def _interactive_input_fn(hparams, decode_hp, Q):
"""Generator that reads from the terminal and yields "interactive inputs".
Due to temporary limitations in tf.learn, if we don't want to reload the
whole graph, then we are stuck encoding all of the input as one fixed-size
numpy array.
We yield int32 arrays with shape [const_array_size]. The format is:
[num_samples, decode_length, len(input ids), <input ids>, <padding>]
Args:
hparams: model hparams
decode_hp: decode hparams
Yields:
numpy arrays
Raises:
Exception: when `input_type` is invalid.
"""
num_samples = decode_hp.num_samples if decode_hp.num_samples > 0 else 1
decode_length = 1 #go one more
input_type = "text"
p_hparams = hparams.problem_hparams
has_input = "inputs" in p_hparams.modality
vocabulary = p_hparams.vocabulary["inputs" if has_input else "targets"]
# This should be longer than the longest input.
const_array_size = 10000
# Import readline if available for command line editing and recall.
print('*' * 100)
print('getting...')
input_string = Q.get()
print('got!', input_string)
input_ids = vocabulary.encode(input_string)
if has_input:
input_ids.append(text_encoder.EOS_ID)
x = [num_samples, decode_length, len(input_ids)] + input_ids
assert len(x) < const_array_size
x += [0] * (const_array_size - len(x))
features = {
"inputs": np.array(x).astype(np.int32),
}
for k, v in six.iteritems(
problem_lib.problem_hparams_to_features(p_hparams)):
features[k] = np.array(v).astype(np.int32)
print('yielded')
yield features
def __interactive_input_fn(self):
num_samples = self.decode_hp.num_samples if self.decode_hp.num_samples > 0\
else 1
decode_length = self.decode_hp.extra_length
input_type = "text"
p_hparams = self.hparams.problem_hparams
has_input = "inputs" in p_hparams.input_modality
vocabulary = p_hparams.vocabulary["inputs" if has_input else "targets"]
# Import readline if available for command line editing and recall.
try:
import readline # pylint: disable=g-import-not-at-top,unused-variable
except ImportError:
pass
while True:
features = {
"inputs": np.array(self.inputs).astype(np.int32),
}
for k, v in six.iteritems(problem_hparams_to_features(p_hparams)):
features[k] = np.array(v).astype(np.int32)
yield features
def _interactive_input_fn(hparams, decode_length=1024, input_string="\documentclass"):
num_samples = 1
input_type = "text"
p_hparams = hparams.problem_hparams
has_input = "inputs" in p_hparams.modality
vocabulary = p_hparams.vocabulary["inputs" if has_input else "targets"]
# This should be longer than the longest input.
const_array_size = 10000
input_ids = vocabulary.encode(input_string)
if has_input:
input_ids.append(text_encoder.EOS_ID)
x = [num_samples, decode_length, len(input_ids)] + input_ids
assert len(x) < const_array_size
x += [0] * (const_array_size - len(x))
features = {
"inputs": np.array(x).astype(np.int32),
}
for k, v in six.iteritems(problem_lib.problem_hparams_to_features(p_hparams)):
features[k] = np.array(v).astype(np.int32)
yield features
def __interactive_input_fn(self):
num_samples = self.decode_hp.num_samples if self.decode_hp.num_samples > 0\
else 1
decode_length = self.decode_hp.extra_length
input_type = "text"
p_hparams = self.hparams.problem_hparams
has_input = "inputs" in p_hparams.input_modality
vocabulary = p_hparams.vocabulary["inputs" if has_input else "targets"]
# This should be longer than the longest input.
const_array_size = 10000
# Import readline if available for command line editing and recall.
try:
import readline # pylint: disable=g-import-not-at-top,unused-variable
except ImportError:
pass
while True:
features = {
"inputs": np.array(self.inputs).astype(np.int32),
}
for k, v in six.iteritems(problem_hparams_to_features(p_hparams)):
features[k] = np.array(v).astype(np.int32)
yield features
def _interactive_input_fn(hparams, decode_hp):
"""Generator that reads from the terminal and yields "interactive inputs".
Due to temporary limitations in tf.learn, if we don't want to reload the
whole graph, then we are stuck encoding all of the input as one fixed-size
numpy array.
We yield int32 arrays with shape [const_array_size]. The format is:
[num_samples, decode_length, len(input ids), <input ids>, <padding>]
Args:
hparams: model hparams
decode_hp: decode hparams
Yields:
numpy arrays
Raises:
Exception: when `input_type` is invalid.
"""
num_samples = decode_hp.num_samples if decode_hp.num_samples > 0 else 1
decode_length = decode_hp.extra_length
input_type = "text"
p_hparams = hparams.problem_hparams
has_input = "inputs" in p_hparams.input_modality
vocabulary = p_hparams.vocabulary["inputs" if has_input else "targets"]
# This should be longer than the longest input.
const_array_size = 10000
# Import readline if available for command line editing and recall.
try:
import readline # pylint: disable=g-import-not-at-top,unused-variable
except ImportError:
pass
while True:
prompt = (
"INTERACTIVE MODE num_samples=%d decode_length=%d \n"
" it=<input_type> ('text' or 'image' or 'label', default: "
"text)\n"
" ns=<num_samples> (changes number of samples, default: 1)\n"
" dl=<decode_length> (changes decode length, default: 100)\n"
" <%s> (decode)\n"
" q (quit)\n"
">" % (num_samples, decode_length,
"source_string" if has_input else "target_prefix"))
input_string = input(prompt)
if input_string == "q":
return
elif input_string[:3] == "ns=":
num_samples = int(input_string[3:])
elif input_string[:3] == "dl=":
decode_length = int(input_string[3:])
elif input_string[:3] == "it=":
input_type = input_string[3:]
else:
if input_type == "text":
input_ids = vocabulary.encode(input_string)
if has_input:
input_ids.append(text_encoder.EOS_ID)
x = [num_samples, decode_length, len(input_ids)] + input_ids
assert len(x) < const_array_size
x += [0] * (const_array_size - len(x))
features = {
"inputs": np.array(x).astype(np.int32),
}
elif input_type == "image":
input_path = input_string
img = vocabulary.encode(input_path)
features = {
"inputs": img.astype(np.int32),
}
elif input_type == "label":
input_ids = [int(input_string)]
x = [num_samples, decode_length, len(input_ids)] + input_ids
features = {
"inputs": np.array(x).astype(np.int32),
}
else:
raise Exception("Unsupported input type.")
for k, v in six.iteritems(
problem_lib.problem_hparams_to_features(p_hparams)):
features[k] = np.array(v).astype(np.int32)
yield features
def _fill_problem_hparams_features(self, features):
if features is not None:
for k, v in six.iteritems(
problem_hparams_to_features(self._problem_hparams)):
if k not in features:
features[k] = tf.constant(v, name=k)
def _interactive_input_fn(hparams, decode_hp):
"""Generator that reads from the terminal and yields "interactive inputs".
Due to temporary limitations in tf.learn, if we don't want to reload the
whole graph, then we are stuck encoding all of the input as one fixed-size
numpy array.
We yield int32 arrays with shape [const_array_size]. The format is:
[num_samples, decode_length, len(input ids), <input ids>, <padding>]
Args:
hparams: model hparams
decode_hp: decode hparams
Yields:
numpy arrays
Raises:
Exception: when `input_type` is invalid.
"""
num_samples = decode_hp.num_samples if decode_hp.num_samples > 0 else 1
decode_length = decode_hp.extra_length
input_type = "text"
p_hparams = hparams.problem_hparams
has_input = "inputs" in p_hparams.modality
vocabulary = p_hparams.vocabulary["inputs" if has_input else "targets"]
# This should be longer than the longest input.
const_array_size = 10000
# Import readline if available for command line editing and recall.
try:
import readline # pylint: disable=g-import-not-at-top,unused-variable
except ImportError:
pass
while True:
prompt = ("INTERACTIVE MODE num_samples=%d decode_length=%d \n"
" it=<input_type> ('text' or 'image' or 'label', default: "
"text)\n"
" ns=<num_samples> (changes number of samples, default: 1)\n"
" dl=<decode_length> (changes decode length, default: 100)\n"
" <%s> (decode)\n"
" q (quit)\n"
">" % (num_samples, decode_length, "source_string"
if has_input else "target_prefix"))
input_string = input(prompt)
if input_string == "q":
return
elif input_string[:3] == "ns=":
num_samples = int(input_string[3:])
elif input_string[:3] == "dl=":
decode_length = int(input_string[3:])
elif input_string[:3] == "it=":
input_type = input_string[3:]
else:
if input_type == "text":
input_ids = vocabulary.encode(input_string)
if has_input:
input_ids.append(text_encoder.EOS_ID)
x = [num_samples, decode_length, len(input_ids)] + input_ids
assert len(x) < const_array_size
x += [0] * (const_array_size - len(x))
features = {
"inputs": np.array(x).astype(np.int32),
}
elif input_type == "image":
input_path = input_string
img = vocabulary.encode(input_path)
features = {
"inputs": img.astype(np.int32),
}
elif input_type == "label":
input_ids = [int(input_string)]
x = [num_samples, decode_length, len(input_ids)] + input_ids
features = {
"inputs": np.array(x).astype(np.int32),
}
else:
raise Exception("Unsupported input type.")
for k, v in six.iteritems(
problem_lib.problem_hparams_to_features(p_hparams)):
features[k] = np.array(v).astype(np.int32)
yield features
def _fill_problem_hparams_features(self, features):
if features is not None:
for k, v in six.iteritems(
problem_hparams_to_features(self._problem_hparams)):
if k not in features:
features[k] = tf.constant(v, name=k)
