INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Adds tags to the stack if this resource is using the serverless app repo | def _get_application_tags(self):
"""Adds tags to the stack if this resource is using the serverless app repo
"""
application_tags = {}
if isinstance(self.Location, dict):
if (self.APPLICATION_ID_KEY in self.Location.keys() and
self.Location[self.APPLICATIO... |
Returns the Lambda layer to which this SAM Layer corresponds.
:param dict kwargs: already-converted resources that may need to be modified when converting this \
macro to pure CloudFormation
:returns: a list of vanilla CloudFormation Resources, to which this Function expands
:rtype: lis... | def to_cloudformation(self, **kwargs):
"""Returns the Lambda layer to which this SAM Layer corresponds.
:param dict kwargs: already-converted resources that may need to be modified when converting this \
macro to pure CloudFormation
:returns: a list of vanilla CloudFormation Resources, ... |
Constructs and returns the Lambda function.
:returns: a list containing the Lambda function and execution role resources
:rtype: list | def _construct_lambda_layer(self, intrinsics_resolver):
"""Constructs and returns the Lambda function.
:returns: a list containing the Lambda function and execution role resources
:rtype: list
"""
# Resolve intrinsics if applicable:
self.LayerName = self._resolve_string_... |
Sets the deletion policy on this resource. The default is 'Retain'.
:return: value for the DeletionPolicy attribute. | def _get_retention_policy_value(self):
"""
Sets the deletion policy on this resource. The default is 'Retain'.
:return: value for the DeletionPolicy attribute.
"""
if self.RetentionPolicy is None or self.RetentionPolicy.lower() == self.RETAIN.lower():
return self.RE... |
Performs dialog management and fulfillment for ordering flowers.
Beyond fulfillment, the implementation of this intent demonstrates the use of the elicitSlot dialog action
in slot validation and re-prompting. | def order_flowers(intent_request):
"""
Performs dialog management and fulfillment for ordering flowers.
Beyond fulfillment, the implementation of this intent demonstrates the use of the elicitSlot dialog action
in slot validation and re-prompting.
"""
flower_type = get_slots(intent_request)["Fl... |
Called when the user specifies an intent for this bot. | def dispatch(intent_request):
"""
Called when the user specifies an intent for this bot.
"""
logger.debug('dispatch userId={}, intentName={}'.format(intent_request['userId'], intent_request['currentIntent']['name']))
intent_name = intent_request['currentIntent']['name']
# Dispatch to your bot... |
Constructs the Lambda Permission resource allowing the source service to invoke the function this event
source triggers.
:returns: the permission resource
:rtype: model.lambda_.LambdaPermission | def _construct_permission(self, function, source_arn=None, source_account=None, suffix="", event_source_token=None):
"""Constructs the Lambda Permission resource allowing the source service to invoke the function this event
source triggers.
:returns: the permission resource
:rtype: mode... |
Returns the CloudWatch Events Rule and Lambda Permission to which this Schedule event source corresponds.
:param dict kwargs: no existing resources need to be modified
:returns: a list of vanilla CloudFormation Resources, to which this pull event expands
:rtype: list | def to_cloudformation(self, **kwargs):
"""Returns the CloudWatch Events Rule and Lambda Permission to which this Schedule event source corresponds.
:param dict kwargs: no existing resources need to be modified
:returns: a list of vanilla CloudFormation Resources, to which this pull event expand... |
Constructs the Target property for the CloudWatch Events Rule.
:returns: the Target property
:rtype: dict | def _construct_target(self, function):
"""Constructs the Target property for the CloudWatch Events Rule.
:returns: the Target property
:rtype: dict
"""
target = {
'Arn': function.get_runtime_attr("arn"),
'Id': self.logical_id + 'LambdaTarget'
... |
Returns the Lambda Permission resource allowing S3 to invoke the function this event source triggers.
:param dict kwargs: S3 bucket resource
:returns: a list of vanilla CloudFormation Resources, to which this S3 event expands
:rtype: list | def to_cloudformation(self, **kwargs):
"""Returns the Lambda Permission resource allowing S3 to invoke the function this event source triggers.
:param dict kwargs: S3 bucket resource
:returns: a list of vanilla CloudFormation Resources, to which this S3 event expands
:rtype: list
... |
Make the S3 bucket depends on Lambda Permissions resource because when S3 adds a Notification Configuration,
it will check whether it has permissions to access Lambda. This will fail if the Lambda::Permissions is not
already applied for this bucket to invoke the Lambda.
:param dict bucket: Dict... | def _depend_on_lambda_permissions(self, bucket, permission):
"""
Make the S3 bucket depends on Lambda Permissions resource because when S3 adds a Notification Configuration,
it will check whether it has permissions to access Lambda. This will fail if the Lambda::Permissions is not
alread... |
Since conditional DependsOn is not supported this undocumented way of
implicitely making dependency through tags is used.
See https://stackoverflow.com/questions/34607476/cloudformation-apply-condition-on-dependson
It is done by using Ref wrapped in a conditional Fn::If. Using Ref implies a
... | def _depend_on_lambda_permissions_using_tag(self, bucket, permission):
"""
Since conditional DependsOn is not supported this undocumented way of
implicitely making dependency through tags is used.
See https://stackoverflow.com/questions/34607476/cloudformation-apply-condition-on-depend... |
Returns the Lambda Permission resource allowing SNS to invoke the function this event source triggers.
:param dict kwargs: no existing resources need to be modified
:returns: a list of vanilla CloudFormation Resources, to which this SNS event expands
:rtype: list | def to_cloudformation(self, **kwargs):
"""Returns the Lambda Permission resource allowing SNS to invoke the function this event source triggers.
:param dict kwargs: no existing resources need to be modified
:returns: a list of vanilla CloudFormation Resources, to which this SNS event expands
... |
If this API Event Source refers to an explicit API resource, resolve the reference and grab
necessary data from the explicit API | def resources_to_link(self, resources):
"""
If this API Event Source refers to an explicit API resource, resolve the reference and grab
necessary data from the explicit API
"""
rest_api_id = self.RestApiId
if isinstance(rest_api_id, dict) and "Ref" in rest_api_id:
... |
If the Api event source has a RestApi property, then simply return the Lambda Permission resource allowing
API Gateway to call the function. If no RestApi is provided, then additionally inject the path, method, and the
x-amazon-apigateway-integration into the Swagger body for a provided implicit API.
... | def to_cloudformation(self, **kwargs):
"""If the Api event source has a RestApi property, then simply return the Lambda Permission resource allowing
API Gateway to call the function. If no RestApi is provided, then additionally inject the path, method, and the
x-amazon-apigateway-integration int... |
Adds the path and method for this Api event source to the Swagger body for the provided RestApi.
:param model.apigateway.ApiGatewayRestApi rest_api: the RestApi to which the path and method should be added. | def _add_swagger_integration(self, api, function):
"""Adds the path and method for this Api event source to the Swagger body for the provided RestApi.
:param model.apigateway.ApiGatewayRestApi rest_api: the RestApi to which the path and method should be added.
"""
swagger_body = api.get... |
Resolves references to parameters within the given dictionary recursively. Other intrinsic functions such as
!GetAtt, !Sub or !Ref to non-parameters will be left untouched.
Result is a dictionary where parameter values are inlined. Don't pass this dictionary directly into
transform's output bec... | def resolve_parameter_refs(self, input):
"""
Resolves references to parameters within the given dictionary recursively. Other intrinsic functions such as
!GetAtt, !Sub or !Ref to non-parameters will be left untouched.
Result is a dictionary where parameter values are inlined. Don't pass... |
Customers can provide a reference to a "derived" SAM resource such as Alias of a Function or Stage of an API
resource. This method recursively walks the tree, converting all derived references to the real resource name,
if it is present.
Example:
{"Ref": "MyFunction.Alias"} -> {"Ref... | def resolve_sam_resource_refs(self, input, supported_resource_refs):
"""
Customers can provide a reference to a "derived" SAM resource such as Alias of a Function or Stage of an API
resource. This method recursively walks the tree, converting all derived references to the real resource name,
... |
Some SAM resources have their logical ids mutated from the original id that the customer writes in the
template. This method recursively walks the tree and updates these logical ids from the old value
to the new value that is generated by SAM.
Example:
{"Ref": "MyLayer"} -> {"Ref": ... | def resolve_sam_resource_id_refs(self, input, supported_resource_id_refs):
"""
Some SAM resources have their logical ids mutated from the original id that the customer writes in the
template. This method recursively walks the tree and updates these logical ids from the old value
to the n... |
Driver method that performs the actual traversal of input and calls the appropriate `resolver_method` when
to perform the resolution.
:param input: Any primitive type (dict, array, string etc) whose value might contain an intrinsic function
:param resolution_data: Data that will help with reso... | def _traverse(self, input, resolution_data, resolver_method):
"""
Driver method that performs the actual traversal of input and calls the appropriate `resolver_method` when
to perform the resolution.
:param input: Any primitive type (dict, array, string etc) whose value might contain a... |
Traverse a dictionary to resolve intrinsic functions on every value
:param input_dict: Input dictionary to traverse
:param resolution_data: Data that the `resolver_method` needs to operate
:param resolver_method: Method that can actually resolve an intrinsic function, if it detects one
... | def _traverse_dict(self, input_dict, resolution_data, resolver_method):
"""
Traverse a dictionary to resolve intrinsic functions on every value
:param input_dict: Input dictionary to traverse
:param resolution_data: Data that the `resolver_method` needs to operate
:param resolve... |
Traverse a list to resolve intrinsic functions on every element
:param input_list: List of input
:param resolution_data: Data that the `resolver_method` needs to operate
:param resolver_method: Method that can actually resolve an intrinsic function, if it detects one
:return: Modified l... | def _traverse_list(self, input_list, resolution_data, resolver_method):
"""
Traverse a list to resolve intrinsic functions on every element
:param input_list: List of input
:param resolution_data: Data that the `resolver_method` needs to operate
:param resolver_method: Method th... |
Try to resolve parameter references on the given input object. The object could be of any type.
If the input is not in the format used by intrinsics (ie. dictionary with one key), input is returned
unmodified. If the single key in dictionary is one of the supported intrinsic function types,
go a... | def _try_resolve_parameter_refs(self, input, parameters):
"""
Try to resolve parameter references on the given input object. The object could be of any type.
If the input is not in the format used by intrinsics (ie. dictionary with one key), input is returned
unmodified. If the single ke... |
Try to resolve SAM resource references on the given template. If the given object looks like one of the
supported intrinsics, it calls the appropriate resolution on it. If not, this method returns the original input
unmodified.
:param dict input: Dictionary that may represent an intrinsic funct... | def _try_resolve_sam_resource_refs(self, input, supported_resource_refs):
"""
Try to resolve SAM resource references on the given template. If the given object looks like one of the
supported intrinsics, it calls the appropriate resolution on it. If not, this method returns the original input
... |
Try to resolve SAM resource id references on the given template. If the given object looks like one of the
supported intrinsics, it calls the appropriate resolution on it. If not, this method returns the original input
unmodified.
:param dict input: Dictionary that may represent an intrinsic fu... | def _try_resolve_sam_resource_id_refs(self, input, supported_resource_id_refs):
"""
Try to resolve SAM resource id references on the given template. If the given object looks like one of the
supported intrinsics, it calls the appropriate resolution on it. If not, this method returns the original... |
Can the input represent an intrinsic function in it?
:param input: Object to be checked
:return: True, if the input contains a supported intrinsic function. False otherwise | def _is_intrinsic_dict(self, input):
"""
Can the input represent an intrinsic function in it?
:param input: Object to be checked
:return: True, if the input contains a supported intrinsic function. False otherwise
"""
# All intrinsic functions are dictionaries with just... |
Returns the CloudWatch Logs Subscription Filter and Lambda Permission to which this CloudWatch Logs event source
corresponds.
:param dict kwargs: no existing resources need to be modified
:returns: a list of vanilla CloudFormation Resources, to which this push event expands
:rtype: list | def to_cloudformation(self, **kwargs):
"""Returns the CloudWatch Logs Subscription Filter and Lambda Permission to which this CloudWatch Logs event source
corresponds.
:param dict kwargs: no existing resources need to be modified
:returns: a list of vanilla CloudFormation Resources, to ... |
Converts the given template to IAM-ready policy statement by substituting template parameters with the given
values.
:param template_name: Name of the template
:param parameter_values: Values for all parameters of the template
:return dict: Dictionary containing policy statement
... | def convert(self, template_name, parameter_values):
"""
Converts the given template to IAM-ready policy statement by substituting template parameters with the given
values.
:param template_name: Name of the template
:param parameter_values: Values for all parameters of the templ... |
Is this a valid policy template dictionary
:param dict policy_templates_dict: Data to be validated
:param dict schema: Optional, dictionary containing JSON Schema representing policy template
:return: True, if it is valid.
:raises ValueError: If the template dictionary doesn't match up ... | def _is_valid_templates_dict(policy_templates_dict, schema=None):
"""
Is this a valid policy template dictionary
:param dict policy_templates_dict: Data to be validated
:param dict schema: Optional, dictionary containing JSON Schema representing policy template
:return: True, if... |
Render a chart or page to local html files.
:param chart: A Chart or Page object
:param path: The destination file which the html code write to
:param template_name: The name of template file. | def render_chart_to_file(self, template_name: str, chart: Any, path: str):
"""
Render a chart or page to local html files.
:param chart: A Chart or Page object
:param path: The destination file which the html code write to
:param template_name: The name of template file.
... |
Decode base64, padding being optional.
:param data: Base64 data as an ASCII byte string
:returns: The decoded byte string. | def decode_base64(data: str) -> bytes:
"""Decode base64, padding being optional.
:param data: Base64 data as an ASCII byte string
:returns: The decoded byte string.
"""
missing_padding = len(data) % 4
if missing_padding != 0:
data += "=" * (4 - missing_padding)
return base64.decodeb... |
间隔折叠节点，当节点过多时可以解决节点显示过杂间隔。
:param data: 节点数据
:param interval: 指定间隔 | def _set_collapse_interval(data, interval):
"""
间隔折叠节点，当节点过多时可以解决节点显示过杂间隔。
:param data: 节点数据
:param interval: 指定间隔
"""
if interval <= 0:
return data
if data and isinstance(data, list):
for d in data:
children = d... |
Parses a string and returns a pin-num. | def parse_pin(name_str):
"""Parses a string and returns a pin-num."""
if len(name_str) < 1:
raise ValueError("Expecting pin name to be at least 4 charcters.")
if name_str[0] != 'P':
raise ValueError("Expecting pin name to start with P")
pin_str = name_str[1:].split('/')[0]
if not pin... |
Returns the numbered function (i.e. USART6) for this AF. | def ptr(self):
"""Returns the numbered function (i.e. USART6) for this AF."""
if self.fn_num is None:
return self.func
return '{:s}{:d}'.format(self.func, self.fn_num) |
Prints the C representation of this AF. | def print(self):
"""Prints the C representation of this AF."""
if self.supported:
print(' AF', end='')
else:
print(' //', end='')
fn_num = self.fn_num
if fn_num is None:
fn_num = 0
print('({:2d}, {:8s}, {:2d}, {:10s}, {:8s}), // {:s}... |
Start the loop.
:param `leds`: Which LEDs to light up upon switch press.
:type `leds`: sequence of LED objects | def run_loop(leds=all_leds):
"""
Start the loop.
:param `leds`: Which LEDs to light up upon switch press.
:type `leds`: sequence of LED objects
"""
print('Loop started.\nPress Ctrl+C to break out of the loop.')
while 1:
try:
if switch():
[led.on() for led... |
Search vpaths for the c file that matches the provided object_file.
:param str obj_file: object file to find the matching c file for
:param List[str] vpath: List of base paths, similar to gcc vpath
:return: str path to c file or None | def find_c_file(obj_file, vpath):
""" Search vpaths for the c file that matches the provided object_file.
:param str obj_file: object file to find the matching c file for
:param List[str] vpath: List of base paths, similar to gcc vpath
:return: str path to c file or None
"""
c_file = None
r... |
Find any MP_REGISTER_MODULE definitions in the provided c file.
:param str c_file: path to c file to check
:return: List[(module_name, obj_module, enabled_define)] | def find_module_registrations(c_file):
""" Find any MP_REGISTER_MODULE definitions in the provided c file.
:param str c_file: path to c file to check
:return: List[(module_name, obj_module, enabled_define)]
"""
global pattern
if c_file is None:
# No c file to match the object file, ski... |
Generate header with module table entries for builtin modules.
:param List[(module_name, obj_module, enabled_define)] modules: module defs
:return: None | def generate_module_table_header(modules):
""" Generate header with module table entries for builtin modules.
:param List[(module_name, obj_module, enabled_define)] modules: module defs
:return: None
"""
# Print header file for all external modules.
mod_defs = []
print("// Automatically ge... |
Reads test files | def readfiles():
""" Reads test files """
tests = list(filter(lambda x: x.endswith('.py'), os.listdir(TESTPATH)))
tests.sort()
files = []
for test in tests:
text = open(TESTPATH + test, 'r').read()
try:
class_, desc, cause, workaround, code = [x.rstrip() for x in \
... |
converts CPython module names into MicroPython equivalents | def uimports(code):
""" converts CPython module names into MicroPython equivalents """
for uimport in UIMPORTLIST:
uimport = bytes(uimport, 'utf8')
code = code.replace(uimport, b'u' + uimport)
return code |
indents paragraphs of text for rst formatting | def indent(block, spaces):
""" indents paragraphs of text for rst formatting """
new_block = ''
for line in block.split('\n'):
new_block += spaces + line + '\n'
return new_block |
creates a table given any set of columns | def gen_table(contents):
""" creates a table given any set of columns """
xlengths = []
ylengths = []
for column in contents:
col_len = 0
for entry in column:
lines = entry.split('\n')
for line in lines:
col_len = max(len(line) + 2, col_len)
... |
creates restructured text documents to display tests | def gen_rst(results):
""" creates restructured text documents to display tests """
# make sure the destination directory exists
try:
os.mkdir(DOCPATH)
except OSError as e:
if e.args[0] != errno.EEXIST and e.args[0] != errno.EISDIR:
raise
toctree = []
class_ = []
... |
Main function | def main():
""" Main function """
# set search path so that test scripts find the test modules (and no other ones)
os.environ['PYTHONPATH'] = TESTPATH
os.environ['MICROPYPATH'] = TESTPATH
files = readfiles()
results = run_tests(files)
gen_rst(results) |
Initializes the found DFU device so that we can program it. | def init():
"""Initializes the found DFU device so that we can program it."""
global __dev, __cfg_descr
devices = get_dfu_devices(idVendor=__VID, idProduct=__PID)
if not devices:
raise ValueError('No DFU device found')
if len(devices) > 1:
raise ValueError("Multiple DFU devices found... |
Performs a MASS erase (i.e. erases the entire device. | def mass_erase():
"""Performs a MASS erase (i.e. erases the entire device."""
# Send DNLOAD with first byte=0x41
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE,
"\x41", __TIMEOUT)
# Execute last command
if get_status() != __DFU_STATE_DFU_DOWNLOAD_BUSY:
ra... |
Erases a single page. | def page_erase(addr):
"""Erases a single page."""
if __verbose:
print("Erasing page: 0x%x..." % (addr))
# Send DNLOAD with first byte=0x41 and page address
buf = struct.pack("<BI", 0x41, addr)
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE, buf, __TIMEOUT)
# Execute last co... |
Sets the address for the next operation. | def set_address(addr):
"""Sets the address for the next operation."""
# Send DNLOAD with first byte=0x21 and page address
buf = struct.pack("<BI", 0x21, addr)
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE, buf, __TIMEOUT)
# Execute last command
if get_status() != __DFU_STATE_DFU_DO... |
Writes a single page. This routine assumes that memory has already
been erased. | def write_page(buf, xfer_offset):
"""Writes a single page. This routine assumes that memory has already
been erased.
"""
xfer_base = 0x08000000
# Set mem write address
set_address(xfer_base+xfer_offset)
# Send DNLOAD with fw data
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 2, __DFU_INTERF... |
Exit DFU mode, and start running the program. | def exit_dfu():
"""Exit DFU mode, and start running the program."""
# set jump address
set_address(0x08000000)
# Send DNLOAD with 0 length to exit DFU
__dev.ctrl_transfer(0x21, __DFU_DNLOAD, 0, __DFU_INTERFACE,
None, __TIMEOUT)
try:
# Execute last command
... |
Writes a buffer into memory. This routine assumes that memory has
already been erased. | def write_memory(addr, buf, progress=None, progress_addr=0, progress_size=0):
"""Writes a buffer into memory. This routine assumes that memory has
already been erased.
"""
xfer_count = 0
xfer_bytes = 0
xfer_total = len(buf)
xfer_base = addr
while xfer_bytes < xfer_total:
if __v... |
Parses the struct defined by `fmt` from `data`, stores the parsed fields
into a named tuple using `names`. Returns the named tuple, and the data
with the struct stripped off. | def consume(fmt, data, names):
"""Parses the struct defined by `fmt` from `data`, stores the parsed fields
into a named tuple using `names`. Returns the named tuple, and the data
with the struct stripped off."""
size = struct.calcsize(fmt)
return named(struct.unpack(fmt, data[:size]), names), data[s... |
Reads a DFU file, and parses the individual elements from the file.
Returns an array of elements. Each element is a dictionary with the
following keys:
num - The element index
address - The address that the element data should be written to.
size - The size of the element ddata.
... | def read_dfu_file(filename):
"""Reads a DFU file, and parses the individual elements from the file.
Returns an array of elements. Each element is a dictionary with the
following keys:
num - The element index
address - The address that the element data should be written to.
size ... |
Returns a list of USB device which are currently in DFU mode.
Additional filters (like idProduct and idVendor) can be passed in to
refine the search. | def get_dfu_devices(*args, **kwargs):
"""Returns a list of USB device which are currently in DFU mode.
Additional filters (like idProduct and idVendor) can be passed in to
refine the search.
"""
# convert to list for compatibility with newer pyusb
return list(usb.core.find(*args, find_all=True,
... |
Returns an array which identifies the memory layout. Each entry
of the array will contain a dictionary with the following keys:
addr - Address of this memory segment
last_addr - Last address contained within the memory segment.
size - size of the segment, in bytes
num... | def get_memory_layout(device):
"""Returns an array which identifies the memory layout. Each entry
of the array will contain a dictionary with the following keys:
addr - Address of this memory segment
last_addr - Last address contained within the memory segment.
size - siz... |
Prints a lits of devices detected in DFU mode. | def list_dfu_devices(*args, **kwargs):
"""Prints a lits of devices detected in DFU mode."""
devices = get_dfu_devices(*args, **kwargs)
if not devices:
print("No DFU capable devices found")
return
for device in devices:
print("Bus {} Device {:03d}: ID {:04x}:{:04x}"
... |
Writes the indicated elements into the target memory,
erasing as needed. | def write_elements(elements, mass_erase_used, progress=None):
"""Writes the indicated elements into the target memory,
erasing as needed.
"""
mem_layout = get_memory_layout(__dev)
for elem in elements:
addr = elem['addr']
size = elem['size']
data = elem['data']
elem_... |
Prints a progress report suitable for use on the command line. | def cli_progress(addr, offset, size):
"""Prints a progress report suitable for use on the command line."""
width = 25
done = offset * width // size
print("\r0x{:08x} {:7d} [{}{}] {:3d}% "
.format(addr, size, '=' * done, ' ' * (width - done),
offset * 100 // size), end="")
... |
Test program for verifying this files functionality. | def main():
"""Test program for verifying this files functionality."""
global __verbose
# Parse CMD args
parser = argparse.ArgumentParser(description='DFU Python Util')
#parser.add_argument("path", help="file path")
parser.add_argument(
"-l", "--list",
help="list available DFU de... |
Parses a string and returns a (port-num, pin-num) tuple. | def parse_port_pin(name_str):
"""Parses a string and returns a (port-num, pin-num) tuple."""
if len(name_str) < 3:
raise ValueError("Expecting pin name to be at least 3 charcters.")
if name_str[0] != 'P':
raise ValueError("Expecting pin name to start with P")
if name_str[1] < 'A' or name... |
Prints the C representation of this AF. | def print(self):
"""Prints the C representation of this AF."""
cond_var = None
if self.supported:
cond_var = conditional_var('{}{}'.format(self.func, self.fn_num))
print_conditional_if(cond_var)
print(' AF', end='')
else:
print(' //', en... |
Parses a string and returns a (port, gpio_bit) tuple. | def parse_port_pin(name_str):
"""Parses a string and returns a (port, gpio_bit) tuple."""
if len(name_str) < 3:
raise ValueError("Expecting pin name to be at least 3 characters")
if name_str[:2] != 'GP':
raise ValueError("Expecting pin name to start with GP")
if not name_str[2:].isdigit(... |
Simple run one operator and return the results.
Args:
outputs_info: a list of tuples, which contains the element type and
shape of each output. First element of the tuple is the dtype, and
the second element is the shape. More use case can be found in
https://gith... | def run_node(cls,
node, # type: NodeProto
inputs, # type: Any
device='CPU', # type: Text
outputs_info=None, # type: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]]
**kwargs # type: Dict[Text, Any]
): # ty... |
Load data from an external file for tensor.
@params
tensor: a TensorProto object.
base_dir: directory that contains the external data. | def load_external_data_for_tensor(tensor, base_dir): # type: (TensorProto, Text) -> None
"""
Load data from an external file for tensor.
@params
tensor: a TensorProto object.
base_dir: directory that contains the external data.
"""
if tensor.HasField("raw_data"): # already loaded
... |
Loads external tensors into model
@params
model: ModelProto to load external data to
base_dir: directory that contains external data | def load_external_data_for_model(model, base_dir): # type: (ModelProto, Text) -> None
"""
Loads external tensors into model
@params
model: ModelProto to load external data to
base_dir: directory that contains external data
"""
for tensor in _get_all_tensors(model):
if uses_external... |
call to set all tensors as external data. save_model saves all the tensors data as external data after calling this function.
@params
model: ModelProto to be converted.
all_tensors_to_one_file: If true, save all tensors to one external file specified by location.
If false, save ... | def convert_model_to_external_data(model, all_tensors_to_one_file=True, location=None):
# type: (ModelProto, bool, Optional[Text]) -> None
"""
call to set all tensors as external data. save_model saves all the tensors data as external data after calling this function.
@params
model: ModelProto to be... |
call to set all tensors data as embedded data. save_model saves all the tensors data as embedded data after calling this function.
@params
model: ModelProto to be converted. | def convert_model_from_external_data(model): # type: (ModelProto) -> None
"""
call to set all tensors data as embedded data. save_model saves all the tensors data as embedded data after calling this function.
@params
model: ModelProto to be converted.
"""
for tensor in _get_all_tensors(model):
... |
Write tensor data to an external file according to information in the `external_data` field.
@params
tensor: Tensor object to be serialized
base_path: System path of a folder where tensor data is to be stored | def save_external_data(tensor, base_path): # type: (TensorProto, Text) -> None
"""
Write tensor data to an external file according to information in the `external_data` field.
@params
tensor: Tensor object to be serialized
base_path: System path of a folder where tensor data is to be stored
""... |
Create an iterator of tensors from node attributes of an ONNX model. | def _get_attribute_tensors(onnx_model_proto): # type: (ModelProto) -> Iterable[TensorProto]
"""Create an iterator of tensors from node attributes of an ONNX model."""
for node in onnx_model_proto.graph.node:
for attribute in node.attribute:
if attribute.HasField("t"):
yield ... |
Remove a field from a Tensor's external_data key-value store.
Modifies tensor object in place.
@params
tensor: Tensor object from which value will be removed
field_key: The key of the field to be removed | def remove_external_data_field(tensor, field_key): # type: (TensorProto, Text) -> None
"""
Remove a field from a Tensor's external_data key-value store.
Modifies tensor object in place.
@params
tensor: Tensor object from which value will be removed
field_key: The key of the field to be remove... |
Write external data of all tensors to files on disk.
Note: This function also strips basepath information from all tensors' external_data fields.
@params
model: Model object which is the source of tensors to serialize.
filepath: System path to the directory which should be treated as base path for ext... | def write_external_data_tensors(model, filepath): # type: (ModelProto, Text) -> ModelProto
"""
Write external data of all tensors to files on disk.
Note: This function also strips basepath information from all tensors' external_data fields.
@params
model: Model object which is the source of tenso... |
Imports a stdlib path and returns a handle to it
eg. self._import("typing", "Optional") -> "Optional" | def _import(self, path, name):
# type: (Text, Text) -> Text
"""Imports a stdlib path and returns a handle to it
eg. self._import("typing", "Optional") -> "Optional"
"""
imp = path.replace('/', '.')
self.imports[imp].add(name)
return name |
Import a referenced message and return a handle | def _import_message(self, type_name):
# type: (d.FieldDescriptorProto) -> Text
"""Import a referenced message and return a handle"""
name = cast(Text, type_name)
if name[0] == '.' and name[1].isupper() and name[2].islower():
# Message defined in this file
return ... |
Run command. | def run(self):
"""Run command."""
onnx_script = os.path.realpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "tools/mypy-onnx.py"))
returncode = subprocess.call([sys.executable, onnx_script])
sys.exit(returncode) |
Construct a NodeProto.
Arguments:
op_type (string): The name of the operator to construct
inputs (list of string): list of input names
outputs (list of string): list of output names
name (string, default None): optional unique identifier for NodeProto
doc_string (string, def... | def make_node(
op_type, # type: Text
inputs, # type: Sequence[Text]
outputs, # type: Sequence[Text]
name=None, # type: Optional[Text]
doc_string=None, # type: Optional[Text]
domain=None, # type: Optional[Text]
**kwargs # type: Any
): # type: (...) -> NodeP... |
Construct an OperatorSetIdProto.
Arguments:
domain (string): The domain of the operator set id
version (integer): Version of operator set id | def make_operatorsetid(
domain, # type: Text
version, # type: int
): # type: (...) -> OperatorSetIdProto
"""Construct an OperatorSetIdProto.
Arguments:
domain (string): The domain of the operator set id
version (integer): Version of operator set id
"""
operatorsetid =... |
An internal graph to convert the input to a bytes or to False.
The criteria for conversion is as follows and should be python 2 and 3
compatible:
- If val is py2 str or py3 bytes: return bytes
- If val is py2 unicode or py3 str: return val.decode('utf-8')
- Otherwise, return False | def _to_bytes_or_false(val): # type: (Union[Text, bytes]) -> Union[bytes, bool]
"""An internal graph to convert the input to a bytes or to False.
The criteria for conversion is as follows and should be python 2 and 3
compatible:
- If val is py2 str or py3 bytes: return bytes
- If val is py2 unicod... |
Makes an AttributeProto based on the value type. | def make_attribute(
key, # type: Text
value, # type: Any
doc_string=None # type: Optional[Text]
): # type: (...) -> AttributeProto
"""Makes an AttributeProto based on the value type."""
attr = AttributeProto()
attr.name = key
if doc_string:
attr.doc_string = doc_strin... |
Makes a ValueInfoProto based on the data type and shape. | def make_tensor_value_info(
name, # type: Text
elem_type, # type: int
shape, # type: Optional[Sequence[Union[Text, int]]]
doc_string="", # type: Text
shape_denotation=None, # type: Optional[List[Text]]
): # type: (...) -> ValueInfoProto
"""Makes a ValueInfoProto based o... |
Empties `doc_string` field on any nested protobuf messages | def strip_doc_string(proto): # type: (google.protobuf.message.Message) -> None
"""
Empties `doc_string` field on any nested protobuf messages
"""
assert isinstance(proto, google.protobuf.message.Message)
for descriptor in proto.DESCRIPTOR.fields:
if descriptor.name == 'doc_string':
... |
Converts a tensor def object to a numpy array.
Inputs:
tensor: a TensorProto object.
Returns:
arr: the converted array. | def to_array(tensor): # type: (TensorProto) -> np.ndarray[Any]
"""Converts a tensor def object to a numpy array.
Inputs:
tensor: a TensorProto object.
Returns:
arr: the converted array.
"""
if tensor.HasField("segment"):
raise ValueError(
"Currently not supporti... |
Converts a numpy array to a tensor def.
Inputs:
arr: a numpy array.
name: (optional) the name of the tensor.
Returns:
tensor_def: the converted tensor def. | def from_array(arr, name=None): # type: (np.ndarray[Any], Optional[Text]) -> TensorProto
"""Converts a numpy array to a tensor def.
Inputs:
arr: a numpy array.
name: (optional) the name of the tensor.
Returns:
tensor_def: the converted tensor def.
"""
tensor = TensorProto()... |
Serialize a in-memory proto to bytes
@params
proto is a in-memory proto, such as a ModelProto, TensorProto, etc
@return
Serialized proto in bytes | def _serialize(proto): # type: (Union[bytes, google.protobuf.message.Message]) -> bytes
'''
Serialize a in-memory proto to bytes
@params
proto is a in-memory proto, such as a ModelProto, TensorProto, etc
@return
Serialized proto in bytes
'''
if isinstance(proto, bytes):
return... |
Parse bytes into a in-memory proto
@params
s is bytes containing serialized proto
proto is a in-memory proto object
@return
The proto instance filled in by s | def _deserialize(s, proto): # type: (bytes, _Proto) -> _Proto
'''
Parse bytes into a in-memory proto
@params
s is bytes containing serialized proto
proto is a in-memory proto object
@return
The proto instance filled in by s
'''
if not isinstance(s, bytes):
raise ValueError... |
Loads a serialized ModelProto into memory
@params
f can be a file-like object (has "read" function) or a string containing a file name
format is for future use
@return
Loaded in-memory ModelProto | def load_model(f, format=None, load_external_data=True): # type: (Union[IO[bytes], Text], Optional[Any], bool) -> ModelProto
'''
Loads a serialized ModelProto into memory
@params
f can be a file-like object (has "read" function) or a string containing a file name
format is for future use
@ret... |
Loads a serialized TensorProto into memory
@params
f can be a file-like object (has "read" function) or a string containing a file name
format is for future use
@return
Loaded in-memory TensorProto | def load_tensor(f, format=None): # type: (Union[IO[bytes], Text], Optional[Any]) -> TensorProto
'''
Loads a serialized TensorProto into memory
@params
f can be a file-like object (has "read" function) or a string containing a file name
format is for future use
@return
Loaded in-memory Ten... |
Saves the ModelProto to the specified path.
@params
proto should be a in-memory ModelProto
f can be a file-like object (has "write" function) or a string containing a file name
format is for future use | def save_model(proto, f, format=None): # type: (Union[ModelProto, bytes], Union[IO[bytes], Text], Optional[Any]) -> None
'''
Saves the ModelProto to the specified path.
@params
proto should be a in-memory ModelProto
f can be a file-like object (has "write" function) or a string containing a file n... |
This function combines several useful utility functions together. | def polish_model(model): # type: (ModelProto) -> ModelProto
'''
This function combines several useful utility functions together.
'''
onnx.checker.check_model(model)
onnx.helper.strip_doc_string(model)
model = onnx.shape_inference.infer_shapes(model)
model = onnx.optimizer.optimize(mode... |
Unrolls an RNN cell across time steps.
Currently, 'TNC' is a preferred layout. unroll on the input of this layout
runs much faster.
Parameters
----------
cell : an object whose base class is RNNCell.
The RNN cell to run on the input sequence.
inputs : Symbol
It should have shap... | def dynamic_unroll(cell, inputs, begin_state, drop_inputs=0, drop_outputs=0,
layout='TNC', valid_length=None):
"""Unrolls an RNN cell across time steps.
Currently, 'TNC' is a preferred layout. unroll on the input of this layout
runs much faster.
Parameters
----------
cell : ... |
Unrolls an RNN cell across time steps.
Parameters
----------
length : int
Number of steps to unroll.
inputs : Symbol, list of Symbol, or None
If `inputs` is a single Symbol (usually the output
of Embedding symbol), it should have shape
(ba... | def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None,
valid_length=None):
"""Unrolls an RNN cell across time steps.
Parameters
----------
length : int
Number of steps to unroll.
inputs : Symbol, list of Symbol, or None
... |
Change attribute names as per values in change_map dictionary.
Parameters
----------
:param attrs : dict Dict of operator attributes
:param change_map : dict Dict of onnx attribute name to mxnet attribute names.
Returns
-------
:return new_attr : dict Converted dict of operator attributes. | def _fix_attribute_names(attrs, change_map):
"""
Change attribute names as per values in change_map dictionary.
Parameters
----------
:param attrs : dict Dict of operator attributes
:param change_map : dict Dict of onnx attribute name to mxnet attribute names.
Returns
-------
:retur... |
Removes attributes in the remove list from the input attribute dict
:param attrs : Dict of operator attributes
:param remove_list : list of attributes to be removed
:return new_attr : Dict of operator attributes without the listed attributes. | def _remove_attributes(attrs, remove_list):
"""
Removes attributes in the remove list from the input attribute dict
:param attrs : Dict of operator attributes
:param remove_list : list of attributes to be removed
:return new_attr : Dict of operator attributes without the listed attributes.
"""
... |
:param attrs: Current Attribute list
:param extraAttrMap: Additional attributes to be added
:return: new_attr | def _add_extra_attributes(attrs, extra_attr_map):
"""
:param attrs: Current Attribute list
:param extraAttrMap: Additional attributes to be added
:return: new_attr
"""
for attr in extra_attr_map:
if attr not in attrs:
attrs[attr] = extra_attr_map[attr]
return attrs |
Changing onnx's pads sequence to match with mxnet's pad_width
mxnet: (x1_begin, x1_end, ... , xn_begin, xn_end)
onnx: (x1_begin, x2_begin, ... , xn_end, xn_end) | def _pad_sequence_fix(attr, kernel_dim=None):
"""Changing onnx's pads sequence to match with mxnet's pad_width
mxnet: (x1_begin, x1_end, ... , xn_begin, xn_end)
onnx: (x1_begin, x2_begin, ... , xn_end, xn_end)"""
new_attr = ()
if len(attr) % 2 == 0:
for index in range(int(len(attr) / 2)):
... |
onnx pooling operator supports asymmetrical padding
Adding pad operator before pooling in mxnet to work with onnx | def _fix_pooling(pool_type, inputs, new_attr):
"""onnx pooling operator supports asymmetrical padding
Adding pad operator before pooling in mxnet to work with onnx"""
stride = new_attr.get('stride')
kernel = new_attr.get('kernel')
padding = new_attr.get('pad')
p_value = new_attr.get('p_value')
... |
A workaround for 'use_bias' attribute since onnx don't provide this attribute,
we have to check the number of inputs to decide it. | def _fix_bias(op_name, attrs, num_inputs):
"""A workaround for 'use_bias' attribute since onnx don't provide this attribute,
we have to check the number of inputs to decide it."""
if num_inputs == 3:
attrs['no_bias'] = False
elif num_inputs == 2:
attrs['no_bias'] = True
else:
... |
A workaround to reshape bias term to (1, num_channel). | def _fix_broadcast(op_name, inputs, broadcast_axis, proto_obj):
"""A workaround to reshape bias term to (1, num_channel)."""
if int(len(proto_obj._params)) > 0:
assert len(list(inputs)) == 2
input0_shape = get_input_shape(inputs[0], proto_obj)
#creating reshape shape
reshape_sha... |
A workaround for getting 'channels' or 'units' since onnx don't provide
these attributes. We check the shape of weights provided to get the number. | def _fix_channels(op_name, attrs, inputs, proto_obj):
"""A workaround for getting 'channels' or 'units' since onnx don't provide
these attributes. We check the shape of weights provided to get the number.
"""
weight_name = inputs[1].name
if not weight_name in proto_obj._params:
raise ValueEr... |
Using FullyConnected operator in place of linalg_gemm to perform same operation | def _fix_gemm(op_name, inputs, old_attr, proto_obj):
"""Using FullyConnected operator in place of linalg_gemm to perform same operation"""
op_sym = getattr(symbol, op_name, None)
alpha = float(old_attr.get('alpha', 1.0))
beta = float(old_attr.get('beta', 1.0))
trans_a = int(old_attr.get('transA', 0)... |
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