Change imports to support usage as module

Note that this does require rerunning the `process_data.py` scripts in the example folders so that the dill-files are updated.

experiments/pedestrians/evaluate.py

@@ -7,11 +7,11 @@ import torch
 import numpy as np
 import pandas as pd
 
-sys.path.append("../../trajectron")
+sys.path.append("../../")
 from tqdm import tqdm
-from model.model_registrar import ModelRegistrar
-from model.trajectron import Trajectron
-import evaluation
+from trajectron.model.model_registrar import ModelRegistrar
+from trajectron.model.trajectron import Trajectron
+import trajectron.evaluation as evaluation
 
 seed = 0
 np.random.seed(seed)

experiments/pedestrians/process_data.py

@@ -4,10 +4,10 @@ import numpy as np
 import pandas as pd
 import dill
 
-sys.path.append("../../trajectron")
-from environment import Environment, Scene, Node
-from utils import maybe_makedirs
-from environment import derivative_of
+sys.path.append("../../")
+from trajectron.environment import Environment, Scene, Node
+from trajectron.utils import maybe_makedirs
+from trajectron.environment import derivative_of
 
 desired_max_time = 100
 pred_indices = [2, 3]

trajectron/environment/map.py

@@ -1,6 +1,6 @@
 import torch
 import numpy as np
-from model.dataset.homography_warper import get_rotation_matrix2d, warp_affine_crop
+from trajectron.model.dataset.homography_warper import get_rotation_matrix2d, warp_affine_crop
 
 
 class Map(object):

trajectron/environment/node.py

@@ -1,7 +1,7 @@
 import random
 import numpy as np
 import pandas as pd
-from environment import DoubleHeaderNumpyArray
+from trajectron.environment import DoubleHeaderNumpyArray
 from ncls import NCLS
 
 

trajectron/evaluation/evaluation.py

@@ -2,8 +2,8 @@ import numpy as np
 from scipy.interpolate import RectBivariateSpline
 from scipy.ndimage import binary_dilation
 from scipy.stats import gaussian_kde
-from utils import prediction_output_to_trajectories
-import visualization
+from trajectron.utils import prediction_output_to_trajectories
+import trajectron.visualization
 from matplotlib import pyplot as plt
 
 

trajectron/model/components/discrete_latent.py

@@ -1,7 +1,7 @@
 import torch
 import torch.distributions as td
 import numpy as np
-from model.model_utils import ModeKeys
+from trajectron.model.model_utils import ModeKeys
 
 
 class DiscreteLatent(object):

trajectron/model/components/gmm2d.py

@@ -1,7 +1,7 @@
 import torch
 import torch.distributions as td
 import numpy as np
-from model.model_utils import to_one_hot
+from trajectron.model.model_utils import to_one_hot
 
 
 class GMM2D(td.Distribution):

trajectron/model/dynamics/linear.py

@@ -1,4 +1,4 @@
-from model.dynamics import Dynamic
+from trajectron.model.dynamics import Dynamic
 
 
 class Linear(Dynamic):

trajectron/model/dynamics/single_integrator.py

@@ -1,7 +1,7 @@
 import torch
-from model.dynamics import Dynamic
-from utils import block_diag
-from model.components import GMM2D
+from trajectron.model.dynamics import Dynamic
+from trajectron.utils import block_diag
+from trajectron.model.components import GMM2D
 
 
 class SingleIntegrator(Dynamic):

trajectron/model/dynamics/unicycle.py

@@ -1,8 +1,8 @@
 import torch
 import torch.nn as nn
-from model.dynamics import Dynamic
-from utils import block_diag
-from model.components import GMM2D
+from trajectron.model.dynamics import Dynamic
+from trajectron.utils import block_diag
+from trajectron.model.components import GMM2D
 
 
 class Unicycle(Dynamic):

trajectron/model/mgcvae.py

@@ -2,10 +2,10 @@ import warnings
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
-from model.components import *
-from model.model_utils import *
-import model.dynamics as dynamic_module
-from environment.scene_graph import DirectedEdge
+from trajectron.model.components import *
+from trajectron.model.model_utils import *
+import trajectron.model.dynamics as dynamic_module
+from trajectron.environment.scene_graph import DirectedEdge
 
 
 class MultimodalGenerativeCVAE(object):

trajectron/model/trajectron.py

@@ -1,7 +1,7 @@
 import torch
 import numpy as np
-from model.mgcvae import MultimodalGenerativeCVAE
-from model.dataset import get_timesteps_data, restore
+from trajectron.model.mgcvae import MultimodalGenerativeCVAE
+from trajectron.model.dataset import get_timesteps_data, restore
 
 
 class Trajectron(object):

trajectron/visualization/visualization.py

@@ -1,4 +1,4 @@
-from utils import prediction_output_to_trajectories
+from trajectron.utils import prediction_output_to_trajectories
 from scipy import linalg
 import matplotlib.pyplot as plt
 import matplotlib.patches as patches