Merge branch 'add_Aug_DiskCachedDataset' of https://github.com/MinaKh…

…/tonic into add_Aug_DiskCachedDataset

merging last minor modifications of the branch with latest tonim master

.github/workflows/ci-pipeline.yml

@@ -9,7 +9,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-2022]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.10", "3.11"]
     steps:
       - uses: actions/checkout@v3
       - if: matrix.os == 'ubuntu-latest'
@@ -21,7 +21,6 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - name: Install requirements
         run: |
-          pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
           pip install -r test/requirements.txt
           pip install -r test/torch_requirements.txt
           pip install .
@@ -43,7 +42,6 @@ jobs:
           python-version: 3.9
       - name: Generate coverage report
         run: |
-          pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
           pip install -r test/requirements.txt
           pip install -r test/torch_requirements.txt
           pip install .
@@ -65,8 +63,8 @@ jobs:
           python-version: 3.9
       - name: Install dependencies
         run: |
-          pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
           pip install -r docs/requirements.txt
+          pip install -r test/torch_requirements.txt
           pip install .
       - name: Build documentation
         run: cd docs && make clean && make html # Use SPHINXOPTS="-W" to fail on warning.

.gitignore

@@ -5,6 +5,8 @@ data
 generated
 auto_examples
 .vscode
+AUTHORS
+ChangeLog
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

README.md

@@ -5,7 +5,7 @@
 [![contributors](https://img.shields.io/github/contributors-anon/neuromorphs/tonic)](https://github.com/neuromorphs/tonic/pulse)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/neuromorphs/tonic/main?labpath=docs%2Ftutorials)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5079802.svg)](https://doi.org/10.5281/zenodo.5079802)
-[![Discord](https://img.shields.io/discord/852094154188259338)](https://discord.gg/V6FHBZURkg)
+[![Discord](https://img.shields.io/discord/1044548629622439977)](https://discord.gg/qubbM4uPuA)
 
 **Tonic** is a tool to facilitate the download, manipulation and loading of event-based/spike-based data. It's like PyTorch Vision but for neuromorphic data!
 

docs/datasets.rst

@@ -53,6 +53,14 @@ Star tracking
 
     EBSSA
 
+Eye tracking
+-------------------
+.. autosummary::
+    :toctree: generated/
+    :template: class_dataset.rst
+
+    ThreeET_Eyetracking
+
 .. currentmodule:: tonic.prototype.datasets
 
 Prototype iterable datasets
@@ -65,4 +73,4 @@ Prototype iterable datasets
     NCARS
     STMNIST
     Gen1AutomotiveDetection
-    Gen4AutomotiveDetectionMini
+    Gen4AutomotiveDetectionMini

docs/getting_involved/communication_channels.rst

@@ -3,11 +3,10 @@ Communication channels
 
 Discord
 -------
-We would be very happy if you got in touch with us, so please don't hesitate!
-The easiest way is to join our Discord channel. There we can reply more or less
-instantly. The #tonic channel is part of SynSense's public space along other channels
+The easiest way to get in touch with us is to via Discord. There we can reply more or less
+instantly. The #tonic channel is part of Open Neuromorphic's public space along other channels
 for all things revolving around SNN training.
-The link to join is https://discord.gg/V6FHBZURkg.
+The link to join is https://discord.gg/qubbM4uPuA
 
 Github
 ------

docs/index.md

@@ -4,6 +4,7 @@
 [![Documentation Status](https://readthedocs.org/projects/tonic/badge/?version=latest)](https://tonic.readthedocs.io/en/latest/?badge=latest)
 [![contributors](https://img.shields.io/github/contributors-anon/neuromorphs/tonic)](https://github.com/neuromorphs/tonic/pulse)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5079802.svg)](https://doi.org/10.5281/zenodo.5079802)
+[![Discord](https://img.shields.io/discord/1044548629622439977)](https://discord.gg/qubbM4uPuA)
 
 **Download and manipulate neuromorphic datasets fast and easily!**
 
@@ -53,4 +54,4 @@ how-tos/how-tos
 reading_material/reading_material
 getting_involved/getting_involved
 about/about
-```
+```

docs/reading_material/intro-snns.rst

@@ -22,7 +22,7 @@ ANN is typically a
 tensor with high data precision, but low temporal resolution, the input
 for an SNN are
 binary flags of spikes with comparatively high temporal precision in the
-order of s. The unit in the SNN integrates all of the incoming spikes,
+order of µs. The unit in the SNN integrates all of the incoming spikes,
 which affect the internal parameters such as membrane potential. The
 unit in the ANN
 merely computes the linear combination for inputs on all synapses and

docs/requirements.txt

@@ -4,8 +4,6 @@ sphinx-book-theme
 sphinx-gallery
 myst_nb
 pbr
-torchvision
 ipywidgets
 matplotlib
-torchdata
 sphinx-autoapi

test/test_datasets.py

@@ -98,13 +98,44 @@ class EBSSATestCase(dataset_utils.DatasetTestCase):
     def inject_fake_data(self, tmpdir):
         testfolder = os.path.join(tmpdir, "EBSSA")
         os.makedirs(testfolder, exist_ok=True)
-        filename = "A5ooN9edo7TnNPx/download/labelled_ebssa.h5"
+        filename = "Jpw3Adae5kReMrN/download/labelled_ebssa.h5"
         download_url(
             url=base_url + filename, root=testfolder, filename="labelled_ebssa.h5"
         )
         return {"n_samples": 1}
 
 
+class ThreeET_EyetrackingTestCase(dataset_utils.DatasetTestCase):
+    DATASET_CLASS = datasets.ThreeET_Eyetracking
+    FEATURE_TYPES = (datasets.ThreeET_Eyetracking.dtype,)
+    TARGET_TYPES = (np.ndarray,)
+    KWARGS = {"split": "train"}
+
+    def inject_fake_data(self, tmpdir):
+        testfolder = os.path.join(tmpdir, "ThreeET_Eyetracking")
+        os.makedirs(testfolder, exist_ok=True)
+        os.makedirs(os.path.join(testfolder, "data"), exist_ok=True)
+        os.makedirs(os.path.join(testfolder, "labels"), exist_ok=True)
+        # write one line of file name into train_files.txt under testfolder
+        os.system("echo testcase > " + os.path.join(testfolder, "train_files.txt"))
+        filename = "testcase"
+
+        # download test h5 file
+        download_url(
+            url=base_url + "4aiA4BAqz5km4Gc/download/" + filename + ".h5",
+            root=os.path.join(testfolder, "data"),
+            filename=filename + ".h5",
+        )
+        # # download test labels
+        download_url(
+            url=base_url + "G6ejNmXNnB2sKyc/download/" + filename + ".txt",
+            root=os.path.join(testfolder, "labels"),
+            filename=filename + ".txt",
+        )
+
+        return {"n_samples": 1}
+
+
 class NCaltech101TestCase(dataset_utils.DatasetTestCase):
     DATASET_CLASS = datasets.NCALTECH101
     FEATURE_TYPES = (datasets.NCALTECH101.dtype,)

test/test_transforms.py

@@ -151,7 +151,7 @@ def test_transform_drop_events_by_area(area_ratio):
                 break
 
     assert (
-        dropped_area_found is True
+            dropped_area_found is True
     ), f"There should be an area with {dropped_events} events dropped in the obtained sequence."
 
 
@@ -223,6 +223,51 @@ def test_transform_drop_pixel(coordinates, hot_pixel_frequency):
     assert events is not orig_events
 
 
+@pytest.mark.parametrize(
+    "hot_pixel_frequency, event_max_freq",
+    [(59, 60), (10, 60)],
+)
+def test_transform_drop_pixel_unequal_sensor(hot_pixel_frequency, event_max_freq):
+    orig_events, sensor_size = create_random_input(
+        n_events=40000, sensor_size=(15, 20, 2)
+    )
+    orig_events = orig_events.tolist()
+    orig_events += [(0, 0, int(t * 1e3), 1) for t in np.arange(1, 1e6, 1e3 / event_max_freq)]
+    orig_events += [(0, 19, int(t * 1e3), 1) for t in np.arange(1, 1e6, 1e3 / event_max_freq)]
+    orig_events += [(14, 0, int(t * 1e3), 1) for t in np.arange(1, 1e6, 1e3 / event_max_freq)]
+    orig_events += [(14, 19, int(t * 1e3), 1) for t in np.arange(1, 1e6, 1e3 / event_max_freq)]
+    # cast back to numpy events
+    orig_events = np.asarray(orig_events, np.dtype([("x", int), ("y", int), ("t", int), ("p", int)]))
+
+    transform = transforms.DropPixel(
+        coordinates=None, hot_pixel_frequency=hot_pixel_frequency
+    )
+
+    events = transform(orig_events)
+    assert len(np.where((events["x"] == 0) & (events["y"] == 0))[0]) == 0
+    assert len(np.where((events["x"] == 14) & (events["y"] == 0))[0]) == 0
+    assert len(np.where((events["x"] == 0) & (events["y"] == 19))[0]) == 0
+    assert len(np.where((events["x"] == 14) & (events["y"] == 19))[0]) == 0
+
+
+@pytest.mark.parametrize(
+    "coordinates, hot_pixel_frequency",
+    [(((9, 11), (10, 12), (11, 13)), None), (None, 10000)],
+)
+def test_transform_drop_pixel_empty(coordinates, hot_pixel_frequency):
+    orig_events, sensor_size = create_random_input(
+        n_events=0, sensor_size=(15, 20, 2)
+    )
+
+    transform = transforms.DropPixel(coordinates=None, hot_pixel_frequency=hot_pixel_frequency)
+    events = transform(orig_events)
+    assert len(events) == len(orig_events)
+
+    transform = transforms.DropPixel(coordinates=coordinates, hot_pixel_frequency=None)
+    events = transform(orig_events)
+    assert len(events) == len(orig_events)
+
+
 @pytest.mark.parametrize(
     "coordinates, hot_pixel_frequency",
     [(((199, 11), (199, 12), (11, 13)), None), (None, 5000)],
@@ -247,7 +292,8 @@ def test_transform_drop_pixel_raster(coordinates, hot_pixel_frequency):
         assert not merged_polarity_raster[merged_polarity_raster > 5000].sum().sum()
 
 
-@pytest.mark.parametrize("time_factor, spatial_factor, target_size", [(1, 0.25, None), (1e-3, (1, 2), None), (1, 1, (5, 5))])
+@pytest.mark.parametrize("time_factor, spatial_factor, target_size",
+                         [(1, 0.25, None), (1e-3, (1, 2), None), (1, 1, (5, 5))])
 def test_transform_downsample(time_factor, spatial_factor, target_size):
     orig_events, sensor_size = create_random_input()
 
@@ -256,43 +302,42 @@ def test_transform_downsample(time_factor, spatial_factor, target_size):
     )
 
     events = transform(orig_events)
-    
+
     if not isinstance(spatial_factor, tuple):
         spatial_factor = (spatial_factor, spatial_factor)
-    
+
     if target_size is None:
         assert np.array_equal(
             (orig_events["t"] * time_factor).astype(orig_events["t"].dtype), events["t"]
         )
         assert np.array_equal(np.floor(orig_events["x"] * spatial_factor[0]), events["x"])
         assert np.array_equal(np.floor(orig_events["y"] * spatial_factor[1]), events["y"])
-    
+
     else:
         spatial_factor_test = np.asarray(target_size) / sensor_size[:-1]
         assert np.array_equal(np.floor(orig_events["x"] * spatial_factor_test[0]), events["x"])
         assert np.array_equal(np.floor(orig_events["y"] * spatial_factor_test[1]), events["y"])
-    
+
     assert events is not orig_events
-    
-    
-@pytest.mark.parametrize("target_size, dt, downsampling_method, noise_threshold, differentiator_time_bins", 
+
+
+@pytest.mark.parametrize("target_size, dt, downsampling_method, noise_threshold, differentiator_time_bins",
                          [((50, 50), 0.05, 'integrator', 1, None),
                           ((20, 15), 5, 'differentiator', 3, 1)])
-def test_transform_event_downsampling(target_size, dt, downsampling_method, noise_threshold, 
+def test_transform_event_downsampling(target_size, dt, downsampling_method, noise_threshold,
                                       differentiator_time_bins):
-
     orig_events, sensor_size = create_random_input()
-    
-    transform = transforms.EventDownsampling(sensor_size=sensor_size, target_size=target_size, dt=dt, 
+
+    transform = transforms.EventDownsampling(sensor_size=sensor_size, target_size=target_size, dt=dt,
                                              downsampling_method=downsampling_method, noise_threshold=noise_threshold,
                                              differentiator_time_bins=differentiator_time_bins)
-    
+
     events = transform(orig_events)
-    
+
     assert len(events) <= len(orig_events)
     assert np.logical_and(np.all(events["x"] <= target_size[0]), np.all(events["y"] <= target_size[1]))
     assert events is not orig_events
-    
+
 
 @pytest.mark.parametrize("target_size", [(50, 50), (10, 5)])
 def test_transform_random_crop(target_size):
@@ -465,13 +510,13 @@ def test_transform_spatial_jitter(variance, clip_outliers):
         assert np.isclose(events["y"].all(), orig_events["y"].all(), atol=2 * variance)
 
         assert (
-            events["x"] - orig_events["x"]
-            == (events["x"] - orig_events["x"]).astype(int)
+                events["x"] - orig_events["x"]
+                == (events["x"] - orig_events["x"]).astype(int)
         ).all()
 
         assert (
-            events["y"] - orig_events["y"]
-            == (events["y"] - orig_events["y"]).astype(int)
+                events["y"] - orig_events["y"]
+                == (events["y"] - orig_events["y"]).astype(int)
         ).all()
 
     else:
@@ -503,8 +548,8 @@ def test_transform_time_jitter(std, clip_negative, sort_timestamps):
         np.testing.assert_array_equal(events["y"], orig_events["y"])
         np.testing.assert_array_equal(events["p"], orig_events["p"])
         assert (
-            events["t"] - orig_events["t"]
-            == (events["t"] - orig_events["t"]).astype(int)
+                events["t"] - orig_events["t"]
+                == (events["t"] - orig_events["t"]).astype(int)
         ).all()
     assert events is not orig_events
 
@@ -562,17 +607,7 @@ def test_transform_time_skew(coefficient, offset):
     assert events is not orig_events
 
 
-@pytest.mark.parametrize(
-    "n",
-    [
-        100,
-        0,
-        (
-            10,
-            100,
-        ),
-    ],
-)
+@pytest.mark.parametrize("n", [100, 0, (10, 100)])
 def test_transform_uniform_noise(n):
     orig_events, sensor_size = create_random_input()
 
@@ -597,6 +632,16 @@ def test_transform_uniform_noise(n):
     assert events is not orig_events
 
 
+@pytest.mark.parametrize("n", [100, 0, (10, 100)])
+def test_transform_uniform_noise_empty(n):
+    orig_events, sensor_size = create_random_input(n_events=0)
+    assert len(orig_events) == 0
+
+    transform = transforms.UniformNoise(sensor_size=sensor_size, n=n)
+    events = transform(orig_events)
+    assert len(events) == 0  # check returns an empty array, independent of n.
+
+
 def test_transform_time_alignment():
     orig_events, sensor_size = create_random_input()
 
@@ -606,3 +651,36 @@ def test_transform_time_alignment():
 
     assert np.min(events["t"]) == 0
     assert events is not orig_events
+
+
+def test_toframe_empty():
+    orig_events, sensor_size = create_random_input(n_events=0)
+    assert len(orig_events) == 0
+
+    with pytest.raises(ValueError):  # check that empty array raises error if no slicing method is specified
+        transform = transforms.ToFrame(sensor_size=sensor_size)
+        frame = transform(orig_events)
+
+    n_event_bins = 100
+    transform = transforms.ToFrame(sensor_size=sensor_size, n_event_bins=n_event_bins)
+    frame = transform(orig_events)
+    assert frame.shape == (n_event_bins, sensor_size[2], sensor_size[0], sensor_size[1])
+    assert frame.sum() == 0
+
+    n_time_bins = 100
+    transform = transforms.ToFrame(sensor_size=sensor_size, n_time_bins=n_time_bins)
+    frame = transform(orig_events)
+    assert frame.shape == (n_time_bins, sensor_size[2], sensor_size[0], sensor_size[1])
+    assert frame.sum() == 0
+
+    event_count = 1e3
+    transform = transforms.ToFrame(sensor_size=sensor_size, event_count=event_count)
+    frame = transform(orig_events)
+    assert frame.shape == (1, sensor_size[2], sensor_size[0], sensor_size[1])
+    assert frame.sum() == 0
+
+    time_window = 1e3
+    transform = transforms.ToFrame(sensor_size=sensor_size, time_window=time_window)
+    frame = transform(orig_events)
+    assert frame.shape == (1, sensor_size[2], sensor_size[0], sensor_size[1])
+    assert frame.sum() == 0