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Revert mise to dataset pipeline #3

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Revert mise to dataset pipeline #3

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472d6e1
Name update to fix conflicts.
mvandermerwe Jul 1, 2020
13f257d
Revert mise.py to dataset pipeline.
mvandermerwe Jul 1, 2020
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3 changes: 2 additions & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -2,4 +2,5 @@
*.pyc
data/
test.dae
mesh_objects.py
mesh_objects.py
out/
23 changes: 6 additions & 17 deletions mise.py
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Expand Up @@ -17,14 +17,10 @@
from visualization import plot_3d_points, plot_voxel, convert_to_sparse_voxel_grid, visualize_points_overlay
from sdf_dataset import get_sdf_dataset, get_pcd

from embedding import cloud_embedding

sys.path.append('/home/markvandermerwe/catkin_ws/src/ll4ma_3d_reconstruction/src/data_generation/')
from generate_view_splits import get_view_splits

_MODEL_FUNC = get_pointconv_model
_MODEL_PATH = '/home/markvandermerwe/models/ICRA_Models/reconstruction/pointconv_mse_cf'
_SAVE_PATH = '/home/markvandermerwe/data/ReconstructedMeshesTest/Real'
_SPLITS_PATH = '/home/markvandermerwe/catkin_ws/src/ll4ma_3d_reconstruction/src/data_generation/data_split/test_fold.txt'
_SAVE_PATH = 'out/test_dir/'
_PCD_DATABASE = '/dataspace/ICRA_Data/PyrenderData/Depth/'
_GRASP_DATABASE = False
_OBJECT_FRAME = False
Expand Down Expand Up @@ -198,7 +194,7 @@ def mise_voxel(get_sdf, bound, initial_voxel_resolution, final_voxel_resolution,

def get_test_meshes(grasp_database=True, ycb_database=False):
meshes = set()
with open('/home/markvandermerwe/catkin_ws/src/ll4ma_3d_reconstruction/src/data_generation/data_split/test_fold.txt') as f:
with open(_SPLITS_PATH) as f:
for view in f:
if grasp_database and 'poisson' in view:
meshes.add('_'.join(view.split('_')[:-1]))
Expand All @@ -207,22 +203,16 @@ def get_test_meshes(grasp_database=True, ycb_database=False):

fin_meshes = []
for mesh in meshes:
fin_meshes.append(mesh + '_10')
fin_meshes.append(mesh + '_10') # Only use the 10th rendered view.

return fin_meshes

def get_real_pt_cld():
real_info = "/home/markvandermerwe/data/GraspTestData/recon_high/mustard_p1_a1/grasp_plan_info.pickle"
obj_dict = pickle.load(open(real_info))
return obj_dict['scaled_object_cloud'], (obj_dict['max_dim'] * (1.03/1.0)), obj_dict['scale'], [0,0,0]

def mesh_objects(model_func, model_path, save_path, pcd_folder, grasp_database=True):
# Setup model.
get_sdf, get_embedding, _ = get_sdf_prediction(model_func, model_path)

# Get names of partial views.
# meshes = get_test_meshes(grasp_database=grasp_database, ycb_database=(not grasp_database))
meshes = ["mustard_real"]
meshes = get_test_meshes(grasp_database=grasp_database, ycb_database=(not grasp_database))

# Bounds of 3D space to evaluate in: [-bound, bound] in each dim.
bound = 0.8
Expand All @@ -234,8 +224,7 @@ def mesh_objects(model_func, model_path, save_path, pcd_folder, grasp_database=T
# Mesh the views.
for mesh in tqdm(meshes):
# Point cloud for this view.
# pc_, length, scale, centroid_diff = get_pcd(mesh, pcd_folder, object_frame=_OBJECT_FRAME, verbose=False);
pc_, length, scale, centroid_diff = get_real_pt_cld()
pc_, length, scale, centroid_diff = get_pcd(mesh, pcd_folder, object_frame=_OBJECT_FRAME, verbose=False);

voxel_size = (2.*bound * length) / float(final_voxel_resolution)
if pc_ is None:
Expand Down
9 changes: 4 additions & 5 deletions run_sdf_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
from helper import get_num_trainable_variables, shuffle_in_unison, get_bn_decay, get_learning_rate
from visualization import plot_3d_points, plot_voxel, convert_to_sparse_voxel_grid

def run(get_model, train_path, validation_path, model_path, logs_path, batch_size=32, epoch_start=0, epochs=100, learning_rate=1e-4, optimizer='adam', train=True, warm_start=False, alpha=0.5, loss_function='mse', sdf_count=64):
def run_sdf(get_model, train_path, validation_path, model_path, logs_path, batch_size=32, epoch_start=0, epochs=100, learning_rate=1e-4, optimizer='adam', train=True, warm_start=False, sdf_count=64, voxel=False):

# Read in training and validation files.
train_files = [os.path.join(train_path, filename) for filename in os.listdir(train_path) if ".tfrecord" in filename]
Expand Down Expand Up @@ -48,7 +48,7 @@ def run(get_model, train_path, validation_path, model_path, logs_path, batch_siz
xyz_in = tf.placeholder(tf.float32, name="query_points")
sdf_labels = tf.placeholder(tf.float32, name="query_labels")
is_training = tf.placeholder(tf.bool, name="is_training")
sdf_prediction, loss, debug = get_model(points, xyz_in, sdf_labels, is_training, bn_decay, batch_size=batch_size, alpha=alpha, loss_function=loss_function)
sdf_prediction, loss, debug = get_model(points, xyz_in, sdf_labels, is_training, bn_decay, batch_size=batch_size)

# Get update ops for the BN.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
Expand Down Expand Up @@ -92,7 +92,6 @@ def run(get_model, train_path, validation_path, model_path, logs_path, batch_siz
# Track loss throughout updates.
total_loss = 0.0
examples = 0

while True:
try:
# Split the given features into batches.
Expand All @@ -115,8 +114,8 @@ def run(get_model, train_path, validation_path, model_path, logs_path, batch_siz

plot_3d_points(point_clouds_[0])
plot_3d_points(pts)
plot_3d_points(pts, truth)
plot_3d_points(pts, pred)
plot_3d_points(pts, signed_distances=truth)
plot_3d_points(pts, signed_distances=pred)

total_loss += loss_

Expand Down