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joint_with_icp.py
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joint_with_icp.py
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import os, sys
BASE_DIR = os.path.normpath(
os.path.join(os.path.dirname(os.path.abspath(__file__))))
import argparse
import numpy as np
import json
import datetime
import time
from collections import defaultdict
from data_utils import *
import torch
import torch.nn as nn
import torch.nn.functional as F
from pytorch3d.loss import chamfer_distance
from dataset import *
from model import *
from losses import *
import gc
parser = argparse.ArgumentParser()
parser.add_argument("--config", default="config_chairs_final.json", help="path to the json config file", type=str)
parser.add_argument("--logdir", default="log_test2", help="path to the log directory", type=str)
parser.add_argument('--dump_dir', default= "dump_test2", type=str)
parser.add_argument('--category', default= "storagefurniture", type=str)
parser.add_argument('--to_train', default= False, type=bool)
parser.add_argument('--part_loss', default= False, type=bool)
parser.add_argument('--use_bn', default= False, type=bool)
parser.add_argument('--update_src_latent', default= False, type=bool)
parser.add_argument('--share_src_latent', default= False, type=bool)
parser.add_argument('--normalize', default= False, type=bool)
parser.add_argument('--init_deformation', default= False, type=bool)
parser.add_argument('--fixed_deformation', default= False, type=bool)
parser.add_argument('--shared_encoder', default= False, type=bool)
parser.add_argument('--clip_vec', default= False, type=bool)
parser.add_argument('--init_joint', default= False, type=bool)
parser.add_argument('--distance_function', default= "mahalanobis", type=str)
parser.add_argument('--loss_function', default= "regression", type=str)
parser.add_argument('--selection', default= "retrieval_candidates", type=str)
parser.add_argument('--use_connectivity', default= False, type=bool)
parser.add_argument('--use_src_encoder', default= False, type=bool)
parser.add_argument('--use_src_encoder_retrieval', default= False, type=bool)
parser.add_argument('--use_symmetry', default= False, type=bool)
parser.add_argument('--use_singleaxis', default= False, type=bool)
parser.add_argument('--use_keypoint', default= False, type=bool)
parser.add_argument('--prop2', default= False, type=bool)
parser.add_argument('--prop2_more', default= False, type=bool)
parser.add_argument('--prop3', default= False, type=bool)
parser.add_argument('--model_init', default= "log_chair500_conn/", type=str)
parser.add_argument('--K', default= 10, type=int)
parser.add_argument('--margin', default= 10.0, type=float)
parser.add_argument('--activation_fn', default= "sigmoid", type=str)
parser.add_argument('--visualize', default= False, type=bool)
parser.add_argument('--eval_selection', default= "retrieval", type=str)
parser.add_argument('--use_icp_pp', default= False, type=bool)
parser.add_argument('--fitting_loss', default= "chamfer", type=str)
parser.add_argument('--num_sources', default= 200, type=int)
parser.add_argument('--complementme', default= False, type=int)
FLAGS = parser.parse_args()
config = FLAGS.config
LOG_DIR = FLAGS.logdir
if not os.path.exists(LOG_DIR):
os.mkdir(LOG_DIR)
TO_TRAIN = FLAGS.to_train
fname = os.path.join(LOG_DIR, "config.json")
if TO_TRAIN:
args = json.load(open(config))
with open(fname, "w") as fp:
json.dump(args, fp, indent=4)
else:
args = json.load(open(fname))
curr_fname = sys.argv[0]
if TO_TRAIN:
os.system('cp %s %s' % (curr_fname, LOG_DIR))
DATA_DIR = args["data_dir"]
# OBJ_CAT = args["category"]
OBJ_CAT = FLAGS.category
DUMP_DIR = FLAGS.dump_dir
if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR)
temp_fol = os.path.join(DUMP_DIR, "tmp")
if not os.path.exists(temp_fol): os.mkdir(temp_fol)
LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
ALPHA = args["alpha"]
SOURCE_LATENT_DIM = args["source_latent_dim"]
TARGET_LATENT_DIM = args["target_latent_dim"]
PART_LATENT_DIM = args["part_latent_dim"]
PART_LOSS = FLAGS.part_loss
K = FLAGS.K
MARGIN = FLAGS.margin
USE_BN = FLAGS.use_bn
ACTIVATION_FN = FLAGS.activation_fn
UPDATE_SRC_LATENT = FLAGS.update_src_latent
SHARE_SRC_LATENT = FLAGS.share_src_latent
NORMALIZE = FLAGS.normalize
INIT_DEFORMATION = FLAGS.init_deformation
FIXED_DEFORMATION = FLAGS.fixed_deformation
SHARED_ENCODER = FLAGS.shared_encoder
MODEL_INIT = FLAGS.model_init
CLIP_VEC = FLAGS.clip_vec
EVAL_SELECTION = FLAGS.eval_selection
LOSS_FUNC = FLAGS.loss_function
DIST_FUNC = FLAGS.distance_function
SELECTION = FLAGS.selection
INIT_JOINT = FLAGS.init_joint
USE_CONNECTIVITY = FLAGS.use_connectivity
USE_SRC_ENCODER = FLAGS.use_src_encoder
USE_SRC_ENCODER_RETRIEVAL = FLAGS.use_src_encoder_retrieval
USE_SYMMETRY = FLAGS.use_symmetry
PROP2 = FLAGS.prop2
PROP2_MORE = FLAGS.prop2_more
PROP3 = FLAGS.prop3
USE_ICP_PP = FLAGS.use_icp_pp
FITTING_LOSS = FLAGS.fitting_loss
USE_SINGLEAXIS = FLAGS.use_singleaxis
USE_KEYPOINT = FLAGS.use_keypoint
NUM_SOURCES = FLAGS.num_sources
print("Num sources: "+str(NUM_SOURCES))
COMPLEMENTME = FLAGS.complementme
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
log_string("Normalize: " + str(NORMALIZE))
log_string("Use connectivity: " + str(USE_CONNECTIVITY))
log_string("Update source vec: " + str(UPDATE_SRC_LATENT))
log_string("Shared src_latent: " + str(SHARE_SRC_LATENT))
log_string("")
log_string("Fixed deformation: " + str(FIXED_DEFORMATION))
log_string("Init Deformation: " + str(INIT_DEFORMATION)+" model: "+MODEL_INIT)
log_string("Shared encoder: " + str(SHARED_ENCODER))
log_string("")
log_string("Loss Func: " + str(LOSS_FUNC))
log_string("Distance Func: " + str(DIST_FUNC))
log_string("Activation Func: " + str(ACTIVATION_FN))
log_string("Selection: " + str(SELECTION))
log_string("Use Property 2: " + str(PROP2))
log_string("Use Property 2 more: " + str(PROP2_MORE))
log_string("Use Property 3: " + str(PROP3))
log_string("")
LOG_FOUT.write(str(FLAGS)+'\n')
TO_VISU = FLAGS.visualize
global DEFORMATION_CANDIDATES
DEFORMATION_CANDIDATES = {}
global DEFORMATION_DISTANCES
DEFORMATION_DISTANCES = {}
if __name__ == "__main__":
if COMPLEMENTME:
print("Using ComplementMe dataset")
src_data_fol = os.path.join(BASE_DIR, "data_complementme_final", OBJ_CAT, "h5_new")
elif USE_SINGLEAXIS:
print("Using single axis constraint")
src_data_fol = os.path.join(BASE_DIR, "data_aabb_constraints_singleaxis", OBJ_CAT, "h5")
elif USE_KEYPOINT:
print("Using keypoint constraint")
src_data_fol = os.path.join(BASE_DIR, "data_aabb_constraints_keypoint", OBJ_CAT, "h5")
else:
src_data_fol = os.path.join(BASE_DIR, "data_aabb_constraints", OBJ_CAT, "h5")
if COMPLEMENTME:
filename_pickle = os.path.join("generated_datasplits_complementme", OBJ_CAT+"_"+str(NUM_SOURCES)+".pickle")
else:
filename_pickle = os.path.join("generated_datasplits", OBJ_CAT+"_"+str(NUM_SOURCES)+".pickle")
sources, _, _ = get_all_selected_models_pickle(filename_pickle)
if (TO_TRAIN):
DATA_SPLIT = "train"
batch_size = args["batch_size"]
else:
DATA_SPLIT = "test"
batch_size = 2
if COMPLEMENTME:
filename = os.path.join("generated_datasplits_complementme", OBJ_CAT+"_"+str(NUM_SOURCES)+"_"+DATA_SPLIT+".h5")
else:
#### Get data for all target models
filename = os.path.join("generated_datasplits", OBJ_CAT+"_"+str(NUM_SOURCES)+"_"+DATA_SPLIT+".h5")
dataset = StructureNetDataset_h5(filename)
to_shuffle = TO_TRAIN
# print(to_shuffle)
loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=args["num_workers"],
pin_memory=True,
shuffle=to_shuffle,
)
#### Torch
device = args["device"]
##Get the data of the sources
## Get max number of params for the embedding size
MAX_NUM_PARAMS = -1
MAX_NUM_PARTS = -1
SOURCE_MODEL_INFO = []
SOURCE_SEMANTICS = []
SOURCE_PART_LATENT_CODES = []
print("Loading sources...")
for source_model in sources:
src_filename = str(source_model) + "_leaves.h5"
if (USE_CONNECTIVITY):
box_params, orig_ids, default_param, points, point_labels, points_mat, point_semantic, \
constraint_mat, constraint_proj_mat = get_model(os.path.join(src_data_fol, src_filename), semantic=True, constraint=True)
else:
box_params, orig_ids, default_param, points, point_labels, points_mat, point_semantic = get_model(os.path.join(src_data_fol, src_filename), semantic=True)
curr_source_dict = {}
curr_source_dict["default_param"] = default_param
curr_source_dict["points"] = points
curr_source_dict["point_labels"] = point_labels
curr_source_dict["points_mat"] = points_mat
curr_source_dict["point_semantic"] = point_semantic
curr_source_dict["model_id"] = source_model
if (USE_CONNECTIVITY):
curr_source_dict["constraint_mat"] = constraint_mat
curr_source_dict["constraint_proj_mat"] = constraint_proj_mat
# Get number of parts of the model
num_parts = len(np.unique(point_labels))
curr_source_dict["num_parts"] = num_parts
curr_num_params = default_param.shape[0]
if (MAX_NUM_PARAMS < curr_num_params):
MAX_NUM_PARAMS = curr_num_params
MAX_NUM_PARTS = int(MAX_NUM_PARAMS/6)
SOURCE_MODEL_INFO.append(curr_source_dict)
# For source semantics also get a list of unique labels
src_semantic = torch.from_numpy(point_semantic)
src_semantic = src_semantic.to(device)
unique_labels = torch.unique(src_semantic)
SOURCE_SEMANTICS.append([src_semantic, unique_labels])
# Create latent code per part for the autodecoder setup
part_latent_codes = torch.autograd.Variable(torch.randn((num_parts,PART_LATENT_DIM), dtype=torch.float, device=device), requires_grad=True)
SOURCE_PART_LATENT_CODES.append(part_latent_codes)
## Create source latent
SOURCE_LATENT_CODES = torch.autograd.Variable(torch.randn((len(sources),SOURCE_LATENT_DIM), dtype=torch.float, device=device), requires_grad=True)
if not SHARE_SRC_LATENT:
RETRIEVAL_SOURCE_LATENT_CODES = torch.autograd.Variable(torch.randn((len(sources),SOURCE_LATENT_DIM), dtype=torch.float, device=device), requires_grad=True)
if (DIST_FUNC == "mahalanobis"):
SOURCE_VARIANCES = torch.autograd.Variable(torch.randn((len(sources),SOURCE_LATENT_DIM), dtype=torch.float, device=device), requires_grad=True)
if (LOSS_FUNC == "regression"):
SOURCE_SIGMAS = torch.autograd.Variable(torch.randn((len(sources),1), dtype=torch.float, device=device), requires_grad=True)
print("Done loading sources.")
print(len(SOURCE_MODEL_INFO))
embedding_size = 6
## Define Networks
## For Deformation
target_encoder = TargetEncoder(
TARGET_LATENT_DIM,
args["input_channels"],
)
target_encoder.to(device, dtype=torch.float)
## For Retrieval
retrieval_encoder = TargetEncoder(
TARGET_LATENT_DIM,
args["input_channels"],
)
retrieval_encoder.to(device, dtype=torch.float)
decoder_input_dim = TARGET_LATENT_DIM + SOURCE_LATENT_DIM + PART_LATENT_DIM
param_decoder = ParamDecoder2(decoder_input_dim, 256, embedding_size, use_bn=USE_BN)
param_decoder.to(device, dtype=torch.float)
## Define loss and optimizer
learning_rate = args["learning_rate"]
n_epochs = args["epochs"]
if FIXED_DEFORMATION:
###Load a model and keep it fixed
fname = os.path.join(MODEL_INIT, "model.pth")
target_encoder.load_state_dict(torch.load(fname)["target_encoder"])
target_encoder.to(device)
target_encoder.eval()
param_decoder.load_state_dict(torch.load(fname)["param_decoder"])
param_decoder.to(device)
param_decoder.eval()
SOURCE_LATENT_CODES = torch.load(fname)["source_latent_codes"]
SOURCE_PART_LATENT_CODES = torch.load(fname)["part_latent_codes"]
for child in target_encoder.children():
if type(child)==nn.BatchNorm1d:
child.track_running_stats = False
elif type(child)==nn.Sequential:
for ii in range(len(child)):
if type(child[ii])==nn.BatchNorm1d:
child[ii].track_running_stats = False
for child in param_decoder.children():
if type(child)==nn.BatchNorm1d:
child.track_running_stats = False
else:
if (INIT_JOINT):
print("Initializing joint model.")
fname = os.path.join(MODEL_INIT, "model.pth")
target_encoder.load_state_dict(torch.load(fname)["target_encoder"])
target_encoder.to(device)
param_decoder.load_state_dict(torch.load(fname)["param_decoder"])
param_decoder.to(device)
if not SHARED_ENCODER:
retrieval_encoder.load_state_dict(torch.load(fname)["retrieval_encoder"])
retrieval_encoder.to(device)
SOURCE_LATENT_CODES = torch.load(fname)["source_latent_codes"]
SOURCE_PART_LATENT_CODES = torch.load(fname)["part_latent_codes"]
if (DIST_FUNC == "mahalanobis"):
SOURCE_VARIANCES = torch.load(fname)["source_variances"]
if not SHARE_SRC_LATENT:
RETRIEVAL_SOURCE_LATENT_CODES = torch.load(fname)["retrieval_source_latent_codes"]
elif (INIT_DEFORMATION):
#Load model
fname = os.path.join(MODEL_INIT, "model.pth")
target_encoder.load_state_dict(torch.load(fname)["target_encoder"])
target_encoder.to(device)
param_decoder.load_state_dict(torch.load(fname)["param_decoder"])
param_decoder.to(device)
SOURCE_LATENT_CODES = torch.load(fname)["source_latent_codes"]
SOURCE_PART_LATENT_CODES = torch.load(fname)["part_latent_codes"]
target_encoder_params = list(target_encoder.parameters())
decoder_params = list(param_decoder.parameters())
all_params = target_encoder_params + decoder_params
## For the deformation
optimizer_deformation = torch.optim.SGD(
all_params,
lr=args["learning_rate"],
momentum=args["momentum"],
weight_decay=args["weight_decay"],
)
optimizer_deformation.add_param_group({"params": SOURCE_LATENT_CODES})
optimizer_deformation.add_param_group({"params": SOURCE_PART_LATENT_CODES})
### Optimizer for retrieval
if not SHARED_ENCODER :
params_embedding = list(retrieval_encoder.parameters())
optimizer_embedding = torch.optim.SGD(
params_embedding,
lr=args["learning_rate"],
momentum=args["momentum"],
weight_decay=args["weight_decay"],
)
if not SHARE_SRC_LATENT:
optimizer_embedding.add_param_group({"params": RETRIEVAL_SOURCE_LATENT_CODES})
elif SHARE_SRC_LATENT and UPDATE_SRC_LATENT:
optimizer_embedding.add_param_group({"params": SOURCE_LATENT_CODES})
if (DIST_FUNC == "mahalanobis"):
optimizer_embedding.add_param_group({"params": SOURCE_VARIANCES})
if (LOSS_FUNC == "regression"):
optimizer_embedding.add_param_group({"params": SOURCE_SIGMAS, "lr": 0.1})
else:
if not SHARE_SRC_LATENT:
optimizer_embedding = torch.optim.SGD(
[RETRIEVAL_SOURCE_LATENT_CODES],
lr=args["learning_rate"],
momentum=args["momentum"],
weight_decay=args["weight_decay"],
)
if (DIST_FUNC == "mahalanobis"):
optimizer_embedding.add_param_group({"params": SOURCE_VARIANCES})
if (LOSS_FUNC == "regression"):
optimizer_embedding.add_param_group({"params": SOURCE_SIGMAS, "lr": 0.1})
elif SHARE_SRC_LATENT and UPDATE_SRC_LATENT:
optimizer_embedding = torch.optim.SGD(
[SOURCE_LATENT_CODES],
lr=args["learning_rate"],
momentum=args["momentum"],
weight_decay=args["weight_decay"],
)
if (DIST_FUNC == "mahalanobis"):
optimizer_embedding.add_param_group({"params": SOURCE_VARIANCES})
if (LOSS_FUNC == "regression"):
optimizer_embedding.add_param_group({"params": SOURCE_SIGMAS, "lr": 0.1})
else:
if (DIST_FUNC == "mahalanobis"):
optimizer_embedding = torch.optim.SGD(
[SOURCE_VARIANCES],
lr=args["learning_rate"],
momentum=args["momentum"],
weight_decay=args["weight_decay"],
)
elif (LOSS_FUNC == "regression"):
optimizer_embedding = torch.optim.SGD(
[SOURCE_SIGMAS],
lr=args["learning_rate"],
momentum=args["momentum"],
weight_decay=args["weight_decay"],
)
def construct_candidates_dict(loader, encoder):
print("Creating candidates dict.")
start_time = time.time()
for i, batch in enumerate(loader):
# target_shapes, target_ids, _, _, _ = batch
target_shapes, target_ids, _, _ = batch
x = [x.to(device, dtype=torch.float) for x in target_shapes]
x = torch.stack(x)
retrieval_latent_codes = encoder(x)
retrieval_latent_codes = retrieval_latent_codes.unsqueeze(0).repeat(len(SOURCE_MODEL_INFO),1,1)
retrieval_latent_codes = retrieval_latent_codes.view(-1, retrieval_latent_codes.shape[-1])
###Get dummy source labels
source_label_shape = torch.zeros(target_shapes.shape[0])
source_labels = source_label_shape.unsqueeze(0).repeat(len(SOURCE_MODEL_INFO),1)
source_labels = source_labels.view(-1)
#Get all labels
source_labels = get_all_source_labels(source_labels, len(SOURCE_MODEL_INFO))
if (NORMALIZE):
retrieval_latent_codes = F.normalize(retrieval_latent_codes)
retrieval_latent_codes = retrieval_latent_codes.view(len(SOURCE_MODEL_INFO), -1, TARGET_LATENT_DIM)
if USE_SRC_ENCODER_RETRIEVAL:
with torch.no_grad():
## Split to two batches for memory
src_latent_codes = []
num_sets = 20
interval = int(len(source_labels)/num_sets)
for j in range(num_sets):
if (j==num_sets-1):
curr_src_latent_codes = get_source_latent_codes_encoder(source_labels[j*interval:], SOURCE_MODEL_INFO, encoder, device=device)
else:
curr_src_latent_codes = get_source_latent_codes_encoder(source_labels[j*interval:(j+1)*interval], SOURCE_MODEL_INFO, encoder, device=device)
src_latent_codes.append(curr_src_latent_codes)
src_latent_codes = torch.cat(src_latent_codes).view(len(SOURCE_MODEL_INFO), -1, SOURCE_LATENT_DIM)
else:
if (SHARE_SRC_LATENT):
src_latent_codes = src_latent_codes.view(len(SOURCE_MODEL_INFO), -1, SOURCE_LATENT_DIM)
else:
src_latent_codes = get_source_latent_codes_fixed(source_labels, RETRIEVAL_SOURCE_LATENT_CODES, device=device)
src_latent_codes = src_latent_codes.view(len(SOURCE_MODEL_INFO), -1, SOURCE_LATENT_DIM)
if (DIST_FUNC == "mahalanobis"):
src_variances = get_source_latent_codes_fixed(source_labels, SOURCE_VARIANCES, device=device)
src_variances = src_variances.view(len(SOURCE_MODEL_INFO), -1, SOURCE_LATENT_DIM)
if (ACTIVATION_FN.lower() == "none"):
distances = compute_mahalanobis(retrieval_latent_codes, src_latent_codes, src_variances, clip_vec=CLIP_VEC)
elif (ACTIVATION_FN == "sigmoid"):
distances = compute_mahalanobis(retrieval_latent_codes, src_latent_codes, src_variances, activation_fn=torch.sigmoid, clip_vec=CLIP_VEC)
elif (ACTIVATION_FN == "relu"):
distances = compute_mahalanobis(retrieval_latent_codes, src_latent_codes, src_variances, activation_fn=torch.relu, clip_vec=CLIP_VEC)
elif (DIST_FUNC == "order"):
if (ACTIVATION_FN.lower() == "none"):
distances = order_embedding_distance(retrieval_latent_codes, src_latent_codes, device=device)
elif (ACTIVATION_FN == "sigmoid"):
distances = order_embedding_distance(retrieval_latent_codes, src_latent_codes, device=device, activation_fn=torch.sigmoid)
elif (ACTIVATION_FN == "relu"):
distances = order_embedding_distance(retrieval_latent_codes, src_latent_codes, device=device, activation_fn=torch.relu)
sorted_distances, sorted_indices = torch.sort(distances, dim=0)
sorted_indices = sorted_indices.to("cpu")
sorted_indices = sorted_indices.detach().numpy().T
distances = distances.to("cpu")
distances = distances.detach().numpy().T
target_ids = target_ids.to("cpu")
target_ids = target_ids.detach().numpy()
# print(sorted_indices)
for j in range(sorted_indices.shape[0]):
DEFORMATION_CANDIDATES[target_ids[j]] = sorted_indices[j]
DEFORMATION_DISTANCES[target_ids[j]] = distances[j]
if (i%20==0):
print("Time elapsed: "+str(time.time()-start_time)+" sec for batch "+str(i)+ "/"+ str(len(loader))+".")
return
def construct_candidates_dict_faster(loader, encoder):
print("Creating candidates dict. Faster version.")
start_time = time.time()
source_labels = np.expand_dims(np.arange(len(SOURCE_MODEL_INFO)), axis=1)
source_labels = np.reshape(source_labels, (-1))
print(source_labels.shape)
##Cache source latent codes
## Source latent codes
if USE_SRC_ENCODER_RETRIEVAL:
with torch.no_grad():
## Split to two batches for memory
src_latent_codes_template = []
num_sets = 20
interval = int(len(SOURCE_MODEL_INFO)/num_sets)
for j in range(num_sets):
if (j==num_sets-1):
curr_src_latent_codes = get_source_latent_codes_encoder(source_labels[j*interval:], SOURCE_MODEL_INFO, retrieval_encoder, device=device)
else:
curr_src_latent_codes = get_source_latent_codes_encoder(source_labels[j*interval:(j+1)*interval], SOURCE_MODEL_INFO, retrieval_encoder, device=device)
src_latent_codes_template.append(curr_src_latent_codes)
src_latent_codes_template = torch.cat(src_latent_codes_template).view(len(SOURCE_MODEL_INFO), -1, SOURCE_LATENT_DIM)
else:
if (SHARE_SRC_LATENT):
print("Error in creating dict with shared latent code.")
else:
src_latent_codes_template = get_source_latent_codes_fixed(source_labels, RETRIEVAL_SOURCE_LATENT_CODES, device=device)
src_latent_codes_template = src_lsrc_latent_codes_templateatent_codes.view(len(SOURCE_MODEL_INFO), -1, SOURCE_LATENT_DIM)
for i, batch in enumerate(loader):
# target_shapes, target_ids, _, _, _ = batch
target_shapes, target_ids, _, _ = batch
x = [x.to(device, dtype=torch.float) for x in target_shapes]
x = torch.stack(x)
retrieval_latent_codes = encoder(x)
retrieval_latent_codes = retrieval_latent_codes.unsqueeze(0).repeat(len(SOURCE_MODEL_INFO),1,1)
retrieval_latent_codes = retrieval_latent_codes.view(-1, retrieval_latent_codes.shape[-1])
###Get dummy source labels
source_label_shape = torch.zeros(target_shapes.shape[0])
source_labels = source_label_shape.unsqueeze(0).repeat(len(SOURCE_MODEL_INFO),1)
source_labels = source_labels.view(-1)
#Get all labels
source_labels = get_all_source_labels(source_labels, len(SOURCE_MODEL_INFO))
if (NORMALIZE):
retrieval_latent_codes = F.normalize(retrieval_latent_codes)
retrieval_latent_codes = retrieval_latent_codes.view(len(SOURCE_MODEL_INFO), -1, TARGET_LATENT_DIM)
##Repeat the src_latent vector tensor
src_latent_codes = src_latent_codes_template.repeat(1, target_shapes.shape[0],1)
if (DIST_FUNC == "mahalanobis"):
src_variances = get_source_latent_codes_fixed(source_labels, SOURCE_VARIANCES, device=device)
src_variances = src_variances.view(len(SOURCE_MODEL_INFO), -1, SOURCE_LATENT_DIM)
if (ACTIVATION_FN.lower() == "none"):
distances = compute_mahalanobis(retrieval_latent_codes, src_latent_codes, src_variances, clip_vec=CLIP_VEC)
elif (ACTIVATION_FN == "sigmoid"):
distances = compute_mahalanobis(retrieval_latent_codes, src_latent_codes, src_variances, activation_fn=torch.sigmoid, clip_vec=CLIP_VEC)
elif (ACTIVATION_FN == "relu"):
distances = compute_mahalanobis(retrieval_latent_codes, src_latent_codes, src_variances, activation_fn=torch.relu, clip_vec=CLIP_VEC)
elif (DIST_FUNC == "order"):
if (ACTIVATION_FN.lower() == "none"):
distances = order_embedding_distance(retrieval_latent_codes, src_latent_codes, device=device)
elif (ACTIVATION_FN == "sigmoid"):
distances = order_embedding_distance(retrieval_latent_codes, src_latent_codes, device=device, activation_fn=torch.sigmoid)
elif (ACTIVATION_FN == "relu"):
distances = order_embedding_distance(retrieval_latent_codes, src_latent_codes, device=device, activation_fn=torch.relu)
sorted_distances, sorted_indices = torch.sort(distances, dim=0)
sorted_indices = sorted_indices.to("cpu")
sorted_indices = sorted_indices.detach().numpy().T
distances = distances.to("cpu")
distances = distances.detach().numpy().T
target_ids = target_ids.to("cpu")
target_ids = target_ids.detach().numpy()
# print(sorted_indices)
for j in range(sorted_indices.shape[0]):
DEFORMATION_CANDIDATES[target_ids[j]] = sorted_indices[j]
DEFORMATION_DISTANCES[target_ids[j]] = distances[j]
if (i%20==0):
print("Time elapsed: "+str(time.time()-start_time)+" sec for batch "+str(i)+ "/"+ str(len(loader))+".")
return
def construct_deformation_candidates_dict(loader, encoder):
print("Creating deformation candidates dict.")
start_time = time.time()
for i, batch in enumerate(loader):
# target_shapes, target_ids, _, _, _ = batch
target_shapes, target_ids, _, _ = batch
x = [x.to(device, dtype=torch.float) for x in target_shapes]
x = torch.stack(x)
target_latent_codes = encoder(x)
target_latent_codes = target_latent_codes.unsqueeze(0).repeat(len(SOURCE_MODEL_INFO),1,1)
source_label_shape = torch.zeros(target_shapes.shape[0])
source_labels = source_label_shape.unsqueeze(0).repeat(len(SOURCE_MODEL_INFO),1)
## Reshape to (K*batch_size, ...) to feed into the network
## Source assignments have to be done accordingly
target_latent_codes = target_latent_codes.view(-1, target_latent_codes.shape[-1])
source_labels = source_labels.view(-1)
#Get all labels
source_labels = get_all_source_labels(source_labels, len(SOURCE_MODEL_INFO))
##Also overwrite x for chamfer distance
x_repeated = x.unsqueeze(0).repeat(source_labels.shape[0],1,1,1)
x_repeated = x_repeated.view(-1, x_repeated.shape[-2], x_repeated.shape[-1])
num_sub_epochs = int(source_labels.shape[0]/batch_size) + 1
all_cd_loss = []
for j in range(num_sub_epochs):
curr_source_labels = source_labels[j*batch_size:(j+1)*batch_size]
num_samples = curr_source_labels.shape[0]
if (num_samples <=0):
break
curr_target_latent_codes = target_latent_codes[j*batch_size:(j+1)*batch_size, :]
curr_x_repeated = x_repeated[j*batch_size:(j+1)*batch_size, :, :]
# #Debug
# print(curr_source_labels.shape)
# print(curr_target_latent_codes.shape)
# print(curr_x_repeated.shape)
# exit()
###Set up source A matrices and default params based on source_labels of the target
src_mats, src_default_params, src_connectivity_mat = get_source_info(curr_source_labels, SOURCE_MODEL_INFO, MAX_NUM_PARAMS, use_connectivity= USE_CONNECTIVITY)
###Set up source latent codes based on source_labels of the target
src_latent_codes = get_source_latent_codes_fixed(curr_source_labels, SOURCE_LATENT_CODES, device=device)
mat = [mat.to(device, dtype=torch.float) for mat in src_mats]
def_param = [def_param.to(device, dtype=torch.float) for def_param in src_default_params]
mat = torch.stack(mat)
def_param = torch.stack(def_param)
## If using connectivity
if (USE_CONNECTIVITY):
conn_mat = [conn_mat.to(device, dtype=torch.float) for conn_mat in src_connectivity_mat]
conn_mat = torch.stack(conn_mat)
concat_latent_code = torch.cat((src_latent_codes, curr_target_latent_codes), dim=1)
##Param Decoder per part
# Make the part latent codes of each source into a (K x PART_LATENT_DIM) tensor
all_params = []
for k in range(concat_latent_code.shape[0]):
curr_num_parts = SOURCE_MODEL_INFO[curr_source_labels[k]]["num_parts"]
curr_code = concat_latent_code[k]
curr_code_repeated = curr_code.view(1,curr_code.shape[0]).repeat(curr_num_parts, 1)
part_latent_codes = SOURCE_PART_LATENT_CODES[curr_source_labels[k]]
full_latent_code = torch.cat((curr_code_repeated, part_latent_codes), dim=1)
params = param_decoder(full_latent_code, use_bn=USE_BN)
## Pad with extra zero rows to cater to max number of parameters
if (curr_num_parts < MAX_NUM_PARTS):
dummy_params = torch.zeros((MAX_NUM_PARTS-curr_num_parts, embedding_size), dtype=torch.float, device=device)
params = torch.cat((params, dummy_params), dim=0)
params = params.view(-1, 1)
all_params.append(params)
params = torch.stack(all_params)
if (USE_CONNECTIVITY):
output_pcs = get_shape(mat, params, def_param, ALPHA, connectivity_mat=conn_mat)
else:
output_pcs = get_shape(mat, params, def_param, ALPHA)
cd_loss, _ = chamfer_distance(output_pcs, curr_x_repeated, batch_reduction=None)
cd_loss = cd_loss.to("cpu").detach().numpy()
all_cd_loss.append(cd_loss)
all_cd_loss = np.array(all_cd_loss)
all_cd_loss = np.reshape(all_cd_loss, (target_shapes.shape[0], -1))
distances = all_cd_loss
sorted_indices = np.argsort(distances, axis=1)
# sorted_distances, sorted_indices = torch.sort(distances, dim=0)
# sorted_indices = sorted_indices.to("cpu")
# sorted_indices = sorted_indices.detach().numpy().T
# distances = distances.to("cpu")
# distances = distances.detach().numpy().T
target_ids = target_ids.to("cpu")
target_ids = target_ids.detach().numpy()
# print(sorted_indices)
for j in range(sorted_indices.shape[0]):
DEFORMATION_CANDIDATES[target_ids[j]] = sorted_indices[j]
DEFORMATION_DISTANCES[target_ids[j]] = distances[j]
if (i%20==0):
print("Time elapsed: "+str(time.time()-start_time)+" sec for batch "+str(i)+ "/"+ str(len(loader))+".")
return
def get_dict_candidates(target_ids, K):
target_ids = target_ids.to("cpu")
target_ids = target_ids.detach().numpy()
all_selected = []
all_weights = []
for target_id in target_ids:
ranked_candidates = np.array(DEFORMATION_CANDIDATES[target_id])
distances_candidates = np.array(DEFORMATION_DISTANCES[target_id])
##softmax of distances
# distances_candidates = distances_candidates/np.min(distances_candidates)
if (SELECTION == "retrieval_candidates"):
distances_candidates = distances_candidates/100.0
distances_normalized = np.exp(-(distances_candidates - np.max(distances_candidates)))
probs = distances_normalized / distances_normalized.sum()
selected_candidates = np.random.choice(np.arange(len(SOURCE_MODEL_INFO)), size=K, replace=False, p= probs)
all_selected.append(selected_candidates)
weights = probs[selected_candidates]/np.sum(probs[selected_candidates])
all_weights.append(weights)
all_selected = np.array(all_selected).T
all_selected = all_selected.flatten()
all_weights = np.array(all_weights).T
print(all_weights.T[0])
print()
all_weights = all_weights.flatten()
return all_selected, all_weights
if (TO_TRAIN):
if not FIXED_DEFORMATION:
target_encoder.train()
param_decoder.train()
else:
target_encoder.eval()
param_decoder.eval()
if not SHARED_ENCODER:
retrieval_encoder.train()
best_loss = np.Inf
## Training loop
for epoch in range(n_epochs):
start = datetime.datetime.now()
scalars = defaultdict(list)
for i, batch in enumerate(loader):
'''
Per batch output:
self.target_points[index], self.target_ids[index], self.target_labels[index], self.target_semantics[index], \
self.corres_source_label[index]
'''
# target_shapes, target_ids, _, semantics, source_label_shape = batch
target_shapes, target_ids, _, semantics = batch
source_label_shape = torch.zeros(target_shapes.shape[0])
if COMPLEMENTME:
target_shapes[:,:,2] = -target_shapes[:,:,2]
x = [x.to(device, dtype=torch.float) for x in target_shapes]
x = torch.stack(x)
##Target Encoder
target_latent_codes = target_encoder(x)
## K is the number of sources selected for each target
## Repeat each K times
target_latent_codes = target_latent_codes.unsqueeze(0).repeat(K,1,1)
semantics = semantics.unsqueeze(0).repeat(K,1,1)
source_labels = source_label_shape.unsqueeze(0).repeat(K,1)
## Reshape to (K*batch_size, ...) to feed into the network
## Source assignments have to be done accordingly
target_latent_codes = target_latent_codes.view(-1, target_latent_codes.shape[-1])
semantics = semantics.view(-1, semantics.shape[-1])
source_labels = source_labels.view(-1)
## Get the source assignments
## For now, we get all sources 1,2,...,K
if (SELECTION == "exhaustive"):
source_labels = get_all_source_labels(source_labels, len(SOURCE_MODEL_INFO))
elif (SELECTION == "random"):
source_labels = get_random_labels(source_labels, len(SOURCE_MODEL_INFO))
elif (SELECTION == "retrieval_candidates"):
#after epochs where retrieval module is trained
if (epoch>10):
# if (epoch>30):
# if (1):
to_select_from_dict=1
else:
to_select_from_dict=0
if (to_select_from_dict):
##Dict has not been constructed
if len(DEFORMATION_CANDIDATES.keys())==0:
if SHARED_ENCODER:
target_encoder.eval()
construct_candidates_dict_faster(loader, target_encoder)
target_encoder.train()
else:
retrieval_encoder.eval()
construct_candidates_dict_faster(loader, retrieval_encoder)
retrieval_encoder.train()
source_labels, weights = get_dict_candidates(target_ids, K)
#get random
else:
source_labels = get_random_labels(source_labels, len(SOURCE_MODEL_INFO))
elif (SELECTION == "deformation_candidates"):
to_select_from_dict=1
if len(DEFORMATION_CANDIDATES.keys())==0:
target_encoder.eval()
construct_deformation_candidates_dict(loader, target_encoder)
target_encoder.train()
source_labels, weights = get_dict_candidates(target_ids, K)
##Also overwrite x for chamfer distance
x_repeat = x.unsqueeze(0).repeat(K,1,1,1)
x_repeat = x_repeat.view(-1, x_repeat.shape[-2], x_repeat.shape[-1])
###Set up source A matrices and default params based on source_labels of the target
src_mats, src_default_params, src_connectivity_mat = get_source_info(source_labels, SOURCE_MODEL_INFO, MAX_NUM_PARAMS, use_connectivity= USE_CONNECTIVITY)
# src_mats, src_default_params = get_source_info(source_labels, SOURCE_MODEL_INFO, MAX_NUM_PARAMS)
if USE_SRC_ENCODER:
##Use the encoder to get the source latent code
src_latent_codes = get_source_latent_codes_encoder(source_labels, SOURCE_MODEL_INFO, target_encoder, device=device)
else:
## Autodecoded: Set up source latent codes based on source_labels of the target
src_latent_codes = get_source_latent_codes_fixed(source_labels, SOURCE_LATENT_CODES, device=device)
mat = [mat.to(device, dtype=torch.float) for mat in src_mats]
def_param = [def_param.to(device, dtype=torch.float) for def_param in src_default_params]
mat = torch.stack(mat)
def_param = torch.stack(def_param)
## If using connectivity
if (USE_CONNECTIVITY):
conn_mat = [conn_mat.to(device, dtype=torch.float) for conn_mat in src_connectivity_mat]
conn_mat = torch.stack(conn_mat)
concat_latent_code = torch.cat((src_latent_codes, target_latent_codes), dim=1)
##Param Decoder per part
all_params = []
for j in range(concat_latent_code.shape[0]):
curr_num_parts = SOURCE_MODEL_INFO[source_labels[j]]["num_parts"]
curr_code = concat_latent_code[j]
curr_code_repeated = curr_code.view(1,curr_code.shape[0]).repeat(curr_num_parts, 1)
part_latent_codes = SOURCE_PART_LATENT_CODES[source_labels[j]]
full_latent_code = torch.cat((curr_code_repeated, part_latent_codes), dim=1)
params = param_decoder(full_latent_code, use_bn=USE_BN)
## Pad with extra zero rows to cater to max number of parameters
if (curr_num_parts < MAX_NUM_PARTS):
dummy_params = torch.zeros((MAX_NUM_PARTS-curr_num_parts, embedding_size), dtype=torch.float, device=device)
params = torch.cat((params, dummy_params), dim=0)
params = params.view(-1, 1)
all_params.append(params)
params = torch.stack(all_params)
### Bool to update fitting network
to_update_fitting = (not FIXED_DEFORMATION) and ((INIT_DEFORMATION and i%2==0) or ((not INIT_DEFORMATION) and (epoch<20 or i%2==0)))
if (USE_ICP_PP) and to_update_fitting:
## Output shape with ICP
'''
Direct fitting via optimization
'''
param_init = get_param_init(params, def_param, ALPHA)
# param_init = torch.tensor(param_init)
param_init = param_init.clone().detach().to(device)
param_init = param_init.view(param_init.shape[0], param_init.shape[2], param_init.shape[1])
param_init.requires_grad_(True)
# icp_learning_rate = 0.05
icp_learning_rate = 0.5
# icp_n_iters = 5000
icp_n_iters = 2000
optimizer = torch.optim.SGD([param_init], lr=icp_learning_rate)
# To check for convergence