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def _construct_deployment(self, rest_api):
"""Constructs and returns the ApiGateway Deployment. :param model.apigateway.ApiGatewayRestApi rest_api: the RestApi f... |
deployment = ApiGatewayDeployment(self.logical_id + 'Deployment',
attributes=self.passthrough_resource_attributes)
deployment.RestApiId = rest_api.get_runtime_attr('rest_api_id')
deployment.StageName = 'Stage'
return deployment |
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def _construct_stage(self, deployment, swagger):
"""Constructs and returns the ApiGateway Stage. :param model.apigateway.ApiGatewayDeployment deployment: the Dep... |
# If StageName is some intrinsic function, then don't prefix the Stage's logical ID
# This will NOT create duplicates because we allow only ONE stage per API resource
stage_name_prefix = self.stage_name if isinstance(self.stage_name, string_types) else ""
stage = ApiGatewayStage(self.... |
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def to_cloudformation(self):
"""Generates CloudFormation resources from a SAM API resource :returns: a tuple containing the RestApi, Deployment, and Stage for an... |
rest_api = self._construct_rest_api()
deployment = self._construct_deployment(rest_api)
swagger = None
if rest_api.Body is not None:
swagger = rest_api.Body
elif rest_api.BodyS3Location is not None:
swagger = rest_api.BodyS3Location
stage = sel... |
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def _add_cors(self):
""" Add CORS configuration to the Swagger file, if necessary """ |
INVALID_ERROR = "Invalid value for 'Cors' property"
if not self.cors:
return
if self.cors and not self.definition_body:
raise InvalidResourceException(self.logical_id,
"Cors works only with inline Swagger specified in "
... |
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def _add_auth(self):
""" Add Auth configuration to the Swagger file, if necessary """ |
if not self.auth:
return
if self.auth and not self.definition_body:
raise InvalidResourceException(self.logical_id,
"Auth works only with inline Swagger specified in "
"'DefinitionBody' prope... |
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def _add_gateway_responses(self):
""" Add Gateway Response configuration to the Swagger file, if necessary """ |
if not self.gateway_responses:
return
if self.gateway_responses and not self.definition_body:
raise InvalidResourceException(
self.logical_id, "GatewayResponses works only with inline Swagger specified in "
"'DefinitionBody' pro... |
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def _get_permission(self, authorizer_name, authorizer_lambda_function_arn):
"""Constructs and returns the Lambda Permission resource allowing the Authorizer to i... |
rest_api = ApiGatewayRestApi(self.logical_id, depends_on=self.depends_on, attributes=self.resource_attributes)
api_id = rest_api.get_runtime_attr('rest_api_id')
partition = ArnGenerator.get_partition_name()
resource = '${__ApiId__}/authorizers/*'
source_arn = fnSub(ArnGenerator... |
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def to_cloudformation(self, **kwargs):
"""Returns the Lambda function, role, and event resources to which this SAM Function corresponds. :param dict kwargs: alre... |
resources = []
intrinsics_resolver = kwargs["intrinsics_resolver"]
if self.DeadLetterQueue:
self._validate_dlq()
lambda_function = self._construct_lambda_function()
resources.append(lambda_function)
lambda_alias = None
if self.AutoPublishAlias:
... |
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def _construct_role(self, managed_policy_map):
"""Constructs a Lambda execution role based on this SAM function's Policies property. :returns: the generated IAM ... |
execution_role = IAMRole(self.logical_id + 'Role', attributes=self.get_passthrough_resource_attributes())
execution_role.AssumeRolePolicyDocument = IAMRolePolicies.lambda_assume_role_policy()
managed_policy_arns = [ArnGenerator.generate_aws_managed_policy_arn('service-role/AWSLambdaBasicExecut... |
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def _generate_event_resources(self, lambda_function, execution_role, event_resources, lambda_alias=None):
"""Generates and returns the resources associated with ... |
resources = []
if self.Events:
for logical_id, event_dict in self.Events.items():
try:
eventsource = self.event_resolver.resolve_resource_type(event_dict).from_dict(
lambda_function.logical_id + logical_id, event_dict, logical_id)
... |
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def _construct_version(self, function, intrinsics_resolver):
"""Constructs a Lambda Version resource that will be auto-published when CodeUri of the function cha... |
code_dict = function.Code
if not code_dict:
raise ValueError("Lambda function code must be a valid non-empty dictionary")
if not intrinsics_resolver:
raise ValueError("intrinsics_resolver is required for versions creation")
# Resolve references to template para... |
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def _construct_alias(self, name, function, version):
"""Constructs a Lambda Alias for the given function and pointing to the given version :param string name: Na... |
if not name:
raise InvalidResourceException(self.logical_id, "Alias name is required to create an alias")
logical_id = "{id}Alias{suffix}".format(id=function.logical_id, suffix=name)
alias = LambdaAlias(logical_id=logical_id, attributes=self.get_passthrough_resource_attributes())
... |
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def to_cloudformation(self, **kwargs):
"""Returns the API Gateway RestApi, Deployment, and Stage to which this SAM Api corresponds. :param dict kwargs: already-c... |
resources = []
api_generator = ApiGenerator(self.logical_id,
self.CacheClusterEnabled,
self.CacheClusterSize,
self.Variables,
self.depends_on,
... |
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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.APPLICATION_ID_KEY] is not None):
application_tags[self._SAR_APP_KEY] = self.Location[self.APPLICATION_ID_KEY]
... |
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def to_cloudformation(self, **kwargs):
"""Returns the Lambda layer to which this SAM Layer corresponds. :param dict kwargs: already-converted resources that may ... |
resources = []
# Append any CFN resources:
intrinsics_resolver = kwargs["intrinsics_resolver"]
resources.append(self._construct_lambda_layer(intrinsics_resolver))
return resources |
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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.RETAIN
elif self.RetentionPolicy.lower() == self.DELETE.lower():
return self.DELETE
elif self.RetentionPolicy.lower() not in self.retention_policy_options:
ra... |
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def order_flowers(intent_request):
""" Performs dialog management and fulfillment for ordering flowers. Beyond fulfillment, the implementation of this intent dem... |
flower_type = get_slots(intent_request)["FlowerType"]
date = get_slots(intent_request)["PickupDate"]
time = get_slots(intent_request)["PickupTime"]
source = intent_request['invocationSource']
if source == 'DialogCodeHook':
# Perform basic validation on the supplied input slots.
# ... |
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def _construct_permission(self, function, source_arn=None, source_account=None, suffix="", event_source_token=None):
"""Constructs the Lambda Permission resource... |
lambda_permission = LambdaPermission(self.logical_id + 'Permission' + suffix,
attributes=function.get_passthrough_resource_attributes())
try:
# Name will not be available for Alias resources
function_name_or_arn = function.get_runtim... |
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def to_cloudformation(self, **kwargs):
"""Returns the CloudWatch Events Rule and Lambda Permission to which this Schedule event source corresponds. :param dict k... |
function = kwargs.get('function')
if not function:
raise TypeError("Missing required keyword argument: function")
resources = []
events_rule = EventsRule(self.logical_id)
resources.append(events_rule)
events_rule.ScheduleExpression = self.Schedule
... |
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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'
}
if self.Input is not None:
target['Input'] = self.Input
if self.InputPath is not None:
target['InputPath'] = self.InputPath
retur... |
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def to_cloudformation(self, **kwargs):
"""Returns the Lambda Permission resource allowing S3 to invoke the function this event source triggers. :param dict kwarg... |
function = kwargs.get('function')
if not function:
raise TypeError("Missing required keyword argument: function")
if 'bucket' not in kwargs or kwargs['bucket'] is None:
raise TypeError("Missing required keyword argument: bucket")
if 'bucket_id' not in kwargs o... |
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def _depend_on_lambda_permissions_using_tag(self, bucket, permission):
""" Since conditional DependsOn is not supported this undocumented way of implicitely maki... |
properties = bucket.get('Properties', None)
if properties is None:
properties = {}
bucket['Properties'] = properties
tags = properties.get('Tags', None)
if tags is None:
tags = []
properties['Tags'] = tags
dep_tag = {
'... |
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def to_cloudformation(self, **kwargs):
"""Returns the Lambda Permission resource allowing SNS to invoke the function this event source triggers. :param dict kwar... |
function = kwargs.get('function')
if not function:
raise TypeError("Missing required keyword argument: function")
return [self._construct_permission(function, source_arn=self.Topic),
self._inject_subscription(function, self.Topic, self.FilterPolicy)] |
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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 e... |
rest_api_id = self.RestApiId
if isinstance(rest_api_id, dict) and "Ref" in rest_api_id:
rest_api_id = rest_api_id["Ref"]
# If RestApiId is a resource in the same template, then we try find the StageName by following the reference
# Otherwise we default to a wildcard. This ... |
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def to_cloudformation(self, **kwargs):
"""If the Api event source has a RestApi property, then simply return the Lambda Permission resource allowing API Gateway ... |
resources = []
function = kwargs.get('function')
if not function:
raise TypeError("Missing required keyword argument: function")
if self.Method is not None:
# Convert to lower case so that user can specify either GET or get
self.Method = self.Metho... |
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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 mod... |
swagger_body = api.get("DefinitionBody")
if swagger_body is None:
return
function_arn = function.get_runtime_attr('arn')
partition = ArnGenerator.get_partition_name()
uri = fnSub('arn:' + partition + ':apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/' +
... |
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def resolve_parameter_refs(self, input):
""" Resolves references to parameters within the given dictionary recursively. Other intrinsic functions such as !GetAtt... |
return self._traverse(input, self.parameters, self._try_resolve_parameter_refs) |
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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 Functio... |
return self._traverse(input, supported_resource_refs, self._try_resolve_sam_resource_refs) |
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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 cu... |
return self._traverse(input, supported_resource_id_refs, self._try_resolve_sam_resource_id_refs) |
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def _traverse(self, input, resolution_data, resolver_method):
""" Driver method that performs the actual traversal of input and calls the appropriate `resolver_m... |
# There is data to help with resolution. Skip the traversal altogether
if len(resolution_data) == 0:
return input
#
# Traversal Algorithm:
#
# Imagine the input dictionary/list as a tree. We are doing a Pre-Order tree traversal here where we first
#... |
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def _traverse_dict(self, input_dict, resolution_data, resolver_method):
""" Traverse a dictionary to resolve intrinsic functions on every value :param input_dict... |
for key, value in input_dict.items():
input_dict[key] = self._traverse(value, resolution_data, resolver_method)
return input_dict |
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def _traverse_list(self, input_list, resolution_data, resolver_method):
""" Traverse a list to resolve intrinsic functions on every element :param input_list: Li... |
for index, value in enumerate(input_list):
input_list[index] = self._traverse(value, resolution_data, resolver_method)
return input_list |
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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 l... |
if not self._is_intrinsic_dict(input):
return input
function_type = list(input.keys())[0]
return self.supported_intrinsics[function_type].resolve_resource_refs(input, supported_resource_refs) |
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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... |
if not self._is_intrinsic_dict(input):
return input
function_type = list(input.keys())[0]
return self.supported_intrinsics[function_type].resolve_resource_id_refs(input, supported_resource_id_refs) |
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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 cont... |
# All intrinsic functions are dictionaries with just one key
return isinstance(input, dict) \
and len(input) == 1 \
and list(input.keys())[0] in self.supported_intrinsics |
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def to_cloudformation(self, **kwargs):
"""Returns the CloudWatch Logs Subscription Filter and Lambda Permission to which this CloudWatch Logs event source corres... |
function = kwargs.get('function')
if not function:
raise TypeError("Missing required keyword argument: function")
source_arn = self.get_source_arn()
permission = self._construct_permission(function, source_arn=source_arn)
subscription_filter = self.get_subscription... |
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def convert(self, template_name, parameter_values):
""" Converts the given template to IAM-ready policy statement by substituting template parameters with the gi... |
if not self.has(template_name):
raise TemplateNotFoundException(template_name)
template = self.get(template_name)
return template.to_statement(parameter_values) |
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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 va... |
if not schema:
schema = PolicyTemplatesProcessor._read_schema()
try:
jsonschema.validate(policy_templates_dict, schema)
except ValidationError as ex:
# Stringifying the exception will give us useful error message
raise ValueError(str(ex))
... |
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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 :... |
tpl = self.env.get_template(template_name)
html = tpl.render(chart=self.generate_js_link(chart))
write_utf8_html_file(path, self._reg_replace(html)) |
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def decode_base64(data: str) -> bytes: """Decode base64, padding being optional. :param data: Base64 data as an ASCII byte string :returns: The decoded byte strin... |
missing_padding = len(data) % 4
if missing_padding != 0:
data += "=" * (4 - missing_padding)
return base64.decodebytes(data.encode("utf-8")) |
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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_str.isdigit():
raise ValueError("Expecting numeric pin number.")... |
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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 in leds]
else:
[led.off() for led in leds]
except OSError: # VCPInterrupt # Ctrl+C in interpreter mode.
break |
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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... |
c_file = None
relative_c_file = os.path.splitext(obj_file)[0] + ".c"
relative_c_file = relative_c_file.lstrip('/\\')
for p in vpath:
possible_c_file = os.path.join(p, relative_c_file)
if os.path.exists(possible_c_file):
c_file = possible_c_file
break
return ... |
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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: Li... |
global pattern
if c_file is None:
# No c file to match the object file, skip
return set()
with io.open(c_file, encoding='utf-8') as c_file_obj:
return set(re.findall(pattern, c_file_obj.read())) |
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def generate_module_table_header(modules):
""" Generate header with module table entries for builtin modules. :param List[(module_name, obj_module, enabled_defin... |
# Print header file for all external modules.
mod_defs = []
print("// Automatically generated by makemoduledefs.py.\n")
for module_name, obj_module, enabled_define in modules:
mod_def = "MODULE_DEF_{}".format(module_name.upper())
mod_defs.append(mod_def)
print((
"#i... |
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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 \
... |
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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 |
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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 |
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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)
xlengths.append(col_len)
for i in range(len(contents[0])):
ymax... |
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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")
__dev = devices[0]
__dev.set_configuration()
# Claim DFU interfac... |
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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:
raise Exception("DFU: erase failed")
# Check command state
if get_status()... |
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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 command
if get_status() != __DFU_STATE_DFU_DOWNLOAD... |
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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_DOWNLOAD_BUSY:
raise Exception("DFU: set address failed")
# Ch... |
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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 e... |
xfer_count = 0
xfer_bytes = 0
xfer_total = len(buf)
xfer_base = addr
while xfer_bytes < xfer_total:
if __verbose and xfer_count % 512 == 0:
print ("Addr 0x%x %dKBs/%dKBs..." % (xfer_base + xfer_bytes,
xfer_bytes // 1024,
... |
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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_INTERFACE, buf, __TIMEOUT)
# Execute last command
if get_status() != __DFU_STATE_DFU_DOWNLOAD_BUSY:
raise Exception("DFU:... |
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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
if get_status() != __DFU_STATE_DFU_MANIFEST:
print("Fail... |
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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 tu... |
size = struct.calcsize(fmt)
return named(struct.unpack(fmt, data[:size]), names), data[size:] |
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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}"
.format(device.bus, device.address,
device.idVendor, device.idProduct))
... |
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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_size = size
elem_addr = addr
if progress:
progress(elem_addr, 0, elem_size)
while size > 0:
write_size = ... |
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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="")
try:
sys.stdout.flush()
except OSError:
pass # Ignore Windows CLI "WinError 87" on Python... |
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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 devices",
action="store_true",
default=False
)
parse... |
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def load_external_data_for_tensor(tensor, base_dir):
# type: (TensorProto, Text) -> None """ Load data from an external file for tensor. @params tensor: a Tensor... |
if tensor.HasField("raw_data"): # already loaded
return
info = ExternalDataInfo(tensor)
file_location = _sanitize_path(info.location)
external_data_file_path = os.path.join(base_dir, file_location)
with open(external_data_file_path, 'rb') as data_file:
if info.offset:
... |
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def load_external_data_for_model(model, base_dir):
# type: (ModelProto, Text) -> None """ Loads external tensors into model @params model: ModelProto to load ext... |
for tensor in _get_all_tensors(model):
if uses_external_data(tensor):
load_external_data_for_tensor(tensor, base_dir) |
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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 te... |
if all_tensors_to_one_file:
file_name = Text(uuid.uuid1())
if location:
file_name = location
for tensor in _get_all_tensors(model):
set_external_data(tensor, file_name)
else:
for tensor in _get_all_tensors(model):
set_external_data(tensor, ten... |
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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 dat... |
for tensor in _get_all_tensors(model):
if uses_external_data(tensor):
if not tensor.HasField("raw_data"):
raise ValueError("raw_data field doesn't exist.")
del tensor.external_data[:]
tensor.data_location = TensorProto.DEFAULT |
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def save_external_data(tensor, base_path):
# type: (TensorProto, Text) -> None """ Write tensor data to an external file according to information in the `externa... |
info = ExternalDataInfo(tensor)
external_data_file_path = os.path.join(base_path, info.location)
# Retrieve the tensor's data from raw_data or load external file
if not tensor.HasField("raw_data"):
raise ValueError("raw_data field doesn't exist.")
# Create file if it doesn't exist
if ... |
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def _get_attribute_tensors(onnx_model_proto):
# type: (ModelProto) -> Iterable[TensorProto] """Create an iterator of tensors from node attributes of an ONNX mode... |
for node in onnx_model_proto.graph.node:
for attribute in node.attribute:
if attribute.HasField("t"):
yield attribute.t
for tensor in attribute.tensors:
yield tensor |
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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... |
for (i, field) in enumerate(tensor.external_data):
if field.key == field_key:
del tensor.external_data[i] |
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def write_external_data_tensors(model, filepath):
# type: (ModelProto, Text) -> ModelProto """ Write external data of all tensors to files on disk. Note: This fu... |
for tensor in _get_all_tensors(model):
if uses_external_data(tensor):
save_external_data(tensor, filepath)
tensor.ClearField(str('raw_data'))
return model |
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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 name[1:]
message_fd = self.descriptors.message_to_fd[name]
if message_fd.name == self.fd.name:
# message defined in thi... |
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def make_node( op_type, # type: Text inputs, # type: Sequence[Text] outputs, # type: Sequence[Text] name=None, # type: Optional[Text] doc_string=None, # type: Opt... |
node = NodeProto()
node.op_type = op_type
node.input.extend(inputs)
node.output.extend(outputs)
if name:
node.name = name
if doc_string:
node.doc_string = doc_string
if domain is not None:
node.domain = domain
if kwargs:
node.attribute.extend(
... |
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def make_operatorsetid( domain, # type: Text version, # type: int """Construct an OperatorSetIdProto. Arguments: domain (string):
The domain of the operator set ... |
operatorsetid = OperatorSetIdProto()
operatorsetid.domain = domain
operatorsetid.version = version
return operatorsetid |
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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 fo... |
if isinstance(val, bytes):
return val
else:
try:
return val.encode('utf-8')
except AttributeError:
return False |
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def make_attribute( key, # type: Text value, # type: Any doc_string=None # type: Optional[Text] """Makes an AttributeProto based on the value type.""" |
attr = AttributeProto()
attr.name = key
if doc_string:
attr.doc_string = doc_string
is_iterable = isinstance(value, collections.Iterable)
bytes_or_false = _to_bytes_or_false(value)
# First, singular cases
# float
if isinstance(value, float):
attr.f = value
attr.... |
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def make_tensor_value_info( name, # type: Text elem_type, # type: int shape, # type: Optional[Sequence[Union[Text, int]]] doc_string="", # type: Text shape_denota... |
value_info_proto = ValueInfoProto()
value_info_proto.name = name
if doc_string:
value_info_proto.doc_string = doc_string
tensor_type_proto = value_info_proto.type.tensor_type
tensor_type_proto.elem_type = elem_type
tensor_shape_proto = tensor_type_proto.shape
if shape is not None... |
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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':
proto.ClearField(descriptor.name)
elif descriptor.type == descriptor.TYPE_MESSAGE:
if descriptor.label == descriptor.LABEL_REPEATED:
... |
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def to_array(tensor):
# type: (TensorProto) -> np.ndarray[Any] """Converts a tensor def object to a numpy array. Inputs: tensor: a TensorProto object. Returns: a... |
if tensor.HasField("segment"):
raise ValueError(
"Currently not supporting loading segments.")
if tensor.data_type == TensorProto.UNDEFINED:
raise ValueError("The data type is not defined.")
tensor_dtype = tensor.data_type
np_dtype = mapping.TENSOR_TYPE_TO_NP_TYPE[tensor_dt... |
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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. n... |
tensor = TensorProto()
tensor.dims.extend(arr.shape)
if name:
tensor.name = name
if arr.dtype == np.object:
# Special care for strings.
tensor.data_type = mapping.NP_TYPE_TO_TENSOR_TYPE[arr.dtype]
# TODO: Introduce full string support.
# We flatten the array in ... |
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| 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... |
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| 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... |
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| 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... |
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| 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... |
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| 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... |
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| 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... |
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def get_input_shape(sym, proto_obj):
"""Helper function to obtain the shape of an array""" |
arg_params = proto_obj.arg_dict
aux_params = proto_obj.aux_dict
model_input_shape = [data[1] for data in proto_obj.model_metadata.get('input_tensor_data')]
data_names = [data[0] for data in proto_obj.model_metadata.get('input_tensor_data')]
# creating dummy inputs
inputs = []
for in_sh... |
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def imresize(src, w, h, *args, **kwargs):
r"""Resize image with OpenCV. .. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built... |
return _internal._cvimresize(src, w, h, *args, **kwargs) |
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def imdecode(buf, *args, **kwargs):
"""Decode an image to an NDArray. .. note:: `imdecode` uses OpenCV (not the CV2 Python library). MXNet must have been built w... |
if not isinstance(buf, nd.NDArray):
if sys.version_info[0] == 3 and not isinstance(buf, (bytes, bytearray, np.ndarray)):
raise ValueError('buf must be of type bytes, bytearray or numpy.ndarray,'
'if you would like to input type str, please convert to bytes')
... |
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def scale_down(src_size, size):
"""Scales down crop size if it's larger than image size. If width/height of the crop is larger than the width/height of the image... |
w, h = size
sw, sh = src_size
if sh < h:
w, h = float(w * sh) / h, sh
if sw < w:
w, h = sw, float(h * sw) / w
return int(w), int(h) |
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def copyMakeBorder(src, top, bot, left, right, *args, **kwargs):
"""Pad image border with OpenCV. Parameters src : NDArray source image top : int, required Top m... |
return _internal._cvcopyMakeBorder(src, top, bot, left, right, *args, **kwargs) |
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def _get_interp_method(interp, sizes=()):
"""Get the interpolation method for resize functions. The major purpose of this function is to wrap a random interp met... |
if interp == 9:
if sizes:
assert len(sizes) == 4
oh, ow, nh, nw = sizes
if nh > oh and nw > ow:
return 2
elif nh < oh and nw < ow:
return 3
else:
return 1
else:
return 2
if in... |
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def resize_short(src, size, interp=2):
"""Resizes shorter edge to size. .. note:: `resize_short` uses OpenCV (not the CV2 Python library). MXNet must have been b... |
h, w, _ = src.shape
if h > w:
new_h, new_w = size * h // w, size
else:
new_h, new_w = size, size * w // h
return imresize(src, new_w, new_h, interp=_get_interp_method(interp, (h, w, new_h, new_w))) |
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def center_crop(src, size, interp=2):
"""Crops the image `src` to the given `size` by trimming on all four sides and preserving the center of the image. Upsample... |
h, w, _ = src.shape
new_w, new_h = scale_down((w, h), size)
x0 = int((w - new_w) / 2)
y0 = int((h - new_h) / 2)
out = fixed_crop(src, x0, y0, new_w, new_h, size, interp)
return out, (x0, y0, new_w, new_h) |
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def color_normalize(src, mean, std=None):
"""Normalize src with mean and std. Parameters src : NDArray Input image mean : NDArray RGB mean to be subtracted std :... |
if mean is not None:
src -= mean
if std is not None:
src /= std
return src |
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def random_size_crop(src, size, area, ratio, interp=2, **kwargs):
"""Randomly crop src with size. Randomize area and aspect ratio. Parameters src : NDArray Input... |
h, w, _ = src.shape
src_area = h * w
if 'min_area' in kwargs:
warnings.warn('`min_area` is deprecated. Please use `area` instead.',
DeprecationWarning)
area = kwargs.pop('min_area')
assert not kwargs, "unexpected keyword arguments for `random_size_crop`."
if ... |
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def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=... |
auglist = []
if resize > 0:
auglist.append(ResizeAug(resize, inter_method))
crop_size = (data_shape[2], data_shape[1])
if rand_resize:
assert rand_crop
auglist.append(RandomSizedCropAug(crop_size, 0.08, (3.0 / 4.0, 4.0 / 3.0), inter_method))
elif rand_crop:
auglist... |
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def dumps(self):
"""Saves the Augmenter to string Returns ------- str JSON formatted string that describes the Augmenter. """ |
return json.dumps([self.__class__.__name__.lower(), self._kwargs]) |
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def dumps(self):
"""Override the default to avoid duplicate dump.""" |
return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]] |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def hard_reset(self):
"""Resets the iterator and ignore roll over data""" |
if self.seq is not None and self.shuffle:
random.shuffle(self.seq)
if self.imgrec is not None:
self.imgrec.reset()
self.cur = 0
self._allow_read = True
self._cache_data = None
self._cache_label = None
self._cache_idx = None |
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Description:
def next_sample(self):
"""Helper function for reading in next sample.""" |
if self._allow_read is False:
raise StopIteration
if self.seq is not None:
if self.cur < self.num_image:
idx = self.seq[self.cur]
else:
if self.last_batch_handle != 'discard':
self.cur = 0
raise Stop... |
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Description:
def _batchify(self, batch_data, batch_label, start=0):
"""Helper function for batchifying data""" |
i = start
batch_size = self.batch_size
try:
while i < batch_size:
label, s = self.next_sample()
data = self.imdecode(s)
try:
self.check_valid_image(data)
except RuntimeError as e:
... |
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