 represent nil, and should be treated as padding_idx in embedding
"""
for entry in self.entries:
tokens = self.dictionary.tokenize(entry['question'], False)
tokens = tokens[:max_length]
if len(tokens) < max_length:
# Note here we pad in front of the sentence
padding = [self.dictionary.padding_idx] * (max_length - len(tokens))
tokens = padding + tokens
utils.assert_eq(len(tokens), max_length)
entry['q_token'] = tokens
def _load_dataset(dataroot, name, img_id2val):
"""Load entries
img_id2val: dict {img_id -> val} val can be used to retrieve image or features
dataroot: root path of dataset
name: 'train', 'val'
"""
question_path = os.path.join(
dataroot, 'v2_OpenEnded_mscoco_%s2014_questions.json' % name)
questions = sorted(json.load(open(question_path))['questions'],
key=lambda x: x['question_id'])
answer_path = os.path.join(dataroot, 'cache', '%s_target.pkl' % name)
answers = cPickle.load(open(answer_path, 'rb'))
answers = sorted(answers, key=lambda x: x['question_id'])
utils.assert_eq(len(questions), len(answers))
entries = []
for question, answer in zip(questions, answers):
utils.assert_eq(question['question_id'], answer['question_id'])
utils.assert_eq(question['image_id'], answer['image_id'])
img_id = question['image_id']
entries.append(_create_entry(img_id2val[img_id], question, answer))
return entries