index int64 | repo_name string | branch_name string | path string | content string | import_graph string |
|---|---|---|---|---|---|
58,667 | hungpham2511/NeuroKit | refs/heads/master | /tests/tests_eeg.py | import numpy as np
import pandas as pd
import neurokit2 as nk
import mne
# =============================================================================
# EEG
# =============================================================================
def test_eeg_add_channel():
raw = mne.io.read_raw_fif(mne.datasets.sample.data_path() + '/MEG/sample/sample_audvis_raw.fif', preload=True)
ecg = nk.ecg_simulate(length=170000)
raw = nk.eeg_add_channel(raw, ecg, channel_type="ecg")
df = raw.to_data_frame()
assert len(df.columns) == 377
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,668 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/stats/__init__.py | """Submodule for NeuroKit."""
from .standardize import standardize
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,669 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/ecg/__init__.py | """Submodule for NeuroKit."""
from .ecg_simulate import ecg_simulate | {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,670 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/stats/standardize.py | # -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import scipy.stats
def _standardize(data, robust=False):
if robust is False:
z = (data - np.mean(data, axis=0))/np.std(data, axis=0)
else:
z = (data - np.median(data, axis=0))/scipy.stats.median_absolute_deviation(data, axis=0, nan_policy="omit")
return(z)
def standardize(data, robust=False):
"""Standardization of data
Performs a standardization of data (Z-scoring), i.e., centering and scaling,
so that the data is expressed in terms of standard deviation
(i.e., mean = 0, SD = 1) or Median Absolute Deviance (median = 0, MAD = 1).
Parameters
----------
data : list, array or Series
Raw data.
robust : bool
If True, centering is done by substracting the median from the
variables and dividing it by the median absolute deviation (MAD).
If False, variables are standardized by substracting the mean and
dividing it by the standard deviation (SD).
Returns
----------
list, array or Series
The standardized values.
Examples
----------
>>> import neurokit2 as nk
>>>
>>> nk.standardize([3, 1, 2, 4, 6])
"""
# Return appropriate type
if isinstance(data, list):
data = list(_standardize(np.array(data), robust=robust))
elif isinstance(data, pd.Series) or isinstance(data, pd.DataFrame):
z = _standardize(data.values, robust=robust)
data[:] = z
else:
data = _standardize(data, robust=robust)
return(data)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,671 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/events/__init__.py | """Submodule for NeuroKit."""
from .events_find import events_find
from .plot_events_in_signal import plot_events_in_signal
from .events_to_mne import events_to_mne | {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,672 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/ecg/ecg_simulate.py | # -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import scipy
from ..signal import signal_resample
def ecg_simulate(duration=10, length=None, sampling_rate=1000, noise=0.01, heart_rate=60):
"""Simulate an ECG/EKG signal
Generate an artificial (synthetic) ECG signal of a given duration and sampling rate. It uses a 'Daubechies' wavelet that roughly approximates a single cardiac cycle.
Parameters
----------
duration : int
Desired recording length in seconds.
sampling_rate, length : int
The desired sampling rate (in Hz, i.e., samples/second) or the desired length of the signal (in samples).
noise : float
Noise level (gaussian noise).
heart_rate : int
Desired simulated heart rate (in beats per minute).
Returns
----------
array
Array containing the ECG signal.
Examples
----------
>>> import neurokit as nk
>>> import pandas as pd
>>>
>>> ecg = nk.ecg_simulate(duration=10, sampling_rate=100)
>>> pd.Series(ecg).plot()
See Also
--------
signal_resample, ppg_simulate, emg_simulate
References
-----------
This function is based on `this script <https://github.com/diarmaidocualain/ecg_simulation>`_.
"""
# The "Daubechies" wavelet is a rough approximation to a real, single, cardiac cycle
cardiac = scipy.signal.wavelets.daub(10)
# Add the gap after the pqrst when the heart is resting.
cardiac = np.concatenate([cardiac, np.zeros(10)])
# Caculate the number of beats in capture time period
num_heart_beats = int(duration * heart_rate / 60)
# Concatenate together the number of heart beats needed
ecg = np.tile(cardiac , num_heart_beats)
# Add random (gaussian distributed) noise
ecg += np.random.normal(0, noise, len(ecg))
# Resample
ecg = signal_resample(ecg,
sampling_rate=int(len(ecg)/10),
desired_length=length,
desired_sampling_rate=sampling_rate)
return(ecg)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,673 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/ppg/ppg_simulate.py | # -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import scipy.stats
from ..signal import signal_resample
def ppg_simulate(duration=10, length=None, sampling_rate=1000, noise=0.01, heart_rate=60):
"""Simulate a PPG (photoplethysmography) signal
Generate an artificial (synthetic) PPG signal of a given duration and sampling rate.
Parameters
----------
duration : int
Desired recording length in seconds.
sampling_rate, length : int
The desired sampling rate (in Hz, i.e., samples/second) or the desired
length of the signal (in samples).
noise : float
Noise level (gaussian noise).
heart_rate : int
Desired simulated heart rate (in beats per minute).
Returns
----------
array
Array containing the PPG signal.
Example
----------
>>> import neurokit as nk
>>> import pandas as pd
>>>
>>> ppg = nk.ppg_simulate(duration=10, n_bursts=3)
>>> pd.Series(ppg).plot()
See Also
--------
signal_resample, ecg_simulate, emg_simulate
References
----------
- `Banerjee et al. (2015). Noise cleaning and Gaussian modeling of smart phone photoplethysmogram to improve blood pressure estimation. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 967-971). IEEE. <http://150.162.46.34:8080/icassp2015/pdfs/0000967.pdf>`_.
"""
index = np.linspace(-9, 9, num=100)
normal1 = scipy.stats.norm.pdf(index, loc=-2, scale=2)/1.5
normal2 = scipy.stats.norm.pdf(index, loc=2, scale=1.5)
ppg = normal1+normal2
pd.Series(ppg).plot()
return(ppg)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,674 | hungpham2511/NeuroKit | refs/heads/master | /tests/tests_events.py | import numpy as np
import pandas as pd
import neurokit2 as nk
# =============================================================================
# Events
# =============================================================================
def test_events_find():
signal = np.cos(np.linspace(start=0, stop=20, num=1000))
events = nk.events_find(signal)
assert list(events["Onset"]) == [0, 236, 550, 864]
events = nk.events_find(signal, duration_min = 150)
assert list(events["Onset"]) == [236, 550]
events = nk.events_find(signal, inter_min = 300)
assert list(events["Onset"]) == [0, 550, 864]
def test_events_to_mne():
signal = np.cos(np.linspace(start=0, stop=20, num=1000))
events = nk.events_find(signal)
events, event_id = nk.events_to_mne(events)
assert event_id == {'Event': 0}
def test_plot_events_in_signal():
signal = np.cos(np.linspace(start=0, stop=20, num=1000))
events = nk.events_find(signal)
data = nk.plot_events_in_signal(signal, events, show=False)
assert len(data['Event_Onset']) == 1000 | {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,675 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/signal/signal_binarize.py | # -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
def _signal_binarize(signal, threshold="auto"):
if threshold == "auto":
threshold = np.mean([np.max(signal), np.min(signal)])
binary = signal.copy()
binary[signal > threshold] = 1
binary[signal <= threshold] = 0
return(binary)
def signal_binarize(signal, threshold="auto"):
"""Binarize a continuous signal.
Parameters
----------
signal : list, array or Series
The signal channel.
threshold : float
The threshold value by which to select the events. If "auto", takes the value between the max and the min.
Returns
-------
list
A list or array depending on the type passed.
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> import neurokit2 as nk
>>>
>>> signal = np.cos(np.linspace(start=0, stop=20, num=1000))
>>> binary = nk.signal_binarize(signal)
>>> pd.DataFrame({"Raw": signal, "Binary": binary}).plot()
"""
# Return appropriate type
if isinstance(signal, list):
binary = _signal_binarize(np.array(signal), threshold=threshold)
signal = list(binary)
elif isinstance(signal, pd.Series):
binary = _signal_binarize(signal.values, threshold=threshold)
signal[:] = binary
else:
signal = _signal_binarize(signal, threshold=threshold)
return(signal)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,676 | hungpham2511/NeuroKit | refs/heads/master | /docs/img/README_examples.py | import numpy as np
import pandas as pd
import neurokit2 as nk
# =============================================================================
# Example 1
# =============================================================================
# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
emg = nk.emg_simulate(duration=10, n_bursts=3)
# Visualise biosignals
pd.DataFrame({"ECG": ecg, "EMG": emg}).plot(subplots=True, layout=(2, 1))
plot = pd.DataFrame({"ECG": ecg, "EMG": emg}).plot(subplots=True, layout=(2, 1))
plot[0][0].get_figure().savefig("README_simulation.png", dpi=300)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,677 | hungpham2511/NeuroKit | refs/heads/master | /tests/tests_ecg.py | import numpy as np
import pandas as pd
import neurokit2 as nk
import scipy.stats
# =============================================================================
# ECG
# =============================================================================
def test_ecg_simulate():
ecg1 = nk.ecg_simulate(duration=20, length=5000)
assert len(ecg1) == 5000
ecg2 = nk.ecg_simulate(duration=20, length=5000, bpm=500)
# pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).plot()
# pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).hist()
assert scipy.stats.median_absolute_deviation(ecg1) < scipy.stats.median_absolute_deviation(ecg2)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,678 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/events/events_to_mne.py | # -*- coding: utf-8 -*-
import numpy as np
def events_to_mne(events, conditions=None):
"""Create MNE compatible events.
Parameters
----------
events : list, ndarray or dict
Events onset location. If a dict is passed (e.g., from 'events_find()'), will select only the 'Onset' list.
conditions : list
A list of same length as events containing the stimuli types/conditions.
Returns
----------
(events, event_id) : tuple
MNE-formatted events, that can be added via 'raw.add_events(events), and a dictionary with event's names.
See Also
--------
events_find
Examples
----------
>>> import numpy as np
>>> import pandas as pd
>>> import neurokit2 as nk
>>>
>>> signal = np.cos(np.linspace(start=0, stop=20, num=1000))
>>> events = nk.events_find(signal)
>>> events, event_id = nk.events_to_mne(events)
>>> event_id
{'Event': 0}
"""
if isinstance(events, dict):
events = events["Onset"]
event_id = {}
if conditions is None:
conditions = ["Event"] * len(events)
# Sanity check
if len(conditions) != len(events):
raise ValueError("NeuroKit error: events_to_mne(): 'conditions' argument of different length than event onsets.")
event_names = list(set(conditions))
event_index = list(range(len(event_names)))
for i in enumerate(event_names):
conditions = [event_index[i[0]] if x==i[1] else x for x in conditions]
event_id[i[1]] = event_index[i[0]]
events = np.array([events, [0]*len(events), conditions]).T
return(events, event_id)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,679 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/emg/__init__.py | """Submodule for NeuroKit."""
from .emg_simulate import emg_simulate
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,680 | hungpham2511/NeuroKit | refs/heads/master | /tests/test_data.py | import numpy as np
import pandas as pd
import neurokit2 as nk
# =============================================================================
# Data
# =============================================================================
#
def test_read_acqknowledge():
df, sampling_rate = nk.read_acqknowledge("../data/example1", sampling_rate=2000)
assert sampling_rate == 2000
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,681 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/ppg/__init__.py | """Submodule for NeuroKit."""
from .ppg_simulate import ppg_simulate
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,682 | hungpham2511/NeuroKit | refs/heads/master | /tests/tests_signal.py | import numpy as np
import pandas as pd
import neurokit2 as nk
# =============================================================================
# Signal
# =============================================================================
def test_signal_binarize():
signal = np.cos(np.linspace(start=0, stop=20, num=1000))
binary = nk.signal_binarize(signal)
assert len(binary) == 1000
binary = nk.signal_binarize(list(signal))
assert len(binary) == 1000
def test_signal_resample():
signal = np.cos(np.linspace(start=0, stop=20, num=50))
downsampled_interpolation = nk.signal_resample(signal, method="interpolation", sampling_rate=1000, desired_sampling_rate=500)
downsampled_numpy = nk.signal_resample(signal, method="numpy", sampling_rate=1000, desired_sampling_rate=500)
downsampled_pandas = nk.signal_resample(signal, method="pandas", sampling_rate=1000, desired_sampling_rate=500)
downsampled_fft = nk.signal_resample(signal, method="FFT", sampling_rate=1000, desired_sampling_rate=500)
downsampled_poly = nk.signal_resample(signal, method="poly", sampling_rate=1000, desired_sampling_rate=500)
# Upsample
upsampled_interpolation = nk.signal_resample(downsampled_interpolation, method="interpolation", sampling_rate=500, desired_sampling_rate=1000)
upsampled_numpy = nk.signal_resample(downsampled_numpy, method="numpy", sampling_rate=500, desired_sampling_rate=1000)
upsampled_pandas = nk.signal_resample(downsampled_pandas, method="pandas", sampling_rate=500, desired_sampling_rate=1000)
upsampled_fft = nk.signal_resample(downsampled_fft, method="FFT", sampling_rate=500, desired_sampling_rate=1000)
upsampled_poly = nk.signal_resample(downsampled_poly, method="poly", sampling_rate=500, desired_sampling_rate=1000)
# Check
rez = pd.DataFrame({"Interpolation": upsampled_interpolation - signal,
"Numpy": upsampled_numpy - signal,
"Pandas": upsampled_pandas - signal,
"FFT": upsampled_fft - signal,
"Poly": upsampled_poly - signal})
assert np.allclose(np.mean(rez.mean()), 0.0001, atol=0.0001)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,683 | hungpham2511/NeuroKit | refs/heads/master | /tests/tests_emg.py | import numpy as np
import pandas as pd
import neurokit2 as nk
import scipy.stats
# =============================================================================
# EMG
# =============================================================================
def test_emg_simulate():
emg1 = nk.emg_simulate(duration=20, length=5000, n_bursts=1)
assert len(emg1) == 5000
emg2 = nk.emg_simulate(duration=20, length=5000, n_bursts=15)
assert scipy.stats.median_absolute_deviation(emg1) < scipy.stats.median_absolute_deviation(emg2)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,684 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/__init__.py | """Top-level package for NeuroKit."""
__author__ = """NeuroKit development team"""
__email__ = 'dom.makowski@gmail.com'
__version__ = '0.0.2'
from .stats import *
from .signal import *
from .events import *
from .data import *
from .ecg import *
from .emg import *
from .ppg import *
from .eeg import *
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,685 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/events/plot_events_in_signal.py | # -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def plot_events_in_signal(signal, events, show=True, color="red", linestyle="--"):
"""
Plot events in signal.
Parameters
----------
signal : array or DataFrame
Signal array (can be a dataframe with many signals).
events : list, ndarray or dict
Events onset location. If a dict is passed (e.g., from 'events_find()'), will select only the 'Onset' list.
show : bool
If True, will return a plot. If False, will return a DataFrame that can be plotted externally.
color, linestyle : str
Other arguments to pass to matplotlib plotting.
See Also
--------
events_find
Examples
----------
>>> import numpy as np
>>> import pandas as pd
>>> import neurokit2 as nk
>>>
>>> signal = np.cos(np.linspace(start=0, stop=20, num=1000))
>>> events = nk.events_find(signal)
>>> nk.plot_events_in_signal(signal, events)
"""
if isinstance(events, dict):
events = events["Onset"]
if isinstance(signal, pd.DataFrame) is False:
df = pd.DataFrame({"Signal": signal})
# Plot if necessary
if show:
df.plot()
for event in events:
plt.axvline(event, color=color, linestyle=linestyle)
else:
df["Event_Onset"] = 0
df.iloc[events] = 1
return(df)
| {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,686 | hungpham2511/NeuroKit | refs/heads/master | /neurokit2/signal/__init__.py | """Submodule for NeuroKit."""
from .signal_binarize import signal_binarize
from .signal_resample import signal_resample | {"/neurokit2/ecg/ecg_ecgsynth.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_eeg.py": ["/neurokit2/__init__.py"], "/neurokit2/stats/__init__.py": ["/neurokit2/stats/standardize.py"], "/neurokit2/ecg/__init__.py": ["/neurokit2/ecg/ecg_simulate.py"], "/neurokit2/events/__init__.py": ["/neurokit2/events/plot_events_in_signal.py", "/neurokit2/events/events_to_mne.py"], "/neurokit2/ecg/ecg_simulate.py": ["/neurokit2/signal/__init__.py"], "/neurokit2/ppg/ppg_simulate.py": ["/neurokit2/signal/__init__.py"], "/tests/tests_events.py": ["/neurokit2/__init__.py"], "/docs/img/README_examples.py": ["/neurokit2/__init__.py"], "/tests/tests_ecg.py": ["/neurokit2/__init__.py"], "/tests/test_data.py": ["/neurokit2/__init__.py"], "/neurokit2/ppg/__init__.py": ["/neurokit2/ppg/ppg_simulate.py"], "/tests/tests_signal.py": ["/neurokit2/__init__.py"], "/tests/tests_emg.py": ["/neurokit2/__init__.py"], "/neurokit2/__init__.py": ["/neurokit2/stats/__init__.py", "/neurokit2/signal/__init__.py", "/neurokit2/events/__init__.py", "/neurokit2/ecg/__init__.py", "/neurokit2/emg/__init__.py", "/neurokit2/ppg/__init__.py"], "/neurokit2/signal/__init__.py": ["/neurokit2/signal/signal_binarize.py"]} |
58,694 | E-Jacques/emojipedia | refs/heads/master | /main.py | from window import MainFrame
from tkinter import Tk
from sys import argv
root = Tk()
root.title("Emojipedia")
root.geometry("300x450")
MainFrame(root, work_dir=argv[1])
root.focus()
root.mainloop() | {"/main.py": ["/window.py"], "/window.py": ["/emote.py"]} |
58,695 | E-Jacques/emojipedia | refs/heads/master | /window.py | from tkinter import *
from tkinter.ttk import *
from string import ascii_lowercase, ascii_uppercase
from emote import Emote
from os import chdir
import json
class MainFrame(Frame):
def __init__(self, master: Tk, work_dir: str = "."):
super().__init__(master)
super().grid(row=0, column=0, sticky="we")
chdir(work_dir)
master.bind("<Key>", self.moveArrow)
self.master = master
self.query: str = ""
self.input = Entry(master)
self.emotes: list[Emote] = []
self.emoteLabels: list[Label] = []
self.emoteFrame = Frame(master)
self.emoteFrameColNum = 6
self.selected: list[int, int] = [-1, -1]
self.lastUsed: list[int] = []
self.setupFrame()
self.loadEmotes("data.json")
self.loadLastUsed("lastused.json")
self.displayEmotes()
def setupFrame(self):
self.input.bind("<Key>", self.displayEmotes)
self.input.grid(row=0, column=0, sticky="nwe", padx=2, pady=2)
self.emoteFrame.grid(row=1, column=0, padx=2, pady=2, sticky="wes")
def getEmoteById(self, id: int) -> Emote:
for e in self.emotes:
if e.id == id:
return e
return None
def moveArrow(self, ev: Event):
maxLen = len(self.getFilteredEmotes(self.query)) if self.query != "" else len(self.lastUsed)
# On retire le bg
idx = self.selected[1] * self.emoteFrameColNum + self.selected[0]
if ev.keysym in ["Down", "Up", "Right", "Left"] and len(self.emoteLabels) > idx >= 0:
emotes = [self.getEmoteById(id) for id in self.lastUsed]
if self.query != "":
emotes = self.getFilteredEmotes(self.query)
l = self.emoteLabels[idx]
self.emoteLabels[idx] = emotes[idx].getLabel(self.emoteFrame, fz=16)
self.emoteLabels[idx].grid(row=self.selected[1] , column=self.selected[0] , padx=5, pady=5)
l.destroy()
if ev.keysym == "Down":
self.selected[0] = max(0, self.selected[0])
if (self.selected[1] + 1) * self.emoteFrameColNum + self.selected[0] < maxLen:
self.selected[1] += 1
elif ev.keysym == "Up":
self.selected[0] = max(0, self.selected[0])
self.selected[1] = max(0, self.selected[1] - 1)
elif ev.keysym == "Right":
if self.selected[1] * self.emoteFrameColNum + self.selected[0] + 1 < maxLen:
self.selected[0] += 1
self.selected[1] = max(0, self.selected[1])
if self.selected[0] // self.emoteFrameColNum > 0:
self.selected[0] = 0
self.selected[1] += 1
elif ev.keysym == "Left":
if self.selected[0] == 0 and self.selected[1] > 0:
self.selected[0] = self.emoteFrameColNum - 1
self.selected[1] = max(0, self.selected[1] - 1)
else:
self.selected[0] = max(0, self.selected[0] - 1)
self.selected[1] = max(0, self.selected[1])
elif ev.keysym == "Escape":
super().destroy()
self.saveLastUsed("lastused.json")
self.master.withdraw()
self.master.destroy()
elif ev.keysym in ["Return", "KP_Enter"]:
idx = self.selected[1] * self.emoteFrameColNum + self.selected[0]
if self.query != "":
self.writeEmote(self.getFilteredEmotes(self.query)[idx])
else:
self.writeEmote(self.getEmoteById(self.lastUsed[idx]))
elif str(self.master.focus_get()) != str(self.input):
self.input.insert(len(self.input.get()), ev.char)
self.input.focus()
if ev.keysym in ["Down", "Up", "Right", "Left"]:
self.emoteLabels[self.selected[1] * self.emoteFrameColNum + self.selected[0]].config(bg="lightblue")
def writeEmote(self, emote: Emote):
if (self.lastUsed.count(emote.id) > 0):
self.lastUsed.remove(emote.id)
self.lastUsed = [emote.id] + self.lastUsed
self.master.clipboard_clear()
self.master.clipboard_append(emote.emote)
self.master.update()
def loadEmotes(self, path: str):
with open(path, "r") as f:
rawData = f.read()
data = json.loads(rawData)
for d in data:
self.emotes.append(Emote(emote=d["emote"], id=d["id"], keywords=d["keywords"]))
def loadLastUsed(self, path:str):
with open(path, "r") as f:
rawData = f.read()
data = json.loads(rawData)
self.lastUsed = data
def saveLastUsed(self, path: str):
with open(path, "w") as f:
f.write(json.dumps(self.lastUsed))
def getFilteredEmotes(self, q="") -> 'list[Emote]':
l = [e for e in self.emotes if e.containsKey(q) or q == ""]
return l
def displayEmotes(self, entry: Event=None):
value = self.query
if entry and entry.keysym not in ["Down", "Up", "Right", "Left", "Escape", "Return", "KP_Enter"]:
value=self.input.get()
if entry.char in ascii_lowercase + ascii_uppercase:
value += entry.char
self.selected = [-1, -1]
elif entry:
super().focus()
self.query = value
for widget in self.emoteFrame.winfo_children():
widget.destroy()
self.emoteLabels = []
emotes = [self.getEmoteById(id) for id in self.lastUsed]
if value != "":
emotes = self.getFilteredEmotes(value)
for idx, emote in enumerate(emotes):
if emote == None:
continue
emoteLabel = emote.getLabel(self.emoteFrame, fz=16)
emoteLabel.grid(row=idx//self.emoteFrameColNum , column=idx%self.emoteFrameColNum , padx=5, pady=5)
self.emoteLabels.append(emoteLabel)
| {"/main.py": ["/window.py"], "/window.py": ["/emote.py"]} |
58,696 | E-Jacques/emojipedia | refs/heads/master | /dataget.py | import requests
from json import dumps
from typing import List
def getData(pathLs: List[str]):
emotes: List[dict] = []
idx = 0
for path in pathLs:
print(path)
data = requests.get(path).text
# print(data)
emoteList = data.split('<ul class="emoji-list">')[1].split("</ul>")[0]
ls = emoteList.split("<li>")
for e in ls:
if e == "":
continue
try:
idx += 1
o = {
"id": idx,
"emote": e.split('<span class="emoji">')[1].split("</span>")[0],
"keywords": [e.split("</span> ")[1].split("</a>")[0]]
}
emotes.append(o)
except IndexError:
print(e)
print(emotes)
json = dumps(emotes, ensure_ascii=False)
with open("./data.json", "w") as f:
f.write(json)
if __name__ == "__main__":
getData(["https://emojipedia.org/people/", "https://emojipedia.org/nature/", "https://emojipedia.org/food-drink/", "https://emojipedia.org/activity/",
"https://emojipedia.org/travel-places/", "https://emojipedia.org/objects/", "https://emojipedia.org/symbols/", "https://emojipedia.org/flags/"])
| {"/main.py": ["/window.py"], "/window.py": ["/emote.py"]} |
58,697 | E-Jacques/emojipedia | refs/heads/master | /emote.py | from tkinter import Frame, Label
class Emote():
def __init__(self, emote: str, id: int, keywords=[]):
self.emote: str = emote
self.id = id
self.keywords: list[str] = keywords
def getLabel(self, master: Frame, fz=12) -> Label:
label = Label(master, text=self.emote, font=("Arial", fz))
return label
def addKeyword(self, k: str):
self.keywords.append(k)
def containsKey(self, k: str) -> bool:
for key in self.keywords:
if (k.lower() in key.lower()):
return True
return False | {"/main.py": ["/window.py"], "/window.py": ["/emote.py"]} |
58,701 | garyfeng/ggplot | refs/heads/master | /ggplot/geoms/geom_segment.py | # Gary Feng, 2016-04
# Implementing something close to the R/ggplot2 geom_segment()
# https://github.com/cran/ggplot2/blob/master/R/geom-segment.r
# based on geom_bar
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import itertools
import numpy as np
import pandas as pd
import matplotlib.cbook as cbook
from matplotlib.patches import *
from matplotlib.lines import *
from .geom import geom
from ggplot.utils import is_string
from ggplot.utils import is_categorical
import warnings
class geom_segment(geom):
# from r:
"""
required_aes = c("x", "y", "xend", "yend"),
non_missing_aes = c("linetype", "size", "shape"),
default_aes = aes(colour = "black", size = 0.5, linetype = 1, alpha = NA),
"""
# DEFAULT_AES = {'alpha': None, 'color': None, 'fill': '#333333',
# 'linetype': 'solid', 'size': 1.0, 'weight': None, 'y': None, 'width' : None}
# REQUIRED_AES = {'x'}
# DEFAULT_PARAMS = {'stat': 'bin', 'position': 'stack'}
DEFAULT_AES = {'alpha': None, 'color': "black",
'fill': '#333333', 'barwidth':2, 'method':"bar",
'linetype': 'solid', 'size': 0.5,
'weight': None, 'y': None, 'width' : None}
REQUIRED_AES = {'x', 'y', 'xend'}
DEFAULT_PARAMS = {'stat': 'identity', 'position': 'identity'}
_extra_requires = {'y', 'yend', 'width', 'barwidth', 'method'}
_aes_renames = {'linetype': 'linestyle', 'size': 'linewidth',
'fill': 'color', 'color': 'edgecolor'}
# NOTE: Currently, geom_bar does not support mapping
# to alpha and linestyle. TODO: raise exception
_units = {'edgecolor', 'color', 'alpha', 'linestyle', 'linewidth'}
def __init__(self, *args, **kwargs):
# TODO: Change self.__class__ to geom_segment
super(geom_segment, self).__init__(*args, **kwargs)
self.ax = None
def _plot_unit(self, pinfo, ax):
# not using weight
pinfo.pop('weight')
# getting the data
x = np.asarray(pinfo.pop('x'))
xend = np.asarray(pinfo.pop('xend'))
# bar width with as a constant or as an aes parameter
barwidth = np.ones(len(x))*2
if "barwidth" in pinfo:
barwidth = np.ones(len(x)) * np.asarray(pinfo.pop('barwidth'))
maxBarWidth = np.max(barwidth)
categoricalY = is_categorical(pinfo['y'])
# TODO:
# if y is categorical:
# make up y coordinates, adjust the barwidth etc
# do not print y ticks or the y axis
y = np.asarray(pinfo.pop('y'))
#warnings.warn(str(len(y)))
if categoricalY:
y = pd.Series(y, dtype="category")
y.cat.categories = range(y.cat.categories.shape[0])
y = np.asarray(y) * max(maxBarWidth,1 )
# y = np.asarray(range(len(x))) * (maxBarWidth +1)
else:
# plot horizontal bars around y; +/- half width
y = y - barwidth/2
#warnings.warn(str(y))
if not "yend" in pinfo:
yend = y + barwidth/2
else:
yend = np.asarray(pinfo.pop('yend')) + barwidth/2
w = xend - x
h = yend - y
# width is the linewidth
width_elem = pinfo.pop('width')
# If width is unspecified, default is an array of 1's
if width_elem is None:
width = np.ones(len(x))
else :
width = np.array(width_elem)
self.ax = ax
# ax.bar(left=x, height=barwidth, width=w, bottom=y, **pinfo)
# consider using rect instead of bar
# matplotlib.patches.Rectangle(xy, width, height, angle=0.0, **kwargs)
# http://matplotlib.org/api/patches_api.html#matplotlib.patches.Rectangle
# ax.add_patch(Rectangle((someX - .1, someY - .1), 0.2, 0.2, fill=True, alpha=1))
method = pinfo.pop('method').lower()
if method == "rect":
# patches.Rectangle() does not take vectors; need to iterate
# use izip for efficient iteration; don't really need enumerate
for ix, iy, ib, iw in itertools.izip(x,y,barwidth,w):
ax.add_patch(Rectangle(xy = (ix,iy), \
height=ib, width=iw, fill=True, **pinfo))
elif method == "line":
# note that linewidth is already set with the `size` aes()
# and Line2D draws a continueous line for (x,y)
# we need to break it apart
# get rid of linewide in the aes(), because we will set here.
pinfo.pop('linewidth')
c = pinfo.pop('color')
for ix, iy, ib, iw in itertools.izip(x,y,barwidth,w):
# ax.add_line(Line2D((ix, ix+iw), (iy, iy), lw=ib, **pinfo))
ax.add_line(Line2D((ix, ix+iw), (iy, iy), lw=ib, color=c))
else:
ax.bar(left=x, height=barwidth, width=w, bottom=y, **pinfo)
ax.autoscale()
| {"/ggplot/themes/__init__.py": ["/ggplot/themes/theme_noy.py"]} |
58,702 | garyfeng/ggplot | refs/heads/master | /ggplot/themes/__init__.py | from .theme import theme
from .theme_538 import theme_538
from .theme_bw import theme_bw
from .theme_gray import theme_gray
from .theme_xkcd import theme_xkcd
from .theme_matplotlib import theme_matplotlib
from .theme_seaborn import theme_seaborn
from .element_text import element_text
# garyfeng
from .theme_noy import theme_noy
__all__ = ["theme", "theme_538", "theme_bw", "theme_gray", "theme_xkcd",
"theme_matplotlib", "theme_seaborn", "element_text",
"theme_noy"]
__all__ = [str(u) for u in __all__]
| {"/ggplot/themes/__init__.py": ["/ggplot/themes/theme_noy.py"]} |
58,703 | garyfeng/ggplot | refs/heads/master | /ggplot/themes/theme_noy.py | from .theme_gray import theme_gray
class theme_noy(theme_gray):
"""
White background w/ black gridlines
"""
def __init__(self):
super(theme_noy, self).__init__()
self._rcParams['axes.facecolor'] = 'white'
self._rcParams["axes.grid"] = "False"
self._rcParams["ytick.color"] = "white"
| {"/ggplot/themes/__init__.py": ["/ggplot/themes/theme_noy.py"]} |
58,715 | achan90/python-automation | refs/heads/master | /pt.py | #! /usr/bin/env python3
import pprint
tableData = [['apples', 'oranges', 'cherries', 'banana'],
['Alice', 'Bob', 'Carol', 'David'],
['dogs', 'cats', 'moose', 'goose']]
def printTable(table):
width = []
for x in range(len(table)):
for y in range(len(table[x])):
width.append(len(table[x][y]))
for x in range(len(table)):
for y in range(len(table[x])):
table[x][y] = str(table[x][y]).rjust(max(width))
for x in range(len(table[0])):
printer(table, x)
def printer(table, listLength):
columns = []
for x in range(len(table)):
columns.append((table[x][listLength]))
print("".join(columns))
printTable(tableData)
| {"/loot.py": ["/inventory.py"]} |
58,716 | achan90/python-automation | refs/heads/master | /comma_code.py | spam = ["apples", "bananas", "tofu", "cats", "mice"]
def function(spam):
spam.insert(len(spam) - 1, "and")
sentence = ", ".join(spam)
print("'{}'".format(sentence))
function(spam)
| {"/loot.py": ["/inventory.py"]} |
58,717 | achan90/python-automation | refs/heads/master | /regexStrip.py | #! /usr/bin/env python3
import re
def stripRegex(*arg):
arguments = []
regexSpace = re.compile(r"^(\s+)|$(\s+)")
for x in arg:
arguments.append(x)
if len(arguments) == 1:
sentence = regexSpace.sub("", arguments[0])
print(sentence)
elif len(arguments) == 2:
regexSpace = re.compile(r"[{}]".format(arguments[1]))
sentence = regexSpace.sub("", arguments[0])
print(sentence)
else:
exit("Too many arguments")
a = " jEEEsus "
b = "su"
stripRegex(a, b)
stripRegex(a)
| {"/loot.py": ["/inventory.py"]} |
58,718 | achan90/python-automation | refs/heads/master | /madLibs.py | #! /usr/bin/env python3
import re
import os
def pathLocator(file):
filePath = os.path.join(file)
return filePath
def madLibs():
regex = re.compile(r"(ADJECTIVE)|(VERB)|(NOUN)")
file = open(pathLocator(input("Enter path name: ")), "r")
fileInput = file.read()
match = regex.search(fileInput)
while match is not None:
if match.group(1) is not None:
fileInput = regex.sub(
input("Enter adjective: "), fileInput, count=1)
match = regex.search(fileInput)
elif match.group(2) is not None:
fileInput = regex.sub(
input("Enter verb: "), fileInput, count=1)
match = regex.search(fileInput)
elif match.group(3) is not None:
fileInput = regex.sub(
input("Enter noun: "), fileInput, count=1)
match = regex.search(fileInput)
else:
break
print(fileInput)
os.chdir(pathLocator(input("Enter directory name: ")))
newFile = open("madLibbed.txt", "w")
newFile.write(fileInput)
file.close()
newFile.close()
madLibs()
| {"/loot.py": ["/inventory.py"]} |
58,719 | achan90/python-automation | refs/heads/master | /loot.py | from inventory import *
loot = ["gold coin", "dagger", "gold coin", "gold coin", "ruby"]
def addToInventory(inventory, loot):
for x in loot:
if x in inventory:
inventory[x] += 1
else:
inventory.setdefault("{}".format(x), 1)
displayInventory(inventory)
addToInventory(inventory, loot)
| {"/loot.py": ["/inventory.py"]} |
58,720 | achan90/python-automation | refs/heads/master | /pw.py | #! /usr/bin/env python3
import pyperclip
import shelve
# Still need to figure out how to encrypt the passwords.
PASSWORDS = shelve.open("passwords")
account = input("Enter account name: ")
if account.lower() in PASSWORDS:
pyperclip.copy(PASSWORDS[account])
print("Password for {} copied to clipboard".format(account))
else:
while True:
print("There is no account named {}".format(account))
add_account = input("Do you wish to add it? Y/N: ")
if add_account.lower() == "y":
PASSWORDS[account] = input(
"Enter {} password: ".format(account))
print("Password for {} saved".format(account))
exit()
elif add_account.lower() == "n":
exit()
else:
continue
PASSWORDS.close()
| {"/loot.py": ["/inventory.py"]} |
58,721 | achan90/python-automation | refs/heads/master | /bp.py | #! /usr/bin/env python3
# Adds bullet points to beginning of each new line of text on clipboard
import pyperclip
text = pyperclip.paste()
def separator(clipboard):
lines = clipboard.split("\n")
for line in range(len(lines)):
lines[line] = "* {}".format(lines[line])
return "\n".join(lines)
# Copies the finished bullet point list to clipboard
pyperclip.copy(separator(text))
print("Bullet list created:")
print(pyperclip.paste())
| {"/loot.py": ["/inventory.py"]} |
58,722 | achan90/python-automation | refs/heads/master | /renameDates.py | #! /usr/bin/env python3
# renameDates.py - Renames filenames with American MM-DD-YYYY date format
# to European DD-MM-YYY
import shutil
import os
import re
def renameDates(mode):
# Regex to match files with american date format
dateRegex = re.compile(r"""
^(.*?)- # all text before the date
( (0|1)? \d )- # one or two digits for the month
( (0|1|2|3)? \d ) # one or two digits for the day
( 20\d\d ) # four digits for the year
(.*?)$ # all text after the date
""", re.VERBOSE)
# Loop over files in working directory
for amerFilename in os.listdir("."):
mo = dateRegex.search(amerFilename)
# Kkip files without date
if mo is None:
continue
# Get parts of the filename
before = mo.group(1)
month = mo.group(2)
day = mo.group(4)
year = mo.group(6)
after = mo.group(7)
# Form the european style filename
euroFilename = ("{}{}-{}-{}{}".format(before, month, day, year, after))
# Get full absolute filepaths
absWorkingDir = os.path.abspath(".")
amerFilename = os.path.join(absWorkingDir, amerFilename)
euroFilename = os.path.join(absWorkingDir, euroFilename)
# Rename the files
print("Renaming '{}' to '{}'".format(amerFilename, euroFilename))
confirmation = input("\nDo you want to continue? [Y/N]: ")
if confirmation.lower() == "y":
print("File renamed")
shutil.move(amerFilename, euroFilename)
elif confirmation.lower() == "n":
print("File skipped".format(amerFilename))
continue
| {"/loot.py": ["/inventory.py"]} |
58,723 | achan90/python-automation | refs/heads/master | /contactSearch.py | #! /usr/bin/env python3
import pyperclip
import re
text = pyperclip.paste()
regexMail = re.compile(r"""
( [a-zA-Z.-_+%0-9]+ ) # username
( @ ) # @ symbol
( [a-zA-Z0-9.-]+ ) # domain
( \. \w{2,4} ) # domain extension
""", re.VERBOSE)
regexPhone = re.compile(r"""
( \d{3} | \( \d{3} \) ) ? # area code
( [-\s.] ) ? # separator
( \d{3} ) # first numbers
( [-\s.] ) # separator
( \d{4} ) # last numbers
( \s* (ext | x | ext.) \s* (\d{2,5}) ) ? # extension
""", re.VERBOSE)
def finder(text, emails, numbers):
matches = [[], []]
substitute = re.compile(r"\.+")
for x in emails.findall(text):
if emails.findall(text) is None:
print("No emails found.")
else:
matches[0].append("".join(x))
for x in numbers.findall(text):
if numbers.findall(text) is None:
print("No phone numbers found.")
else:
matches[1].append("".join(x))
if matches is None:
print("Search complete,no matches found.")
else:
formattedMatches = []
for x in matches[0]:
formattedMatches.append(x)
for x in range(len(matches[1])):
matches[1][x] = substitute.sub("-", matches[1][x])
formattedMatches.append(matches[1][x])
print("\n".join(formattedMatches))
pyperclip.copy("\n".join(formattedMatches))
print("Search complete, matches copied to clipboard.")
finder(text, regexMail, regexPhone)
| {"/loot.py": ["/inventory.py"]} |
58,724 | achan90/python-automation | refs/heads/master | /mcb.py | #! /usr/bin/env python3
# mcb.py - Saves and loads pieces of text to the clipboard.
# Usage:
# ./mcb.py save <keyword> - Saves clipboard to keyword.
# ./mcb.py <keyword> - Loads data from keyword to clipboard.
# ./mcb.py list - Loads all keywords to clipboard.
# ./mcb.py delete <keyword> - Deletes keyword data.
# -/mcb.py reset - Deletes data from all keywords.
# This script is not tested, might not work as intended.
import shelve
import pyperclip
import sys
mcbShelf = shelve.open("mcb")
# Save clipboard content.
if len(sys.argv) == 3 and sys.argv[1].lower() == "save":
mcbShelf[sys.argv[2]] = pyperclip.paste()
# Delete keyword data
elif len(sys.argv) == 3 and sys.argv[1].lower == "delete":
del mcbShelf[sys.argv[2]]
# Delete all keyword data
elif len(sys.argv) == 2 and sys.argv[1] == "reset":
del mcbShelf
# List keywords and load content.
elif len(sys.argv) == 2:
if sys.argv[1].lower() == "list":
pyperclip.copy(str(list(mcbShelf.keys())))
elif sys.argv[1] in mcbShelf:
pyperclip.copy(mcbShelf[sys.argv[1]])
mcbShelf.close()
| {"/loot.py": ["/inventory.py"]} |
58,725 | achan90/python-automation | refs/heads/master | /inventory.py | inventory = {"arrow": 12, "gold coin": 42, "rope": 1, "torch": 6, "dagger": 1}
def displayInventory(items):
total = 0
print("Inventory:")
for x, y in items.items():
if y == 1:
print(y, x)
total += y
elif y > 1 and x == "torch":
print(y, "torches")
total += y
else:
print(y, "{}s".format(x))
total += y
print("Total number of items: {}".format(total))
| {"/loot.py": ["/inventory.py"]} |
58,726 | achan90/python-automation | refs/heads/master | /regexSearch/regexSearch.py | #! /usr/bin/env python3
# Searches through all text files on cwd for user supplied regex.
import re
import os
def regexSearch():
regex = re.compile(input("Enter regex pattern: "))
fileList = os.listdir(os.getcwd())
for file in fileList:
open(file, "r")
matches = regex.findall(file.read())
print("Searching {}".format(file))
if matches is not None:
for match in matches:
print("Match found: {}".format("".join(match)))
else:
print("No matches found on {}".format(file))
regexSearch()
| {"/loot.py": ["/inventory.py"]} |
58,727 | achan90/python-automation | refs/heads/master | /ipn.py | #! /usr/bin/env python3
import re
def isPhoneNumber(message):
regex = re.compile(
r"""
( \d{3} [-\s)] | \( \d{3} \) ) ? # Area code
( [-\s] ) ? # Separator
( \d{3} ) # First three numbers
( [-\s] ) ? # Separator
( \d{4} ) # Last four numbers
""", re.VERBOSE)
match = regex.findall(message)
for x in match:
print("Found phone number: {}".format("".join(x)))
message = "Call me at 415-555-1011 tomorrow. (415) 555 9999 is my office."
isPhoneNumber(message)
| {"/loot.py": ["/inventory.py"]} |
58,732 | ddrkt/forms-validation | refs/heads/master | /forms/urls.py | from django.contrib import admin
from django.urls import path, include
from forms import views
from django.views.generic import TemplateView
urlpatterns = [
path('values/', views.FormViewSet.as_view()),
]
| {"/forms/views.py": ["/forms/serializers.py"]} |
58,733 | ddrkt/forms-validation | refs/heads/master | /forms/views.py | from django.shortcuts import render
from forms.serializers import FormSerializers
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.exceptions import ValidationError
# Create your views here.
class FormViewSet(APIView):
def post(self, request, format=None):
serializer = FormSerializers(data=request.data)
if serializer.is_valid():
return Response(dict(result="input is valid"))
raise ValidationError(serializer.errors, 400)
| {"/forms/views.py": ["/forms/serializers.py"]} |
58,734 | ddrkt/forms-validation | refs/heads/master | /forms/serializers.py | from rest_framework import serializers
from django.conf import settings
import requests
import re
class FormSerializers(serializers.Serializer):
email = serializers.EmailField()
phone = serializers.CharField()
fname = serializers.CharField(required=False)
lname = serializers.CharField(required=False)
cardn = serializers.CharField(min_length=16, max_length=16)
def validate_phone(self, value):
if re.match(r"^((8|\+7)[\- ]?)?(\(?\d{3}\)?[\- ]?)?[\d\- ]{7,10}$", value) == None:
raise serializers.ValidationError("There must be valid phone number")
return value
def validate_cardn(self, value):
if not value.isdecimal():
raise serializers.ValidationError("card number is not valid")
api_key = getattr(settings, 'CARD_INFO_API_KEY', None)
allowed_banks = getattr(settings, 'ALLOWED_BANKS', [])
request = requests.get(f"https://api.cardinfo.online/?input={value[0:6]}&apiKey={api_key}")
if request.json().get('bankName', False) not in allowed_banks:
raise serializers.ValidationError("Bank is not in allowed banks")
return value
| {"/forms/views.py": ["/forms/serializers.py"]} |
58,753 | pombredanne/gevent-utils | refs/heads/master | /gevent_utils.py | """
gevent-utils
============
Debugging utilities for gevent.
"""
import math
import signal
import gevent
arm_alarm = None
if hasattr(signal, 'setitimer'):
def alarm_itimer(seconds):
signal.setitimer(signal.ITIMER_REAL, seconds)
arm_alarm = alarm_itimer
else:
try:
import itimer
arm_alarm = itimer.alarm
except ImportError:
def alarm_signal(seconds):
signal.alarm(math.ceil(seconds))
arm_alarm = alarm_signal
class AlarmInterrupt(BaseException): pass
class BlockingDetector(object):
"""
Utility class to detect thread blocking. Intended for debugging only.
``timeout`` is the number of seconds to wait before considering the thread
blocked.
Operates by setting and attempt to clear an alarm signal. The alarm signal
cannot be cleared then the thread is considered blocked and
``BlockingDetector.alarm_handler`` is invoked with the signal and current
frame. An ``AlarmInterrupt`` exception will be raised if the signal
actually gets raised.
Invoke via: gevent.spawn(BlockingDetector())
"""
def __init__(self, timeout=1):
self.timeout = timeout
def __call__(self):
"""
Loop for 95% of our detection time and attempt to clear the signal.
"""
while True:
self.set_signal()
gevent.sleep(self.timeout * 0.95)
self.clear_signal()
# sleep the rest of the time
gevent.sleep(self.timeout * 0.05)
def alarm_handler(self, signum, frame):
raise AlarmInterrupt('Blocking detected')
def set_signal(self):
tmp = signal.signal(signal.SIGALRM, self.alarm_handler)
if tmp != self.alarm_handler:
self._old_signal_handler = tmp
arm_alarm(self.timeout)
def clear_signal(self):
if (hasattr(self, "_old_signal_handler") and self._old_signal_handler):
signal.signal(signal.SIGALRM, self._old_signal_handler)
signal.alarm(0)
| {"/tests.py": ["/gevent_utils.py"]} |
58,754 | pombredanne/gevent-utils | refs/heads/master | /docs/conf.py | import sys, os
try:
import sphinxtogithub
optional_extensions = ['sphinxtogithub']
except ImportError:
optional_extensions = []
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.autosummary'] + optional_extensions
master_doc = 'index'
project = u'gevent-utils'
copyright = u'2011, Travis Cline'
version = '0.0.2'
release = '0.0.2'
exclude_patterns = []
add_module_names = True
pygments_style = 'sphinx'
html_show_sourcelink = False
html_show_sphinx = False
htmlhelp_basename = 'gevent-utilsdoc'
latex_documents = [
('index', 'gevent-utils.tex', u'gevent-utils Documentation',
u'Travis Cline', 'manual'),
]
man_pages = [
('index', 'gevent-utils', u'gevent-utils Documentation',
[u'Travis Cline'], 1)
]
| {"/tests.py": ["/gevent_utils.py"]} |
58,755 | pombredanne/gevent-utils | refs/heads/master | /tests.py | import time
import mock
import gevent
from unittest import TestCase
from gevent_utils import BlockingDetector, AlarmInterrupt
class TestBlockingDetector(TestCase):
@mock.patch.object(BlockingDetector, 'alarm_handler')
def test_triggered_when_blocking(self, mock_alarm_handler):
detector_greenlet = gevent.spawn(BlockingDetector(2))
gevent.sleep()
self.assertFalse(mock_alarm_handler.called)
time.sleep(1)
self.assertFalse(mock_alarm_handler.called)
time.sleep(1)
# after two seconds of sleep the alarm_handler should have been called
self.assertTrue(mock_alarm_handler.called)
detector_greenlet.kill()
def test_actual_exception_raised(self):
detector_greenlet = gevent.spawn(BlockingDetector())
gevent.sleep()
self.assertRaises(AlarmInterrupt, time.sleep, 1)
detector_greenlet.kill()
@mock.patch.object(BlockingDetector, 'alarm_handler')
def test_not_triggered_when_cooperating(self, mock_alarm_handler):
detector_greenlet = gevent.spawn(BlockingDetector())
gevent.sleep()
self.assertFalse(mock_alarm_handler.called)
gevent.sleep(2)
self.assertFalse(mock_alarm_handler.called)
detector_greenlet.kill()
| {"/tests.py": ["/gevent_utils.py"]} |
58,768 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/sign.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Sign(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Sign",
inputs=["x"],
outputs=["y"],
)
x = np.array(range(-5, 6)).astype(np.float32)
y = np.sign(x)
expect(node, inputs=[x], outputs=[y], name="test_sign")
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_non_zero.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/prelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_conv.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/lppool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/hardswish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/thresholdedrelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_leaky_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sigmoid.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_pool_common.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_backend_test.py": ["/onnx/backend/base.py", "/onnx/backend/test/__init__.py", "/onnx/shape_inference.py", "/onnx/version_converter.py", "/onnx/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/backend/test/case/__init__.py": ["/onnx/backend/test/case/base.py", "/onnx/backend/test/case/utils.py"], "/onnx/reference/ops/op_instance_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/sequence_map.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_hamming_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_unique.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sqrt.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/serialization_test.py": ["/onnx/__init__.py"], "/onnx/defs/gen_shape_inference_information.py": ["/onnx/__init__.py"], "/onnx/test/elu_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/bitwiseor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/conv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/pow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_scan.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_reduce_mean.py": ["/onnx/reference/ops/_op.py"], "/onnx/tools/net_drawer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/momentum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_shape.py": ["/onnx/reference/op_run.py"], "/onnx/test/reference_evaluator_backend_test.py": ["/onnx/__init__.py", "/onnx/backend/test/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/reference/ops/op_string_normalizer.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/adam.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/defs/__init__.py"], "/onnx/backend/test/case/node/ceil.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bitwiseand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/onehot.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/cumsum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reshape.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_qlinear_conv.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/gathernd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/negativeloglikelihoodloss.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/function_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/clip.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_l1.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mod.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], 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58,769 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/dft.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class DFT(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=1)
x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
y = np.fft.fft(x, axis=0)
x = x.reshape(1, 10, 10, 1)
y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
expect(node, inputs=[x], outputs=[y], name="test_dft")
node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=2)
x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
y = np.fft.fft(x, axis=1)
x = x.reshape(1, 10, 10, 1)
y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
expect(node, inputs=[x], outputs=[y], name="test_dft_axis")
node = onnx.helper.make_node(
"DFT", inputs=["x"], outputs=["y"], inverse=1, axis=1
)
x = np.arange(0, 100, dtype=np.complex64).reshape(
10,
10,
)
y = np.fft.ifft(x, axis=0)
x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
expect(node, inputs=[x], outputs=[y], name="test_dft_inverse")
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58,770 | onnx/onnx | refs/heads/main | /onnx/parser.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import onnx
import onnx.onnx_cpp2py_export.parser as C # noqa: N812
class ParseError(Exception):
pass
def parse_model(model_text: str) -> onnx.ModelProto:
"""Parse a string to build a ModelProto.
Arguments:
model_text (string): formatted string
Returns:
ModelProto
"""
(success, msg, model_proto_str) = C.parse_model(model_text)
if success:
return onnx.load_from_string(model_proto_str)
raise ParseError(msg)
def parse_graph(graph_text: str) -> onnx.GraphProto:
"""Parse a string to build a GraphProto.
Arguments:
graph_text (string): formatted string
Returns:
GraphProto
"""
(success, msg, graph_proto_str) = C.parse_graph(graph_text)
if success:
graph_proto = onnx.GraphProto()
graph_proto.ParseFromString(graph_proto_str)
return graph_proto
raise ParseError(msg)
def parse_function(function_text: str) -> onnx.FunctionProto:
"""Parse a string to build a FunctionProto.
Arguments:
function_text (string): formatted string
Returns:
FunctionProto
"""
(success, msg, function_proto_str) = C.parse_function(function_text)
if success:
function_proto = onnx.FunctionProto()
function_proto.ParseFromString(function_proto_str)
return function_proto
raise ParseError(msg)
def parse_node(node_text: str) -> onnx.NodeProto:
"""Parse a string to build a NodeProto.
Arguments:
node_text: formatted string
Returns:
NodeProto
"""
(success, msg, node_proto_str) = C.parse_node(node_text)
if success:
node_proto = onnx.NodeProto()
node_proto.ParseFromString(node_proto_str)
return node_proto
raise ParseError(msg)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,771 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/constantofshape.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ConstantOfShape(Base):
@staticmethod
def export_float_ones() -> None:
x = np.array([4, 3, 2]).astype(np.int64)
tensor_value = onnx.helper.make_tensor(
"value", onnx.TensorProto.FLOAT, [1], [1]
)
node = onnx.helper.make_node(
"ConstantOfShape",
inputs=["x"],
outputs=["y"],
value=tensor_value,
)
y = np.ones(x, dtype=np.float32)
expect(node, inputs=[x], outputs=[y], name="test_constantofshape_float_ones")
@staticmethod
def export_int32_zeros() -> None:
x = np.array([10, 6]).astype(np.int64)
tensor_value = onnx.helper.make_tensor(
"value", onnx.TensorProto.INT32, [1], [0]
)
node = onnx.helper.make_node(
"ConstantOfShape",
inputs=["x"],
outputs=["y"],
value=tensor_value,
)
y = np.zeros(x, dtype=np.int32)
expect(node, inputs=[x], outputs=[y], name="test_constantofshape_int_zeros")
@staticmethod
def export_int32_shape_zero() -> None:
x = np.array(
[
0,
]
).astype(np.int64)
tensor_value = onnx.helper.make_tensor(
"value", onnx.TensorProto.INT32, [1], [0]
)
node = onnx.helper.make_node(
"ConstantOfShape",
inputs=["x"],
outputs=["y"],
value=tensor_value,
)
y = np.zeros(x, dtype=np.int32)
expect(
node, inputs=[x], outputs=[y], name="test_constantofshape_int_shape_zero"
)
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,772 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/averagepool.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.reference.ops.op_pool_common import (
get_output_shape_auto_pad,
get_output_shape_explicit_padding,
get_pad_shape,
pool,
)
class AveragePool(Base):
@staticmethod
def export_averagepool_2d_precomputed_pads() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array(
[
[
[
[7, 7.5, 8, 8.5, 9],
[9.5, 10, 10.5, 11, 11.5],
[12, 12.5, 13, 13.5, 14],
[14.5, 15, 15.5, 16, 16.5],
[17, 17.5, 18, 18.5, 19],
]
]
]
).astype(np.float32)
expect(
node, inputs=[x], outputs=[y], name="test_averagepool_2d_precomputed_pads"
)
@staticmethod
def export_averagepool_2d_precomputed_pads_count_include_pad() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 5, 5]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
pads=[2, 2, 2, 2],
count_include_pad=1,
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array(
[
[
[
[2.5200, 3.6000, 4.8000, 4.0800, 3.2400],
[4.5600, 6.4000, 8.4000, 7.0400, 5.5200],
[7.2000, 10.0000, 13.0000, 10.8000, 8.4000],
[6.9600, 9.6000, 12.4000, 10.2400, 7.9200],
[6.1200, 8.4000, 10.8000, 8.8800, 6.8400],
]
]
]
).astype(np.float32)
expect(
node,
inputs=[x],
outputs=[y],
name="test_averagepool_2d_precomputed_pads_count_include_pad",
)
@staticmethod
def export_averagepool_2d_precomputed_strides() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[2, 2],
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[4, 6], [14, 16]]]]).astype(np.float32)
expect(
node,
inputs=[x],
outputs=[y],
name="test_averagepool_2d_precomputed_strides",
)
@staticmethod
def export_averagepool_2d_precomputed_same_upper() -> None:
"""
input_shape: [1, 1, 5, 5]
output_shape: [1, 1, 3, 3]
pad_shape: [2, 2] -> [1, 1, 1, 1] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[2, 2],
auto_pad="SAME_UPPER",
)
x = np.array(
[
[
[
[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
[16, 17, 18, 19, 20],
[21, 22, 23, 24, 25],
]
]
]
).astype(np.float32)
y = np.array([[[[4, 5.5, 7], [11.5, 13, 14.5], [19, 20.5, 22]]]]).astype(
np.float32
)
expect(
node,
inputs=[x],
outputs=[y],
name="test_averagepool_2d_precomputed_same_upper",
)
@staticmethod
def export_averagepool_1d_default() -> None:
"""
input_shape: [1, 3, 32]
output_shape: [1, 3, 31]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2],
)
x = np.random.randn(1, 3, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = [2]
strides = [1]
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_1d_default")
@staticmethod
def export_averagepool_2d_default() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 31, 31]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = (2, 2)
strides = (1, 1)
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_default")
@staticmethod
def export_averagepool_3d_default() -> None:
"""
input_shape: [1, 3, 32, 32, 32]
output_shape: [1, 3, 31, 31, 31]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2, 2],
)
x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = [2, 2, 2]
strides = [1, 1, 1]
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG")
expect(node, inputs=[x], outputs=[y], name="test_averagepool_3d_default")
@staticmethod
def export_averagepool_2d_same_upper() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_UPPER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape_auto_pad(
"SAME_UPPER", x_shape[2:], kernel_shape, strides
)
pad_shape = get_pad_shape(
"SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_top = pad_shape[0] // 2
pad_bottom = pad_shape[0] - pad_top
pad_left = pad_shape[1] // 2
pad_right = pad_shape[1] - pad_left
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
pads = (pad_top, pad_left, pad_bottom, pad_right)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads)
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_upper")
@staticmethod
def export_averagepool_2d_same_lower() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_LOWER",
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape_auto_pad(
"SAME_LOWER", x_shape[2:], kernel_shape, strides
)
pad_shape = get_pad_shape(
"SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_bottom = pad_shape[0] // 2
pad_top = pad_shape[0] - pad_bottom
pad_right = pad_shape[1] // 2
pad_left = pad_shape[1] - pad_right
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=np.nan,
)
pads = (pad_top, pad_left, pad_bottom, pad_right)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads)
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_same_lower")
@staticmethod
def export_averagepool_2d_pads() -> None:
"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2],
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = 2
pad_top = 2
pad_right = 2
pad_left = 2
pads = [pad_top, pad_left, pad_bottom, pad_right]
out_shape, pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides, ceil_mode=False
)
padded = np.pad(
x,
((0, 0), (0, 0), (pads[0], pads[2]), (pads[1], pads[3])),
mode="constant",
constant_values=np.nan,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads)
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_pads")
@staticmethod
def export_averagepool_2d_pads_count_include_pad() -> None:
"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2],
count_include_pad=1,
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
dilations = (1, 1)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = 2
pad_top = 2
pad_right = 2
pad_left = 2
pads = [pad_top, pad_left, pad_bottom, pad_right]
out_shape, pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides, dilations, ceil_mode=False
)
padded = np.pad(
x,
((0, 0), (0, 0), (pads[0], pads[2]), (pads[1], pads[3])),
mode="constant",
constant_values=0,
)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
"AVG",
pads,
count_include_pad=1,
)
expect(
node,
inputs=[x],
outputs=[y],
name="test_averagepool_2d_pads_count_include_pad",
)
@staticmethod
def export_averagepool_2d_strides() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 10, 10]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
strides=[3, 3],
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (5, 5)
strides = (3, 3)
out_shape, pads = get_output_shape_explicit_padding(
None, x_shape[2:], kernel_shape, strides, ceil_mode=False
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "AVG", pads)
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_strides")
@staticmethod
def export_averagepool_2d_ceil() -> None:
"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
strides=[2, 2],
ceil_mode=True,
)
x = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
]
).astype(np.float32)
y = np.array([[[[6, 7.5], [12, 13.5]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_ceil")
@staticmethod
def export_averagepool_2d_dilations() -> None:
"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[1, 1],
dilations=[2, 2],
ceil_mode=True,
)
# input shape: [1, 1, 4, 4]
x = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
]
).astype(np.float32)
y = np.array([[[[6, 7], [10, 11]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_averagepool_2d_dilations")
@staticmethod
def export_averagepool_3d_dilations() -> None:
"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2, 2],
strides=[1, 1, 1],
dilations=[2, 2, 2],
ceil_mode=True,
)
# input shape: [1, 1, 4, 4, 4]
x = np.array(
[
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
],
]
]
]
).astype(np.float32)
y = np.array([[[[[6, 7], [10, 11]], [[6, 7], [10, 11]]]]]).astype(np.float32)
expect(
node, inputs=[x], outputs=[y], name="test_averagepool_3d_dilations_small"
)
@staticmethod
def export_averagepool_3d_dilations_large() -> None:
x_shape = (32, 32, 32)
dilations = (2, 2, 2)
kernel_shape = (5, 5, 5)
strides = (3, 3, 3)
count_include_pad = 0
for count_include_pad in (0, 1):
for ceil_mode in (True, False):
node = onnx.helper.make_node(
"AveragePool",
inputs=["x"],
outputs=["y"],
kernel_shape=kernel_shape,
strides=strides,
dilations=dilations,
count_include_pad=count_include_pad,
ceil_mode=ceil_mode,
)
x = np.random.randn(1, 1, *x_shape).astype(np.float32)
out_shape, pads = get_output_shape_explicit_padding(
None,
x_shape,
kernel_shape,
strides,
dilations=dilations,
ceil_mode=ceil_mode,
)
padded = np.pad(
x,
(
(0, 0),
(0, 0),
(pads[0], pads[3]),
(pads[1], pads[4]),
(pads[2], pads[5]),
),
mode="constant",
constant_values=0 if count_include_pad == 1 else np.nan,
)
y = pool(
padded,
(1, 1, *x_shape),
kernel_shape,
strides,
out_shape,
"AVG",
pads=pads,
dilations=dilations,
count_include_pad=count_include_pad,
)
test_name = f"test_averagepool_3d_dilations_large_count_include_pad_is_{count_include_pad}_ceil_mode_is_{ceil_mode}"
expect(node, inputs=[x], outputs=[y], name=test_name)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": 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"/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_string_concat.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_batch_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cum_sum.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_prelu.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_unsqueeze.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/globalaveragepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/__init__.py": ["/onnx/backend/test/runner/__init__.py"], "/onnx/test/parser_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_regex_full_match.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/xor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/shape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_scatternd.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_optional.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/rnn.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/image_decoder.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_tree_ensemble_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_tree_ensemble_helper.py"], "/workflow_scripts/test_model_zoo.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/printer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/nonzero.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,773 | onnx/onnx | refs/heads/main | /onnx/backend/test/runner/__init__.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import functools
import glob
import os
import re
import shutil
import sys
import tarfile
import tempfile
import time
import unittest
from collections import defaultdict
from typing import Any, Callable, Iterable, Pattern, Sequence
from urllib.request import urlretrieve
import numpy as np
import onnx
import onnx.reference
from onnx import ONNX_ML, ModelProto, NodeProto, TypeProto, ValueInfoProto, numpy_helper
from onnx.backend.base import Backend
from onnx.backend.test.case.test_case import TestCase
from onnx.backend.test.loader import load_model_tests
from onnx.backend.test.runner.item import TestItem
class BackendIsNotSupposedToImplementIt(unittest.SkipTest):
pass
def retry_execute(times: int) -> Callable[[Callable[..., Any]], Callable[..., Any]]:
assert times >= 1
def wrapper(func: Callable[..., Any]) -> Callable[..., Any]:
@functools.wraps(func)
def wrapped(*args: Any, **kwargs: Any) -> Any:
for i in range(1, times + 1):
try:
return func(*args, **kwargs)
except Exception:
print(f"{i} times tried")
if i == times:
raise
time.sleep(5 * i)
return wrapped
return wrapper
class Runner:
def __init__(
self, backend: type[Backend], parent_module: str | None = None
) -> None:
self.backend = backend
self._parent_module = parent_module
self._include_patterns: set[Pattern[str]] = set()
self._exclude_patterns: set[Pattern[str]] = set()
self._xfail_patterns: set[Pattern[str]] = set()
# This is the source of the truth of all test functions.
# Properties `test_cases`, `test_suite` and `tests` will be
# derived from it.
# {category: {name: func}}
self._test_items: dict[str, dict[str, TestItem]] = defaultdict(dict)
for rt in load_model_tests(kind="node"):
self._add_model_test(rt, "Node")
for rt in load_model_tests(kind="real"):
self._add_model_test(rt, "Real")
for rt in load_model_tests(kind="simple"):
self._add_model_test(rt, "Simple")
for ct in load_model_tests(kind="pytorch-converted"):
self._add_model_test(ct, "PyTorchConverted")
for ot in load_model_tests(kind="pytorch-operator"):
self._add_model_test(ot, "PyTorchOperator")
def _get_test_case(self, name: str) -> type[unittest.TestCase]:
test_case = type(str(name), (unittest.TestCase,), {})
if self._parent_module:
test_case.__module__ = self._parent_module
return test_case
def include(self, pattern: str) -> Runner:
self._include_patterns.add(re.compile(pattern))
return self
def exclude(self, pattern: str) -> Runner:
self._exclude_patterns.add(re.compile(pattern))
return self
def xfail(self, pattern: str) -> Runner:
self._xfail_patterns.add(re.compile(pattern))
return self
def enable_report(self) -> Runner:
import pytest
for category, items_map in self._test_items.items():
for item in items_map.values():
item.func = pytest.mark.onnx_coverage(item.proto, category)(item.func)
return self
@property
def _filtered_test_items(self) -> dict[str, dict[str, TestItem]]:
filtered: dict[str, dict[str, TestItem]] = {}
for category, items_map in self._test_items.items():
filtered[category] = {}
for name, item in items_map.items():
if self._include_patterns and (
not any(include.search(name) for include in self._include_patterns)
):
item.func = unittest.skip("no matched include pattern")(item.func)
for exclude in self._exclude_patterns:
if exclude.search(name):
item.func = unittest.skip(
f'matched exclude pattern "{exclude.pattern}"'
)(item.func)
for xfail in self._xfail_patterns:
if xfail.search(name):
item.func = unittest.expectedFailure(item.func)
filtered[category][name] = item
return filtered
@property
def test_cases(self) -> dict[str, type[unittest.TestCase]]:
"""
List of test cases to be applied on the parent scope
Example usage:
globals().update(BackendTest(backend).test_cases)
"""
test_cases = {}
for category, items_map in self._filtered_test_items.items():
test_case_name = f"OnnxBackend{category}Test"
test_case = self._get_test_case(test_case_name)
for name, item in sorted(items_map.items()):
setattr(test_case, name, item.func)
test_cases[test_case_name] = test_case
return test_cases
@property
def test_suite(self) -> unittest.TestSuite:
"""
TestSuite that can be run by TestRunner
Example usage:
unittest.TextTestRunner().run(BackendTest(backend).test_suite)
"""
suite = unittest.TestSuite()
for case in sorted(
self.test_cases.values(), key=lambda cl: cl.__class__.__name__
):
suite.addTests(unittest.defaultTestLoader.loadTestsFromTestCase(case))
return suite
# For backward compatibility (we used to expose `.tests`)
@property
def tests(self) -> type[unittest.TestCase]:
"""
One single unittest.TestCase that hosts all the test functions
Example usage:
onnx_backend_tests = BackendTest(backend).tests
"""
tests = self._get_test_case("OnnxBackendTest")
for items_map in sorted(
self._filtered_test_items.values(), key=lambda cl: cl.__class__.__name__
):
for name, item in sorted(items_map.items()):
setattr(tests, name, item.func)
return tests
@classmethod
def assert_similar_outputs(
cls,
ref_outputs: Sequence[Any],
outputs: Sequence[Any],
rtol: float,
atol: float,
) -> None:
np.testing.assert_equal(len(outputs), len(ref_outputs))
for i in range(len(outputs)):
if isinstance(outputs[i], (list, tuple)):
for j in range(len(outputs[i])):
cls.assert_similar_outputs(
ref_outputs[i][j], outputs[i][j], rtol, atol
)
else:
np.testing.assert_equal(outputs[i].dtype, ref_outputs[i].dtype)
if ref_outputs[i].dtype == object: # type: ignore[attr-defined]
np.testing.assert_array_equal(outputs[i], ref_outputs[i])
else:
np.testing.assert_allclose(
outputs[i], ref_outputs[i], rtol=rtol, atol=atol
)
@classmethod
@retry_execute(3)
def download_model(
cls, model_test: TestCase, model_dir: str, models_dir: str
) -> None:
# On Windows, NamedTemporaryFile can not be opened for a
# second time
download_file = tempfile.NamedTemporaryFile(delete=False)
try:
download_file.close()
assert model_test.url
print(
f"Start downloading model {model_test.model_name} from {model_test.url}"
)
urlretrieve(model_test.url, download_file.name)
print("Done")
with tarfile.open(download_file.name) as t:
t.extractall(models_dir)
except Exception as e:
print(f"Failed to prepare data for model {model_test.model_name}: {e}")
raise
finally:
os.remove(download_file.name)
@classmethod
def prepare_model_data(cls, model_test: TestCase) -> str:
onnx_home = os.path.expanduser(
os.getenv("ONNX_HOME", os.path.join("~", ".onnx"))
)
models_dir = os.getenv("ONNX_MODELS", os.path.join(onnx_home, "models"))
model_dir: str = os.path.join(models_dir, model_test.model_name)
if not os.path.exists(os.path.join(model_dir, "model.onnx")):
if os.path.exists(model_dir):
bi = 0
while True:
dest = f"{model_dir}.old.{bi}"
if os.path.exists(dest):
bi += 1
continue
shutil.move(model_dir, dest)
break
os.makedirs(model_dir)
cls.download_model(
model_test=model_test, model_dir=model_dir, models_dir=models_dir
)
return model_dir
def _add_test(
self,
category: str,
test_name: str,
test_func: Callable[..., Any],
report_item: list[ModelProto | NodeProto | None],
devices: Iterable[str] = ("CPU", "CUDA"),
) -> None:
# We don't prepend the 'test_' prefix to improve greppability
if not test_name.startswith("test_"):
raise ValueError(f"Test name must start with test_: {test_name}")
def add_device_test(device: str) -> None:
device_test_name = f"{test_name}_{device.lower()}"
if device_test_name in self._test_items[category]:
raise ValueError(
f'Duplicated test name "{device_test_name}" in category "{category}"'
)
@unittest.skipIf( # type: ignore
not self.backend.supports_device(device),
f"Backend doesn't support device {device}",
)
@functools.wraps(test_func)
def device_test_func(*args: Any, **kwargs: Any) -> Any:
try:
return test_func(*args, device=device, **kwargs)
except BackendIsNotSupposedToImplementIt as e:
# hacky verbose reporting
if "-v" in sys.argv or "--verbose" in sys.argv:
print(f"Test {device_test_name} is effectively skipped: {e}")
self._test_items[category][device_test_name] = TestItem(
device_test_func, report_item
)
for device in devices:
add_device_test(device)
@staticmethod
def generate_dummy_data(
x: ValueInfoProto, seed: int = 0, name: str = "", random: bool = False
) -> np.ndarray:
"""
Generates a random tensor based on the input definition.
"""
if not x.type.tensor_type:
raise NotImplementedError(
f"Input expected to have tensor type. "
f"Unable to generate random data for model {name!r} and input {x}."
)
if x.type.tensor_type.elem_type != 1:
raise NotImplementedError(
f"Currently limited to float tensors. "
f"Unable to generate random data for model {name!r} and input {x}."
)
shape = tuple(
d.dim_value if d.HasField("dim_value") else 1
for d in x.type.tensor_type.shape.dim
)
if random:
gen = np.random.default_rng(seed=seed)
return gen.random(shape, np.float32)
n = np.prod(shape)
return (np.arange(n).reshape(shape) / n).astype(np.float32)
def _add_model_test(self, model_test: TestCase, kind: str) -> None:
# model is loaded at runtime, note sometimes it could even
# never loaded if the test skipped
model_marker: list[ModelProto | NodeProto | None] = [None]
def run(test_self: Any, device: str) -> None:
if model_test.url is not None and model_test.url.startswith(
"onnx/backend/test/data/light/"
):
# testing local files
model_pb_path = os.path.normpath(
os.path.join(
os.path.dirname(__file__),
"..",
"..",
"..",
"..",
model_test.url,
)
)
if not os.path.exists(model_pb_path):
raise FileNotFoundError(f"Unable to find model {model_pb_path!r}.")
onnx_home = os.path.expanduser(
os.getenv("ONNX_HOME", os.path.join("~", ".onnx"))
)
models_dir = os.getenv(
"ONNX_MODELS", os.path.join(onnx_home, "models", "light")
)
model_dir: str = os.path.join(models_dir, model_test.model_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
use_dummy = True
else:
if model_test.model_dir is None:
model_dir = self.prepare_model_data(model_test)
else:
model_dir = model_test.model_dir
model_pb_path = os.path.join(model_dir, "model.onnx")
use_dummy = False
if not ONNX_ML and "ai_onnx_ml" in model_dir:
return
model = onnx.load(model_pb_path)
model_marker[0] = model
if (
hasattr(self.backend, "is_compatible")
and callable(self.backend.is_compatible)
and not self.backend.is_compatible(model)
):
raise unittest.SkipTest("Not compatible with backend")
prepared_model = self.backend.prepare(model, device)
assert prepared_model is not None
if use_dummy:
# When the backend test goes through a test involving a
# model stored in onnx/backend/test/data/light,
# this function generates expected output coming from
# from ReferenceEvaluator run with random inputs.
# A couple of models include many Conv operators and the
# python implementation is slow (such as test_bvlc_alexnet).
with open(model_pb_path, "rb") as f:
onx = onnx.load(f)
test_data_set = os.path.join(model_dir, "test_data_set_0")
if not os.path.exists(test_data_set):
os.mkdir(test_data_set)
feeds = {}
inits = {i.name for i in onx.graph.initializer}
n_input = 0
inputs = []
for i in range(len(onx.graph.input)):
if onx.graph.input[i].name in inits:
continue
name = os.path.join(test_data_set, f"input_{n_input}.pb")
inputs.append(name)
n_input += 1
x = onx.graph.input[i]
value = self.generate_dummy_data(
x, seed=0, name=model_test.model_name, random=False
)
feeds[x.name] = value
with open(name, "wb") as f:
f.write(onnx.numpy_helper.from_array(value).SerializeToString())
# loads expected output if any available
prefix = os.path.splitext(model_pb_path)[0]
expected_outputs = []
for i in range(len(onx.graph.output)):
name = f"{prefix}_output_{i}.pb"
if os.path.exists(name):
expected_outputs.append(name)
continue
expected_outputs = None
break
if expected_outputs is None:
ref = onnx.reference.ReferenceEvaluator(onx)
outputs = ref.run(None, feeds)
for i, o in enumerate(outputs):
name = os.path.join(test_data_set, f"output_{i}.pb")
with open(name, "wb") as f:
f.write(onnx.numpy_helper.from_array(o).SerializeToString())
else:
for i, o in enumerate(expected_outputs):
name = os.path.join(test_data_set, f"output_{i}.pb")
shutil.copy(o, name)
else:
# TODO after converting all npz files to protobuf, we can delete this.
for test_data_npz in glob.glob(
os.path.join(model_dir, "test_data_*.npz")
):
test_data = np.load(test_data_npz, encoding="bytes")
inputs = list(test_data["inputs"])
outputs = list(prepared_model.run(inputs))
ref_outputs = test_data["outputs"]
self.assert_similar_outputs(
ref_outputs, outputs, rtol=model_test.rtol, atol=model_test.atol
)
for test_data_dir in glob.glob(os.path.join(model_dir, "test_data_set*")):
inputs = []
inputs_num = len(glob.glob(os.path.join(test_data_dir, "input_*.pb")))
for i in range(inputs_num):
input_file = os.path.join(test_data_dir, f"input_{i}.pb")
self._load_proto(input_file, inputs, model.graph.input[i].type)
ref_outputs = []
ref_outputs_num = len(
glob.glob(os.path.join(test_data_dir, "output_*.pb"))
)
for i in range(ref_outputs_num):
output_file = os.path.join(test_data_dir, f"output_{i}.pb")
self._load_proto(
output_file, ref_outputs, model.graph.output[i].type
)
outputs = list(prepared_model.run(inputs))
self.assert_similar_outputs(
ref_outputs, outputs, rtol=model_test.rtol, atol=model_test.atol
)
self._add_test(kind + "Model", model_test.name, run, model_marker)
def _load_proto(
self,
proto_filename: str,
target_list: list[np.ndarray | list[Any]],
model_type_proto: TypeProto,
) -> None:
with open(proto_filename, "rb") as f:
protobuf_content = f.read()
if model_type_proto.HasField("sequence_type"):
sequence = onnx.SequenceProto()
sequence.ParseFromString(protobuf_content)
target_list.append(numpy_helper.to_list(sequence))
elif model_type_proto.HasField("tensor_type"):
tensor = onnx.TensorProto()
tensor.ParseFromString(protobuf_content)
target_list.append(numpy_helper.to_array(tensor))
elif model_type_proto.HasField("optional_type"):
optional = onnx.OptionalProto()
optional.ParseFromString(protobuf_content)
target_list.append(numpy_helper.to_optional(optional)) # type: ignore[arg-type]
else:
print(
"Loading proto of that specific type (Map/Sparse Tensor) is currently not supported"
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_non_zero.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/prelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_conv.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/lppool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/hardswish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/thresholdedrelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_leaky_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sigmoid.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_pool_common.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_backend_test.py": ["/onnx/backend/base.py", "/onnx/backend/test/__init__.py", "/onnx/shape_inference.py", "/onnx/version_converter.py", "/onnx/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/backend/test/case/__init__.py": ["/onnx/backend/test/case/base.py", "/onnx/backend/test/case/utils.py"], "/onnx/reference/ops/op_instance_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/sequence_map.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_hamming_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_unique.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sqrt.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/serialization_test.py": ["/onnx/__init__.py"], "/onnx/defs/gen_shape_inference_information.py": ["/onnx/__init__.py"], "/onnx/test/elu_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/bitwiseor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/conv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/pow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_scan.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_reduce_mean.py": ["/onnx/reference/ops/_op.py"], "/onnx/tools/net_drawer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/momentum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_shape.py": ["/onnx/reference/op_run.py"], "/onnx/test/reference_evaluator_backend_test.py": ["/onnx/__init__.py", "/onnx/backend/test/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/reference/ops/op_string_normalizer.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/adam.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/defs/__init__.py"], "/onnx/backend/test/case/node/ceil.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bitwiseand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/onehot.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/cumsum.py": 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58,774 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_topk.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221,W0622
import numpy as np
from onnx.reference.op_run import OpRun
def topk_sorted_implementation(X, k, axis, largest): # type: ignore
"""
See function `_kneighbors_reduce_func
<https://github.com/scikit-learn/scikit-learn/blob/main/
sklearn/neighbors/_base.py#L304>`_.
"""
if isinstance(k, np.ndarray):
if k.size != 1:
raise RuntimeError(f"k must be an integer not {k!r}.")
k = k[0]
# This conversion is needed for distribution x86.
k = int(k)
if len(X.shape) == 2 and axis == 1:
sample_range = np.arange(X.shape[0])[:, None]
if largest == 0:
sorted_indices = np.argpartition(X, axis=axis, kth=k - 1)
sorted_indices = sorted_indices[:, :k]
# argpartition doesn't guarantee sorted order, so we sort again
sorted_indices = sorted_indices[
sample_range, np.argsort(X[sample_range, sorted_indices])
]
else:
sorted_indices = np.argpartition(-X, axis=axis, kth=k - 1)
sorted_indices = sorted_indices[:, :k]
# argpartition doesn't guarantee sorted order, so we sort again
sorted_indices = sorted_indices[
sample_range, np.argsort(-X[sample_range, sorted_indices])
]
sorted_distances = X[sample_range, sorted_indices]
return sorted_distances, sorted_indices
sorted_indices = np.argsort(X, axis=axis)
sorted_values = np.sort(X, axis=axis)
if largest:
sorted_indices = np.flip(sorted_indices, axis=axis)
sorted_values = np.flip(sorted_values, axis=axis)
ark = np.arange(k)
topk_sorted_indices = np.take(sorted_indices, ark, axis=axis)
topk_sorted_values = np.take(sorted_values, ark, axis=axis)
return topk_sorted_values, topk_sorted_indices
class _CommonTopK(OpRun):
def _common_run(self, data, ink, axis, largest=1): # type: ignore
"""
Runtime for operator *TopK*.
The implementation is not the most efficient
as it sorts everything then extracts the top *k*
values.
.. warning::
ONNX specifications may be imprecise in case of negative value
for axis. The implementation follows what `onnxruntime`
does in `top_k.cc
<https://github.com/Microsoft/onnxruntime/blob/main/onnxruntime/core/providers/cpu/math/top_k.cc#L63>`_.
"""
k = ink[0]
axis = axis if axis >= 0 else (axis + len(data.shape)) # type: ignore
sort, sorti = topk_sorted_implementation(data, k, axis, largest)
return (sort, sorti.astype(np.int64))
class TopK_1(_CommonTopK):
def _run(self, data, k=None, axis=None): # type: ignore
"""
Runtime for operator *TopK*.
The implementation is not the most efficient
as it sorts everything then extracts the top *k*
values.
.. warning::
ONNX specifications may be imprecise in case of negative value
for axis. The implementation follows what `onnxruntime`
does in `top_k.cc
<https://github.com/Microsoft/onnxruntime/blob/main/onnxruntime/core/providers/cpu/math/top_k.cc#L63>`_.
"""
return _CommonTopK._common_run(self, data, [k], axis=axis) # type: ignore
class TopK_10(_CommonTopK):
def _run(self, data, ink, axis=None): # type: ignore
"""
Runtime for operator *TopK*.
The implementation is not the most efficient
as it sorts everything then extracts the top *k*
values.
.. warning::
ONNX specifications may be imprecise in case of negative value
for axis. The implementation follows what `onnxruntime`
does in `top_k.cc
<https://github.com/Microsoft/onnxruntime/blob/main/onnxruntime/core/providers/cpu/math/top_k.cc#L63>`_.
"""
return _CommonTopK._common_run(self, data, ink, axis=axis)
class TopK_11(_CommonTopK):
def _run(self, data, ink, axis=None, largest=None, sorted=None): # type: ignore
"""
Runtime for operator *TopK*.
The implementation is not the most efficient
as it sorts everything then extracts the top *k*
values.
.. warning::
ONNX specifications may be imprecise in case of negative value
for axis. The implementation follows what `onnxruntime`
does in `top_k.cc
<https://github.com/Microsoft/onnxruntime/blob/main/onnxruntime/core/providers/cpu/math/top_k.cc#L63>`_.
"""
if sorted not in (True, 1):
raise RuntimeError("TopK does not implement anything for sorted=0.")
return _CommonTopK._common_run(self, data, ink, axis=axis, largest=largest)
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58,775 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_image_decoder.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0123,C3001,R0912,R0913,R0914,R1730,W0221,W0613
import numpy as np
from onnx.reference.op_run import OpRun
class ImageDecoder(OpRun):
def _run( # type: ignore
self,
encoded,
pixel_format="RGB",
):
try:
# pylint: disable=import-outside-toplevel`
import cv2
except ImportError as e:
raise ImportError(
"opencv-python must be installed to use the reference implementation of the ImageDecoder operator"
) from e
decoded = None
if pixel_format == "BGR":
decoded = cv2.imdecode(encoded, cv2.IMREAD_COLOR)
elif pixel_format == "RGB":
decoded = cv2.imdecode(encoded, cv2.IMREAD_COLOR)
decoded = cv2.cvtColor(decoded, cv2.COLOR_BGR2RGB)
elif pixel_format == "Grayscale":
decoded = cv2.imdecode(encoded, cv2.IMREAD_GRAYSCALE)
decoded = np.expand_dims(decoded, axis=2) # (H, W) to (H, W, 1)
else:
raise RuntimeError(f"pixel_format={pixel_format!r} is not supported.")
return (decoded,)
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"/onnx/reference/ops/op_unique.py", "/onnx/reference/ops/op_unsqueeze.py", "/onnx/reference/ops/op_upsample.py", "/onnx/reference/ops/op_where.py"], "/onnx/backend/test/case/model/gradient.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py", "/onnx/defs/__init__.py"], "/onnx/compose.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_det.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sequence_empty.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/topk.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_log_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_linear_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/center_crop_pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", 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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], 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"/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,776 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_non_max_suppression.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0902,R0911,R0912,R0913,R0914,W0221
import dataclasses
from typing import Optional, Tuple
import numpy as np
from onnx.reference.op_run import OpRun
@dataclasses.dataclass
class PrepareContext:
boxes_data_: Optional[np.ndarray] = None
boxes_size_: int = 0
scores_data_: Optional[np.ndarray] = None
scores_size_: int = 0
max_output_boxes_per_class_: Optional[np.ndarray] = None
score_threshold_: Optional[np.ndarray] = None
iou_threshold_: Optional[np.ndarray] = None
num_batches_: int = 0
num_classes_: int = 0
num_boxes_: int = 0
class SelectedIndex:
__slots__ = ("batch_index_", "class_index_", "box_index_")
def __init__(
self, batch_index: int = 0, class_index: int = 0, box_index: int = 0
) -> None:
self.batch_index_ = batch_index
self.class_index_ = class_index
self.box_index_ = box_index
def max_min(lhs: float, rhs: float) -> Tuple[float, float]:
if lhs >= rhs:
return rhs, lhs
return lhs, rhs
def suppress_by_iou(
boxes_data: np.ndarray,
box_index1: int,
box_index2: int,
center_point_box: int,
iou_threshold: float,
) -> bool:
box1 = boxes_data[box_index1]
box2 = boxes_data[box_index2]
# center_point_box_ only support 0 or 1
if center_point_box == 0:
# boxes data format [y1, x1, y2, x2]
x1_min, x1_max = max_min(box1[1], box1[3])
x2_min, x2_max = max_min(box2[1], box2[3])
intersection_x_min = max(x1_min, x2_min)
intersection_x_max = min(x1_max, x2_max)
if intersection_x_max <= intersection_x_min:
return False
y1_min, y1_max = max_min(box1[0], box1[2])
y2_min, y2_max = max_min(box2[0], box2[2])
intersection_y_min = max(y1_min, y2_min)
intersection_y_max = min(y1_max, y2_max)
if intersection_y_max <= intersection_y_min:
return False
else:
# 1 == center_point_box_ => boxes data format [x_center, y_center, width, height]
box1_width_half = box1[2] / 2
box1_height_half = box1[3] / 2
box2_width_half = box2[2] / 2
box2_height_half = box2[3] / 2
x1_min = box1[0] - box1_width_half
x1_max = box1[0] + box1_width_half
x2_min = box2[0] - box2_width_half
x2_max = box2[0] + box2_width_half
intersection_x_min = max(x1_min, x2_min)
intersection_x_max = min(x1_max, x2_max)
if intersection_x_max <= intersection_x_min:
return False
y1_min = box1[1] - box1_height_half
y1_max = box1[1] + box1_height_half
y2_min = box2[1] - box2_height_half
y2_max = box2[1] + box2_height_half
intersection_y_min = max(y1_min, y2_min)
intersection_y_max = min(y1_max, y2_max)
if intersection_y_max <= intersection_y_min:
return False
intersection_area = (intersection_x_max - intersection_x_min) * (
intersection_y_max - intersection_y_min
)
if intersection_area <= 0:
return False
area1 = (x1_max - x1_min) * (y1_max - y1_min)
area2 = (x2_max - x2_min) * (y2_max - y2_min)
union_area = area1 + area2 - intersection_area
if area1 <= 0 or area2 <= 0 or union_area <= 0:
return False
intersection_over_union = intersection_area / union_area
return intersection_over_union > iou_threshold
class BoxInfo:
def __init__(self, score: float = 0, idx: int = -1):
self.score_ = score
self.idx_ = idx
def __lt__(self, rhs) -> bool: # type: ignore
return self.score_ < rhs.score_ or ( # type: ignore
self.score_ == rhs.score_ and self.idx_ > rhs.idx_
)
def __repr__(self) -> str:
return f"BoxInfo({self.score_}, {self.idx_})"
class NonMaxSuppression(OpRun):
def get_thresholds_from_inputs(
self,
pc: PrepareContext,
max_output_boxes_per_class: int,
iou_threshold: float,
score_threshold: float,
) -> Tuple[int, float, float]:
if pc.max_output_boxes_per_class_ is not None:
max_output_boxes_per_class = max(pc.max_output_boxes_per_class_[0], 0)
if pc.iou_threshold_ is not None:
iou_threshold = pc.iou_threshold_[0]
if pc.score_threshold_ is not None:
score_threshold = pc.score_threshold_[0]
return max_output_boxes_per_class, iou_threshold, score_threshold
def prepare_compute( # type: ignore
self,
pc: PrepareContext,
boxes_tensor: np.ndarray, # float
scores_tensor: np.ndarray, # float
max_output_boxes_per_class_tensor: np.ndarray, # int
iou_threshold_tensor: np.ndarray, # float
score_threshold_tensor: np.ndarray, # float
):
pc.boxes_data_ = boxes_tensor
pc.scores_data_ = scores_tensor
if max_output_boxes_per_class_tensor.size != 0:
pc.max_output_boxes_per_class_ = max_output_boxes_per_class_tensor
if iou_threshold_tensor.size != 0:
pc.iou_threshold_ = iou_threshold_tensor
if score_threshold_tensor.size != 0:
pc.score_threshold_ = score_threshold_tensor
pc.boxes_size_ = boxes_tensor.size
pc.scores_size_ = scores_tensor.size
boxes_dims = boxes_tensor.shape
scores_dims = scores_tensor.shape
pc.num_batches_ = boxes_dims[0]
pc.num_classes_ = scores_dims[1]
pc.num_boxes_ = boxes_dims[1]
def _run( # type: ignore
self,
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
center_point_box,
):
center_point_box = center_point_box or self.center_point_box # type: ignore
pc = PrepareContext()
self.prepare_compute(
pc,
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
)
(
max_output_boxes_per_class,
iou_threshold,
score_threshold,
) = self.get_thresholds_from_inputs(pc, 0, 0, 0)
if max_output_boxes_per_class.size == 0:
return (np.empty((0,), dtype=np.int64),)
boxes_data = pc.boxes_data_
scores_data = pc.scores_data_
selected_indices = []
# std::vector<BoxInfo> selected_boxes_inside_class;
# selected_boxes_inside_class.reserve(
# std::min<size_t>(static_cast<size_t>(max_output_boxes_per_class), pc.num_boxes_));
for batch_index in range(pc.num_batches_):
for class_index in range(pc.num_classes_):
box_score_offset = (batch_index, class_index)
batch_boxes = boxes_data[batch_index] # type: ignore
# std::vector<BoxInfo> candidate_boxes;
# candidate_boxes.reserve(pc.num_boxes_);
# Filter by score_threshold_
candidate_boxes = []
class_scores = scores_data[box_score_offset] # type: ignore
if pc.score_threshold_ is not None:
for box_index in range(pc.num_boxes_):
if class_scores[box_index] > score_threshold:
candidate_boxes.append(
BoxInfo(class_scores[box_index], box_index)
)
else:
for box_index in range(pc.num_boxes_):
candidate_boxes.append(
BoxInfo(class_scores[box_index], box_index)
)
sorted_boxes = sorted(candidate_boxes)
selected_boxes_inside_class = [] # type: ignore
# Get the next box with top score, filter by iou_threshold.
while (
len(sorted_boxes) > 0
and len(selected_boxes_inside_class) < max_output_boxes_per_class
):
next_top_score = sorted_boxes[-1]
selected = True
# Check with existing selected boxes for this class,
# suppress if exceed the IOU (Intersection Over Union) threshold.
for selected_index in selected_boxes_inside_class:
if suppress_by_iou(
batch_boxes,
next_top_score.idx_,
selected_index.idx_,
center_point_box,
iou_threshold,
):
selected = False
break
if selected:
selected_boxes_inside_class.append(next_top_score)
selected_indices.append(
SelectedIndex(batch_index, class_index, next_top_score.idx_)
)
sorted_boxes.pop()
result = np.empty((len(selected_indices), 3), dtype=np.int64)
for i in range(result.shape[0]):
result[i, 0] = selected_indices[i].batch_index_
result[i, 1] = selected_indices[i].class_index_
result[i, 2] = selected_indices[i].box_index_
return (result,)
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58,777 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/logsoftmax.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def logsoftmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
x_max = np.max(x, axis=axis, keepdims=True)
tmp = np.exp(x - x_max)
s = np.sum(tmp, axis=axis, keepdims=True)
return (x - x_max) - np.log(s)
class LogSoftmax(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
)
x = np.array([[-1, 0, 1]]).astype(np.float32)
# expected output
# [[-2.4076061 -1.407606 -0.407606 ]]
y = logsoftmax(x)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_example_1")
@staticmethod
def export_logsoftmax_axis() -> None:
x = np.array([[0, 1, 2, 3], [10000, 10001, 10002, 10003]]).astype(np.float32)
# expected output
# [[-3.4401896 -2.4401896 -1.4401896 -0.44018966]
# [-3.4401896 -2.4401896 -1.4401896 -0.44018966]]
y = logsoftmax(x)
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_large_number")
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
axis=0,
)
y = logsoftmax(x, axis=0)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_0")
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
axis=1,
)
y = logsoftmax(x, axis=1)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_1")
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
axis=2,
)
y = logsoftmax(x, axis=2)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_axis_2")
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
axis=-1,
)
y = logsoftmax(x, axis=-1)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_negative_axis")
# default axis is -1
node = onnx.helper.make_node(
"LogSoftmax",
inputs=["x"],
outputs=["y"],
)
expect(node, inputs=[x], outputs=[y], name="test_logsoftmax_default_axis")
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58,778 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnx_preview_training/op_adam.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnx_preview_training._op_run_training import OpRunTraining
def _apply_adam( # type: ignore
r, t, x, g, v, h, norm_coefficient, norm_coefficient_post, alpha, beta, epsilon
): # type: ignore
# Add gradient of regularization term.
g_regularized = norm_coefficient * x + g
# Update momentum.
v_new = alpha * v + (1 - alpha) * g_regularized
# Update second-order momentum.
h_new = beta * h + (1 - beta) * (g_regularized * g_regularized)
# Compute element-wise square root.
h_sqrt = np.sqrt(h_new) + epsilon
# Adjust learning rate.
r_adjusted = None
if t > 0:
# Consider bias correction on momentums.
r_adjusted = r * np.sqrt(1 - beta**t) / (1 - alpha**t)
else:
# No bias correction on momentums.
r_adjusted = r
# Apply Adam update rule.
x_new = x - r_adjusted * (v_new / h_sqrt)
# It's possible to apply regularization in the end.
x_final = (1 - norm_coefficient_post) * x_new
return x_final, v_new, h_new
class Adam(OpRunTraining):
def _run( # type: ignore
self,
*data,
alpha=None,
beta=None,
epsilon=None,
norm_coefficient=None,
norm_coefficient_post=None,
):
if len(data) == 6:
return self._run1( # type: ignore
*data,
alpha=alpha,
beta=beta,
epsilon=epsilon,
norm_coefficient=norm_coefficient,
norm_coefficient_post=norm_coefficient_post,
)
n = (len(data) - 2) // 4
xs = []
vs = []
hs = []
for i in range(0, n):
a, b, c = self._run1( # type: ignore
*data[:2],
data[2 + i],
data[2 + n + i],
data[2 + n * 2 + i],
data[2 + n * 3 + i],
alpha=alpha,
beta=beta,
epsilon=epsilon,
norm_coefficient=norm_coefficient,
norm_coefficient_post=norm_coefficient_post,
)
xs.append(a.astype(np.float32))
vs.append(b.astype(np.float32))
hs.append(c.astype(np.float32))
return tuple(xs + vs + hs)
def _run1( # type: ignore
self,
r,
t,
x,
g,
v,
h,
alpha=None,
beta=None,
epsilon=None,
norm_coefficient=None,
norm_coefficient_post=None,
):
x_new, v_new, h_new = _apply_adam(
r,
t,
x,
g,
v,
h,
norm_coefficient,
norm_coefficient_post,
alpha,
beta,
epsilon,
)
return x_new, v_new, h_new
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58,779 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_affine_grid.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,R0915,R1702,R1716,W0221
import numpy as np
from onnx.reference.op_run import OpRun
def construct_original_grid(data_size, align_corners):
is_2d = len(data_size) == 2
size_zeros = np.zeros(data_size)
original_grid = [np.ones(data_size)]
for dim, dim_size in enumerate(data_size):
if align_corners == 1:
step = 2.0 / (dim_size - 1)
start = -1
stop = 1 + 0.0001
a = np.arange(start, stop, step)
else:
step = 2.0 / dim_size
start = -1 + step / 2
stop = 1
a = np.arange(start, stop, step)
if dim == 0:
if is_2d:
y = np.reshape(a, (dim_size, 1)) + size_zeros
original_grid = [y, *original_grid]
else:
z = np.reshape(a, (dim_size, 1, 1)) + size_zeros
original_grid = [z, *original_grid]
elif dim == 1:
if is_2d:
x = np.reshape(a, (1, dim_size)) + size_zeros
original_grid = [x, *original_grid]
else:
y = np.reshape(a, (1, dim_size, 1)) + size_zeros
original_grid = [y, *original_grid]
else:
x = np.reshape(a, (1, dim_size)) + size_zeros
original_grid = [x, *original_grid]
return np.stack(original_grid, axis=2 if is_2d else 3)
def apply_affine_transform(theta_n, original_grid_homo):
# theta_n: (N, 2, 3) for 2D, (N, 3, 4) for 3D
# original_grid_homo: (H, W, 3) for 2D, (D, H, W, 4) for 3D
assert (
theta_n.ndim == 3
), "theta_n shall have shape of (N, 2, 3) for 2D, (N, 3, 4) for 3D"
if original_grid_homo.ndim == 3:
N, dim_2d, dim_homo = theta_n.shape
assert dim_2d == 2 and dim_homo == 3
H, W, dim_homo = original_grid_homo.shape
assert dim_homo == 3
# reshape to [H * W, dim_homo] and then transpose to [dim_homo, H * W]
original_grid_transposed = np.transpose(
np.reshape(original_grid_homo, (H * W, dim_homo))
)
grid_n = np.matmul(
theta_n, original_grid_transposed
) # shape (N, dim_2d, H * W)
# transpose to (N, H * W, dim_2d) and then reshape to (N, H, W, dim_2d)
grid = np.reshape(np.transpose(grid_n, (0, 2, 1)), (N, H, W, dim_2d))
return grid
else:
assert original_grid_homo.ndim == 4
N, dim_3d, dim_homo = theta_n.shape
assert dim_3d == 3 and dim_homo == 4
D, H, W, dim_homo = original_grid_homo.shape
assert dim_homo == 4
# reshape to [D * H * W, dim_homo] and then transpose to [dim_homo, D * H * W]
original_grid_transposed = np.transpose(
np.reshape(original_grid_homo, (D * H * W, dim_homo))
)
grid_n = np.matmul(
theta_n, original_grid_transposed
) # shape (N, dim_3d, D * H * W)
# transpose to (N, D * H * W, dim_3d) and then reshape to (N, D, H, W, dim_3d)
grid = np.reshape(np.transpose(grid_n, (0, 2, 1)), (N, D, H, W, dim_3d))
return grid
class AffineGrid(OpRun):
def _run(self, theta, size, align_corners=None): # type: ignore
align_corners = align_corners or self.align_corners # type: ignore
_, _, *data_size = size
original_grid = construct_original_grid(data_size, align_corners)
grid = apply_affine_transform(theta, original_grid)
return (grid,)
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58,780 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_lp_normalization.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class LpNormalization(OpRunUnaryNum):
def _run(self, x, axis=None, p=None): # type: ignore
axis = axis or self.axis # type: ignore
p = p or self.p # type: ignore
norm = np.power(np.power(x, p).sum(axis=axis), 1.0 / p) # type: ignore
norm = np.expand_dims(norm, axis) # type: ignore
return ((x / norm).astype(x.dtype),)
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58,781 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_rnn.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221,W0613
import numpy as np
from onnx.reference.op_run import OpRun
class CommonRNN(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
if self.direction in ("forward", "reverse"): # type: ignore
self.num_directions = 1 # type: ignore
elif self.direction == "bidirectional": # type: ignore
self.num_directions = 2 # type: ignore
else:
raise RuntimeError(f"Unknown direction {self.direction!r}.") # type: ignore
if (
self.activation_alpha is not None # type: ignore
and len(self.activation_alpha) != self.num_directions # type: ignore
):
raise RuntimeError(
f"activation_alpha must have the same size as num_directions={self.num_directions}." # type: ignore
)
if (
self.activation_beta is not None # type: ignore
and len(self.activation_beta) != self.num_directions # type: ignore
):
raise RuntimeError(
f"activation_beta must have the same size as num_directions={self.num_directions}." # type: ignore
)
self.f1 = self.choose_act(
self.activations[0], # type: ignore
self.activation_alpha[0] # type: ignore
if self.activation_alpha is not None and len(self.activation_alpha) > 0 # type: ignore
else None,
self.activation_beta[0] # type: ignore
if self.activation_beta is not None and len(self.activation_beta) > 0 # type: ignore
else None,
)
if len(self.activations) > 1: # type: ignore
self.f2 = self.choose_act(
self.activations[1], # type: ignore
self.activation_alpha[1] # type: ignore
if self.activation_alpha is not None and len(self.activation_alpha) > 1 # type: ignore
else None,
self.activation_beta[1] # type: ignore
if self.activation_beta is not None and len(self.activation_beta) > 1 # type: ignore
else None,
)
self.n_outputs = len(onnx_node.output)
def choose_act(self, name, alpha, beta): # type: ignore
if name in ("Tanh", "tanh"):
return self._f_tanh
if name in ("Affine", "affine"):
return lambda x: x * alpha + beta
raise RuntimeError(f"Unknown activation function {name!r}.")
def _f_tanh(self, x): # type: ignore
return np.tanh(x)
def _step(self, X, R, B, W, H_0): # type: ignore
h_list = []
H_t = H_0
for x in np.split(X, X.shape[0], axis=0):
H = self.f1(
np.dot(x, np.transpose(W))
+ np.dot(H_t, np.transpose(R))
+ np.add(*np.split(B, 2))
)
h_list.append(H)
H_t = H
concatenated = np.concatenate(h_list)
if self.num_directions == 1:
output = np.expand_dims(concatenated, 1)
return output, h_list[-1]
def _run( # type: ignore
self,
X,
W,
R,
B=None,
sequence_lens=None,
initial_h=None,
activation_alpha=None,
activation_beta=None,
activations=None,
clip=None,
direction=None,
hidden_size=None,
layout=None,
):
# TODO: support overridden attributes.
self.num_directions = W.shape[0]
if self.num_directions == 1:
R = np.squeeze(R, axis=0)
W = np.squeeze(W, axis=0)
if B is not None:
B = np.squeeze(B, axis=0)
if sequence_lens is not None:
sequence_lens = np.squeeze(sequence_lens, axis=0)
if initial_h is not None:
initial_h = np.squeeze(initial_h, axis=0)
hidden_size = R.shape[-1]
batch_size = X.shape[1]
X = X if layout == 0 else np.swapaxes(X, 0, 1)
b = B if B is not None else np.zeros(2 * hidden_size, dtype=X.dtype)
h_0 = (
initial_h
if initial_h is not None
else np.zeros((batch_size, hidden_size), dtype=X.dtype)
)
B = b
H_0 = h_0
else:
raise NotImplementedError(
f"Unsupported value {self.num_directions} for num_directions and operator {self.__class__.__name__!r}."
)
Y, Y_h = self._step(X, R, B, W, H_0)
if layout == 1:
Y = np.transpose(Y, [2, 0, 1, 3])
Y_h = Y[:, :, -1, :]
Y = Y.astype(X.dtype)
return (Y,) if self.n_outputs == 1 else (Y, Y_h)
class RNN_7(CommonRNN):
pass
class RNN_14(CommonRNN):
pass
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58,782 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/not.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Not(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Not",
inputs=["x"],
outputs=["not"],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(bool)
expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_2d")
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_3d")
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
expect(node, inputs=[x], outputs=[np.logical_not(x)], name="test_not_4d")
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58,783 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_reduce_sum.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunReduceNumpy
class ReduceSum_1(OpRunReduceNumpy):
def _run(self, x, axes=None, keepdims=None): # type: ignore # pylint: disable=W0221
axes = tuple(axes) if axes is not None else None
res = np.sum(x, axis=axes, keepdims=keepdims, dtype=x.dtype)
if keepdims == 0 and not isinstance(res, np.ndarray):
# The runtime must return a numpy array of a single float.
res = np.array(res)
return (res,)
class ReduceSum_13(OpRunReduceNumpy):
def _run(self, x, axes=None, keepdims=None, noop_with_empty_axes=None): # type: ignore
if (axes is None or axes.shape == (0,)) and noop_with_empty_axes:
return (x,)
axes = self.handle_axes(axes)
try:
res = np.sum(x, axis=axes, keepdims=keepdims, dtype=x.dtype)
if keepdims == 0 and not isinstance(res, np.ndarray):
# The runtime must return a numpy array of a single float.
res = np.array(res)
return (res,) # type: ignore
except TypeError as e:
raise TypeError(
f"Unable to reduce shape {x.shape!r} with axes={axes!r} and keepdims={keepdims}."
) from e
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,784 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_mean.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.reference.op_run import OpRun
class Mean(OpRun):
def _run(self, *args): # type: ignore
res = args[0].copy()
for m in args[1:]:
res += m
return ((res / len(args)).astype(args[0].dtype),)
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58,785 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_roi_align.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0902,R0912,R0913,R0914,R0915,R1702,W0221
from typing import Tuple
import numpy as np
from onnx.reference.op_run import OpRun
class PreCalc:
def __init__(self, pos1=0, pos2=0, pos3=0, pos4=0, w1=0, w2=0, w3=0, w4=0): # type: ignore
self.pos1 = pos1
self.pos2 = pos2
self.pos3 = pos3
self.pos4 = pos4
self.w1 = w1
self.w2 = w2
self.w3 = w3
self.w4 = w4
def __repr__(self) -> str:
return f"PreCalc({self.pos1},{self.pos2},{self.pos3},{self.pos4},{self.w1},{self.w2},{self.w3},{self.w4})"
class RoiAlign(OpRun):
@staticmethod
def pre_calc_for_bilinear_interpolate( # type: ignore
height: int,
width: int,
pooled_height: int,
pooled_width: int,
iy_upper: int,
ix_upper: int,
roi_start_h,
roi_start_w,
bin_size_h,
bin_size_w,
roi_bin_grid_h: int,
roi_bin_grid_w: int,
pre_calc,
):
pre_calc_index = 0
for ph in range(pooled_height):
for pw in range(pooled_width):
for iy in range(iy_upper):
yy = (
roi_start_h
+ ph * bin_size_h
+ (iy + 0.5) * bin_size_h / roi_bin_grid_h
)
for ix in range(ix_upper):
xx = (
roi_start_w
+ pw * bin_size_w
+ (ix + 0.5) * bin_size_w / roi_bin_grid_w
)
x = xx
y = yy
# deal with: inverse elements are out of feature map boundary
if y < -1.0 or y > height or x < -1.0 or x > width:
pc = pre_calc[pre_calc_index]
pc.pos1 = 0
pc.pos2 = 0
pc.pos3 = 0
pc.pos4 = 0
pc.w1 = 0
pc.w2 = 0
pc.w3 = 0
pc.w4 = 0
pre_calc_index += 1
continue
y = max(y, 0)
x = max(x, 0)
y_low = int(y)
x_low = int(x)
if y_low >= height - 1:
y_high = y_low = height - 1
y = y_low
else:
y_high = y_low + 1
if x_low >= width - 1:
x_high = x_low = width - 1
x = x_low
else:
x_high = x_low + 1
ly = y - y_low
lx = x - x_low
hy = 1.0 - ly
hx = 1.0 - lx
w1 = hy * hx
w2 = hy * lx
w3 = ly * hx
w4 = ly * lx
# save weights and indeces
pc = PreCalc()
pc.pos1 = y_low * width + x_low
pc.pos2 = y_low * width + x_high
pc.pos3 = y_high * width + x_low
pc.pos4 = y_high * width + x_high
pc.w1 = w1
pc.w2 = w2
pc.w3 = w3
pc.w4 = w4
pre_calc[pre_calc_index] = pc
pre_calc_index += 1
@staticmethod
def roi_align_forward( # type: ignore
output_shape: Tuple[int, int, int, int],
bottom_data,
spatial_scale,
height: int,
width: int,
sampling_ratio,
bottom_rois,
num_roi_cols: int,
top_data,
mode,
half_pixel: bool,
batch_indices_ptr,
):
n_rois = output_shape[0]
channels = output_shape[1]
pooled_height = output_shape[2]
pooled_width = output_shape[3]
# 100 is a random chosed value, need be tuned
for n in range(n_rois):
index_n = n * channels * pooled_width * pooled_height
# bottom_rois
offset_bottom_rois = n * num_roi_cols
roi_batch_ind = batch_indices_ptr[n]
# Do not using rounding; this implementation detail is critical.
offset = 0.5 if half_pixel else 0.0
roi_start_w = bottom_rois[offset_bottom_rois + 0] * spatial_scale - offset
roi_start_h = bottom_rois[offset_bottom_rois + 1] * spatial_scale - offset
roi_end_w = bottom_rois[offset_bottom_rois + 2] * spatial_scale - offset
roi_end_h = bottom_rois[offset_bottom_rois + 3] * spatial_scale - offset
roi_width = roi_end_w - roi_start_w
roi_height = roi_end_h - roi_start_h
if not half_pixel:
# Force malformed ROIs to be 1x1
roi_width = max(roi_width, 1.0)
roi_height = max(roi_height, 1.0)
bin_size_h = roi_height / pooled_height
bin_size_w = roi_width / pooled_width
# We use roi_bin_grid to sample the grid and mimic integral
roi_bin_grid_h = (
int(sampling_ratio)
if sampling_ratio > 0
else int(np.ceil(roi_height / pooled_height))
)
roi_bin_grid_w = (
int(sampling_ratio)
if sampling_ratio > 0
else int(np.ceil(roi_width / pooled_width))
)
# We do average (integral) pooling inside a bin
count = int(max(roi_bin_grid_h * roi_bin_grid_w, 1))
# we want to precalculate indices and weights shared by all channels,
# this is the key point of optimization
pre_calc = [
PreCalc()
for i in range(
roi_bin_grid_h * roi_bin_grid_w * pooled_width * pooled_height
)
]
RoiAlign.pre_calc_for_bilinear_interpolate(
height,
width,
pooled_height,
pooled_width,
roi_bin_grid_h,
roi_bin_grid_w,
roi_start_h,
roi_start_w,
bin_size_h,
bin_size_w,
roi_bin_grid_h,
roi_bin_grid_w,
pre_calc,
)
for c in range(channels):
index_n_c = index_n + c * pooled_width * pooled_height
# bottom_data
offset_bottom_data = int(
(roi_batch_ind * channels + c) * height * width
)
pre_calc_index = 0
for ph in range(pooled_height):
for pw in range(pooled_width):
index = index_n_c + ph * pooled_width + pw
output_val = 0.0
if mode == "avg": # avg pooling
for _iy in range(roi_bin_grid_h):
for _ix in range(roi_bin_grid_w):
pc = pre_calc[pre_calc_index]
output_val += (
pc.w1
* bottom_data[offset_bottom_data + pc.pos1]
+ pc.w2
* bottom_data[offset_bottom_data + pc.pos2]
+ pc.w3
* bottom_data[offset_bottom_data + pc.pos3]
+ pc.w4
* bottom_data[offset_bottom_data + pc.pos4]
)
pre_calc_index += 1
output_val /= count
else: # max pooling
max_flag = False
for _iy in range(roi_bin_grid_h):
for _ix in range(roi_bin_grid_w):
pc = pre_calc[pre_calc_index]
val = max(
pc.w1
* bottom_data[offset_bottom_data + pc.pos1],
pc.w2
* bottom_data[offset_bottom_data + pc.pos2],
pc.w3
* bottom_data[offset_bottom_data + pc.pos3],
pc.w4
* bottom_data[offset_bottom_data + pc.pos4],
)
if not max_flag:
output_val = val
max_flag = True
else:
output_val = max(output_val, val)
pre_calc_index += 1
top_data[index] = output_val
def _run( # type: ignore
self,
X,
rois,
batch_indices,
coordinate_transformation_mode=None,
mode=None,
output_height=None,
output_width=None,
sampling_ratio=None,
spatial_scale=None,
):
coordinate_transformation_mode = coordinate_transformation_mode or self.coordinate_transformation_mode # type: ignore
mode = mode or self.mode # type: ignore
output_height = output_height or self.output_height # type: ignore
output_width = output_width or self.output_width # type: ignore
sampling_ratio = sampling_ratio or self.sampling_ratio # type: ignore
spatial_scale = spatial_scale or self.spatial_scale # type: ignore
num_channels = X.shape[1]
num_rois = batch_indices.shape[0]
num_roi_cols = rois.shape[1]
y_dims = (num_rois, num_channels, output_height, output_width)
Y = np.empty(y_dims, dtype=X.dtype).flatten()
self.roi_align_forward(
y_dims,
X.flatten(),
spatial_scale,
X.shape[2], # height, 3
X.shape[3], # width, 4
sampling_ratio,
rois.flatten(),
num_roi_cols,
Y,
mode.lower(),
coordinate_transformation_mode.lower() == "half_pixel",
batch_indices.flatten(),
)
return (Y.reshape(y_dims).astype(X.dtype),)
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58,786 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_center_crop_pad.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0914,R1702,W0221
import numpy as np
from onnx.reference.op_run import OpRun
class CenterCropPad(OpRun):
def _run(self, input_data, shape, axes=None): # type: ignore
axes = axes or self.axes # type: ignore
input_rank = len(input_data.shape)
if axes is None:
axes = list(range(input_rank))
else:
axes = [axis if axis >= 0 else axis + input_rank for axis in axes]
pad_slices = [slice(0, s) for s in input_data.shape]
crop_slices = [slice(0, s) for s in input_data.shape]
new_shape = list(input_data.shape)
for a, sh in zip(axes, shape):
dim = input_data.shape[a]
if sh == a:
pass
elif sh < dim:
new_shape[a] = sh
d = dim - sh
if d % 2 == 0:
d //= 2
sl = slice(d, dim - d)
else:
d //= 2
sl = slice(d, dim - d - 1)
crop_slices[a] = sl
else: # sh > dim
new_shape[a] = sh
d = sh - dim
if d % 2 == 0:
d //= 2
sl = slice(d, sh - d)
else:
d //= 2
sl = slice(d, sh - d - 1)
pad_slices[a] = sl
res = np.zeros(tuple(new_shape), dtype=input_data.dtype)
cropped = input_data[tuple(crop_slices)]
res[tuple(pad_slices)] = cropped
return (res,)
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58,787 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/nonmaxsuppression.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class NonMaxSuppression(Base):
@staticmethod
def export_nonmaxsuppression_suppress_by_IOU() -> None:
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_suppress_by_IOU",
)
@staticmethod
def export_nonmaxsuppression_suppress_by_IOU_and_scores() -> None:
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.4]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_suppress_by_IOU_and_scores",
)
@staticmethod
def export_nonmaxsuppression_flipped_coordinates() -> None:
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[1.0, 1.0, 0.0, 0.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, 0.9, 1.0, -0.1],
[0.0, 10.0, 1.0, 11.0],
[1.0, 10.1, 0.0, 11.1],
[1.0, 101.0, 0.0, 100.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_flipped_coordinates",
)
@staticmethod
def export_nonmaxsuppression_limit_output_size() -> None:
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([2]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_limit_output_size",
)
@staticmethod
def export_nonmaxsuppression_single_box() -> None:
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array([[[0.0, 0.0, 1.0, 1.0]]]).astype(np.float32)
scores = np.array([[[0.9]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_single_box",
)
@staticmethod
def export_nonmaxsuppression_identical_boxes() -> None:
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.0, 1.0, 1.0],
]
]
).astype(np.float32)
scores = np.array(
[[[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]]]
).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 0]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_identical_boxes",
)
@staticmethod
def export_nonmaxsuppression_center_point_box_format() -> None:
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
center_point_box=1,
)
boxes = np.array(
[
[
[0.5, 0.5, 1.0, 1.0],
[0.5, 0.6, 1.0, 1.0],
[0.5, 0.4, 1.0, 1.0],
[0.5, 10.5, 1.0, 1.0],
[0.5, 10.6, 1.0, 1.0],
[0.5, 100.5, 1.0, 1.0],
]
]
).astype(np.float32)
scores = np.array([[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]).astype(np.float32)
max_output_boxes_per_class = np.array([3]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array([[0, 0, 3], [0, 0, 0], [0, 0, 5]]).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_center_point_box_format",
)
@staticmethod
def export_nonmaxsuppression_two_classes() -> None:
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
]
]
).astype(np.float32)
scores = np.array(
[[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3], [0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]
).astype(np.float32)
max_output_boxes_per_class = np.array([2]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array(
[[0, 0, 3], [0, 0, 0], [0, 1, 3], [0, 1, 0]]
).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_two_classes",
)
@staticmethod
def export_nonmaxsuppression_two_batches() -> None:
node = onnx.helper.make_node(
"NonMaxSuppression",
inputs=[
"boxes",
"scores",
"max_output_boxes_per_class",
"iou_threshold",
"score_threshold",
],
outputs=["selected_indices"],
)
boxes = np.array(
[
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
],
[
[0.0, 0.0, 1.0, 1.0],
[0.0, 0.1, 1.0, 1.1],
[0.0, -0.1, 1.0, 0.9],
[0.0, 10.0, 1.0, 11.0],
[0.0, 10.1, 1.0, 11.1],
[0.0, 100.0, 1.0, 101.0],
],
]
).astype(np.float32)
scores = np.array(
[[[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]], [[0.9, 0.75, 0.6, 0.95, 0.5, 0.3]]]
).astype(np.float32)
max_output_boxes_per_class = np.array([2]).astype(np.int64)
iou_threshold = np.array([0.5]).astype(np.float32)
score_threshold = np.array([0.0]).astype(np.float32)
selected_indices = np.array(
[[0, 0, 3], [0, 0, 0], [1, 0, 3], [1, 0, 0]]
).astype(np.int64)
expect(
node,
inputs=[
boxes,
scores,
max_output_boxes_per_class,
iou_threshold,
score_threshold,
],
outputs=[selected_indices],
name="test_nonmaxsuppression_two_batches",
)
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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_non_zero.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/prelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_conv.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/lppool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/hardswish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/thresholdedrelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_leaky_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sigmoid.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_pool_common.py": 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58,788 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/dropout.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx import helper
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def dropout(X, drop_probability=0.5, seed=0, training_mode=False, return_mask=False): # type: ignore
if drop_probability == 0 or training_mode is False:
if return_mask is True:
return X, np.ones(X.shape, dtype=bool)
else:
return X
np.random.seed(seed)
mask = np.random.uniform(0, 1.0, X.shape) >= drop_probability
scale = 1 / (1 - drop_probability)
if return_mask:
return mask * X * scale, mask.astype(bool)
return mask * X * scale
class Dropout(Base):
# Inferencing tests.
@staticmethod
def export_default() -> None:
seed = np.int64(0)
node = onnx.helper.make_node("Dropout", inputs=["x"], outputs=["y"], seed=seed)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = dropout(x)
expect(node, inputs=[x], outputs=[y], name="test_dropout_default")
@staticmethod
def export_default_ratio() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r"], outputs=["y"], seed=seed
)
r = np.float32(0.1)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = dropout(x, r)
expect(node, inputs=[x, r], outputs=[y], name="test_dropout_default_ratio")
@staticmethod
def export_default_mask() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y, z = dropout(x, return_mask=True)
expect(node, inputs=[x], outputs=[y, z], name="test_dropout_default_mask")
@staticmethod
def export_default_mask_ratio() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r"], outputs=["y", "z"], seed=seed
)
r = np.float32(0.1)
x = np.random.randn(3, 4, 5).astype(np.float32)
y, z = dropout(x, r, return_mask=True)
expect(
node, inputs=[x, r], outputs=[y, z], name="test_dropout_default_mask_ratio"
)
# Training tests.
@staticmethod
def export_training_default() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.5)
t = np.bool_(True)
y = dropout(x, r, training_mode=t)
expect(
node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_default"
)
@staticmethod
def export_training_default_ratio_mask() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.5)
t = np.bool_(True)
y, z = dropout(x, r, training_mode=t, return_mask=True)
expect(
node,
inputs=[x, r, t],
outputs=[y, z],
name="test_training_dropout_default_mask",
)
@staticmethod
def export_training() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.75)
t = np.bool_(True)
y = dropout(x, r, training_mode=t)
expect(node, inputs=[x, r, t], outputs=[y], name="test_training_dropout")
@staticmethod
def export_training_ratio_mask() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.75)
t = np.bool_(True)
y, z = dropout(x, r, training_mode=t, return_mask=True)
expect(
node, inputs=[x, r, t], outputs=[y, z], name="test_training_dropout_mask"
)
@staticmethod
def export_training_default_zero_ratio() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.0)
t = np.bool_(True)
y = dropout(x, r, training_mode=t)
expect(
node, inputs=[x, r, t], outputs=[y], name="test_training_dropout_zero_ratio"
)
@staticmethod
def export_training_default_zero_ratio_mask() -> None:
seed = np.int64(0)
node = onnx.helper.make_node(
"Dropout", inputs=["x", "r", "t"], outputs=["y", "z"], seed=seed
)
x = np.random.randn(3, 4, 5).astype(np.float32)
r = np.float32(0.0)
t = np.bool_(True)
y, z = dropout(x, r, training_mode=t, return_mask=True)
expect(
node,
inputs=[x, r, t],
outputs=[y, z],
name="test_training_dropout_zero_ratio_mask",
)
# Old dropout tests
@staticmethod
def export_default_old() -> None:
node = onnx.helper.make_node(
"Dropout",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = x
expect(
node,
inputs=[x],
outputs=[y],
name="test_dropout_default_old",
opset_imports=[helper.make_opsetid("", 11)],
)
@staticmethod
def export_random_old() -> None:
node = onnx.helper.make_node(
"Dropout",
inputs=["x"],
outputs=["y"],
ratio=0.2,
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = x
expect(
node,
inputs=[x],
outputs=[y],
name="test_dropout_random_old",
opset_imports=[helper.make_opsetid("", 11)],
)
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["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,789 | onnx/onnx | refs/heads/main | /onnx/test/model_inference_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import typing
import unittest
import onnx
import onnx.parser
import onnx.shape_inference
class TestModelInference(unittest.TestCase):
def _check(self, model_text: str, *expected: int):
"""Check that the model inference infers the expected types for outputs.
Restricted to the simple case of tensor types, so expected types specify
only the element type (ints corresponding to onnx.TensorProto.DataType).
"""
model = onnx.parser.parse_model(model_text)
inferred = onnx.shape_inference.infer_shapes(model)
outputs = inferred.graph.output
for output, expected_elem_type in zip(outputs, expected):
inferred_type = output.type
self.assertTrue(inferred_type.HasField("tensor_type"))
tensor_type = inferred_type.tensor_type
self.assertTrue(tensor_type.HasField("elem_type"))
elem_type = tensor_type.elem_type
self.assertEqual(elem_type, expected_elem_type)
def test_mi_basic(self):
"""Test that model inference infers model output type."""
model = """
<
ir_version: 7,
opset_import: [ "" : 17]
>
agraph (float[N] x) => (y)
{
y = Cast<to=6> (x)
}
"""
self._check(model, onnx.TensorProto.INT32)
def test_mi_function(self):
"""Test use of functions."""
model = """
<
ir_version: 7,
opset_import: [ "" : 17, "local" : 1]
>
agraph (float[N] x) => (y)
{
y = local.cast(x)
}
<
opset_import: [ "" : 17 ],
domain: "local"
>
cast (x) => (y)
{
y = Cast<to=6> (x)
}
"""
self._check(model, onnx.TensorProto.INT32)
def test_mi_function_attr(self):
"""Test use of functions with attribute parameters."""
model = """
<
ir_version: 7,
opset_import: [ "" : 17, "local" : 1]
>
agraph (float[N] x) => (y)
{
y = local.cast<target=6>(x)
}
<
opset_import: [ "" : 17 ],
domain: "local"
>
cast<target>(x) => (y)
{
y = Cast<to:int = @target> (x)
}
"""
self._check(model, onnx.TensorProto.INT32)
def test_mi_function_subgraph_attr(self):
"""Test use of function attributes within subgraphs."""
model = """
<
ir_version: 7,
opset_import: [ "" : 17, "local" : 1]
>
agraph (float[N] x, bool flag) => (y)
{
y = local.cast<target=6>(x, flag)
}
<
opset_import: [ "" : 17 ],
domain: "local"
>
cast<target>(x, flag) => (y)
{
y = If (flag) <
then_branch = g1 () => (z_then) { z_then = Cast<to:int = @target> (x) },
else_branch = g2 () => (z_else) { z_else = Cast<to:int = @target> (x) }
>
}
"""
self._check(model, onnx.TensorProto.INT32)
def test_mi_function_multiple_calls(self):
"""Test use of multiple invocation of functions."""
model = """
<
ir_version: 7,
opset_import: [ "" : 17, "local" : 1]
>
agraph (float[N] x, bool flag) => (y, z)
{
y = local.cast<target=6>(x, flag)
z = local.cast<target=7>(x, flag)
}
<
opset_import: [ "" : 17 ],
domain: "local"
>
cast<target>(x, flag) => (y)
{
y = If (flag) <
then_branch = g1 () => (z_then) { z_then = Cast<to:int = @target> (x) },
else_branch = g2 () => (z_else) { z_else = Cast<to:int = @target> (x) }
>
}
"""
self._check(model, onnx.TensorProto.INT32, onnx.TensorProto.INT64)
def _check_shape(self, model_text: str, *expected: typing.Sequence[int]):
"""Check that the model inference infers the expected shapes for outputs.
Restricted to the simple case of tensor type outputs with completely
known shapes.
"""
model = onnx.parser.parse_model(model_text)
inferred = onnx.shape_inference.infer_shapes(model, True, True, True)
outputs = inferred.graph.output
for output, expected_shape in zip(outputs, expected):
inferred_type = output.type
self.assertTrue(inferred_type.HasField("tensor_type"))
tensor_type = inferred_type.tensor_type
self.assertTrue(tensor_type.HasField("shape"))
inferred_shape = tensor_type.shape
self.assertEqual(len(inferred_shape.dim), len(expected_shape))
for inferred_dim, expected_dim in zip(inferred_shape.dim, expected_shape):
self.assertTrue(inferred_dim.HasField("dim_value"))
self.assertEqual(inferred_dim.dim_value, expected_dim)
def test_mi_constant(self):
model = """
<
ir_version: 7,
opset_import: [ "" : 17]
>
mymodel (float[4, 8, 16] x) => (y) {
shape = Constant<value_ints=[8,4,16]>()
y = Reshape(x, shape)
}
"""
self._check_shape(model, [8, 4, 16])
def test_mi_constant_2(self):
model = """
<
ir_version: 7,
opset_import: [ "" : 17]
>
mymodel (float[4, 8, 16] x) => (y) {
shape = Constant<value_ints=[4,2,8]>()
two = Constant<value_int=2>()
shape2 = Mul(shape, two)
y = Reshape(x, shape2)
}
"""
self._check_shape(model, [8, 4, 16])
def test_mi_constant_in_function(self):
model = """
<
ir_version: 7,
opset_import: [ "" : 17, "local" : 1]
>
main (float x) => (y, z) {
y, z = local.expand(x)
}
<
opset_import: [ "" : 17 ],
domain: "local"
>
expand (x) => (y, z) {
shape1 = Constant<value = int64[2] {4,4}>()
shape2 = Constant<value = int64[3] {8,8,8}>()
z = Expand (x, shape2)
y = Expand (x, shape1)
}
"""
self._check_shape(model, [4, 4], [8, 8, 8])
def test_mi_function_default_attr(self):
"""Test use of default values of function attributes."""
model = """
<ir_version: 7, opset_import: [ "" : 17, "local" : 1]>
agraph (float[N] x) => (y, z)
{
y = local.cast <target=6> (x) # casts to INT32 type (encoding value 6)
z = local.cast (x) # uses default-attribute value of 1 (FLOAT type)
}
<opset_import: [ "" : 17 ], domain: "local">
cast <target: int = 1> (x) => (y)
{
y = Cast <to:int = @target> (x)
}
"""
self._check(model, onnx.TensorProto.INT32, onnx.TensorProto.FLOAT)
if __name__ == "__main__":
unittest.main()
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": 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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,790 | onnx/onnx | refs/heads/main | /onnx/test/inliner_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from onnx import inliner, parser
class InlinerTest(unittest.TestCase):
def test_basic(self):
model = parser.parse_model(
"""
<ir_version: 8, opset_import: [ "" : 17, "local" : 1 ]>
agraph (float[N] X) => (float[N] Y)
{
Y = local.foo (X)
}
<opset_import: [ "" : 17, "local" : 1 ], domain: "local">
foo (x) => (y) {
temp = Add(x, x)
y = local.bar(temp)
}
<opset_import: [ "" : 17 ], domain: "local">
bar (x) => (y) {
y = Mul (x, x)
}
"""
)
inlined = inliner.inline_local_functions(model)
inlined_nodes = inlined.graph.node
# function-call should be replaced by Add, followed by Mul
self.assertEqual(len(inlined_nodes), 2)
self.assertEqual(inlined_nodes[0].op_type, "Add")
self.assertEqual(inlined_nodes[1].op_type, "Mul")
if __name__ == "__main__":
unittest.main()
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58,791 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/argmin.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def argmin_use_numpy(data: np.ndarray, axis: int = 0, keepdims: int = 1) -> np.ndarray:
result = np.argmin(data, axis=axis)
if keepdims == 1:
result = np.expand_dims(result, axis)
return result.astype(np.int64)
def argmin_use_numpy_select_last_index(
data: np.ndarray, axis: int = 0, keepdims: int = True
) -> np.ndarray:
data = np.flip(data, axis)
result = np.argmin(data, axis=axis)
result = data.shape[axis] - result - 1
if keepdims:
result = np.expand_dims(result, axis)
return result.astype(np.int64)
class ArgMin(Base):
@staticmethod
def export_no_keepdims() -> None:
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
node = onnx.helper.make_node(
"ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# The content of result is : [[1, 0]]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_no_keepdims_example",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 4]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node, inputs=[data], outputs=[result], name="test_argmin_no_keepdims_random"
)
@staticmethod
def export_keepdims() -> None:
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
node = onnx.helper.make_node(
"ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# The content of result is : [[1], [0]]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node, inputs=[data], outputs=[result], name="test_argmin_keepdims_example"
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 1, 4]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node, inputs=[data], outputs=[result], name="test_argmin_keepdims_random"
)
@staticmethod
def export_default_axes_keepdims() -> None:
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
keepdims = 1
node = onnx.helper.make_node(
"ArgMin", inputs=["data"], outputs=["result"], keepdims=keepdims
)
# The content of result is : [[0], [0]]
result = argmin_use_numpy(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_default_axis_example",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [1, 3, 4]
result = argmin_use_numpy(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_default_axis_random",
)
@staticmethod
def export_negative_axis_keepdims() -> None:
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
node = onnx.helper.make_node(
"ArgMin", inputs=["data"], outputs=["result"], axis=axis, keepdims=keepdims
)
# The content of result is : [[1], [0]]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_negative_axis_keepdims_example",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 3, 1]
result = argmin_use_numpy(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_negative_axis_keepdims_random",
)
@staticmethod
def export_no_keepdims_select_last_index() -> None:
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
node = onnx.helper.make_node(
"ArgMin",
inputs=["data"],
outputs=["result"],
axis=axis,
keepdims=keepdims,
select_last_index=True,
)
# result: [[1, 0]]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_no_keepdims_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 4]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_no_keepdims_random_select_last_index",
)
@staticmethod
def export_keepdims_select_last_index() -> None:
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
node = onnx.helper.make_node(
"ArgMin",
inputs=["data"],
outputs=["result"],
axis=axis,
keepdims=keepdims,
select_last_index=True,
)
# result: [[1], [0]]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_keepdims_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 1, 4]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_keepdims_random_select_last_index",
)
@staticmethod
def export_default_axes_keepdims_select_last_index() -> None:
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
keepdims = 1
node = onnx.helper.make_node(
"ArgMin",
inputs=["data"],
outputs=["result"],
keepdims=keepdims,
select_last_index=True,
)
# result: [[0, 0]]
result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_default_axis_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [1, 3, 4]
result = argmin_use_numpy_select_last_index(data, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_default_axis_random_select_last_index",
)
@staticmethod
def export_negative_axis_keepdims_select_last_index() -> None:
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
node = onnx.helper.make_node(
"ArgMin",
inputs=["data"],
outputs=["result"],
axis=axis,
keepdims=keepdims,
select_last_index=True,
)
# result: [[1], [0]]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_negative_axis_keepdims_example_select_last_index",
)
data = np.random.uniform(-10, 10, [2, 3, 4]).astype(np.float32)
# result's shape: [2, 3, 1]
result = argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
expect(
node,
inputs=[data],
outputs=[result],
name="test_argmin_negative_axis_keepdims_random_select_last_index",
)
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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,792 | onnx/onnx | refs/heads/main | /onnx/reference/op_run.py | # SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0415,R0912
from __future__ import annotations
import abc
from typing import Any, ClassVar, Iterable
import numpy as np
from onnx import TensorProto
from onnx.defs import get_all_schemas_with_history, get_schema, onnx_opset_version
from onnx.helper import make_node, make_tensor_type_proto, np_dtype_to_tensor_dtype
from onnx.numpy_helper import to_array
from onnx.onnx_pb import AttributeProto, GraphProto, NodeProto, TypeProto
from onnx.reference.custom_element_types import (
bfloat16,
float8e4m3fn,
float8e4m3fnuz,
float8e5m2,
float8e5m2fnuz,
)
def _split_class_name(name): # type: ignore
if "_" in name:
prefix, vers = name.rsplit("_", maxsplit=1)
try:
v = int(vers)
except ValueError:
return name, None
return prefix, v
return name, None
class RuntimeTypeError(RuntimeError):
"""
Raised when a type of a variable is unexpected.
"""
class RuntimeContextError(RuntimeError):
"""
Raised when the context is missing but an context dependent implementation is defined for an operator.
"""
class RuntimeImplementationError(NotImplementedError):
"""
Raised when no implementation was found for an operator.
"""
class DefaultNone:
"""
Default value for parameters when the parameter is not set
but the operator has a default behavior for it.
"""
class RefAttrName:
"""
Implements a link between a parameter of a function
and an attribute in node.
:param name: name of the input
"""
def __init__(self, name: str):
self.name = name
def __repr__(self) -> str:
"usual"
return f"{self.__class__.__name__}({self.name!r})"
def _build_schemas() -> dict[str, type]:
res: dict[str, type] = {}
for schema in get_all_schemas_with_history():
# Multiple version can coexist. The last one is kept.
if schema.name in res:
if schema.domain != res[schema.name].domain: # type: ignore
raise NotImplementedError(
f"This function assumes every operator has a unique name {schema.name!r} " # type: ignore
f"even accross multiple domains {schema.domain!r} and {res[schema.name].domain!r}." # type: ignore
)
if schema.since_version > res[schema.name].since_version: # type: ignore
# We keep the most recent one.
res[schema.name] = schema # type: ignore
else:
res[schema.name] = schema # type: ignore
res[schema.name + "_" + str(schema.since_version)] = schema # type: ignore
return res
_schemas = _build_schemas()
class OnnxType:
def __init__(self, type_proto: TypeProto):
if not isinstance(type_proto, TypeProto):
raise TypeError(f"type_proto {type(type_proto)} must be of type TypeProto.")
self.type_proto = type_proto
def __repr__(self) -> str:
return f"OnnxType({self.type_proto!r})"
class SparseTensor:
"""
Simple representation of a sparse tensor.
It is based on numpy but does not require scipy.
"""
def __init__(
self, values: np.ndarray, indices: np.ndarray, shape: tuple[int]
) -> None:
self.values = values
self.indices = indices
self.shape = shape
@property
def dtype(self) -> Any:
return self.values.dtype
def to_sparse_tensor(att: AttributeProto) -> SparseTensor:
"""
Hosts a sparse tensor.
"""
shape = tuple(d for d in att.dims) # type: ignore[attr-defined]
return SparseTensor(to_array(att.values), to_array(att.indices), shape) # type: ignore
def to_array_extended(tensor: TensorProto) -> np.ndarray:
"""
Similar to :func:`to_array` but deals with bfloat16,
float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz.
"""
elem_type = tensor.data_type
if elem_type == TensorProto.BFLOAT16:
data = tensor.int32_data
shape = tuple(tensor.dims)
y = np.empty(shape, dtype=bfloat16).ravel()
for i, d in enumerate(data):
y[i] = d
return y.reshape(shape)
if elem_type in (
TensorProto.FLOAT8E4M3FN,
TensorProto.FLOAT8E4M3FNUZ,
TensorProto.FLOAT8E5M2,
TensorProto.FLOAT8E5M2FNUZ,
):
m = {
TensorProto.FLOAT8E4M3FN: float8e4m3fn,
TensorProto.FLOAT8E4M3FNUZ: float8e4m3fnuz,
TensorProto.FLOAT8E5M2: float8e5m2,
TensorProto.FLOAT8E5M2FNUZ: float8e5m2fnuz,
}
if tensor.HasField("raw_data"):
data = tensor.raw_data # type: ignore[assignment]
else:
data = tensor.int32_data
shape = tuple(tensor.dims)
y = np.empty(shape, dtype=m[elem_type]).ravel() # type: ignore[index]
for i, d in enumerate(data):
y[i] = d
return y.reshape(shape)
return to_array(tensor)
class Graph:
__slots__ = ("g",)
def __init__(self, g: GraphProto) -> None:
self.g = g
class OpRun(abc.ABC):
"""
Ancestor to all operators in this subfolder.
:param onnx_node: `onnx` node
:param run_params: additional parameters such as `verbose`, `opsets`
(it can be more than one if the operator has a subgraph),
`log` for a logging function
:param schema: operator schema
"""
op_domain = ""
_attribute_conversion_functions: ClassVar[dict[Any, Any]] = {
AttributeProto.FLOAT: lambda att: np.float32(att.f),
AttributeProto.FLOATS: lambda att: [np.float32(f) for f in att.floats],
AttributeProto.GRAPH: lambda att: Graph(att.g),
AttributeProto.GRAPHS: lambda att: [Graph(g) for g in att.graphs],
AttributeProto.INT: lambda att: int(att.i),
AttributeProto.INTS: lambda att: [int(i) for i in att.ints],
AttributeProto.SPARSE_TENSOR: lambda att: to_sparse_tensor(att.sparse_tensor),
AttributeProto.SPARSE_TENSORS: lambda att: [
to_sparse_tensor(t) for t in att.sparse_tensors
],
AttributeProto.STRING: lambda att: att.s.decode("utf-8"),
AttributeProto.STRINGS: lambda att: [s.decode("utf-8") for s in att.strings],
AttributeProto.TENSOR: lambda att: to_array_extended(att.t),
AttributeProto.TENSORS: lambda att: [to_array_extended(t) for t in att.tensors],
AttributeProto.TYPE_PROTO: lambda att: OnnxType(att.tp),
AttributeProto.TYPE_PROTOS: lambda att: [OnnxType(t) for t in att.type_protos],
}
def __init__(
self, onnx_node: NodeProto, run_params: dict[str, Any], schema: Any = None
):
if not isinstance(run_params, dict):
raise TypeError(f"run_params must be a dictionary not {type(run_params)}.")
for att in ["opsets", "new_ops"]:
if att not in run_params:
raise RuntimeError(
f"Attribute {att!r} must be in run_params, only "
f"{sorted(run_params)} was found."
)
if "log" not in run_params:
raise KeyError("run_params must contains key 'log'.")
self.onnx_node = onnx_node
self.run_params = run_params
if schema is None:
if hasattr(self.__class__, "op_schema"):
self._schema = self.__class__.op_schema
elif self.__class__.__name__ in _schemas:
self._schema = _schemas[self.__class__.__name__]
elif onnx_node.op_type in _schemas:
self._schema = _schemas[onnx_node.op_type]
else:
self._schema = None # type: ignore
else:
self._schema = schema
self.has_subgraph = False
self._load_attributes()
def _log(self, pattern, *args): # type: ignore
self.run_params["log"](pattern, *args)
def _extract_attribute_value(
self, att: AttributeProto, ref_att: AttributeProto | None = None
) -> Any:
"""
Converts an attribute value into a python value.
"""
if att.type == AttributeProto.GRAPH:
from onnx.reference.reference_evaluator import (
ReferenceEvaluator, # type: ignore
)
new_ops = self.run_params.get("new_ops", None)
return ReferenceEvaluator(
att.g,
opsets=self.run_params["opsets"],
verbose=max(0, self.run_params.get("verbose", 0) - 2),
new_ops=None if new_ops is None else list(new_ops.values()),
)
if att.type in OpRun._attribute_conversion_functions:
return OpRun._attribute_conversion_functions[att.type](att) # type: ignore
if ref_att is None:
raise AttributeError(
f"Unable to convert attribute {att.name!r} type {att.type!r} "
f"from node type {self.onnx_node.op_type!r}, "
f"domain {self.onnx_node.domain!r}\n{att}."
)
raise AttributeError(
f"Unable to convert default value for {ref_att.name!r} type {att.type!r} "
f"from node type {self.onnx_node.op_type!r}, "
f"domain {self.onnx_node.domain!r}\n{att}\n{ref_att}."
)
@staticmethod
def _evaluate_subgraph(context, value, attributes):
return value.run(None, context or {}, attributes=attributes)
def _load_attributes(self) -> None:
"Checks and loads attributes."
self.has_linked_attribute = False
added_attributes = []
for att in self.onnx_node.attribute:
name = att.name
if att.ref_attr_name:
value = RefAttrName(att.ref_attr_name)
self.has_linked_attribute = True
else:
value = self._extract_attribute_value(att)
setattr(self, name, value)
added_attributes.append(name)
if att.type == AttributeProto.GRAPH:
self.has_subgraph = True
self.has_linked_attribute |= value.has_linked_attribute # type: ignore
setattr(
self,
f"_run_{att.name}",
lambda context, value=value, attributes=None: OpRun._evaluate_subgraph(
context, value, attributes
),
)
if self._schema and self.onnx_node.op_type not in {"Constant"}:
for k, v in self._schema.attributes.items(): # type: ignore
if not hasattr(self, k):
if getattr(v, "required", True):
raise RuntimeError(
f"Attribute {k!r} is expected based on ONNX specifications "
f"for node {self.onnx_node.op_type!r}."
)
if hasattr(v, "default_value"):
if v.default_value.type == 0:
# default value is undefined, it depends on the inputs
value = None # type: ignore
else:
value = self._extract_attribute_value(v.default_value, v)
setattr(self, k, value)
added_attributes.append(k)
self.attributes_names_ = set(added_attributes)
@staticmethod
def implicit_inputs(graph: GraphProto) -> list[str]:
"""
Returns all varibles not registered as inputs and not produced by
an node inside the graph. This inputs are part of the context
existing in the graph calling this one.
"""
if not isinstance(graph, GraphProto):
raise TypeError(f"Unexpected type {type(graph)!r}.")
local = set()
known = set()
for init in graph.initializer:
known.add(init.name)
for sparse_init in graph.sparse_initializer:
known.add(sparse_init.name) # type: ignore
for inp in graph.input:
known.add(inp.name)
for node in graph.node:
for o in node.output:
known.add(o)
for i in node.input:
if i not in known:
local.add(i)
return list(local)
@property
def input(self) -> Iterable[str]:
"Returns node attribute `input`."
return self.onnx_node.input # type: ignore
@property
def output(self) -> Iterable[str]:
"Returns node attribute `output`."
return self.onnx_node.output # type: ignore
@property
def op_type(self) -> str:
"Returns node attribute `op_type`."
return self.onnx_node.op_type # type: ignore
@property
def domain(self) -> str:
"Returns node attribute `domain`."
return self.onnx_node.domain # type: ignore
def need_context(self) -> bool:
"""
Tells the runtime if this node needs the context
(all the results produced so far) as it may silently access
one of them (operator Scan, If, Loop).
The default answer is `False`.
"""
return False
def __str__(self) -> str:
atts = [self.__class__.__name__ + "(", f" op_type={self.onnx_node.op_type}"]
for k, v in sorted(self.__dict__.items()):
if k in {"desc", "onnx_node"}:
continue
if "a" <= k[0] <= "z" and k[-1] != "_":
atts.append(f" {k}={v},")
atts.append(")")
return "\n".join(atts)
@abc.abstractmethod
def _run(self, *args, **kwargs): # type: ignore
"""
Should be overwritten.
:param args: operator inputs
:param kwargs: optional inputs and overriden attributes,
an attribute may be overridden if it belongs to a function,
in this case, the same instance of OpRun can be called
with different values of the same attribute.
:return: outputs
"""
raise NotImplementedError(
f"Method '_run' must be overwritten for operator {self.__class__.__name__!r}."
)
def run(self, *args, linked_attributes=None, context=None): # type: ignore
"""
Calls method ``_run``, catches exceptions,
displays a longer error message.
:param args: inputs
:param linked_attributes: used if this has an attriute linked
to the attribute of the function it belongs to
:param context: if this node is part of the subgraph, `context`
is a dictionary with the values this node may use
:return: tuple of results
"""
if self.need_context():
if context is None:
raise RuntimeError(
f"This node if type {type(self)} needs context to be filled."
)
elif context is not None:
raise RuntimeError(
f"This node if type {type(self)} does not need any contextbut one is given."
)
if self.has_linked_attribute and linked_attributes is None:
raise ValueError(
f"This node {type(self)} has linked attributes but None are given in parameter 'linked_attributes'."
)
if not self.has_linked_attribute and linked_attributes is not None:
raise ValueError(
f"This node {type(self)} has no linked attribute but some are given in parameter "
f"'linked_attributes' {set(linked_attributes)}."
)
overridden_attributes = {}
if self.has_linked_attribute:
if linked_attributes is None:
raise AttributeError(
f"One attribute is linked but no linked value is provided, "
f"in class {type(self)}."
)
for att in self.attributes_names_:
v = getattr(self, att)
if isinstance(v, RefAttrName):
if v.name not in linked_attributes:
raise ValueError(
f"Unable to find a value for linked attribute {att!r} in {linked_attributes!r} "
f"in node {type(self)}."
)
overridden_attributes[att] = linked_attributes[v.name]
self._log("-- begin %s.run(%d inputs)", self.__class__.__name__, len(args))
kwargs = {}
for att in self.attributes_names_:
if att in overridden_attributes:
continue
if not hasattr(self, att):
raise NameError(
f"Attribute {att!r} is missing in operator {self.__class__.__name__!r}."
)
kwargs[att] = getattr(self, att)
if self.has_subgraph:
if self.has_linked_attribute and not linked_attributes:
raise RuntimeError(
f"A subgraph has linked attribute but none was given to {type(self)}."
)
kwargs["attributes"] = linked_attributes
if context is not None:
kwargs["context"] = context
try:
if overridden_attributes:
res = self._run(*args, **overridden_attributes, **kwargs)
else:
res = self._run(*args, **kwargs)
except (TypeError, AttributeError) as e:
raise TypeError(
f"Issues with types {[type(_) for _ in args]} and attributes "
f"{sorted(kwargs)} and linked attributes={sorted(overridden_attributes)} "
f"(operator {self.__class__.__name__!r})."
) from e
self._log("-- done %s.run -> %d outputs", self.__class__.__name__, len(res))
if not isinstance(res, tuple):
raise TypeError(
f"Method '_run' of class {self.__class__.__name__!r} does not return a tuple but {type(res)}."
)
if len(res) == 0:
raise ValueError(
f"Method '_run' of class {self.__class__.__name__!r} does not return any result."
)
if any(isinstance(t, tuple) for t in res):
dtypes = [type(t) for t in res]
raise TypeError(
f"One of the results returned by method '_run' of class {self.__class__.__name__!r} "
f"is a tuple, this is no onnx correponding type (Map, List, Tensor, SparseTensor). "
f"All returned types: {dtypes!r}."
)
return res
@classmethod
def infer_name(cls):
name = cls.__name__
if "_" not in name:
return name, onnx_opset_version()
name, vers = name.rsplit("_", 1)
try:
i_vers = int(vers)
except ValueError:
return cls.__name__, onnx_opset_version()
return name, i_vers
@classmethod
def make_node(
cls,
n_inputs: int | None = None,
n_outputs: int | None = None,
**kwargs: Any,
) -> NodeProto: # type: ignore
"""
Creates an ONNX node for this class based on the given information.
:param n_inputs: number of inputs (default is defined by the operator schema)
:param n_outputs: number of outputs (default is defined by the operator schema)
:param verbose: verbosity
:param kwargs: node attributes
:return: NodeProto
Method :meth:`eval <onnx.reference.op_run.OpRun.eval>` creates an onnx node
returned by method :meth:`make_node <onnx.reference.op_run.OpRun.make_node>`.
.. exec_code::
import numpy as np
from onnx.reference.ops._op_list import Celu
onnx_node = Celu.make_node(alpha=0.5)
print(onnx_node)
"""
op_type, opset = cls.infer_name()
domain = cls.op_domain
schema = None
if n_inputs is None:
if schema is None:
schema = get_schema(op_type, opset, domain)
n_inputs = schema.min_input
if n_outputs is None:
if schema is None:
schema = get_schema(op_type, opset, domain)
n_outputs = schema.min_output
names_in = [f"x{i}" for i in range(n_inputs)]
names_out = [f"y{i}" for i in range(n_outputs)]
node = make_node(op_type, names_in, names_out, **kwargs)
return node
@classmethod
def create(
cls,
n_inputs: int | None = None,
n_outputs: int | None = None,
verbose: int = 0,
**kwargs: Any,
) -> Any:
"""
Instantiates this class based on the given information.
:param n_inputs: number of inputs (default is defined by the operator schema)
:param n_outputs: number of outputs (default is defined by the operator schema)
:param verbose: verbosity
:param kwargs: node attributes
:return: NodeProto
"""
def log_function(pattern: str, *args: Any) -> None:
if verbose > 1:
print(pattern % tuple(args))
node = cls.make_node(n_inputs, n_outputs, **kwargs)
run_params = {
"verbose": verbose,
"log": log_function,
"new_ops": None,
"opsets": {"": onnx_opset_version()},
}
cl = cls(node, run_params)
return cl
@classmethod
def eval(
cls,
*args: list[Any],
n_outputs: int | None = None,
verbose: int = 0,
**kwargs: Any,
) -> Any: # type: ignore
"""
Evaluates this operator.
:param args: inputs
:param n_outputs: number of outputs (default is defined by the operator schema)
:param verbose: verbosity
:param kwargs: node attributes
:return: NodeProto
"""
inst = cls.create(len(args), n_outputs=n_outputs, verbose=verbose, **kwargs)
res = inst.run(*args)
if len(res) == 1:
return res[0]
return res
class OpRunExpand(OpRun):
"""
Class any operator to avoid must inherit from.
"""
def __init__(
self, onnx_node: NodeProto, log_function: Any, impl: Any = None
): # pylint: disable=super-init-not-called
raise RuntimeError(
f"The reference implementation must not use this node ({type(self)})."
)
def _run(self, *inputs, **kwargs):
raise RuntimeError(
f"The reference implementation must not use this node ({type(self)})."
)
class OpFunction(OpRun):
"""
Runs a custom function.
"""
def __init__(
self,
onnx_node: NodeProto,
log_function: Any,
impl: Any = None,
attributes: dict[str, Any] | None = None,
):
if impl is None:
raise RuntimeError(
f"impl cannot be None for node type {onnx_node.op_type!r} "
f"from domain {onnx_node.domain!r}."
)
OpRun.__init__(self, onnx_node, log_function)
self.impl_ = impl
# The function implementation is the same whenever the function is called
# but the attributes may be different at every call.
self.attributes_ = {
name: getattr(self, name)
for name in getattr(self.impl_, "attributes_", attributes) # type: ignore[union-attr]
}
def _run(self, *inputs, **kwargs): # type: ignore # pylint: disable=W0221
return self._run_impl(self.impl_, *inputs, **kwargs)
def _run_impl(self, impl, *inputs, **kwargs): # type: ignore # pylint: disable=W0221
if len(impl.input_names) != len(inputs):
raise RuntimeError(
f"Mismatch lengths between the number of inputs {len(inputs)} "
f"and the expected number of inputs {len(impl.inputs)} "
f"for node {self.op_type!r} from domain {self.domain!r}."
)
feeds = dict(zip(impl.input_names, inputs))
attributes = self.attributes_.copy()
attributes.update(kwargs)
results = impl.run(None, feeds, attributes=attributes)
if len(impl.output_names) != len(results):
raise RuntimeError(
f"Mismatch lengths between the number of outputs {len(results)} "
f"and the expected number of outputs {len(impl.output_names)} "
f"for node {self.op_type!r} from domain {self.domain!r}."
)
return tuple(results)
class OpFunctionContextDependant(OpFunction):
"""
The function can be instantiated but only at execution time.
An instance of OpFunction is created everytime to node is executed.
This is needed when the schema of an operator defines a context dependant function.
"""
def __init__(self, onnx_node: NodeProto, log_function: Any, parent: Any = None):
OpFunction.__init__(self, onnx_node, log_function, impl=self, attributes={})
self.parent = parent
version = parent.opsets[onnx_node.domain]
self.schema_ = get_schema(onnx_node.op_type, version, onnx_node.domain)
def _run(self, *inputs, **kwargs):
# Input types are known. They are used to properly
# created the body for this operator.
types = []
for t in inputs:
try:
ttype = np_dtype_to_tensor_dtype(t.dtype)
except KeyError as e:
if t.dtype == float8e4m3fn:
ttype = TensorProto.FLOAT8E4M3FN # type: ignore[attr-defined]
elif t.dtype == float8e4m3fnuz:
ttype = TensorProto.FLOAT8E4M3FNUZ # type: ignore[attr-defined]
elif t.dtype == float8e5m2:
ttype = TensorProto.FLOAT8E5M2 # type: ignore[attr-defined]
elif t.dtype == float8e5m2fnuz:
ttype = TensorProto.FLOAT8E5M2FNUZ # type: ignore[attr-defined]
elif t.dtype == bfloat16:
ttype = TensorProto.BLOFAT16 # type: ignore[attr-defined]
else:
raise e
types.append(make_tensor_type_proto(ttype, t.shape))
cl = self.parent._load_impl( # pylint: disable=protected-access
self.onnx_node, types
)
inst = cl(self.onnx_node, self.run_params)
return self._run_impl(inst.impl_, *inputs, **kwargs)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,793 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_max_unpool.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0200,R0913,R0914,W0221
import numpy as np
from onnx.reference.op_run import OpRun
class MaxUnpool(OpRun):
def _run(self, X, indices, output_shape=None, kernel_shape=None, pads=None, strides=None): # type: ignore
pooling_dims = len(X.shape) - 2
if pooling_dims > 3:
raise NotImplementedError(
f"Unsupported pooling size {pooling_dims} for operator MaxUnpool."
)
kernel_shape = kernel_shape or self.kernel_shape # type: ignore
pads = pads or self.pads # type: ignore
strides = strides or self.strides # type: ignore
if strides is None:
strides = [1 for d in kernel_shape]
if pads is None:
pads = [0 for d in range(len(kernel_shape) * 2)]
inferred_shape = np.empty((len(X.shape),), dtype=np.int64)
inferred_shape[0] = X.shape[0]
inferred_shape[1] = X.shape[1]
for dim in range(0, len(kernel_shape)):
inferred_shape[dim + 2] = (
(X.shape[dim + 2] - 1) * strides[dim]
- (pads[dim] + pads[len(kernel_shape) + dim])
+ kernel_shape[dim]
)
if output_shape is None:
shape = inferred_shape
else:
shape = output_shape
total_elements = np.prod(X.shape)
Y = np.zeros((np.prod(inferred_shape),), dtype=X.dtype)
I_data = indices.flatten()
X_data = X.flatten()
for cur_elem in range(total_elements):
Y[I_data[cur_elem]] = X_data[cur_elem]
Y = Y.reshape(tuple(inferred_shape))
res = np.zeros(shape, dtype=Y.dtype)
slices = tuple(slice(0, i) for i in inferred_shape)
res[slices] = Y
return (res,)
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58,794 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reversesequence.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ReverseSequence(Base):
@staticmethod
def export_reversesequence_time() -> None:
node = onnx.helper.make_node(
"ReverseSequence",
inputs=["x", "sequence_lens"],
outputs=["y"],
time_axis=0,
batch_axis=1,
)
x = np.array(
[
[0.0, 4.0, 8.0, 12.0],
[1.0, 5.0, 9.0, 13.0],
[2.0, 6.0, 10.0, 14.0],
[3.0, 7.0, 11.0, 15.0],
],
dtype=np.float32,
)
sequence_lens = np.array([4, 3, 2, 1], dtype=np.int64)
y = np.array(
[
[3.0, 6.0, 9.0, 12.0],
[2.0, 5.0, 8.0, 13.0],
[1.0, 4.0, 10.0, 14.0],
[0.0, 7.0, 11.0, 15.0],
],
dtype=np.float32,
)
expect(
node,
inputs=[x, sequence_lens],
outputs=[y],
name="test_reversesequence_time",
)
@staticmethod
def export_reversesequence_batch() -> None:
node = onnx.helper.make_node(
"ReverseSequence",
inputs=["x", "sequence_lens"],
outputs=["y"],
time_axis=1,
batch_axis=0,
)
x = np.array(
[
[0.0, 1.0, 2.0, 3.0],
[4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0],
[12.0, 13.0, 14.0, 15.0],
],
dtype=np.float32,
)
sequence_lens = np.array([1, 2, 3, 4], dtype=np.int64)
y = np.array(
[
[0.0, 1.0, 2.0, 3.0],
[5.0, 4.0, 6.0, 7.0],
[10.0, 9.0, 8.0, 11.0],
[15.0, 14.0, 13.0, 12.0],
],
dtype=np.float32,
)
expect(
node,
inputs=[x, sequence_lens],
outputs=[y],
name="test_reversesequence_batch",
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": 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58,795 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/celu.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Celu(Base):
@staticmethod
def export() -> None:
alpha = 2.0
node = onnx.helper.make_node(
"Celu",
inputs=["X"],
outputs=["Y"],
alpha=alpha,
)
input_data = np.array(
[
[
[[0.8439683], [0.5665144], [0.05836735]],
[[0.02916367], [0.12964272], [0.5060197]],
[[0.79538304], [0.9411346], [0.9546573]],
],
[
[[0.17730942], [0.46192095], [0.26480448]],
[[0.6746842], [0.01665257], [0.62473077]],
[[0.9240844], [0.9722341], [0.11965699]],
],
[
[[0.41356155], [0.9129373], [0.59330076]],
[[0.81929934], [0.7862604], [0.11799799]],
[[0.69248444], [0.54119414], [0.07513223]],
],
],
dtype=np.float32,
)
# Calculate expected output data
positive_input = np.maximum(0, input_data)
negative_input = np.minimum(0, alpha * (np.exp(input_data / alpha) - 1))
expected_output = positive_input + negative_input
expect(node, inputs=[input_data], outputs=[expected_output], name="test_celu")
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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,796 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_where.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Where(OpRun):
def _run(self, condition, x, y): # type: ignore
if (
x.dtype != y.dtype
and x.dtype not in (object,)
and x.dtype.type is not np.str_
and y.dtype.type is not np.str_
):
raise RuntimeError(
f"x and y should share the same dtype {x.dtype} != {y.dtype}"
)
return (np.where(condition, x, y).astype(x.dtype),)
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58,797 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnx_preview_training/op_momentum.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
from onnx.reference.ops.aionnx_preview_training._op_run_training import OpRunTraining
def _apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta): # type: ignore
# Add gradient of regularization term.
g_regularized = norm_coefficient * x + g
# Coefficient of gradient should be 1 at the first iteration.
beta_adjusted = beta if t > 0 else 1
# Update momentum.
v_new = alpha * v + beta_adjusted * g_regularized
# Apply SG with momentum update rule.
x_new = x - r * v_new
return x_new, v_new
def _apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta): # type: ignore
# Add gradient of regularization term.
g_regularized = norm_coefficient * x + g
# Coefficient of gradient should be 1 at the first iteration.
beta_adjusted = beta if t > 0 else 1
# Update momentum.
v_new = alpha * v + beta_adjusted * g_regularized
# Apply Nesterov with momentum update rule.
x_new = x - r * (g_regularized + alpha * v_new)
return x_new, v_new
class Momentum(OpRunTraining):
def _run(self, *data, alpha=None, beta=None, mode=None, norm_coefficient=None): # type: ignore
if len(data) == 5:
r, t, x, g, v = data
return self._run1( # type: ignore
r,
t,
x,
g,
v,
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
mode=mode,
)
n = (len(data) - 2) // 3
xs = []
vs = []
for i in range(0, n):
a, b = self._run1( # type: ignore
*data[:2],
data[2 + i],
data[2 + n + i],
data[2 + n * 2 + i],
norm_coefficient=norm_coefficient,
alpha=alpha,
beta=beta,
mode=mode,
)
xs.append(a)
vs.append(b)
return tuple(xs + vs)
def _run1(self, r, t, x, g, v, mode="standard", norm_coefficient=None, alpha=None, beta=None): # type: ignore
if mode == "standard":
x_new, v_new = _apply_momentum(r, t, x, g, v, norm_coefficient, alpha, beta)
else:
x_new, v_new = _apply_nesterov(r, t, x, g, v, norm_coefficient, alpha, beta)
return x_new, v_new
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58,798 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/test_case.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from dataclasses import dataclass
from typing import Optional, Sequence, Tuple
import numpy as np
import onnx
@dataclass
class TestCase:
name: str
model_name: str
url: Optional[str]
model_dir: Optional[str]
model: Optional[onnx.ModelProto]
data_sets: Optional[Sequence[Tuple[Sequence[np.ndarray], Sequence[np.ndarray]]]]
kind: str
rtol: float
atol: float
# Tell PyTest this isn't a real test.
__test__: bool = False
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58,799 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_erf.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from math import erf
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Erf(OpRunUnaryNum):
def __init__(self, onnx_node, run_params): # type: ignore
OpRunUnaryNum.__init__(self, onnx_node, run_params)
self._erf = np.vectorize(erf)
def _run(self, x): # type: ignore
return (self._erf(x).astype(x.dtype),)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,800 | onnx/onnx | refs/heads/main | /onnx/test/function_inference_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from typing import Sequence
from shape_inference_test import TestShapeInferenceHelper
import onnx
import onnx.helper
import onnx.parser
import onnx.shape_inference
from onnx import AttributeProto, TypeProto
float_type_ = onnx.helper.make_tensor_type_proto(1, None)
uint8_type_ = onnx.helper.make_tensor_type_proto(2, None)
int8_type_ = onnx.helper.make_tensor_type_proto(3, None)
int32_type_ = onnx.helper.make_tensor_type_proto(6, None)
float16_type_ = onnx.helper.make_tensor_type_proto(10, None)
no_type_ = TypeProto()
class TestFunctionInference(TestShapeInferenceHelper):
def _check(
self,
function_text: str,
input_types: Sequence[TypeProto],
attributes: Sequence[AttributeProto],
expected_output_types: Sequence[TypeProto],
):
function = onnx.parser.parse_function(function_text)
result = onnx.shape_inference.infer_function_output_types(
function, input_types, attributes
)
self.assertEqual(len(expected_output_types), len(result))
for expected, actual in zip(expected_output_types, result):
self._compare_value_infos(expected, actual)
def _check_fails(
self,
function_text: str,
input_types: Sequence[TypeProto],
attributes: Sequence[AttributeProto],
):
function = onnx.parser.parse_function(function_text)
def invoke_inference():
onnx.shape_inference.infer_function_output_types(
function, input_types, attributes
)
self.assertRaises(onnx.shape_inference.InferenceError, invoke_inference)
def test_fi_basic(self):
code = """
<opset_import: [ "" : 18 ], domain: "local">
f (y, z) => (w) {
x = Add(y, z)
w = Mul(x, y)
}
"""
self._check(code, [float_type_, float_type_], [], [float_type_])
self._check(code, [int32_type_, int32_type_], [], [int32_type_])
self._check_fails(code, [float_type_, int32_type_], [])
def test_fi_attribute(self):
code = """
<opset_import: [ "" : 18 ], domain: "local">
CastTo <dtype> (x) => (y) {
y = Cast <to : int = @dtype> (x)
}
"""
dtype_6 = onnx.helper.make_attribute("dtype", 6)
self._check(code, [float_type_], [dtype_6], [int32_type_])
dtype_10 = onnx.helper.make_attribute("dtype", 10)
self._check(code, [float_type_], [dtype_10], [float16_type_])
def test_fi_optional_input(self):
code = """
<opset_import: [ "" : 18 ], domain: "local">
DoReduce (x, axes) => (y) {
y = ReduceMax (x, axes)
}
"""
# We can omit the type for a missing trailing optional parameter
self._check(code, [float_type_], [], [float_type_])
# Or, we can pass in a default-value of TypeProto() for a missing optional parameter
self._check(code, [float_type_, no_type_], [], [float_type_])
code = """
<opset_import: [ "" : 18 ], domain: "local">
Quantize (x, scale, zero_point) => (y) {
y = QuantizeLinear (x, scale, zero_point)
}
"""
# If the optional third parameter is specified, it determines the output type.
self._check(code, [float_type_, float_type_, int8_type_], [], [int8_type_])
self._check(code, [float_type_, float_type_, uint8_type_], [], [uint8_type_])
# If the optional third parameter is omitted, the output type is uint8 (default).
self._check(code, [float_type_, float_type_, no_type_], [], [uint8_type_])
code = """
<opset_import: [ "" : 18 ], domain: "local">
DoClip (x, min, max) => (y) {
y = Clip (x, min, max)
}
"""
# A test-case with a non-trailing missing optional parameter
self._check(code, [float_type_, no_type_, float_type_], [], [float_type_])
# A failing test-case with a non-trailing missing optional parameter
self._check_fails(code, [float_type_, no_type_, int8_type_], [])
if __name__ == "__main__":
unittest.main()
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58,801 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_dict_vectorizer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class DictVectorizer(OpRunAiOnnxMl):
def _run(self, x, int64_vocabulary=None, string_vocabulary=None): # type: ignore
if isinstance(x, (np.ndarray, list)):
dict_labels = {}
if int64_vocabulary:
for i, v in enumerate(int64_vocabulary):
dict_labels[v] = i
else:
for i, v in enumerate(string_vocabulary):
dict_labels[v] = i
if not dict_labels:
raise RuntimeError(
"int64_vocabulary and string_vocabulary cannot be both empty."
)
values_list = []
rows_list = []
cols_list = []
for i, row in enumerate(x):
for k, v in row.items():
values_list.append(v)
rows_list.append(i)
cols_list.append(dict_labels[k])
values = np.array(values_list)
rows = np.array(rows_list)
cols = np.array(cols_list)
res = np.zeros((len(x), len(dict_labels)), dtype=values.dtype) # type: ignore
for r, c, v in zip(rows, cols, values):
res[r, c] = v
return (res,)
# return (
# coo_matrix(
# (values, (rows, cols)), shape=(len(x), len(dict_labels))
# ).todense(),
# )
if isinstance(x, dict):
keys = int64_vocabulary or string_vocabulary
result = []
for k in keys:
result.append(x.get(k, 0))
return (np.array(result),)
raise TypeError(f"x must be iterable not {type(x)}.")
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58,802 | onnx/onnx | refs/heads/main | /onnx/inliner.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import onnx
import onnx.onnx_cpp2py_export.inliner as C # noqa: N812
def inline_local_functions(
model: onnx.ModelProto, convert_version: bool = False
) -> onnx.ModelProto:
"""Inline model-local functions in given model.
Arguments:
model: an ONNX ModelProto
convert_version: if true, try to apply automatic version-conversion to functions requiring a
different (ONNX) opset version from the model.
Returns:
ModelProto with all calls to model-local functions inlined (recursively)
"""
result = C.inline_local_functions(model.SerializeToString(), convert_version)
inlined_model = onnx.ModelProto()
inlined_model.ParseFromString(result)
return inlined_model
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,803 | onnx/onnx | refs/heads/main | /onnx/test/reference_evaluator_ml_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# type: ignore
# pylint: disable=C3001,C0302,C0415,R0904,R0913,R0914,R0915,W0221,W0707
import unittest
from functools import wraps
from os import getenv
import numpy as np # type: ignore
from numpy.testing import assert_allclose # type: ignore
from onnx import ONNX_ML, TensorProto, TypeProto, ValueInfoProto
from onnx.checker import check_model
from onnx.defs import onnx_opset_version
from onnx.helper import (
make_graph,
make_model_gen_version,
make_node,
make_opsetid,
make_tensor_value_info,
)
from onnx.reference import ReferenceEvaluator
# TODO (https://github.com/microsoft/onnxruntime/issues/14932): Get max supported version from onnxruntime directly
# For now, bump the version in CIs whenever there is a new onnxruntime release
ORT_MAX_IR_SUPPORTED_VERSION = int(getenv("ORT_MAX_IR_SUPPORTED_VERSION", "8"))
ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION = int(
getenv("ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION", "18")
)
ORT_MAX_ML_OPSET_SUPPORTED_VERSION = int(
getenv("ORT_MAX_ML_OPSET_SUPPORTED_VERSION", "3")
)
TARGET_OPSET = onnx_opset_version() - 2
TARGET_OPSET_ML = 3
OPSETS = [make_opsetid("", TARGET_OPSET), make_opsetid("ai.onnx.ml", TARGET_OPSET_ML)]
def has_onnxruntime():
try:
import onnxruntime # pylint: disable=W0611
del onnxruntime
return True
except ImportError:
return False
def skip_if_no_onnxruntime(fn):
@wraps(fn)
def wrapper(*args, **kwargs):
if not has_onnxruntime():
raise unittest.SkipTest("onnxruntime not installed")
fn(*args, **kwargs)
return wrapper
class TestReferenceEvaluatorAiOnnxMl(unittest.TestCase):
@staticmethod
def _check_ort(onx, feeds, atol=0, rtol=0, equal=False, rev=False):
if not has_onnxruntime():
return
from onnxruntime import InferenceSession
onnx_domain_opset = ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION
ml_domain_opset = ORT_MAX_ML_OPSET_SUPPORTED_VERSION
for opset in onx.opset_import:
if opset.domain in ("", "ai.onnx"):
onnx_domain_opset = opset.version
break
for opset in onx.opset_import:
if opset.domain == "ai.onnx.ml":
ml_domain_opset = opset.version
break
# The new IR or opset version is not supported by onnxruntime yet
if (
onx.ir_version > ORT_MAX_IR_SUPPORTED_VERSION
or onnx_domain_opset > ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION
or ml_domain_opset > ORT_MAX_ML_OPSET_SUPPORTED_VERSION
):
return
ort = InferenceSession(
onx.SerializeToString(), providers=["CPUExecutionProvider"]
)
sess = ReferenceEvaluator(onx)
expected = ort.run(None, feeds)
got = sess.run(None, feeds)
if len(expected) != len(got):
raise AssertionError(
f"onnxruntime returns a different number of output "
f"{len(expected)} != {len(sess)} (ReferenceEvaluator)."
)
look = zip(reversed(expected), reversed(got)) if rev else zip(expected, got)
for i, (e, g) in enumerate(look):
if e.shape != g.shape:
raise AssertionError(
f"Unexpected shape {g.shape} for output {i} "
f"(expecting {e.shape})\n{e!r}\n---\n{g!r}."
)
if equal:
if e.tolist() != g.tolist():
raise AssertionError(
f"Discrepancies for output {i}"
f"\nexpected=\n{e}\n!=\nresults=\n{g}"
)
else:
assert_allclose(
actual=g,
desired=e,
atol=atol,
rtol=rtol,
err_msg=f"Discrepancies for output {i} expected[0]={e.ravel()[0]}.",
)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_binarizer(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node("Binarizer", ["X"], ["Y"], threshold=5.5, domain="ai.onnx.ml")
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.arange(12).reshape((3, 4)).astype(np.float32)
expected = np.array(
[[0, 0, 0, 0], [0, 0, 1, 1], [1, 1, 1, 1]], dtype=np.float32
)
self._check_ort(onx, {"X": x})
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
assert_allclose(expected, got)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_scaler(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"Scaler", ["X"], ["Y"], scale=[0.5], offset=[-4.5], domain="ai.onnx.ml"
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.arange(12).reshape((3, 4)).astype(np.float32)
expected = np.array(
[
[2.25, 2.75, 3.25, 3.75],
[4.25, 4.75, 5.25, 5.75],
[6.25, 6.75, 7.25, 7.75],
],
dtype=np.float32,
)
self._check_ort(onx, {"X": x})
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
assert_allclose(expected, got)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_array_feature_extractor(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
A = make_tensor_value_info("A", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"ArrayFeatureExtractor", ["X", "A"], ["Y"], domain="ai.onnx.ml"
)
graph = make_graph([node1], "ml", [X, A], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.arange(12).reshape((3, 4)).astype(np.float32)
expected = np.array([[0, 4, 8]], dtype=np.float32).T
feeds = {"X": x, "A": np.array([0], dtype=np.int64)}
self._check_ort(onx, feeds)
sess = ReferenceEvaluator(onx)
got = sess.run(None, feeds)[0]
assert_allclose(expected, got)
expected = np.array([[0, 4, 8], [1, 5, 9]], dtype=np.float32).T
feeds = {"X": x, "A": np.array([0, 1], dtype=np.int64)}
self._check_ort(onx, feeds)
sess = ReferenceEvaluator(onx)
got = sess.run(None, feeds)[0]
assert_allclose(expected, got)
expected = np.array(
[[0, 4, 8], [1, 5, 9], [0, 4, 8], [1, 5, 9], [0, 4, 8], [1, 5, 9]],
dtype=np.float32,
).T
feeds = {"X": x, "A": np.array([0, 1, 0, 1, 0, 1], dtype=np.int64)}
self._check_ort(onx, feeds)
sess = ReferenceEvaluator(onx)
got = sess.run(None, feeds)[0]
assert_allclose(expected, got)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_normalizer(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
x = np.arange(12).reshape((3, 4)).astype(np.float32)
expected = {
"MAX": x / x.max(axis=1, keepdims=1),
"L1": x / np.abs(x).sum(axis=1, keepdims=1),
"L2": x / (x**2).sum(axis=1, keepdims=1) ** 0.5,
}
for norm, value in expected.items():
with self.subTest(norm=norm):
node1 = make_node(
"Normalizer", ["X"], ["Y"], norm=norm, domain="ai.onnx.ml"
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
feeds = {"X": x}
self._check_ort(onx, feeds, atol=1e-6)
sess = ReferenceEvaluator(onx)
got = sess.run(None, feeds)[0]
assert_allclose(value, got, atol=1e-6)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_feature_vectorizer(self):
X = [
make_tensor_value_info("X0", TensorProto.FLOAT, [None, None]),
make_tensor_value_info("X1", TensorProto.FLOAT, [None, None]),
]
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
x = [
np.arange(9).reshape((3, 3)).astype(np.float32),
np.arange(9).reshape((3, 3)).astype(np.float32) + 0.5,
]
expected = {
(1,): np.array([[0], [3], [6]], dtype=np.float32),
(2,): np.array([[0, 1], [3, 4], [6, 7]], dtype=np.float32),
(4,): np.array(
[[0, 1, 2, 0], [3, 4, 5, 0], [6, 7, 8, 0]], dtype=np.float32
),
(1, 1): np.array([[0, 0.5], [3, 3.5], [6, 6.5]], dtype=np.float32),
(0, 1): np.array([[0.5], [3.5], [6.5]], dtype=np.float32),
}
for inputdimensions, value in expected.items():
att = (
list(inputdimensions)
if isinstance(inputdimensions, tuple)
else inputdimensions
)
with self.subTest(inputdimensions=att):
node1 = make_node(
"FeatureVectorizer",
[f"X{i}" for i in range(len(att))],
["Y"],
inputdimensions=att,
domain="ai.onnx.ml",
)
graph = make_graph([node1], "ml", X[: len(att)], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
feeds = {f"X{i}": v for i, v in enumerate(x[: len(att)])}
self._check_ort(onx, feeds, atol=1e-6)
sess = ReferenceEvaluator(onx)
got = sess.run(None, feeds)[0]
assert_allclose(value, got, atol=1e-6)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_imputer_float(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"Imputer",
["X"],
["Y"],
domain="ai.onnx.ml",
imputed_value_floats=np.array([0], dtype=np.float32),
replaced_value_float=np.nan,
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.array([[0, 1, np.nan, 3]], dtype=np.float32).T
expected = np.array([[0, 1, 0, 3]], dtype=np.float32).T
self._check_ort(onx, {"X": x})
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
assert_allclose(expected, got)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_imputer_float_2d(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"Imputer",
["X"],
["Y"],
domain="ai.onnx.ml",
imputed_value_floats=np.array([0, 0.1], dtype=np.float32),
replaced_value_float=np.nan,
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.array([[0, 1, np.nan, 3], [0, 1, np.nan, 3]], dtype=np.float32).T
expected = np.array([[0, 1, 0, 3], [0, 1, 0.1, 3]], dtype=np.float32).T
self._check_ort(onx, {"X": x})
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
assert_allclose(expected, got)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_imputer_int(self):
X = make_tensor_value_info("X", TensorProto.INT64, [None, None])
Y = make_tensor_value_info("Y", TensorProto.INT64, [None, None])
node1 = make_node(
"Imputer",
["X"],
["Y"],
domain="ai.onnx.ml",
imputed_value_int64s=np.array([0], dtype=np.int64),
replaced_value_int64=-1,
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.array([[0, 1, -1, 3]], dtype=np.int64).T
expected = np.array([[0, 1, 0, 3]], dtype=np.int64).T
self._check_ort(onx, {"X": x})
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
assert_allclose(expected, got)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_label_encoder_float_int(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.INT64, [None, None])
node1 = make_node(
"LabelEncoder",
["X"],
["Y"],
domain="ai.onnx.ml",
default_int64=-5,
keys_floats=[4.0, 1.0, 2.0, 3.0],
values_int64s=[0, 1, 2, 3],
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.array([[0, 1, np.nan, 3, 4]], dtype=np.float32).T
expected = np.array([[-5, 1, -5, 3, 0]], dtype=np.int64).T
self._check_ort(onx, {"X": x})
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
assert_allclose(expected, got)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_label_encoder_int_string(self):
X = make_tensor_value_info("X", TensorProto.INT64, [None, None])
Y = make_tensor_value_info("Y", TensorProto.STRING, [None, None])
node1 = make_node(
"LabelEncoder",
["X"],
["Y"],
domain="ai.onnx.ml",
default_string="NONE",
keys_int64s=[1, 2, 3, 4],
values_strings=["a", "b", "cc", "ddd"],
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.array([[0, 1, 3, 4]], dtype=np.int64).T
expected = np.array([["NONE"], ["a"], ["cc"], ["ddd"]])
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
self.assertEqual(expected.tolist(), got.tolist())
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_dict_vectorizer(self):
value_type = TypeProto()
value_type.tensor_type.elem_type = TensorProto.INT64
onnx_type = TypeProto()
onnx_type.map_type.key_type = TensorProto.STRING
onnx_type.map_type.value_type.CopyFrom(value_type)
value_info = ValueInfoProto()
value_info.name = "X"
value_info.type.CopyFrom(onnx_type)
X = value_info
Y = make_tensor_value_info("Y", TensorProto.INT64, [None, None])
node1 = make_node(
"DictVectorizer",
["X"],
["Y"],
domain="ai.onnx.ml",
string_vocabulary=["a", "c", "b", "z"],
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = {"a": np.array(4, dtype=np.int64), "c": np.array(8, dtype=np.int64)}
expected = np.array([4, 8, 0, 0], dtype=np.int64)
# Unexpected input data type. Actual: ((map(string,tensor(float)))) , expected: ((map(string,tensor(int64))))
# self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
self.assertEqual(expected.tolist(), got.tolist())
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_one_hot_encoder_int(self):
X = make_tensor_value_info("X", TensorProto.INT64, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None])
node1 = make_node(
"OneHotEncoder",
["X"],
["Y"],
domain="ai.onnx.ml",
zeros=1,
cats_int64s=[1, 2, 3],
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.array([[5, 1, 3], [2, 1, 3]], dtype=np.int64)
expected = np.array(
[[[0, 0, 0], [1, 0, 0], [0, 0, 1]], [[0, 1, 0], [1, 0, 0], [0, 0, 1]]],
dtype=np.float32,
)
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
self.assertEqual(expected.tolist(), got.tolist())
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_one_hot_encoder_string(self):
X = make_tensor_value_info("X", TensorProto.STRING, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None])
node1 = make_node(
"OneHotEncoder",
["X"],
["Y"],
domain="ai.onnx.ml",
zeros=1,
cats_strings=["c1", "c2", "c3"],
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.array([["c5", "c1", "c3"], ["c2", "c1", "c3"]])
expected = np.array(
[[[0, 0, 0], [1, 0, 0], [0, 0, 1]], [[0, 1, 0], [1, 0, 0], [0, 0, 1]]],
dtype=np.float32,
)
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
self.assertEqual(expected.tolist(), got.tolist())
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_one_hot_encoder_zeros(self):
X = make_tensor_value_info("X", TensorProto.INT64, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None])
node1 = make_node(
"OneHotEncoder",
["X"],
["Y"],
domain="ai.onnx.ml",
zeros=0,
cats_int64s=[1, 2, 3],
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.array([[2, 1, 3], [2, 1, 3]], dtype=np.int64)
expected = np.array(
[[[0, 1, 0], [1, 0, 0], [0, 0, 1]], [[0, 1, 0], [1, 0, 0], [0, 0, 1]]],
dtype=np.float32,
)
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})[0]
self.assertEqual(expected.tolist(), got.tolist())
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_linear_regressor(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"LinearRegressor",
["X"],
["Y"],
domain="ai.onnx.ml",
coefficients=[0.3, -0.77],
intercepts=[0.5],
post_transform="NONE",
targets=1,
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.arange(6).reshape((-1, 2)).astype(np.float32)
expected = np.array([[-0.27], [-1.21], [-2.15]], dtype=np.float32)
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected, got[0], atol=1e-6)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_linear_regressor_2(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"LinearRegressor",
["X"],
["Y"],
domain="ai.onnx.ml",
coefficients=[0.3, -0.77, 0.3, -0.77],
intercepts=[0.5, 0.7],
post_transform="NONE",
targets=2,
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.arange(6).reshape((-1, 2)).astype(np.float32)
expected = np.array(
[[-0.27, -0.07], [-1.21, -1.01], [-2.15, -1.95]], dtype=np.float32
)
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected, got[0], atol=1e-6)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_linear_classifier_multi(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
In = make_tensor_value_info("I", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
expected_post = {
"NONE": [
np.array([0, 2, 2], dtype=np.int64),
np.array(
[[2.41, -2.12, 0.59], [0.67, -1.14, 1.35], [-1.07, -0.16, 2.11]],
dtype=np.float32,
),
],
"LOGISTIC": [
np.array([0, 2, 2], dtype=np.int64),
np.array(
[
[0.917587, 0.107168, 0.643365],
[0.661503, 0.24232, 0.79413],
[0.255403, 0.460085, 0.891871],
],
dtype=np.float32,
),
],
"SOFTMAX": [
np.array([0, 2, 2], dtype=np.int64),
np.array(
[
[0.852656, 0.009192, 0.138152],
[0.318722, 0.05216, 0.629118],
[0.036323, 0.090237, 0.87344],
],
dtype=np.float32,
),
],
"SOFTMAX_ZERO": [
np.array([0, 2, 2], dtype=np.int64),
np.array(
[
[0.852656, 0.009192, 0.138152],
[0.318722, 0.05216, 0.629118],
[0.036323, 0.090237, 0.87344],
],
dtype=np.float32,
),
],
"PROBIT": [
np.array([1, 1, 1], dtype=np.int64),
np.array(
[
[-0.527324, -0.445471, -1.080504],
[-0.067731, 0.316014, -0.310748],
[0.377252, 1.405167, 0.295001],
],
dtype=np.float32,
),
],
}
for post in ("SOFTMAX", "NONE", "LOGISTIC", "SOFTMAX_ZERO", "PROBIT"):
if post == "PROBIT":
coefficients = [0.058, 0.029, 0.09, 0.058, 0.029, 0.09]
intercepts = [0.27, 0.27, 0.05]
else:
coefficients = [-0.58, -0.29, -0.09, 0.58, 0.29, 0.09]
intercepts = [2.7, -2.7, 0.5]
with self.subTest(post_transform=post):
node1 = make_node(
"LinearClassifier",
["X"],
["I", "Y"],
domain="ai.onnx.ml",
classlabels_ints=[0, 1, 2],
coefficients=coefficients,
intercepts=intercepts,
multi_class=0,
post_transform=post,
)
graph = make_graph([node1], "ml", [X], [In, Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.arange(6).reshape((-1, 2)).astype(np.float32)
self._check_ort(onx, {"X": x}, rev=True, atol=1e-4)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
expected = expected_post[post]
assert_allclose(expected[1], got[1], atol=1e-4)
assert_allclose(expected[0], got[0])
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_linear_classifier_binary(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
In = make_tensor_value_info("I", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
expected_post = {
"NONE": [
np.array([1, 1], dtype=np.int64),
np.array([[-9.53, 9.53], [-6.65, 6.65]], dtype=np.float32),
],
"LOGISTIC": [
np.array([1, 1], dtype=np.int64),
np.array(
[[7.263436e-05, 9.999274e-01], [1.292350e-03, 9.987077e-01]],
dtype=np.float32,
),
],
"SOFTMAX": [
np.array([1, 1], dtype=np.int64),
np.array(
[[5.276517e-09, 1.000000e00], [1.674492e-06, 9.999983e-01]],
dtype=np.float32,
),
],
"SOFTMAX_ZERO": [
np.array([1, 1], dtype=np.int64),
np.array(
[[5.276517e-09, 1.000000e00], [1.674492e-06, 9.999983e-01]],
dtype=np.float32,
),
],
}
x = np.arange(6).reshape((-1, 3)).astype(np.float32)
for post in ("SOFTMAX", "NONE", "LOGISTIC", "SOFTMAX_ZERO"):
expected = expected_post[post]
with self.subTest(post_transform=post):
node1 = make_node(
"LinearClassifier",
["X"],
["I", "Y"],
domain="ai.onnx.ml",
classlabels_ints=[0, 1],
coefficients=[-0.58, -0.29, -0.09],
intercepts=[10.0],
multi_class=0,
post_transform=post,
)
graph = make_graph([node1], "ml", [X], [In, Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
# onnxruntime answer seems odd.
# self._check_ort(onx, {"X": x}, rev=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[1], got[1], atol=1e-6)
assert_allclose(expected[0], got[0])
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_linear_classifier_unary(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
In = make_tensor_value_info("I", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
expected_post = {
"NONE": [
np.array([1, 0], dtype=np.int64),
np.array([[2.23], [-0.65]], dtype=np.float32),
],
"LOGISTIC": [
np.array([1, 0], dtype=np.int64),
np.array([[0.902911], [0.34299]], dtype=np.float32),
],
"SOFTMAX": [
np.array([1, 1], dtype=np.int64),
np.array([[1.0], [1.0]], dtype=np.float32),
],
"SOFTMAX_ZERO": [
np.array([1, 1], dtype=np.int64),
np.array([[1.0], [1.0]], dtype=np.float32),
],
}
x = np.arange(6).reshape((-1, 3)).astype(np.float32)
for post in ("NONE", "LOGISTIC", "SOFTMAX_ZERO", "SOFTMAX"):
expected = expected_post[post]
with self.subTest(post_transform=post):
node1 = make_node(
"LinearClassifier",
["X"],
["I", "Y"],
domain="ai.onnx.ml",
classlabels_ints=[1],
coefficients=[-0.58, -0.29, -0.09],
intercepts=[2.7],
multi_class=0,
post_transform=post,
)
graph = make_graph([node1], "ml", [X], [In, Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
# onnxruntime answer seems odd.
# self._check_ort(onx, {"X": x}, rev=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[1], got[1], atol=1e-6)
assert_allclose(expected[0], got[0])
@staticmethod
def _get_test_tree_ensemble_regressor(
aggregate_function, rule="BRANCH_LEQ", unique_targets=False
):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
if unique_targets:
targets = [
1.0,
10.0,
100.0,
1000.0,
10000.0,
100000.0,
]
else:
targets = [
0.07692307978868484,
0.5,
0.5,
0.0,
0.2857142984867096,
0.5,
]
node1 = make_node(
"TreeEnsembleRegressor",
["X"],
["Y"],
domain="ai.onnx.ml",
n_targets=1,
aggregate_function=aggregate_function,
nodes_falsenodeids=[4, 3, 0, 0, 0, 2, 0, 4, 0, 0],
nodes_featureids=[0, 2, 0, 0, 0, 0, 0, 2, 0, 0],
nodes_hitrates=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
nodes_missing_value_tracks_true=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
nodes_modes=[
rule,
rule,
"LEAF",
"LEAF",
"LEAF",
rule,
"LEAF",
rule,
"LEAF",
"LEAF",
],
nodes_nodeids=[0, 1, 2, 3, 4, 0, 1, 2, 3, 4],
nodes_treeids=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
nodes_truenodeids=[1, 2, 0, 0, 0, 1, 0, 3, 0, 0],
nodes_values=[
0.26645058393478394,
0.6214364767074585,
0.0,
0.0,
0.0,
-0.7208403944969177,
0.0,
-0.5592705607414246,
0.0,
0.0,
],
post_transform="NONE",
target_ids=[0, 0, 0, 0, 0, 0],
target_nodeids=[2, 3, 4, 1, 3, 4],
target_treeids=[0, 0, 0, 1, 1, 1],
target_weights=targets,
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
return onx
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_tree_ensemble_regressor(self):
x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
expected_agg = {
"SUM": np.array([[0.576923], [0.576923], [0.576923]], dtype=np.float32),
"AVERAGE": np.array([[0.288462], [0.288462], [0.288462]], dtype=np.float32),
"MIN": np.array([[0.076923], [0.076923], [0.076923]], dtype=np.float32),
"MAX": np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
}
for agg in ("SUM", "AVERAGE", "MIN", "MAX"):
expected = expected_agg[agg]
with self.subTest(aggregate_function=agg):
onx = self._get_test_tree_ensemble_regressor(agg)
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected, got[0], atol=1e-6)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_tree_ensemble_regressor_rule(self):
x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
expected_agg = {
"BRANCH_LEQ": np.array(
[[0.576923], [0.576923], [0.576923]], dtype=np.float32
),
"BRANCH_GT": np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
"BRANCH_LT": np.array(
[[0.576923], [0.576923], [0.576923]], dtype=np.float32
),
"BRANCH_GTE": np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
"BRANCH_EQ": np.array([[1.0], [1.0], [1.0]], dtype=np.float32),
"BRANCH_NEQ": np.array(
[[0.076923], [0.076923], [0.076923]], dtype=np.float32
),
}
for rule, expected in expected_agg.items():
with self.subTest(rule=rule):
onx = self._get_test_tree_ensemble_regressor("SUM", rule)
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected, got[0], atol=1e-6)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_tree_ensemble_regressor_2_targets(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"TreeEnsembleRegressor",
["X"],
["Y"],
domain="ai.onnx.ml",
n_targets=2,
nodes_falsenodeids=[4, 3, 0, 0, 6, 0, 0, 4, 3, 0, 0, 6, 0, 0],
nodes_featureids=[0, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 2, 0, 0],
nodes_hitrates=[
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
],
nodes_missing_value_tracks_true=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
nodes_modes=[
"BRANCH_LEQ",
"BRANCH_LEQ",
"LEAF",
"LEAF",
"BRANCH_LEQ",
"LEAF",
"LEAF",
"BRANCH_LEQ",
"BRANCH_LEQ",
"LEAF",
"LEAF",
"BRANCH_LEQ",
"LEAF",
"LEAF",
],
nodes_nodeids=[0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6],
nodes_treeids=[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
nodes_truenodeids=[1, 2, 0, 0, 5, 0, 0, 1, 2, 0, 0, 5, 0, 0],
nodes_values=[
-0.3367232382297516,
1.5326381921768188,
0.0,
0.0,
-0.24646544456481934,
0.0,
0.0,
-0.3367232382297516,
0.6671845316886902,
0.0,
0.0,
-0.24646544456481934,
0.0,
0.0,
],
post_transform="NONE",
target_ids=[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],
target_nodeids=[2, 2, 3, 3, 5, 5, 6, 6, 2, 2, 3, 3, 5, 5, 6, 6],
target_treeids=[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
target_weights=[
0.0,
2.5,
0.5,
3.0,
0.15000000596046448,
2.6500000953674316,
0.5,
3.0,
0.02777777798473835,
2.527777671813965,
0.5,
3.0,
0.20000000298023224,
2.700000047683716,
0.5,
3.0,
],
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
expected = np.array(
[[0.027778, 5.027778], [1.0, 6.0], [1.0, 6.0]], dtype=np.float32
)
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected, got[0], atol=1e-6)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_tree_ensemble_regressor_missing(self):
x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
x[2, 0] = 5
x[1, :] = np.nan
expected = np.array([[100001.0], [100100.0], [100100.0]], dtype=np.float32)
onx = self._get_test_tree_ensemble_regressor("SUM", unique_targets=True)
self._check_ort(onx, {"X": x}, equal=True)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected, got[0], atol=1e-6)
self.assertIn("op_type=TreeEnsembleRegressor", str(sess.rt_nodes_[0]))
@staticmethod
def _get_test_svm_regressor(kernel_type, kernel_params):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"SVMRegressor",
["X"],
["Y"],
domain="ai.onnx.ml",
coefficients=[
1.0,
-1.0,
0.8386201858520508,
-0.8386201858520508,
0.4470679759979248,
-1.0,
0.5529320240020752,
],
kernel_params=kernel_params,
kernel_type=kernel_type,
n_supports=7,
post_transform="NONE",
rho=[0.5460880398750305],
support_vectors=[
-0.12850627303123474,
0.08915442228317261,
0.06881910562515259,
-0.07938569784164429,
-0.22557435929775238,
-0.26520243287086487,
0.9246066212654114,
-0.025557516142725945,
-0.5900523662567139,
0.9735698699951172,
-1.3385062217712402,
0.3393094539642334,
0.9432410001754761,
-0.5228781700134277,
0.5557093620300293,
0.4191802740097046,
0.43368014693260193,
-1.0569839477539062,
2.3318440914154053,
0.06202844902873039,
-0.9502395987510681,
],
)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
return onx
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_svm_regressor(self):
x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
expected_kernel = {
"LINEAR": (
[0.42438405752182007, 0.0, 3.0],
np.array([[-0.468206], [0.227487], [0.92318]], dtype=np.float32),
),
"POLY": (
[0.3426632285118103, 0.0, 3.0],
np.array([[0.527084], [0.543578], [0.546506]], dtype=np.float32),
),
"RBF": (
[0.30286383628845215, 0.0, 3.0],
np.array([[0.295655], [0.477876], [0.695292]], dtype=np.float32),
),
"SIGMOID": (
[0.30682486295700073, 0.0, 3.0],
np.array([[0.239304], [0.448929], [0.661689]], dtype=np.float32),
),
}
for kernel, (params, expected) in expected_kernel.items():
with self.subTest(kernel=kernel):
onx = self._get_test_svm_regressor(kernel, params)
self._check_ort(onx, {"X": x}, atol=1e-6)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected, got[0], atol=1e-6)
@staticmethod
def _get_test_tree_ensemble_classifier_binary(post_transform):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
In = make_tensor_value_info("I", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"TreeEnsembleClassifier",
["X"],
["I", "Y"],
domain="ai.onnx.ml",
class_ids=[0, 0, 0, 0, 0, 0, 0],
class_nodeids=[2, 3, 5, 6, 1, 3, 4],
class_treeids=[0, 0, 0, 0, 1, 1, 1],
class_weights=[
0.0,
0.1764705926179886,
0.0,
0.5,
0.0,
0.0,
0.4285714328289032,
],
classlabels_int64s=[0, 1],
nodes_falsenodeids=[4, 3, 0, 0, 6, 0, 0, 2, 0, 4, 0, 0],
nodes_featureids=[2, 2, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0],
nodes_hitrates=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
nodes_missing_value_tracks_true=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
nodes_modes=[
"BRANCH_LEQ",
"BRANCH_LEQ",
"LEAF",
"LEAF",
"BRANCH_LEQ",
"LEAF",
"LEAF",
"BRANCH_LEQ",
"LEAF",
"BRANCH_LEQ",
"LEAF",
"LEAF",
],
nodes_nodeids=[0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4],
nodes_treeids=[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
nodes_truenodeids=[1, 2, 0, 0, 5, 0, 0, 1, 0, 3, 0, 0],
nodes_values=[
0.6874135732650757,
-0.3654803931713104,
0.0,
0.0,
-1.926770806312561,
0.0,
0.0,
-0.3654803931713104,
0.0,
-2.0783839225769043,
0.0,
0.0,
],
post_transform=post_transform,
)
graph = make_graph([node1], "ml", [X], [In, Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
return onx
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_tree_ensemble_classifier_binary(self):
x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
expected_post = {
"NONE": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[1.0, 0.0], [0.394958, 0.605042], [0.394958, 0.605042]],
dtype=np.float32,
),
),
"LOGISTIC": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.5, 0.5], [0.353191, 0.646809], [0.353191, 0.646809]],
dtype=np.float32,
),
),
"SOFTMAX": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.5, 0.5], [0.229686, 0.770314], [0.229686, 0.770314]],
dtype=np.float32,
),
),
"SOFTMAX_ZERO": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.5, 0.5], [0.229686, 0.770314], [0.229686, 0.770314]],
dtype=np.float32,
),
),
"PROBIT": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.0, 0.0], [-0.266426, 0.266426], [-0.266426, 0.266426]],
dtype=np.float32,
),
),
}
for post, expected in expected_post.items():
with self.subTest(post_transform=post):
onx = self._get_test_tree_ensemble_classifier_binary(post)
if post in ("NONE",):
self._check_ort(onx, {"X": x})
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[1], got[1], atol=1e-6)
assert_allclose(expected[0], got[0])
@staticmethod
def _get_test_tree_ensemble_classifier_multi(post_transform):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
In = make_tensor_value_info("I", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node(
"TreeEnsembleClassifier",
["X"],
["I", "Y"],
domain="ai.onnx.ml",
class_ids=[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2],
class_nodeids=[2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 3, 3, 3, 4, 4, 4],
class_treeids=[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1],
class_weights=[
0.46666666865348816,
0.0,
0.03333333507180214,
0.20000000298023224,
0.23999999463558197,
0.05999999865889549,
0.0,
0.5,
0.0,
0.5,
0.0,
0.0,
0.44999998807907104,
0.0,
0.05000000074505806,
0.10294117778539658,
0.19117647409439087,
0.20588235557079315,
],
classlabels_int64s=[0, 1, 2],
nodes_falsenodeids=[4, 3, 0, 0, 0, 2, 0, 4, 0, 0],
nodes_featureids=[1, 0, 0, 0, 0, 1, 0, 0, 0, 0],
nodes_hitrates=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
nodes_missing_value_tracks_true=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
nodes_modes=[
"BRANCH_LEQ",
"BRANCH_LEQ",
"LEAF",
"LEAF",
"LEAF",
"BRANCH_LEQ",
"LEAF",
"BRANCH_LEQ",
"LEAF",
"LEAF",
],
nodes_nodeids=[0, 1, 2, 3, 4, 0, 1, 2, 3, 4],
nodes_treeids=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
nodes_truenodeids=[1, 2, 0, 0, 0, 1, 0, 3, 0, 0],
nodes_values=[
1.2495747804641724,
-0.3050493597984314,
0.0,
0.0,
0.0,
-1.6830512285232544,
0.0,
-0.6751254796981812,
0.0,
0.0,
],
post_transform=post_transform,
)
graph = make_graph([node1], "ml", [X], [In, Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
return onx
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_tree_ensemble_classifier_multi(self):
x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
expected_post = {
"NONE": (
np.array([0, 0, 1], dtype=np.int64),
np.array(
[
[0.916667, 0.0, 0.083333],
[0.569608, 0.191176, 0.239216],
[0.302941, 0.431176, 0.265882],
],
dtype=np.float32,
),
),
"LOGISTIC": (
np.array([0, 0, 1], dtype=np.int64),
np.array(
[
[0.714362, 0.5, 0.520821],
[0.638673, 0.547649, 0.55952],
[0.575161, 0.606155, 0.566082],
],
dtype=np.float32,
),
),
"SOFTMAX": (
np.array([0, 0, 1], dtype=np.int64),
np.array(
[
[0.545123, 0.217967, 0.23691],
[0.416047, 0.284965, 0.298988],
[0.322535, 0.366664, 0.310801],
],
dtype=np.float32,
),
),
"SOFTMAX_ZERO": (
np.array([0, 0, 1], dtype=np.int64),
np.array(
[
[0.697059, 0.0, 0.302941],
[0.416047, 0.284965, 0.298988],
[0.322535, 0.366664, 0.310801],
],
dtype=np.float32,
),
),
"PROBIT": (
np.array([0, 0, 1], dtype=np.int64),
np.array(
[
[1.383104, 0, -1.383105],
[0.175378, -0.873713, -0.708922],
[-0.516003, -0.173382, -0.625385],
],
dtype=np.float32,
),
),
}
for post, expected in expected_post.items():
with self.subTest(post_transform=post):
onx = self._get_test_tree_ensemble_classifier_multi(post)
if post != "PROBIT":
self._check_ort(onx, {"X": x}, atol=1e-5)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[1], got[1], atol=1e-6)
assert_allclose(expected[0], got[0])
@staticmethod
def _get_test_svm_classifier_binary(post_transform, probability=True, linear=False):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
In = make_tensor_value_info("I", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
if linear:
kwargs = {
"classlabels_ints": [0, 1, 2, 3],
"coefficients": [
-1.55181212e-01,
2.42698956e-01,
7.01893432e-03,
4.07614474e-01,
-3.24927823e-02,
2.79897536e-04,
-1.95771302e-01,
-3.52437368e-01,
-2.15973096e-02,
-4.38190277e-01,
4.56869105e-02,
-1.29375499e-02,
],
"kernel_params": [0.001, 0.0, 3.0],
"kernel_type": "LINEAR",
"prob_a": [-5.139118194580078],
"prob_b": [0.06399919837713242],
"rho": [-0.07489691, -0.1764396, -0.21167431, -0.51619097],
"post_transform": post_transform,
}
else:
kwargs = {
"classlabels_ints": [0, 1],
"coefficients": [1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0],
"kernel_params": [0.3824487328529358, 0.0, 3.0],
"kernel_type": "RBF",
"prob_a": [-5.139118194580078],
"prob_b": [0.06399919837713242],
"rho": [0.16708599030971527],
"support_vectors": [
0.19125767052173615,
-1.062204122543335,
0.5006636381149292,
-0.5892484784126282,
-0.3196830451488495,
0.0984845906496048,
0.24746321141719818,
-1.1535362005233765,
0.4109955430030823,
-0.5937694907188416,
-1.3183348178863525,
-1.6423596143722534,
0.558641254901886,
-0.9218668341636658,
0.6264089345932007,
-0.16060839593410492,
-0.6365169882774353,
0.8335472345352173,
0.7539799213409424,
-0.3970031440258026,
-0.1780400276184082,
-0.616622805595398,
0.49261474609375,
0.4470972716808319,
],
"vectors_per_class": [4, 4],
"post_transform": post_transform,
}
if not probability:
del kwargs["prob_a"]
del kwargs["prob_b"]
node1 = make_node(
"SVMClassifier", ["X"], ["I", "Y"], domain="ai.onnx.ml", **kwargs
)
graph = make_graph([node1], "ml", [X], [In, Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
return onx
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_svm_classifier_binary(self):
x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
expected_post = {
"NONE": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.993287, 0.006713], [0.469401, 0.530599], [0.014997, 0.985003]],
dtype=np.float32,
),
),
"LOGISTIC": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.729737, 0.501678], [0.615242, 0.629623], [0.503749, 0.7281]],
dtype=np.float32,
),
),
"SOFTMAX": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.728411, 0.271589], [0.484705, 0.515295], [0.274879, 0.725121]],
dtype=np.float32,
),
),
"SOFTMAX_ZERO": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.728411, 0.271589], [0.484705, 0.515295], [0.274879, 0.725121]],
dtype=np.float32,
),
),
"PROBIT": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[2.469393, -2.469391], [-0.076776, 0.076776], [-2.16853, 2.16853]],
dtype=np.float32,
),
),
}
for post, expected in expected_post.items():
with self.subTest(post_transform=post):
onx = self._get_test_svm_classifier_binary(post)
self._check_ort(onx, {"X": x}, rev=True, atol=1e-5)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[1], got[1], atol=1e-5)
assert_allclose(expected[0], got[0])
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_svm_classifier_binary_noprob(self):
x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
expected_post = {
"NONE": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[
[-0.986073, 0.986073],
[0.011387, -0.011387],
[0.801808, -0.801808],
],
dtype=np.float32,
),
),
"LOGISTIC": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.271688, 0.728312], [0.502847, 0.497153], [0.690361, 0.309639]],
dtype=np.float32,
),
),
"SOFTMAX": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.122158, 0.877842], [0.505693, 0.494307], [0.832523, 0.167477]],
dtype=np.float32,
),
),
"SOFTMAX_ZERO": (
np.array([0, 1, 1], dtype=np.int64),
np.array(
[[0.122158, 0.877842], [0.505693, 0.494307], [0.832523, 0.167477]],
dtype=np.float32,
),
),
}
for post, expected in expected_post.items():
with self.subTest(post_transform=post):
onx = self._get_test_svm_classifier_binary(post, probability=False)
if post not in {"LOGISTIC", "SOFTMAX", "SOFTMAX_ZERO", "PROBIT"}:
self._check_ort(onx, {"X": x}, rev=True, atol=1e-5)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[1], got[1], atol=1e-6)
assert_allclose(expected[0], got[0])
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_svm_classifier_noprob_linear(self):
x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
nan = np.nan
expected_post = {
"NONE": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[-0.118086, -0.456685, 0.415783, 0.334506],
[-0.061364, -0.231444, 0.073899, 0.091242],
[-0.004642, -0.006203, -0.267985, -0.152023],
],
dtype=np.float32,
),
),
"LOGISTIC": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[0.470513, 0.387773, 0.602474, 0.582855],
[0.484664, 0.442396, 0.518466, 0.522795],
[0.498839, 0.498449, 0.433402, 0.462067],
],
dtype=np.float32,
),
),
"SOFTMAX": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[0.200374, 0.14282, 0.341741, 0.315065],
[0.240772, 0.203115, 0.275645, 0.280467],
[0.275491, 0.275061, 0.211709, 0.237739],
],
dtype=np.float32,
),
),
"SOFTMAX_ZERO": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[0.200374, 0.14282, 0.341741, 0.315065],
[0.240772, 0.203115, 0.275645, 0.280467],
[0.275491, 0.275061, 0.211709, 0.237739],
],
dtype=np.float32,
),
),
"PROBIT": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[nan, nan, -0.212698, -0.427529],
[nan, nan, -1.447414, -1.333286],
[nan, nan, nan, nan],
],
dtype=np.float32,
),
),
}
for post, expected in expected_post.items():
with self.subTest(post_transform=post):
onx = self._get_test_svm_classifier_binary(
post, probability=False, linear=True
)
self._check_ort(onx, {"X": x}, rev=True, atol=1e-5)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[1], got[1], atol=1e-6)
assert_allclose(expected[0], got[0])
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_svm_classifier_linear(self):
# prob_a, prob_b are not used in this case.
x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
nan = np.nan
expected_post = {
"NONE": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[-0.118086, -0.456685, 0.415783, 0.334506],
[-0.061364, -0.231444, 0.073899, 0.091242],
[-0.004642, -0.006203, -0.267985, -0.152023],
],
dtype=np.float32,
),
),
"LOGISTIC": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[0.470513, 0.387773, 0.602474, 0.582855],
[0.484664, 0.442396, 0.518466, 0.522795],
[0.498839, 0.498449, 0.433402, 0.462067],
],
dtype=np.float32,
),
),
"SOFTMAX": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[0.200374, 0.14282, 0.341741, 0.315065],
[0.240772, 0.203115, 0.275645, 0.280467],
[0.275491, 0.275061, 0.211709, 0.237739],
],
dtype=np.float32,
),
),
"SOFTMAX_ZERO": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[0.200374, 0.14282, 0.341741, 0.315065],
[0.240772, 0.203115, 0.275645, 0.280467],
[0.275491, 0.275061, 0.211709, 0.237739],
],
dtype=np.float32,
),
),
"PROBIT": (
np.array([2, 3, 0], dtype=np.int64),
np.array(
[
[nan, nan, -0.212698, -0.427529],
[nan, nan, -1.447414, -1.333286],
[nan, nan, nan, nan],
],
dtype=np.float32,
),
),
}
for post, expected in expected_post.items():
with self.subTest(post_transform=post):
onx = self._get_test_svm_classifier_binary(
post, probability=True, linear=True
)
self._check_ort(onx, {"X": x}, rev=True, atol=1e-5)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[1], got[1], atol=1e-6)
assert_allclose(expected[0], got[0])
@staticmethod
def _get_test_svm_classifier_linear_sv(post_transform, probability=True):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
In = make_tensor_value_info("I", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
kwargs = {
"classlabels_ints": [0, 1],
"coefficients": [
0.766398549079895,
0.0871576070785522,
0.110420741140842,
-0.963976919651031,
],
"support_vectors": [
4.80000019073486,
3.40000009536743,
1.89999997615814,
5.0,
3.0,
1.60000002384186,
4.5,
2.29999995231628,
1.29999995231628,
5.09999990463257,
2.5,
3.0,
],
"kernel_params": [0.122462183237076, 0.0, 3.0],
"kernel_type": "LINEAR",
"prob_a": [-5.139118194580078],
"prob_b": [0.06399919837713242],
"rho": [2.23510527610779],
"post_transform": post_transform,
"vectors_per_class": [3, 1],
}
if not probability:
del kwargs["prob_a"]
del kwargs["prob_b"]
node1 = make_node(
"SVMClassifier", ["X"], ["I", "Y"], domain="ai.onnx.ml", **kwargs
)
graph = make_graph([node1], "ml", [X], [In, Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
return onx
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_svm_classifier_binary_noprob_linear_sv(self):
x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
expected_post = {
"NONE": (
np.array([0, 0, 0], dtype=np.int64),
np.array(
[[-2.662655, 2.662655], [-2.21481, 2.21481], [-1.766964, 1.766964]],
dtype=np.float32,
),
),
"LOGISTIC": (
np.array([0, 0, 0], dtype=np.int64),
np.array(
[[0.065213, 0.934787], [0.098428, 0.901572], [0.14592, 0.85408]],
dtype=np.float32,
),
),
"SOFTMAX": (
np.array([0, 0, 0], dtype=np.int64),
np.array(
[[0.004843, 0.995157], [0.011779, 0.988221], [0.028362, 0.971638]],
dtype=np.float32,
),
),
"SOFTMAX_ZERO": (
np.array([0, 0, 0], dtype=np.int64),
np.array(
[[0.004843, 0.995157], [0.011779, 0.988221], [0.028362, 0.971638]],
dtype=np.float32,
),
),
}
for post, expected in expected_post.items():
with self.subTest(post_transform=post):
onx = self._get_test_svm_classifier_linear_sv(post, probability=False)
if post not in {"LOGISTIC", "SOFTMAX", "SOFTMAX_ZERO"}:
self._check_ort(onx, {"X": x}, rev=True, atol=1e-5)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[1], got[1], atol=1e-6)
assert_allclose(expected[0], got[0])
@staticmethod
def _get_test_svm_regressor_linear(post_transform, one_class=0):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
kwargs = {
"coefficients": [0.28290501, -0.0266512, 0.01674867],
"kernel_params": [0.001, 0.0, 3.0],
"kernel_type": "LINEAR",
"rho": [1.24032312],
"post_transform": post_transform,
"n_supports": 0,
"one_class": one_class,
}
node1 = make_node("SVMRegressor", ["X"], ["Y"], domain="ai.onnx.ml", **kwargs)
graph = make_graph([node1], "ml", [X], [Y])
onx = make_model_gen_version(graph, opset_imports=OPSETS)
check_model(onx)
return onx
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_svm_regressor_linear(self):
x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
expected_post = {
"NONE": (
np.array(
[[0.96869], [1.132491], [1.296293]],
dtype=np.float32,
),
),
}
for post, expected in expected_post.items():
with self.subTest(post_transform=post):
onx = self._get_test_svm_regressor_linear(post)
self._check_ort(onx, {"X": x}, rev=True, atol=1e-5)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[0], got[0], atol=1e-6)
@unittest.skipIf(not ONNX_ML, reason="onnx not compiled with ai.onnx.ml")
def test_svm_regressor_linear_one_class(self):
x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
expected_post = {
"NONE": (
np.array(
[[1.0], [1.0], [1.0]],
dtype=np.float32,
),
),
}
for post, expected in expected_post.items():
with self.subTest(post_transform=post):
onx = self._get_test_svm_regressor_linear(post, one_class=1)
self._check_ort(onx, {"X": x}, rev=True, atol=1e-5)
sess = ReferenceEvaluator(onx)
got = sess.run(None, {"X": x})
assert_allclose(expected[0], got[0], atol=1e-6)
if __name__ == "__main__":
unittest.main(verbosity=2)
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58,804 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_softmax.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Softmax(OpRunUnaryNum):
def _run(self, X, axis=None): # type: ignore
axis = axis or self.axis # type: ignore
tmp = X - X.max(axis=axis, keepdims=1) # type: ignore
Y = np.exp(tmp)
Y /= Y.sum(axis=axis, keepdims=1) # type: ignore
return (Y.astype(X.dtype),)
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58,805 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reduce_log_sum_exp.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ReduceLogSumExp(Base):
@staticmethod
def export_do_not_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceLogSumExp",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
# print(reduced)
# [[20., 2.31326175]
# [40.00004578, 2.31326175]
# [60.00671387, 2.31326175]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.double)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_do_not_keepdims_random",
)
@staticmethod
def export_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceLogSumExp",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
# print(reduced)
# [[[20., 2.31326175]]
# [[40.00004578, 2.31326175]]
# [[60.00671387, 2.31326175]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.double)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_keepdims_random",
)
@staticmethod
def export_default_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceLogSumExp",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
)
reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1))
# print(reduced)
# [[[60.00671387]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.double)
reduced = np.log(np.sum(np.exp(data), axis=None, keepdims=keepdims == 1))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_default_axes_keepdims_random",
)
@staticmethod
def export_negative_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceLogSumExp",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]], dtype=np.double
)
reduced = np.log(np.sum(np.exp(data), axis=tuple(axes), keepdims=keepdims == 1))
# print(reduced)
# [[[20., 2.31326175]]
# [[40.00004578, 2.31326175]]
# [[60.00671387, 2.31326175]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.double)
reduced = np.log(
np.sum(np.exp(data), axis=tuple(axes.tolist()), keepdims=keepdims == 1)
)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_exp_negative_axes_keepdims_random",
)
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"/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", 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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": 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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,806 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/utils.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import importlib
import pkgutil
from types import ModuleType
from typing import List, Optional
import numpy as np
from onnx import ONNX_ML
all_numeric_dtypes = [
np.int8,
np.int16,
np.int32,
np.int64,
np.uint8,
np.uint16,
np.uint32,
np.uint64,
np.float16,
np.float32,
np.float64,
]
def import_recursive(package: ModuleType) -> None:
"""
Takes a package and imports all modules underneath it
"""
pkg_dir: Optional[List[str]] = None
pkg_dir = package.__path__ # type: ignore
module_location = package.__name__
for _module_loader, name, ispkg in pkgutil.iter_modules(pkg_dir):
module_name = f"{module_location}.{name}" # Module/package
if not ONNX_ML and module_name.startswith(
"onnx.backend.test.case.node.ai_onnx_ml"
):
continue
module = importlib.import_module(module_name)
if ispkg:
import_recursive(module)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], 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58,807 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_non_zero.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class NonZero(OpRun):
def _run(self, x): # type: ignore
# Specify np.int64 for Windows x86 machines
res = np.vstack(np.nonzero(x)).astype(np.int64)
return (res,)
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58,808 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/prelu.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class PRelu(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"PRelu",
inputs=["x", "slope"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
slope = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_example")
@staticmethod
def export_prelu_broadcast() -> None:
node = onnx.helper.make_node(
"PRelu",
inputs=["x", "slope"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
slope = np.random.randn(5).astype(np.float32)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
expect(node, inputs=[x, slope], outputs=[y], name="test_prelu_broadcast")
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"/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_string_concat.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_batch_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cum_sum.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_prelu.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_unsqueeze.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/globalaveragepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/__init__.py": ["/onnx/backend/test/runner/__init__.py"], "/onnx/test/parser_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_regex_full_match.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/xor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/shape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], 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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], 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58,809 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_conv.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,R0915,R1702,W0221
import numpy as np
from onnx.reference.op_run import OpRun
def _conv_implementation( # type: ignore
X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides
):
if dilations is None:
dilations = [1 for s in X.shape[2:]]
if kernel_shape is None:
kernel_shape = W.shape[2:]
if pads is None:
pads = [0 for s in X.shape[2:]] * 2
if strides is None:
strides = [1 for s in X.shape[2:]]
if X.shape[1] != W.shape[1] * group or W.shape[0] % group != 0:
raise ValueError(
f"Shape inconsistencies, X.shape={X.shape}, W.shape={W.shape}, group={group}, "
f"W should be {(W.shape[0], X.shape[1] // group, np.prod(W.shape[1:]) // X.shape[1] * group)}."
)
if group > 1:
res = []
td = 0
mg = W.shape[0] // group
dw = W.shape[1]
for b in range(X.shape[0]):
for g in range(group):
gx = X[b : b + 1, g * dw : (g + 1) * dw]
gw = W[g * mg : (g + 1) * mg]
try:
cv = _conv_implementation(
gx,
gw,
None,
auto_pad,
dilations,
1,
kernel_shape,
pads,
strides,
)
except (ValueError, RuntimeError) as e:
raise ValueError(
f"Shape inconsistencies, X.shape={X.shape}, W.shape={W.shape}, group={g}/{group}, "
f"gx.shape={gx.shape}, gw.shape={gw.shape}, auto_pad={auto_pad}, "
f"dilations={dilations}, kernel_shape={kernel_shape}, pads={pads}, "
f"strides={strides}."
) from e
if b == 0:
td += cv.shape[1]
res.append((b, cv))
new_shape = [X.shape[0], *list(res[0][1].shape[1:])]
new_shape[1] = td
final = np.zeros(tuple(new_shape), dtype=res[0][1].dtype)
p = 0
for b, cv in res:
final[b : b + 1, p : p + cv.shape[1]] = cv
p += cv.shape[1]
if p >= final.shape[1]:
p = 0
if B is not None:
new_shape = [1 for s in final.shape]
new_shape[1] = B.shape[0]
b = B.reshape(tuple(new_shape))
final += b
return final
if dilations[0] != 1 or min(dilations) != max(dilations):
# Let's compute the dilated kernel.
nd = len(dilations)
new_kernel_shape = []
new_shape = list(W.shape[:-nd])
for i, d in enumerate(dilations):
di = len(W.shape) - nd + i
new_shape.append(W.shape[di] + (W.shape[di] - 1) * (d - 1))
new_kernel_shape.append(kernel_shape[i] + (kernel_shape[i] - 1) * (d - 1))
new_w = np.zeros(tuple(new_shape), dtype=W.dtype)
indices = [slice(0, new_w.shape[0]), slice(0, new_w.shape[1])]
for i, d in enumerate(dilations):
di = len(W.shape) - nd + i
indices.append(slice(0, new_w.shape[di], d))
new_w[tuple(indices)] = W
W = new_w
kernel_shape = new_kernel_shape
if auto_pad in {"SAME_LOWER", "SAME_UPPER", "VALID"}:
head = []
tail = []
for i in range(len(X.shape) - 2):
d = X.shape[i]
target_size = (d + strides[i] - 1) // strides[i]
pad_needed = (target_size - 1) * strides[i] + kernel_shape[i] - d
if auto_pad == "SAME_LOWER":
pad_head = (pad_needed + 1) // 2
else:
pad_head = pad_needed // 2
pad_tail = pad_needed - pad_head
head.append(pad_head)
tail.append(pad_tail)
pads = head + tail
if len(X.shape) == 3:
sN, sC, sH = X.shape
# M, C_group, kH, kW = W.shape
(kh,) = kernel_shape
(sth,) = strides
h_out = int(((sH - kh + pads[0] + pads[1]) / sth) + 1)
h0 = pads[0]
oh = -1 * (kh % 2)
bh = -h0
eh = h_out * sth
res = np.zeros((X.shape[0], W.shape[0], h_out)) # type: ignore[assignment]
if B is not None:
res[:, :, :] += B.reshape((1, -1, 1)) # type: ignore
for n in range(0, sN):
for nw in range(W.shape[0]):
for c in range(0, sC):
w = W[nw : nw + 1, c : c + 1]
for io in range(bh, eh, sth):
hr = (io - bh) // sth
if hr >= h_out:
continue
i = io + kh % 2
ih1, ih2 = max(0, i + oh), min(i + oh + kh, sH)
img = X[n : n + 1, c : c + 1, ih1:ih2]
if img.shape != w.shape:
jh1, jh2 = max(-oh - i, 0), min(kh, kh + sH - (i + oh + kh))
w_ = w[:1, :1, jh1:jh2]
if img.shape != w_.shape:
raise RuntimeError(
f"Unexpected shape {img.shape} != {w_.shape}, oh={oh}, "
f"i={i}, kh={kh}, sH={sH}, sth={sth}."
)
s = np.dot(img.reshape((1, -1)), w_.reshape((-1, 1)))[
0, 0
] # (img * w_).sum()
else:
s = np.dot(img.reshape((1, -1)), w.reshape((-1, 1)))[
0, 0
] # (img * w).sum()
res[n, nw, hr] += s # type: ignore
return res
if len(X.shape) == 4:
sN, sC, sH, sW = X.shape
# M, C_group, kH, kW = W.shape
kh, kw = kernel_shape
sth, stw = strides
h_out = int(((sH - kh + pads[0] + pads[2]) / sth) + 1)
w_out = int(((sW - kw + pads[1] + pads[3]) / stw) + 1)
h0, w0 = pads[0], pads[1]
oh, ow = -1 * (kh % 2), -1 * (kw % 2)
bh, bw = -h0, -w0
eh, ew = h_out * sth, w_out * stw
res = np.zeros((X.shape[0], W.shape[0], h_out, w_out)) # type: ignore[assignment]
if B is not None:
res[:, :, :, :] = B.reshape((1, -1, 1, 1)) # type: ignore
for n in range(0, sN):
for nw in range(W.shape[0]):
for c in range(0, sC):
w = W[nw : nw + 1, c : c + 1]
for io in range(bh, eh, sth):
hr = (io - bh) // sth
if hr >= h_out:
continue
i = io + kh % 2
ih1, ih2 = max(0, i + oh), min(i + oh + kh, sH)
for jo in range(bw, ew, stw):
wr = (jo - bw) // stw
if wr >= w_out:
continue
j = jo + kw % 2
iw1, iw2 = max(0, j + ow), min(j + ow + kw, sW)
img = X[n : n + 1, c : c + 1, ih1:ih2, iw1:iw2]
if img.shape != w.shape:
jh1, jh2 = max(-oh - i, 0), min(
kh, kh + sH - (i + oh + kh)
)
jw1, jw2 = max(-ow - j, 0), min(
kw, kw + sW - (j + ow + kw)
)
w_ = w[:1, :1, jh1:jh2, jw1:jw2]
if img.shape != w_.shape:
raise RuntimeError(
f"Unexpected shape {img.shape} != {w_.shape}, oh={oh}, ow={ow}, "
f"i={i}, j={j}, kh={kh}, kw={kw}, sH={sH}, sW={sW}, sth={sth}, stw={stw}."
)
s = np.dot(img.reshape((1, -1)), w_.reshape((-1, 1)))[
0, 0
] # (img * w_).sum()
else:
s = np.dot(img.reshape((1, -1)), w.reshape((-1, 1)))[
0, 0
] # (img * w).sum()
res[n, nw, hr, wr] += s # type: ignore
return res
if len(X.shape) == 5:
sN, sC, sH, sW, sZ = X.shape
kh, kw, kz = kernel_shape
sth, stw, stz = strides
h_out = int(((sH - kh + pads[0] + pads[3]) / sth) + 1)
w_out = int(((sW - kw + pads[1] + pads[4]) / stw) + 1)
z_out = int(((sZ - kz + pads[2] + pads[5]) / stz) + 1)
h0, w0, z0 = pads[0], pads[1], pads[2]
oh, ow, oz = -1 * (kh % 2), -1 * (kw % 2), -1 * (kz % 2)
bh, bw, bz = -h0, -w0, -z0
eh, ew, ez = h_out * sth, w_out * stw, z_out * stz
res = np.zeros((X.shape[0], W.shape[0], h_out, w_out, z_out)) # type: ignore[assignment]
if B is not None:
res[:, :, :, :, :] = B.reshape((1, -1, 1, 1, 1)) # type: ignore
for n in range(0, sN):
for nw in range(W.shape[0]):
for c in range(0, sC):
w = W[nw : nw + 1, c : c + 1]
for io in range(bh, eh, sth):
hr = (io - bh) // sth
if hr >= h_out:
continue
i = io + kh % 2
ih1, ih2 = max(0, i + oh), min(i + oh + kh, sH)
for jo in range(bw, ew, stw):
wr = (jo - bw) // stw
if wr >= w_out:
continue
j = jo + kw % 2
iw1, iw2 = max(0, j + ow), min(j + ow + kw, sW)
for zo in range(bz, ez, stz):
zr = (zo - bz) // stz
if zr >= z_out:
continue
z = zo + kz % 2
iz1, iz2 = max(0, z + oz), min(z + oz + kz, sZ)
img = X[n : n + 1, c : c + 1, ih1:ih2, iw1:iw2, iz1:iz2]
if img.shape != w.shape:
jh1, jh2 = max(-oh - i, 0), min(
kh, kh + sH - (i + oh + kh)
)
jw1, jw2 = max(-ow - j, 0), min(
kw, kw + sW - (j + ow + kw)
)
jz1, jz2 = max(-oz - z, 0), min(
kz, kz + sZ - (z + oz + kz)
)
w_ = w[:1, :1, jh1:jh2, jw1:jw2, jz1:jz2]
if img.shape != w_.shape:
raise RuntimeError(
f"Unexpected shape {img.shape} != {w_.shape}, oh={oh}, ow={ow}, oz={oz}, "
f"i={i}, j={j}, z={z}, kh={kh}, kw={kw}, kz={kz}, "
f"sH={sH}, sW={sW}, sZ={sZ}, sth={sth}, stw={stw}, stz={stz}."
)
s = np.dot(
img.reshape((1, -1)), w_.reshape((-1, 1))
)[
0, 0
] # (img * w_).sum()
else:
s = np.dot(
img.reshape((1, -1)), w.reshape((-1, 1))
)[
0, 0
] # (img * w).sum()
res[n, nw, hr, wr, zr] += s # type: ignore
return res
raise RuntimeError(
f"The convolution for X.shape={X.shape}, W.shape={W.shape}, "
f"kernel_shape={kernel_shape} is not implemented yet."
)
class Conv(OpRun):
def _run( # type: ignore
self,
X,
W,
B=None,
auto_pad=None,
dilations=None,
group=None,
kernel_shape=None,
pads=None,
strides=None,
):
if len(X.shape) < 3:
raise ValueError(
f"X must have at least 3 dimensions but its shape is {X.shape}."
)
return (
# _conv_implementation(
_conv_implementation(
X, W, B, auto_pad, dilations, group, kernel_shape, pads, strides
).astype(X.dtype),
)
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"/onnx/reference/ops/op_unique.py", "/onnx/reference/ops/op_unsqueeze.py", "/onnx/reference/ops/op_upsample.py", "/onnx/reference/ops/op_where.py"], "/onnx/backend/test/case/model/gradient.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py", "/onnx/defs/__init__.py"], "/onnx/compose.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_det.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sequence_empty.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/topk.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_log_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_linear_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/center_crop_pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", 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58,810 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/lppool.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.reference.ops.op_pool_common import (
get_output_shape_auto_pad,
get_output_shape_explicit_padding,
get_pad_shape,
pool,
)
class LpPool(Base):
@staticmethod
def export_lppool_1d_default() -> None:
"""
input_shape: [1, 3, 32]
output_shape: [1, 3, 31]
"""
p = 3
kernel_shape = [2]
strides = [1]
node = onnx.helper.make_node(
"LpPool",
inputs=["x"],
outputs=["y"],
kernel_shape=kernel_shape,
strides=strides,
p=p,
)
x = np.random.randn(1, 3, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p)
expect(node, inputs=[x], outputs=[y], name="test_lppool_1d_default")
@staticmethod
def export_lppool_2d_default() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 31, 31]
"""
p = 4
node = onnx.helper.make_node(
"LpPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
p=p,
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = (2, 2)
strides = (1, 1)
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p)
expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_default")
@staticmethod
def export_lppool_3d_default() -> None:
"""
input_shape: [1, 3, 32, 32, 32]
output_shape: [1, 3, 31, 31, 31]
"""
p = 3
node = onnx.helper.make_node(
"LpPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2, 2],
p=p,
)
x = np.random.randn(1, 3, 32, 32, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = [2, 2, 2]
strides = [1, 1, 1]
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p)
expect(node, inputs=[x], outputs=[y], name="test_lppool_3d_default")
@staticmethod
def export_lppool_2d_same_upper() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [0, 1, 0, 1] by axis
"""
p = 2
node = onnx.helper.make_node(
"LpPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_UPPER",
p=p,
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape_auto_pad(
"SAME_UPPER", x_shape[2:], kernel_shape, strides
)
pad_shape = get_pad_shape(
"SAME_UPPER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_top = pad_shape[0] // 2
pad_bottom = pad_shape[0] - pad_top
pad_left = pad_shape[1] // 2
pad_right = pad_shape[1] - pad_left
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=0,
)
pads = [pad_top, pad_left, pad_bottom, pad_right]
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, p=p)
expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_upper")
@staticmethod
def export_lppool_2d_same_lower() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 32, 32]
pad_shape: [1, 1] -> [1, 0, 1, 0] by axis
"""
p = 4
node = onnx.helper.make_node(
"LpPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
auto_pad="SAME_LOWER",
p=p,
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (2, 2)
strides = (1, 1)
out_shape = get_output_shape_auto_pad(
"SAME_LOWER", x_shape[2:], kernel_shape, strides
)
pad_shape = get_pad_shape(
"SAME_LOWER", x_shape[2:], kernel_shape, strides, out_shape
)
pad_bottom = pad_shape[0] // 2
pad_top = pad_shape[0] - pad_bottom
pad_right = pad_shape[1] // 2
pad_left = pad_shape[1] - pad_right
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=0,
)
pads = [pad_top, pad_left, pad_bottom, pad_right]
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, p=p)
expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_same_lower")
@staticmethod
def export_lppool_2d_pads() -> None:
"""
input_shape: [1, 3, 28, 28]
output_shape: [1, 3, 30, 30]
pad_shape: [4, 4] -> [2, 2, 2, 2] by axis
"""
p = 3
node = onnx.helper.make_node(
"LpPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[3, 3],
pads=[2, 2, 2, 2],
p=p,
)
x = np.random.randn(1, 3, 28, 28).astype(np.float32)
x_shape = np.shape(x)
kernel_shape = (3, 3)
strides = (1, 1)
pad_bottom = pad_top = pad_right = pad_left = 2
pads = [pad_top, pad_left, pad_bottom, pad_right]
out_shape, pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = np.pad(
x,
((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)),
mode="constant",
constant_values=0,
)
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", pads, p=p)
expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_pads")
@staticmethod
def export_lppool_2d_strides() -> None:
"""
input_shape: [1, 3, 32, 32]
output_shape: [1, 3, 10, 10]
"""
p = 2
node = onnx.helper.make_node(
"LpPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[5, 5],
strides=[3, 3],
p=p,
)
x = np.random.randn(1, 3, 32, 32).astype(np.float32)
x_shape = np.shape(x)
pads = None
kernel_shape = (5, 5)
strides = (3, 3)
out_shape, _ = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides
)
padded = x
y = pool(padded, x_shape, kernel_shape, strides, out_shape, "LPPOOL", p=p)
expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_strides")
@staticmethod
def export_lppool_2d_dilations() -> None:
"""
input_shape: [1, 1, 4, 4]
output_shape: [1, 1, 2, 2]
"""
p = 2
node = onnx.helper.make_node(
"LpPool",
inputs=["x"],
outputs=["y"],
kernel_shape=[2, 2],
strides=[1, 1],
dilations=[2, 2],
p=p,
)
x = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
]
).astype(np.float32)
y = np.array(
[
[
[
[14.560219778561036, 16.24807680927192],
[21.633307652783937, 23.49468024894146],
]
]
]
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_lppool_2d_dilations")
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58,811 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/hardswish.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def hardswish(x: np.ndarray) -> np.ndarray:
alfa = float(1 / 6)
beta = 0.5
return x * np.maximum(0, np.minimum(1, alfa * x + beta))
class HardSwish(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"HardSwish",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = hardswish(x)
expect(node, inputs=[x], outputs=[y], name="test_hardswish")
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58,812 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/thresholdedrelu.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ThresholdedRelu(Base):
@staticmethod
def export() -> None:
alpha = 2.0
node = onnx.helper.make_node(
"ThresholdedRelu", inputs=["x"], outputs=["y"], alpha=alpha
)
x = np.array([-1.5, 0.0, 1.2, 2.0, 2.2]).astype(np.float32)
y = np.clip(x, alpha, np.inf) # expected output [0., 0., 0., 0., 2.2]
y[y == alpha] = 0
expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, alpha, np.inf)
y[y == alpha] = 0
expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu")
@staticmethod
def export_default() -> None:
default_alpha = 1.0
node = onnx.helper.make_node("ThresholdedRelu", inputs=["x"], outputs=["y"])
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.clip(x, default_alpha, np.inf)
y[y == default_alpha] = 0
expect(node, inputs=[x], outputs=[y], name="test_thresholdedrelu_default")
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58,813 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_leaky_relu.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
def _leaky_relu(x: np.ndarray, alpha: float) -> np.ndarray:
sign = (x > 0).astype(x.dtype)
sign -= ((sign - 1) * alpha).astype(x.dtype)
return x * sign # type: ignore
class LeakyRelu(OpRunUnaryNum):
def _run(self, x, alpha=None): # type: ignore
alpha = alpha or self.alpha # type: ignore
return (_leaky_relu(x, alpha).astype(x.dtype),)
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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,814 | onnx/onnx | refs/heads/main | /onnx/test/helper_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=unnecessary-lambda
import random
import struct
import unittest
from typing import Any, List, Tuple
import numpy as np
import pytest
from onnx import (
AttributeProto,
GraphProto,
ModelProto,
OptionalProto,
SequenceProto,
TensorProto,
TypeProto,
checker,
defs,
helper,
numpy_helper,
)
class TestHelperAttributeFunctions(unittest.TestCase):
def test_attr_float(self) -> None:
# float
attr = helper.make_attribute("float", 1.0)
self.assertEqual(attr.name, "float")
self.assertEqual(attr.f, 1.0)
checker.check_attribute(attr)
# float with scientific
attr = helper.make_attribute("float", 1e10)
self.assertEqual(attr.name, "float")
self.assertEqual(attr.f, 1e10)
checker.check_attribute(attr)
def test_attr_int(self) -> None:
# integer
attr = helper.make_attribute("int", 3)
self.assertEqual(attr.name, "int")
self.assertEqual(attr.i, 3)
checker.check_attribute(attr)
# long integer
attr = helper.make_attribute("int", 5)
self.assertEqual(attr.name, "int")
self.assertEqual(attr.i, 5)
checker.check_attribute(attr)
# octinteger
attr = helper.make_attribute("int", 0o1701)
self.assertEqual(attr.name, "int")
self.assertEqual(attr.i, 0o1701)
checker.check_attribute(attr)
# hexinteger
attr = helper.make_attribute("int", 0x1701)
self.assertEqual(attr.name, "int")
self.assertEqual(attr.i, 0x1701)
checker.check_attribute(attr)
def test_attr_doc_string(self) -> None:
attr = helper.make_attribute("a", "value")
self.assertEqual(attr.name, "a")
self.assertEqual(attr.doc_string, "")
attr = helper.make_attribute("a", "value", "doc")
self.assertEqual(attr.name, "a")
self.assertEqual(attr.doc_string, "doc")
def test_attr_string(self) -> None:
# bytes
attr = helper.make_attribute("str", b"test")
self.assertEqual(attr.name, "str")
self.assertEqual(attr.s, b"test")
checker.check_attribute(attr)
# unspecified
attr = helper.make_attribute("str", "test")
self.assertEqual(attr.name, "str")
self.assertEqual(attr.s, b"test")
checker.check_attribute(attr)
# unicode
attr = helper.make_attribute("str", "test")
self.assertEqual(attr.name, "str")
self.assertEqual(attr.s, b"test")
checker.check_attribute(attr)
# empty str
attr = helper.make_attribute("str", "")
self.assertEqual(attr.name, "str")
self.assertEqual(helper.get_attribute_value(attr), b"")
checker.check_attribute(attr)
def test_attr_repeated_float(self) -> None:
attr = helper.make_attribute("floats", [1.0, 2.0])
self.assertEqual(attr.name, "floats")
self.assertEqual(list(attr.floats), [1.0, 2.0])
checker.check_attribute(attr)
def test_attr_repeated_int(self) -> None:
attr = helper.make_attribute("ints", [1, 2])
self.assertEqual(attr.name, "ints")
self.assertEqual(list(attr.ints), [1, 2])
checker.check_attribute(attr)
def test_attr_repeated_mixed_floats_and_ints(self) -> None:
attr = helper.make_attribute("mixed", [1, 2, 3.0, 4.5])
self.assertEqual(attr.name, "mixed")
self.assertEqual(list(attr.floats), [1.0, 2.0, 3.0, 4.5])
checker.check_attribute(attr)
def test_attr_repeated_str(self) -> None:
attr = helper.make_attribute("strings", ["str1", "str2"])
self.assertEqual(attr.name, "strings")
self.assertEqual(list(attr.strings), [b"str1", b"str2"])
checker.check_attribute(attr)
def test_attr_repeated_tensor_proto(self) -> None:
tensors = [
helper.make_tensor(
name="a", data_type=TensorProto.FLOAT, dims=(1,), vals=np.ones(1)
),
helper.make_tensor(
name="b", data_type=TensorProto.FLOAT, dims=(1,), vals=np.ones(1)
),
]
attr = helper.make_attribute("tensors", tensors)
self.assertEqual(attr.name, "tensors")
self.assertEqual(list(attr.tensors), tensors)
checker.check_attribute(attr)
def test_attr_sparse_tensor_proto(self) -> None:
dense_shape = [3, 3]
sparse_values = [1.764052391052246, 0.40015721321105957, 0.978738009929657]
values_tensor = helper.make_tensor(
name="sparse_values",
data_type=TensorProto.FLOAT,
dims=[len(sparse_values)],
vals=np.array(sparse_values).astype(np.float32),
raw=False,
)
linear_indices = [2, 3, 5]
indices_tensor = helper.make_tensor(
name="indices",
data_type=TensorProto.INT64,
dims=[len(linear_indices)],
vals=np.array(linear_indices).astype(np.int64),
raw=False,
)
sparse_tensor = helper.make_sparse_tensor(
values_tensor, indices_tensor, dense_shape
)
attr = helper.make_attribute("sparse_attr", sparse_tensor)
self.assertEqual(attr.name, "sparse_attr")
checker.check_sparse_tensor(helper.get_attribute_value(attr))
checker.check_attribute(attr)
def test_attr_sparse_tensor_repeated_protos(self) -> None:
dense_shape = [3, 3]
sparse_values = [1.764052391052246, 0.40015721321105957, 0.978738009929657]
values_tensor = helper.make_tensor(
name="sparse_values",
data_type=TensorProto.FLOAT,
dims=[len(sparse_values)],
vals=np.array(sparse_values).astype(np.float32),
raw=False,
)
linear_indices = [2, 3, 5]
indices_tensor = helper.make_tensor(
name="indices",
data_type=TensorProto.INT64,
dims=[len(linear_indices)],
vals=np.array(linear_indices).astype(np.int64),
raw=False,
)
sparse_tensor = helper.make_sparse_tensor(
values_tensor, indices_tensor, dense_shape
)
repeated_sparse = [sparse_tensor, sparse_tensor]
attr = helper.make_attribute("sparse_attrs", repeated_sparse)
self.assertEqual(attr.name, "sparse_attrs")
checker.check_attribute(attr)
for s in helper.get_attribute_value(attr):
checker.check_sparse_tensor(s)
def test_attr_repeated_graph_proto(self) -> None:
graphs = [GraphProto(), GraphProto()]
graphs[0].name = "a"
graphs[1].name = "b"
attr = helper.make_attribute("graphs", graphs)
self.assertEqual(attr.name, "graphs")
self.assertEqual(list(attr.graphs), graphs)
checker.check_attribute(attr)
def test_attr_type_proto(self) -> None:
# type_proto
type_proto = TypeProto()
attr = helper.make_attribute("type_proto", type_proto)
self.assertEqual(attr.name, "type_proto")
self.assertEqual(attr.tp, type_proto)
self.assertEqual(attr.type, AttributeProto.TYPE_PROTO)
# type_protos
types = [TypeProto(), TypeProto()]
attr = helper.make_attribute("type_protos", types)
self.assertEqual(attr.name, "type_protos")
self.assertEqual(list(attr.type_protos), types)
self.assertEqual(attr.type, AttributeProto.TYPE_PROTOS)
def test_attr_empty_list(self) -> None:
attr = helper.make_attribute("empty", [], attr_type=AttributeProto.STRINGS)
self.assertEqual(attr.type, AttributeProto.STRINGS)
self.assertEqual(len(attr.strings), 0)
self.assertRaises(ValueError, helper.make_attribute, "empty", [])
def test_is_attr_legal(self) -> None:
# no name, no field
attr = AttributeProto()
self.assertRaises(checker.ValidationError, checker.check_attribute, attr)
# name, but no field
attr = AttributeProto()
attr.name = "test"
self.assertRaises(checker.ValidationError, checker.check_attribute, attr)
# name, with two fields
attr = AttributeProto()
attr.name = "test"
attr.f = 1.0
attr.i = 2
self.assertRaises(checker.ValidationError, checker.check_attribute, attr)
def test_is_attr_legal_verbose(self) -> None:
def _set(
attr: AttributeProto,
type_: AttributeProto.AttributeType,
var: str,
value: Any,
) -> None:
setattr(attr, var, value)
attr.type = type_
def _extend(
attr: AttributeProto,
type_: AttributeProto.AttributeType,
var: List[Any],
value: Any,
) -> None:
var.extend(value)
attr.type = type_
SET_ATTR = [
(lambda attr: _set(attr, AttributeProto.FLOAT, "f", 1.0)),
(lambda attr: _set(attr, AttributeProto.INT, "i", 1)),
(lambda attr: _set(attr, AttributeProto.STRING, "s", b"str")),
(
lambda attr: _extend(
attr, AttributeProto.FLOATS, attr.floats, [1.0, 2.0]
)
),
(lambda attr: _extend(attr, AttributeProto.INTS, attr.ints, [1, 2])),
(
lambda attr: _extend(
attr, AttributeProto.STRINGS, attr.strings, [b"a", b"b"]
)
),
]
# Randomly set one field, and the result should be legal.
for _i in range(100):
attr = AttributeProto()
attr.name = "test"
random.choice(SET_ATTR)(attr)
checker.check_attribute(attr)
# Randomly set two fields, and then ensure helper function catches it.
for _i in range(100):
attr = AttributeProto()
attr.name = "test"
for func in random.sample(SET_ATTR, 2):
func(attr)
self.assertRaises(checker.ValidationError, checker.check_attribute, attr)
class TestHelperNodeFunctions(unittest.TestCase):
def test_node_no_arg(self) -> None:
self.assertTrue(defs.has("Relu"))
node_def = helper.make_node("Relu", ["X"], ["Y"], name="test")
self.assertEqual(node_def.op_type, "Relu")
self.assertEqual(node_def.name, "test")
self.assertEqual(list(node_def.input), ["X"])
self.assertEqual(list(node_def.output), ["Y"])
def test_attr_doc_string(self) -> None:
node_def = helper.make_node("Relu", ["X"], ["Y"], name="test", doc_string="doc")
self.assertEqual(node_def.doc_string, "doc")
def test_node_with_arg(self) -> None:
self.assertTrue(defs.has("Relu"))
# Note: Relu actually does not need an arg, but let's
# test it.
node_def = helper.make_node("Relu", ["X"], ["Y"], arg_value=1)
self.assertEqual(node_def.op_type, "Relu")
self.assertEqual(list(node_def.input), ["X"])
self.assertEqual(list(node_def.output), ["Y"])
self.assertEqual(len(node_def.attribute), 1)
self.assertEqual(node_def.attribute[0], helper.make_attribute("arg_value", 1))
def test_node_domain(self) -> None:
node_def = helper.make_node(
"Relu", ["X"], ["Y"], name="test", doc_string="doc", domain="test.domain"
)
self.assertEqual(node_def.domain, "test.domain")
def test_graph(self) -> None:
node_def1 = helper.make_node("Relu", ["X"], ["Y"])
node_def2 = helper.make_node("Add", ["X", "Y"], ["Z"])
value_info = [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])]
graph = helper.make_graph(
[node_def1, node_def2],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
doc_string=None,
value_info=value_info,
)
self.assertEqual(graph.name, "test")
self.assertEqual(len(graph.node), 2)
self.assertEqual(graph.node[0], node_def1)
self.assertEqual(graph.node[1], node_def2)
self.assertEqual(graph.doc_string, "")
self.assertEqual(graph.value_info[0], value_info[0])
def test_graph_docstring(self) -> None:
graph = helper.make_graph([], "my graph", [], [], None, "my docs")
self.assertEqual(graph.name, "my graph")
self.assertEqual(graph.doc_string, "my docs")
def test_model(self) -> None:
node_def = helper.make_node("Relu", ["X"], ["Y"])
graph_def = helper.make_graph(
[node_def],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
self.assertRaises(AttributeError, helper.make_model, graph_def, xxx=1)
model_def = helper.make_model(graph_def, producer_name="test")
self.assertEqual(model_def.producer_name, "test")
def test_model_docstring(self) -> None:
graph = helper.make_graph([], "my graph", [], [])
model_def = helper.make_model(graph, doc_string="test")
# models may have their own documentation, but don't have a name
# their name is the domain-qualified name of the underlying graph.
self.assertFalse(hasattr(model_def, "name"))
self.assertEqual(model_def.doc_string, "test")
def test_model_metadata_props(self) -> None:
graph = helper.make_graph([], "my graph", [], [])
model_def = helper.make_model(graph, doc_string="test")
helper.set_model_props(
model_def, {"Title": "my graph", "Keywords": "test;graph"}
)
checker.check_model(model_def)
helper.set_model_props(
model_def, {"Title": "my graph", "Keywords": "test;graph"}
)
checker.check_model(model_def) # helper replaces, so no dupe
dupe = model_def.metadata_props.add()
dupe.key = "Title"
dupe.value = "Other"
self.assertRaises(checker.ValidationError, checker.check_model, model_def)
def test_model_irversion(self) -> None:
def mk_model(opset_versions: List[Tuple[str, int]]) -> ModelProto:
graph = helper.make_graph([], "my graph", [], [])
return helper.make_model_gen_version(
graph,
opset_imports=[helper.make_opsetid(*pair) for pair in opset_versions],
)
def test(opset_versions: List[Tuple[str, int]], ir_version: int) -> None:
model = mk_model(opset_versions)
self.assertEqual(model.ir_version, ir_version)
# opset version 9 requires minimum ir_version 4
test([("", 9)], 4)
test([("", 10)], 5)
test([("", 11)], 6)
test([("", 12)], 7)
test([("", 13)], 7)
test([("", 14)], 7)
test([("", 15)], 8)
test([("", 16)], 8)
test([("", 17)], 8)
test([("", 18)], 8)
test([("", 19)], 9)
# standard opset can be referred to using empty-string or "ai.onnx"
test([("ai.onnx", 9)], 4)
test([("ai.onnx.ml", 2)], 6)
test([("ai.onnx.ml", 3)], 8)
test([("ai.onnx.training", 1)], 7)
# helper should pick *max* IR version required from all opsets specified.
test([("", 10), ("ai.onnx.ml", 2)], 6)
self.assertRaises(ValueError, mk_model, [("", 100)])
class TestHelperTensorFunctions(unittest.TestCase):
def test_make_string_tensor(self) -> None:
string_list = [s.encode("utf-8") for s in ["Amy", "Billy", "Cindy", "David"]]
tensor = helper.make_tensor(
name="test",
data_type=TensorProto.STRING,
dims=(2, 2),
vals=string_list,
raw=False,
)
self.assertEqual(string_list, list(tensor.string_data))
def test_make_bfloat16_tensor(self) -> None:
# numpy doesn't support bf16, so we have to compute the correct result manually
np_array = np.array(
[
[1.0, 2.0],
[3.0, 4.0],
[0.099853515625, 0.099365234375],
[0.0998535081744, 0.1],
[np.nan, np.inf],
],
dtype=np.float32,
)
np_results = np.array(
[
[
struct.unpack("!f", bytes.fromhex("3F800000"))[0], # 1.0
struct.unpack("!f", bytes.fromhex("40000000"))[0],
], # 2.0
[
struct.unpack("!f", bytes.fromhex("40400000"))[0], # 3.0
struct.unpack("!f", bytes.fromhex("40800000"))[0],
], # 4.0
[
struct.unpack("!f", bytes.fromhex("3DCC0000"))[
0
], # round-to-nearest-even rounds down (0x8000)
struct.unpack("!f", bytes.fromhex("3DCC0000"))[0],
], # round-to-nearest-even rounds up (0x8000)
[
struct.unpack("!f", bytes.fromhex("3DCC0000"))[
0
], # round-to-nearest-even rounds down (0x7fff)
struct.unpack("!f", bytes.fromhex("3DCD0000"))[0],
], # round-to-nearest-even rounds up (0xCCCD)
[
struct.unpack("!f", bytes.fromhex("7FC00000"))[0], # NaN
struct.unpack("!f", bytes.fromhex("7F800000"))[0],
], # inf
]
)
tensor = helper.make_tensor(
name="test",
data_type=TensorProto.BFLOAT16,
dims=np_array.shape,
vals=np_array,
)
self.assertEqual(tensor.name, "test")
np.testing.assert_equal(np_results, numpy_helper.to_array(tensor))
def test_make_float8e4m3fn_tensor(self) -> None:
y = helper.make_tensor(
"zero_point", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 50000, 10.1]
)
ynp = numpy_helper.to_array(y)
expected = np.array([0, 0.5, 1, 448, 10], dtype=np.float32)
np.testing.assert_equal(expected, ynp)
def test_make_float8e4m3fnuz_tensor(self) -> None:
y = helper.make_tensor(
"zero_point", TensorProto.FLOAT8E4M3FNUZ, [5], [0, 0.5, 1, 50000, 10.1]
)
ynp = numpy_helper.to_array(y)
expected = np.array([0, 0.5, 1, 240, 10], dtype=np.float32)
np.testing.assert_equal(expected, ynp)
def test_make_float8e5m2_tensor(self) -> None:
y = helper.make_tensor(
"zero_point", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 50000, 96]
)
ynp = numpy_helper.to_array(y)
expected = np.array([0, 0.5, 1, 49152, 96], dtype=np.float32)
np.testing.assert_equal(expected, ynp)
def test_make_float8e5m2fnuz_tensor(self) -> None:
y = helper.make_tensor(
"zero_point", TensorProto.FLOAT8E5M2FNUZ, [5], [0, 0.5, 1, 50000, 96]
)
ynp = numpy_helper.to_array(y)
expected = np.array([0, 0.5, 1, 49152, 96], dtype=np.float32)
np.testing.assert_equal(expected, ynp)
def test_make_bfloat16_tensor_raw(self) -> None:
# numpy doesn't support bf16, so we have to compute the correct result manually
np_array = np.array(
[
[1.0, 2.0],
[3.0, 4.0],
[0.099853515625, 0.099365234375],
[0.0998535081744, 0.1],
[np.nan, np.inf],
],
dtype=np.float32,
)
np_results = np.array(
[
[
struct.unpack("!f", bytes.fromhex("3F800000"))[0], # 1.0
struct.unpack("!f", bytes.fromhex("40000000"))[0],
], # 2.0
[
struct.unpack("!f", bytes.fromhex("40400000"))[0], # 3.0
struct.unpack("!f", bytes.fromhex("40800000"))[0],
], # 4.0
[
struct.unpack("!f", bytes.fromhex("3DCC0000"))[0], # truncated
struct.unpack("!f", bytes.fromhex("3DCB0000"))[0],
], # truncated
[
struct.unpack("!f", bytes.fromhex("3DCC0000"))[0], # truncated
struct.unpack("!f", bytes.fromhex("3DCC0000"))[0],
], # truncated
[
struct.unpack("!f", bytes.fromhex("7FC00000"))[0], # NaN
struct.unpack("!f", bytes.fromhex("7F800000"))[0],
], # inf
]
)
# write out 16-bit of fp32 to create bf16 using truncation, no rounding
def truncate(x):
return x >> 16
values_as_ints = np_array.astype(np.float32).view(np.uint32).flatten()
packed_values = truncate(values_as_ints).astype(np.uint16).tobytes()
tensor = helper.make_tensor(
name="test",
data_type=TensorProto.BFLOAT16,
dims=np_array.shape,
vals=packed_values,
raw=True,
)
self.assertEqual(tensor.name, "test")
np.testing.assert_equal(np_results, numpy_helper.to_array(tensor))
def test_make_float8e4m3fn_tensor_raw(self) -> None:
expected = np.array([0, 0.5, 1, 448, 10], dtype=np.float32)
f8 = np.array(
[helper.float32_to_float8e4m3(x) for x in expected], dtype=np.uint8
)
packed_values = f8.tobytes()
y = helper.make_tensor(
name="test",
data_type=TensorProto.FLOAT8E4M3FN,
dims=list(expected.shape),
vals=packed_values,
raw=True,
)
ynp = numpy_helper.to_array(y)
np.testing.assert_equal(expected, ynp)
def test_make_float8e4m3fnuz_tensor_raw(self) -> None:
expected = np.array([0, 0.5, 1, 240, 10], dtype=np.float32)
f8 = np.array(
[helper.float32_to_float8e4m3(x, uz=True) for x in expected], dtype=np.uint8
)
packed_values = f8.tobytes()
y = helper.make_tensor(
name="test",
data_type=TensorProto.FLOAT8E4M3FNUZ,
dims=list(expected.shape),
vals=packed_values,
raw=True,
)
ynp = numpy_helper.to_array(y)
np.testing.assert_equal(expected, ynp)
def test_make_float8e5m2_tensor_raw(self) -> None:
expected = np.array([0, 0.5, 1, 49152, 10], dtype=np.float32)
f8 = np.array(
[helper.float32_to_float8e5m2(x) for x in expected], dtype=np.uint8
)
packed_values = f8.tobytes()
y = helper.make_tensor(
name="test",
data_type=TensorProto.FLOAT8E5M2,
dims=list(expected.shape),
vals=packed_values,
raw=True,
)
ynp = numpy_helper.to_array(y)
np.testing.assert_equal(expected, ynp)
def test_make_float8e5m2fnuz_tensor_raw(self) -> None:
expected = np.array([0, 0.5, 1, 49152, 10], dtype=np.float32)
f8 = np.array(
[helper.float32_to_float8e5m2(x, fn=True, uz=True) for x in expected],
dtype=np.uint8,
)
packed_values = f8.tobytes()
y = helper.make_tensor(
name="test",
data_type=TensorProto.FLOAT8E5M2FNUZ,
dims=list(expected.shape),
vals=packed_values,
raw=True,
)
ynp = numpy_helper.to_array(y)
np.testing.assert_equal(expected, ynp)
def test_make_sparse_tensor(self) -> None:
values = [1.1, 2.2, 3.3, 4.4, 5.5]
values_tensor = helper.make_tensor(
name="test", data_type=TensorProto.FLOAT, dims=(5,), vals=values
)
indices = [1, 3, 5, 7, 9]
indices_tensor = helper.make_tensor(
name="test_indices", data_type=TensorProto.INT64, dims=(5,), vals=indices
)
dense_shape = [10]
sparse = helper.make_sparse_tensor(values_tensor, indices_tensor, dense_shape)
self.assertEqual(sparse.values, values_tensor)
self.assertEqual(sparse.indices, indices_tensor)
self.assertEqual(sparse.dims, dense_shape)
def test_make_tensor_value_info(self) -> None:
vi = helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 4))
checker.check_value_info(vi)
# scalar value
vi = helper.make_tensor_value_info("Y", TensorProto.FLOAT, ())
checker.check_value_info(vi)
def test_make_sparse_tensor_value_info(self) -> None:
vi = helper.make_sparse_tensor_value_info("X", TensorProto.FLOAT, (2, 3))
checker.check_value_info(vi)
# scalar value
vi = helper.make_sparse_tensor_value_info("Y", TensorProto.FLOAT, ())
checker.check_value_info(vi)
class TestHelperOptionalAndSequenceFunctions(unittest.TestCase):
def test_make_optional(self) -> None:
values = [1.1, 2.2, 3.3, 4.4, 5.5]
values_tensor = helper.make_tensor(
name="test", data_type=TensorProto.FLOAT, dims=(5,), vals=values
)
optional = helper.make_optional(
name="test", elem_type=OptionalProto.TENSOR, value=values_tensor
)
self.assertEqual(optional.name, "test")
self.assertEqual(optional.elem_type, OptionalProto.TENSOR)
self.assertEqual(optional.tensor_value, values_tensor)
# Test Sequence
values_sequence = helper.make_sequence(
name="test",
elem_type=SequenceProto.TENSOR,
values=[values_tensor, values_tensor],
)
optional = helper.make_optional(
name="test", elem_type=OptionalProto.SEQUENCE, value=values_sequence
)
self.assertEqual(optional.name, "test")
self.assertEqual(optional.elem_type, OptionalProto.SEQUENCE)
self.assertEqual(optional.sequence_value, values_sequence)
# Test None
optional_none = helper.make_optional(
name="test", elem_type=OptionalProto.UNDEFINED, value=None
)
self.assertEqual(optional_none.name, "test")
self.assertEqual(optional_none.elem_type, OptionalProto.UNDEFINED)
self.assertFalse(optional_none.HasField("tensor_value"))
def test_make_optional_value_info(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(elem_type=2, shape=[5])
tensor_val_into = helper.make_value_info(
name="test", type_proto=tensor_type_proto
)
optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
optional_val_info = helper.make_value_info(
name="test", type_proto=optional_type_proto
)
self.assertEqual(optional_val_info.name, "test")
self.assertTrue(optional_val_info.type.optional_type)
self.assertEqual(
optional_val_info.type.optional_type.elem_type, tensor_val_into.type
)
# Test Sequence
sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
optional_val_info = helper.make_value_info(
name="test", type_proto=optional_type_proto
)
self.assertEqual(optional_val_info.name, "test")
self.assertTrue(optional_val_info.type.optional_type)
sequence_value_info = helper.make_value_info(
name="test", type_proto=tensor_type_proto
)
self.assertEqual(
optional_val_info.type.optional_type.elem_type.sequence_type.elem_type,
sequence_value_info.type,
)
def test_make_seuence_value_info(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(elem_type=2, shape=None)
sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
sequence_val_info = helper.make_value_info(
name="test", type_proto=sequence_type_proto
)
sequence_val_info_prim = helper.make_tensor_sequence_value_info(
name="test", elem_type=2, shape=None
)
self.assertEqual(sequence_val_info, sequence_val_info_prim)
class TestPrintableGraph(unittest.TestCase):
def test_initializer_with_matching_graph_input(self) -> None:
add = helper.make_node("Add", ["X", "Y_Initializer"], ["Z"])
value_info = [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1])]
graph = helper.make_graph(
[add],
"test",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, [1]),
helper.make_tensor_value_info("Y_Initializer", TensorProto.FLOAT, [1]),
], # inputs
[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1])], # outputs
[
helper.make_tensor("Y_Initializer", TensorProto.FLOAT, [1], [1])
], # initializers
doc_string=None,
value_info=value_info,
)
graph_str = helper.printable_graph(graph)
self.assertTrue(
""") optional inputs with matching initializers (
%Y_Initializer[FLOAT, 1]"""
in graph_str,
graph_str,
)
def test_initializer_no_matching_graph_input(self) -> None:
add = helper.make_node("Add", ["X", "Y_Initializer"], ["Z"])
value_info = [helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1])]
graph = helper.make_graph(
[add],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1])], # inputs
[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1])], # outputs
[
helper.make_tensor("Y_Initializer", TensorProto.FLOAT, [1], [1])
], # initializers
doc_string=None,
value_info=value_info,
)
graph_str = helper.printable_graph(graph)
self.assertTrue(
""") initializers (
%Y_Initializer[FLOAT, 1]"""
in graph_str,
graph_str,
)
@pytest.mark.parametrize(
"tensor_dtype",
[
t
for t in helper.get_all_tensor_dtypes()
if t
not in {
TensorProto.BFLOAT16,
TensorProto.FLOAT8E4M3FN,
TensorProto.FLOAT8E4M3FNUZ,
TensorProto.FLOAT8E5M2,
TensorProto.FLOAT8E5M2FNUZ,
TensorProto.STRING,
TensorProto.COMPLEX64,
TensorProto.COMPLEX128,
}
],
ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype),
)
def test_make_tensor_vals(tensor_dtype: int) -> None:
np_array = np.random.randn(2, 3).astype(
helper.tensor_dtype_to_np_dtype(tensor_dtype)
)
tensor = helper.make_tensor(
name="test", data_type=tensor_dtype, dims=np_array.shape, vals=np_array
)
np.testing.assert_equal(np_array, numpy_helper.to_array(tensor))
@pytest.mark.parametrize(
"tensor_dtype",
[
t
for t in helper.get_all_tensor_dtypes()
if t
not in {
TensorProto.BFLOAT16,
TensorProto.STRING,
TensorProto.FLOAT8E4M3FN,
TensorProto.FLOAT8E4M3FNUZ,
TensorProto.FLOAT8E5M2,
TensorProto.FLOAT8E5M2FNUZ,
}
],
ids=lambda tensor_dtype: helper.tensor_dtype_to_string(tensor_dtype),
)
def test_make_tensor_raw(tensor_dtype: int) -> None:
np_array = np.random.randn(2, 3).astype(
helper.tensor_dtype_to_np_dtype(tensor_dtype)
)
tensor = helper.make_tensor(
name="test",
data_type=tensor_dtype,
dims=np_array.shape,
vals=np_array.tobytes(),
raw=True,
)
np.testing.assert_equal(np_array, numpy_helper.to_array(tensor))
class TestHelperMappingFunctions(unittest.TestCase):
# TODO (#4554): remove these tests about catching warnings after the deprecation period
# Test these new functions should not raise any deprecation warnings
@pytest.mark.filterwarnings("error::DeprecationWarning")
def test_tensor_dtype_to_np_dtype_not_throw_warning(self) -> None:
_ = helper.tensor_dtype_to_np_dtype(TensorProto.FLOAT)
@pytest.mark.filterwarnings("error::DeprecationWarning")
def test_tensor_dtype_to_storage_tensor_dtype_not_throw_warning(self) -> None:
_ = helper.tensor_dtype_to_storage_tensor_dtype(TensorProto.FLOAT)
@pytest.mark.filterwarnings("error::DeprecationWarning")
def test_tensor_dtype_to_field_not_throw_warning(self) -> None:
_ = helper.tensor_dtype_to_field(TensorProto.FLOAT)
@pytest.mark.filterwarnings("error::DeprecationWarning")
def test_np_dtype_to_tensor_dtype_not_throw_warning(self) -> None:
_ = helper.np_dtype_to_tensor_dtype(np.dtype("float32"))
def test_tensor_dtype_to_np_dtype_bfloat16(self) -> None:
self.assertEqual(
helper.tensor_dtype_to_np_dtype(TensorProto.BFLOAT16), np.dtype("float32")
)
def test_tensor_dtype_to_storage_tensor_dtype_bfloat16(self) -> None:
self.assertEqual(
helper.tensor_dtype_to_storage_tensor_dtype(TensorProto.BFLOAT16),
TensorProto.UINT16,
)
# BFloat16 tensor uses TensorProto.UINT16 as storage type;
# And the field name for TensorProto.UINT16 is int32_data
def test_tensor_dtype_to_field_bfloat16(self) -> None:
self.assertEqual(
helper.tensor_dtype_to_field(TensorProto.BFLOAT16), "int32_data"
)
if __name__ == "__main__":
unittest.main()
pytest.main([__file__])
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58,815 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sigmoid.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
def sigmoid(x): # type: ignore
if x > 0:
return 1 / (1 + np.exp(-x))
return np.exp(x) / (1 + np.exp(x))
class Sigmoid(OpRunUnaryNum):
def __init__(self, onnx_node, run_params): # type: ignore
OpRunUnaryNum.__init__(self, onnx_node, run_params)
self.vf = np.vectorize(sigmoid)
def _run(self, X): # type: ignore
if len(X.shape) == 0:
return (sigmoid(X).astype(X.dtype),)
if X.size == 0:
return (X,)
return (self.vf(X).astype(X.dtype),)
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"/onnx/reference/ops/op_unique.py", "/onnx/reference/ops/op_unsqueeze.py", "/onnx/reference/ops/op_upsample.py", "/onnx/reference/ops/op_where.py"], "/onnx/backend/test/case/model/gradient.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py", "/onnx/defs/__init__.py"], "/onnx/compose.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_det.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sequence_empty.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/topk.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_log_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_linear_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/center_crop_pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_string_concat.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_batch_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cum_sum.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_prelu.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_unsqueeze.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/globalaveragepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/__init__.py": ["/onnx/backend/test/runner/__init__.py"], "/onnx/test/parser_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_regex_full_match.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/xor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/shape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], 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"/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,816 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_pool_common.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import itertools
import math
from typing import Sequence, Tuple, Union
import numpy as np
from onnx.reference.op_run import OpRun
def get_pad_shape(
auto_pad: str,
input_spatial_shape: Sequence[int],
kernel_spatial_shape: Sequence[int],
strides_spatial: Sequence[int],
output_spatial_shape: Sequence[int],
) -> Sequence[int]:
spatial_dims = len(input_spatial_shape)
pad_shape = [0] * spatial_dims
strides_spatial = strides_spatial or [1] * spatial_dims
if auto_pad in ("SAME_UPPER", "SAME_LOWER"):
for i in range(spatial_dims):
pad_shape[i] = (
(output_spatial_shape[i] - 1) * strides_spatial[i]
+ kernel_spatial_shape[i]
- input_spatial_shape[i]
)
elif auto_pad == "VALID":
pass
return pad_shape
def get_pad_with_auto_pad(auto_pad: str, pad_shape: Sequence[int]) -> Sequence[int]:
spatial_dims = len(pad_shape)
if auto_pad == "SAME_UPPER":
pads = [pad_shape[i] // 2 for i in range(spatial_dims)] + [
pad_shape[i] - pad_shape[i] // 2 for i in range(spatial_dims)
]
elif auto_pad == "SAME_LOWER":
pads = [pad_shape[i] - pad_shape[i] // 2 for i in range(spatial_dims)] + [
pad_shape[i] // 2 for i in range(spatial_dims)
]
else:
pads = [0] * spatial_dims * 2 # no padding
return pads
def get_output_shape_explicit_padding(
pads: Sequence[int],
input_spatial_shape: Sequence[int],
kernel_spatial_shape: Sequence[int],
strides_spatial: Sequence[int],
dilations: Union[Sequence[int], None] = None,
ceil_mode: bool = False,
) -> Tuple[Sequence[int], Sequence[int]]:
"""
compute output shape according to:
https://pytorch.org/docs/stable/generated/torch.nn.MaxPool1d.html?highlight=max+pool#torch.nn.MaxPool1d
Pads are used to calculate output shape. Use output shape in turn to calculate the actual pads
that are used to pad the input tensor so that computation in pool() will not cause out of bound error.
Here is the detail. Thinking kernel as a sliding window, its size:
sw = dilation * (kernel - 1) + 1
l_out = (l_in + pad[0] + pad[1] - sw) / stride + 1 # (ceiled if ceil_mode is True)
l_in_required = (l_out - 1) * stride + sw
l_in_required is used to for computation in pool() which may be larger than padded l_in, because of ceiling.
as an example, l_in = 3, kernel = 2, stride = 2, dilation = 1, pad = [0, 0], then
sw = dilation * (kernel - 1) + 1 = 1 * (2 - 1) + 1 = 2
l_out = ceil((l_in + pad[0] + pad[1] - sw) / stride + 1) = ceil((3 + 0 + 0 - 1 * (2 - 1) - 1) / 2 + 1) = 2
l_in_required = (l_out - 1) * stride + sw = (2 - 1) * 2 + 2 = 4
l_in_required (= 4) is not equal to l_in (= 3), so we need to pad the input tensor to l_in_required to make sure that
the sliding window does not go out-of-bound w.r.t. input tensor. Otherwise pool() will fail.
"""
output_spatial_shape = [0] * len(input_spatial_shape)
pads = pads or [0] * len(input_spatial_shape) * 2
strides_spatial = strides_spatial or [1] * len(input_spatial_shape)
dims = len(input_spatial_shape)
if dilations is None:
dilations = np.ones([dims], dtype=np.int64)
for dim in range(dims):
dim_size = (
input_spatial_shape[dim]
+ pads[dim]
+ pads[dims + dim]
- dilations[dim] * (kernel_spatial_shape[dim] - 1)
- 1
) / strides_spatial[dim] + 1
if ceil_mode:
output_spatial_shape[dim] = int(np.ceil(dim_size))
else:
output_spatial_shape[dim] = int(np.floor(dim_size))
pads_spatial_shape_new = pads[:]
for dim in range(dims):
sliding_window_size = (kernel_spatial_shape[dim] - 1) * dilations[dim] + 1
actual_padded_input_size = (output_spatial_shape[dim] - 1) * strides_spatial[
dim
] + sliding_window_size
extra_pad = (
actual_padded_input_size
- input_spatial_shape[dim]
- pads[dim]
- pads[dims + dim]
)
if extra_pad > 0:
pads_spatial_shape_new[dim] += extra_pad // 2
pads_spatial_shape_new[dims + dim] += extra_pad - extra_pad // 2
return output_spatial_shape, pads_spatial_shape_new
def get_output_shape_auto_pad(
auto_pad: str,
input_spatial_shape: Sequence[int],
kernel_spatial_shape: Sequence[int],
strides_spatial: Sequence[int],
) -> Sequence[int]:
"""
https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D
output_shape = math.floor((input_shape - 1) / strides) + 1 (SAME)
output_shape = math.floor((input_shape - pool_size) / strides) + 1 (VALID)
IMPORTANT: this function assumes ceil_mode is False. In tenforflow, ceil_mode is always False.
However, ONNX spec allow ceil_mode to be True because ORT does handle the case.
"""
strides_spatial = strides_spatial or [1] * len(input_spatial_shape)
out_shape = [0] * len(input_spatial_shape)
for i in range( # pylint: disable=consider-using-enumerate
len(input_spatial_shape)
):
if auto_pad in ("SAME_UPPER", "SAME_LOWER"):
out_shape[i] = (
math.floor((input_spatial_shape[i] - 1) / strides_spatial[i]) + 1
)
elif auto_pad == "VALID":
out_shape[i] = (
math.floor(
(input_spatial_shape[i] - kernel_spatial_shape[i])
/ strides_spatial[i]
)
+ 1
)
# if auto_pad is NOTSET, explicite padding should be used
else:
raise ValueError(
"auto_pad can only be NOTSET, SAME_UPPER, SAME_LOWER, or VALID"
)
# pads = get_pad_shape(auto_pad, input_spatial_shape, kernel_shape, strides_spatial, out_shape)
return out_shape
def lp_pool(x: np.array, p: int) -> float:
y = 0
for v in np.nditer(x):
y += abs(v) ** p
return y ** (1.0 / p)
def pool(
padded: np.ndarray,
x_shape: Sequence[int],
kernel: Sequence[int],
strides: Sequence[int],
out_shape: Sequence[int],
pooling_type: str,
pads: Union[Sequence[int], None] = None,
dilations: Union[Sequence[int], None] = None,
count_include_pad: int = 0,
p: int = 1,
) -> np.ndarray:
"""
this function is used to calculate the pooling result of a padded tensor
padded: the padded tensor
x_shape: the shape of the original tensor in [N, C, *spatial_shape]
kernel: the pooling kernel
strides: the strides
out_shape: the shape of the output tensor
pooling_type: the pooling type, can be "AVG", "LPPOOL", or "MAX"
pads: the padding in an order of head_pad_1, head_pad_2, ..., tail_pad_1, tail_pad_2, ...
dilations: the dilation
count_include_pad: whether to include the padding in the calculation of average and lp pooling
p: the p value for lp pooling
"""
spatial_size = len(x_shape) - 2
y = np.zeros([x_shape[0], x_shape[1], *list(out_shape)], dtype=padded.dtype)
if dilations is None:
dilations = np.ones([spatial_size], dtype=np.int64)
if pads is None:
pads = np.zeros([spatial_size * 2], dtype=np.int64)
elif len(pads) == 1:
pads = pads * spatial_size * 2
strides = strides or [1] * spatial_size
def lp_pool_p(x):
return lp_pool(x, p)
for shape in itertools.product(
range(x_shape[0]),
range(x_shape[1]),
*[
range(
int(
(
x_shape[i + 2]
+ pads[i]
+ pads[i + spatial_size]
- (1 + (kernel[i] - 1) * dilations[i])
)
/ strides[i]
+ 1
)
)
for i in range(spatial_size)
],
):
window = padded[shape[0], shape[1]]
window_vals = np.array(
[
window[i]
for i in list(
itertools.product(
*[
range(
strides[i] * shape[i + 2],
strides[i] * shape[i + 2]
+ (1 + (kernel[i] - 1) * dilations[i]),
dilations[i],
)
for i in range(spatial_size)
]
)
)
]
)
if pooling_type == "AVG":
f = np.average
elif pooling_type == "MAX":
f = np.max
elif pooling_type == "LPPOOL":
f = lp_pool_p
else:
raise NotImplementedError(
f"Pooling type {pooling_type} does not support. Should be AVG, MAX"
)
if count_include_pad == 1 and (pooling_type in {"AVG", "LPPOOL"}):
y[shape] = f(window_vals)
else:
y[shape] = f(window_vals[np.where(~np.isnan(window_vals))])
return y
class CommonPool(OpRun):
def _run( # pylint: disable=arguments-differ
self,
pooling_type,
count_include_pad,
x,
auto_pad=None,
ceil_mode=None,
dilations=None,
kernel_shape=None,
pads=None,
strides=None,
p=None,
):
x_shape = np.shape(x)
pading_value = np.nan if pooling_type == "MAX" or count_include_pad == 0 else 0
if auto_pad in ["SAME_UPPER", "SAME_LOWER", "VALID"]:
assert (
ceil_mode is None or ceil_mode == 0
), "ceil_mode is not supported with auto_pad"
out_shape = get_output_shape_auto_pad(
auto_pad, x.shape[2:], kernel_shape, strides
)
pads_shape = get_pad_shape(
auto_pad, x_shape[2:], kernel_shape, strides, out_shape
)
pads = get_pad_with_auto_pad(auto_pad, pads_shape)
n_dims = len(pads) // 2
pads_np = [(pads[i], pads[i + n_dims]) for i in range(n_dims)]
padded = np.pad(
x,
((0, 0), (0, 0), *pads_np),
mode="constant",
constant_values=pading_value,
)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
pooling_type,
pads,
dilations,
count_include_pad,
p,
)
return (y,)
else:
out_shape, pads = get_output_shape_explicit_padding(
pads, x_shape[2:], kernel_shape, strides, dilations, ceil_mode
)
# convert pads from [x1_begin, x2_begin,...,x1_end, x2_end,...] to [(x1_begin, x1_end), (x2_begin, x2_end),...]
n_dims = len(pads) // 2
pads_np = [(pads[i], pads[i + n_dims]) for i in range(n_dims)]
padded = np.pad(
x,
((0, 0), (0, 0), *pads_np),
mode="constant",
constant_values=pading_value,
)
y = pool(
padded,
x_shape,
kernel_shape,
strides,
out_shape,
pooling_type,
pads,
dilations,
count_include_pad,
p,
)
return (y,)
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58,817 | onnx/onnx | refs/heads/main | /onnx/test/test_backend_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import itertools
import os
import platform
import unittest
from typing import Any, Optional, Sequence, Tuple
import numpy
import onnx.backend.base
import onnx.backend.test
import onnx.shape_inference
import onnx.version_converter
from onnx import ModelProto, NodeProto, TensorProto
from onnx.backend.base import Device, DeviceType
from onnx.backend.test.runner import BackendIsNotSupposedToImplementIt
# The following just executes the fake backend through the backend test
# infrastructure. Since we don't have full reference implementation of all ops
# in ONNX repo, it's impossible to produce the proper results. However, we can
# run 'checker' (that's what base Backend class does) to verify that all tests
# fed are actually well-formed ONNX models.
#
# If everything is fine, all the tests would be marked as "skipped".
#
# We don't enable report in this test because the report collection logic itself
# fails when models are mal-formed.
class DummyBackend(onnx.backend.base.Backend):
@classmethod
def prepare(
cls, model: ModelProto, device: str = "CPU", **kwargs: Any
) -> Optional[onnx.backend.base.BackendRep]:
super().prepare(model, device, **kwargs)
# test strict shape inference
onnx.checker.check_model(model)
model = onnx.shape_inference.infer_shapes(
model, check_type=True, strict_mode=True
)
value_infos = {
vi.name: vi
for vi in itertools.chain(model.graph.value_info, model.graph.output)
}
if do_enforce_test_coverage_safelist(model):
for node in model.graph.node:
for i, output in enumerate(node.output):
if node.op_type == "Dropout" and i != 0:
continue
assert output in value_infos
tt = value_infos[output].type.tensor_type
assert tt.elem_type != TensorProto.UNDEFINED
for dim in tt.shape.dim:
assert dim.WhichOneof("value") == "dim_value"
raise BackendIsNotSupposedToImplementIt(
"This is the dummy backend test that doesn't verify the results but does run the checker"
)
@classmethod
def run_node(
cls,
node: NodeProto,
inputs: Any,
device: str = "CPU",
outputs_info: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]] = None,
**kwargs: Any,
) -> Optional[Tuple[Any, ...]]:
super().run_node(node, inputs, device=device, outputs_info=outputs_info)
raise BackendIsNotSupposedToImplementIt(
"This is the dummy backend test that doesn't verify the results but does run the checker"
)
@classmethod
def supports_device(cls, device: str) -> bool:
d = Device(device)
if d.type == DeviceType.CPU:
return True
return False
test_coverage_safelist = {
"bvlc_alexnet",
"densenet121",
"inception_v1",
"inception_v2",
"resnet50",
"shufflenet",
"SingleRelu",
"squeezenet_old",
"vgg19",
"zfnet",
}
def do_enforce_test_coverage_safelist(model: ModelProto) -> bool:
if model.graph.name not in test_coverage_safelist:
return False
return all(node.op_type not in {"RNN", "LSTM", "GRU"} for node in model.graph.node)
backend_test = onnx.backend.test.BackendTest(DummyBackend, __name__)
if os.getenv("APPVEYOR"):
backend_test.exclude(r"(test_vgg19|test_zfnet)")
if platform.architecture()[0] == "32bit":
backend_test.exclude(r"(test_vgg19|test_zfnet|test_bvlc_alexnet)")
# Needs investigation on onnxruntime.
backend_test.exclude("test_dequantizelinear_e4m3fn_float16")
# import all test cases at global scope to make them visible to python.unittest
globals().update(backend_test.test_cases)
if __name__ == "__main__":
unittest.main()
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,818 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/__init__.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import sys
from typing import Dict, List, Tuple
from onnx.backend.test.case.base import Snippets
from onnx.backend.test.case.utils import import_recursive
def collect_snippets() -> Dict[str, List[Tuple[str, str]]]:
import_recursive(sys.modules[__name__])
return Snippets
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58,819 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_instance_normalization.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.reference.op_run import OpRun
class InstanceNormalization(OpRun):
def _run(self, x, s, bias, epsilon=None): # type: ignore
dims_x = len(x.shape)
axis = tuple(range(2, dims_x))
mean = np.mean(x, axis=axis, keepdims=True)
var = np.var(x, axis=axis, keepdims=True)
dim_ones = (1,) * (dims_x - 2)
s = s.reshape(-1, *dim_ones)
bias = bias.reshape(-1, *dim_ones)
y = s * (x - mean) / np.sqrt(var + epsilon) + bias
return (y.astype(x.dtype),)
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
58,820 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/sequence_map.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class SequenceMap(Base):
@staticmethod
def export_sequence_map_identity_1_sequence(): # type: () -> None
body = onnx.helper.make_graph(
[onnx.helper.make_node("Identity", ["in0"], ["out0"])],
"seq_map_body",
[onnx.helper.make_tensor_value_info("in0", onnx.TensorProto.FLOAT, ["N"])],
[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["M"])],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x"], outputs=["y"], body=body
)
x = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for _ in range(3)]
y = x
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
expect(
node,
inputs=[x],
outputs=[y],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_identity_1_sequence",
)
@staticmethod
def export_sequence_map_identity_2_sequences(): # type: () -> None
body = onnx.helper.make_graph(
[
onnx.helper.make_node("Identity", ["in0"], ["out0"]),
onnx.helper.make_node("Identity", ["in1"], ["out1"]),
],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"in0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"in1", onnx.TensorProto.FLOAT, ["M"]
),
],
[
onnx.helper.make_tensor_value_info(
"out0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"out1", onnx.TensorProto.FLOAT, ["M"]
),
],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
)
x0 = [
np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
for _ in range(3)
]
x1 = [
np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
for _ in range(3)
]
y0 = x0
y1 = x1
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
),
]
expect(
node,
inputs=[x0, x1],
outputs=[y0, y1],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_identity_2_sequences",
)
@staticmethod
def export_sequence_map_identity_1_sequence_1_tensor(): # type: () -> None
body = onnx.helper.make_graph(
[
onnx.helper.make_node("Identity", ["in0"], ["out0"]),
onnx.helper.make_node("Identity", ["in1"], ["out1"]),
],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"in0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"in1", onnx.TensorProto.FLOAT, ["M"]
),
],
[
onnx.helper.make_tensor_value_info(
"out0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"out1", onnx.TensorProto.FLOAT, ["M"]
),
],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x0", "x1"], outputs=["y0", "y1"], body=body
)
x0 = [
np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
for _ in range(3)
]
x1 = np.random.uniform(0.0, 1.0, np.random.randint(1, 10)).astype(np.float32)
y0 = x0
y1 = [x1 for _ in range(3)]
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"]),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["M"])
),
]
expect(
node,
inputs=[x0, x1],
outputs=[y0, y1],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_identity_1_sequence_1_tensor",
)
@staticmethod
def export_sequence_map_add_2_sequences(): # type: () -> None
body = onnx.helper.make_graph(
[onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"in0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"in1", onnx.TensorProto.FLOAT, ["N"]
),
],
[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
)
N = [np.random.randint(1, 10) for _ in range(3)]
x0 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
x1 = [np.random.uniform(0.0, 1.0, N[k]).astype(np.float32) for k in range(3)]
y0 = [x0[k] + x1[k] for k in range(3)]
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
expect(
node,
inputs=[x0, x1],
outputs=[y0],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_add_2_sequences",
)
@staticmethod
def export_sequence_map_add_1_sequence_1_tensor(): # type: () -> None
body = onnx.helper.make_graph(
[onnx.helper.make_node("Add", ["in0", "in1"], ["out0"])],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"in0", onnx.TensorProto.FLOAT, ["N"]
),
onnx.helper.make_tensor_value_info(
"in1", onnx.TensorProto.FLOAT, ["N"]
),
],
[onnx.helper.make_tensor_value_info("out0", onnx.TensorProto.FLOAT, ["N"])],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["x0", "x1"], outputs=["y0"], body=body
)
x0 = [np.random.uniform(0.0, 1.0, 10).astype(np.float32) for k in range(3)]
x1 = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y0 = [x0[i] + x1 for i in range(3)]
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"]),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, ["N"])
),
]
expect(
node,
inputs=[x0, x1],
outputs=[y0],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_add_1_sequence_1_tensor",
)
@staticmethod
def export_sequence_map_extract_shapes(): # type: () -> None
body = onnx.helper.make_graph(
[onnx.helper.make_node("Shape", ["x"], ["shape"])],
"seq_map_body",
[
onnx.helper.make_tensor_value_info(
"x", onnx.TensorProto.FLOAT, ["H", "W", "C"]
)
],
[onnx.helper.make_tensor_value_info("shape", onnx.TensorProto.INT64, [3])],
)
node = onnx.helper.make_node(
"SequenceMap", inputs=["in_seq"], outputs=["shapes"], body=body
)
shapes = [
np.array([40, 30, 3], dtype=np.int64),
np.array([20, 10, 3], dtype=np.int64),
np.array([10, 5, 3], dtype=np.int64),
]
x0 = [np.zeros(shape, dtype=np.float32) for shape in shapes]
input_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, ["H", "W", "C"]
)
),
]
output_type_protos = [
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.INT64, [3])
),
]
expect(
node,
inputs=[x0],
outputs=[shapes],
input_type_protos=input_type_protos,
output_type_protos=output_type_protos,
name="test_sequence_map_extract_shapes",
)
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58,821 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reshape.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def reshape_reference_implementation(
data: np.ndarray, shape: np.ndarray, allowzero: int = 0
) -> np.ndarray:
# replace zeros with corresponding dim size
# we need to do this because np.reshape doesn't support 0 by default unless 'allowzero' is set
new_shape = np.copy(shape)
if allowzero == 0:
zeros_index = np.where(shape == 0)
new_shape[zeros_index] = np.array(data.shape)[zeros_index]
reshaped = np.reshape(data, new_shape)
return reshaped
class Reshape(Base):
@staticmethod
def export_reshape() -> None:
original_shape = [2, 3, 4]
test_cases = {
"reordered_all_dims": np.array([4, 2, 3], dtype=np.int64),
"reordered_last_dims": np.array([2, 4, 3], dtype=np.int64),
"reduced_dims": np.array([2, 12], dtype=np.int64),
"extended_dims": np.array([2, 3, 2, 2], dtype=np.int64),
"one_dim": np.array([24], dtype=np.int64),
"negative_dim": np.array([2, -1, 2], dtype=np.int64),
"negative_extended_dims": np.array([-1, 2, 3, 4], dtype=np.int64),
"zero_dim": np.array([2, 0, 4, 1], dtype=np.int64),
"zero_and_negative_dim": np.array([2, 0, 1, -1], dtype=np.int64),
}
data = np.random.random_sample(original_shape).astype(np.float32)
for test_name, shape in test_cases.items():
node = onnx.helper.make_node(
"Reshape",
inputs=["data", "shape"],
outputs=["reshaped"],
)
reshaped = reshape_reference_implementation(data, shape)
expect(
node,
inputs=[data, shape],
outputs=[reshaped],
name="test_reshape_" + test_name,
)
@staticmethod
def export_allowzero() -> None:
original_shape = [0, 3, 4]
test_cases = {
"allowzero_reordered": np.array([3, 4, 0], dtype=np.int64),
}
data = np.random.random_sample(original_shape).astype(np.float32)
for test_name, shape in test_cases.items():
node = onnx.helper.make_node(
"Reshape",
inputs=["data", "shape"],
outputs=["reshaped"],
allowzero=1, # if allowzero=1, final shape = (3, 4, 0)
# if allowzero=0, final shape = (3, 4, 4)
)
reshaped = reshape_reference_implementation(data, shape, allowzero=1)
expect(
node,
inputs=[data, shape],
outputs=[reshaped],
name="test_reshape_" + test_name,
)
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