id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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227,700 | NetEaseGame/ATX | atx/base.py | list_images | def list_images(path=['.']):
""" Return list of image files """
for image_dir in set(path):
if not os.path.isdir(image_dir):
continue
for filename in os.listdir(image_dir):
bname, ext = os.path.splitext(filename)
if ext.lower() not in VALID_IMAGE_EXTS:
... | python | def list_images(path=['.']):
""" Return list of image files """
for image_dir in set(path):
if not os.path.isdir(image_dir):
continue
for filename in os.listdir(image_dir):
bname, ext = os.path.splitext(filename)
if ext.lower() not in VALID_IMAGE_EXTS:
... | [
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227,701 | NetEaseGame/ATX | atx/base.py | list_all_image | def list_all_image(path, valid_exts=VALID_IMAGE_EXTS):
"""List all images under path
@return unicode list
"""
for filename in os.listdir(path):
bname, ext = os.path.splitext(filename)
if ext.lower() not in VALID_IMAGE_EXTS:
continue
filepath = os.path.join(path, file... | python | def list_all_image(path, valid_exts=VALID_IMAGE_EXTS):
"""List all images under path
@return unicode list
"""
for filename in os.listdir(path):
bname, ext = os.path.splitext(filename)
if ext.lower() not in VALID_IMAGE_EXTS:
continue
filepath = os.path.join(path, file... | [
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227,702 | NetEaseGame/ATX | atx/base.py | search_image | def search_image(name=None, path=['.']):
"""
look for the image real path, if name is None, then return all images under path.
@return system encoded path string
FIXME(ssx): this code is just looking wired.
"""
name = strutils.decode(name)
for image_dir in path:
if not os.path.isdir... | python | def search_image(name=None, path=['.']):
"""
look for the image real path, if name is None, then return all images under path.
@return system encoded path string
FIXME(ssx): this code is just looking wired.
"""
name = strutils.decode(name)
for image_dir in path:
if not os.path.isdir... | [
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227,703 | NetEaseGame/ATX | atx/adbkit/device.py | Device.run_cmd | def run_cmd(self, *args, **kwargs):
"""
Unix style output, already replace \r\n to \n
Args:
- timeout (float): timeout for a command exec
"""
timeout = kwargs.pop('timeout', None)
p = self.raw_cmd(*args, **kwargs)
return p.communicate(timeout=timeout)... | python | def run_cmd(self, *args, **kwargs):
"""
Unix style output, already replace \r\n to \n
Args:
- timeout (float): timeout for a command exec
"""
timeout = kwargs.pop('timeout', None)
p = self.raw_cmd(*args, **kwargs)
return p.communicate(timeout=timeout)... | [
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227,704 | NetEaseGame/ATX | atx/adbkit/device.py | Device.shell | def shell(self, *args, **kwargs):
"""
Run command `adb shell`
"""
args = ['shell'] + list(args)
return self.run_cmd(*args, **kwargs) | python | def shell(self, *args, **kwargs):
"""
Run command `adb shell`
"""
args = ['shell'] + list(args)
return self.run_cmd(*args, **kwargs) | [
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227,705 | NetEaseGame/ATX | atx/adbkit/device.py | Device.remove | def remove(self, filename):
"""
Remove file from device
"""
output = self.shell('rm', filename)
# any output means rm failed.
return False if output else True | python | def remove(self, filename):
"""
Remove file from device
"""
output = self.shell('rm', filename)
# any output means rm failed.
return False if output else True | [
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227,706 | NetEaseGame/ATX | atx/adbkit/device.py | Device.display | def display(self):
'''
Return device width, height, rotation
'''
w, h = (0, 0)
for line in self.shell('dumpsys', 'display').splitlines():
m = _DISPLAY_RE.search(line, 0)
if not m:
continue
w = int(m.group('width'))
h... | python | def display(self):
'''
Return device width, height, rotation
'''
w, h = (0, 0)
for line in self.shell('dumpsys', 'display').splitlines():
m = _DISPLAY_RE.search(line, 0)
if not m:
continue
w = int(m.group('width'))
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227,707 | NetEaseGame/ATX | atx/adbkit/device.py | Device.packages | def packages(self):
"""
Show all packages
"""
pattern = re.compile(r'package:(/[^=]+\.apk)=([^\s]+)')
packages = []
for line in self.shell('pm', 'list', 'packages', '-f').splitlines():
m = pattern.match(line)
if not m:
continue
... | python | def packages(self):
"""
Show all packages
"""
pattern = re.compile(r'package:(/[^=]+\.apk)=([^\s]+)')
packages = []
for line in self.shell('pm', 'list', 'packages', '-f').splitlines():
m = pattern.match(line)
if not m:
continue
... | [
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227,708 | NetEaseGame/ATX | atx/adbkit/device.py | Device._adb_screencap | def _adb_screencap(self, scale=1.0):
"""
capture screen with adb shell screencap
"""
remote_file = tempfile.mktemp(dir='/data/local/tmp/', prefix='screencap-', suffix='.png')
local_file = tempfile.mktemp(prefix='atx-screencap-', suffix='.png')
self.shell('screencap', '-p'... | python | def _adb_screencap(self, scale=1.0):
"""
capture screen with adb shell screencap
"""
remote_file = tempfile.mktemp(dir='/data/local/tmp/', prefix='screencap-', suffix='.png')
local_file = tempfile.mktemp(prefix='atx-screencap-', suffix='.png')
self.shell('screencap', '-p'... | [
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227,709 | NetEaseGame/ATX | atx/adbkit/device.py | Device._adb_minicap | def _adb_minicap(self, scale=1.0):
"""
capture screen with minicap
https://github.com/openstf/minicap
"""
remote_file = tempfile.mktemp(dir='/data/local/tmp/', prefix='minicap-', suffix='.jpg')
local_file = tempfile.mktemp(prefix='atx-minicap-', suffix='.jpg')
(w... | python | def _adb_minicap(self, scale=1.0):
"""
capture screen with minicap
https://github.com/openstf/minicap
"""
remote_file = tempfile.mktemp(dir='/data/local/tmp/', prefix='minicap-', suffix='.jpg')
local_file = tempfile.mktemp(prefix='atx-minicap-', suffix='.jpg')
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227,710 | NetEaseGame/ATX | atx/adbkit/device.py | Device.screenshot | def screenshot(self, filename=None, scale=1.0, method=None):
"""
Take device screenshot
Args:
- filename(string): optional, save int filename
- scale(float): scale size
- method(string): one of minicap,screencap
Return:
PIL.Image
... | python | def screenshot(self, filename=None, scale=1.0, method=None):
"""
Take device screenshot
Args:
- filename(string): optional, save int filename
- scale(float): scale size
- method(string): one of minicap,screencap
Return:
PIL.Image
... | [
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227,711 | NetEaseGame/ATX | atx/record/android_layout.py | AndroidLayout.get_index_node | def get_index_node(self, idx):
'''get node with iterindex `idx`'''
idx = self.node_index.index(idx)
return self.nodes[idx] | python | def get_index_node(self, idx):
'''get node with iterindex `idx`'''
idx = self.node_index.index(idx)
return self.nodes[idx] | [
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227,712 | NetEaseGame/ATX | atx/record/base.py | BaseRecorder.start | def start(self):
'''start running in background.'''
self.update_device_info()
self.get_device_status(0) # start addons.
self.hook()
self.thread = threading.Thread(target=self._run)
self.thread.start()
self.running = True | python | def start(self):
'''start running in background.'''
self.update_device_info()
self.get_device_status(0) # start addons.
self.hook()
self.thread = threading.Thread(target=self._run)
self.thread.start()
self.running = True | [
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227,713 | NetEaseGame/ATX | atx/record/base.py | BaseRecorder.analyze_frames | def analyze_frames(cls, workdir):
'''generate draft from recorded frames'''
record = cls(None, workdir)
obj = {}
with open(os.path.join(workdir, 'frames', 'frames.json')) as f:
obj = json.load(f)
record.device_info = obj['device']
record.frames = obj['f... | python | def analyze_frames(cls, workdir):
'''generate draft from recorded frames'''
record = cls(None, workdir)
obj = {}
with open(os.path.join(workdir, 'frames', 'frames.json')) as f:
obj = json.load(f)
record.device_info = obj['device']
record.frames = obj['f... | [
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227,714 | NetEaseGame/ATX | atx/record/base.py | BaseRecorder.process_casefile | def process_casefile(cls, workdir):
'''generate code from case.json'''
record = cls(None, workdir)
obj = {}
with open(os.path.join(workdir, 'frames', 'frames.json')) as f:
obj = json.load(f)
record.device_info = obj['device']
record.frames = obj['frames... | python | def process_casefile(cls, workdir):
'''generate code from case.json'''
record = cls(None, workdir)
obj = {}
with open(os.path.join(workdir, 'frames', 'frames.json')) as f:
obj = json.load(f)
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227,715 | NetEaseGame/ATX | atx/adbkit/client.py | Client.adb_path | def adb_path(cls):
"""return adb binary full path"""
if cls.__adb_cmd is None:
if "ANDROID_HOME" in os.environ:
filename = "adb.exe" if os.name == 'nt' else "adb"
adb_dir = os.path.join(os.environ["ANDROID_HOME"], "platform-tools")
adb_cmd = os... | python | def adb_path(cls):
"""return adb binary full path"""
if cls.__adb_cmd is None:
if "ANDROID_HOME" in os.environ:
filename = "adb.exe" if os.name == 'nt' else "adb"
adb_dir = os.path.join(os.environ["ANDROID_HOME"], "platform-tools")
adb_cmd = os... | [
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] | f4415c57b45cb0730e08899cbc92a2af1c047ffb | https://github.com/NetEaseGame/ATX/blob/f4415c57b45cb0730e08899cbc92a2af1c047ffb/atx/adbkit/client.py#L52-L72 |
227,716 | NetEaseGame/ATX | atx/adbkit/client.py | Client.connect | def connect(self, addr):
'''
Call adb connect
Return true when connect success
'''
if addr.find(':') == -1:
addr += ':5555'
output = self.run_cmd('connect', addr)
return 'unable to connect' not in output | python | def connect(self, addr):
'''
Call adb connect
Return true when connect success
'''
if addr.find(':') == -1:
addr += ':5555'
output = self.run_cmd('connect', addr)
return 'unable to connect' not in output | [
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227,717 | NetEaseGame/ATX | atx/cmds/screencap.py | main | def main(host=None, port=None, serial=None, scale=1.0, out='screenshot.png', method='minicap'):
"""
If minicap not avaliable then use uiautomator instead
Disable scale for now.
Because -s scale is conflict of -s serial
"""
print('Started screencap')
start = time.time()
client... | python | def main(host=None, port=None, serial=None, scale=1.0, out='screenshot.png', method='minicap'):
"""
If minicap not avaliable then use uiautomator instead
Disable scale for now.
Because -s scale is conflict of -s serial
"""
print('Started screencap')
start = time.time()
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227,718 | NetEaseGame/ATX | atx/drivers/android.py | AndroidDevice.rotation | def rotation(self):
"""
Rotaion of the phone
0: normal
1: home key on the right
2: home key on the top
3: home key on the left
"""
if self.screen_rotation in range(4):
return self.screen_rotation
return self.adb_device.rotation() or se... | python | def rotation(self):
"""
Rotaion of the phone
0: normal
1: home key on the right
2: home key on the top
3: home key on the left
"""
if self.screen_rotation in range(4):
return self.screen_rotation
return self.adb_device.rotation() or se... | [
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227,719 | NetEaseGame/ATX | atx/drivers/android.py | AndroidDevice.properties | def properties(self):
'''
Android Properties, extracted from `adb shell getprop`
Returns:
dict of props, for
example:
{'ro.bluetooth.dun': 'true'}
'''
props = {}
for line in self.adb_shell(['getprop']).splitlines():
m ... | python | def properties(self):
'''
Android Properties, extracted from `adb shell getprop`
Returns:
dict of props, for
example:
{'ro.bluetooth.dun': 'true'}
'''
props = {}
for line in self.adb_shell(['getprop']).splitlines():
m ... | [
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227,720 | NetEaseGame/ATX | atx/drivers/android.py | AndroidDevice.type | def type(self, s, enter=False, clear=False):
"""Input some text, this method has been tested not very stable on some device.
"Hi world" maybe spell into "H iworld"
Args:
- s: string (text to input), better to be unicode
- enter(bool): input enter at last
- ne... | python | def type(self, s, enter=False, clear=False):
"""Input some text, this method has been tested not very stable on some device.
"Hi world" maybe spell into "H iworld"
Args:
- s: string (text to input), better to be unicode
- enter(bool): input enter at last
- ne... | [
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"Hi world" maybe spell into "H iworld"
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- enter(bool): input enter at last
- next(bool): perform editor action Next
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227,721 | NetEaseGame/ATX | atx/drivers/android.py | AndroidDevice.input_methods | def input_methods(self):
"""
Get all input methods
Return example: ['com.sohu.inputmethod.sogou/.SogouIME', 'android.unicode.ime/.Utf7ImeService']
"""
imes = []
for line in self.adb_shell(['ime', 'list', '-s', '-a']).splitlines():
line = line.strip()
... | python | def input_methods(self):
"""
Get all input methods
Return example: ['com.sohu.inputmethod.sogou/.SogouIME', 'android.unicode.ime/.Utf7ImeService']
"""
imes = []
for line in self.adb_shell(['ime', 'list', '-s', '-a']).splitlines():
line = line.strip()
... | [
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227,722 | NetEaseGame/ATX | atx/drivers/android.py | AndroidDevice.current_ime | def current_ime(self):
''' Get current input method '''
dumpout = self.adb_shell(['dumpsys', 'input_method'])
m = _INPUT_METHOD_RE.search(dumpout)
if m:
return m.group(1) | python | def current_ime(self):
''' Get current input method '''
dumpout = self.adb_shell(['dumpsys', 'input_method'])
m = _INPUT_METHOD_RE.search(dumpout)
if m:
return m.group(1) | [
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227,723 | NetEaseGame/ATX | atx/imutils.py | open_as_pillow | def open_as_pillow(filename):
""" This way can delete file immediately """
with __sys_open(filename, 'rb') as f:
data = BytesIO(f.read())
return Image.open(data) | python | def open_as_pillow(filename):
""" This way can delete file immediately """
with __sys_open(filename, 'rb') as f:
data = BytesIO(f.read())
return Image.open(data) | [
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227,724 | NetEaseGame/ATX | atx/imutils.py | from_pillow | def from_pillow(pil_image):
""" Convert from pillow image to opencv """
# convert PIL to OpenCV
pil_image = pil_image.convert('RGB')
cv2_image = np.array(pil_image)
# Convert RGB to BGR
cv2_image = cv2_image[:, :, ::-1].copy()
return cv2_image | python | def from_pillow(pil_image):
""" Convert from pillow image to opencv """
# convert PIL to OpenCV
pil_image = pil_image.convert('RGB')
cv2_image = np.array(pil_image)
# Convert RGB to BGR
cv2_image = cv2_image[:, :, ::-1].copy()
return cv2_image | [
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227,725 | NetEaseGame/ATX | atx/imutils.py | url_to_image | def url_to_image(url, flag=cv2.IMREAD_COLOR):
""" download the image, convert it to a NumPy array, and then read
it into OpenCV format """
resp = urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype="uint8")
image = cv2.imdecode(image, flag)
return image | python | def url_to_image(url, flag=cv2.IMREAD_COLOR):
""" download the image, convert it to a NumPy array, and then read
it into OpenCV format """
resp = urlopen(url)
image = np.asarray(bytearray(resp.read()), dtype="uint8")
image = cv2.imdecode(image, flag)
return image | [
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227,726 | NetEaseGame/ATX | atx/imutils.py | mark_point | def mark_point(img, x, y):
"""
Mark a point
Args:
- img(numpy): the source image
- x, y(int): position
"""
overlay = img.copy()
output = img.copy()
alpha = 0.5
radius = max(5, min(img.shape[:2])//15)
center = int(x), int(y)
color = (0, 0, 255)
cv2.circle(ov... | python | def mark_point(img, x, y):
"""
Mark a point
Args:
- img(numpy): the source image
- x, y(int): position
"""
overlay = img.copy()
output = img.copy()
alpha = 0.5
radius = max(5, min(img.shape[:2])//15)
center = int(x), int(y)
color = (0, 0, 255)
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227,727 | NetEaseGame/ATX | atx/drivers/ios_webdriveragent.py | IOSDevice.display | def display(self):
""" Get screen width and height """
w, h = self.session.window_size()
return Display(w*self.scale, h*self.scale) | python | def display(self):
""" Get screen width and height """
w, h = self.session.window_size()
return Display(w*self.scale, h*self.scale) | [
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227,728 | NetEaseGame/ATX | scripts/adb_old.py | Adb.forward | def forward(self, device_port, local_port=None):
'''adb port forward. return local_port'''
if local_port is None:
for s, lp, rp in self.forward_list():
if s == self.device_serial() and rp == 'tcp:%d' % device_port:
return int(lp[4:])
return sel... | python | def forward(self, device_port, local_port=None):
'''adb port forward. return local_port'''
if local_port is None:
for s, lp, rp in self.forward_list():
if s == self.device_serial() and rp == 'tcp:%d' % device_port:
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227,729 | NetEaseGame/ATX | atx/record/android.py | touch2screen | def touch2screen(w, h, o, x, y):
'''convert touch position'''
if o == 0:
return x, y
elif o == 1: # landscape-right
return y, w-x
elif o == 2: # upsidedown
return w-x, h-y
elif o == 3: # landscape-left
return h-y, x
return x, y | python | def touch2screen(w, h, o, x, y):
'''convert touch position'''
if o == 0:
return x, y
elif o == 1: # landscape-right
return y, w-x
elif o == 2: # upsidedown
return w-x, h-y
elif o == 3: # landscape-left
return h-y, x
return x, y | [
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227,730 | NetEaseGame/ATX | atx/ext/report/__init__.py | Report.patch_wda | def patch_wda(self):
"""
Record steps of WebDriverAgent
"""
import wda
def _click(that):
rawx, rawy = that.bounds.center
x, y = self.d.scale*rawx, self.d.scale*rawy
screen_before = self._save_screenshot()
orig_click = pt.get_origin... | python | def patch_wda(self):
"""
Record steps of WebDriverAgent
"""
import wda
def _click(that):
rawx, rawy = that.bounds.center
x, y = self.d.scale*rawx, self.d.scale*rawy
screen_before = self._save_screenshot()
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227,731 | NetEaseGame/ATX | atx/ext/report/__init__.py | Report._take_screenshot | def _take_screenshot(self, screenshot=False, name_prefix='unknown'):
"""
This is different from _save_screenshot.
The return value maybe None or the screenshot path
Args:
screenshot: bool or PIL image
"""
if isinstance(screenshot, bool):
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"""
This is different from _save_screenshot.
The return value maybe None or the screenshot path
Args:
screenshot: bool or PIL image
"""
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227,732 | NetEaseGame/ATX | atx/ext/report/__init__.py | Report._add_assert | def _add_assert(self, **kwargs):
"""
if screenshot is None, only failed case will take screenshot
"""
# convert screenshot to relative path from <None|True|False|PIL.Image>
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"""
if screenshot is None, only failed case will take screenshot
"""
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227,733 | Phylliade/ikpy | src/ikpy/plot_utils.py | plot_chain | def plot_chain(chain, joints, ax, target=None, show=False):
"""Plots the chain"""
# LIst of nodes and orientations
nodes = []
axes = []
transformation_matrixes = chain.forward_kinematics(joints, full_kinematics=True)
# Get the nodes and the orientation from the tranformation matrix
for (in... | python | def plot_chain(chain, joints, ax, target=None, show=False):
"""Plots the chain"""
# LIst of nodes and orientations
nodes = []
axes = []
transformation_matrixes = chain.forward_kinematics(joints, full_kinematics=True)
# Get the nodes and the orientation from the tranformation matrix
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227,734 | Phylliade/ikpy | src/ikpy/plot_utils.py | plot_target | def plot_target(target, ax):
"""Ajoute la target au plot"""
ax.scatter(target[0], target[1], target[2], c="red", s=80) | python | def plot_target(target, ax):
"""Ajoute la target au plot"""
ax.scatter(target[0], target[1], target[2], c="red", s=80) | [
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227,735 | Phylliade/ikpy | src/ikpy/geometry_utils.py | Rx_matrix | def Rx_matrix(theta):
"""Rotation matrix around the X axis"""
return np.array([
[1, 0, 0],
[0, np.cos(theta), -np.sin(theta)],
[0, np.sin(theta), np.cos(theta)]
]) | python | def Rx_matrix(theta):
"""Rotation matrix around the X axis"""
return np.array([
[1, 0, 0],
[0, np.cos(theta), -np.sin(theta)],
[0, np.sin(theta), np.cos(theta)]
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227,736 | Phylliade/ikpy | src/ikpy/geometry_utils.py | Rz_matrix | def Rz_matrix(theta):
"""Rotation matrix around the Z axis"""
return np.array([
[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]
]) | python | def Rz_matrix(theta):
"""Rotation matrix around the Z axis"""
return np.array([
[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]
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227,737 | Phylliade/ikpy | src/ikpy/geometry_utils.py | symbolic_Rz_matrix | def symbolic_Rz_matrix(symbolic_theta):
"""Matrice symbolique de rotation autour de l'axe Z"""
return sympy.Matrix([
[sympy.cos(symbolic_theta), -sympy.sin(symbolic_theta), 0],
[sympy.sin(symbolic_theta), sympy.cos(symbolic_theta), 0],
[0, 0, 1]
]) | python | def symbolic_Rz_matrix(symbolic_theta):
"""Matrice symbolique de rotation autour de l'axe Z"""
return sympy.Matrix([
[sympy.cos(symbolic_theta), -sympy.sin(symbolic_theta), 0],
[sympy.sin(symbolic_theta), sympy.cos(symbolic_theta), 0],
[0, 0, 1]
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227,738 | Phylliade/ikpy | src/ikpy/geometry_utils.py | Ry_matrix | def Ry_matrix(theta):
"""Rotation matrix around the Y axis"""
return np.array([
[np.cos(theta), 0, np.sin(theta)],
[0, 1, 0],
[-np.sin(theta), 0, np.cos(theta)]
]) | python | def Ry_matrix(theta):
"""Rotation matrix around the Y axis"""
return np.array([
[np.cos(theta), 0, np.sin(theta)],
[0, 1, 0],
[-np.sin(theta), 0, np.cos(theta)]
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227,739 | Phylliade/ikpy | src/ikpy/geometry_utils.py | rpy_matrix | def rpy_matrix(roll, pitch, yaw):
"""Returns a rotation matrix described by the extrinsinc roll, pitch, yaw coordinates"""
return np.dot(Rz_matrix(yaw), np.dot(Ry_matrix(pitch), Rx_matrix(roll))) | python | def rpy_matrix(roll, pitch, yaw):
"""Returns a rotation matrix described by the extrinsinc roll, pitch, yaw coordinates"""
return np.dot(Rz_matrix(yaw), np.dot(Ry_matrix(pitch), Rx_matrix(roll))) | [
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227,740 | Phylliade/ikpy | src/ikpy/geometry_utils.py | cartesian_to_homogeneous | def cartesian_to_homogeneous(cartesian_matrix, matrix_type="numpy"):
"""Converts a cartesian matrix to an homogenous matrix"""
dimension_x, dimension_y = cartesian_matrix.shape
# Square matrix
# Manage different types fo input matrixes
if matrix_type == "numpy":
homogeneous_matrix = np.eye(d... | python | def cartesian_to_homogeneous(cartesian_matrix, matrix_type="numpy"):
"""Converts a cartesian matrix to an homogenous matrix"""
dimension_x, dimension_y = cartesian_matrix.shape
# Square matrix
# Manage different types fo input matrixes
if matrix_type == "numpy":
homogeneous_matrix = np.eye(d... | [
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227,741 | Phylliade/ikpy | src/ikpy/geometry_utils.py | cartesian_to_homogeneous_vectors | def cartesian_to_homogeneous_vectors(cartesian_vector, matrix_type="numpy"):
"""Converts a cartesian vector to an homogenous vector"""
dimension_x = cartesian_vector.shape[0]
# Vector
if matrix_type == "numpy":
homogeneous_vector = np.zeros(dimension_x + 1)
# Last item is a 1
hom... | python | def cartesian_to_homogeneous_vectors(cartesian_vector, matrix_type="numpy"):
"""Converts a cartesian vector to an homogenous vector"""
dimension_x = cartesian_vector.shape[0]
# Vector
if matrix_type == "numpy":
homogeneous_vector = np.zeros(dimension_x + 1)
# Last item is a 1
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227,742 | Phylliade/ikpy | src/ikpy/URDF_utils.py | find_next_joint | def find_next_joint(root, current_link, next_joint_name):
"""
Find the next joint in the URDF tree
Parameters
----------
root
current_link: xml.etree.ElementTree
The current URDF link
next_joint_name: str
Optional : The name of the next joint. If not provided, find it automa... | python | def find_next_joint(root, current_link, next_joint_name):
"""
Find the next joint in the URDF tree
Parameters
----------
root
current_link: xml.etree.ElementTree
The current URDF link
next_joint_name: str
Optional : The name of the next joint. If not provided, find it automa... | [
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227,743 | Phylliade/ikpy | src/ikpy/URDF_utils.py | find_next_link | def find_next_link(root, current_joint, next_link_name):
"""
Find the next link in the URDF tree
Parameters
----------
root
current_joint: xml.etree.ElementTree
The current URDF joint
next_link_name: str
Optional : The name of the next link. If not provided, find it automati... | python | def find_next_link(root, current_joint, next_link_name):
"""
Find the next link in the URDF tree
Parameters
----------
root
current_joint: xml.etree.ElementTree
The current URDF joint
next_link_name: str
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227,744 | Phylliade/ikpy | src/ikpy/URDF_utils.py | get_urdf_parameters | def get_urdf_parameters(urdf_file, base_elements=None, last_link_vector=None, base_element_type="link"):
"""
Returns translated parameters from the given URDF file
Parameters
----------
urdf_file: str
The path of the URDF file
base_elements: list of strings
List of the links beg... | python | def get_urdf_parameters(urdf_file, base_elements=None, last_link_vector=None, base_element_type="link"):
"""
Returns translated parameters from the given URDF file
Parameters
----------
urdf_file: str
The path of the URDF file
base_elements: list of strings
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227,745 | Phylliade/ikpy | src/ikpy/URDF_utils.py | _convert_angle_limit | def _convert_angle_limit(angle, joint, **kwargs):
"""Converts the limit angle of the PyPot JSON file to the internal format"""
angle_pypot = angle
# No need to take care of orientation
if joint["orientation"] == "indirect":
angle_pypot = 1 * angle_pypot
# angle_pypot = angle_pypot + offset... | python | def _convert_angle_limit(angle, joint, **kwargs):
"""Converts the limit angle of the PyPot JSON file to the internal format"""
angle_pypot = angle
# No need to take care of orientation
if joint["orientation"] == "indirect":
angle_pypot = 1 * angle_pypot
# angle_pypot = angle_pypot + offset... | [
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227,746 | Phylliade/ikpy | scripts/hand_follow.py | follow_hand | def follow_hand(poppy, delta):
"""Tell the right hand to follow the left hand"""
right_arm_position = poppy.l_arm_chain.end_effector + delta
poppy.r_arm_chain.goto(right_arm_position, 0.5, wait=True) | python | def follow_hand(poppy, delta):
"""Tell the right hand to follow the left hand"""
right_arm_position = poppy.l_arm_chain.end_effector + delta
poppy.r_arm_chain.goto(right_arm_position, 0.5, wait=True) | [
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227,747 | Phylliade/ikpy | src/ikpy/inverse_kinematics.py | inverse_kinematic_optimization | def inverse_kinematic_optimization(chain, target_frame, starting_nodes_angles, regularization_parameter=None, max_iter=None):
"""
Computes the inverse kinematic on the specified target with an optimization method
Parameters
----------
chain: ikpy.chain.Chain
The chain used for the Inverse k... | python | def inverse_kinematic_optimization(chain, target_frame, starting_nodes_angles, regularization_parameter=None, max_iter=None):
"""
Computes the inverse kinematic on the specified target with an optimization method
Parameters
----------
chain: ikpy.chain.Chain
The chain used for the Inverse k... | [
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227,748 | Phylliade/ikpy | src/ikpy/chain.py | Chain.forward_kinematics | def forward_kinematics(self, joints, full_kinematics=False):
"""Returns the transformation matrix of the forward kinematics
Parameters
----------
joints: list
The list of the positions of each joint. Note : Inactive joints must be in the list.
full_kinematics: bool
... | python | def forward_kinematics(self, joints, full_kinematics=False):
"""Returns the transformation matrix of the forward kinematics
Parameters
----------
joints: list
The list of the positions of each joint. Note : Inactive joints must be in the list.
full_kinematics: bool
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227,749 | Phylliade/ikpy | src/ikpy/chain.py | Chain.inverse_kinematics | def inverse_kinematics(self, target, initial_position=None, **kwargs):
"""Computes the inverse kinematic on the specified target
Parameters
----------
target: numpy.array
The frame target of the inverse kinematic, in meters. It must be 4x4 transformation matrix
initi... | python | def inverse_kinematics(self, target, initial_position=None, **kwargs):
"""Computes the inverse kinematic on the specified target
Parameters
----------
target: numpy.array
The frame target of the inverse kinematic, in meters. It must be 4x4 transformation matrix
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227,750 | Phylliade/ikpy | src/ikpy/chain.py | Chain.plot | def plot(self, joints, ax, target=None, show=False):
"""Plots the Chain using Matplotlib
Parameters
----------
joints: list
The list of the positions of each joint
ax: matplotlib.axes.Axes
A matplotlib axes
target: numpy.array
An optio... | python | def plot(self, joints, ax, target=None, show=False):
"""Plots the Chain using Matplotlib
Parameters
----------
joints: list
The list of the positions of each joint
ax: matplotlib.axes.Axes
A matplotlib axes
target: numpy.array
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An optional target
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227,751 | Phylliade/ikpy | src/ikpy/chain.py | Chain.from_urdf_file | def from_urdf_file(cls, urdf_file, base_elements=None, last_link_vector=None, base_element_type="link", active_links_mask=None, name="chain"):
"""Creates a chain from an URDF file
Parameters
----------
urdf_file: str
The path of the URDF file
base_elements: list of s... | python | def from_urdf_file(cls, urdf_file, base_elements=None, last_link_vector=None, base_element_type="link", active_links_mask=None, name="chain"):
"""Creates a chain from an URDF file
Parameters
----------
urdf_file: str
The path of the URDF file
base_elements: list of s... | [
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urdf_file: str
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List of the links beginning the chain
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Optional : The translation vector of the tip.
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227,752 | blockchain-certificates/cert-issuer | cert_issuer/signer.py | check_internet_off | def check_internet_off(secrets_file_path):
"""If internet off and USB plugged in, returns true. Else, continues to wait..."""
while True:
if internet_on() is False and os.path.exists(secrets_file_path):
break
else:
print("Turn off your internet and plug in your USB to con... | python | def check_internet_off(secrets_file_path):
"""If internet off and USB plugged in, returns true. Else, continues to wait..."""
while True:
if internet_on() is False and os.path.exists(secrets_file_path):
break
else:
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227,753 | blockchain-certificates/cert-issuer | cert_issuer/signer.py | check_internet_on | def check_internet_on(secrets_file_path):
"""If internet on and USB unplugged, returns true. Else, continues to wait..."""
while True:
if internet_on() is True and not os.path.exists(secrets_file_path):
break
else:
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"""If internet on and USB unplugged, returns true. Else, continues to wait..."""
while True:
if internet_on() is True and not os.path.exists(secrets_file_path):
break
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227,754 | blockchain-certificates/cert-issuer | cert_issuer/blockchain_handlers/bitcoin/signer.py | verify_signature | def verify_signature(uid, signed_cert_file_name, issuing_address):
"""
Verify the certificate signature matches the expected. Double-check the uid field in the certificate and use
VerifyMessage to confirm that the signature in the certificate matches the issuing_address.
Raises error is verification fa... | python | def verify_signature(uid, signed_cert_file_name, issuing_address):
"""
Verify the certificate signature matches the expected. Double-check the uid field in the certificate and use
VerifyMessage to confirm that the signature in the certificate matches the issuing_address.
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227,755 | blockchain-certificates/cert-issuer | cert_issuer/blockchain_handlers/ethereum/connectors.py | EtherscanBroadcaster.get_balance | def get_balance(self, address, api_token):
"""
returns the balance in wei
with some inspiration from PyWallet
"""
broadcast_url = self.base_url + '?module=account&action=balance'
broadcast_url += '&address=%s' % address
broadcast_url += '&tag=latest'
if ap... | python | def get_balance(self, address, api_token):
"""
returns the balance in wei
with some inspiration from PyWallet
"""
broadcast_url = self.base_url + '?module=account&action=balance'
broadcast_url += '&address=%s' % address
broadcast_url += '&tag=latest'
if ap... | [
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227,756 | blockchain-certificates/cert-issuer | cert_issuer/blockchain_handlers/ethereum/connectors.py | EtherscanBroadcaster.get_address_nonce | def get_address_nonce(self, address, api_token):
"""
Looks up the address nonce of this address
Neccesary for the transaction creation
"""
broadcast_url = self.base_url + '?module=proxy&action=eth_getTransactionCount'
broadcast_url += '&address=%s' % address
broad... | python | def get_address_nonce(self, address, api_token):
"""
Looks up the address nonce of this address
Neccesary for the transaction creation
"""
broadcast_url = self.base_url + '?module=proxy&action=eth_getTransactionCount'
broadcast_url += '&address=%s' % address
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227,757 | tensorflow/mesh | mesh_tensorflow/tpu_variables.py | ReplicatedVariable._dense_var_to_tensor | def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False):
"""Converts a variable to a tensor."""
# pylint: disable=protected-access
if _enclosing_tpu_context() is None:
if hasattr(self._primary_var, '_dense_var_to_tensor'):
return self._primary_var._dense_var_to_tensor(dtype, name, ... | python | def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False):
"""Converts a variable to a tensor."""
# pylint: disable=protected-access
if _enclosing_tpu_context() is None:
if hasattr(self._primary_var, '_dense_var_to_tensor'):
return self._primary_var._dense_var_to_tensor(dtype, name, ... | [
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227,758 | tensorflow/mesh | mesh_tensorflow/auto_mtf/memory_estimator.py | MemoryEstimator._compute_layout_validator | def _compute_layout_validator(self):
"""Computes self._layout_validator."""
self._layout_validator = valid_layouts.LayoutValidator(self.mtf_graph,
self.mesh_shape) | python | def _compute_layout_validator(self):
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self._layout_validator = valid_layouts.LayoutValidator(self.mtf_graph,
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227,759 | tensorflow/mesh | mesh_tensorflow/auto_mtf/memory_estimator.py | MemoryEstimator._compute_graph_interface | def _compute_graph_interface(self):
"""Computes self._graph_interface."""
self._graph_interface = graph_interface.GraphInterface(self.mtf_graph)
for mtf_output in self.mtf_outputs:
self._graph_interface.set_tensor_final(mtf_output.name) | python | def _compute_graph_interface(self):
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227,760 | tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | make_layer_stack | def make_layer_stack(layers=gin.REQUIRED, num_layers=6):
"""Configurable layer stack.
Args:
layers: a list of subclasses of TransformerLayer
num_layers: an integer
Returns:
a LayerStack
"""
return LayerStack([cls() for cls in layers] * num_layers) | python | def make_layer_stack(layers=gin.REQUIRED, num_layers=6):
"""Configurable layer stack.
Args:
layers: a list of subclasses of TransformerLayer
num_layers: an integer
Returns:
a LayerStack
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227,761 | tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | make_bitransformer | def make_bitransformer(
input_vocab_size=gin.REQUIRED,
output_vocab_size=gin.REQUIRED,
layout=None,
mesh_shape=None):
"""Gin-configurable bitransformer constructor.
In your config file you need to set the encoder and decoder layers like this:
encoder/make_layer_stack.layers = [
@transformer_l... | python | def make_bitransformer(
input_vocab_size=gin.REQUIRED,
output_vocab_size=gin.REQUIRED,
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mesh_shape=None):
"""Gin-configurable bitransformer constructor.
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227,762 | tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | Context.get_states | def get_states(self, n):
"""Get the next n recurrent states.
Called by layers in "incremental" mode.
Args:
n: an integer
Returns:
a list of n Tensors
"""
return self.states[len(self.new_states):len(self.new_states) + n] | python | def get_states(self, n):
"""Get the next n recurrent states.
Called by layers in "incremental" mode.
Args:
n: an integer
Returns:
a list of n Tensors
"""
return self.states[len(self.new_states):len(self.new_states) + n] | [
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227,763 | tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | Context.get_constant_state | def get_constant_state(self):
"""Read state that was written in "first_part" mode.
Returns:
a structure
"""
ret = self.constant_states[self.next_constant_state]
self.next_constant_state += 1
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"""Read state that was written in "first_part" mode.
Returns:
a structure
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ret = self.constant_states[self.next_constant_state]
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227,764 | tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | Context.nonpadding | def nonpadding(self):
"""Tensor with zeros in padding positions and ones elsewhere."""
if self.sequence_id is None:
return None
if self.sequence_id == 1:
return 1
else:
return mtf.cast(
mtf.not_equal(self.sequence_id, 0), self.activation_dtype) | python | def nonpadding(self):
"""Tensor with zeros in padding positions and ones elsewhere."""
if self.sequence_id is None:
return None
if self.sequence_id == 1:
return 1
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227,765 | tensorflow/mesh | mesh_tensorflow/transformer/metrics.py | sequence_accuracy | def sequence_accuracy(labels, outputs):
"""Compute the sequence-level accuracy.
A sequence is only considered correct if all of its entries were predicted
correctly.
Args:
labels: ground-truth labels, shape=(batch, packed_seq_length)
outputs: predicted tokens, shape=(batch, seq_length)
Returns:
... | python | def sequence_accuracy(labels, outputs):
"""Compute the sequence-level accuracy.
A sequence is only considered correct if all of its entries were predicted
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Args:
labels: ground-truth labels, shape=(batch, packed_seq_length)
outputs: predicted tokens, shape=(batch, seq_length)
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227,766 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_operation_input_names | def get_operation_input_names(self, operation_name):
"""Generates the names of all input tensors of an operation.
Args:
operation_name: a string, the name of an operation in the graph.
Yields:
a string, the name of an input tensor.
"""
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"""Generates the names of all input tensors of an operation.
Args:
operation_name: a string, the name of an operation in the graph.
Yields:
a string, the name of an input tensor.
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227,767 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_operation_output_names | def get_operation_output_names(self, operation_name):
"""Generates the names of all output tensors of an operation.
Args:
operation_name: a string, the name of an operation in the graph.
Yields:
a string, the name of an output tensor.
"""
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"""Generates the names of all output tensors of an operation.
Args:
operation_name: a string, the name of an operation in the graph.
Yields:
a string, the name of an output tensor.
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227,768 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_shape | def get_tensor_shape(self, tensor_name):
"""The tf.TensorShape of a tensor.
Args:
tensor_name: string, the name of a tensor in the graph.
Returns:
a tf.TensorShape
"""
tensor = self._name_to_tensor(tensor_name)
if isinstance(tensor, mtf.Tensor):
return tf.TensorShape(tensor.... | python | def get_tensor_shape(self, tensor_name):
"""The tf.TensorShape of a tensor.
Args:
tensor_name: string, the name of a tensor in the graph.
Returns:
a tf.TensorShape
"""
tensor = self._name_to_tensor(tensor_name)
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227,769 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_num_entries | def get_tensor_num_entries(self, tensor_name, partial_layout=None,
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"""The number of entries in a tensor.
If partial_layout is specified, then mesh_dimension_to_size must also be. In
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"""The number of entries in a tensor.
If partial_layout is specified, then mesh_dimension_to_size must also be. In
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227,770 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_size | def get_tensor_size(self, tensor_name, partial_layout=None,
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"""The size of a tensor in bytes.
If partial_layout is specified, then mesh_dimension_to_size must also be. In
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Args:
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mesh_dimension_to_size=None):
"""The size of a tensor in bytes.
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227,771 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_device | def get_tensor_device(self, tensor_name):
"""The device of a tensor.
Note that only tf tensors have device assignments.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a string or None, representing the device name.
"""
tensor = self._name_to_tensor(tensor_nam... | python | def get_tensor_device(self, tensor_name):
"""The device of a tensor.
Note that only tf tensors have device assignments.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a string or None, representing the device name.
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227,772 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_operation_device | def get_operation_device(self, operation_name):
"""The device of an operation.
Note that only tf operations have device assignments.
Args:
operation_name: a string, name of an operation in the graph.
Returns:
a string or None, representing the device name.
"""
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"""The device of an operation.
Note that only tf operations have device assignments.
Args:
operation_name: a string, name of an operation in the graph.
Returns:
a string or None, representing the device name.
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227,773 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_mtf_dimension_names | def get_tensor_mtf_dimension_names(self, tensor_name):
"""The Mesh TensorFlow dimensions associated with a tensor.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a [string], the names of Mesh TensorFlow dimensions.
"""
tensor = self._name_to_tensor(tensor_name)
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"""The Mesh TensorFlow dimensions associated with a tensor.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a [string], the names of Mesh TensorFlow dimensions.
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227,774 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_operation_mtf_dimension_names | def get_operation_mtf_dimension_names(self, operation_name):
"""The Mesh TensorFlow dimensions associated with an operation.
Args:
operation_name: a string, name of an operation in the graph.
Returns:
a set(string), the names of Mesh TensorFlow dimensions.
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"""The Mesh TensorFlow dimensions associated with an operation.
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operation_name: a string, name of an operation in the graph.
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a set(string), the names of Mesh TensorFlow dimensions.
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227,775 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.set_tensor_final | def set_tensor_final(self, tensor_name):
"""Denotes a tensor as a final output of the computation.
Args:
tensor_name: a string, name of a tensor in the graph.
"""
tensor = self._name_to_tensor(tensor_name)
self._final_tensors.add(tensor) | python | def set_tensor_final(self, tensor_name):
"""Denotes a tensor as a final output of the computation.
Args:
tensor_name: a string, name of a tensor in the graph.
"""
tensor = self._name_to_tensor(tensor_name)
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227,776 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.is_tensor_final | def is_tensor_final(self, tensor_name):
"""Whether a tensor is a final output of the computation.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a boolean indicating whether the tensor was a final output.
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tensor_name: a string, name of a tensor in the graph.
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227,777 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.compute_cost_graph | def compute_cost_graph(self, devices=None):
"""Computes a CostGraphDef protobuf based on this graph.
Defined in tensorflow/core/framework/cost_graph.proto.
Args:
devices: optional [string], the names of devices to consider. If
specified, any tensor on a device not listed is given a size of... | python | def compute_cost_graph(self, devices=None):
"""Computes a CostGraphDef protobuf based on this graph.
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227,778 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.compute_memory_contents_under_schedule | def compute_memory_contents_under_schedule(self, schedule):
"""The in-memory tensors present when executing each operation in schedule.
Simulates running operations in the order given by a schedule. Keeps track
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227,779 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._initialize_operations | def _initialize_operations(self):
"""Initializer for _operations.
Raises:
TypeError: _graph is not a tf.Graph or mtf.Graph.
Returns:
a list of (tf.Operation or mtf.Operation)
"""
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"""Initializer for _operations.
Raises:
TypeError: _graph is not a tf.Graph or mtf.Graph.
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a list of (tf.Operation or mtf.Operation)
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227,780 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._initialize_operation_name_to_id | def _initialize_operation_name_to_id(self):
"""Initializer for _operation_name_to_id.
Returns:
a {string: int}, mapping operation names to their index in _operations.
"""
operation_name_to_id = {}
for i, operation in enumerate(self._operations):
operation_name_to_id[operation.name] = i
... | python | def _initialize_operation_name_to_id(self):
"""Initializer for _operation_name_to_id.
Returns:
a {string: int}, mapping operation names to their index in _operations.
"""
operation_name_to_id = {}
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227,781 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._initialize_tensor_name_to_ids | def _initialize_tensor_name_to_ids(self):
"""Initializer for _tensor_name_to_ids.
Returns:
a {string: (int, int)}, mapping the name of tensor T to the index of T's
operation in _operations and T's index in T's operation's outputs.
"""
tensor_name_to_ids = {}
for i, operation in enum... | python | def _initialize_tensor_name_to_ids(self):
"""Initializer for _tensor_name_to_ids.
Returns:
a {string: (int, int)}, mapping the name of tensor T to the index of T's
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227,782 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._name_to_tensor | def _name_to_tensor(self, tensor_name):
"""The tensor with the given name.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a tf.Tensor or mtf.Tensor
"""
id1, id2 = self._tensor_name_to_ids[tensor_name]
return self._operations[id1].outputs[id2] | python | def _name_to_tensor(self, tensor_name):
"""The tensor with the given name.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a tf.Tensor or mtf.Tensor
"""
id1, id2 = self._tensor_name_to_ids[tensor_name]
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227,783 | tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._compute_initial_out_degree | def _compute_initial_out_degree(self):
"""The number of operations which use each tensor as input.
Returns:
a {string, int} mapping tensor name to the number of operations which use
it as input, or one plus that quantity if the tensor is final.
"""
out_degree = collections.defaultdict(int)
... | python | def _compute_initial_out_degree(self):
"""The number of operations which use each tensor as input.
Returns:
a {string, int} mapping tensor name to the number of operations which use
it as input, or one plus that quantity if the tensor is final.
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out_degree = collections.defaultdict(int)
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227,784 | tensorflow/mesh | mesh_tensorflow/layers.py | layer_norm | def layer_norm(x, dim, epsilon=1e-6, name="layer_prepostprocess"):
"""Layer normalization over dimension dim.
Args:
x: a mtf.Tensor whose shape contains dim.
dim: a mtf.Dimension
epsilon: a floating point number
name: a string. variable scope.
Returns:
a mtf.Tensor with same shape as x.
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"""Layer normalization over dimension dim.
Args:
x: a mtf.Tensor whose shape contains dim.
dim: a mtf.Dimension
epsilon: a floating point number
name: a string. variable scope.
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a mtf.Tensor with same shape as x.
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227,785 | tensorflow/mesh | mesh_tensorflow/layers.py | softmax_cross_entropy_with_logits | def softmax_cross_entropy_with_logits(logits, targets, vocab_dim, z_loss=0.0):
"""Per-example softmax loss.
if z_loss is nonzero, we add a loss equal to z_loss*log(z)^2, where z is the
partition function. Example value: z_loss=1e-4. Two uses of z_loss are:
- To keep the logits from drifting too far from zero... | python | def softmax_cross_entropy_with_logits(logits, targets, vocab_dim, z_loss=0.0):
"""Per-example softmax loss.
if z_loss is nonzero, we add a loss equal to z_loss*log(z)^2, where z is the
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227,786 | tensorflow/mesh | mesh_tensorflow/layers.py | sigmoid_cross_entropy_with_logits | def sigmoid_cross_entropy_with_logits(logits, targets):
"""Sigmoid cross-entropy loss.
Args:
logits: a mtf.Tensor
targets: a mtf.Tensor with the same shape as logits
Returns:
a mtf.Tensor whose shape is equal to logits.shape
Raises:
ValueError: if the shapes do not match.
"""
if logits.sh... | python | def sigmoid_cross_entropy_with_logits(logits, targets):
"""Sigmoid cross-entropy loss.
Args:
logits: a mtf.Tensor
targets: a mtf.Tensor with the same shape as logits
Returns:
a mtf.Tensor whose shape is equal to logits.shape
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227,787 | tensorflow/mesh | mesh_tensorflow/layers.py | dense_relu_dense | def dense_relu_dense(x,
hidden_channels,
dropout=0.0,
dropout_broadcast_dims=None,
master_dtype=tf.float32,
slice_dtype=tf.float32, name=None):
"""Hidden layer with ReLU activation followed by linear projection.
... | python | def dense_relu_dense(x,
hidden_channels,
dropout=0.0,
dropout_broadcast_dims=None,
master_dtype=tf.float32,
slice_dtype=tf.float32, name=None):
"""Hidden layer with ReLU activation followed by linear projection.
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227,788 | tensorflow/mesh | mesh_tensorflow/layers.py | local_1d_halo_exchange | def local_1d_halo_exchange(k, v, num_w_blocks, w_dim, mask_right):
"""Halo exchange for keys and values for Local 1D attention."""
if num_w_blocks is not None:
if mask_right:
k = mtf.left_halo_exchange(k, num_w_blocks, w_dim, w_dim.size)
v = mtf.left_halo_exchange(v, num_w_blocks, w_dim, w_dim.size)... | python | def local_1d_halo_exchange(k, v, num_w_blocks, w_dim, mask_right):
"""Halo exchange for keys and values for Local 1D attention."""
if num_w_blocks is not None:
if mask_right:
k = mtf.left_halo_exchange(k, num_w_blocks, w_dim, w_dim.size)
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227,789 | tensorflow/mesh | mesh_tensorflow/layers.py | local_2d_halo_exchange | def local_2d_halo_exchange(k, v, num_h_blocks, h_dim,
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"""Halo exchange for keys and values for Local 2D attention."""
for blocks_dim, block_size_dim, halo_size in [
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# s... | python | def local_2d_halo_exchange(k, v, num_h_blocks, h_dim,
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227,790 | tensorflow/mesh | mesh_tensorflow/layers.py | local_2d_self_attention_spatial_blocks | def local_2d_self_attention_spatial_blocks(query_antecedent,
kv_channels,
heads,
memory_h_dim=None,
memory_w_dim=None,
... | python | def local_2d_self_attention_spatial_blocks(query_antecedent,
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memory_w_dim=None,
... | [
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] | 3921196e5e43302e820da0a87329f25d7e2a3016 | https://github.com/tensorflow/mesh/blob/3921196e5e43302e820da0a87329f25d7e2a3016/mesh_tensorflow/layers.py#L560-L642 |
227,791 | tensorflow/mesh | mesh_tensorflow/layers.py | multihead_attention_vars | def multihead_attention_vars(
mesh, heads, io_channels, kv_channels,
master_dtype, slice_dtype, activation_dtype):
"""Deprecated version of multihead_attention_params with combine=True."""
return multihead_attention_params(
mesh, heads, io_channels, kv_channels,
mtf.VariableDType(master_dtype, s... | python | def multihead_attention_vars(
mesh, heads, io_channels, kv_channels,
master_dtype, slice_dtype, activation_dtype):
"""Deprecated version of multihead_attention_params with combine=True."""
return multihead_attention_params(
mesh, heads, io_channels, kv_channels,
mtf.VariableDType(master_dtype, s... | [
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227,792 | tensorflow/mesh | mesh_tensorflow/layers.py | multihead_attention_params | def multihead_attention_params(mesh, heads, io_channels, kv_channels,
variable_dtype, combine=False):
"""Create Parameters for Multihead Attention.
If the combine flag is set to True, then we create only one variable
which stacks together all of the parameters. Otherwise, we creat... | python | def multihead_attention_params(mesh, heads, io_channels, kv_channels,
variable_dtype, combine=False):
"""Create Parameters for Multihead Attention.
If the combine flag is set to True, then we create only one variable
which stacks together all of the parameters. Otherwise, we creat... | [
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227,793 | tensorflow/mesh | mesh_tensorflow/layers.py | attention_mask_ignore_padding | def attention_mask_ignore_padding(inputs, dtype=tf.float32):
"""Bias for encoder-decoder attention.
Args:
inputs: a mtf.Tensor with shape [..., length_dim]
dtype: a tf.dtype
Returns:
a mtf.Tensor with shape [..., memory_length_dim]
"""
inputs = rename_length_to_memory_length(inputs)
return mtf... | python | def attention_mask_ignore_padding(inputs, dtype=tf.float32):
"""Bias for encoder-decoder attention.
Args:
inputs: a mtf.Tensor with shape [..., length_dim]
dtype: a tf.dtype
Returns:
a mtf.Tensor with shape [..., memory_length_dim]
"""
inputs = rename_length_to_memory_length(inputs)
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227,794 | tensorflow/mesh | mesh_tensorflow/layers.py | attention_mask_autoregressive | def attention_mask_autoregressive(query_pos, dtype=tf.float32):
"""Bias for self-attention where attention to the right is disallowed.
Args:
query_pos: a mtf.Tensor with shape [..., length_dim]
dtype: a tf.dtype
Returns:
a mtf.Tensor with shape [..., length_dim, memory_length_dim]
"""
memory_pos... | python | def attention_mask_autoregressive(query_pos, dtype=tf.float32):
"""Bias for self-attention where attention to the right is disallowed.
Args:
query_pos: a mtf.Tensor with shape [..., length_dim]
dtype: a tf.dtype
Returns:
a mtf.Tensor with shape [..., length_dim, memory_length_dim]
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227,795 | tensorflow/mesh | mesh_tensorflow/layers.py | attention_mask_same_segment | def attention_mask_same_segment(
query_segment, memory_segment=None, dtype=tf.float32):
"""Bias for attention where attention between segments is disallowed.
Args:
query_segment: a mtf.Tensor with shape [..., length_dim]
memory_segment: a mtf.Tensor with shape [..., memory_length_dim]
dtype: a tf.d... | python | def attention_mask_same_segment(
query_segment, memory_segment=None, dtype=tf.float32):
"""Bias for attention where attention between segments is disallowed.
Args:
query_segment: a mtf.Tensor with shape [..., length_dim]
memory_segment: a mtf.Tensor with shape [..., memory_length_dim]
dtype: a tf.d... | [
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227,796 | tensorflow/mesh | mesh_tensorflow/layers.py | multiplicative_jitter | def multiplicative_jitter(x, epsilon=1e-2):
"""Multiply values by a random number between 1-epsilon and 1+epsilon.
Makes models more resilient to rounding errors introduced by bfloat16.
This seems particularly important for logits.
Args:
x: a mtf.Tensor
epsilon: a floating point value
Returns:
... | python | def multiplicative_jitter(x, epsilon=1e-2):
"""Multiply values by a random number between 1-epsilon and 1+epsilon.
Makes models more resilient to rounding errors introduced by bfloat16.
This seems particularly important for logits.
Args:
x: a mtf.Tensor
epsilon: a floating point value
Returns:
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x: a mtf.Tensor
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227,797 | tensorflow/mesh | mesh_tensorflow/layers.py | multihead_self_attention_memory_compressed | def multihead_self_attention_memory_compressed(x,
mask_right,
compression_factor,
kv_channels,
heads,
... | python | def multihead_self_attention_memory_compressed(x,
mask_right,
compression_factor,
kv_channels,
heads,
... | [
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kv_channels: a mtf.Dimension ... | [
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227,798 | tensorflow/mesh | mesh_tensorflow/layers.py | compress_mean | def compress_mean(x, dim, compression_factor):
"""Compress by taking group means.
Args:
x: a Tensor
dim: a dimension in x.shape
compression_factor: an integer
Returns:
a Tensor
"""
dims = x.shape.dims
pos = dims.index(dim)
compressed_dim = mtf.Dimension(dim.name, dim.size // compression_... | python | def compress_mean(x, dim, compression_factor):
"""Compress by taking group means.
Args:
x: a Tensor
dim: a dimension in x.shape
compression_factor: an integer
Returns:
a Tensor
"""
dims = x.shape.dims
pos = dims.index(dim)
compressed_dim = mtf.Dimension(dim.name, dim.size // compression_... | [
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227,799 | tensorflow/mesh | mesh_tensorflow/layers.py | embedding | def embedding(indices, vocab_dim, output_dim, variable_dtype, name="embedding"):
"""Embedding layer."""
weights = embedding_weights(
indices.mesh, vocab_dim, output_dim, variable_dtype, name)
return mtf.gather(weights, indices, vocab_dim) | python | def embedding(indices, vocab_dim, output_dim, variable_dtype, name="embedding"):
"""Embedding layer."""
weights = embedding_weights(
indices.mesh, vocab_dim, output_dim, variable_dtype, name)
return mtf.gather(weights, indices, vocab_dim) | [
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