def __getitem__(self, index):
if hyp.dataset_name == 'kitti' or hyp.dataset_name == 'clevr' or hyp.dataset_name == 'real' or hyp.dataset_name == "bigbird" or hyp.dataset_name == "carla" or hyp.dataset_name == "carla_mix" or hyp.dataset_name == "carla_det" or hyp.dataset_name == "replica" or hyp.dataset_name == "clevr_vqa":
# print(index)
filename = self.records[index]
d = pickle.load(open(filename, "rb"))
d = dict(d)
# elif hyp.dataset_name=="carla":
# filename = self.records[index]
# d = np.load(filename)
# d = dict(d)
# d['rgb_camXs_raw'] = d['rgb_camXs']
# d['pix_T_cams_raw'] = d['pix_T_cams']
# d['tree_seq_filename'] = "dummy_tree_filename"
# d['origin_T_camXs_raw'] = d['origin_T_camXs']
# d['camR_T_origin_raw'] = utils_geom.safe_inverse(torch.from_numpy(d['origin_T_camRs'])).numpy()
# d['xyz_camXs_raw'] = d['xyz_camXs']
else:
assert (False) # reader not ready yet
# st()
# if hyp.save_gt_occs:
# pickle.dump(d,open(filename, "wb"))
# st()
# st()
if hyp.use_gt_occs:
__p = lambda x: utils_basic.pack_seqdim(x, 1)
__u = lambda x: utils_basic.unpack_seqdim(x, 1)
B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N
PH, PW = hyp.PH, hyp.PW
K = hyp.K
BOX_SIZE = hyp.BOX_SIZE
Z, Y, X = hyp.Z, hyp.Y, hyp.X
Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2)
Z4, Y4, X4 = int(Z / 4), int(Y / 4), int(X / 4)
D = 9
pix_T_cams = torch.from_numpy(
d["pix_T_cams_raw"]).unsqueeze(0).cuda().to(torch.float)
camRs_T_origin = torch.from_numpy(
d["camR_T_origin_raw"]).unsqueeze(0).cuda().to(torch.float)
origin_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_origin)))
origin_T_camXs = torch.from_numpy(
d["origin_T_camXs_raw"]).unsqueeze(0).cuda().to(torch.float)
camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0)
camRs_T_camXs = __u(
torch.matmul(utils_geom.safe_inverse(__p(origin_T_camRs)),
__p(origin_T_camXs)))
camXs_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_camXs)))
camX0_T_camRs = camXs_T_camRs[:, 0]
camX1_T_camRs = camXs_T_camRs[:, 1]
camR_T_camX0 = utils_geom.safe_inverse(camX0_T_camRs)
xyz_camXs = torch.from_numpy(
d["xyz_camXs_raw"]).unsqueeze(0).cuda().to(torch.float)
xyz_camRs = __u(
utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs)))
depth_camXs_, valid_camXs_ = utils_geom.create_depth_image(
__p(pix_T_cams), __p(xyz_camXs), H, W)
dense_xyz_camXs_ = utils_geom.depth2pointcloud(
depth_camXs_, __p(pix_T_cams))
occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X))
occRs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z2, Y2,
X2))
occRs_half = torch.max(occRs_half, dim=1).values.squeeze(0)
occ_complete = occRs_half.cpu().numpy()
# st()
if hyp.do_empty:
item_names = [
'pix_T_cams_raw',
'origin_T_camXs_raw',
'camR_T_origin_raw',
'rgb_camXs_raw',
'xyz_camXs_raw',
'empty_rgb_camXs_raw',
'empty_xyz_camXs_raw',
]
else:
item_names = [
'pix_T_cams_raw',
'origin_T_camXs_raw',
'camR_T_origin_raw',
'rgb_camXs_raw',
'xyz_camXs_raw',
]
# if hyp.do_time_flip:
# d = random_time_flip_single(d,item_names)
# if the sequence length > 2, select S frames
# filename = d['raw_seq_filename']
original_filename = filename
if hyp.dataset_name == "carla_mix" or hyp.dataset_name == "carla_det":
bbox_origin_gt = d['bbox_origin']
if 'bbox_origin_predicted' in d:
bbox_origin_predicted = d['bbox_origin_predicted']
else:
bbox_origin_predicted = []
classes = d['obj_name']
if isinstance(classes, str):
classes = [classes]
# st()
d['tree_seq_filename'] = "temp"
if hyp.dataset_name == "replica":
d['tree_seq_filename'] = "temp"
object_category = d['object_category_names']
bbox_origin = d['bbox_origin']
if hyp.dataset_name == "clevr_vqa":
d['tree_seq_filename'] = "temp"
pix_T_cams = d['pix_T_cams_raw']
num_cams = pix_T_cams.shape[0]
# padding_1 = torch.zeros([num_cams,1,3])
# padding_2 = torch.zeros([num_cams,4,1])
# padding_2[:,3] = 1.0
# st()
# pix_T_cams = torch.cat([pix_T_cams,padding_1],dim=1)
# pix_T_cams = torch.cat([pix_T_cams,padding_2],dim=2)
# st()
shape_name = d['shape_list']
color_name = d['color_list']
material_name = d['material_list']
all_name = []
all_style = []
for index in range(len(shape_name)):
name = shape_name[index] + "/" + color_name[
index] + "_" + material_name[index]
style_name = color_name[index] + "_" + material_name[index]
all_name.append(name)
all_style.append(style_name)
# st()
if hyp.do_shape:
class_name = shape_name
elif hyp.do_color:
class_name = color_name
elif hyp.do_material:
class_name = material_name
elif hyp.do_style:
class_name = all_style
else:
class_name = all_name
object_category = class_name
bbox_origin = d['bbox_origin']
# st()
if hyp.dataset_name == "carla":
camR_index = d['camR_index']
rgb_camtop = d['rgb_camXs_raw'][camR_index:camR_index + 1]
origin_T_camXs_top = d['origin_T_camXs_raw'][
camR_index:camR_index + 1]
# predicted_box = d['bbox_origin_predicted']
predicted_box = []
filename = d['tree_seq_filename']
if hyp.do_2d_style_munit:
d, indexes = non_random_select_single(d,
item_names,
num_samples=hyp.S)
# st()
if hyp.fixed_view:
d, indexes = non_random_select_single(d,
item_names,
num_samples=hyp.S)
elif self.shuffle or hyp.randomly_select_views:
d, indexes = random_select_single(d, item_names, num_samples=hyp.S)
else:
d, indexes = non_random_select_single(d,
item_names,
num_samples=hyp.S)
filename_g = "/".join([original_filename, str(indexes[0])])
filename_e = "/".join([original_filename, str(indexes[1])])
rgb_camXs = d['rgb_camXs_raw']
# move channel dim inward, like pytorch wants
# rgb_camRs = np.transpose(rgb_camRs, axes=[0, 3, 1, 2])
rgb_camXs = np.transpose(rgb_camXs, axes=[0, 3, 1, 2])
rgb_camXs = rgb_camXs[:, :3]
rgb_camXs = utils_improc.preprocess_color(rgb_camXs)
if hyp.dataset_name == "carla":
rgb_camtop = np.transpose(rgb_camtop, axes=[0, 3, 1, 2])
rgb_camtop = rgb_camtop[:, :3]
rgb_camtop = utils_improc.preprocess_color(rgb_camtop)
d['rgb_camtop'] = rgb_camtop
d['origin_T_camXs_top'] = origin_T_camXs_top
if len(predicted_box) == 0:
predicted_box = np.zeros([hyp.N, 6])
score = np.zeros([hyp.N]).astype(np.float32)
else:
num_boxes = predicted_box.shape[0]
score = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes])
predicted_box = np.pad(predicted_box,
[[0, hyp.N - num_boxes], [0, 0]])
d['predicted_box'] = predicted_box.astype(np.float32)
d['predicted_scores'] = score.astype(np.float32)
if hyp.dataset_name == "clevr_vqa":
num_boxes = bbox_origin.shape[0]
bbox_origin = np.array(bbox_origin)
score = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes])
bbox_origin = np.pad(bbox_origin,
[[0, hyp.N - num_boxes], [0, 0], [0, 0]])
object_category = np.pad(object_category, [[0, hyp.N - num_boxes]],
lambda x, y, z, m: "0")
d['gt_box'] = bbox_origin.astype(np.float32)
d['gt_scores'] = score.astype(np.float32)
d['classes'] = list(object_category)
if hyp.dataset_name == "replica":
if len(bbox_origin) == 0:
score = np.zeros([hyp.N])
bbox_origin = np.zeros([hyp.N, 6])
object_category = ["0"] * hyp.N
object_category = np.array(object_category)
else:
num_boxes = len(bbox_origin)
bbox_origin = torch.stack(bbox_origin).numpy().squeeze(
1).squeeze(1).reshape([num_boxes, 6])
bbox_origin = np.array(bbox_origin)
score = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes])
bbox_origin = np.pad(bbox_origin,
[[0, hyp.N - num_boxes], [0, 0]])
object_category = np.pad(object_category,
[[0, hyp.N - num_boxes]],
lambda x, y, z, m: "0")
d['gt_box'] = bbox_origin.astype(np.float32)
d['gt_scores'] = score.astype(np.float32)
d['classes'] = list(object_category)
# st()
if hyp.dataset_name == "carla_mix" or hyp.dataset_name == "carla_det":
bbox_origin_predicted = bbox_origin_predicted[:3]
if len(bbox_origin_gt.shape) == 1:
bbox_origin_gt = np.expand_dims(bbox_origin_gt, 0)
num_boxes = bbox_origin_gt.shape[0]
# st()
score_gt = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes])
bbox_origin_gt = np.pad(bbox_origin_gt,
[[0, hyp.N - num_boxes], [0, 0]])
# st()
classes = np.pad(classes, [[0, hyp.N - num_boxes]],
lambda x, y, z, m: "0")
if len(bbox_origin_predicted) == 0:
bbox_origin_predicted = np.zeros([hyp.N, 6])
score_pred = np.zeros([hyp.N]).astype(np.float32)
else:
num_boxes = bbox_origin_predicted.shape[0]
score_pred = np.pad(np.ones([num_boxes]),
[0, hyp.N - num_boxes])
bbox_origin_predicted = np.pad(
bbox_origin_predicted, [[0, hyp.N - num_boxes], [0, 0]])
d['predicted_box'] = bbox_origin_predicted.astype(np.float32)
d['predicted_scores'] = score_pred.astype(np.float32)
d['gt_box'] = bbox_origin_gt.astype(np.float32)
d['gt_scores'] = score_gt.astype(np.float32)
d['classes'] = list(classes)
d['rgb_camXs_raw'] = rgb_camXs
if hyp.dataset_name != "carla" and hyp.do_empty:
empty_rgb_camXs = d['empty_rgb_camXs_raw']
# move channel dim inward, like pytorch wants
empty_rgb_camXs = np.transpose(empty_rgb_camXs, axes=[0, 3, 1, 2])
empty_rgb_camXs = empty_rgb_camXs[:, :3]
empty_rgb_camXs = utils_improc.preprocess_color(empty_rgb_camXs)
d['empty_rgb_camXs_raw'] = empty_rgb_camXs
# st()
if hyp.use_gt_occs:
d['occR_complete'] = occ_complete
d['tree_seq_filename'] = filename
d['filename_e'] = filename_e
d['filename_g'] = filename_g
return d
def __getitem__(self, index):
if hyp.dataset_name == 'kitti' or hyp.dataset_name == 'clevr' or hyp.dataset_name == 'real' or hyp.dataset_name == "bigbird" or hyp.dataset_name == "carla" or hyp.dataset_name == "carla_mix" or hyp.dataset_name == "replica" or hyp.dataset_name == "clevr_vqa" or hyp.dataset_name == "carla_det":
# print(index)
# st()
filename = self.records[index]
d = pickle.load(open(filename, "rb"))
d = dict(d)
d_empty = pickle.load(open(self.empty_scene, "rb"))
d_empty = dict(d_empty)
# st()
# elif hyp.dataset_name=="carla":
# filename = self.records[index]
# d = np.load(filename)
# d = dict(d)
# d['rgb_camXs_raw'] = d['rgb_camXs']
# d['pix_T_cams_raw'] = d['pix_T_cams']
# d['tree_seq_filename'] = "dummy_tree_filename"
# d['origin_T_camXs_raw'] = d['origin_T_camXs']
# d['camR_T_origin_raw'] = utils_geom.safe_inverse(torch.from_numpy(d['origin_T_camRs'])).numpy()
# d['xyz_camXs_raw'] = d['xyz_camXs']
else:
assert (False) # reader not ready yet
if hyp.do_empty:
item_names = [
'pix_T_cams_raw',
'origin_T_camXs_raw',
'camR_T_origin_raw',
'rgb_camXs_raw',
'xyz_camXs_raw',
'empty_rgb_camXs_raw',
'empty_xyz_camXs_raw',
]
else:
item_names = [
'pix_T_cams_raw',
'origin_T_camXs_raw',
'camR_T_origin_raw',
'rgb_camXs_raw',
'xyz_camXs_raw',
]
if hyp.use_gt_occs:
__p = lambda x: utils_basic.pack_seqdim(x, 1)
__u = lambda x: utils_basic.unpack_seqdim(x, 1)
B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N
PH, PW = hyp.PH, hyp.PW
K = hyp.K
BOX_SIZE = hyp.BOX_SIZE
Z, Y, X = hyp.Z, hyp.Y, hyp.X
Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2)
Z4, Y4, X4 = int(Z / 4), int(Y / 4), int(X / 4)
D = 9
pix_T_cams = torch.from_numpy(
d["pix_T_cams_raw"]).unsqueeze(0).cuda().to(torch.float)
camRs_T_origin = torch.from_numpy(
d["camR_T_origin_raw"]).unsqueeze(0).cuda().to(torch.float)
origin_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_origin)))
origin_T_camXs = torch.from_numpy(
d["origin_T_camXs_raw"]).unsqueeze(0).cuda().to(torch.float)
camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0)
camRs_T_camXs = __u(
torch.matmul(utils_geom.safe_inverse(__p(origin_T_camRs)),
__p(origin_T_camXs)))
camXs_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_camXs)))
camX0_T_camRs = camXs_T_camRs[:, 0]
camX1_T_camRs = camXs_T_camRs[:, 1]
camR_T_camX0 = utils_geom.safe_inverse(camX0_T_camRs)
xyz_camXs = torch.from_numpy(
d["xyz_camXs_raw"]).unsqueeze(0).cuda().to(torch.float)
xyz_camRs = __u(
utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs)))
depth_camXs_, valid_camXs_ = utils_geom.create_depth_image(
__p(pix_T_cams), __p(xyz_camXs), H, W)
dense_xyz_camXs_ = utils_geom.depth2pointcloud(
depth_camXs_, __p(pix_T_cams))
occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X))
occRs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z2, Y2,
X2))
occRs_half = torch.max(occRs_half, dim=1).values.squeeze(0)
occ_complete = occRs_half.cpu().numpy()
# if hyp.do_time_flip:
# d = random_time_flip_single(d,item_names)
# if the sequence length > 2, select S frames
# filename = d['raw_seq_filename']
original_filename = filename
original_filename_empty = self.empty_scene
# st()
if hyp.dataset_name == "clevr_vqa":
d['tree_seq_filename'] = "temp"
pix_T_cams = d['pix_T_cams_raw']
num_cams = pix_T_cams.shape[0]
# padding_1 = torch.zeros([num_cams,1,3])
# padding_2 = torch.zeros([num_cams,4,1])
# padding_2[:,3] = 1.0
# st()
# pix_T_cams = torch.cat([pix_T_cams,padding_1],dim=1)
# pix_T_cams = torch.cat([pix_T_cams,padding_2],dim=2)
# st()
shape_name = d['shape_list']
color_name = d['color_list']
material_name = d['material_list']
all_name = []
all_style = []
for index in range(len(shape_name)):
name = shape_name[index] + "/" + color_name[
index] + "_" + material_name[index]
style_name = color_name[index] + "_" + material_name[index]
all_name.append(name)
all_style.append(style_name)
# st()
if hyp.do_shape:
class_name = shape_name
elif hyp.do_color:
class_name = color_name
elif hyp.do_material:
class_name = material_name
elif hyp.do_style:
class_name = all_style
else:
class_name = all_name
object_category = class_name
bbox_origin = d['bbox_origin']
# bbox_origin = torch.cat([bbox_origin],dim=0)
# object_category = object_category
bbox_origin_empty = np.zeros_like(bbox_origin)
object_category_empty = ['0']
# st()
if not hyp.dataset_name == "clevr_vqa":
filename = d['tree_seq_filename']
filename_empty = d_empty['tree_seq_filename']
if hyp.fixed_view:
d, indexes = non_random_select_single(d,
item_names,
num_samples=hyp.S)
d_empty, indexes_empty = specific_select_single_empty(
d_empty,
item_names,
d['origin_T_camXs_raw'],
num_samples=hyp.S)
filename_g = "/".join([original_filename, str(indexes[0])])
filename_e = "/".join([original_filename, str(indexes[1])])
filename_g_empty = "/".join([original_filename_empty, str(indexes[0])])
filename_e_empty = "/".join([original_filename_empty, str(indexes[1])])
rgb_camXs = d['rgb_camXs_raw']
rgb_camXs_empty = d_empty['rgb_camXs_raw']
# move channel dim inward, like pytorch wants
# rgb_camRs = np.transpose(rgb_camRs, axes=[0, 3, 1, 2])
rgb_camXs = np.transpose(rgb_camXs, axes=[0, 3, 1, 2])
rgb_camXs = rgb_camXs[:, :3]
rgb_camXs = utils_improc.preprocess_color(rgb_camXs)
rgb_camXs_empty = np.transpose(rgb_camXs_empty, axes=[0, 3, 1, 2])
rgb_camXs_empty = rgb_camXs_empty[:, :3]
rgb_camXs_empty = utils_improc.preprocess_color(rgb_camXs_empty)
if hyp.dataset_name == "clevr_vqa":
num_boxes = bbox_origin.shape[0]
bbox_origin = np.array(bbox_origin)
score = np.pad(np.ones([num_boxes]), [0, hyp.N - num_boxes])
bbox_origin = np.pad(bbox_origin,
[[0, hyp.N - num_boxes], [0, 0], [0, 0]])
object_category = np.pad(object_category, [[0, hyp.N - num_boxes]],
lambda x, y, z, m: "0")
object_category_empty = np.pad(object_category_empty,
[[0, hyp.N - 1]],
lambda x, y, z, m: "0")
# st()
score_empty = np.zeros_like(score)
bbox_origin_empty = np.zeros_like(bbox_origin)
d['gt_box'] = np.stack(
[bbox_origin.astype(np.float32), bbox_origin_empty])
d['gt_scores'] = np.stack([score.astype(np.float32), score_empty])
try:
d['classes'] = np.stack(
[object_category, object_category_empty]).tolist()
except Exception as e:
st()
d['rgb_camXs_raw'] = np.stack([rgb_camXs, rgb_camXs_empty])
d['pix_T_cams_raw'] = np.stack(
[d["pix_T_cams_raw"], d_empty["pix_T_cams_raw"]])
d['origin_T_camXs_raw'] = np.stack(
[d["origin_T_camXs_raw"], d_empty["origin_T_camXs_raw"]])
d['camR_T_origin_raw'] = np.stack(
[d["camR_T_origin_raw"], d_empty["camR_T_origin_raw"]])
d['xyz_camXs_raw'] = np.stack(
[d["xyz_camXs_raw"], d_empty["xyz_camXs_raw"]])
# d['rgb_camXs_raw'] = rgb_camXs
# d['tree_seq_filename'] = filename
if not hyp.dataset_name == "clevr_vqa":
d['tree_seq_filename'] = [filename, "invalid_tree"]
else:
d['tree_seq_filename'] = ["temp"]
# st()
d['filename_e'] = ["temp"]
d['filename_g'] = ["temp"]
if hyp.use_gt_occs:
d['occR_complete'] = np.expand_dims(occ_complete, axis=0)
return d
def assemble(bkg_feat0, obj_feat0, origin_T_camRs, camRs_T_zoom):
# let's first assemble the seq of background tensors
# this should effectively CREATE egomotion
# i fully expect we can do this all in one shot
# note it makes sense to create egomotion here, because
# we want to predict each view
B, C, Z, Y, X = list(bkg_feat0.shape)
B2, C2, Z2, Y2, X2 = list(obj_feat0.shape)
assert (B == B2)
assert (C == C2)
B, S, _, _ = list(origin_T_camRs.shape)
# ok, we have everything we need
# for each timestep, we want to warp the bkg to this timestep
# utils for packing/unpacking along seq dim
__p = lambda x: pack_seqdim(x, B)
__u = lambda x: unpack_seqdim(x, B)
# we in fact have utils for this already
cam0s_T_camRs = utils_geom.get_camM_T_camXs(origin_T_camRs, ind=0)
camRs_T_cam0s = __u(utils_geom.safe_inverse(__p(cam0s_T_camRs)))
bkg_feat0s = bkg_feat0.unsqueeze(1).repeat(1, S, 1, 1, 1, 1)
bkg_featRs = apply_4x4s_to_voxs(camRs_T_cam0s, bkg_feat0s)
# now for the objects
# we want to sample for each location in the bird grid
xyz_mems_ = utils_basic.gridcloud3D(B * S, Z, Y, X, norm=False)
# this is B*S x Z*Y*X x 3
xyz_camRs_ = Mem2Ref(xyz_mems_, Z, Y, X)
camRs_T_zoom_ = __p(camRs_T_zoom)
zoom_T_camRs_ = camRs_T_zoom_.inverse(
) # note this is not a rigid transform
xyz_zooms_ = utils_geom.apply_4x4(zoom_T_camRs_, xyz_camRs_)
# we will do the whole traj at once (per obj)
# note we just have one feat for the whole traj, so we tile up
obj_feats = obj_feat0.unsqueeze(1).repeat(1, S, 1, 1, 1, 1)
obj_feats_ = __p(obj_feats)
# this is B*S x Z x Y x X x C
# to sample, we need feats_ in ZYX order
obj_featRs_ = utils_samp.sample3D(obj_feats_, xyz_zooms_, Z, Y, X)
obj_featRs = __u(obj_featRs_)
# overweigh objects, so that we essentially overwrite
# featRs = 0.05*bkg_featRs + 0.95*obj_featRs
# overwrite the bkg at the object
obj_mask = (bkg_featRs > 0).float()
featRs = obj_featRs + (1.0 - obj_mask) * bkg_featRs
# note the normalization (next) will restore magnitudes for the bkg
# # featRs = bkg_featRs
# featRs = obj_featRs
# l2 normalize on chans
featRs = l2_normalize(featRs, dim=2)
validRs = 1.0 - (featRs == 0).all(dim=2, keepdim=True).float().cuda()
return featRs, validRs, bkg_featRs, obj_featRs
def prepare_common_tensors(self, feed, prep_summ=True):
results = dict()
if prep_summ:
self.summ_writer = utils_improc.Summ_writer(
writer=feed['writer'],
global_step=feed['global_step'],
log_freq=feed['set_log_freq'],
fps=8,
just_gif=feed['just_gif'],
)
else:
self.summ_writer = None
self.include_vis = hyp.do_include_vis
self.B = feed["set_batch_size"]
self.S = feed["set_seqlen"]
__p = lambda x: utils_basic.pack_seqdim(x, self.B)
__u = lambda x: utils_basic.unpack_seqdim(x, self.B)
self.H, self.W, self.V, self.N = hyp.H, hyp.W, hyp.V, hyp.N
self.PH, self.PW = hyp.PH, hyp.PW
self.K = hyp.K
self.set_name = feed['set_name']
# print('set_name', self.set_name)
if self.set_name == 'test':
self.Z, self.Y, self.X = hyp.Z_test, hyp.Y_test, hyp.X_test
else:
self.Z, self.Y, self.X = hyp.Z, hyp.Y, hyp.X
# print('Z, Y, X = %d, %d, %d' % (self.Z, self.Y, self.X))
self.Z2, self.Y2, self.X2 = int(self.Z / 2), int(self.Y / 2), int(
self.X / 2)
self.Z4, self.Y4, self.X4 = int(self.Z / 4), int(self.Y / 4), int(
self.X / 4)
self.rgb_camXs = feed["rgb_camXs"]
self.pix_T_cams = feed["pix_T_cams"]
self.origin_T_camXs = feed["origin_T_camXs"]
self.cams_T_velos = feed["cams_T_velos"]
self.camX0s_T_camXs = utils_geom.get_camM_T_camXs(self.origin_T_camXs,
ind=0)
self.camXs_T_camX0s = __u(
utils_geom.safe_inverse(__p(self.camX0s_T_camXs)))
self.xyz_veloXs = feed["xyz_veloXs"]
self.xyz_camXs = __u(
utils_geom.apply_4x4(__p(self.cams_T_velos), __p(self.xyz_veloXs)))
self.xyz_camX0s = __u(
utils_geom.apply_4x4(__p(self.camX0s_T_camXs),
__p(self.xyz_camXs)))
if self.set_name == 'test':
self.boxlist_camXs = feed["boxlists"]
self.scorelist_s = feed["scorelists"]
self.tidlist_s = feed["tidlists"]
boxlist_camXs_ = __p(self.boxlist_camXs)
scorelist_s_ = __p(self.scorelist_s)
tidlist_s_ = __p(self.tidlist_s)
boxlist_camXs_, tidlist_s_, scorelist_s_ = utils_misc.shuffle_valid_and_sink_invalid_boxes(
boxlist_camXs_, tidlist_s_, scorelist_s_)
self.boxlist_camXs = __u(boxlist_camXs_)
self.scorelist_s = __u(scorelist_s_)
self.tidlist_s = __u(tidlist_s_)
# self.boxlist_camXs[:,0], self.scorelist_s[:,0], self.tidlist_s[:,0] = utils_misc.shuffle_valid_and_sink_invalid_boxes(
# self.boxlist_camXs[:,0], self.tidlist_s[:,0], self.scorelist_s[:,0])
# self.score_s = feed["scorelists"]
# self.tid_s = torch.ones_like(self.score_s).long()
# self.lrt_camRs = utils_geom.convert_boxlist_to_lrtlist(self.box_camRs)
# self.lrt_camXs = utils_geom.apply_4x4s_to_lrts(self.camXs_T_camRs, self.lrt_camRs)
# self.lrt_camX0s = utils_geom.apply_4x4s_to_lrts(self.camX0s_T_camXs, self.lrt_camXs)
# self.lrt_camR0s = utils_geom.apply_4x4s_to_lrts(self.camR0s_T_camRs, self.lrt_camRs)
# boxlist_camXs_ = __p(self.boxlist_camXs)
# boxlist_camXs_ = __p(self.boxlist_camXs)
# lrtlist_camXs = __u(utils_geom.convert_boxlist_to_lrtlist(__p(self.boxlist_camXs))).reshape(
# self.B, self.S, self.N, 19)
self.lrtlist_camXs = __u(
utils_geom.convert_boxlist_to_lrtlist(__p(self.boxlist_camXs)))
# print('lrtlist_camXs', lrtlist_camXs.shape)
# # self.B, self.S, self.N, 19)
# # lrtlist_camXs = __u(utils_geom.apply_4x4_to_lrtlist(__p(camXs_T_camRs), __p(lrtlist_camRs)))
# self.summ_writer.summ_lrtlist('2D_inputs/lrtlist_camX0', self.rgb_camXs[:,0], lrtlist_camXs[:,0],
# self.scorelist_s[:,0], self.tidlist_s[:,0], self.pix_T_cams[:,0])
# self.summ_writer.summ_lrtlist('2D_inputs/lrtlist_camX1', self.rgb_camXs[:,1], lrtlist_camXs[:,1],
# self.scorelist_s[:,1], self.tidlist_s[:,1], self.pix_T_cams[:,1])
(
self.lrt_camXs,
self.box_camXs,
self.score_s,
) = utils_misc.collect_object_info(self.lrtlist_camXs,
self.boxlist_camXs,
self.tidlist_s,
self.scorelist_s,
1,
mod='X',
do_vis=False,
summ_writer=None)
self.lrt_camXs = self.lrt_camXs.squeeze(0)
self.score_s = self.score_s.squeeze(0)
self.tid_s = torch.ones_like(self.score_s).long()
self.lrt_camX0s = utils_geom.apply_4x4s_to_lrts(
self.camX0s_T_camXs, self.lrt_camXs)
if prep_summ and self.include_vis:
visX_g = []
for s in list(range(self.S)):
visX_g.append(
self.summ_writer.summ_lrtlist('',
self.rgb_camXs[:, s],
self.lrtlist_camXs[:, s],
self.scorelist_s[:, s],
self.tidlist_s[:, s],
self.pix_T_cams[:, 0],
only_return=True))
self.summ_writer.summ_rgbs('2D_inputs/box_camXs', visX_g)
# visX_g = []
# for s in list(range(self.S)):
# visX_g.append(self.summ_writer.summ_lrtlist(
# 'track/box_camX%d_g' % s, self.rgb_camXs[:,s], self.lrt_camXs[:,s:s+1],
# self.score_s[:,s:s+1], self.tid_s[:,s:s+1], self.pix_T_cams[:,0], only_return=True))
# self.summ_writer.summ_rgbs('track/box_camXs_g', visX_g)
if self.set_name == 'test':
# center on an object, so that it does not fall out of bounds
self.scene_centroid = utils_geom.get_clist_from_lrtlist(
self.lrt_camXs)[:, 0]
self.vox_util = vox_util.Vox_util(
self.Z,
self.Y,
self.X,
self.set_name,
scene_centroid=self.scene_centroid,
assert_cube=True)
else:
# center randomly
scene_centroid_x = np.random.uniform(-8.0, 8.0)
scene_centroid_y = np.random.uniform(-1.5, 3.0)
scene_centroid_z = np.random.uniform(10.0, 26.0)
scene_centroid = np.array(
[scene_centroid_x, scene_centroid_y,
scene_centroid_z]).reshape([1, 3])
self.scene_centroid = torch.from_numpy(
scene_centroid).float().cuda()
# center on a random non-outlier point
all_ok = False
num_tries = 0
while not all_ok:
scene_centroid_x = np.random.uniform(-8.0, 8.0)
scene_centroid_y = np.random.uniform(-1.5, 3.0)
scene_centroid_z = np.random.uniform(10.0, 26.0)
scene_centroid = np.array(
[scene_centroid_x, scene_centroid_y,
scene_centroid_z]).reshape([1, 3])
self.scene_centroid = torch.from_numpy(
scene_centroid).float().cuda()
num_tries += 1
# try to vox
self.vox_util = vox_util.Vox_util(
self.Z,
self.Y,
self.X,
self.set_name,
scene_centroid=self.scene_centroid,
assert_cube=True)
all_ok = True
# we want to ensure this gives us a few points inbound for each batch el
inb = __u(
self.vox_util.get_inbounds(__p(self.xyz_camX0s),
self.Z4,
self.Y4,
self.X,
already_mem=False))
num_inb = torch.sum(inb.float(), axis=2)
if torch.min(num_inb) < 100:
all_ok = False
if num_tries > 100:
return False
self.summ_writer.summ_scalar('zoom_sampling/num_tries', num_tries)
self.summ_writer.summ_scalar('zoom_sampling/num_inb',
torch.mean(num_inb).cpu().item())
self.occ_memXs = __u(
self.vox_util.voxelize_xyz(__p(self.xyz_camXs), self.Z, self.Y,
self.X))
self.occ_memX0s = __u(
self.vox_util.voxelize_xyz(__p(self.xyz_camX0s), self.Z, self.Y,
self.X))
self.occ_memX0s_half = __u(
self.vox_util.voxelize_xyz(__p(self.xyz_camX0s), self.Z2, self.Y2,
self.X2))
self.unp_memXs = __u(
self.vox_util.unproject_rgb_to_mem(__p(self.rgb_camXs), self.Z,
self.Y, self.X,
__p(self.pix_T_cams)))
self.unp_memX0s = self.vox_util.apply_4x4s_to_voxs(
self.camX0s_T_camXs, self.unp_memXs)
if prep_summ and self.include_vis:
self.summ_writer.summ_rgbs('2D_inputs/rgb_camXs',
torch.unbind(self.rgb_camXs, dim=1))
self.summ_writer.summ_occs('3D_inputs/occ_memXs',
torch.unbind(self.occ_memXs, dim=1))
self.summ_writer.summ_occs('3D_inputs/occ_memX0s',
torch.unbind(self.occ_memX0s, dim=1))
self.summ_writer.summ_rgb('2D_inputs/rgb_camX0', self.rgb_camXs[:,
0])
# self.summ_writer.summ_oned('2D_inputs/depth_camX0', self.depth_camXs[:,0], maxval=20.0)
# self.summ_writer.summ_oned('2D_inputs/valid_camX0', self.valid_camXs[:,0], norm=False)
return True
def forward(self, feed):
results = dict()
summ_writer = utils_improc.Summ_writer(writer=feed['writer'],
global_step=feed['global_step'],
set_name=feed['set_name'],
fps=8)
writer = feed['writer']
global_step = feed['global_step']
total_loss = torch.tensor(0.0)
__p = lambda x: pack_seqdim(x, B)
__u = lambda x: unpack_seqdim(x, B)
B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N
PH, PW = hyp.PH, hyp.PW
K = hyp.K
Z, Y, X = hyp.Z, hyp.Y, hyp.X
Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2)
D = 9
rgb_camRs = feed["rgb_camRs"]
rgb_camXs = feed["rgb_camXs"]
pix_T_cams = feed["pix_T_cams"]
cam_T_velos = feed["cam_T_velos"]
boxlist_camRs = feed["boxes3D"]
tidlist_s = feed["tids"] # coordinate-less and plural
scorelist_s = feed["scores"] # coordinate-less and plural
# # postproc the boxes:
# scorelist_s = __u(utils_misc.rescore_boxlist_with_inbound(__p(boxlist_camRs), __p(tidlist_s), Z, Y, X))
boxlist_camRs_, tidlist_s_, scorelist_s_ = __p(boxlist_camRs), __p(
tidlist_s), __p(scorelist_s)
boxlist_camRs_, tidlist_s_, scorelist_s_ = utils_misc.shuffle_valid_and_sink_invalid_boxes(
boxlist_camRs_, tidlist_s_, scorelist_s_)
boxlist_camRs = __u(boxlist_camRs_)
tidlist_s = __u(tidlist_s_)
scorelist_s = __u(scorelist_s_)
origin_T_camRs = feed["origin_T_camRs"]
origin_T_camRs_ = __p(origin_T_camRs)
origin_T_camXs = feed["origin_T_camXs"]
origin_T_camXs_ = __p(origin_T_camXs)
camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0)
camX0_T_camXs_ = __p(camX0_T_camXs)
camRs_T_camXs_ = torch.matmul(origin_T_camRs_.inverse(),
origin_T_camXs_)
camXs_T_camRs_ = camRs_T_camXs_.inverse()
camRs_T_camXs = __u(camRs_T_camXs_)
camXs_T_camRs = __u(camXs_T_camRs_)
xyz_veloXs = feed["xyz_veloXs"]
xyz_camXs = __u(utils_geom.apply_4x4(__p(cam_T_velos),
__p(xyz_veloXs)))
xyz_camRs = __u(
utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs)))
xyz_camX0s = __u(
utils_geom.apply_4x4(__p(camX0_T_camXs), __p(xyz_camXs)))
occRs = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z, Y, X))
occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X))
occX0s = __u(utils_vox.voxelize_xyz(__p(xyz_camX0s), Z, Y, X))
occRs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z2, Y2, X2))
occXs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z2, Y2, X2))
occX0s_half = __u(utils_vox.voxelize_xyz(__p(xyz_camX0s), Z2, Y2, X2))
unpRs = __u(
utils_vox.unproject_rgb_to_mem(
__p(rgb_camXs), Z, Y, X,
__p(torch.matmul(pix_T_cams, camXs_T_camRs))))
unpXs = __u(
utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z, Y, X,
__p(pix_T_cams)))
unpX0s = utils_vox.apply_4x4_to_voxs(camX0_T_camXs, unpXs)
unpRs_half = __u(
utils_vox.unproject_rgb_to_mem(
__p(rgb_camXs), Z2, Y2, X2,
__p(torch.matmul(pix_T_cams, camXs_T_camRs))))
#####################
## visualize what we got
#####################
summ_writer.summ_rgbs('2D_inputs/rgb_camRs',
torch.unbind(rgb_camRs, dim=1))
summ_writer.summ_rgbs('2D_inputs/rgb_camXs',
torch.unbind(rgb_camXs, dim=1))
summ_writer.summ_occs('3D_inputs/occRs', torch.unbind(occRs, dim=1))
summ_writer.summ_occs('3D_inputs/occXs', torch.unbind(occXs, dim=1))
summ_writer.summ_unps('3D_inputs/unpRs', torch.unbind(unpRs, dim=1),
torch.unbind(occRs, dim=1))
summ_writer.summ_unps('3D_inputs/unpXs', torch.unbind(unpXs, dim=1),
torch.unbind(occXs, dim=1))
summ_writer.summ_unps('3D_inputs/unpX0s', torch.unbind(unpX0s, dim=1),
torch.unbind(occX0s, dim=1))
lrtlist_camRs = __u(
utils_geom.convert_boxlist_to_lrtlist(boxlist_camRs_)).reshape(
B, S, N, 19)
lrtlist_camXs = __u(
utils_geom.apply_4x4_to_lrtlist(__p(camXs_T_camRs),
__p(lrtlist_camRs)))
# stabilize boxes for ego/cam motion
lrtlist_camX0s = __u(
utils_geom.apply_4x4_to_lrtlist(__p(camX0_T_camXs),
__p(lrtlist_camXs)))
# these are is B x S x N x 19
summ_writer.summ_lrtlist('lrtlist_camR0', rgb_camRs[:, 0],
lrtlist_camRs[:, 0], scorelist_s[:, 0],
tidlist_s[:, 0], pix_T_cams[:, 0])
summ_writer.summ_lrtlist('lrtlist_camR1', rgb_camRs[:, 1],
lrtlist_camRs[:, 1], scorelist_s[:, 1],
tidlist_s[:, 1], pix_T_cams[:, 1])
summ_writer.summ_lrtlist('lrtlist_camX0', rgb_camXs[:, 0],
lrtlist_camXs[:, 0], scorelist_s[:, 0],
tidlist_s[:, 0], pix_T_cams[:, 0])
summ_writer.summ_lrtlist('lrtlist_camX1', rgb_camXs[:, 1],
lrtlist_camXs[:, 1], scorelist_s[:, 1],
tidlist_s[:, 1], pix_T_cams[:, 1])
(
obj_lrtlist_camXs,
obj_scorelist_s,
) = utils_misc.collect_object_info(lrtlist_camXs,
tidlist_s,
scorelist_s,
pix_T_cams,
K,
mod='X',
do_vis=True,
summ_writer=summ_writer)
(
obj_lrtlist_camRs,
obj_scorelist_s,
) = utils_misc.collect_object_info(lrtlist_camRs,
tidlist_s,
scorelist_s,
pix_T_cams,
K,
mod='R',
do_vis=True,
summ_writer=summ_writer)
(
obj_lrtlist_camX0s,
obj_scorelist_s,
) = utils_misc.collect_object_info(lrtlist_camX0s,
tidlist_s,
scorelist_s,
pix_T_cams,
K,
mod='X0',
do_vis=False)
masklist_memR = utils_vox.assemble_padded_obj_masklist(
lrtlist_camRs[:, 0], scorelist_s[:, 0], Z, Y, X, coeff=1.0)
masklist_memX = utils_vox.assemble_padded_obj_masklist(
lrtlist_camXs[:, 0], scorelist_s[:, 0], Z, Y, X, coeff=1.0)
# obj_mask_memR is B x N x 1 x Z x Y x X
summ_writer.summ_occ('obj/masklist_memR',
torch.sum(masklist_memR, dim=1))
summ_writer.summ_occ('obj/masklist_memX',
torch.sum(masklist_memX, dim=1))
# to do tracking or whatever, i need to be able to extract a 3d object crop
cropX0_obj0 = utils_vox.crop_zoom_from_mem(occXs[:, 0],
lrtlist_camXs[:, 0, 0], Z2,
Y2, X2)
cropX0_obj1 = utils_vox.crop_zoom_from_mem(occXs[:, 0],
lrtlist_camXs[:, 0, 1], Z2,
Y2, X2)
cropR0_obj0 = utils_vox.crop_zoom_from_mem(occRs[:, 0],
lrtlist_camRs[:, 0, 0], Z2,
Y2, X2)
cropR0_obj1 = utils_vox.crop_zoom_from_mem(occRs[:, 0],
lrtlist_camRs[:, 0, 1], Z2,
Y2, X2)
# print('got it:')
# print(cropX00.shape)
# summ_writer.summ_occ('crops/cropX0_obj0', cropX0_obj0)
# summ_writer.summ_occ('crops/cropX0_obj1', cropX0_obj1)
summ_writer.summ_feat('crops/cropX0_obj0', cropX0_obj0, pca=False)
summ_writer.summ_feat('crops/cropX0_obj1', cropX0_obj1, pca=False)
summ_writer.summ_feat('crops/cropR0_obj0', cropR0_obj0, pca=False)
summ_writer.summ_feat('crops/cropR0_obj1', cropR0_obj1, pca=False)
if hyp.do_feat:
if hyp.flow_do_synth_rt:
result = utils_misc.get_synth_flow(unpRs_half,
occRs_half,
obj_lrtlist_camX0s,
obj_scorelist_s,
occXs_half,
feed['set_name'],
K=K,
summ_writer=summ_writer,
sometimes_zero=True,
sometimes_real=False)
occXs, unpXs, flowX0, camX1_T_camX0, is_synth = result
else:
# ego-stabilized flow from X00 to X01
flowX0 = utils_misc.get_gt_flow(
obj_lrtlist_camX0s,
obj_scorelist_s,
utils_geom.eye_4x4s(B, S),
occXs_half[:, 0],
K=K,
occ_only=False, # get the dense flow
mod='X0',
summ_writer=summ_writer)
# occXs is B x S x 1 x H x W x D
# unpXs is B x S x 3 x H x W x D
# featXs_input = torch.cat([occXs, occXs*unpXs], dim=2)
featX0s_input = torch.cat([occX0s, occX0s * unpX0s], dim=2)
featX0s_input_ = __p(featX0s_input)
featX0s_, validX0s_, feat_loss = self.featnet(
featX0s_input_, summ_writer)
total_loss += feat_loss
featX0s = __u(featX0s_)
# _featX00 = featXs[:,0:1]
# _featX01 = utils_vox.apply_4x4_to_voxs(camX0_T_camXs[:,1:], featXs[:,1:])
# featX0s = torch.cat([_featX00, _featX01], dim=1)
validX0s = 1.0 - (featX0s == 0).all(
dim=2,
keepdim=True).float() #this shall be B x S x 1 x H x W x D
summ_writer.summ_feats('3D_feats/featX0s_input',
torch.unbind(featX0s_input, dim=1),
pca=True)
# summ_writer.summ_feats('3D_feats/featXs_output', torch.unbind(featXs, dim=1), pca=True)
summ_writer.summ_feats('3D_feats/featX0s_output',
torch.unbind(featX0s, dim=1),
pca=True)
if hyp.do_flow:
# total flow from X0 to X1
flowX = utils_misc.get_gt_flow(
obj_lrtlist_camXs,
obj_scorelist_s,
camX0_T_camXs,
occXs_half[:, 0],
K=K,
occ_only=False, # get the dense flow
mod='X',
vis=False,
summ_writer=None)
# # vis this to confirm it's ok (it is)
# unpX0_e = utils_samp.backwarp_using_3D_flow(unpXs[:,1], flowX)
# occX0_e = utils_samp.backwarp_using_3D_flow(occXs[:,1], flowX)
# summ_writer.summ_unps('flow/backwarpX', [unpX0s[:,0], unpX0_e], [occXs[:,0], occX0_e])
# unpX0_e = utils_samp.backwarp_using_3D_flow(unpX0s[:,1], flowX0)
# occX0_e = utils_samp.backwarp_using_3D_flow(occX0s[:,1], flowX0, binary_feat=True)
# summ_writer.summ_unps('flow/backwarpX0', [unpX0s[:,0], unpX0_e], [occXs[:,0], occX0_e])
flow_loss, flowX0_pred = self.flownet(
featX0s[:, 0],
featX0s[:, 1],
flowX0, # gt flow
torch.max(validX0s[:, 1:], dim=1)[0],
is_synth,
summ_writer)
total_loss += flow_loss
# g = flowX.reshape(-1)
# summ_writer.summ_histogram('flowX_g_nonzero_hist', g[torch.abs(g)>0.01])
# g = flowX0.reshape(-1)
# e = flowX0_pred.reshape(-1)
# summ_writer.summ_histogram('flowX0_g_nonzero_hist', g[torch.abs(g)>0.01])
# summ_writer.summ_histogram('flowX0_e_nonzero_hist', e[torch.abs(g)>0.01])
summ_writer.summ_scalar('loss', total_loss.cpu().item())
return total_loss, results
def forward(self, feed):
results = dict()
if 'log_freq' not in feed.keys():
feed['log_freq'] = None
start_time = time.time()
summ_writer = utils_improc.Summ_writer(writer=feed['writer'],
global_step=feed['global_step'],
set_name=feed['set_name'],
log_freq=feed['log_freq'],
fps=8)
writer = feed['writer']
global_step = feed['global_step']
total_loss = torch.tensor(0.0).cuda()
__p = lambda x: utils_basic.pack_seqdim(x, B)
__u = lambda x: utils_basic.unpack_seqdim(x, B)
__pb = lambda x: utils_basic.pack_boxdim(x, hyp.N)
__ub = lambda x: utils_basic.unpack_boxdim(x, hyp.N)
if hyp.aug_object_ent_dis:
__pb_a = lambda x: utils_basic.pack_boxdim(
x, hyp.max_obj_aug + hyp.max_obj_aug_dis)
__ub_a = lambda x: utils_basic.unpack_boxdim(
x, hyp.max_obj_aug + hyp.max_obj_aug_dis)
else:
__pb_a = lambda x: utils_basic.pack_boxdim(x, hyp.max_obj_aug)
__ub_a = lambda x: utils_basic.unpack_boxdim(x, hyp.max_obj_aug)
B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N
PH, PW = hyp.PH, hyp.PW
K = hyp.K
BOX_SIZE = hyp.BOX_SIZE
Z, Y, X = hyp.Z, hyp.Y, hyp.X
Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2)
Z4, Y4, X4 = int(Z / 4), int(Y / 4), int(X / 4)
D = 9
tids = torch.from_numpy(np.reshape(np.arange(B * N), [B, N]))
rgb_camXs = feed["rgb_camXs_raw"]
pix_T_cams = feed["pix_T_cams_raw"]
camRs_T_origin = feed["camR_T_origin_raw"]
origin_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_origin)))
origin_T_camXs = feed["origin_T_camXs_raw"]
camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0)
camRs_T_camXs = __u(
torch.matmul(utils_geom.safe_inverse(__p(origin_T_camRs)),
__p(origin_T_camXs)))
camXs_T_camRs = __u(utils_geom.safe_inverse(__p(camRs_T_camXs)))
camX0_T_camRs = camXs_T_camRs[:, 0]
camX1_T_camRs = camXs_T_camRs[:, 1]
camR_T_camX0 = utils_geom.safe_inverse(camX0_T_camRs)
xyz_camXs = feed["xyz_camXs_raw"]
depth_camXs_, valid_camXs_ = utils_geom.create_depth_image(
__p(pix_T_cams), __p(xyz_camXs), H, W)
dense_xyz_camXs_ = utils_geom.depth2pointcloud(depth_camXs_,
__p(pix_T_cams))
xyz_camRs = __u(
utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs)))
xyz_camX0s = __u(
utils_geom.apply_4x4(__p(camX0_T_camXs), __p(xyz_camXs)))
occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X))
occXs_to_Rs = utils_vox.apply_4x4s_to_voxs(camRs_T_camXs, occXs)
occXs_to_Rs_45 = cross_corr.rotate_tensor_along_y_axis(occXs_to_Rs, 45)
occXs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z2, Y2, X2))
occRs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z2, Y2, X2))
occX0s_half = __u(utils_vox.voxelize_xyz(__p(xyz_camX0s), Z2, Y2, X2))
unpXs = __u(
utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z, Y, X,
__p(pix_T_cams)))
unpXs_half = __u(
utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z2, Y2, X2,
__p(pix_T_cams)))
unpX0s_half = __u(
utils_vox.unproject_rgb_to_mem(
__p(rgb_camXs), Z2, Y2, X2,
utils_basic.matmul2(
__p(pix_T_cams),
utils_geom.safe_inverse(__p(camX0_T_camXs)))))
unpRs = __u(
utils_vox.unproject_rgb_to_mem(
__p(rgb_camXs), Z, Y, X,
utils_basic.matmul2(
__p(pix_T_cams),
utils_geom.safe_inverse(__p(camRs_T_camXs)))))
unpRs_half = __u(
utils_vox.unproject_rgb_to_mem(
__p(rgb_camXs), Z2, Y2, X2,
utils_basic.matmul2(
__p(pix_T_cams),
utils_geom.safe_inverse(__p(camRs_T_camXs)))))
dense_xyz_camRs_ = utils_geom.apply_4x4(__p(camRs_T_camXs),
dense_xyz_camXs_)
inbound_camXs_ = utils_vox.get_inbounds(dense_xyz_camRs_, Z, Y,
X).float()
inbound_camXs_ = torch.reshape(inbound_camXs_, [B * S, 1, H, W])
depth_camXs = __u(depth_camXs_)
valid_camXs = __u(valid_camXs_) * __u(inbound_camXs_)
summ_writer.summ_oneds('2D_inputs/depth_camXs',
torch.unbind(depth_camXs, dim=1),
maxdepth=21.0)
summ_writer.summ_oneds('2D_inputs/valid_camXs',
torch.unbind(valid_camXs, dim=1))
summ_writer.summ_rgbs('2D_inputs/rgb_camXs',
torch.unbind(rgb_camXs, dim=1))
summ_writer.summ_occs('3D_inputs/occXs', torch.unbind(occXs, dim=1))
summ_writer.summ_unps('3D_inputs/unpXs', torch.unbind(unpXs, dim=1),
torch.unbind(occXs, dim=1))
occRs = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z, Y, X))
if hyp.do_eval_boxes:
if hyp.dataset_name == "clevr_vqa":
gt_boxes_origin_corners = feed['gt_box']
gt_scores_origin = feed['gt_scores'].detach().cpu().numpy()
classes = feed['classes']
scores = gt_scores_origin
tree_seq_filename = feed['tree_seq_filename']
gt_boxes_origin = nlu.get_ends_of_corner(
gt_boxes_origin_corners)
gt_boxes_origin_end = torch.reshape(gt_boxes_origin,
[hyp.B, hyp.N, 2, 3])
gt_boxes_origin_theta = nlu.get_alignedboxes2thetaformat(
gt_boxes_origin_end)
gt_boxes_origin_corners = utils_geom.transform_boxes_to_corners(
gt_boxes_origin_theta)
gt_boxesR_corners = __ub(
utils_geom.apply_4x4(camRs_T_origin[:, 0],
__pb(gt_boxes_origin_corners)))
gt_boxesR_theta = utils_geom.transform_corners_to_boxes(
gt_boxesR_corners)
gt_boxesR_end = nlu.get_ends_of_corner(gt_boxesR_corners)
else:
tree_seq_filename = feed['tree_seq_filename']
tree_filenames = [
join(hyp.root_dataset, i) for i in tree_seq_filename
if i != "invalid_tree"
]
invalid_tree_filenames = [
join(hyp.root_dataset, i) for i in tree_seq_filename
if i == "invalid_tree"
]
num_empty = len(invalid_tree_filenames)
trees = [pickle.load(open(i, "rb")) for i in tree_filenames]
len_valid = len(trees)
if len_valid > 0:
gt_boxesR, scores, classes = nlu.trees_rearrange(trees)
if num_empty > 0:
gt_boxesR = np.concatenate([
gt_boxesR, empty_gt_boxesR
]) if len_valid > 0 else empty_gt_boxesR
scores = np.concatenate([
scores, empty_scores
]) if len_valid > 0 else empty_scores
classes = np.concatenate([
classes, empty_classes
]) if len_valid > 0 else empty_classes
gt_boxesR = torch.from_numpy(
gt_boxesR).cuda().float() # torch.Size([2, 3, 6])
gt_boxesR_end = torch.reshape(gt_boxesR, [hyp.B, hyp.N, 2, 3])
gt_boxesR_theta = nlu.get_alignedboxes2thetaformat(
gt_boxesR_end) #torch.Size([2, 3, 9])
gt_boxesR_corners = utils_geom.transform_boxes_to_corners(
gt_boxesR_theta)
class_names_ex_1 = "_".join(classes[0])
summ_writer.summ_text('eval_boxes/class_names', class_names_ex_1)
gt_boxesRMem_corners = __ub(
utils_vox.Ref2Mem(__pb(gt_boxesR_corners), Z2, Y2, X2))
gt_boxesRMem_end = nlu.get_ends_of_corner(gt_boxesRMem_corners)
gt_boxesRMem_theta = utils_geom.transform_corners_to_boxes(
gt_boxesRMem_corners)
gt_boxesRUnp_corners = __ub(
utils_vox.Ref2Mem(__pb(gt_boxesR_corners), Z, Y, X))
gt_boxesRUnp_end = nlu.get_ends_of_corner(gt_boxesRUnp_corners)
gt_boxesX0_corners = __ub(
utils_geom.apply_4x4(camX0_T_camRs, __pb(gt_boxesR_corners)))
gt_boxesX0Mem_corners = __ub(
utils_vox.Ref2Mem(__pb(gt_boxesX0_corners), Z2, Y2, X2))
gt_boxesX0Mem_theta = utils_geom.transform_corners_to_boxes(
gt_boxesX0Mem_corners)
gt_boxesX0Mem_end = nlu.get_ends_of_corner(gt_boxesX0Mem_corners)
gt_boxesX0_end = nlu.get_ends_of_corner(gt_boxesX0_corners)
gt_cornersX0_pix = __ub(
utils_geom.apply_pix_T_cam(pix_T_cams[:, 0],
__pb(gt_boxesX0_corners)))
rgb_camX0 = rgb_camXs[:, 0]
rgb_camX1 = rgb_camXs[:, 1]
summ_writer.summ_box_by_corners('eval_boxes/gt_boxescamX0',
rgb_camX0, gt_boxesX0_corners,
torch.from_numpy(scores), tids,
pix_T_cams[:, 0])
unps_vis = utils_improc.get_unps_vis(unpX0s_half, occX0s_half)
unp_vis = torch.mean(unps_vis, dim=1)
unps_visRs = utils_improc.get_unps_vis(unpRs_half, occRs_half)
unp_visRs = torch.mean(unps_visRs, dim=1)
unps_visRs_full = utils_improc.get_unps_vis(unpRs, occRs)
unp_visRs_full = torch.mean(unps_visRs_full, dim=1)
summ_writer.summ_box_mem_on_unp('eval_boxes/gt_boxesR_mem',
unp_visRs, gt_boxesRMem_end,
scores, tids)
unpX0s_half = torch.mean(unpX0s_half, dim=1)
unpX0s_half = nlu.zero_out(unpX0s_half, gt_boxesX0Mem_end, scores)
occX0s_half = torch.mean(occX0s_half, dim=1)
occX0s_half = nlu.zero_out(occX0s_half, gt_boxesX0Mem_end, scores)
summ_writer.summ_unp('3D_inputs/unpX0s', unpX0s_half, occX0s_half)
if hyp.do_feat:
featXs_input = torch.cat([occXs, occXs * unpXs], dim=2)
featXs_input_ = __p(featXs_input)
freeXs_ = utils_vox.get_freespace(__p(xyz_camXs), __p(occXs_half))
freeXs = __u(freeXs_)
visXs = torch.clamp(occXs_half + freeXs, 0.0, 1.0)
mask_ = None
if (type(mask_) != type(None)):
assert (list(mask_.shape)[2:5] == list(
featXs_input_.shape)[2:5])
featXs_, feat_loss = self.featnet(featXs_input_,
summ_writer,
mask=__p(occXs)) #mask_)
total_loss += feat_loss
validXs = torch.ones_like(visXs)
_validX00 = validXs[:, 0:1]
_validX01 = utils_vox.apply_4x4s_to_voxs(camX0_T_camXs[:, 1:],
validXs[:, 1:])
validX0s = torch.cat([_validX00, _validX01], dim=1)
validRs = utils_vox.apply_4x4s_to_voxs(camRs_T_camXs, validXs)
visRs = utils_vox.apply_4x4s_to_voxs(camRs_T_camXs, visXs)
featXs = __u(featXs_)
_featX00 = featXs[:, 0:1]
_featX01 = utils_vox.apply_4x4s_to_voxs(camX0_T_camXs[:, 1:],
featXs[:, 1:])
featX0s = torch.cat([_featX00, _featX01], dim=1)
emb3D_e = torch.mean(featX0s[:, 1:], dim=1)
vis3D_e_R = torch.max(visRs[:, 1:], dim=1)[0]
emb3D_g = featX0s[:, 0]
vis3D_g_R = visRs[:, 0]
validR_combo = torch.min(validRs, dim=1).values
summ_writer.summ_feats('3D_feats/featXs_input',
torch.unbind(featXs_input, dim=1),
pca=True)
summ_writer.summ_feats('3D_feats/featXs_output',
torch.unbind(featXs, dim=1),
valids=torch.unbind(validXs, dim=1),
pca=True)
summ_writer.summ_feats('3D_feats/featX0s_output',
torch.unbind(featX0s, dim=1),
valids=torch.unbind(
torch.ones_like(validRs), dim=1),
pca=True)
summ_writer.summ_feats('3D_feats/validRs',
torch.unbind(validRs, dim=1),
pca=False)
summ_writer.summ_feat('3D_feats/vis3D_e_R', vis3D_e_R, pca=False)
summ_writer.summ_feat('3D_feats/vis3D_g_R', vis3D_g_R, pca=False)
if hyp.do_munit:
object_classes, filenames = nlu.create_object_classes(
classes, [tree_seq_filename, tree_seq_filename], scores)
if hyp.do_munit_fewshot:
emb3D_e_R = utils_vox.apply_4x4_to_vox(camR_T_camX0, emb3D_e)
emb3D_g_R = utils_vox.apply_4x4_to_vox(camR_T_camX0, emb3D_g)
emb3D_R = emb3D_e_R
emb3D_e_R_object, emb3D_g_R_object, validR_combo_object = nlu.create_object_tensors(
[emb3D_e_R, emb3D_g_R], [validR_combo], gt_boxesRMem_end,
scores, [BOX_SIZE, BOX_SIZE, BOX_SIZE])
emb3D_R_object = (emb3D_e_R_object + emb3D_g_R_object) / 2
content, style = self.munitnet.net.gen_a.encode(emb3D_R_object)
objects_taken, _ = self.munitnet.net.gen_a.decode(
content, style)
styles = style
contents = content
elif hyp.do_3d_style_munit:
emb3D_e_R = utils_vox.apply_4x4_to_vox(camR_T_camX0, emb3D_e)
emb3D_g_R = utils_vox.apply_4x4_to_vox(camR_T_camX0, emb3D_g)
emb3D_R = emb3D_e_R
# st()
emb3D_e_R_object, emb3D_g_R_object, validR_combo_object = nlu.create_object_tensors(
[emb3D_e_R, emb3D_g_R], [validR_combo], gt_boxesRMem_end,
scores, [BOX_SIZE, BOX_SIZE, BOX_SIZE])
emb3D_R_object = (emb3D_e_R_object + emb3D_g_R_object) / 2
camX1_T_R = camXs_T_camRs[:, 1]
camX0_T_R = camXs_T_camRs[:, 0]
assert hyp.B == 2
assert emb3D_e_R_object.shape[0] == 2
munit_loss, sudo_input_0, sudo_input_1, recon_input_0, recon_input_1, sudo_input_0_cycle, sudo_input_1_cycle, styles, contents, adin = self.munitnet(
emb3D_R_object[0:1], emb3D_R_object[1:2])
if hyp.store_content_style_range:
if self.max_content == None:
self.max_content = torch.zeros_like(
contents[0][0]).cuda() - 100000000
if self.min_content == None:
self.min_content = torch.zeros_like(
contents[0][0]).cuda() + 100000000
if self.max_style == None:
self.max_style = torch.zeros_like(
styles[0][0]).cuda() - 100000000
if self.min_style == None:
self.min_style = torch.zeros_like(
styles[0][0]).cuda() + 100000000
self.max_content = torch.max(
torch.max(self.max_content, contents[0][0]),
contents[1][0])
self.min_content = torch.min(
torch.min(self.min_content, contents[0][0]),
contents[1][0])
self.max_style = torch.max(
torch.max(self.max_style, styles[0][0]), styles[1][0])
self.min_style = torch.min(
torch.min(self.min_style, styles[0][0]), styles[1][0])
data_to_save = {
'max_content': self.max_content.cpu().numpy(),
'min_content': self.min_content.cpu().numpy(),
'max_style': self.max_style.cpu().numpy(),
'min_style': self.min_style.cpu().numpy()
}
with open('content_style_range.p', 'wb') as f:
pickle.dump(data_to_save, f)
elif hyp.is_contrastive_examples:
if hyp.normalize_contrast:
content0 = (contents[0] - self.min_content) / (
self.max_content - self.min_content + 1e-5)
content1 = (contents[1] - self.min_content) / (
self.max_content - self.min_content + 1e-5)
style0 = (styles[0] - self.min_style) / (
self.max_style - self.min_style + 1e-5)
style1 = (styles[1] - self.min_style) / (
self.max_style - self.min_style + 1e-5)
else:
content0 = contents[0]
content1 = contents[1]
style0 = styles[0]
style1 = styles[1]
# euclid_dist_content = torch.sum(torch.sqrt((content0 - content1)**2))/torch.prod(torch.tensor(content0.shape))
# euclid_dist_style = torch.sum(torch.sqrt((style0-style1)**2))/torch.prod(torch.tensor(style0.shape))
euclid_dist_content = (content0 - content1).norm(2) / (
content0.numel())
euclid_dist_style = (style0 -
style1).norm(2) / (style0.numel())
content_0_pooled = torch.mean(
content0.reshape(list(content0.shape[:2]) + [-1]),
dim=-1)
content_1_pooled = torch.mean(
content1.reshape(list(content1.shape[:2]) + [-1]),
dim=-1)
euclid_dist_content_pooled = (content_0_pooled -
content_1_pooled).norm(2) / (
content_0_pooled.numel())
content_0_normalized = content0 / content0.norm()
content_1_normalized = content1 / content1.norm()
style_0_normalized = style0 / style0.norm()
style_1_normalized = style1 / style1.norm()
content_0_pooled_normalized = content_0_pooled / content_0_pooled.norm(
)
content_1_pooled_normalized = content_1_pooled / content_1_pooled.norm(
)
cosine_dist_content = torch.sum(content_0_normalized *
content_1_normalized)
cosine_dist_style = torch.sum(style_0_normalized *
style_1_normalized)
cosine_dist_content_pooled = torch.sum(
content_0_pooled_normalized *
content_1_pooled_normalized)
print("euclid dist [content, pooled-content, style]: ",
euclid_dist_content, euclid_dist_content_pooled,
euclid_dist_style)
print("cosine sim [content, pooled-content, style]: ",
cosine_dist_content, cosine_dist_content_pooled,
cosine_dist_style)
if hyp.run_few_shot_on_munit:
if (global_step % 300) == 1 or (global_step % 300) == 0:
wrong = False
try:
precision_style = float(self.tp_style) / self.all_style
precision_content = float(
self.tp_content) / self.all_content
except ZeroDivisionError:
wrong = True
if not wrong:
summ_writer.summ_scalar(
'precision/unsupervised_precision_style',
precision_style)
summ_writer.summ_scalar(
'precision/unsupervised_precision_content',
precision_content)
# st()
self.embed_list_style = defaultdict(lambda: [])
self.embed_list_content = defaultdict(lambda: [])
self.tp_style = 0
self.all_style = 0
self.tp_content = 0
self.all_content = 0
self.check = False
elif not self.check and not nlu.check_fill_dict(
self.embed_list_content, self.embed_list_style):
print("Filling \n")
for index, class_val in enumerate(object_classes):
if hyp.dataset_name == "clevr_vqa":
class_val_content, class_val_style = class_val.split(
"/")
else:
class_val_content, class_val_style = [
class_val.split("/")[0],
class_val.split("/")[0]
]
print(len(self.embed_list_style.keys()), "style class",
len(self.embed_list_content), "content class",
self.embed_list_content.keys())
if len(self.embed_list_style[class_val_style]
) < hyp.few_shot_nums:
self.embed_list_style[class_val_style].append(
styles[index].squeeze())
if len(self.embed_list_content[class_val_content]
) < hyp.few_shot_nums:
if hyp.avg_3d:
content_val = contents[index]
content_val = torch.mean(content_val.reshape(
[content_val.shape[1], -1]),
dim=-1)
# st()
self.embed_list_content[
class_val_content].append(content_val)
else:
self.embed_list_content[
class_val_content].append(
contents[index].reshape([-1]))
else:
self.check = True
try:
print(float(self.tp_content) / self.all_content)
print(float(self.tp_style) / self.all_style)
except Exception as e:
pass
average = True
if average:
for key, val in self.embed_list_style.items():
if isinstance(val, type([])):
self.embed_list_style[key] = torch.mean(
torch.stack(val, dim=0), dim=0)
for key, val in self.embed_list_content.items():
if isinstance(val, type([])):
self.embed_list_content[key] = torch.mean(
torch.stack(val, dim=0), dim=0)
else:
for key, val in self.embed_list_style.items():
if isinstance(val, type([])):
self.embed_list_style[key] = torch.stack(val,
dim=0)
for key, val in self.embed_list_content.items():
if isinstance(val, type([])):
self.embed_list_content[key] = torch.stack(
val, dim=0)
for index, class_val in enumerate(object_classes):
class_val = class_val
if hyp.dataset_name == "clevr_vqa":
class_val_content, class_val_style = class_val.split(
"/")
else:
class_val_content, class_val_style = [
class_val.split("/")[0],
class_val.split("/")[0]
]
style_val = styles[index].squeeze().unsqueeze(0)
if not average:
embed_list_val_style = torch.cat(list(
self.embed_list_style.values()),
dim=0)
embed_list_key_style = list(
np.repeat(
np.expand_dims(
list(self.embed_list_style.keys()), 1),
hyp.few_shot_nums, 1).reshape([-1]))
else:
embed_list_val_style = torch.stack(list(
self.embed_list_style.values()),
dim=0)
embed_list_key_style = list(
self.embed_list_style.keys())
embed_list_val_style = utils_basic.l2_normalize(
embed_list_val_style, dim=1).permute(1, 0)
style_val = utils_basic.l2_normalize(style_val, dim=1)
scores_styles = torch.matmul(style_val,
embed_list_val_style)
index_key = torch.argmax(scores_styles,
dim=1).squeeze()
selected_class_style = embed_list_key_style[index_key]
self.styles_prediction[class_val_style].append(
selected_class_style)
if class_val_style == selected_class_style:
self.tp_style += 1
self.all_style += 1
if hyp.avg_3d:
content_val = contents[index]
content_val = torch.mean(content_val.reshape(
[content_val.shape[1], -1]),
dim=-1).unsqueeze(0)
else:
content_val = contents[index].reshape(
[-1]).unsqueeze(0)
if not average:
embed_list_val_content = torch.cat(list(
self.embed_list_content.values()),
dim=0)
embed_list_key_content = list(
np.repeat(
np.expand_dims(
list(self.embed_list_content.keys()),
1), hyp.few_shot_nums,
1).reshape([-1]))
else:
embed_list_val_content = torch.stack(list(
self.embed_list_content.values()),
dim=0)
embed_list_key_content = list(
self.embed_list_content.keys())
embed_list_val_content = utils_basic.l2_normalize(
embed_list_val_content, dim=1).permute(1, 0)
content_val = utils_basic.l2_normalize(content_val,
dim=1)
scores_content = torch.matmul(content_val,
embed_list_val_content)
index_key = torch.argmax(scores_content,
dim=1).squeeze()
selected_class_content = embed_list_key_content[
index_key]
self.content_prediction[class_val_content].append(
selected_class_content)
if class_val_content == selected_class_content:
self.tp_content += 1
self.all_content += 1
# st()
munit_loss = hyp.munit_loss_weight * munit_loss
recon_input_obj = torch.cat([recon_input_0, recon_input_1], dim=0)
recon_emb3D_R = nlu.update_scene_with_objects(
emb3D_R, recon_input_obj, gt_boxesRMem_end, scores)
sudo_input_obj = torch.cat([sudo_input_0, sudo_input_1], dim=0)
styled_emb3D_R = nlu.update_scene_with_objects(
emb3D_R, sudo_input_obj, gt_boxesRMem_end, scores)
styled_emb3D_e_X1 = utils_vox.apply_4x4_to_vox(
camX1_T_R, styled_emb3D_R)
styled_emb3D_e_X0 = utils_vox.apply_4x4_to_vox(
camX0_T_R, styled_emb3D_R)
emb3D_e_X1 = utils_vox.apply_4x4_to_vox(camX1_T_R, recon_emb3D_R)
emb3D_e_X0 = utils_vox.apply_4x4_to_vox(camX0_T_R, recon_emb3D_R)
emb3D_e_X1_og = utils_vox.apply_4x4_to_vox(camX1_T_R, emb3D_R)
emb3D_e_X0_og = utils_vox.apply_4x4_to_vox(camX0_T_R, emb3D_R)
emb3D_R_aug_diff = torch.abs(emb3D_R - recon_emb3D_R)
summ_writer.summ_feat(f'aug_feat/og', emb3D_R)
summ_writer.summ_feat(f'aug_feat/og_gen', recon_emb3D_R)
summ_writer.summ_feat(f'aug_feat/og_aug_diff', emb3D_R_aug_diff)
if hyp.cycle_style_view_loss:
sudo_input_obj_cycle = torch.cat(
[sudo_input_0_cycle, sudo_input_1_cycle], dim=0)
styled_emb3D_R_cycle = nlu.update_scene_with_objects(
emb3D_R, sudo_input_obj_cycle, gt_boxesRMem_end, scores)
styled_emb3D_e_X0_cycle = utils_vox.apply_4x4_to_vox(
camX0_T_R, styled_emb3D_R_cycle)
styled_emb3D_e_X1_cycle = utils_vox.apply_4x4_to_vox(
camX1_T_R, styled_emb3D_R_cycle)
summ_writer.summ_scalar('munit_loss', munit_loss.cpu().item())
total_loss += munit_loss
if hyp.do_occ and hyp.occ_do_cheap:
occX0_sup, freeX0_sup, _, freeXs = utils_vox.prep_occs_supervision(
camX0_T_camXs, xyz_camXs, Z2, Y2, X2, agg=True)
summ_writer.summ_occ('occ_sup/occ_sup', occX0_sup)
summ_writer.summ_occ('occ_sup/free_sup', freeX0_sup)
summ_writer.summ_occs('occ_sup/freeXs_sup',
torch.unbind(freeXs, dim=1))
summ_writer.summ_occs('occ_sup/occXs_sup',
torch.unbind(occXs_half, dim=1))
occ_loss, occX0s_pred_ = self.occnet(
torch.mean(featX0s[:, 1:], dim=1), occX0_sup, freeX0_sup,
torch.max(validX0s[:, 1:], dim=1)[0], summ_writer)
occX0s_pred = __u(occX0s_pred_)
total_loss += occ_loss
if hyp.do_view:
assert (hyp.do_feat)
PH, PW = hyp.PH, hyp.PW
sy = float(PH) / float(hyp.H)
sx = float(PW) / float(hyp.W)
assert (sx == 0.5) # else we need a fancier downsampler
assert (sy == 0.5)
projpix_T_cams = __u(
utils_geom.scale_intrinsics(__p(pix_T_cams), sx, sy))
# st()
if hyp.do_munit:
feat_projX00 = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 0],
camX0_T_camXs[:, 1],
emb3D_e_X1, # use feat1 to predict rgb0
hyp.view_depth,
PH,
PW)
feat_projX00_og = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 0],
camX0_T_camXs[:, 1],
emb3D_e_X1_og, # use feat1 to predict rgb0
hyp.view_depth,
PH,
PW)
# only for checking the style
styled_feat_projX00 = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 0],
camX0_T_camXs[:, 1],
styled_emb3D_e_X1, # use feat1 to predict rgb0
hyp.view_depth,
PH,
PW)
if hyp.cycle_style_view_loss:
styled_feat_projX00_cycle = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 0],
camX0_T_camXs[:, 1],
styled_emb3D_e_X1_cycle, # use feat1 to predict rgb0
hyp.view_depth,
PH,
PW)
else:
feat_projX00 = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 0],
camX0_T_camXs[:, 1],
featXs[:, 1], # use feat1 to predict rgb0
hyp.view_depth,
PH,
PW)
rgb_X00 = utils_basic.downsample(rgb_camXs[:, 0], 2)
rgb_X01 = utils_basic.downsample(rgb_camXs[:, 1], 2)
valid_X00 = utils_basic.downsample(valid_camXs[:, 0], 2)
view_loss, rgb_e, emb2D_e = self.viewnet(feat_projX00, rgb_X00,
valid_X00, summ_writer,
"rgb")
if hyp.do_munit:
_, rgb_e, emb2D_e = self.viewnet(feat_projX00_og, rgb_X00,
valid_X00, summ_writer,
"rgb_og")
if hyp.do_munit:
styled_view_loss, styled_rgb_e, styled_emb2D_e = self.viewnet(
styled_feat_projX00, rgb_X00, valid_X00, summ_writer,
"recon_style")
if hyp.cycle_style_view_loss:
styled_view_loss_cycle, styled_rgb_e_cycle, styled_emb2D_e_cycle = self.viewnet(
styled_feat_projX00_cycle, rgb_X00, valid_X00,
summ_writer, "recon_style_cycle")
rgb_input_1 = torch.cat(
[rgb_X01[1], rgb_X01[0], styled_rgb_e[0]], dim=2)
rgb_input_2 = torch.cat(
[rgb_X01[0], rgb_X01[1], styled_rgb_e[1]], dim=2)
complete_vis = torch.cat([rgb_input_1, rgb_input_2], dim=1)
summ_writer.summ_rgb('munit/munit_recons_vis',
complete_vis.unsqueeze(0))
if not hyp.do_munit:
total_loss += view_loss
else:
if hyp.basic_view_loss:
total_loss += view_loss
if hyp.style_view_loss:
total_loss += styled_view_loss
if hyp.cycle_style_view_loss:
total_loss += styled_view_loss_cycle
summ_writer.summ_scalar('loss', total_loss.cpu().item())
if hyp.save_embed_tsne:
for index, class_val in enumerate(object_classes):
class_val_content, class_val_style = class_val.split("/")
style_val = styles[index].squeeze().unsqueeze(0)
self.cluster_pool.update(style_val, [class_val_style])
print(self.cluster_pool.num)
if self.cluster_pool.is_full():
embeds, classes = self.cluster_pool.fetch()
with open("offline_cluster" + '/%st.txt' % 'classes',
'w') as f:
for index, embed in enumerate(classes):
class_val = classes[index]
f.write("%s\n" % class_val)
f.close()
with open("offline_cluster" + '/%st.txt' % 'embeddings',
'w') as f:
for index, embed in enumerate(embeds):
# embed = utils_basic.l2_normalize(embed,dim=0)
print("writing {} embed".format(index))
embed_l_s = [str(i) for i in embed.tolist()]
embed_str = '\t'.join(embed_l_s)
f.write("%s\n" % embed_str)
f.close()
st()
return total_loss, results
def forward(self, feed):
results = dict()
summ_writer = utils_improc.Summ_writer(writer=feed['writer'],
global_step=feed['global_step'],
set_name=feed['set_name'],
fps=8)
writer = feed['writer']
global_step = feed['global_step']
total_loss = torch.tensor(0.0).cuda()
__p = lambda x: pack_seqdim(x, B)
__u = lambda x: unpack_seqdim(x, B)
B, H, W, V, S, N = hyp.B, hyp.H, hyp.W, hyp.V, hyp.S, hyp.N
PH, PW = hyp.PH, hyp.PW
K = hyp.K
Z, Y, X = hyp.Z, hyp.Y, hyp.X
Z2, Y2, X2 = int(Z / 2), int(Y / 2), int(X / 2)
D = 9
rgb_camRs = feed["rgb_camRs"]
rgb_camXs = feed["rgb_camXs"]
pix_T_cams = feed["pix_T_cams"]
cam_T_velos = feed["cam_T_velos"]
origin_T_camRs = feed["origin_T_camRs"]
origin_T_camRs_ = __p(origin_T_camRs)
origin_T_camXs = feed["origin_T_camXs"]
origin_T_camXs_ = __p(origin_T_camXs)
camX0_T_camXs = utils_geom.get_camM_T_camXs(origin_T_camXs, ind=0)
camX0_T_camXs_ = __p(camX0_T_camXs)
camRs_T_camXs_ = torch.matmul(utils_geom.safe_inverse(origin_T_camRs_),
origin_T_camXs_)
camXs_T_camRs_ = utils_geom.safe_inverse(camRs_T_camXs_)
camRs_T_camXs = __u(camRs_T_camXs_)
camXs_T_camRs = __u(camXs_T_camRs_)
xyz_veloXs = feed["xyz_veloXs"]
xyz_camXs = __u(utils_geom.apply_4x4(__p(cam_T_velos),
__p(xyz_veloXs)))
xyz_camRs = __u(
utils_geom.apply_4x4(__p(camRs_T_camXs), __p(xyz_camXs)))
xyz_camX0s = __u(
utils_geom.apply_4x4(__p(camX0_T_camXs), __p(xyz_camXs)))
occXs = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z, Y, X))
occXs_half = __u(utils_vox.voxelize_xyz(__p(xyz_camXs), Z2, Y2, X2))
occX0s_half = __u(utils_vox.voxelize_xyz(__p(xyz_camX0s), Z2, Y2, X2))
unpXs = __u(
utils_vox.unproject_rgb_to_mem(__p(rgb_camXs), Z, Y, X,
__p(pix_T_cams)))
## projected depth, and inbound mask
depth_camXs_, valid_camXs_ = utils_geom.create_depth_image(
__p(pix_T_cams), __p(xyz_camXs), H, W)
dense_xyz_camXs_ = utils_geom.depth2pointcloud(depth_camXs_,
__p(pix_T_cams))
dense_xyz_camX0s_ = utils_geom.apply_4x4(__p(camX0_T_camXs),
dense_xyz_camXs_)
inbound_camXs_ = utils_vox.get_inbounds(dense_xyz_camX0s_, Z, Y,
X).float()
inbound_camXs_ = torch.reshape(inbound_camXs_, [B * S, 1, H, W])
depth_camXs = __u(depth_camXs_)
valid_camXs = __u(valid_camXs_) * __u(inbound_camXs_)
#####################
## visualize what we got
#####################
summ_writer.summ_oneds('2D_inputs/depth_camXs',
torch.unbind(depth_camXs, dim=1))
summ_writer.summ_oneds('2D_inputs/valid_camXs',
torch.unbind(valid_camXs, dim=1))
summ_writer.summ_oneds('2D_inputs/valid_camXs',
torch.unbind(valid_camXs, dim=1))
summ_writer.summ_rgbs('2D_inputs/rgb_camRs',
torch.unbind(rgb_camRs, dim=1))
summ_writer.summ_rgbs('2D_inputs/rgb_camXs',
torch.unbind(rgb_camXs, dim=1))
summ_writer.summ_occs('3D_inputs/occXs', torch.unbind(occXs, dim=1))
summ_writer.summ_unps('3D_inputs/unpXs', torch.unbind(unpXs, dim=1),
torch.unbind(occXs, dim=1))
if summ_writer.save_this:
unpRs = __u(
utils_vox.unproject_rgb_to_mem(
__p(rgb_camXs), Z, Y, X,
matmul2(__p(pix_T_cams),
utils_geom.safe_inverse(__p(camRs_T_camXs)))))
occRs = __u(utils_vox.voxelize_xyz(__p(xyz_camRs), Z, Y, X))
summ_writer.summ_occs('3D_inputs/occRs', torch.unbind(occRs,
dim=1))
summ_writer.summ_unps('3D_inputs/unpRs', torch.unbind(unpRs,
dim=1),
torch.unbind(occRs, dim=1))
#####################
## run the nets
#####################
mask_ = None
if hyp.do_occ and (not hyp.occ_do_cheap):
'''
occRs_sup, freeRs_sup, freeXs = utils_vox.prep_occs_supervision(xyz_camXs,
occRs_half,
occXs_half,
camRs_T_camXs,
agg=True)
featRs_input = torch.cat([occRs, occRs*unpRs], dim=2)
featRs_input_ = __p(featRs_input)
occRs_sup_ = __p(occRs_sup)
freeRs_sup_ = __p(freeRs_sup)
occ_loss, occRs_pred_ = self.occnet(featRs_input_,
occRs_sup_,
freeRs_sup_,
summ_writer
)
occRs_pred = __u(occRs_pred_)
total_loss += occ_loss
mask_ = F.upsample(occRs_pred_, scale_factor=2)
'''
occXs_ = __p(occXs)
mask_ = occXs_
if hyp.do_feat:
# occXs is B x S x 1 x H x W x D
# unpXs is B x S x 3 x H x W x D
featXs_input = torch.cat([occXs, occXs * unpXs], dim=2)
featXs_input_ = __p(featXs_input)
# it is useful to keep track of what was visible from each viewpoint
freeXs_ = utils_vox.get_freespace(__p(xyz_camXs), __p(occXs_half))
freeXs = __u(freeXs_)
visXs = torch.clamp(occXs_half + freeXs, 0.0, 1.0)
if (type(mask_) != type(None)):
assert (list(mask_.shape)[2:5] == list(
featXs_input_.shape)[2:5])
featXs_, validXs_, feat_loss = self.featnet(
featXs_input_, summ_writer, mask=__p(occXs)) #mask_)
total_loss += feat_loss
validXs = __u(validXs_)
_validX00 = validXs[:, 0:1]
_validX01 = utils_vox.apply_4x4_to_voxs(camX0_T_camXs[:, 1:],
validXs[:, 1:])
validX0s = torch.cat([_validX00, _validX01], dim=1)
_visX00 = visXs[:, 0:1]
_visX01 = utils_vox.apply_4x4_to_voxs(camX0_T_camXs[:, 1:],
visXs[:, 1:])
visX0s = torch.cat([_visX00, _visX01], dim=1)
featXs = __u(featXs_)
_featX00 = featXs[:, 0:1]
_featX01 = utils_vox.apply_4x4_to_voxs(camX0_T_camXs[:, 1:],
featXs[:, 1:])
featX0s = torch.cat([_featX00, _featX01], dim=1)
emb3D_e = torch.mean(featX0s[:, 1:], dim=1) # context
emb3D_g = featX0s[:, 0] # obs
vis3D_e = torch.max(validX0s[:, 1:], dim=1)[0] * torch.max(
visX0s[:, 1:], dim=1)[0]
vis3D_g = validX0s[:, 0] * visX0s[:, 0] # obs
if hyp.do_eval_recall:
results['emb3D_e'] = emb3D_e
results['emb3D_g'] = emb3D_g
summ_writer.summ_feats('3D_feats/featXs_input',
torch.unbind(featXs_input, dim=1),
pca=True)
summ_writer.summ_feats('3D_feats/featXs_output',
torch.unbind(featXs, dim=1),
pca=True)
summ_writer.summ_feats('3D_feats/featX0s_output',
torch.unbind(featX0s, dim=1),
pca=True)
summ_writer.summ_feats('3D_feats/validX0s',
torch.unbind(validX0s, dim=1),
pca=False)
summ_writer.summ_feat('3D_feats/vis3D_e', vis3D_e, pca=False)
summ_writer.summ_feat('3D_feats/vis3D_g', vis3D_g, pca=False)
if hyp.do_occ and hyp.occ_do_cheap:
occX0_sup, freeX0_sup, freeXs = utils_vox.prep_occs_supervision(
xyz_camXs, occX0s_half, occXs_half, camX0_T_camXs, agg=True)
summ_writer.summ_occ('occ_sup/occ_sup', occX0_sup)
summ_writer.summ_occ('occ_sup/free_sup', freeX0_sup)
summ_writer.summ_occs('occ_sup/freeXs_sup',
torch.unbind(freeXs, dim=1))
summ_writer.summ_occs('occ_sup/occXs_sup',
torch.unbind(occXs_half, dim=1))
occ_loss, occRs_pred_ = self.occnet(
torch.mean(featX0s[:, 1:], dim=1), occX0_sup, freeX0_sup,
torch.max(validX0s[:, 1:], dim=1)[0], summ_writer)
occRs_pred = __u(occRs_pred_)
total_loss += occ_loss
if hyp.do_view:
assert (hyp.do_feat)
# we warped the features into the canonical view
# now we resample to the target view and decode
PH, PW = hyp.PH, hyp.PW
sy = float(PH) / float(hyp.H)
sx = float(PW) / float(hyp.W)
assert (sx == 0.5) # else we need a fancier downsampler
assert (sy == 0.5)
projpix_T_cams = __u(
utils_geom.scale_intrinsics(__p(pix_T_cams), sx, sy))
assert (S == 2) # else we should warp each feat in 1:
feat_projX00 = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 0], camX0_T_camXs[:, 1], featXs[:, 1],
hyp.view_depth, PH, PW)
# feat_projX0 is B x hyp.feat_dim x hyp.view_depth x PH x PW
rgb_X00 = downsample(rgb_camXs[:, 0], 2)
if summ_writer.save_this:
# for vis, let's also project some rgb
rgb_projX00 = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 0], camXs_T_camRs[:, 0], unpRs[:, 0],
hyp.view_depth, PH, PW)
rgb_projX01 = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 1], camXs_T_camRs[:, 1], unpRs[:, 1],
hyp.view_depth, PH, PW)
occ_projX00 = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 0], camXs_T_camRs[:, 0], occRs[:, 0],
hyp.view_depth, PH, PW)
occ_projX01 = utils_vox.apply_pixX_T_memR_to_voxR(
projpix_T_cams[:, 1], camXs_T_camRs[:, 1], occRs[:, 1],
hyp.view_depth, PH, PW)
rgb_projX00_vis = reduce_masked_mean(rgb_projX00,
occ_projX00.repeat(
[1, 3, 1, 1, 1]),
dim=2)
rgb_projX01_vis = reduce_masked_mean(rgb_projX01,
occ_projX01.repeat(
[1, 3, 1, 1, 1]),
dim=2)
summ_writer.summ_rgbs('projection/rgb_projX',
[rgb_projX00_vis, rgb_projX01_vis])
rgb_X01 = downsample(rgb_camXs[:, 1], 2)
summ_writer.summ_rgbs('projection/rgb_origX',
[rgb_X00, rgb_X01])
# decode the perspective volume into an image
view_loss, rgb_e, emb2D_e = self.viewnet(feat_projX00, rgb_X00,
summ_writer)
total_loss += view_loss
if hyp.do_emb2D:
assert (hyp.do_view)
# create an embedding image, representing the bottom-up 2D feature tensor
emb_loss_2D, emb2D_g = self.embnet2D(rgb_camXs[:, 0], emb2D_e,
valid_camXs[:,
0], summ_writer)
total_loss += emb_loss_2D
if hyp.do_emb3D:
occX0_sup, freeX0_sup, freeXs = utils_vox.prep_occs_supervision(
xyz_camXs, occX0s_half, occXs_half, camX0_T_camXs, agg=True)
emb_loss_3D = self.embnet3D(emb3D_e, emb3D_g, vis3D_e, vis3D_g,
summ_writer)
total_loss += emb_loss_3D
if hyp.do_eval_recall:
results['emb2D_e'] = None
results['emb2D_g'] = None
summ_writer.summ_scalar('loss', total_loss.cpu().item())
return total_loss, results