code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def mock_remote_command(self, cmd: str | list[str], **kwargs) -> ReturnValues:
"""Mocks a command, returning the next response for the command."""
if isinstance(cmd, list):
try:
cmd = ' '.join(cmd)
except TypeError as ex:
logging.warning(
'Tried joining command %s but not... | Mocks a command, returning the next response for the command. | mock_remote_command | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/mock_command.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/mock_command.py | Apache-2.0 |
def MockIssueCommand(
self,
call_to_response: dict[str, list[tuple[str, str, int]]],
) -> mock_command.MockIssueCommand:
"""Mocks IssueCommand, returning response for the given call.
Args:
call_to_response: A dictionary of commands to a list of responses.
Commands just need to be a ... | Mocks IssueCommand, returning response for the given call.
Args:
call_to_response: A dictionary of commands to a list of responses.
Commands just need to be a substring of the actual command. Each
response is given in order, like with mock's normal iterating
side_effect.
Returns:... | MockIssueCommand | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/pkb_common_test_case.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/pkb_common_test_case.py | Apache-2.0 |
def MockRemoteCommand(
self,
call_to_response: dict[str, list[tuple[str, str]]],
vm: virtual_machine.BaseVirtualMachine | None = None,
) -> virtual_machine.BaseVirtualMachine:
"""Mocks vm.RemoteCommand, returning response for the given call.
Args:
call_to_response: A dictionary of com... | Mocks vm.RemoteCommand, returning response for the given call.
Args:
call_to_response: A dictionary of commands to a list of responses.
Commands just need to be a substring of the actual command. Each
response is given in order, like with mock's normal iterating
side_effect.
vm:... | MockRemoteCommand | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/pkb_common_test_case.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/pkb_common_test_case.py | Apache-2.0 |
def MockRunCommand(
self,
call_to_response: dict[str, list[tuple[str, str, int]]],
cli: command_interface.CommandInterface | None = None,
) -> command_interface.CommandInterface:
"""Mocks RunCommand, returning response for the given call.
Args:
call_to_response: A dictionary of comman... | Mocks RunCommand, returning response for the given call.
Args:
call_to_response: A dictionary of commands to a list of responses.
Commands just need to be a substring of the actual command. Each
response is given in order, like with mock's normal iterating
side_effect.
cli: A mo... | MockRunCommand | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/pkb_common_test_case.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/pkb_common_test_case.py | Apache-2.0 |
def testScratchDisks(self):
"""Test for creating and deleting scratch disks.
This test creates two scratch disks on a vm and deletes them, ensuring
that the proper calls to create, format, mount, and delete are made.
"""
vm = self._CreateVm()
disk_spec = self.GetDiskSpec(mount_point='/mountpoi... | Test for creating and deleting scratch disks.
This test creates two scratch disks on a vm and deletes them, ensuring
that the proper calls to create, format, mount, and delete are made.
| testScratchDisks | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/scratch_disk_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/scratch_disk_test.py | Apache-2.0 |
def _GetScratchDiskName(
self, vm_group_name: str, multi_writer_group_name: str
) -> str:
"""Get the name of the scratch disk.
Args:
vm_group_name: name of the vm_group
multi_writer_group_name: name of the multiwriter group name (optional)
Returns:
scratch disk name
"""
... | Get the name of the scratch disk.
Args:
vm_group_name: name of the vm_group
multi_writer_group_name: name of the multiwriter group name (optional)
Returns:
scratch disk name
| _GetScratchDiskName | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/scratch_disk_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/scratch_disk_test.py | Apache-2.0 |
def testMultiWriterDiskName(self):
"""Test Case: Multiwriter Disk Name.
Scenario:
Verify that the multiwriter disk name adheres to the following convention:
'pkb-xxxx-multiwriter-{groupName}-{xx}'.
If 'disk_spec.multi_writer_group_name' is provided, ensure 'groupName' in
the disk name i... | Test Case: Multiwriter Disk Name.
Scenario:
Verify that the multiwriter disk name adheres to the following convention:
'pkb-xxxx-multiwriter-{groupName}-{xx}'.
If 'disk_spec.multi_writer_group_name' is provided, ensure 'groupName' in
the disk name is set to this specified value.
If 'd... | testMultiWriterDiskName | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/scratch_disk_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/scratch_disk_test.py | Apache-2.0 |
def testInvalidValue(self):
"""Passing an unrecognized value is not allowed."""
exp_str = 'test: Invalid value for --timing_measurements'
exp_regex = r'^%s$' % re.escape(exp_str)
with self.assertRaisesRegex(flags.ValidationError, exp_regex):
timing_util.ValidateMeasurementsFlag(['test']) | Passing an unrecognized value is not allowed. | testInvalidValue | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/timing_util_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/timing_util_test.py | Apache-2.0 |
def testNoneWithAnother(self):
"""Passing none with another value is not allowed."""
exp_str = 'none: Cannot combine with other --timing_measurements options'
exp_regex = r'^%s$' % re.escape(exp_str)
with self.assertRaisesRegex(flags.ValidationError, exp_regex):
timing_util.ValidateMeasurementsFla... | Passing none with another value is not allowed. | testNoneWithAnother | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/timing_util_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/timing_util_test.py | Apache-2.0 |
def testMeasureSequential(self):
"""Verify correct interval tuple generation in sequential measurements."""
timer = timing_util.IntervalTimer()
self.assertEqual(timer.intervals, [])
with timer.Measure('First Interval'):
pass
with timer.Measure('Second Interval'):
pass
self.assertEqua... | Verify correct interval tuple generation in sequential measurements. | testMeasureSequential | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/timing_util_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/timing_util_test.py | Apache-2.0 |
def testMeasureNested(self):
"""Verify correct interval tuple generation in nested measurements."""
timer = timing_util.IntervalTimer()
self.assertEqual(timer.intervals, [])
with timer.Measure('Outer Interval'):
with timer.Measure('Inner Interval'):
pass
self.assertEqual(len(timer.inte... | Verify correct interval tuple generation in nested measurements. | testMeasureNested | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/timing_util_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/timing_util_test.py | Apache-2.0 |
def testGenerateSamplesMeasureNotCalled(self):
"""GenerateSamples should return an empty list if Measure was not called."""
timer = timing_util.IntervalTimer()
self.assertEqual(timer.intervals, [])
samples = timer.GenerateSamples()
self.assertEqual(timer.intervals, [])
self.assertEqual(samples, ... | GenerateSamples should return an empty list if Measure was not called. | testGenerateSamplesMeasureNotCalled | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/timing_util_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/timing_util_test.py | Apache-2.0 |
def testGenerateSamplesRuntimeNoTimestamps(self):
"""Test generating runtime sample but no timestamp samples."""
timer = timing_util.IntervalTimer()
with timer.Measure('First'):
pass
with timer.Measure('Second'):
pass
start0 = timer.intervals[0][1]
stop0 = timer.intervals[0][2]
s... | Test generating runtime sample but no timestamp samples. | testGenerateSamplesRuntimeNoTimestamps | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/timing_util_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/timing_util_test.py | Apache-2.0 |
def testGenerateSamplesRuntimeAndTimestamps(self):
"""Test generating both runtime and timestamp samples."""
timer = timing_util.IntervalTimer()
with timer.Measure('First'):
pass
with timer.Measure('Second'):
pass
start0 = timer.intervals[0][1]
stop0 = timer.intervals[0][2]
start... | Test generating both runtime and timestamp samples. | testGenerateSamplesRuntimeAndTimestamps | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/timing_util_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/timing_util_test.py | Apache-2.0 |
def HaveSleepSubprocess():
"""Checks if the current process has a sleep subprocess."""
for child in psutil.Process(os.getpid()).children(recursive=True):
if 'sleep' in child.cmdline():
return True
return False | Checks if the current process has a sleep subprocess. | HaveSleepSubprocess | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/vm_util_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/vm_util_test.py | Apache-2.0 |
def _CheckBucket(self, bucket):
"""Make sure that we are only passed one bucket name.
Args:
bucket: the name of a bucket.
Raises: ValueError, if this object has been passed a different
bucket name previously.
"""
if self.bucket is None:
self.bucket = bucket
elif self.bucket !=... | Make sure that we are only passed one bucket name.
Args:
bucket: the name of a bucket.
Raises: ValueError, if this object has been passed a different
bucket name previously.
| _CheckBucket | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/integration/scripts/object_storage_worker_integration_tests.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/integration/scripts/object_storage_worker_integration_tests.py | Apache-2.0 |
def _run_throughput_directly(
command: str,
throughput_script: str,
vm: virtual_machine.BaseVirtualMachine,
burst_requests: int,
) -> list[throughput_load_driver.CommandResponse]:
"""Calls throughput_load_driver directly rather than via VM."""
del throughput_script
... | Calls throughput_load_driver directly rather than via VM. | _run_throughput_directly | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/ai_model_throughput_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/ai_model_throughput_benchmark_test.py | Apache-2.0 |
def _AssertSamples(self, results, delta, values):
""" "Asserts that the samples matching delta correspond to values."""
delta_metric = [r for r in results if r[1] == delta]
self.assertEqual(1, len(delta_metric), delta)
# The bidirectional_network_tests run in parallel and are grouped by
# <test nu... | "Asserts that the samples matching delta correspond to values. | _AssertSamples | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/bidirectional_network_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/bidirectional_network_benchmark_test.py | Apache-2.0 |
def _AssertSample(self, results, metric, value, unit):
"""Asserts results contains a sample matching metric/value/unit."""
match = [
r
for r in results
if (
r.metric == metric
and r.unit == unit
and abs(r.value - value) < TOLERANCE
)
]
... | Asserts results contains a sample matching metric/value/unit. | _AssertSample | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/bidirectional_network_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/bidirectional_network_benchmark_test.py | Apache-2.0 |
def vm_mock(index: int, timestamp: float) -> mock.Mock:
"""Creates a mock vm and Adds the needed vm attributes to the mock vm.
Args:
index: an integer which specifies index of vm in the list of vms.
timestamp: a timestamp which is used to determine delete start time and end
time.
Returns:
A mo... | Creates a mock vm and Adds the needed vm attributes to the mock vm.
Args:
index: an integer which specifies index of vm in the list of vms.
timestamp: a timestamp which is used to determine delete start time and end
time.
Returns:
A mock vm.
| vm_mock | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/cluster_boot_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/cluster_boot_benchmark_test.py | Apache-2.0 |
def vm_mock_given_delete_times(
index: int,
delete_start_time: float | None = None,
delete_end_time: float | None = None,
) -> mock.Mock:
"""Creates a mock vm with a provided delete_start_time and delete_end_time.
Args:
index: an integer which specifies index of vm in the list of vms.
delete_st... | Creates a mock vm with a provided delete_start_time and delete_end_time.
Args:
index: an integer which specifies index of vm in the list of vms.
delete_start_time: mock timestamp indicating the start of the VM delete
process. May be passed in as None to test MeasureDelete logic.
delete_end_time: mo... | vm_mock_given_delete_times | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/cluster_boot_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/cluster_boot_benchmark_test.py | Apache-2.0 |
def testMeasureDeleteNoValidVMs(self):
"""MeasureDelete test where no VMs have valid delete measurements."""
timestamp = 1625863325.003580
vm_with_neither = vm_mock_given_delete_times(index=0)
vm_with_start_only = vm_mock_given_delete_times(
index=1, delete_start_time=timestamp
)
# We ne... | MeasureDelete test where no VMs have valid delete measurements. | testMeasureDeleteNoValidVMs | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/cluster_boot_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/cluster_boot_benchmark_test.py | Apache-2.0 |
def testMeasureDeleteAttributeChecks(self):
"""MeasureDelete test where some VMs do not have valid delete measurements."""
timestamp = 1625863325.003580
vm_with_neither = vm_mock_given_delete_times(index=0)
vm_with_start_only = vm_mock_given_delete_times(
index=1, delete_start_time=timestamp
... | MeasureDelete test where some VMs do not have valid delete measurements. | testMeasureDeleteAttributeChecks | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/cluster_boot_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/cluster_boot_benchmark_test.py | Apache-2.0 |
def testParseOutput(self):
"""Tests parsing valid output from running Coremark."""
with open(_SAMPLE_OUTPUT_FILE) as f:
output = f.read()
samples = coremark_benchmark._ParseOutputForSamples(output, 8)
self.assertEqual('Coremark Score', samples[0].metric)
self.assertAlmostEqual(160665.153736, s... | Tests parsing valid output from running Coremark. | testParseOutput | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/coremark_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/coremark_benchmark_test.py | Apache-2.0 |
def testParseInvalidOutput(self):
"""Tests failing when Coremark does not report valid output."""
with open(_SAMPLE_OUTPUT_FILE) as f:
output = f.read()
output = output.replace('Correct operation validated', 'Invalid run')
with self.assertRaises(errors.Benchmarks.RunError):
coremark_benchmar... | Tests failing when Coremark does not report valid output. | testParseInvalidOutput | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/coremark_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/coremark_benchmark_test.py | Apache-2.0 |
def testGetGccVersion(self):
"""Tests regex calls to parse gcc version."""
mock_vm = mock.Mock()
mock_vm.RemoteCommand.return_value = None, 'gcc version 7.4.0 20190909'
self.assertEqual(_TEST_GCC_VERSION, glibc.GetGccVersion(mock_vm)) | Tests regex calls to parse gcc version. | testGetGccVersion | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/glibc_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/glibc_benchmark_test.py | Apache-2.0 |
def CallParseOutput(self, filename, upper_key, results, metadata):
"""Read sample outputs of glibc_benchmark and call ParseOutput function.
Args:
filename: The name of the sample output file required to run the
benchmark.
upper_key: The first dimension key of the glibc_output dict.
re... | Read sample outputs of glibc_benchmark and call ParseOutput function.
Args:
filename: The name of the sample output file required to run the
benchmark.
upper_key: The first dimension key of the glibc_output dict.
results: A list to which the ParseOutput function will append new samples
... | CallParseOutput | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/glibc_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/glibc_benchmark_test.py | Apache-2.0 |
def testParseOutputAttachesCorrectCommonMetadata(self):
"""Tests that a run of ParseOutput attaches the correct common metadata."""
metadata = {
'gcc': _TEST_GCC_VERSION,
'glibc_benchset': glibc_benchmark.glibc_default_benchset,
'glibc_version': glibc.GLIBC_VERSION,
'num_machines... | Tests that a run of ParseOutput attaches the correct common metadata. | testParseOutputAttachesCorrectCommonMetadata | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/glibc_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/glibc_benchmark_test.py | Apache-2.0 |
def MockVm() -> mock.Mock:
"""Mocks test VM and returns sample results."""
vm = mock.Mock()
vm.hostname = 'localhost'
path = os.path.join(
os.path.dirname(__file__), '..', 'data', 'gpu_pingpong_output.txt'
)
with open(path) as reader:
stderr = reader.read()
vm.RemoteCommand.return_value = '', st... | Mocks test VM and returns sample results. | MockVm | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/gpu_pingpong_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/gpu_pingpong_benchmark_test.py | Apache-2.0 |
def testCmd(self) -> None:
"""Tests GPU PingPong test command on the remote VM."""
vm = MockVm()
gpu_pingpong_benchmark.Run(mock.Mock(vms=[vm, MockVm()]))
vm.RemoteCommand.assert_called_with(
'PATH=/opt/conda/bin:$PATH python gpu_pingpong_test.py localhost:2000'
) | Tests GPU PingPong test command on the remote VM. | testCmd | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/gpu_pingpong_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/gpu_pingpong_benchmark_test.py | Apache-2.0 |
def testSample(self) -> None:
"""Tests GPU PingPong test parsing code."""
samples = gpu_pingpong_benchmark.Run(mock.Mock(vms=[MockVm(), MockVm()]))
expected = sample.Sample(
metric='latency',
value=618.9995654384239,
unit='microseconds',
metadata={'ping': 305.00009531252977, ... | Tests GPU PingPong test parsing code. | testSample | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/gpu_pingpong_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/gpu_pingpong_benchmark_test.py | Apache-2.0 |
def testFilename(self):
"""Tests the objects written filename can be parsed for an age."""
FLAGS.object_storage_region = 'us-central1-a'
FLAGS.object_storage_objects_written_file_prefix = 'prefix'
write_time = datetime.datetime.now() - datetime.timedelta(hours=72)
with mock.patch.object(
obj... | Tests the objects written filename can be parsed for an age. | testFilename | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/object_storage_service_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/object_storage_service_benchmark_test.py | Apache-2.0 |
def create_mock_vm(
index: int,
operation_value: float,
) -> mock.Mock:
"""Create mock VMs which will be used by the unit tests.
Args:
index: an integer which specifies index of vm in the list of vms.
operation_value: a float which indicates the resume or suspend duration
Returns:
A mock VM.... | Create mock VMs which will be used by the unit tests.
Args:
index: an integer which specifies index of vm in the list of vms.
operation_value: a float which indicates the resume or suspend duration
Returns:
A mock VM.
| create_mock_vm | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/suspend_resume_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/suspend_resume_benchmark_test.py | Apache-2.0 |
def testMeasureOperation(self, operation: str):
"""Unit test for Suspend and Resume functions."""
num_vms = 3
# Create mock vms
vms_to_test = [create_mock_vm(i, 50.0 + i) for i in range(num_vms)]
# Call suspend function on mock vms - generates actual samples for the test.
actual_samples = geta... | Unit test for Suspend and Resume functions. | testMeasureOperation | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/suspend_resume_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/suspend_resume_benchmark_test.py | Apache-2.0 |
def vm_mock(index: int, operation_return_value: int) -> mock.Mock:
"""Creates a mock vm and Adds the needed vm attributes to the mock vm.
Args:
index: an integer which specifies index of vm in the list of vms.
operation_return_value: value to return when vm operation are called
Returns:
A mock vm.
... | Creates a mock vm and Adds the needed vm attributes to the mock vm.
Args:
index: an integer which specifies index of vm in the list of vms.
operation_return_value: value to return when vm operation are called
Returns:
A mock vm.
| vm_mock | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/vm_stop_start_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/vm_stop_start_benchmark_test.py | Apache-2.0 |
def testMeasureOperation(self, operation: str):
"""Unit test for Stop and Start functions."""
num_vms = 3
# create mock vms
vms_to_test = [vm_mock(0, 5), vm_mock(1, 6), vm_mock(2, 7)]
# call Stop on vms
actual_samples = getattr(vm_stop_start_benchmark, f'_Measure{operation}')(
vms_to_t... | Unit test for Stop and Start functions. | testMeasureOperation | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_benchmarks/vm_stop_start_benchmark_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_benchmarks/vm_stop_start_benchmark_test.py | Apache-2.0 |
def assertCallArgsEqual(self, call_args_singles, mock_method):
"""Compare the list of single arguments to all mocked calls in mock_method.
Mock calls can be tested like this:
(('x',),) == call('x')
As all the mocked method calls have one single argument (ie 'x') they need
to be converted into the... | Compare the list of single arguments to all mocked calls in mock_method.
Mock calls can be tested like this:
(('x',),) == call('x')
As all the mocked method calls have one single argument (ie 'x') they need
to be converted into the tuple of positional arguments tuple that mock
expects.
Args:... | assertCallArgsEqual | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_packages/azure_cli_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_packages/azure_cli_test.py | Apache-2.0 |
def _ExtractHistogramFromMetric(results, sample_name):
"""Search results for given metric and extract the embedded histogram."""
for r in results:
if r.metric == sample_name:
return json.loads(r.metadata['histogram'])
return None | Search results for given metric and extract the embedded histogram. | _ExtractHistogramFromMetric | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_packages/fio_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_packages/fio_test.py | Apache-2.0 |
def testParseTpcCTPMResultsFromFile(self):
"""Tests output from TPCC time profile."""
FLAGS['hammerdbcli_tpcc_time_profile'].parse(True)
FLAGS['hammerdbcli_tpcc_rampup'].parse(0)
output = ParseTpcCTPMResultsFromFile(TPM_PER_SECOND_4_3_LOG)
self.assertSampleListsEqualUpToTimestamp(
output,
... | Tests output from TPCC time profile. | testParseTpcCTPMResultsFromFile | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_packages/hammerdb_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_packages/hammerdb_test.py | Apache-2.0 |
def testParseTPCCWithoutTimeProfile(self):
"""Tests parsing metadata from the ANSYS benchmark output."""
FLAGS['hammerdbcli_tpcc_time_profile'].parse(False)
vm = mock.Mock()
output = ParseTpcCResults(TPCC_LOG, vm)
self.assertCountEqual(
output,
[
sample.Sample('TPM', 2477... | Tests parsing metadata from the ANSYS benchmark output. | testParseTPCCWithoutTimeProfile | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_packages/hammerdb_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_packages/hammerdb_test.py | Apache-2.0 |
def _ValidateRunLimitBenchmarksToRun(self, expected_lines):
"""Validates that expected lines are found in the modified hpcc.c file.
Args:
expected_lines: An iterable of expected lines of code.
"""
expected_lines = set(expected_lines)
with open(os.path.join(self._temp_dir, 'hpcc.c')) as f:
... | Validates that expected lines are found in the modified hpcc.c file.
Args:
expected_lines: An iterable of expected lines of code.
| _ValidateRunLimitBenchmarksToRun | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_packages/hpcc_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_packages/hpcc_test.py | Apache-2.0 |
def testParseUserTotalTimes(self):
"""Test kernel time, total time parsing."""
self.assertEqual(
linux_boot.ParseUserTotalTimes(
'Startup finished in 2.2s (kernel) + 1min 12.5s (userspace) '
'= 1min 14.774s'
),
(2.2, 74.7),
)
self.assertEqual(
linu... | Test kernel time, total time parsing. | testParseUserTotalTimes | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_packages/linux_boot_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_packages/linux_boot_test.py | Apache-2.0 |
def testParseSystemDCriticalChain(self):
"""Test systemd critical-chain output parsing."""
with open(os.path.join(self.data_dir, 'systemd1.output')) as f:
self.assertEqual(
linux_boot.ParseSystemDCriticalChainOutput(f.read()), 2.741
)
with open(os.path.join(self.data_dir, 'systemd2.out... | Test systemd critical-chain output parsing. | testParseSystemDCriticalChain | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_packages/linux_boot_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_packages/linux_boot_test.py | Apache-2.0 |
def testCollectVmToVmSamples(self):
"""Test vm to vm networking result parsing."""
# Load startup script data, which ingress timestamps.
with open(os.path.join(self.data_dir, 'boot.output')) as f:
boot_output = f.read()
# Load tcpdump output, which contains egress timestamps.
self.enter_contex... | Test vm to vm networking result parsing. | testCollectVmToVmSamples | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_packages/linux_boot_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_packages/linux_boot_test.py | Apache-2.0 |
def assertCallArgsEqual(self, call_args_singles, mock_method):
"""Compare the list of single arguments to all mocked calls in mock_method.
Mock calls can be tested like this:
(('x',),) == call('x')
As all the mocked method calls have one single argument (ie 'x') they need
to be converted into the... | Compare the list of single arguments to all mocked calls in mock_method.
Mock calls can be tested like this:
(('x',),) == call('x')
As all the mocked method calls have one single argument (ie 'x') they need
to be converted into the tuple of positional arguments tuple that mock
expects.
Args:
... | assertCallArgsEqual | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/linux_packages/maven_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/linux_packages/maven_test.py | Apache-2.0 |
def testReadableVersion(self):
"""Test normal cases work as they should."""
with self.subTest('6.x'):
self.redis.version = '6.x'
self.assertEqual(self.redis.GetReadableVersion(), '6.x')
with self.subTest('4.0.10'):
self.redis.version = '4.0.10'
self.assertEqual(self.redis.GetReadable... | Test normal cases work as they should. | testReadableVersion | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/providers/aws/aws_elasticache_redis_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/providers/aws/aws_elasticache_redis_test.py | Apache-2.0 |
def testReadableVersionExtraneous(self):
"""Test weird cases just return the version number as is."""
with self.subTest('redis.8'):
self.redis.version = 'redis.8'
self.assertEqual(self.redis.GetReadableVersion(), 'redis.8')
with self.subTest('redis 9_7_5'):
self.redis.version = 'redis 9_7_... | Test weird cases just return the version number as is. | testReadableVersionExtraneous | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/providers/aws/aws_elasticache_redis_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/providers/aws/aws_elasticache_redis_test.py | Apache-2.0 |
def AsyncTest(test_method):
"""Run an async method synchronously for testing."""
@functools.wraps(test_method)
def Wrapped(self, *args, **kwargs):
return asyncio.run(test_method(self, *args, **kwargs))
return Wrapped | Run an async method synchronously for testing. | AsyncTest | python | GoogleCloudPlatform/PerfKitBenchmarker | tests/scripts/messaging_service_scripts_e2e_main_process_test.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tests/scripts/messaging_service_scripts_e2e_main_process_test.py | Apache-2.0 |
def connect(self):
'connect to all the events we need'
self.cidpress = self.figure.canvas.mpl_connect(
'button_press_event', self.on_press
)
self.cidrelease = self.figure.canvas.mpl_connect(
'button_release_event', self.on_release
)
self.cidmotion = self.figure.canvas.mpl_connect... | connect to all the events we need | connect | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/object_storage_timeline.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/object_storage_timeline.py | Apache-2.0 |
def on_press(self, event):
'on button press we will see if the mouse is over us and store some data'
if event.button != 3:
# Only continue for right mouse button
return
if self.span is not None:
return
self.start = event.xdata
self.end = event.xdata
self.span = plt.axvspan(sel... | on button press we will see if the mouse is over us and store some data | on_press | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/object_storage_timeline.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/object_storage_timeline.py | Apache-2.0 |
def on_motion(self, event):
'on motion we will move the rect if the mouse is over us'
if self.span is None:
return
self.span.remove()
self.end = event.xdata
self.span = plt.axvspan(self.start, self.end, color='blue', alpha=0.5)
canvas = self.figure.canvas
axes = self.span.axes
#... | on motion we will move the rect if the mouse is over us | on_motion | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/object_storage_timeline.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/object_storage_timeline.py | Apache-2.0 |
def on_release(self, event):
'on release we reset the press data'
if event.button != 3:
# Only continue for right mouse button
return
if self.span is None:
return
self.span.remove()
self.start = None
self.end = None
self.span = None
self.background = None
# redra... | on release we reset the press data | on_release | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/object_storage_timeline.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/object_storage_timeline.py | Apache-2.0 |
def disconnect(self):
'disconnect all the stored connection ids'
self.figure.canvas.mpl_disconnect(self.cidpress)
self.figure.canvas.mpl_disconnect(self.cidrelease)
self.figure.canvas.mpl_disconnect(self.cidmotion) | disconnect all the stored connection ids | disconnect | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/object_storage_timeline.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/object_storage_timeline.py | Apache-2.0 |
def TempDir(delete=True, **kwargs):
"""Directory equivalent of tempfile.NamedTemporaryFile.
When used as a context manager, yields a temporary directory which by default
is removed when the context manager goes our of scope.
Example usage:
>>> with TempDir(prefix='perfkit') as td:
... shutil.copy('... | Directory equivalent of tempfile.NamedTemporaryFile.
When used as a context manager, yields a temporary directory which by default
is removed when the context manager goes our of scope.
Example usage:
>>> with TempDir(prefix='perfkit') as td:
... shutil.copy('test.txt', td)
Args:
delete: Delet... | TempDir | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def _GitCommandPrefix():
"""Prefix for all git commands.
Returns:
list of strings; 'git' with an appropriate '--git-dir' flag.
"""
git_dir = os.path.join(os.path.dirname(__file__), '..', '..', '.git')
return ['git', '--git-dir', git_dir] | Prefix for all git commands.
Returns:
list of strings; 'git' with an appropriate '--git-dir' flag.
| _GitCommandPrefix | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def _GitRevParse(revision):
"""Returns the output of 'git rev-parse' for 'revision'."""
output = subprocess.check_output(
_GitCommandPrefix() + ['rev-parse', revision]
)
return output.rstrip() | Returns the output of 'git rev-parse' for 'revision'. | _GitRevParse | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def _GitDescribe(revision):
"""Returns the output of 'git describe' for 'revision'."""
output = subprocess.check_output(
_GitCommandPrefix() + ['describe', '--always', revision]
)
return output.rstrip() | Returns the output of 'git describe' for 'revision'. | _GitDescribe | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def PerfKitBenchmarkerCheckout(revision):
"""Yields a directory with PerfKitBenchmarker checked out to 'revision'."""
archive_cmd = _GitCommandPrefix() + ['archive', revision]
logging.info('Running: %s', archive_cmd)
p_archive = subprocess.Popen(archive_cmd, stdout=subprocess.PIPE)
with TempDir(prefix='pkb-te... | Yields a directory with PerfKitBenchmarker checked out to 'revision'. | PerfKitBenchmarkerCheckout | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def _SplitLabels(labels):
"""Parse the 'labels' key from a PerfKitBenchmarker record.
Labels are recorded in '|key:value|,|key:value|' form.
This function transforms them to a dict.
Args:
labels: string. labels to parse.
Returns:
dict. Parsed 'labels'.
"""
result = {}
for item in labels.strip... | Parse the 'labels' key from a PerfKitBenchmarker record.
Labels are recorded in '|key:value|,|key:value|' form.
This function transforms them to a dict.
Args:
labels: string. labels to parse.
Returns:
dict. Parsed 'labels'.
| _SplitLabels | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def _CompareSamples(a, b, context=True, numlines=1):
"""Generate an HTML table showing differences between 'a' and 'b'.
Args:
a: dict, as output by PerfKitBenchmarker.
b: dict, as output by PerfKitBenchmarker.
context: boolean. Show context in diff? If False, all lines are output, even
those whic... | Generate an HTML table showing differences between 'a' and 'b'.
Args:
a: dict, as output by PerfKitBenchmarker.
b: dict, as output by PerfKitBenchmarker.
context: boolean. Show context in diff? If False, all lines are output, even
those which are equal.
numlines: int. Passed to difflib.Htmldiff... | _CompareSamples | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def _MatchSamples(base_samples, head_samples):
"""Match items from base_samples with items from head_samples.
Rows are matched using 'test', 'metric', and 'unit' fields.
Args:
base_samples: List of dicts.
head_samples: List of dicts.
Returns:
List of pairs, each item of the pair containing either... | Match items from base_samples with items from head_samples.
Rows are matched using 'test', 'metric', and 'unit' fields.
Args:
base_samples: List of dicts.
head_samples: List of dicts.
Returns:
List of pairs, each item of the pair containing either a dict or None.
| _MatchSamples | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def RenderResults(base_result, head_result, template_name=TEMPLATE, **kwargs):
"""Render the results of a comparison as an HTML page.
Args:
base_result: PerfKitBenchmarkerResult. Result of running against base
revision.
head_result: PerfKitBenchmarkerResult. Result of running against head
revis... | Render the results of a comparison as an HTML page.
Args:
base_result: PerfKitBenchmarkerResult. Result of running against base
revision.
head_result: PerfKitBenchmarkerResult. Result of running against head
revision.
template_name: string. The filename of the template.
kwargs: Additional... | RenderResults | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def _ClassForPercentDifference(percent_diff):
"""Crude highlighting of differences between runs.
Samples varying by >25% are colored red.
Samples varying by 5-25% are colored orange.
Other samples are colored green.
Args:
percent_diff: float. percent difference between values.
"""
if... | Crude highlighting of differences between runs.
Samples varying by >25% are colored red.
Samples varying by 5-25% are colored orange.
Other samples are colored green.
Args:
percent_diff: float. percent difference between values.
| _ClassForPercentDifference | python | GoogleCloudPlatform/PerfKitBenchmarker | tools/side-by-side/side_by_side.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/tools/side-by-side/side_by_side.py | Apache-2.0 |
def main():
"""
Main function to spawn the train and test process.
"""
cfg = Config(load=True)
if hasattr(cfg, "TASK_TYPE") and cfg.TASK_TYPE == "MULTI_TASK":
logging.info("TASK TYPE: %s " % cfg.TASK_TYPE)
inference_multi(cfg.cfg_dict)
elif hasattr(cfg, "TASK_TYPE") and cfg.TASK... |
Main function to spawn the train and test process.
| main | python | ali-vilab/videocomposer | run_net.py | https://github.com/ali-vilab/videocomposer/blob/master/run_net.py | MIT |
def DOWNLOAD_TO_CACHE(oss_key,
file_or_dirname=None,
cache_dir=osp.join('/'.join(osp.abspath(__file__).split('/')[:-2]), 'model_weights')):
r"""Download OSS [file or folder] to the cache folder.
Only the 0th process on each node will run the downloading.
B... | Download OSS [file or folder] to the cache folder.
Only the 0th process on each node will run the downloading.
Barrier all processes until the downloading is completed.
| DOWNLOAD_TO_CACHE | python | ali-vilab/videocomposer | artist/__init__.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/__init__.py | MIT |
def forward(self, imgs, txt_tokens):
r"""imgs: [B, C, H, W] of torch.float32.
txt_tokens: [B, T] of torch.long.
"""
xi = self.visual(imgs)
xt = self.textual(txt_tokens)
# normalize features
xi = F.normalize(xi, p=2, dim=1)
xt = F.normalize(xt, p... | imgs: [B, C, H, W] of torch.float32.
txt_tokens: [B, T] of torch.long.
| forward | python | ali-vilab/videocomposer | artist/models/clip.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/models/clip.py | MIT |
def rgb2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot... | same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
| rgb2ycbcr | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def bgr2ycbcr(img, only_y=True):
'''bgr version of rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot... | bgr version of rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
| bgr2ycbcr | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def calculate_ssim(img1, img2, border=0):
'''calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
'''
#img1 = img1.squeeze()
#img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
... | calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
| calculate_ssim | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def modcrop_np(img, sf):
'''
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
'''
w, h = img.shape[:2]
im = np.copy(img)
return im[:w - w % sf, :h - h % sf, ...] |
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
| modcrop_np | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def analytic_kernel(k):
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
k_size = k.shape[0]
# Calculate the big kernels size
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
# Loop over the small kernel to fill the big one
for r in range(k_size):
for ... | Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper) | analytic_kernel | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
""" generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
l1 : [0.1,50], scaling of eigenvalues
l2 : [0.1,l1], scaling of eigenvalues
If l1 = l2... | generate an anisotropic Gaussian kernel
Args:
ksize : e.g., 15, kernel size
theta : [0, pi], rotation angle range
l1 : [0.1,50], scaling of eigenvalues
l2 : [0.1,l1], scaling of eigenvalues
If l1 = l2, will get an isotropic Gaussian kernel.
Returns:
k ... | anisotropic_Gaussian | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
... | shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
| shift_pixel | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
""""
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var an... | "
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
# max_var = 2.5 * sf
| gen_kernel | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if... |
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
| fspecial | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def srmd_degradation(x, k, sf=3):
''' blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-res... | blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degradations... | srmd_degradation | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def dpsr_degradation(x, k, sf=3):
''' bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbit... | bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zha... | dpsr_degradation | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def classical_degradation(x, k, sf=3):
''' blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correla... | blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
| classical_degradation | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def add_sharpening(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (1 - Mask) * I
Args:
img (Numpy arr... | USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (1 - Mask) * I
Args:
img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
weight (float): ... | add_sharpening | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def degradation_bsrgan_light(image, sf=4, isp_model=None):
"""
This is the variant of the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
... |
This is the variant of the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range:... | degradation_bsrgan_light | python | ali-vilab/videocomposer | artist/ops/degration.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/degration.py | MIT |
def p_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None):
r"""Sample from p(x_{t-1} | x_t).
- condition_fn: for classifier-based guidance (guided-diffusion).
- guide_scale: for classifier-free guidance (glide/dalle-2).
""... | Sample from p(x_{t-1} | x_t).
- condition_fn: for classifier-based guidance (guided-diffusion).
- guide_scale: for classifier-free guidance (glide/dalle-2).
| p_sample | python | ali-vilab/videocomposer | artist/ops/diffusion.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/diffusion.py | MIT |
def ddim_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, ddim_timesteps=20, eta=0.0):
r"""Sample from p(x_{t-1} | x_t) using DDIM.
- condition_fn: for classifier-based guidance (guided-diffusion).
- guide_scale: for classifier... | Sample from p(x_{t-1} | x_t) using DDIM.
- condition_fn: for classifier-based guidance (guided-diffusion).
- guide_scale: for classifier-free guidance (glide/dalle-2).
| ddim_sample | python | ali-vilab/videocomposer | artist/ops/diffusion.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/diffusion.py | MIT |
def ddim_reverse_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, guide_scale=None, ddim_timesteps=20):
r"""Sample from p(x_{t+1} | x_t) using DDIM reverse ODE (deterministic).
"""
stride = self.num_timesteps // ddim_timesteps
# predict distribution of p(x_{t-1} ... | Sample from p(x_{t+1} | x_t) using DDIM reverse ODE (deterministic).
| ddim_reverse_sample | python | ali-vilab/videocomposer | artist/ops/diffusion.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/diffusion.py | MIT |
def plms_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, plms_timesteps=20):
r"""Sample from p(x_{t-1} | x_t) using PLMS.
- condition_fn: for classifier-based guidance (guided-diffusion).
- guide_scale: for classifier-free gui... | Sample from p(x_{t-1} | x_t) using PLMS.
- condition_fn: for classifier-based guidance (guided-diffusion).
- guide_scale: for classifier-free guidance (glide/dalle-2).
| plms_sample | python | ali-vilab/videocomposer | artist/ops/diffusion.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/diffusion.py | MIT |
def variational_lower_bound_loop(self, x0, model, model_kwargs={}, clamp=None, percentile=None):
r"""Compute the entire variational lower bound, measured in bits-per-dim.
"""
# prepare input and output
b = x0.size(0)
metrics = {'vlb': [], 'mse': [], 'x0_mse': []}
# loop
... | Compute the entire variational lower bound, measured in bits-per-dim.
| variational_lower_bound_loop | python | ali-vilab/videocomposer | artist/ops/diffusion.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/diffusion.py | MIT |
def dpm_solver_sample_loop(self, noise, model, model_kwargs={}, order=2, skip_type='logSNR', method='multistep', clamp=None, percentile=None, condition_fn=None, guide_scale=None,
dpm_solver_timesteps=20, algorithm_type='dpmsolver++', t_start=None, t_end=None, lower_order_final=True, denoi... | Sample using DPM-Solver-based method.
- condition_fn: for classifier-based guidance (guided-diffusion).
- guide_scale: for classifier-free guidance (glide/dalle-2).
Please check all the parameters in `dpm_solver.sample` before using.
| dpm_solver_sample_loop | python | ali-vilab/videocomposer | artist/ops/diffusion.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/diffusion.py | MIT |
def inpaint_mcg_p_sample(self, xt, t, y, mask, model, model_kwargs={}, clamp=None, percentile=None, guide_scale=None, mcg_scale=1.0):
r"""DDPM sampling step for inpainting, with Manifold Constrained Gradient (MCG) correction.
"""
dtype = xt.dtype
# predict distribution of p(x_{t-1} | x_... | DDPM sampling step for inpainting, with Manifold Constrained Gradient (MCG) correction.
| inpaint_mcg_p_sample | python | ali-vilab/videocomposer | artist/ops/diffusion.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/diffusion.py | MIT |
def inpaint_mcg_p_sample_loop(self, noise, y, mask, model, model_kwargs={}, clamp=None, percentile=None, guide_scale=None, mcg_scale=1.0):
r"""DDPM sampling loop for inpainting, with Manifold Constrained Gradient (MCG) correction.
"""
# prepare input
b = noise.size(0)
xt = noise
... | DDPM sampling loop for inpainting, with Manifold Constrained Gradient (MCG) correction.
| inpaint_mcg_p_sample_loop | python | ali-vilab/videocomposer | artist/ops/diffusion.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/diffusion.py | MIT |
def scatter(data, scatter_list=None, src=0, group=None, **kwargs):
r"""NOTE: only supports CPU tensor communication.
"""
if get_world_size(group) > 1:
return dist.scatter(data, scatter_list, src, group, **kwargs) | NOTE: only supports CPU tensor communication.
| scatter | python | ali-vilab/videocomposer | artist/ops/distributed.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/distributed.py | MIT |
def _i(tensor, t, x):
r"""Index tensor using t and format the output according to x.
"""
shape = (x.size(0), ) + (1, ) * (x.ndim - 1)
device = x.device
return tensor[t.to(device)].view(shape).to(device) | Index tensor using t and format the output according to x.
| _i | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def __init__(
self,
schedule='discrete',
betas=None,
alphas_cumprod=None,
continuous_beta_0=0.1,
continuous_beta_1=20.,
dtype=torch.float32):
r"""Create a wrapper class for the forward SDE (VP type).
***
Update:... | Create a wrapper class for the forward SDE (VP type).
***
Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution ima... | __init__ | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def marginal_log_mean_coeff(self, t):
"""
Compute log(alpha_t) of a given continuous-time label t in [0, T].
"""
if self.schedule == 'discrete':
return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
elif ... |
Compute log(alpha_t) of a given continuous-time label t in [0, T].
| marginal_log_mean_coeff | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def marginal_lambda(self, t):
"""
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
"""
log_mean_coeff = self.marginal_log_mean_coeff(t)
log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
return log_mean_coeff - log_s... |
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
| marginal_lambda | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def inverse_lambda(self, lamb):
"""
Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
"""
if self.schedule == 'linear':
tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
Delta = self.beta_... |
Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
| inverse_lambda | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def model_wrapper(
model,
noise_schedule,
model_type="noise",
model_kwargs={},
guidance_type="uncond",
condition=None,
unconditional_condition=None,
guidance_scale=1.,
classifier_fn=None,
classifier_kwargs={}):
r"""Create a wrapper function for the noise prediction model.
... | Create a wrapper function for the noise prediction model.
DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
We support four types of t... | model_wrapper | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def get_model_input_time(t_continuous):
"""
Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
For continuous-time DPMs, we just use `t_continuous`.... |
Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
For continuous-time DPMs, we just use `t_continuous`.
| get_model_input_time | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def cond_grad_fn(x, t_input):
"""
Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
"""
with torch.enable_grad():
x_in = x.detach().requires_grad_(True)
log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
r... |
Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
| cond_grad_fn | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def model_fn(x, t_continuous):
"""
The noise predicition model function that is used for DPM-Solver.
"""
if guidance_type == "uncond":
return noise_pred_fn(x, t_continuous)
elif guidance_type == "classifier":
assert classifier_fn is not None
t_... |
The noise predicition model function that is used for DPM-Solver.
| model_fn | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def model_wrapper_guided_diffusion(
model,
noise_schedule,
var_type,
mean_type,
model_kwargs={},
rescale_timesteps=False,
num_timesteps=1000,
guide_scale=None,
condition_fn=None):
"""Create a wrapper function for the noise prediction model guided diffusion.
DPM-Solver needs ... | Create a wrapper function for the noise prediction model guided diffusion.
DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
We suppor... | model_wrapper_guided_diffusion | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
def __init__(
self,
model_fn,
noise_schedule,
algorithm_type="dpmsolver++",
percentile=None,
thresholding_max_val=1.,
clamp=None):
r"""Construct a DPM-Solver.
We support both the noise prediction model ("predicting epsilon") and the data predi... | Construct a DPM-Solver.
We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
If `predict_x0` is True, we use the solver for the data prediction... | __init__ | python | ali-vilab/videocomposer | artist/ops/dpm_solver.py | https://github.com/ali-vilab/videocomposer/blob/master/artist/ops/dpm_solver.py | MIT |
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