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85e5f25e687a3bf64dce7990c378a34266dcba0b | niosus/homework_checker | homework_checker/core/tools.py | [
"Apache-2.0"
] | Python | succeeded | bool | def succeeded(self: CmdResult) -> bool:
"""Check if the command succeeded."""
if self.returncode is not None:
return self.returncode == CmdResult.SUCCESS
if self.stderr:
return False
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85e5f25e687a3bf64dce7990c378a34266dcba0b | niosus/homework_checker | homework_checker/core/tools.py | [
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timeout: float,
shell: bool = True,
cwd: Path = Path.cwd(),
env: Optional[Mapping[str, Any]] = None,
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command (str): command to run
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str: raw command o... | Run a generic command in a subprocess.
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timeout: float,
shell: bool = True,
cwd: Path = Path.cwd(),
env: Optional[Mapping[str, Any]] = None,
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try:
startupinfo = None
if shell and isinstance(command, list):
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85e5f25e687a3bf64dce7990c378a34266dcba0b | niosus/homework_checker | homework_checker/core/tools.py | [
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str_input: str = None,
timeout: float = None,
check: bool = False,
**kwargs
) -> subprocess.CompletedProcess:
"""Run a command as a subprocess.
Using the guide from StackOverflow:
https://stackoverflow.com/a/36955420/1763680
This... | Run a command as a subprocess.
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https://stackoverflow.com/a/36955420/1763680
This command has been adapted from:
https://github.com/python/cpython/blob/3.5/Lib/subprocess.py#L352-L399
This code does essentially the same as subprocess.run(...) but makes sure to
... | Run a command as a subprocess.
This code does essentially the same as subprocess.run(...) but makes sure to
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244c45569d5682ea0858d59e4c6d48764f7d9a8c | niosus/homework_checker | homework_checker/core/checker.py | [
"Apache-2.0"
] | Python | check_homework | HomeworkResultDict | def check_homework(self: "Checker", homework_node: dict) -> HomeworkResultDict:
"""Run over all Tasks in a single homework."""
results: HomeworkResultDict = {}
current_folder = Path(self._checked_code_folder, homework_node[Tags.FOLDER_TAG])
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results: HomeworkResultDict = {}
current_folder = Path(self._checked_code_folder, homework_node[Tags.FOLDER_TAG])
log.debug("current_folder: %s", current_folder)
if not current_folder.exists():
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244c45569d5682ea0858d59e4c6d48764f7d9a8c | niosus/homework_checker | homework_checker/core/checker.py | [
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] | Python | check_all_homeworks | Dict[str, HomeworkResultDict] | def check_all_homeworks(self: "Checker") -> Dict[str, HomeworkResultDict]:
"""Run over all Tasks in all homeworks."""
results: Dict[str, HomeworkResultDict] = {}
for idx, homework_node in enumerate(self._base_node[Tags.HOMEWORKS_TAG]):
hw_name = tools.add_number_to_name(idx, homework... | Run over all Tasks in all homeworks. | Run over all Tasks in all homeworks. | [
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results: Dict[str, HomeworkResultDict] = {}
for idx, homework_node in enumerate(self._base_node[Tags.HOMEWORKS_TAG]):
hw_name = tools.add_number_to_name(idx, homework_node[Tags.NAME_TAG])
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6a282dac266526afcb7f6b79cbc4cc9b09467ec6 | niosus/homework_checker | homework_checker/core/md_writer.py | [
"Apache-2.0"
] | Python | update | null | def update(self: "MdWriter", hw_results: Dict[str, HomeworkResultDict]):
"""Update the table of completion."""
for hw_name, hw_dict in sorted(hw_results.items()):
hw_name = remove_number_from_name(hw_name)
need_hw_name = True
expired = False
if EXPIRED_TAG... | Update the table of completion. | Update the table of completion. | [
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hw_name = remove_number_from_name(hw_name)
need_hw_name = True
expired = False
if EXPIRED_TAG in hw_dict:
expired = True
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6a282dac266526afcb7f6b79cbc4cc9b09467ec6 | niosus/homework_checker | homework_checker/core/md_writer.py | [
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] | Python | write_md_file | null | def write_md_file(self: "MdWriter", md_file_path: Path):
"""Write all the added content to the md file."""
md_file_content = "# Test results\n"
md_file_content += self._md_table
if self._errors:
md_file_content += "\n# Encountered errors\n"
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md_file_content = "# Test results\n"
md_file_content += self._md_table
if self._errors:
md_file_content += "\n# Encountered errors\n"
md_file_content += self._errors
md_file_content += SEPARATOR
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6a282dac266526afcb7f6b79cbc4cc9b09467ec6 | niosus/homework_checker | homework_checker/core/md_writer.py | [
"Apache-2.0"
] | Python | _add_error | <not_specific> | def _add_error(
self: "MdWriter",
hw_name: str,
task_name: str,
test_name: str,
test_result: CmdResult,
expired: bool,
):
"""Add a section of errors to the md file."""
if test_result.succeeded():
return
if expired:
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task_name: str,
test_name: str,
test_result: CmdResult,
expired: bool,
):
if test_result.succeeded():
return
if expired:
self._errors += EXPIRED_TEMPLATE.format(hw_name=hw_name)
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79cb5feaae56469411cf713b687f91b7e3939f5d | williwacker/django-qr-code | qr_code/qrcode/maker.py | [
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] | Python | make_qr_code_image | <not_specific> | def make_qr_code_image(text, image_factory, qr_code_options=QRCodeOptions()):
"""
Generates an image object (from the qrcode library) representing the QR code for the given text.
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valid_version = _get_valid_ver... |
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valid_size = _get_valid_size_or_default(qr_code_options.size)
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79cb5feaae56469411cf713b687f91b7e3939f5d | williwacker/django-qr-code | qr_code/qrcode/maker.py | [
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] | Python | make_embedded_qr_code | <not_specific> | def make_embedded_qr_code(text, qr_code_options=QRCodeOptions()):
"""
Generates a <svg> or <img> tag representing the QR code for the given text. This tag can be embedded into an
HTML document.
"""
image_format = qr_code_options.image_format
img = make_qr_code_image(text, SvgEmbeddedInHtmlImage ... |
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image_format = qr_code_options.image_format
img = make_qr_code_image(text, SvgEmbeddedInHtmlImage if image_format == SVG_FORMAT_NAME else PilImageOrFallback, qr_code_options=qr_code_options)
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a4c956d5ea31af324d9d6a5d51f2b28c3bcd5c00 | SriramPingali/Image-To-Text | utils.py | [
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Args:
text (str or list of str): texts to convert.
Returns:
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torch.LongTensor [n]: length of each text.
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text (str or list of str): texts to convert.
Returns:
torch.LongTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.
torch.LongTensor [n]: length of each text.
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length.append(len(item))
r = []
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"""Decode encoded texts back into strs.
Args:
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torch.LongTensor [n]: length of each text.
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314d7ac5c6fe5a1510384297827fec9d5191fb88 | nala-cub/prost | src/baselines/unifiedqa.py | [
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""" Prepare PROST example for the T5 Colab Notebook """
template = '{ex_question} \\n (A) {A} (B) {B} (C) {C} (D) {D} \\n {context}'
instance = {
'input': template.format_map(example),
'target': example[list('ABCD')[example['label']]],
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instance = {
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314d7ac5c6fe5a1510384297827fec9d5191fb88 | nala-cub/prost | src/baselines/unifiedqa.py | [
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import string
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
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314d7ac5c6fe5a1510384297827fec9d5191fb88 | nala-cub/prost | src/baselines/unifiedqa.py | [
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""" Prepare PIQA example for scoring preds from the T5 notebook """
match = re.match(r'^(.+) \\n \(A\) (.+) \(B\) (.+)$', example['input'])
instance = {}
instance['sol1'] = match[2].strip().lower()
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instance = {}
instance['sol1'] = match[2].strip().lower()
instance['sol2'] = match[3].strip().lower()
example['target'] = example['target'].strip().lower()
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19e27ccfa1d43321c3ed4d96a1b9ccfc59bb1b88 | nala-cub/prost | src/baselines/results.py | [
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""" Takes in a pivoted DF and produces a LaTeX Table
Each row should be a model and each column a Task.
"""
dfl = df.copy(deep=True)
header = r'\begin{tabular}{rl' + 'c' * (len(dfl.columns) + 2) + '}\n'
header += r'\toprule' + '\n'
# column heade... | Takes in a pivoted DF and produces a LaTeX Table
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19e27ccfa1d43321c3ed4d96a1b9ccfc59bb1b88 | nala-cub/prost | src/baselines/results.py | [
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""" Processes all Predictions into Longform DF with Dataset Examples + Correct Tags.
"""
dfp = to_wide_rankings(df)
dfp = dfp[[('example_idx', ''), ('model_name', ''), ('pred_idx', 'final')]]
dfp.columns = dfp.columns.droplevel(1)
# add unifiedqa
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dfp.columns = dfp.columns.droplevel(1)
uqa_df = df[df['model_name'].apply(lambda x: x.startswith('allenai'))]
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e19108ea6f5b1b380f9a1577730a8bdd0783385d | nala-cub/prost | src/baselines/input_pipeline.py | [
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Albert uses whole-word masking, so [MASK] should be replaced with the
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00aabec21d9e6f2a9a6ac8695441981020a528f2 | nala-cub/prost | src/prost/prost.py | [
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remove_duplicates=True):
"""Construct Test cases for a task.
Args:
config: raw dictionary configurary read in from scenario yml file.
variables: global and scenario lexicons.
Returns:
Formatted examples for... | Construct Test cases for a task.
Args:
config: raw dictionary configurary read in from scenario yml file.
variables: global and scenario lexicons.
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expect_fn = globals()[config.pop('expect_fn')]
enum_variables = T.itemmap(lambda x: make_enum_vars(*x), variables)
logging.debug('variables: %s, ev: %s', variables, enum_variables)
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00aabec21d9e6f2a9a6ac8695441981020a528f2 | nala-cub/prost | src/prost/prost.py | [
"Apache-2.0"
] | Python | preprocess_meta | <not_specific> | def preprocess_meta(fn: Callable):
""" Preprocess meta dicts -> IntEnum objects"""
@wraps(fn)
@T.curry
def _preprocess_meta(ex, meta, ev, **kwargs):
# enumerate meta
meta = enum_meta(meta, ev)
# get options
options = []
for i, k in enumerate('ABCD'):
option_meta = T.valfilter(lambda x... | Preprocess meta dicts -> IntEnum objects | Preprocess meta dicts -> IntEnum objects | [
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"objects"
] | def preprocess_meta(fn: Callable):
@wraps(fn)
@T.curry
def _preprocess_meta(ex, meta, ev, **kwargs):
meta = enum_meta(meta, ev)
options = []
for i, k in enumerate('ABCD'):
option_meta = T.valfilter(lambda x: x['text'] == ex[k], meta)
option_meta = list(option_meta.values())
if len(op... | [
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} |
00aabec21d9e6f2a9a6ac8695441981020a528f2 | nala-cub/prost | src/prost/prost.py | [
"Apache-2.0"
] | Python | find_all_keys | set[str] | def find_all_keys(obj) -> set[str]:
"""Finds all tag keys in object (with options) """
return T.pipe(obj, tree.flatten, set,
T.mapcat(lambda x: string.Formatter().parse(x)),
T.filter(T.get(1)),
T.map(lambda x: x[1] if not x[2] else '%s:%s' % (x[1], x[2])),
... | Finds all tag keys in object (with options) | Finds all tag keys in object (with options) | [
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] | def find_all_keys(obj) -> set[str]:
return T.pipe(obj, tree.flatten, set,
T.mapcat(lambda x: string.Formatter().parse(x)),
T.filter(T.get(1)),
T.map(lambda x: x[1] if not x[2] else '%s:%s' % (x[1], x[2])),
list, set) | [
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} |
00aabec21d9e6f2a9a6ac8695441981020a528f2 | nala-cub/prost | src/prost/prost.py | [
"Apache-2.0"
] | Python | recursive_format | TemplateObj | def recursive_format(obj: TemplateObj, mapping: Dict,
ignore_missing: bool = False) -> TemplateObj:
"""Formats all strings within an object, using mapping
Args:
obj: Object (leaves must be strings, regardless of type)
mapping: format dictionary, maps keys to values
ignore_missi... | Formats all strings within an object, using mapping
Args:
obj: Object (leaves must be strings, regardless of type)
mapping: format dictionary, maps keys to values
ignore_missing: If True, will not throw exception if a string contains a
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] | def recursive_format(obj: TemplateObj, mapping: Dict,
ignore_missing: bool = False) -> TemplateObj:
def formatfn(x):
fmt = SafeFormatter()
formatz = (lambda x, m: x.format(**m)
if not ignore_missing else fmt.format(x, **m))
options = re.compile(r'{([^}]+):([^}]+)}')
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12f692b8b2f9ec20ed743a31c3a5adca9f6bf0cd | JoySkipper/GBT_RFI_pipeline | GBT_RFI_pipeline/process_new_RFI_files.py | [
"MIT"
] | Python | find_parameters_to_process_file | <not_specific> | def find_parameters_to_process_file(RFI_files_to_be_processed: list,path_to_current_RFI_files):
"""
param: RFI_files_to_be_processed: List of all RFI files that need to be processed by the GBTIDL processing script
param: path_to_current_RFI_files: String containing the path to all current RFI files, in whic... |
param: RFI_files_to_be_processed: List of all RFI files that need to be processed by the GBTIDL processing script
param: path_to_current_RFI_files: String containing the path to all current RFI files, in which the files to be processed are contained
return: data_to_process; which is a list of lists contain... | List of all RFI files that need to be processed by the GBTIDL processing script
param: path_to_current_RFI_files: String containing the path to all current RFI files, in which the files to be processed are contained
return: data_to_process; which is a list of lists containing each file with the information needed to ru... | [
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data_to_process = []
for file_to_be_processed in RFI_files_to_be_processed:
try:
_, line_to_start_reader = read_header(file_to_be_processed, path_to_current_RFI_files)
except FileNotFoundE... | [
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12f692b8b2f9ec20ed743a31c3a5adca9f6bf0cd | JoySkipper/GBT_RFI_pipeline | GBT_RFI_pipeline/process_new_RFI_files.py | [
"MIT"
] | Python | analyze_file | null | def analyze_file(file_to_process,output_directory):
"""
param: file_to_process:: if the data has passed all checks up to this point, it is a dictionary containing metadata needed to process the RFI file.
"""
if file_to_process['list_of_scans'] == []:
raise(EmptyScans)
# The parameters for ru... |
param: file_to_process:: if the data has passed all checks up to this point, it is a dictionary containing metadata needed to process the RFI file.
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if file_to_process['list_of_scans'] == []:
raise(EmptyScans)
path = str(pathlib.Path(__file__).parent.absolute())+'/'
temp_path = tempfile.gettempdir()+'/'
temp_file = open(temp_path+"temp_file.pro","w+")
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... |
0e4f69c90aa6d102727975337d04cb852845e341 | ajyong/CMPUT404-assignment-websockets | sockets.py | [
"Apache-2.0"
] | Python | read_ws | <not_specific> | def read_ws(ws,client):
'''A greenlet function that reads from the websocket and updates the world'''
try:
while True:
msg = ws.receive()
# print "WS RECV: %s" % msg
if (msg is not None):
packet = json.loads(msg)
# print "Packet: %s" % ... | A greenlet function that reads from the websocket and updates the world | A greenlet function that reads from the websocket and updates the world | [
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] | def read_ws(ws,client):
try:
while True:
msg = ws.receive()
if (msg is not None):
packet = json.loads(msg)
for name, data in packet.iteritems():
entity = myWorld.get(name)
for k, v in data.iteritems():
... | [
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0e4f69c90aa6d102727975337d04cb852845e341 | ajyong/CMPUT404-assignment-websockets | sockets.py | [
"Apache-2.0"
] | Python | subscribe_socket | null | def subscribe_socket(ws):
'''Fufill the websocket URL of /subscribe, every update notify the
websocket and read updates from the websocket '''
# print "A client has subscribed."
client = Client()
clients.append(client)
# Give the new client the current world data
client.put(json.dumps(my... | Fufill the websocket URL of /subscribe, every update notify the
websocket and read updates from the websocket | Fufill the websocket URL of /subscribe, every update notify the
websocket and read updates from the websocket | [
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"websocket"
] | def subscribe_socket(ws):
client = Client()
clients.append(client)
client.put(json.dumps(myWorld.world()));
g = gevent.spawn( read_ws, ws, client )
try:
while True:
msg = client.get()
ws.send(msg)
except Exception as e:
print "WS Error: %s" % e
finally... | [
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} |
0e4f69c90aa6d102727975337d04cb852845e341 | ajyong/CMPUT404-assignment-websockets | sockets.py | [
"Apache-2.0"
] | Python | update | <not_specific> | def update(entity):
'''update the entities via this interface'''
data = flask_post_json(request)
for key, value in data.iteritems():
myWorld.update(entity, key, value);
return make_json_response(myWorld.get(entity)) | update the entities via this interface | update the entities via this interface | [
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"entities",
"via",
"this",
"interface"
] | def update(entity):
data = flask_post_json(request)
for key, value in data.iteritems():
myWorld.update(entity, key, value);
return make_json_response(myWorld.get(entity)) | [
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6c5986a7ab164a554e0a20a8fc700446685830fb | ShobhitMaheshwari/sign-language1 | feature.py | [
"MIT"
] | Python | features_2D_predict_generator | <not_specific> | def features_2D_predict_generator(sFrameBaseDir:str, sFeatureBaseDir:str, keModel:keras.Model,
nFramesNorm:int = 40):
"""
Used by the MobileNet-LSTM NN architecture.
The (video) frames (2-dimensional) in sFrameBaseDir are fed into keModel (eg MobileNet without top layers)
and the resulting features... |
Used by the MobileNet-LSTM NN architecture.
The (video) frames (2-dimensional) in sFrameBaseDir are fed into keModel (eg MobileNet without top layers)
and the resulting features are save to sFeatureBaseDir.
| Used by the MobileNet-LSTM NN architecture.
The (video) frames (2-dimensional) in sFrameBaseDir are fed into keModel
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nFramesNorm:int = 40):
_, h, w, c = keModel.input_shape
genFrames = FramesGenerator(sFrameBaseDir, 1, nFramesNorm, h, w, c,
liClassesFull = None, bShuffle=False)
print("Predict features with %s ... "... | [
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... |
6c5986a7ab164a554e0a20a8fc700446685830fb | ShobhitMaheshwari/sign-language1 | feature.py | [
"MIT"
] | Python | features_3D_predict_generator | <not_specific> | def features_3D_predict_generator(sFrameBaseDir:str, sFeatureBaseDir:str,
keModel:keras.Model, nBatchSize:int = 16):
"""
Used by I3D-top-only model.
The videos (frames) are fed into keModel (=I3D without top layers) and
resulting features are saved to disc.
(Later these features are used to tr... |
Used by I3D-top-only model.
The videos (frames) are fed into keModel (=I3D without top layers) and
resulting features are saved to disc.
(Later these features are used to train a small model containing
only the adjusted I3D top layers.)
| Used by I3D-top-only model.
The videos (frames) are fed into keModel (=I3D without top layers) and
resulting features are saved to disc.
(Later these features are used to train a small model containing
only the adjusted I3D top layers.) | [
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keModel:keras.Model, nBatchSize:int = 16):
_, nFramesModel, h, w, c = keModel.input_shape
genFrames = FramesGenerator(sFrameBaseDir, nBatchSize, nFramesModel, h, w, c,
liClassesFull = None, bShuffle=False)
print("Predict... | [
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... |
ac46ee13662eee8bc8b3ddf0817779cf1502dc49 | ShobhitMaheshwari/sign-language1 | prepare_chalearn.py | [
"MIT"
] | Python | unzip_sort_videos | <not_specific> | def unzip_sort_videos(sVideoDir, sZipFile, sListFile):
""" Unzip videos use Label information defined in sListFile
to move videos into folders=labels
"""
print("Unzipping and sorting ChaLearn videos from %s into %s" % (sZipFile, sVideoDir))
# save current directory
sCurrentDir = os.getcwd()
... | Unzip videos use Label information defined in sListFile
to move videos into folders=labels
| Unzip videos use Label information defined in sListFile
to move videos into folders=labels | [
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] | def unzip_sort_videos(sVideoDir, sZipFile, sListFile):
print("Unzipping and sorting ChaLearn videos from %s into %s" % (sZipFile, sVideoDir))
sCurrentDir = os.getcwd()
sTmpDir = sVideoDir + "/tmp"
print("Unzipping videos to {} ...".format(sTmpDir))
if os.path.exists(sTmpDir): raise ValueError("Folde... | [
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ac46ee13662eee8bc8b3ddf0817779cf1502dc49 | ShobhitMaheshwari/sign-language1 | prepare_chalearn.py | [
"MIT"
] | Python | move_videos | <not_specific> | def move_videos(sSourceDir, sTargetDir, fFrac = 0.2):
""" Move fraction of the videos to another folder eg 20% from train to val """
# stop if new folder already exists
assert os.path.exists(sTargetDir) == False
sCurrentDir = os.getcwd()
# get list of all directories = classes
os.chdir(sSource... | Move fraction of the videos to another folder eg 20% from train to val | Move fraction of the videos to another folder eg 20% from train to val | [
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] | def move_videos(sSourceDir, sTargetDir, fFrac = 0.2):
assert os.path.exists(sTargetDir) == False
sCurrentDir = os.getcwd()
os.chdir(sSourceDir)
liClasses = glob.glob("*")
print("Found %d classes in %s. Move %.0f%% videos to %s ..." % \
(len(liClasses), sSourceDir, fFrac*100, sTargetDir))
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1048f0b7c8cd6007cb2c0b1dc7d34ae341ea12bf | ShobhitMaheshwari/sign-language1 | videocapture.py | [
"MIT"
] | Python | rectangle_text | <not_specific> | def rectangle_text(arImage, sColor, sUpper, sLower = None, tuRectangle = (224, 224)):
""" Returns new image (not altering arImage)
"""
nHeigth, nWidth, _ = arImage.shape
nRectHeigth, nRectWidth = tuRectangle
x1 = int((nWidth - nRectWidth) / 2)
y1 = int((nHeigth - nRectHeigth) / 2)
if sColor == "green": bgr = (... | Returns new image (not altering arImage)
| Returns new image (not altering arImage) | [
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] | def rectangle_text(arImage, sColor, sUpper, sLower = None, tuRectangle = (224, 224)):
nHeigth, nWidth, _ = arImage.shape
nRectHeigth, nRectWidth = tuRectangle
x1 = int((nWidth - nRectWidth) / 2)
y1 = int((nHeigth - nRectHeigth) / 2)
if sColor == "green": bgr = (84, 175, 25)
elif sColor == "orange": bgr = (60, 125... | [
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1048f0b7c8cd6007cb2c0b1dc7d34ae341ea12bf | ShobhitMaheshwari/sign-language1 | videocapture.py | [
"MIT"
] | Python | frame_show | <not_specific> | def frame_show(oStream, sColor:str, sText:str, tuRectangle = (224, 224)):
""" Read frame from webcam and display it with box+text """
(bGrabbed, oFrame) = oStream.read()
oFrame = rectangle_text(cv2.flip(oFrame, 1), sColor, sText, "", tuRectangle)
cv2.imshow("Video", oFrame)
cv2.waitKey(1)
return | Read frame from webcam and display it with box+text | Read frame from webcam and display it with box+text | [
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] | def frame_show(oStream, sColor:str, sText:str, tuRectangle = (224, 224)):
(bGrabbed, oFrame) = oStream.read()
oFrame = rectangle_text(cv2.flip(oFrame, 1), sColor, sText, "", tuRectangle)
cv2.imshow("Video", oFrame)
cv2.waitKey(1)
return | [
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8d3b8362bddac80d6b742e73f19923bb78906925 | dtedder/WPDx-Toolset | WPDx_Toolset.pyt | [
"MIT"
] | Python | queryWPDx | <not_specific> | def queryWPDx(zone):
"""Fetches all the water points from WPDx in given administrative area"""
# First 2000 results, remove limit and get login if neccessary
start = time.clock()
client = Socrata("data.waterpointdata.org", None)
# Set output fields (this only affects the Repair Priority tool)
fi... | Fetches all the water points from WPDx in given administrative area | Fetches all the water points from WPDx in given administrative area | [
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] | def queryWPDx(zone):
start = time.clock()
client = Socrata("data.waterpointdata.org", None)
fields = 'adm1,adm2,country_id,country_name,created,data_lnk,fecal_coliform_presence,install_year,installer,photo_lnk,photo_lnk_description,report_date,source,status_id,subjective_quality,updated,water_source,water_t... | [
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} |
8d3b8362bddac80d6b742e73f19923bb78906925 | dtedder/WPDx-Toolset | WPDx_Toolset.pyt | [
"MIT"
] | Python | execute | null | def execute(self, parameters, messages):
"""Calculates percentage of population unserved in each administrative area."""
#scratchworkspace = "in_memory"
# Get Paramters
global scratch
scratch = tempfile.mkdtemp()
zone = parameters[0].valueAsText
num = parameters[... | Calculates percentage of population unserved in each administrative area. | Calculates percentage of population unserved in each administrative area. | [
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"of",
"population",
"unserved",
"in",
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] | def execute(self, parameters, messages):
global scratch
scratch = tempfile.mkdtemp()
zone = parameters[0].valueAsText
num = parameters[1].valueAsText
buff_dist = parameters[2].valueAsText
pop_grid = parameters[3].value
out_path = parameters[4].value
query_... | [
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8d3b8362bddac80d6b742e73f19923bb78906925 | dtedder/WPDx-Toolset | WPDx_Toolset.pyt | [
"MIT"
] | Python | isLicensed | <not_specific> | def isLicensed(self):
"""Set whether tool is licensed to execute."""
if arcpy.CheckExtension("Spatial") == "Available":
return True
else:
return False | Set whether tool is licensed to execute. | Set whether tool is licensed to execute. | [
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if arcpy.CheckExtension("Spatial") == "Available":
return True
else:
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8d3b8362bddac80d6b742e73f19923bb78906925 | dtedder/WPDx-Toolset | WPDx_Toolset.pyt | [
"MIT"
] | Python | calcPriority | <not_specific> | def calcPriority(self, pnts_buff, pop_grid):
"""Uses zonal statistics to calculate population served by each point"""
# create list of non-functioning points
pnts = list()
with arcpy.da.SearchCursor(pnts_buff, 'wpdx_id', "status_id='no'") as cursor:
for row in cursor:
... | Uses zonal statistics to calculate population served by each point | Uses zonal statistics to calculate population served by each point | [
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pnts = list()
with arcpy.da.SearchCursor(pnts_buff, 'wpdx_id', "status_id='no'") as cursor:
for row in cursor:
pnts.append(row[0])
start = time.clock()
pop_dict = dict()
with arcpy.da.SearchCursor(incr_pop,... | [
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8d3b8362bddac80d6b742e73f19923bb78906925 | dtedder/WPDx-Toolset | WPDx_Toolset.pyt | [
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"""The source code of the tool."""
# Get Parameters
global scratch
scratch = tempfile.mkdtemp()
zone = parameters[0].valueAsText
buff_dist = parameters[1].valueAsText
pop_grid = parameters[2].value
out_path = param... | The source code of the tool. | The source code of the tool. | [
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zone = parameters[0].valueAsText
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pop_grid = parameters[2].value
out_path = parameters[3].value
query_response, mask = queryWPDx(zone)
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8d3b8362bddac80d6b742e73f19923bb78906925 | dtedder/WPDx-Toolset | WPDx_Toolset.pyt | [
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] | Python | calcUnserved | <not_specific> | def calcUnserved(self, admin_zones, unserved_population):
"""Uses zonal statistics to calculate population unserved in each zone"""
start = time.clock()
pop_dict = dict()
pop_by_region = arcpy.gp.ZonalStatisticsAsTable_sa(admin_zones, 'Name',
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start = time.clock()
pop_dict = dict()
pop_by_region = arcpy.gp.ZonalStatisticsAsTable_sa(admin_zones, 'Name',
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8d3b8362bddac80d6b742e73f19923bb78906925 | dtedder/WPDx-Toolset | WPDx_Toolset.pyt | [
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] | Python | execute | null | def execute(self, parameters, messages):
"""Calculates percentage of population unserved in each administrative area."""
# Get Paramters
global scratch
scratch = tempfile.mkdtemp()
country = parameters[0].valueAsText
buff_dist = parameters[1].valueAsText
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scratch = tempfile.mkdtemp()
country = parameters[0].valueAsText
buff_dist = parameters[1].valueAsText
pop_grid = parameters[2].value
out_path = "in_memory\ServiceOverview"
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8d3b8362bddac80d6b742e73f19923bb78906925 | dtedder/WPDx-Toolset | WPDx_Toolset.pyt | [
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"""Removes urban areas and areas near a functioning well from population raster."""
# Get Paramters
global scratch
scratch = tempfile.mkdtemp()
zone = parameters[0].valueAsText
buff_dist = parameters[1].valueAsText
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zone = parameters[0].valueAsText
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pop_grid = parameters[2].value
out_path = parameters[3].value
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8d3b8362bddac80d6b742e73f19923bb78906925 | dtedder/WPDx-Toolset | WPDx_Toolset.pyt | [
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] | Python | execute | null | def execute(self, parameters, messages):
"""Calculates rural population in each administrative area."""
admin = join(dirname(__file__), "Data", "ToolData.gdb", "Admin")
#set up a scratch workspace and set it as env
scratch = tempfile.mkdtemp()
gdb = arcpy.CreateFileGDB_manageme... | Calculates rural population in each administrative area. | Calculates rural population in each administrative area. | [
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admin = join(dirname(__file__), "Data", "ToolData.gdb", "Admin")
scratch = tempfile.mkdtemp()
gdb = arcpy.CreateFileGDB_management(scratch, "temp").getOutput(0)
country = parameters[0].valueAsText
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604b333cb7656eaddb5801d8662bf8982ce0141d | eugenevinitsky/sequential_social_dilemma_games | models/moa_model.py | [
"MIT"
] | Python | create_moa_encoder_model | <not_specific> | def create_moa_encoder_model(obs_space, model_config):
"""
Creates the convolutional part of the MOA model.
Also casts the input uint8 observations to float32 and normalizes them to the range [0,1].
:param obs_space: The agent's observation space.
:param model_config: The config ... |
Creates the convolutional part of the MOA model.
Also casts the input uint8 observations to float32 and normalizes them to the range [0,1].
:param obs_space: The agent's observation space.
:param model_config: The config dict containing parameters for the convolution type/shape.
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original_obs_dims = obs_space.original_space.spaces["curr_obs"].shape
inputs = tf.keras.layers.Input(original_obs_dims, name="observations", dtype=tf.uint8)
last_layer = tf.keras.backend.cast(inputs, tf.float32)
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604b333cb7656eaddb5801d8662bf8982ce0141d | eugenevinitsky/sequential_social_dilemma_games | models/moa_model.py | [
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] | Python | forward | <not_specific> | def forward(self, input_dict, state, seq_lens):
"""
First evaluate non-LSTM parts of model. Then add a time dimension to the batch before
sending inputs to forward_rnn(), which evaluates the LSTM parts of the model.
:param input_dict: The input tensors.
:param state: The model st... |
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:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequence lengths.
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604b333cb7656eaddb5801d8662bf8982ce0141d | eugenevinitsky/sequential_social_dilemma_games | models/moa_model.py | [
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] | Python | forward_rnn | <not_specific> | def forward_rnn(self, input_dict, state, seq_lens):
"""
Forward pass through the MOA LSTMs.
Implicitly assigns the value function output to self_value_out, and does not return this.
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM... |
Forward pass through the MOA LSTMs.
Implicitly assigns the value function output to self_value_out, and does not return this.
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequence lengths.
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(self._model_out, self._value_out, output_h1, output_c1,) = self.actions_model.forward_rnn(
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604b333cb7656eaddb5801d8662bf8982ce0141d | eugenevinitsky/sequential_social_dilemma_games | models/moa_model.py | [
"MIT"
] | Python | compute_influence_reward | null | def compute_influence_reward(self, input_dict, prev_action_logits, counterfactual_logits):
"""
Compute influence of this agent on other agents.
:param input_dict: The model input tensors.
:param prev_action_logits: Logits for the agent's own policy/actions at t-1
:param counterfa... |
Compute influence of this agent on other agents.
:param input_dict: The model input tensors.
:param prev_action_logits: Logits for the agent's own policy/actions at t-1
:param counterfactual_logits: The counterfactual action logits for actions made by other
agents at t.
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prev_agent_actions = tf.cast(tf.reshape(input_dict["prev_actions"], [-1, 1]), tf.int32)
predicted_logits = tf.gather_nd(
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604b333cb7656eaddb5801d8662bf8982ce0141d | eugenevinitsky/sequential_social_dilemma_games | models/moa_model.py | [
"MIT"
] | Python | marginalize_predictions_over_own_actions | <not_specific> | def marginalize_predictions_over_own_actions(self, prev_action_logits, counterfactual_logits):
"""
Calculates marginal policies for all other agents.
:param prev_action_logits: The agent's own policy logits at time t-1 .
:param counterfactual_logits: The counterfactual action predictions... |
Calculates marginal policies for all other agents.
:param prev_action_logits: The agent's own policy logits at time t-1 .
:param counterfactual_logits: The counterfactual action predictions made at time t-1 for
other agents' actions at t.
:return: The marginal policies for all o... | Calculates marginal policies for all other agents. | [
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logits = tf.nn.softmax(prev_action_logits)
logits = logits / tf.reduce_sum(logits, axis=-1, keepdims=True)
counterfactual_logits = tf.reshape(
counterfactual_logits, [-1, self.num_outputs, ... | [
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604b333cb7656eaddb5801d8662bf8982ce0141d | eugenevinitsky/sequential_social_dilemma_games | models/moa_model.py | [
"MIT"
] | Python | kl_div | <not_specific> | def kl_div(x, y):
"""
Calculate KL divergence between two distributions.
:param x: A distribution
:param y: A distribution
:return: The KL-divergence between x and y. Returns zeros if the KL-divergence contains NaN
or Infinity.
"""
dist_x = tf.distribution... |
Calculate KL divergence between two distributions.
:param x: A distribution
:param y: A distribution
:return: The KL-divergence between x and y. Returns zeros if the KL-divergence contains NaN
or Infinity.
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dist_x = tf.distributions.Categorical(probs=x)
dist_y = tf.distributions.Categorical(probs=y)
result = tf.distributions.kl_divergence(dist_x, dist_y)
is_finite = tf.reduce_all(tf.is_finite(result))
def true_fn():
return result
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604b333cb7656eaddb5801d8662bf8982ce0141d | eugenevinitsky/sequential_social_dilemma_games | models/moa_model.py | [
"MIT"
] | Python | _reshaped_one_hot_actions | <not_specific> | def _reshaped_one_hot_actions(self, actions_tensor, name):
"""
Converts the collection of all actions from a number encoding to a one-hot encoding.
Then, flattens the one-hot encoding so that all concatenated one-hot vectors are the same
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Converts the collection of all actions from a number encoding to a one-hot encoding.
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_reshaped_one_hot_actions([0,1,2]) returns [1,0,0,0,1,... | Converts the collection of all actions from a number encoding to a one-hot encoding.
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batch_time_dims = [
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604b333cb7656eaddb5801d8662bf8982ce0141d | eugenevinitsky/sequential_social_dilemma_games | models/moa_model.py | [
"MIT"
] | Python | predicted_actions | <not_specific> | def predicted_actions(self):
""" :returns Predicted actions. NB: Since the agent's own true action is not known when
evaluating this model, the timestep is off by one (too late). Thus, for any index n > 0,
the value at n is a prediction made at n-1, about the actions taken at n.
predi... | :returns Predicted actions. NB: Since the agent's own true action is not known when
evaluating this model, the timestep is off by one (too late). Thus, for any index n > 0,
the value at n is a prediction made at n-1, about the actions taken at n.
predicted_actions[0] contains no sensible val... | :returns Predicted actions. NB: Since the agent's own true action is not known when
evaluating this model, the timestep is off by one (too late). Thus, for any index n > 0,
the value at n is a prediction made at n-1, about the actions taken at n.
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7df5be47d7287e9549829cbb1e0567df775dd7e0 | eugenevinitsky/sequential_social_dilemma_games | visualization/rollout.py | [
"MIT"
] | Python | rollout | <not_specific> | def rollout(self, horizon=50, save_path=None):
""" Rollout several timesteps of an episode of the environment.
Args:
horizon: The number of timesteps to roll out.
save_path: If provided, will save each frame to disk at this
location.
"""
rewards =... | Rollout several timesteps of an episode of the environment.
Args:
horizon: The number of timesteps to roll out.
save_path: If provided, will save each frame to disk at this
location.
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rewards = []
observations = []
shape = self.env.world_map.shape
full_obs = [np.zeros((shape[0], shape[1], 3), dtype=np.uint8) for i in range(horizon)]
for i in range(horizon):
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... | [
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7df5be47d7287e9549829cbb1e0567df775dd7e0 | eugenevinitsky/sequential_social_dilemma_games | visualization/rollout.py | [
"MIT"
] | Python | render_rollout | null | def render_rollout(self, horizon=50, path=None, render_type="pretty", fps=8):
""" Render a rollout into a video.
Args:
horizon: The number of timesteps to roll out.
path: Directory where the video will be saved.
render_type: Can be 'pretty' or 'fast'. Impliciations o... | Render a rollout into a video.
Args:
horizon: The number of timesteps to roll out.
path: Directory where the video will be saved.
render_type: Can be 'pretty' or 'fast'. Impliciations obvious.
fps: Integer frames per second.
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if path is None:
path = os.path.abspath(os.path.dirname(__file__)) + "/videos"
print(path)
if not os.path.exists(path):
os.makedirs(path)
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6f664ee63abf537ccb5a97d053350af3c352a1d9 | eugenevinitsky/sequential_social_dilemma_games | models/actor_critic_lstm.py | [
"MIT"
] | Python | forward_rnn | <not_specific> | def forward_rnn(self, input_dict, state, seq_lens):
"""
Forward pass through the LSTM.
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequence lengths.
:return: The model output.
"""
input = [input_dict["curr_obs... |
Forward pass through the LSTM.
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequence lengths.
:return: The model output.
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input = [input_dict["curr_obs"], seq_lens] + state
model_out, self._value_out, h, c = self.rnn_model(input)
return model_out, self._value_out, h, c | [
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3c1fa27068afc445fc34b15e460dc24141bb933c | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/cleanup.py | [
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] | Python | custom_reset | null | def custom_reset(self):
"""Initialize the walls and the waste"""
for waste_start_point in self.waste_start_points:
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for river_point in self.river_points:
self.single_update_map(river_point[0], river_point[1], b"R")
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3c1fa27068afc445fc34b15e460dc24141bb933c | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/cleanup.py | [
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updates = []
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agent.fire_beam(b"F")
updates = self.update_map_fire(
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5bcfcf7d6ca467ab8e59645e9fb19d11fa24b83b | eugenevinitsky/sequential_social_dilemma_games | tests/test_envs.py | [
"MIT"
] | Python | convert_empty_cells | <not_specific> | def convert_empty_cells(view):
"""Change all empty cells marked with '0' to ' ' for consistency."""
# No mask because it doesn't work correctly on byte arrays
for x in range(len(view)):
for y in range(len(view[0])):
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for x in range(len(view)):
for y in range(len(view[0])):
view[x, y] = b" " if view[x, y] == b"0" else view[x, y]
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0c7b2e08c3e5be643b4bec0599ea7bf635fed5c6 | eugenevinitsky/sequential_social_dilemma_games | algorithms/common_funcs_scm.py | [
"MIT"
] | Python | compute_curiosity_reward_weight | <not_specific> | def compute_curiosity_reward_weight(self):
""" Computes multiplier for social_curiosity reward based on training steps
taken and schedule parameters.
"""
weight = np.interp(
self.timestep,
self.curiosity_reward_schedule_steps,
self.curiosity_reward_sch... | Computes multiplier for social_curiosity reward based on training steps
taken and schedule parameters.
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] | def compute_curiosity_reward_weight(self):
weight = np.interp(
self.timestep,
self.curiosity_reward_schedule_steps,
self.curiosity_reward_schedule_weights,
)
return weight * self.baseline_curiosity_reward_weight | [
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0c7b2e08c3e5be643b4bec0599ea7bf635fed5c6 | eugenevinitsky/sequential_social_dilemma_games | algorithms/common_funcs_scm.py | [
"MIT"
] | Python | weigh_and_add_curiosity_reward | <not_specific> | def weigh_and_add_curiosity_reward(policy, sample_batch):
"""Compute curiosity of this agent and add to rewards.
"""
cur_curiosity_reward_weight = policy.compute_curiosity_reward_weight()
# Align the reward, as the reward for timestep n is calculated at timestep n+1.
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cur_curiosity_reward_weight = policy.compute_curiosity_reward_weight()
curiosity_reward = np.concatenate((sample_batch[SOCIAL_CURIOSITY_REWARD][1:], [0]))
reward = np.clip(curiosity_reward, -policy.curiosity_reward_clip, policy.curiosity_reward_clip)... | [
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | ascii_to_numpy | <not_specific> | def ascii_to_numpy(self, ascii_list):
"""converts a list of strings into a numpy array
Parameters
----------
ascii_list: list of strings
List describing what the map should look like
Returns
-------
arr: np.ndarray
numpy array describing ... | converts a list of strings into a numpy array
Parameters
----------
ascii_list: list of strings
List describing what the map should look like
Returns
-------
arr: np.ndarray
numpy array describing the map with ' ' indicating an empty space
... | converts a list of strings into a numpy array
Parameters
list of strings
List describing what the map should look like
Returns
np.ndarray
numpy array describing the map with ' ' indicating an empty space | [
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arr = np.full((len(ascii_list), len(ascii_list[0])), b" ", dtype="c")
for row in range(arr.shape[0]):
for col in range(arr.shape[1]):
arr[row, col] = ascii_list[row][col]
return arr | [
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | step | <not_specific> | def step(self, actions):
"""Takes in a dict of actions and converts them to a map update
Parameters
----------
actions: dict {agent-id: int}
dict of actions, keyed by agent-id that are passed to the agent. The agent
interprets the int and converts it to a command... | Takes in a dict of actions and converts them to a map update
Parameters
----------
actions: dict {agent-id: int}
dict of actions, keyed by agent-id that are passed to the agent. The agent
interprets the int and converts it to a command
Returns
-------
... | Takes in a dict of actions and converts them to a map update
Parameters
dict {agent-id: int}
dict of actions, keyed by agent-id that are passed to the agent. The agent
interprets the int and converts it to a command
Returns
dict of arrays representing agent observations
rewards: dict of rewards for each agent
dones:... | [
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self.beam_pos = []
agent_actions = {}
for agent_id, action in actions.items():
agent_action = self.agents[agent_id].action_map(action)
agent_actions[agent_id] = agent_action
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | reset | <not_specific> | def reset(self):
"""Reset the environment.
This method is performed in between rollouts. It resets the state of
the environment.
Returns
-------
observation: dict of numpy ndarray
the initial observation of the space. The initial reward is assumed
... | Reset the environment.
This method is performed in between rollouts. It resets the state of
the environment.
Returns
-------
observation: dict of numpy ndarray
the initial observation of the space. The initial reward is assumed
to be zero.
| Reset the environment.
This method is performed in between rollouts. It resets the state of
the environment.
Returns
dict of numpy ndarray
the initial observation of the space. The initial reward is assumed
to be zero. | [
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self.beam_pos = []
self.agents = {}
self.setup_agents()
self.reset_map()
self.custom_map_update()
map_with_agents = self.get_map_with_agents()
observations = {}
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} |
039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | check_agent_map | <not_specific> | def check_agent_map(self, agent_map):
"""Checks the map to make sure agents aren't duplicated"""
unique, counts = np.unique(agent_map, return_counts=True)
count_dict = dict(zip(unique, counts))
# check for multiple agents
for i in range(self.num_agents):
if count_dic... | Checks the map to make sure agents aren't duplicated | Checks the map to make sure agents aren't duplicated | [
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unique, counts = np.unique(agent_map, return_counts=True)
count_dict = dict(zip(unique, counts))
for i in range(self.num_agents):
if count_dict[chr(i + 1)] != 1:
print("Error! Wrong number of agent", i, "in map!")
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | map_to_colors | <not_specific> | def map_to_colors(self, mmap, color_map, rgb_arr, orientation="UP"):
"""Converts a map to an array of RGB values.
Parameters
----------
mmap: np.ndarray
map to convert to colors
Double m to avoid shadowing map.
color_map: dict
mapping between a... | Converts a map to an array of RGB values.
Parameters
----------
mmap: np.ndarray
map to convert to colors
Double m to avoid shadowing map.
color_map: dict
mapping between array elements and desired colors
rgb_arr: np.array
Variable ... | Converts a map to an array of RGB values.
Parameters
np.ndarray
map to convert to colors
Double m to avoid shadowing map.
color_map: dict
mapping between array elements and desired colors
rgb_arr: np.array
Variable to store the mapping in
orientation:
The way in which the output should be oriented.
UP = no rotation.
R... | [
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x_len = mmap.shape[0]
y_len = mmap.shape[1]
if orientation == "UP":
for row_elem in range(x_len):
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | render | null | def render(self, filename=None):
""" Creates an image of the map to plot or save.
Args:
filename: If a string is passed, will save the image
to disk at this location.
"""
rgb_arr = self.full_map_to_colors()
plt.cla()
plt.imshow(rgb_arr, ... | Creates an image of the map to plot or save.
Args:
filename: If a string is passed, will save the image
to disk at this location.
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] | def render(self, filename=None):
rgb_arr = self.full_map_to_colors()
plt.cla()
plt.imshow(rgb_arr, interpolation="nearest")
if filename is None:
plt.show()
else:
plt.savefig(filename) | [
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | update_moves | null | def update_moves(self, agent_actions):
"""Converts agent action tuples into a new map and new agent positions.
Also resolves conflicts over multiple agents wanting a cell.
This method works by finding all conflicts over a cell and randomly assigning them
to one of the agents that desires... | Converts agent action tuples into a new map and new agent positions.
Also resolves conflicts over multiple agents wanting a cell.
This method works by finding all conflicts over a cell and randomly assigning them
to one of the agents that desires the slot. It then sets all of the other agents
... | Converts agent action tuples into a new map and new agent positions.
Also resolves conflicts over multiple agents wanting a cell.
This method works by finding all conflicts over a cell and randomly assigning them
to one of the agents that desires the slot. It then sets all of the other agents
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reserved_slots = []
for agent_id, action in agent_actions.items():
agent = self.agents[agent_id]
selected_action = self.all_actions[action]
if "MOVE" in action or "STAY" in action:
rot_action = self.rotate_action(... | [
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | update_map | null | def update_map(self, new_points):
"""For points in new_points, place desired char on the map
Update the color map as well"""
for point in new_points:
self.single_update_map(*point) | For points in new_points, place desired char on the map
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | reset_map | null | def reset_map(self):
"""Resets the map to be empty as well as a custom reset set by subclasses"""
self.world_map = np.full((len(self.base_map), len(self.base_map[0])), b" ", dtype="c")
self.world_map_color = np.full(
(len(self.base_map) + self.view_len * 2, len(self.base_map[0]) + se... | Resets the map to be empty as well as a custom reset set by subclasses | Resets the map to be empty as well as a custom reset set by subclasses | [
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self.world_map = np.full((len(self.base_map), len(self.base_map[0])), b" ", dtype="c")
self.world_map_color = np.full(
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | update_map_fire | <not_specific> | def update_map_fire(
self,
firing_pos,
firing_orientation,
fire_len,
fire_char,
cell_types=[],
update_char=[],
blocking_cells=b"P",
beam_width=3,
):
"""From a firing position, fire a beam that may clean or hit agents
Notes:
... | From a firing position, fire a beam that may clean or hit agents
Notes:
(1) Beams are blocked by agents
(2) A beam travels along until it hits a blocking cell at which beam the beam
covers that cell and stops
(3) If a beam hits a cell whose character is in ce... | From a firing position, fire a beam that may clean or hit agents
Notes:
(1) Beams are blocked by agents
(2) A beam travels along until it hits a blocking cell at which beam the beam
covers that cell and stops
(3) If a beam hits a cell whose character is in cell_types, it replaces it with
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fire_len,
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cell_types=[],
update_char=[],
blocking_cells=b"P",
beam_width=3,
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agent_by_pos = {tuple(agent.pos): agent_id for agent_id, agent in self.agents.items()}
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | spawn_point | <not_specific> | def spawn_point(self):
"""Returns a randomly selected spawn point."""
spawn_index = 0
is_free_cell = False
curr_agent_pos = [agent.pos.tolist() for agent in self.agents.values()]
random.shuffle(self.spawn_points)
for i, spawn_point in enumerate(self.spawn_points):
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spawn_index = 0
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curr_agent_pos = [agent.pos.tolist() for agent in self.agents.values()]
random.shuffle(self.spawn_points)
for i, spawn_point in enumerate(self.spawn_points):
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039f1bbaedcf4cc61ebdd92b168649a6bc1d2943 | eugenevinitsky/sequential_social_dilemma_games | social_dilemmas/envs/map_env.py | [
"MIT"
] | Python | find_visible_agents | <not_specific> | def find_visible_agents(self, agent_id):
"""Returns all the agents that can be seen by agent with agent_id
Args
----
agent_id: str
The id of the agent whose visible agents we are asking about
Returns
-------
visible_agents: list
which agent... | Returns all the agents that can be seen by agent with agent_id
Args
----
agent_id: str
The id of the agent whose visible agents we are asking about
Returns
-------
visible_agents: list
which agents can be seen by the agent with id "agent_id"
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lower_lim = int(agent_pos[0] - self.agents[agent_id].row_size)
left_lim = int(agent_pos[1] - self.agents[agent_id].col_size)
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91d04eef66c75d6a8cd200d4e79d419ebef6301a | eugenevinitsky/sequential_social_dilemma_games | algorithms/impala_moa.py | [
"MIT"
] | Python | _make_time_major | <not_specific> | def _make_time_major(policy, seq_lens, tensor, drop_last=False):
"""Swaps batch and trajectory axis.
Arguments:
policy: Policy reference
seq_lens: Sequence lengths if recurrent or None
tensor: A tensor or list of tensors to reshape.
drop_last: A bool indicating whether to drop t... | Swaps batch and trajectory axis.
Arguments:
policy: Policy reference
seq_lens: Sequence lengths if recurrent or None
tensor: A tensor or list of tensors to reshape.
drop_last: A bool indicating whether to drop the last
trajectory item.
Returns:
res: A tensor wit... | Swaps batch and trajectory axis.
Arguments:
policy: Policy reference
seq_lens: Sequence lengths if recurrent or None
tensor: A tensor or list of tensors to reshape.
drop_last: A bool indicating whether to drop the last
trajectory item.
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1aab01bb575856ae6deec27233d6fe81b1901184 | eugenevinitsky/sequential_social_dilemma_games | models/moa_lstm.py | [
"MIT"
] | Python | forward_rnn | <not_specific> | def forward_rnn(self, input_dict, state, seq_lens):
"""
Forward pass through the MOA LSTM.
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequence lengths.
:return: The MOA predictions and new state.
"""
rnn_inpu... |
Forward pass through the MOA LSTM.
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequence lengths.
:return: The MOA predictions and new state.
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rnn_input = [input_dict["curr_obs"], seq_lens] + state
rnn_input.insert(1, input_dict["prev_total_actions"])
model_out, h, c = self.rnn_model(rnn_input)
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9aaa70882d69b906827c7c7efe9bc06d3a998f46 | eugenevinitsky/sequential_social_dilemma_games | algorithms/ppo_scm.py | [
"MIT"
] | Python | extra_scm_fetches | <not_specific> | def extra_scm_fetches(policy):
"""
Adds value function, logits, moa predictions, SCM loss/reward to experience train_batches.
:return: Updated fetches
"""
ppo_fetches = extra_moa_fetches(policy)
ppo_fetches.update(scm_fetches(policy))
return ppo_fetches |
Adds value function, logits, moa predictions, SCM loss/reward to experience train_batches.
:return: Updated fetches
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ppo_fetches = extra_moa_fetches(policy)
ppo_fetches.update(scm_fetches(policy))
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9aaa70882d69b906827c7c7efe9bc06d3a998f46 | eugenevinitsky/sequential_social_dilemma_games | algorithms/ppo_scm.py | [
"MIT"
] | Python | postprocess_ppo_scm | <not_specific> | def postprocess_ppo_scm(policy, sample_batch, other_agent_batches=None, episode=None):
"""
Add the influence and curiosity reward to the trajectory.
Then, add the policy logits, VF preds, and advantages to the trajectory.
:return: Updated trajectory (batch)
"""
batch = moa_postprocess_trajectory... |
Add the influence and curiosity reward to the trajectory.
Then, add the policy logits, VF preds, and advantages to the trajectory.
:return: Updated trajectory (batch)
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batch = moa_postprocess_trajectory(policy, sample_batch)
batch = scm_postprocess_trajectory(policy, batch)
batch = postprocess_ppo_gae(policy, batch)
return batch | [
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9aaa70882d69b906827c7c7efe9bc06d3a998f46 | eugenevinitsky/sequential_social_dilemma_games | algorithms/ppo_scm.py | [
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"""
Validates the PPO+MOA+SCM config
:param config: The config to validate
"""
validate_scm_config(config)
validate_moa_config(config)
validate_config(config) |
Validates the PPO+MOA+SCM config
:param config: The config to validate
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validate_scm_config(config)
validate_moa_config(config)
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9aaa70882d69b906827c7c7efe9bc06d3a998f46 | eugenevinitsky/sequential_social_dilemma_games | algorithms/ppo_scm.py | [
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] | Python | build_ppo_scm_trainer | <not_specific> | def build_ppo_scm_trainer(scm_config):
"""
Creates a SCM+MOA+PPO policy class, then creates a trainer with this policy.
:param scm_config: The configuration dictionary.
:return: A new SCM+MOA+PPO trainer.
"""
tf.keras.backend.set_floatx("float32")
trainer_name = "SCMPPOTrainer"
scm_ppo... |
Creates a SCM+MOA+PPO policy class, then creates a trainer with this policy.
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trainer_name = "SCMPPOTrainer"
scm_ppo_policy = build_tf_policy(
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d4ed970da1a44133f90c8c4f3afed06ffdbb2f34 | eugenevinitsky/sequential_social_dilemma_games | models/baseline_model.py | [
"MIT"
] | Python | forward | <not_specific> | def forward(self, input_dict, state, seq_lens):
"""
Evaluate the model.
Adds time dimension to batch before sending inputs to forward_rnn()
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequence lengths.
:return: The po... |
Evaluate the model.
Adds time dimension to batch before sending inputs to forward_rnn()
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequence lengths.
:return: The policy logits and state.
| Evaluate the model.
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trunk = self.encoder_model(input_dict["obs"]["curr_obs"])
new_dict = {"curr_obs": add_time_dimension(trunk, seq_lens)}
output, new_state = self.forward_rnn(new_dict, state, seq_lens)
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d4ed970da1a44133f90c8c4f3afed06ffdbb2f34 | eugenevinitsky/sequential_social_dilemma_games | models/baseline_model.py | [
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] | Python | forward_rnn | <not_specific> | def forward_rnn(self, input_dict, state, seq_lens):
"""
Forward pass through the LSTM.
Implicitly assigns the value function output to self_value_out, and does not return this.
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequ... |
Forward pass through the LSTM.
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:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: LSTM sequence lengths.
:return: The policy logits and new state.... | Forward pass through the LSTM.
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h1, c1 = state
(self._model_out, self._value_out, output_h1, output_c1,) = self.policy_model.forward_rnn(
input_dict, [h1, c1], seq_lens
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return self._model_out, [output_h1, output_c1] | [
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da2c1e5065ca76e0d9a9922ba232e482bb5d1af0 | eugenevinitsky/sequential_social_dilemma_games | algorithms/a3c_moa.py | [
"MIT"
] | Python | postprocess_a3c_moa | <not_specific> | def postprocess_a3c_moa(policy, sample_batch, other_agent_batches=None, episode=None):
"""Adds the policy logits, VF preds, and advantages to the trajectory."""
batch = moa_postprocess_trajectory(policy, sample_batch)
batch = postprocess_advantages(policy, batch)
return batch | Adds the policy logits, VF preds, and advantages to the trajectory. | Adds the policy logits, VF preds, and advantages to the trajectory. | [
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batch = moa_postprocess_trajectory(policy, sample_batch)
batch = postprocess_advantages(policy, batch)
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601ec833c8daf89c1e84bc255d13cdd9e02b1a78 | eugenevinitsky/sequential_social_dilemma_games | models/scm_model.py | [
"MIT"
] | Python | create_scm_encoder_model | <not_specific> | def create_scm_encoder_model(obs_space, model_config):
"""
Create the encoder submodel, which is part of the SCM.
:param obs_space: A single agent's observation space.
:param model_config: The model config dict.
:return: A new encoder model.
"""
original_obs_dims ... |
Create the encoder submodel, which is part of the SCM.
:param obs_space: A single agent's observation space.
:param model_config: The model config dict.
:return: A new encoder model.
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original_obs_dims = obs_space.original_space.spaces["curr_obs"].shape
input_layer = tf.keras.layers.Input(original_obs_dims, name="observations", dtype=tf.uint8)
last_layer = tf.keras.backend.cast(input_layer, tf.float32)
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601ec833c8daf89c1e84bc255d13cdd9e02b1a78 | eugenevinitsky/sequential_social_dilemma_games | models/scm_model.py | [
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] | Python | forward | <not_specific> | def forward(self, input_dict, state, seq_lens):
"""
The forward pass through the SCM network.
:param input_dict: The input tensors.
:param state: The model state.
:param seq_lens: The LSTM sequence lengths.
:return: The SCM output and new model state.
"""
... |
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output, new_state = super(SocialCuriosityModule, self).forward(input_dict, state, seq_lens)
encoded_state = self.scm_encoder_model(input_dict["obs"]["curr_obs"])
new_state.append(encoded_state)
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601ec833c8daf89c1e84bc255d13cdd9e02b1a78 | eugenevinitsky/sequential_social_dilemma_games | models/scm_model.py | [
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"""
Calculate the mean square error on a batched tensor.
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b91c618b8b488b06d1a22133b2b3dd1a61195a16 | mightyBroccoli/xmpp-chatbot | common/misc.py | [
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] | Python | validate | <not_specific> | def validate(keyword, target):
"""
validation method to reduce malformed querys and unnecessary connection attempts
:param keyword: used keyword
:param target: provided target
:return: true if valid
"""
# if keyword in domain_keywords list
if keyword in StaticAnswers().keys('domain_keywords'):
# if target is ... |
validation method to reduce malformed querys and unnecessary connection attempts
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if keyword in StaticAnswers().keys('domain_keywords'):
if validators.domain(target) or validators.email(target):
return True
elif keyword in StaticAnswers().keys('number_keywords'):
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f5fae61879a1bdefe098f72cda4c92cc18f541e3 | mightyBroccoli/xmpp-chatbot | classes/xep.py | [
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] | Python | req_xeplist | null | def req_xeplist(self):
"""
query and save the current xep list to reduce network bandwidth
"""
# check if etag header is present if not set local_etag to ""
if os.path.isfile("./common/.etag"):
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local_etag = file.read()
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query and save the current xep list to reduce network bandwidth
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local_etag = file.read()
else:
local_etag = ""
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head = s.head("https://xmpp.org/extensions/xeplist.xml")
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50f9b03431b8c70e8d3c283257e567434cfb55b3 | mightyBroccoli/xmpp-chatbot | main.py | [
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"""
:param event -- An empty dictionary. The session_start event does not provide any additional data.
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self.send_presence()
self.get_roster()
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if self.room:
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:param event -- An empty dictionary. The session_start event does not provide any additional data.
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31f050ccb9a80b948aecf66ee178b8ca9d152fae | gizmag/django-haystack | haystack/admin.py | [
"BSD-3-Clause"
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"""
Returns the maximum amount of results a changelist can have for the
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1.4 and 1.3. See Django ticket #15997 for details.
"""
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# This import is available in Django 1.3 and belo... |
Returns the maximum amount of results a changelist can have for the
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248a514da2fae6ba69dfe88cf6e4e55511a4045e | Hawkpath/he-music-extractor | HE_music_extractor.py | [
"MIT"
] | Python | write_pcm_to_file | <not_specific> | def write_pcm_to_file(
output_path: Path, payload: bytes, samplerate: int,
output_options: List[str] = None
):
"""
Write unsigned 8-bit audio data to an audio file.
:param output_path: path to write audio file
:param payload: raw PCM samples
:param sa... |
Write unsigned 8-bit audio data to an audio file.
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output_options: List[str] = None
):
output_options = output_options or []
ffmpeg = Popen([
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248a514da2fae6ba69dfe88cf6e4e55511a4045e | Hawkpath/he-music-extractor | HE_music_extractor.py | [
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artist: str = None,
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248a514da2fae6ba69dfe88cf6e4e55511a4045e | Hawkpath/he-music-extractor | HE_music_extractor.py | [
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] | Python | try_int_coerce | Union[int, str] | def try_int_coerce(string: str) -> Union[int, str]:
"""Try to convert string to integer, otherwise keep as string"""
try:
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except ValueError:
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3d032fb67c80a8b1262120dba41572c8a27c456b | internetofwater/geoconnex.us | PID-server/yourls_client.py | [
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] | Python | walk_path | <not_specific> | def walk_path(path):
"""
Walks os directory path collecting all CSV files.
:param path: required, string. os directory.
:return: list. List of csv paths.
"""
file_list = []
for root, _, files in os.walk(path, topdown=False):
for name in files:
if name.startswit... |
Walks os directory path collecting all CSV files.
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for name in files:
if name.startswith('example'):
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3047b75e307dc268638edb54852db9cfac31d92a | internetofwater/geoconnex.us | PID-server/yourls_api.py | [
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"""
if isinstance(file, list):
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3047b75e307dc268638edb54852db9cfac31d92a | internetofwater/geoconnex.us | PID-server/yourls_api.py | [
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88bc8ba18596ca219e4195a1b59d0a366c07db9d | onespacemedia/cms | cms/apps/media/models.py | [
"BSD-3-Clause"
] | Python | embed_html | <not_specific> | def embed_html(self, loop=False, autoplay=False, controls=False, mute=False, youtube_parameters=None):
'''
Returns the HTML code for embedding the video.
Expects youtube_parameters as a dictionary in the form {parameter:value}
When using, this is a function so call with {{ video.embed_ht... |
Returns the HTML code for embedding the video.
Expects youtube_parameters as a dictionary in the form {parameter:value}
When using, this is a function so call with {{ video.embed_html|safe }}
| Returns the HTML code for embedding the video.
Expects youtube_parameters as a dictionary in the form {parameter:value}
When using, this is a function so call with {{ video.embed_html|safe }} | [
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if self.external_video_service == 'youtube':
return render_to_string('videos/youtube.html', {
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94853dfb50404566c66776a922c87d186fe617d9 | onespacemedia/cms | cms/apps/media/admin.py | [
"BSD-3-Clause"
] | Python | response_add | <not_specific> | def response_add(self, request, obj, post_url_continue=None):
'''Returns the response for a successful add action.'''
if '_tinymce' in request.GET:
context = {'permalink': permalinks.create(obj),
'title': obj.title}
return render(request, 'admin/media/file/... | Returns the response for a successful add action. | Returns the response for a successful add action. | [
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"for",
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"successful",
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"."
] | def response_add(self, request, obj, post_url_continue=None):
if '_tinymce' in request.GET:
context = {'permalink': permalinks.create(obj),
'title': obj.title}
return render(request, 'admin/media/file/filebrowser_add_success.html', context)
return super().r... | [
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94853dfb50404566c66776a922c87d186fe617d9 | onespacemedia/cms | cms/apps/media/admin.py | [
"BSD-3-Clause"
] | Python | media_library_changelist_view | <not_specific> | def media_library_changelist_view(self, request, extra_context=None):
'''Renders the change list, but sets 'is_popup=True' into the template
context to make it render the media library sans navigation, without
needing _popup in the URL (which causes an exception with Jet's
Javascript, wh... | Renders the change list, but sets 'is_popup=True' into the template
context to make it render the media library sans navigation, without
needing _popup in the URL (which causes an exception with Jet's
Javascript, which assumes that if _popup is in the URL that it is a
related item popup)... | Renders the change list, but sets 'is_popup=True' into the template
context to make it render the media library sans navigation, without
needing _popup in the URL (which causes an exception with Jet's
Javascript, which assumes that if _popup is in the URL that it is a
related item popup). | [
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"... | def media_library_changelist_view(self, request, extra_context=None):
context = extra_context or {}
context.setdefault('changelist_template_parent', 'reversion/change_list.html')
context['is_popup'] = True
context['is_media_library_iframe'] = True
return super().changelist_view(r... | [
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"docstring_tokens"... |
ef9a0a80dc67a65e872fd1e8c27837f3bd63e4d4 | onespacemedia/cms | cms/models/base.py | [
"BSD-3-Clause"
] | Python | render | <not_specific> | def render(self, request, template, context=None, **kwargs):
"""Renders a template as a HttpResponse using the context of this page."""
page_context = self.get_context_data()
page_context.update(context or {})
return render(request, template, page_context, **kwargs) | Renders a template as a HttpResponse using the context of this page. | Renders a template as a HttpResponse using the context of this page. | [
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] | def render(self, request, template, context=None, **kwargs):
page_context = self.get_context_data()
page_context.update(context or {})
return render(request, template, page_context, **kwargs) | [
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"docstring_tokens"... |
31fcfd60953ddecfa02f7fdf36e0b680889a5aa4 | onespacemedia/cms | cms/apps/pages/utils.py | [
"BSD-3-Clause"
] | Python | duplicate_page | <not_specific> | def duplicate_page(original_page, page_changes=None):
'''
Takes a page and duplicates it as a child of the original's parent page.
Expects to be passed the original page and an optional function
'''
from .admin import page_admin
original_content = original_page.content
with update_i... |
Takes a page and duplicates it as a child of the original's parent page.
Expects to be passed the original page and an optional function
| Takes a page and duplicates it as a child of the original's parent page.
Expects to be passed the original page and an optional function | [
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] | def duplicate_page(original_page, page_changes=None):
from .admin import page_admin
original_content = original_page.content
with update_index():
page = deepcopy(original_page)
page.pk = None
if callable(page_changes):
page = page_changes(page, original_page)
page... | [
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] | {
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31fcfd60953ddecfa02f7fdf36e0b680889a5aa4 | onespacemedia/cms | cms/apps/pages/utils.py | [
"BSD-3-Clause"
] | Python | overlay_page_obj | <not_specific> | def overlay_page_obj(original_page, overlay_page, commit=False):
'''
A function that takes a page and overlay the fields and linked objects from a different page.
'''
from .admin import page_admin
original_content = original_page.content
page_fields_exclude = ['pk', 'id', 'version_for', 'lef... |
A function that takes a page and overlay the fields and linked objects from a different page.
| A function that takes a page and overlay the fields and linked objects from a different page. | [
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] | def overlay_page_obj(original_page, overlay_page, commit=False):
from .admin import page_admin
original_content = original_page.content
page_fields_exclude = ['pk', 'id', 'version_for', 'left', 'right']
content_fields_exclude = ['pk', 'id', 'page']
checked_models = []
related_fields = []
def... | [
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a2cbbea4b9c4a421a71d226474a2557564ca262e | onespacemedia/cms | cms/html.py | [
"BSD-3-Clause"
] | Python | process | <not_specific> | def process(text):
"""
Expands permalinks in <a/> and <img/> tags.
Images will also be automatically thumbnailed to fit their specified width
and height.
"""
resolved_permalinks = {}
def sub_tag(match):
tagname = match.group(1)
attrs = dict(RE_ATTR.findall(match.group(2)))
... |
Expands permalinks in <a/> and <img/> tags.
Images will also be automatically thumbnailed to fit their specified width
and height.
| Expands permalinks in and tags.
Images will also be automatically thumbnailed to fit their specified width
and height. | [
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] | def process(text):
resolved_permalinks = {}
def sub_tag(match):
tagname = match.group(1)
attrs = dict(RE_ATTR.findall(match.group(2)))
def get_obj(attr_name):
if attr_name in attrs:
value = attrs[attr_name][1:-1]
if value not in resolved_permal... | [
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} |
11d52e4299c578f11412f2b0e7a997fb4db08942 | onespacemedia/cms | cms/apps/pages/admin.py | [
"BSD-3-Clause"
] | Python | move_page_view | <not_specific> | def move_page_view(self, request):
'''Moves a page up or down.'''
# Check that the user has permission to move pages.
if not self.has_change_permission(request):
return HttpResponseForbidden('You do not have permission to move this page.')
# Lock entire table.
existi... | Moves a page up or down. | Moves a page up or down. | [
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"."
] | def move_page_view(self, request):
if not self.has_change_permission(request):
return HttpResponseForbidden('You do not have permission to move this page.')
existing_pages_list = Page.objects.all().exclude(
is_canonical_page=False,
).select_for_update().values(
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... | [
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