text_prompt stringlengths 157 13.1k | code_prompt stringlengths 7 19.8k ⌀ |
|---|---|
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def save(self, path_info, checksum):
"""Save checksum for the specified path info. Args: path_info (dict):
path_info to save checksum for. checksum (str):
chec... |
assert path_info["scheme"] == "local"
assert checksum is not None
path = path_info["path"]
assert os.path.exists(path)
actual_mtime, actual_size = get_mtime_and_size(path)
actual_inode = get_inode(path)
existing_record = self.get_state_record_for_inode(actual_... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get(self, path_info):
"""Gets the checksum for the specified path info. Checksum will be retrieved from the state database if available. Args: path_info (dic... |
assert path_info["scheme"] == "local"
path = path_info["path"]
if not os.path.exists(path):
return None
actual_mtime, actual_size = get_mtime_and_size(path)
actual_inode = get_inode(path)
existing_record = self.get_state_record_for_inode(actual_inode)
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def save_link(self, path_info):
"""Adds the specified path to the list of links created by dvc. This list is later used on `dvc checkout` to cleanup old links. A... |
assert path_info["scheme"] == "local"
path = path_info["path"]
if not os.path.exists(path):
return
mtime, _ = get_mtime_and_size(path)
inode = get_inode(path)
relpath = os.path.relpath(path, self.root_dir)
cmd = (
"REPLACE INTO {}(path,... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def remove_unused_links(self, used):
"""Removes all saved links except the ones that are used. Args: used (list):
list of used links that should not be removed.... |
unused = []
self._execute("SELECT * FROM {}".format(self.LINK_STATE_TABLE))
for row in self.cursor:
relpath, inode, mtime = row
inode = self._from_sqlite(inode)
path = os.path.join(self.root_dir, relpath)
if path in used:
continu... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def lock(self):
"""Acquire lock for dvc repo.""" |
try:
self._do_lock()
return
except LockError:
time.sleep(self.TIMEOUT)
self._do_lock() |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def set_scrollregion(self, event=None):
""" Set the scroll region on the canvas""" |
self.canvas.configure(scrollregion=self.canvas.bbox('all')) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _show_selection(self, text, bbox):
"""Configure canvas for a new selection.""" |
x, y, width, height = bbox
textw = self._font.measure(text)
canvas = self._canvas
canvas.configure(width=width, height=height)
canvas.coords(canvas.text, width - textw, height / 2 - 1)
canvas.itemconfigure(canvas.text, text=text)
canvas.place(in_=self._calendar... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _prev_month(self):
"""Updated calendar to show the previous month.""" |
self._canvas.place_forget()
self._date = self._date - self.timedelta(days=1)
self._date = self.datetime(self._date.year, self._date.month, 1)
self._build_calendar() |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _next_month(self):
"""Update calendar to show the next month.""" |
self._canvas.place_forget()
year, month = self._date.year, self._date.month
self._date = self._date + self.timedelta(
days=calendar.monthrange(year, month)[1] + 1)
self._date = self.datetime(self._date.year, self._date.month, 1)
self._build_calendar() |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def selection(self):
"""Return a datetime representing the current selected date.""" |
if not self._selection:
return None
year, month = self._date.year, self._date.month
return self.datetime(year, month, int(self._selection[0])) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
| def AddRow(self, *args):
''' Parms are a variable number of Elements '''
NumRows = len(self.Rows) # number of existing rows is our row number
CurrentRowNumber = NumRows # this row's number
CurrentRow = [] # start with a blank row and build up
# ------------------------- Add t... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
| def setColor(self, color):
'''Sets Card's color and escape code.'''
if color == 'blue':
self.color = 'blue'
self.colorCode = self.colors['blue']
self.colorCodeDark = self.colors['dblue']
elif color == 'red':
self.color = 'red'
self.colo... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_img_data(f, maxsize = (1200, 850), first = False):
"""Generate image data using PIL """ |
img = Image.open(f)
img.thumbnail(maxsize)
if first: # tkinter is inactive the first time
bio = io.BytesIO()
img.save(bio, format = "PNG")
del img
return bio.getvalue()
return ImageTk.PhotoImage(img) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def quiet_ping(hostname, timeout=WAIT_TIMEOUT, count=NUM_PACKETS, packet_size=PACKET_SIZE, path_finder=False):
""" Same as verbose_ping, but the results are retu... |
myStats = MyStats() # Reset the stats
mySeqNumber = 0 # Starting value
try:
destIP = socket.gethostbyname(hostname)
except socket.gaierror as e:
return 0,0,0,0
myStats.thisIP = destIP
# This will send packet that we dont care about 0.5 seconds before it starts
# acrutally... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_page(pno, zoom = False, max_size = None, first = False):
"""Return a PNG image for a document page number. """ |
dlist = dlist_tab[pno] # get display list of page number
if not dlist: # create if not yet there
dlist_tab[pno] = doc[pno].getDisplayList()
dlist = dlist_tab[pno]
r = dlist.rect # the page rectangle
clip = r
# ensure image fits screen:
# exploit, but do no... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def play(self):
""" Play Conway's Game of Life. """ |
# Write the initial configuration to file.
self.t = 1 # Current time level
while self.t <= self.T: # Evolve!
# print( "At time level %d" % t)
# Loop over each cell of the grid and apply Conway's rules.
for i in range(self.N):
for j in ran... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def human_size(bytes, units=[' bytes','KB','MB','GB','TB', 'PB', 'EB']):
""" Returns a human readable string reprentation of bytes""" |
return str(bytes) + units[0] if bytes < 1024 else human_size(bytes>>10, units[1:]) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def list_view_on_selected(self, widget, selected_item_key):
""" The selection event of the listView, returns a key of the clicked event. You can retrieve the ite... |
self.lbl.set_text('List selection: ' + self.listView.children[selected_item_key].get_text()) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def receive_one_ping(mySocket, myID, timeout):
""" Receive the ping from the socket. Timeout = in ms """ |
timeLeft = timeout/1000
while True: # Loop while waiting for packet or timeout
startedSelect = default_timer()
whatReady = select.select([mySocket], [], [], timeLeft)
howLongInSelect = (default_timer() - startedSelect)
if whatReady[0] == []: # Timeout
return None, 0... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def export(self, path, _sentinel=None, # pylint: disable=invalid-name checkpoint_path=None, name_transform_fn=None):
"""Exports a ModuleSpec with weights taken f... |
from tensorflow_hub.module import export_module_spec # pylint: disable=g-import-not-at-top
if not checkpoint_path:
raise ValueError("Missing mandatory `checkpoint_path` parameter")
name_transform_fn = name_transform_fn or (lambda x: x)
export_module_spec(self, path, checkpoint_path, name_transfo... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_attached_message(self, key, message_type, tags=None, required=False):
"""Returns the message attached to the module under the given key, or None. Module ... |
attached_bytes = self._get_attached_bytes(key, tags)
if attached_bytes is None:
if required:
raise KeyError("No attached message for key '%s' in graph version %s "
"of Hub Module" % (key, sorted(tags or [])))
else:
return None
message = message_type()
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def create_image_lists(image_dir, testing_percentage, validation_percentage):
"""Builds a list of training images from the file system. Analyzes the sub folders ... |
if not tf.gfile.Exists(image_dir):
tf.logging.error("Image directory '" + image_dir + "' not found.")
return None
result = collections.OrderedDict()
sub_dirs = sorted(x[0] for x in tf.gfile.Walk(image_dir))
# The root directory comes first, so skip it.
is_root_dir = True
for sub_dir in sub_dirs:
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_image_path(image_lists, label_name, index, image_dir, category):
"""Returns a path to an image for a label at the given index. Args: image_lists: Ordered... |
if label_name not in image_lists:
tf.logging.fatal('Label does not exist %s.', label_name)
label_lists = image_lists[label_name]
if category not in label_lists:
tf.logging.fatal('Category does not exist %s.', category)
category_list = label_lists[category]
if not category_list:
tf.logging.fatal('... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir, category, module_name):
"""Returns a path to a bottleneck file for a label at the given i... |
module_name = (module_name.replace('://', '~') # URL scheme.
.replace('/', '~') # URL and Unix paths.
.replace(':', '~').replace('\\', '~')) # Windows paths.
return get_image_path(image_lists, label_name, index, bottleneck_dir,
category) + '_' + module_n... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def create_module_graph(module_spec):
"""Creates a graph and loads Hub Module into it. Args: module_spec: the hub.ModuleSpec for the image module being used. Ret... |
height, width = hub.get_expected_image_size(module_spec)
with tf.Graph().as_default() as graph:
resized_input_tensor = tf.placeholder(tf.float32, [None, height, width, 3])
m = hub.Module(module_spec)
bottleneck_tensor = m(resized_input_tensor)
wants_quantization = any(node.op in FAKE_QUANT_OPS
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def run_bottleneck_on_image(sess, image_data, image_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor):
"""Runs inference on an image to... |
# First decode the JPEG image, resize it, and rescale the pixel values.
resized_input_values = sess.run(decoded_image_tensor,
{image_data_tensor: image_data})
# Then run it through the recognition network.
bottleneck_values = sess.run(bottleneck_tensor,
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def create_bottleneck_file(bottleneck_path, image_lists, label_name, index, image_dir, category, sess, jpeg_data_tensor, decoded_image_tensor, resized_input_tenso... |
tf.logging.debug('Creating bottleneck at ' + bottleneck_path)
image_path = get_image_path(image_lists, label_name, index,
image_dir, category)
if not tf.gfile.Exists(image_path):
tf.logging.fatal('File does not exist %s', image_path)
image_data = tf.gfile.GFile(image_path, 'rb... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir, category, bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tens... |
label_lists = image_lists[label_name]
sub_dir = label_lists['dir']
sub_dir_path = os.path.join(bottleneck_dir, sub_dir)
ensure_dir_exists(sub_dir_path)
bottleneck_path = get_bottleneck_path(image_lists, label_name, index,
bottleneck_dir, category, module_name)
if not... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor, module_name):... |
how_many_bottlenecks = 0
ensure_dir_exists(bottleneck_dir)
for label_name, label_lists in image_lists.items():
for category in ['training', 'testing', 'validation']:
category_list = label_lists[category]
for index, unused_base_name in enumerate(category_list):
get_or_create_bottleneck(
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_random_cached_bottlenecks(sess, image_lists, how_many, category, bottleneck_dir, image_dir, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, ... |
class_count = len(image_lists.keys())
bottlenecks = []
ground_truths = []
filenames = []
if how_many >= 0:
# Retrieve a random sample of bottlenecks.
for unused_i in range(how_many):
label_index = random.randrange(class_count)
label_name = list(image_lists.keys())[label_index]
image... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_random_distorted_bottlenecks( sess, image_lists, how_many, category, image_dir, input_jpeg_tensor, distorted_image, resized_input_tensor, bottleneck_tenso... |
class_count = len(image_lists.keys())
bottlenecks = []
ground_truths = []
for unused_i in range(how_many):
label_index = random.randrange(class_count)
label_name = list(image_lists.keys())[label_index]
image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1)
image_path = get_image_path(imag... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def add_input_distortions(flip_left_right, random_crop, random_scale, random_brightness, module_spec):
"""Creates the operations to apply the specified distortio... |
input_height, input_width = hub.get_expected_image_size(module_spec)
input_depth = hub.get_num_image_channels(module_spec)
jpeg_data = tf.placeholder(tf.string, name='DistortJPGInput')
decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
# Convert from full range of uint8 to range [0,1] of f... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor, quantize_layer, is_training):
"""Adds a new softmax and fully-connected layer for tr... |
batch_size, bottleneck_tensor_size = bottleneck_tensor.get_shape().as_list()
assert batch_size is None, 'We want to work with arbitrary batch size.'
with tf.name_scope('input'):
bottleneck_input = tf.placeholder_with_default(
bottleneck_tensor,
shape=[batch_size, bottleneck_tensor_size],
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def add_evaluation_step(result_tensor, ground_truth_tensor):
"""Inserts the operations we need to evaluate the accuracy of our results. Args: result_tensor: The ... |
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
prediction = tf.argmax(result_tensor, 1)
correct_prediction = tf.equal(prediction, ground_truth_tensor)
with tf.name_scope('accuracy'):
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.su... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def run_final_eval(train_session, module_spec, class_count, image_lists, jpeg_data_tensor, decoded_image_tensor, resized_image_tensor, bottleneck_tensor):
"""Run... |
test_bottlenecks, test_ground_truth, test_filenames = (
get_random_cached_bottlenecks(train_session, image_lists,
FLAGS.test_batch_size,
'testing', FLAGS.bottleneck_dir,
FLAGS.image_dir, jpeg_data_tensor... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def build_eval_session(module_spec, class_count):
"""Builds an restored eval session without train operations for exporting. Args: module_spec: The hub.ModuleSpe... |
# If quantized, we need to create the correct eval graph for exporting.
eval_graph, bottleneck_tensor, resized_input_tensor, wants_quantization = (
create_module_graph(module_spec))
eval_sess = tf.Session(graph=eval_graph)
with eval_graph.as_default():
# Add the new layer for exporting.
(_, _, b... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def save_graph_to_file(graph_file_name, module_spec, class_count):
"""Saves an graph to file, creating a valid quantized one if necessary.""" |
sess, _, _, _, _, _ = build_eval_session(module_spec, class_count)
graph = sess.graph
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
with tf.gfile.GFile(graph_file_name, 'wb') as f:
f.write(output_graph_def.SerializeToString()... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def add_jpeg_decoding(module_spec):
"""Adds operations that perform JPEG decoding and resizing to the graph.. Args: module_spec: The hub.ModuleSpec for the image... |
input_height, input_width = hub.get_expected_image_size(module_spec)
input_depth = hub.get_num_image_channels(module_spec)
jpeg_data = tf.placeholder(tf.string, name='DecodeJPGInput')
decoded_image = tf.image.decode_jpeg(jpeg_data, channels=input_depth)
# Convert from full range of uint8 to range [0,1] of fl... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def export_model(module_spec, class_count, saved_model_dir):
"""Exports model for serving. Args: module_spec: The hub.ModuleSpec for the image module being used.... |
# The SavedModel should hold the eval graph.
sess, in_image, _, _, _, _ = build_eval_session(module_spec, class_count)
with sess.graph.as_default() as graph:
tf.saved_model.simple_save(
sess,
saved_model_dir,
inputs={'image': in_image},
outputs={'prediction': graph.get_tensor_... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def logging_level_verbosity(logging_verbosity):
"""Converts logging_level into TensorFlow logging verbosity value Args: logging_level: String value representing ... |
name_to_level = {
'FATAL': tf.logging.FATAL,
'ERROR': tf.logging.ERROR,
'WARN': tf.logging.WARN,
'INFO': tf.logging.INFO,
'DEBUG': tf.logging.DEBUG
}
try:
return name_to_level[logging_verbosity]
except Exception as e:
raise RuntimeError('Not supported logs verbosity (%s). Use one o... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_image_module_info(module_or_spec, required=False):
"""Returns the module's attached ImageModuleInfo message, or None.""" |
return module_or_spec.get_attached_message(
IMAGE_MODULE_INFO_KEY, ImageModuleInfo, required=required) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_num_image_channels(module_or_spec, signature=None, input_name=None):
"""Returns expected num_channels dimensions of an image input. This is for advanced ... |
if input_name is None:
input_name = "images"
input_info_dict = module_or_spec.get_input_info_dict(signature)
try:
shape = input_info_dict[input_name].get_shape()
except KeyError:
raise ValueError("Module is missing input '%s' in signature '%s'." %
(input_name, signature or "def... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _parse_tensor_info_proto(tensor_info):
"""Returns a ParsedTensorInfo instance from a TensorInfo proto.""" |
encoding = tensor_info.WhichOneof("encoding")
dtype = tf.DType(tensor_info.dtype)
shape = tf.TensorShape(tensor_info.tensor_shape)
if encoding == "name":
return ParsedTensorInfo(dtype=dtype, shape=shape, is_sparse=False)
elif encoding == "coo_sparse":
return ParsedTensorInfo(dtype=dtype, shape=shape,... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _is_sparse(x):
"""Returns whether x is a SparseTensor or a parsed sparse tensor info.""" |
return (
isinstance(x, (tf.SparseTensor, tf_v1.SparseTensorValue)) or
(hasattr(x, "is_sparse") and x.is_sparse)) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _convert_to_compatible_tensor(value, target, error_prefix):
"""Converts `value` into a tensor that can be feed into `tensor_info`. Args: value: A value to co... |
try:
tensor = tf_v1.convert_to_tensor_or_indexed_slices(value, target.dtype)
except TypeError as e:
raise TypeError("%s: %s" % (error_prefix, e))
if _is_sparse(tensor) != _is_sparse(target):
if _is_sparse(tensor):
raise TypeError("%s: Is sparse. Expected dense." % error_prefix)
else:
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def convert_dict_to_compatible_tensor(values, targets):
"""Converts dict `values` in tensors that are compatible with `targets`. Args: values: A dict to objects ... |
result = {}
for key, value in sorted(values.items()):
result[key] = _convert_to_compatible_tensor(
value, targets[key], error_prefix="Can't convert %r" % key)
return result |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def build_input_map(protomap, inputs):
"""Builds a map to feed tensors in `protomap` using `inputs`. Args: protomap: A proto map<string,TensorInfo>. inputs: A ma... |
if set(protomap.keys()) != set(inputs.keys()):
raise ValueError("build_input_map: keys do not match.")
input_map = {}
for key, tensor_info in protomap.items():
arg = inputs[key]
encoding = tensor_info.WhichOneof("encoding")
if encoding == "name":
input_map[tensor_info.name] = arg
elif e... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def build_output_map(protomap, get_tensor_by_name):
"""Builds a map of tensors from `protomap` using `get_tensor_by_name`. Args: protomap: A proto map<string,Ten... |
def get_output_from_tensor_info(tensor_info):
encoding = tensor_info.WhichOneof("encoding")
if encoding == "name":
return get_tensor_by_name(tensor_info.name)
elif encoding == "coo_sparse":
return tf.SparseTensor(
get_tensor_by_name(tensor_info.coo_sparse.indices_tensor_name),
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def parse_line(line):
"""Parses a line of a text embedding file. Args: line: (str) One line of the text embedding file. Returns: A token string and its embedding... |
columns = line.split()
token = columns.pop(0)
values = [float(column) for column in columns]
return token, values |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def load(file_path, parse_line_fn):
"""Loads a text embedding into memory as a numpy matrix. Args: file_path: Path to the text embedding file. parse_line_fn: cal... |
vocabulary = []
embeddings = []
embeddings_dim = None
for line in tf.gfile.GFile(file_path):
token, embedding = parse_line_fn(line)
if not embeddings_dim:
embeddings_dim = len(embedding)
elif embeddings_dim != len(embedding):
raise ValueError(
"Inconsistent embedding dimension... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def make_module_spec(vocabulary_file, vocab_size, embeddings_dim, num_oov_buckets, preprocess_text):
"""Makes a module spec to simply perform token to embedding ... |
def module_fn():
"""Spec function for a token embedding module."""
tokens = tf.placeholder(shape=[None], dtype=tf.string, name="tokens")
embeddings_var = tf.get_variable(
initializer=tf.zeros([vocab_size + num_oov_buckets, embeddings_dim]),
name=EMBEDDINGS_VAR_NAME,
dtype=tf.flo... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def export(export_path, vocabulary, embeddings, num_oov_buckets, preprocess_text):
"""Exports a TF-Hub module that performs embedding lookups. Args: export_path:... |
# Write temporary vocab file for module construction.
tmpdir = tempfile.mkdtemp()
vocabulary_file = os.path.join(tmpdir, "tokens.txt")
with tf.gfile.GFile(vocabulary_file, "w") as f:
f.write("\n".join(vocabulary))
vocab_size = len(vocabulary)
embeddings_dim = embeddings.shape[1]
spec = make_module_sp... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def maybe_append_oov_vectors(embeddings, num_oov_buckets):
"""Adds zero vectors for oov buckets if num_oov_buckets > 0. Since we are assigning zero vectors, addi... |
num_embeddings = np.shape(embeddings)[0]
embedding_dim = np.shape(embeddings)[1]
embeddings.resize(
[num_embeddings + num_oov_buckets, embedding_dim], refcheck=False) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def register_module_for_export(module, export_name):
"""Register a Module to be exported under `export_name`. This function registers `module` to be exported by ... |
for used_name, _ in tf_v1.get_collection(_EXPORT_MODULES_COLLECTION):
if used_name == export_name:
raise ValueError(
"There is already a module registered to be exported as %r"
% export_name)
tf_v1.add_to_collection(_EXPORT_MODULES_COLLECTION, (export_name, module)) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _make_estimator_serving_session(estimator, serving_input_fn, checkpoint_path):
"""Returns a session constructed using `estimator` and `serving_input_fn`. The... |
with tf.Graph().as_default() as g:
mode = tf_v1.estimator.ModeKeys.PREDICT
tf_v1.train.create_global_step(g)
tf_v1.set_random_seed(estimator.config.tf_random_seed)
serving_input_receiver = serving_input_fn()
estimator_spec = estimator.model_fn(
features=serving_input_receiver.features,
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def create_module_spec(module_fn, tags_and_args=None, drop_collections=None):
"""Creates a ModuleSpec from a function that builds the module's graph. The `module... |
if not drop_collections:
drop_collections = []
report_tags = True
if not tags_and_args:
tags_and_args = [(set(), {})]
report_tags = False
saved_model_handler = saved_model_lib.SavedModelHandler()
for tags, args in tags_and_args:
with tf.Graph().as_default() as graph:
with tf_v1.variab... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def add_signature(name=None, inputs=None, outputs=None):
"""Adds a signature to the module definition. NOTE: This must be called within a `module_fn` that is def... |
if not name:
name = "default"
if inputs is None:
inputs = {}
if outputs is None:
outputs = {}
if not isinstance(inputs, dict):
inputs = {"default": inputs}
if not isinstance(outputs, dict):
outputs = {"default": outputs}
message = find_signature_inputs_from_multivalued_ops(inputs)
if ... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def attach_message(key, message):
"""Adds an attached message to the module definition. NOTE: This must be called within a `module_fn` that is defining a Module.... |
if not re.match(r"[a-zA-Z][a-zA-Z0-9_]*$", key):
raise ValueError(
"hub.attach_message() called with malformed key '%s'" % key)
saved_model_lib.attach_bytes(key, message.SerializeToString()) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def list_registered_stateful_ops_without_inputs():
"""Returns set of registered stateful ops that do not expect inputs. This list is used to identify the ops to ... |
return set([
name
for name, op in op_def_registry.get_registered_ops().items()
if op.is_stateful and not op.input_arg
]) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_state_map(meta_graph, state_ops, unsupported_state_ops, get_tensor_by_name):
"""Returns a map from tensor names to tensors that hold the state.""" |
state_map = {}
for node in meta_graph.graph_def.node:
if node.op in state_ops:
tensor_name = node.name + ":0"
tensor = get_tensor_by_name(tensor_name)
num_outputs = len(tensor.op.outputs)
if num_outputs != 1:
raise ValueError("Stateful op %s has %d outputs, expected 1" %
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def replace_apply_state(meta_graph, state_ops, feed_map):
"""Replaces state ops with non state Placeholder ops for the apply graph.""" |
for node in meta_graph.graph_def.node:
keys_to_purge = []
tensor_name = node.name + ":0"
# Verify that the node is a state op and that its due to be rewired
# in the feedmap.
if node.op in state_ops and tensor_name in feed_map:
node.op = "Placeholder"
for key in node.attr:
# O... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _extract_variable_parts(variable_key, variable):
"""Matches a variable to individual parts. Args: variable_key: String identifier of the variable in the modu... |
name, offset, partitioned = None, None, False
# pylint: disable=protected-access
if variable._save_slice_info:
name = variable_key[:variable_key.rfind("/")]
if not variable._save_slice_info.full_name.endswith(name):
raise RuntimeError("Unexpected handling of partitioned variable.")
offset = var... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def recover_partitioned_variable_map(var_node_map):
"""Builds a proper variable map if it contains PartitionedVariables. Args: var_node_map: A map to tf.Variable... |
offset_variables_map = {}
for var_key, var_tensor in var_node_map.items():
match, var_name, offset = _extract_variable_parts(var_key, var_tensor)
if not match:
# This is a standard variable, so we can safely add it to the output.
if var_key in offset_variables_map:
raise RuntimeError(
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def check_unique_tags(tag_list):
"""Checks that tag list contains each set of tags only once.""" |
frozen_tags_seen = set()
for tags in tag_list:
frozen_tags = frozenset(tags)
if frozen_tags in frozen_tags_seen:
raise ValueError("Tags %r used repeatedly" % tags)
frozen_tags_seen.add(frozen_tags) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def check_collections_are_supported(saved_model_handler, supported):
"""Checks that SavedModelHandler only uses supported collections.""" |
for meta_graph in saved_model_handler.meta_graphs:
used_collection_keys = set(meta_graph.collection_def.keys())
unsupported = used_collection_keys - supported
if unsupported:
raise ValueError("Unsupported collections in graph: %s\n"
"Use hub.create_module_spec(..., drop_colle... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def register_ops_if_needed(graph_ops):
"""Register graph ops absent in op_def_registry, if present in c++ registry. Args: graph_ops: set with graph op names to r... |
missing_ops = graph_ops - set(op_def_registry.get_registered_ops().keys())
if not missing_ops:
return
p_buffer = c_api.TF_GetAllOpList()
cpp_op_list = op_def_pb2.OpList()
cpp_op_list.ParseFromString(c_api.TF_GetBuffer(p_buffer))
cpp_registry_ops = {op.name: op for op in cpp_op_list.op}
missing_op_... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def fix_colocation_after_import(input_map, absolute_import_scope):
"""Fixes colocation attributes after import according to input_map. This function is meant to ... |
attr_map = _build_colocation_attr_map(input_map, absolute_import_scope)
_apply_colocation_attr_map(attr_map, absolute_import_scope) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _build_colocation_attr_map(input_map, absolute_import_scope):
"""Returns a dict mapping from pre-import to post-import colocation attrs. Args: input_map: as ... |
colocation_attr_map = collections.defaultdict(_ConsistentValue)
used_outputs_of_imported_ops = collections.defaultdict(set)
# Collect mappings from the input_map.
for imported_tensor_name, mapped_tensor in input_map.items():
imported_tensor_name = absolute_import_scope + "/" + imported_tensor_name
impo... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _apply_colocation_attr_map(colocation_attr_map, absolute_import_scope):
"""Rewrites colocation constraints in the current default graph. Nodes in `absolute_i... |
graph = tf_v1.get_default_graph()
for op in graph.get_operations():
# Rewrite the values of the "_class" attr that store colocation constraints.
# NOTE: The colocation_group loc:@X of a node with itself is not stored
# explicitly as an attr, so rewrite errors for loc:@X are not triggered
# by the m... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def find_state_op_colocation_error(graph, reported_tags=None):
"""Returns error message for colocation of state ops, or None if ok.""" |
state_op_types = list_registered_stateful_ops_without_inputs()
state_op_map = {op.name: op for op in graph.get_operations()
if op.type in state_op_types}
for op in state_op_map.values():
for colocation_group in op.colocation_groups():
if not (colocation_group.startswith(tf.compat.as_b... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def find_signature_input_colocation_error(signature_name, inputs):
"""Returns error message for colocation of signature inputs, or None if ok.""" |
for input_name, tensor in inputs.items():
expected_colocation_groups = [tf.compat.as_bytes("loc:@" + tensor.op.name)]
if tensor.op.colocation_groups() != expected_colocation_groups:
return (
"A tensor x used as input in a signature must not be subject to a "
"tf.colocate_with(y) con... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def find_signature_inputs_from_multivalued_ops(inputs):
"""Returns error message for module inputs from ops with multiple outputs.""" |
dense_inputs = [] # List of (str, Tensor), with SparseTensors decomposed.
for name, tensor in sorted(inputs.items()):
if isinstance(tensor, tf.SparseTensor):
dense_inputs.extend(("%s.%s" % (name, attr), getattr(tensor, attr))
for attr in ("indices", "values", "dense_shape"))
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _create_state_graph(self, name):
"""Creates the graph nodes that hold the state of the Module. Args: name: name scope to create the state graph in. Returns: ... |
import_collections = [
tf_v1.GraphKeys.GLOBAL_VARIABLES,
tf_v1.GraphKeys.MODEL_VARIABLES,
tf_v1.GraphKeys.TABLE_INITIALIZERS,
tf_v1.GraphKeys.ASSET_FILEPATHS, # Typically used to initialize tables.
tf_v1.GraphKeys.COND_CONTEXT,
tf_v1.GraphKeys.WHILE_CONTEXT,
]
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def create_apply_graph(self, signature, input_tensors, name):
"""See `ModuleImpl.create_apply_graph`.""" |
signature_def = self._meta_graph.signature_def.get(signature)
meta_graph = meta_graph_pb2.MetaGraphDef()
meta_graph.CopyFrom(self._meta_graph)
apply_graph = tf_v1.get_default_graph()
infeed_map = tensor_info.build_input_map(signature_def.inputs,
input_te... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def export(self, path, session):
"""See `Module.export`.""" |
def variables_saver(variables_path):
if self._saver:
self._saver.save(
session, variables_path,
write_meta_graph=False,
write_state=False)
self._spec._export(path, variables_saver) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def Set(self, value, context=None):
"""Receives a value for the object and some context on its source.""" |
if self.has_error: return
if self.value is None:
self.value = value
self._context["old_value"] = value
self._context.update({"old_" + k: v for k, v in context.items()})
elif self.value != value:
self.has_error = True
self._context["new_value"] = value
self._context.updat... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def GetConsistentValueOrRaise(self, error_format, context=None):
"""Gets consistent value or raises ValueError with formatted contexts.""" |
if self.has_error:
full_context = dict(self._context)
if context: full_context.update(context)
raise ValueError(error_format.format(**full_context))
return self.value |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _module_dir(handle):
"""Returns the directory where to cache the module.""" |
cache_dir = resolver.tfhub_cache_dir(use_temp=True)
return resolver.create_local_module_dir(
cache_dir,
hashlib.sha1(handle.encode("utf8")).hexdigest()) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_variables_path(export_dir):
"""Returns the path for storing variables checkpoints.""" |
return os.path.join(
tf.compat.as_bytes(export_dir),
tf.compat.as_bytes(tf_v1.saved_model.constants.VARIABLES_DIRECTORY),
tf.compat.as_bytes(tf_v1.saved_model.constants.VARIABLES_FILENAME)) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def add_signature(key, inputs, outputs):
"""Adds a signature to current graph. Args: key: Signature key as a string. inputs: Signature inputs as a map from strin... |
_check_dict_maps_to_tensors_or_sparse_tensors(inputs)
_check_dict_maps_to_tensors_or_sparse_tensors(outputs)
input_info = {
input_name: tf_v1.saved_model.utils.build_tensor_info(tensor)
for input_name, tensor in inputs.items()
}
output_info = {
output_name: tf_v1.saved_model.utils.build_ten... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _export_signatures(meta_graph):
"""Exports signatures from current graph into a MetaGraphDef.""" |
named_signatures = tf_v1.get_collection(_SIGNATURE_COLLECTION)
if not named_signatures:
raise ValueError("No signatures present. Please call hub.add_signature(...)"
"at least once in the module_fn.")
for key, signature in named_signatures:
meta_graph.signature_def[key].CopyFrom(signa... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def attach_bytes(key, the_bytes):
"""Adds a ModuleAttachment to the current graph. Args: key: A string with the unique key of the attachment. the_bytes: A bytes ... |
tf_v1.add_to_collection(
_ATTACHMENT_COLLECTION_INTERNAL,
module_attachment_pb2.ModuleAttachment(key=key, value=the_bytes)) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _export_module_attachments(meta_graph):
"""Exports ModuleAttachments from the current tf.Graph into `meta_graph`.""" |
added_attachments = tf_v1.get_collection(_ATTACHMENT_COLLECTION_INTERNAL)
if not added_attachments: return # Don't touch `meta_graph`.
unique_attachments = collections.OrderedDict( # Avoid indeterminism.
(attachment.key, attachment)
for attachment in added_attachments)
meta_graph.collection_def[A... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_attached_bytes_map(meta_graph):
"""Returns the dict of ModuleAttachments stored in `meta_graph`. Args: meta_graph: A MetaGraphDef, as built by SavedModel... |
result = {}
if ATTACHMENT_COLLECTION_SAVED not in meta_graph.collection_def:
return result
collection_def = meta_graph.collection_def[ATTACHMENT_COLLECTION_SAVED]
if collection_def.WhichOneof("kind") != "bytes_list":
raise ValueError(
"Internal CollectionDef for attached messages has kind %s, "... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _check_asset_node_def(node_def):
"""Raises TypeError if `node_def` does not match the expectations.""" |
if node_def.op != "Const":
raise TypeError("Asset node must be of type constant.")
if tf.as_dtype(node_def.attr["dtype"].type) != tf.string:
raise TypeError("Asset node must be of dtype string.")
if len(node_def.attr["value"].tensor.string_val) != 1:
raise TypeError("Asset node must be a scalar.") |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _merge_assets_key_collection(saved_model_proto, path):
"""Merges the ASSETS_KEY collection into the GraphDefs in saved_model_proto. Removes the ASSETS_KEY co... |
for meta_graph in saved_model_proto.meta_graphs:
node_asset_map = {}
if tf_v1.saved_model.constants.ASSETS_KEY in meta_graph.collection_def:
assets_any_proto = meta_graph.collection_def[
tf_v1.saved_model.constants.ASSETS_KEY].any_list.value
for asset_any_proto in assets_any_proto:
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _make_assets_key_collection(saved_model_proto, export_path):
"""Creates an ASSETS_KEY collection in the GraphDefs in saved_model_proto. Adds an ASSETS_KEY co... |
asset_filenames = {}
used_asset_filenames = set()
def _make_asset_filename(original_filename):
"""Returns the asset filename to use for the file."""
if original_filename in asset_filenames:
return asset_filenames[original_filename]
basename = os.path.basename(original_filename)
suggestion... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _parse_saved_model(path):
"""Reads the savedmodel.pb file containing `SavedModel`.""" |
# Based on tensorflow/python/saved_model/loader.py implementation.
path_to_pb = _get_saved_model_proto_path(path)
file_content = tf_v1.gfile.Open(path_to_pb, "rb").read()
saved_model = saved_model_pb2.SavedModel()
try:
saved_model.ParseFromString(file_content)
except message.DecodeError as e:
raise... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def load(path):
"""Creates a SavedModelHandler from a SavedModel in `path`.""" |
proto = _parse_saved_model(path)
_merge_assets_key_collection(proto, path)
handler = SavedModelHandler()
handler._proto = proto # pylint: disable=protected-access
return handler |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def add_graph_copy(self, graph, tags=None):
"""Adds a copy of Graph with the specified set of tags.""" |
with graph.as_default():
# Remove default attrs so that Modules created by a tensorflow version
# with ops that have new attrs that are left to their default values can
# still be loaded by older versions unware of those attributes.
meta_graph = tf_v1.train.export_meta_graph(strip_default_a... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_meta_graph_copy(self, tags=None):
"""Returns a copy of a MetaGraph with the identical set of tags.""" |
meta_graph = self.get_meta_graph(tags)
copy = tf_v1.MetaGraphDef()
copy.CopyFrom(meta_graph)
return copy |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_tags(self):
"""Returns a list of set of tags.""" |
return sorted([frozenset(meta_graph.meta_info_def.tags)
for meta_graph in self.meta_graphs]) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def export(self, path, variables_saver=None):
"""Exports to SavedModel directory. Args: path: path where to export the SavedModel to. variables_saver: lambda tha... |
# Operate on a copy of self._proto since it needs to be modified.
proto = saved_model_pb2.SavedModel()
proto.CopyFrom(self._proto)
assets_map = _make_assets_key_collection(proto, path)
self._save_all_assets(path, assets_map)
self._save_variables(path, variables_saver)
self._save_proto(path... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_meta_graph(self, tags=None):
"""Returns the matching MetaGraphDef or raises KeyError.""" |
matches = [meta_graph
for meta_graph in self.meta_graphs
if set(meta_graph.meta_info_def.tags) == set(tags or [])]
if not matches:
raise KeyError("SavedModelHandler has no graph with tags: %r" % tags)
if len(matches) != 1:
raise KeyError(
"SavedModelHandl... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def _convert_dict_inputs(inputs, tensor_info_map):
"""Converts from inputs into dict of input tensors. This handles: - putting inputs into a dict, per _prepare_d... |
dict_inputs = _prepare_dict_inputs(inputs, tensor_info_map)
return tensor_info.convert_dict_to_compatible_tensor(dict_inputs,
tensor_info_map) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def eval_function_for_module(spec, tags=None):
"""Context manager that yields a function to directly evaluate a Module. This creates a separate graph, in which a... |
# We create a separate graph and add all the signatures of the module to it.
original_graph = tf_v1.get_default_graph()
with tf.Graph().as_default():
module = Module(spec, tags=tags)
input_tensors_per_signature = {}
output_tensors_per_signature = {}
for signature in module.get_signature_names():
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_input_info_dict(self, signature=None):
"""Describes the inputs required by a signature. Args: signature: A string with the signature to get inputs inform... |
return self._spec.get_input_info_dict(signature=signature, tags=self._tags) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def get_output_info_dict(self, signature=None):
"""Describes the outputs provided by a signature. Args: signature: A string with the signature to get ouputs info... |
return self._spec.get_output_info_dict(signature=signature, tags=self._tags) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def export(self, path, session):
"""Exports the module with the variables from the session in `path`. Note that it is the module definition in the ModuleSpec use... |
if self._graph is not tf_v1.get_default_graph():
raise RuntimeError("default graph differs from the graph where the "
"module was instantiated.")
if self._graph is not session.graph:
raise RuntimeError("session graph differs from the graph where the "
... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def variables(self):
"""Returns the list of all tf.Variables created by module instantiation.""" |
result = []
for _, value in sorted(self.variable_map.items()):
if isinstance(value, list):
result.extend(value)
else:
result.append(value)
return result |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.