reformat and add colab running button

Tianhao Wu · Tianhao Wu · commit 57d6d2373b2c · 2022-10-20T12:57:32.000-04:00
diff --git a/Makefile b/Makefile
@@ -18,3 +18,12 @@ help:
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+html-noplot:
+	$(SPHINXBUILD) -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(SOURCEDIR) $(BUILDDIR)/html
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+clean:
+	rm -rf $(BUILDDIR)/*
+	rm -rf beginner/ advanced/ intermediate/ imu/ dynamics/ pgo/
diff --git a/conf.py b/conf.py
@@ -22,7 +22,8 @@
 author = 'PyPose contributors'
 
 # The full version, including alpha/beta/rc tags
-release = 'v0.1'
+import pypose
+version = pypose.__version__
 
 
 # -- General configuration ---------------------------------------------------
@@ -36,9 +37,9 @@
 
 
 sphinx_gallery_conf = {
-    'examples_dirs': ['beginner_source', 'intermediate_source',
-                      'advanced_source'],
-    'gallery_dirs': ['beginner', 'intermediate', 'advanced'],
+    'examples_dirs': ['beginner_source', 'dynamics_source', 'imu_source', 
+                      'pgo_source'],
+    'gallery_dirs': ['beginner', 'dynamics', 'imu', 'pgo'],
     'filename_pattern': 'tutorial.py',
 }
 
@@ -60,7 +61,11 @@
 
 html_theme = 'pypose_sphinx_theme'
 html_theme_path = [pypose_sphinx_theme.get_html_theme_path()]
-
+html_theme_options = {
+    'pytorch_project': 'tutorials',
+    'collapse_navigation': False,
+    'display_version': True,
+}
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
diff --git a/dynamics_source/cartpole_tutorial.py b/dynamics_source/cartpole_tutorial.py
@@ -71,6 +71,7 @@ def createTimePlot(x, y, figname="Un-named plot", title=None, xlabel=None, ylabe
 
 time  = torch.arange(0, N+1) * dt
 input = torch.sin(time)
+
 ######################################################################
 # Initial state
 
@@ -100,6 +101,7 @@ def createTimePlot(x, y, figname="Un-named plot", title=None, xlabel=None, ylabe
 
 ######################################################################
 # Jacobian computation - Find jacobians at the last step
+
 jacob_state, jacob_input = state_all[-1,:].T, input[-1]
 cartPoleSolver.set_refpoint(state=jacob_state, input=jacob_input.unsqueeze(0), t=time[-1])
 A = cartPoleSolver.A
diff --git a/dynamics_source/floquet_tutorial.py b/dynamics_source/floquet_tutorial.py
@@ -48,13 +48,13 @@ def createTimePlot(x, y, figname="Un-named plot", title=None, xlabel=None, ylabe
 ######################################################################
 # Number of time steps
 # ---------------------
+
 N = 100    
 
 
 ######################################################################
 # Time, Input, Initial state
 
-
 time  = torch.arange(0, N+1)
 input = torch.sin(2*math.pi*time/50)
 state = torch.tensor([1., 1.])
@@ -78,6 +78,7 @@ def createTimePlot(x, y, figname="Un-named plot", title=None, xlabel=None, ylabe
 
 ######################################################################
 # Create time plots to show dynamics
+
 f, ax = plt.subplots(nrows=4, sharex=True)
 for _i in range(2):
     ax[_i].plot(time, state_all[:,_i], label='pp')
@@ -90,12 +91,14 @@ def createTimePlot(x, y, figname="Un-named plot", title=None, xlabel=None, ylabe
 
 ######################################################################
 # Jacobian computation - Find jacobians at the last step
+
 vars = ['A', 'B', 'C', 'D', 'c1', 'c2']
 solver.set_refpoint()
 [print(_v, getattr(solver, _v)) for _v in vars]
 
 ######################################################################
 # Jacobian computation - Find jacobians at the 5th step
+
 idx = 5
 solver.set_refpoint(state=state_all[idx], input=input[idx], t=time[idx])
 [print(_v, getattr(solver, _v)) for _v in vars]
diff --git a/dynamics_source/neuralnet_tutorial.py b/dynamics_source/neuralnet_tutorial.py
@@ -39,11 +39,13 @@ def createTimePlot(x, y, figname="Un-named plot", title=None, xlabel=None, ylabe
 
 ######################################################################
 # Time Step
+
 dt = 0.01  # Time step size
 N  = 1000  # Number of time steps
 
 ######################################################################
 # Time and input
+
 time  = torch.arange(0, N + 1) * dt
 input = torch.sin(time)
 
diff --git a/imu_source/imu_corrector_tutorial.py b/imu_source/imu_corrector_tutorial.py
@@ -10,12 +10,12 @@
 import tqdm, argparse
 import torch.utils.data as Data
 from torch.optim.lr_scheduler import ReduceLROnPlateau
-from imu_dataset import KITTI_IMU, imu_collate, move_to
+from imu_dataset_tutorial import KITTI_IMU, imu_collate, move_to
 
 
 ######################################################################
-# Preparation
-# ------------
+# Define IMU Corrector
+# -----------------------
 
 class IMUCorrector(nn.Module):
     def __init__(self, size_list= [6, 64, 128, 128, 128, 6]):
@@ -102,7 +102,7 @@ def test(network, loader, device = "cuda:0"):
 parser.add_argument("--device", type=str, default='cuda:0', help="cuda or cpu")
 parser.add_argument("--batch-size", type=int, default=4, help="batch size")
 parser.add_argument("--max_epoches", type=int, default=100, help="max_epoches")
-parser.add_argument("--dataroot", type=str, default='./examples/module/imu', help="dataset location downloaded")
+parser.add_argument("--dataroot", type=str, default='../dataset', help="dataset location downloaded")
 parser.add_argument("--dataname", type=str, default='2011_09_26', help="dataset name")
 parser.add_argument("--datadrive", nargs='+', type=str, default=[ "0001"], help="data sequences")
 parser.add_argument('--load_ckpt', default=False, action="store_true")
@@ -114,7 +114,7 @@ def test(network, loader, device = "cuda:0"):
 test_loader = Data.DataLoader(dataset=test_dataset, batch_size=args.batch_size, collate_fn=imu_collate, shuffle=False)
 
 ######################################################################
-# optimizer
+# Optimizer
 
 network = IMUCorrector().to(args.device)
 optimizer = torch.optim.Adam(network.parameters(), lr = 5e-6)  # to use with ViTs
diff --git a/imu_source/imu_dataset_tutorial.py b/imu_source/imu_dataset_tutorial.py
diff --git a/imu_source/imu_integrator_tutorial.py b/imu_source/imu_integrator_tutorial.py
@@ -15,7 +15,7 @@
 import matplotlib.pyplot as plt
 from matplotlib.patches import Ellipse
 from matplotlib.collections import PatchCollection
-from imu_dataset import KITTI_IMU, imu_collate, move_to
+from imu_dataset_tutorial import KITTI_IMU, imu_collate, move_to
 
 
 ######################################################################
@@ -42,8 +42,8 @@ def plot_gaussian(ax, means, covs, color=None, sigma=3):
 parser.add_argument("--integrating-step", type=int, default=1, help="number of integrated steps")
 parser.add_argument("--batch-size", type=int, default=1, help="batch size, only support 1 now")
 parser.add_argument("--step-size", type=int, default=2, help="the size of the integration for one interval")
-parser.add_argument("--save", type=str, default='./examples/module/imu/save/', help="location of png files to save")
-parser.add_argument("--dataroot", type=str, default='./examples/module/imu/', help="dataset location downloaded")
+parser.add_argument("--save", type=str, default='../dataset/save/', help="location of png files to save")
+parser.add_argument("--dataroot", type=str, default='../dataset/', help="dataset location downloaded")
 parser.add_argument("--dataname", type=str, default='2011_09_26', help="dataset name")
 parser.add_argument("--datadrive", nargs='+', type=str, default=["0001","0002","0005","0009","0011",
                     "0013","0014","0015","0017","0018","0019","0020","0022","0005"], help="data sequences")
diff --git a/pgo_source/pgo_dataset_tutorial.py b/pgo_source/pgo_dataset_tutorial.py
diff --git a/pgo_source/pgo_tutorial.py b/pgo_source/pgo_tutorial.py
@@ -10,7 +10,7 @@
 import argparse
 import pypose as pp
 from torch import nn
-from pgo_dataset import G2OPGO
+from pgo_dataset_tutorial import G2OPGO
 import matplotlib.pyplot as plt
 import pypose.optim.solver as ppos
 import pypose.optim.kernel as ppok
@@ -20,8 +20,9 @@
 
 
 ######################################################################
-# Preparation
-# ------------
+# Define Pose Graph
+# ------------------
+
 class PoseGraph(nn.Module):
 
     def __init__(self, nodes):
@@ -54,18 +55,22 @@ def plot_and_save(points, pngname, title='', axlim=None):
 parser = argparse.ArgumentParser(description='Pose Graph Optimization')
 parser.add_argument("--device", type=str, default='cuda:0', help="cuda or cpu")
 parser.add_argument("--radius", type=float, default=1e4, help="trust region radius")
-parser.add_argument("--save", type=str, default='./examples/module/pgo/save/', help="files location to save")
-parser.add_argument("--dataroot", type=str, default='./examples/module/pgo/pgodata', help="dataset location")
+parser.add_argument("--save", type=str, default='../dataset/pgo/save/', help="files location to save")
+parser.add_argument("--dataroot", type=str, default='../dataset/pgo', help="dataset location")
 parser.add_argument("--dataname", type=str, default='parking-garage.g2o', help="dataset name")
 parser.add_argument('--no-vectorize', dest='vectorize', action='store_false', help="to save memory")
 parser.add_argument('--vectorize', action='store_true', help='to accelerate computation')
-parser.set_defaults(vectorize=True)
+parser.set_defaults(vectorize=False)
 args = parser.parse_args(); print(args)
 os.makedirs(os.path.join(args.save), exist_ok=True)
 
 data = G2OPGO(args.dataroot, args.dataname, device=args.device)
 edges, poses, infos = data.edges, data.poses, data.infos
 
+######################################################################
+# Define Optimizer
+# -------------------------------------------------------------
+
 graph = PoseGraph(data.nodes).to(args.device)
 solver = ppos.Cholesky()
 strategy = ppost.TrustRegion(radius=args.radius)
@@ -91,4 +96,5 @@ def plot_and_save(points, pngname, title='', axlim=None):
 ######################################################################
 # The 2nd implementation: equivalent to the 1st one, but more compact
 # --------------------------------------------------------------------
+
 scheduler.optimize(input=(edges, poses), weight=infos)
