Datasets:

ArtifactAI

/

arxiv_python_research_code

like 16

kge_ecotox_regression

kge_ecotox_regression-main/main.py

""" TODO: - Train embedding model. - Apply embeddings to data. - Encode data. - Train,valid,test model """ from autoencoder import create_auto_encoder from model import create_model, CorrelelatedFeatures, ApproxKerasSVM, coeff_determination import numpy as np import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV from sklearn.preprocessing import OneHotEncoder from tensorflow.keras.callbacks import EarlyStopping from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import KFold from random import shuffle from collections import defaultdict import tensorflow as tf from sklearn.svm import SVR from sklearn.neural_network import MLPRegressor from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression, LinearRegression, HuberRegressor, BayesianRidge from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, VotingRegressor, BaggingRegressor, ExtraTreesRegressor, GradientBoostingRegressor from sklearn.compose import TransformedTargetRegressor from sklearn.preprocessing import QuantileTransformer, RobustScaler from sklearn.tree import DecisionTreeRegressor from itertools import product from random import choice, choices from sklearn.pipeline import Pipeline from tqdm import tqdm from matplotlib import pyplot as plt from sklearn.decomposition import PCA,FastICA from sklearn.cluster import FeatureAgglomeration from sklearn.feature_selection import RFE from sklearn.metrics import r2_score from sklearn.isotonic import IsotonicRegression from sklearn.feature_selection import VarianceThreshold from sklearn.dummy import DummyRegressor from sklearn.experimental import enable_hist_gradient_boosting # noqa from sklearn.ensemble import HistGradientBoostingRegressor from sklearn.neighbors import KNeighborsRegressor from sklearn.model_selection import cross_val_score, LeaveOneOut MAX_ENCODER_EPOCHS = 1000 MAX_EPOCHS = 1000 EPSILON = 1e-10 MODEL = 'ComplEx' hidden_dim = (128,) SEED = 42 np.random.seed(SEED) import tensorflow as tf tf.get_logger().setLevel('ERROR') import warnings warnings.filterwarnings('ignore') def load_fingerprints(filename): df = pd.read_csv(filename,index_col='chemical') l = len(df.iloc[0]['fingerprint']) out = {} for c in df.index: fp = df.loc[c]['fingerprint'] v = [int(f) for f in fp] out[c] = np.asarray(v) return out def load_features(filename): df = pd.read_csv(filename,index_col='chemical') df = df.dropna() columns = df.columns out = {} for c in df.index: v = [df.loc[c][col] for col in columns] out[c] = np.asarray(v) return out def load_one_hot(entities): all_entities = list(set(entities)) out = {} for e in entities: v = np.zeros((len(all_entities),)) v[all_entities.index(e)] = 1 out[e] = np.asarray(v) return out def load_embeddings(filename,filename_ids): df = np.load(filename) ids = dict(np.load(filename_ids)) return {k:df[int(ids[k])] for k in ids} def load_data(filename,filter_chemicals=None, filter_species=None): df = pd.read_csv(filename) X,y = [],[] if filter_chemicals: to_drop = set(df.chemical) - filter_chemicals for c in to_drop: df = df.drop(df[df.chemical == c].index) if filter_species: to_drop = set(df.species) - filter_species for s in to_drop: df = df.drop(df[df.species == s].index) df = df.drop(df[df.study_duration > 24*14].index) df = df.groupby(['chemical','species'],as_index=False).mean() X = list(zip(df['chemical'],df['species'])) y = np.log(df.concentration+EPSILON) tmp = np.asarray(df.study_duration).reshape((-1,1)) mms = StandardScaler() tmp = mms.fit_transform(tmp) experimental_features = dict(zip(X,tmp.reshape(-1,1))) y = np.asarray(y).reshape((-1,1)) #y = MinMaxScaler().fit_transform(y) return X, y, experimental_features def data_split(X,Y,restrictions=None,method = 1, variant = 1, prop=0.33): """ C_x - chemical set S_x - species set t,v - training,validation 1. C_t \cap C_v == Ø and S_t \cap S_v != Ø, 2. C_t \cap C_v == Ø and S_t \cap S_v == Ø, 3. C_t \cap C_v != Ø and S_t \cap S_v != Ø, 4. C_t \cap C_v != Ø and S_t \cap S_v == Ø, Variants where C_t \cap C_v != Ø (same for S_x): 1. C_t == C_v 2. |C_t \cap C_v| < |C_t \cup C_v| Restrictions: Retriction of a set. eg. s_1 \in S_v and |S_v|=1, {'S_v':{'content:[s_1],'max_len',1}} """ C_t,C_v,S_t,S_v=map(set,[[]]*4) restrictions = {**{'C_t':{},'C_v':{},'S_t':{},'S_v':{}},**restrictions} def filter_restrictions(C_t,C_v,S_t,S_v): for _set,_inv_set,k in zip([C_t,C_v,S_t,S_v],[C_v,C_t,S_v,S_t],['C_t','C_v','S_t','S_v']): if k in restrictions: if 'content' in restrictions[k]: _set |= restrictions[k]['content'] if 'not content' in restrictions[k]: _set -= restrictions[k]['not content'] if 'max_len' in restrictions[k]: while restrictions[k]['max_len'] < len(_set): entity = choice(list(_set)) if not ('content' in restrictions[k] and entity in restrictions[k]['content']): _set.remove(entity) return C_t,C_v,S_t,S_v def check_restrictions(C_t,C_v,S_t,S_v): for _set,k,inv_k in zip([C_t,C_v,S_t,S_v],['C_t','C_v','S_t','S_v'],['C_v','C_t','S_v','S_t']): if k in restrictions: if 'content' in restrictions[k] and 'not content' in restrictions[k]: try: assert len(restrictions[k]['content'].intersection(restrictions[k]['not content'])) < 1 except AssertionError: raise AssertionError('Set %s content conflict.' % k) if 'content' in restrictions[k] and 'max_len' in restrictions[k]: try: assert len(restrictions[k]['content']) <= restrictions[k]['max_len'] except AssertionError: raise AssertionError('Set %s content is longer than max length' % k) if ((method == 1 and 'C' in k) or (method == 4 and 'S' in k) or method == 2) and 'content' in restrictions[inv_k]: try: assert restrictions[k]['content'].intersection(restrictions[inv_k]['content']) == set() except AssertionError: raise AssertionError('Intersection in %s content is not allowed in method %s.' % ('chemical' if method==1 else 'species',str(method))) if method == 3 and 'content' in restrictions[inv_k]: try: assert restrictions[k]['content'].intersection(restrictions[inv_k]['content']) == set() except AssertionError: raise AssertionError('Intersection in set content is not allowed in method 3.') C,S = map(set,zip(*X)) if method == 1: C_t,C_v = train_test_split(list(C),test_size=prop) if variant == 1: S_t,S_v = S, S else: S_t = choices(list(S),k=int((1-prop)*len(S))) S_v = choices(list(S),k=int(prop*len(S))) if method == 2: S_t,S_v = train_test_split(list(S),test_size=prop) C_t,C_v = train_test_split(list(C),test_size=prop) if method == 3: X_t, X_v = train_test_split(X,test_size=prop) C_t,S_t = map(set,zip(*X_t)) C_v,S_v = map(set,zip(*X_v)) if method == 4: S_t,S_v = train_test_split(list(S),test_size=prop) if variant == 1: C_t,C_v = C, C else: C_t = choices(list(C),k=int((1-prop)*len(C))) C_v = choices(list(C),k=int(prop*len(C))) C_t,C_v,S_t,S_v = map(set,[C_t,C_v,S_t,S_v]) C_t,C_v,S_t,S_v = filter_restrictions(C_t,C_v,S_t,S_v) if method == 1: C_t -= C_v if method == 2: C_t -= C_v S_t -= S_v if method == 4: S_t -= S_v if method == 1: assert C_t.intersection(C_v) == set() if variant == 1: S_t = S_v assert S_t == S_v else: assert len(S_t.intersection(S_v)) < len(S_t.union(S_v)) if method == 2: assert C_t.intersection(C_v) == set() and S_t.intersection(S_v) == set() if method == 3: assert len(C_t.intersection(C_v)) > 0 and len(S_t.intersection(S_v)) > 0 if method == 4: assert S_t.intersection(S_v) == set() if variant == 1: C_t = C_v assert C_t == C_v else: assert len(C_t.intersection(C_v)) < len(C_t.union(C_v)) check_restrictions(C_t,C_v,S_t,S_v) Xtr = [] Xte = [] ytr = [] yte = [] for x,y in zip(X,Y): c,s = x if c in C_t and s in S_t: Xtr.append(x) ytr.append(y) if c in C_v and s in S_v: Xte.append(x) yte.append(y) return Xtr,Xte,ytr,yte class FilterFingerprints: def __init__(self): pass def fit(self,X): idx = [] for i,a in enumerate(X.T): if len(np.unique(a)) > 1: idx.append(i) self.idx = idx def transform(self,X): if len(X.shape) > 1: return X[:,self.idx] else: return X[self.idx] def fit_transform(self,X): self.fit(X) return self.transform(X) def compile_model(model): model.compile(optimizer='adagrad',loss='log_cosh',metrics=['mae','mse',R2(name='r2')]) import math def lcm(a, b): return abs(a*b) // math.gcd(a, b) def combine(Xs): n = map(len,Xs) l = max(*map(lambda x: lcm(len(x[0]),len(x[1])),product(Xs,Xs))) r = [l//a for a in n] tmp = [] for X,a in zip(Xs,r): tmp.append(np.repeat(X,a,axis=0)) return np.concatenate(tmp,axis=1) def list_duplicates(seq): tally = defaultdict(list) for i,item in enumerate(seq): tally[item].append(i) return ((key,locs) for key,locs in tally.items() if len(locs)>1) def run_model(C_t,C_v,S_t,S_v,y, experimental_features, fingerprints, chemical_embedding, species_embedding, chemical_features, merge_species=False): """ Take four classes of chemicals, two pairs of siblings, test these on one-two species, combine siblings, combine cusins, see performance drop. Repeat on species side. Repeat with embeddings for chemicals and species and see the same performance on lower levels, but imporved over baseline on higher levels. """ """ 5-fold validation + 1-fold test set """ keys = set(y.keys()) keys_t = keys.intersection(set(product(C_t,S_t))) keys_v = keys.intersection(set(product(C_v,S_v))) ytr,yte = map(lambda x:np.asarray([y[i] for i in x]),[keys_t,keys_v]) if len(yte) < 1 or len(ytr) < 1: return None,None,None fingerprints_train,fingerprints_test = map(lambda x:np.asarray([fingerprints[i] for i,_ in x]),[keys_t,keys_v]) chemical_embedding_train,chemical_embedding_test = map(lambda x:np.asarray([chemical_embedding[i] for i,_ in x]),[keys_t,keys_v]) chemical_features_train,chemical_features_test = map(lambda x:np.asarray([chemical_features[i] for i,_ in x]),[keys_t,keys_v]) species_embedding_train,species_embedding_test = map(lambda x:np.asarray([species_embedding[i] for _,i in x]),[keys_t,keys_v]) experimental_features_train,experimental_features_test = map(lambda x:np.asarray([experimental_features[i] for i in x]),[keys_t,keys_v]) species_one_hot_encoder = OneHotEncoder(sparse=False) sp_t = set(list(zip(*keys_t))[1]) sp_v = set(list(zip(*keys_v))[1]) sp = np.asarray(list(sp_t|sp_v)).reshape((-1,1)) species_one_hot_encoder.fit(sp) species_one_hot_train,species_one_hot_test = map(lambda x:species_one_hot_encoder.transform(np.asarray(list(zip(*x))[1]).reshape((-1,1))),[keys_t,keys_v]) if merge_species: for array in [species_embedding_train,species_one_hot_train,ytr]: for elem,loc in list_duplicates([c for c,_ in keys_t]): #i.e. mean where c is the same array[loc] = np.mean(array[loc]) for array in [species_embedding_test,species_one_hot_test,yte]: for elem,loc in list_duplicates([c for c,_ in keys_v]): array[loc] = np.mean(array[loc]) n_tr = ytr.shape[1] n_te = yte.shape[1] train_1 = combine([fingerprints_train,chemical_features_train,species_one_hot_train,experimental_features_train,ytr]) train_2 = combine([fingerprints_train,chemical_features_train,species_embedding_train,chemical_embedding_train,experimental_features_train,ytr]) test_1 = combine([fingerprints_test,chemical_features_test,species_one_hot_test,experimental_features_test,yte]) test_2 = combine([fingerprints_test,chemical_features_test,species_embedding_test,chemical_embedding_test,experimental_features_test,yte]) Xtr_1,ytr = train_1[:,:-n_tr],train_1[:,-n_tr:] Xtr_2,ytr = train_2[:,:-n_tr],train_2[:,-n_tr:] Xte_1,yte = test_1[:,:-n_te],test_1[:,-n_te:] Xte_2,yte = test_2[:,:-n_te],test_2[:,-n_te:] res1 = np.zeros(yte.ravel().shape) res2 = np.zeros(yte.ravel().shape) params = {'n_neighbors':[2,5,10,25,50,100], 'weights':['uniform','distance']} n = min(len(ytr),5) FOLDS = 10 for Xtr,Xte,res in zip([Xtr_1,Xtr_2],[Xte_1,Xte_2],[res1,res2]): for _ in range(FOLDS): regr = AdaBoostRegressor(n_estimators=10,loss='square') regr.fit(Xtr,ytr.ravel()) res += regr.predict(Xte)/FOLDS return res1,res2,yte from SPARQLWrapper import SPARQLWrapper, JSON sparql = SPARQLWrapper("https://query.wikidata.org/sparql") sparql.setReturnFormat(JSON) def get_species_name(ncbi_id): q = """ select ?label where { ?s wdt:P685 "%s" ; wdt:P225 ?label . } """ % ncbi_id sparql.setQuery(q) try: results = sparql.query().convert() for result in results["results"]["bindings"]: out = result["label"]["value"] return out except: return ncbi_id def encode_fingerprints(fingerprints_all): fingerprint_encoder, fingerprint_ae = create_auto_encoder(input_size=len(fingerprints_all[0]),dense_layers=(128,),noise=0.1) fingerprint_ae.compile(optimizer='adagrad',loss='binary_crossentropy') fingerprint_ae.fit(fingerprints_all,fingerprints_all, epochs=MAX_ENCODER_EPOCHS, callbacks=[EarlyStopping('loss',min_delta=1e-5)], verbose=0) return fingerprint_encoder.predict(fingerprints_all) from sklearn.cluster import KMeans # function returns WSS score for k values from 1 to kmax def calculate_WSS(points, kmax): sse = [] for k in range(1, kmax+1): kmeans = KMeans(n_clusters = k).fit(points) centroids = kmeans.cluster_centers_ pred_clusters = kmeans.predict(points) curr_sse = 0 # calculate square of Euclidean distance of each point from its cluster center and add to current WSS for i in range(len(points)): curr_center = centroids[pred_clusters[i]] curr_sse += (points[i, 0] - curr_center[0]) ** 2 + (points[i, 1] - curr_center[1]) ** 2 sse.append(curr_sse) return sse def define_chemical_clusters(fingerprints,k=15,use_pca=True): if not isinstance(fingerprints,list): fingerprints = [fingerprints] keys = set.intersection(*[set(f.keys()) for f in fingerprints]) array = np.concatenate([np.asarray([v[k] for k in keys]) for v in fingerprints],axis=1) if use_pca: array = PCA(2).fit_transform(array) if k < 0: sse = calculate_WSS(array,25) k = np.argmin(sse) + 1 plt.plot(sse) plt.show() clusters = defaultdict(set) kmeans = KMeans(n_clusters = k).fit(array) cp = kmeans.predict(array) for k,v in zip(keys,cp): clusters[v].add(k) return clusters, kmeans.cluster_centers_ def merge_closest(clusters,cluster_centers,ord=2): dist = {} for i,cc1 in enumerate(cluster_centers): for j,cc2 in enumerate(cluster_centers): if i == j: continue dist[(i,j)] = np.linalg.norm(cc1-cc2,ord=ord) if len(dist) > 1: merge,_ = sorted(dist.items(),key=lambda x:x[1])[0] else: merge = (i,j) k1,k2 = merge cluster_centers[k1] = np.mean([cluster_centers[k1],cluster_centers[k2]],axis=0) cluster_centers = np.delete(cluster_centers,k2,axis=0) clusters[k1] |= clusters[k2] clusters.pop(k2,None) return clusters, cluster_centers def filter_data(X,Y,C_t,C_v,S_t,S_v): Xtr,Xte,ytr,yte = [],[],[],[] for x,y in zip(X,Y): c,s = x if c in C_t and s in S_t: Xtr.append(x) ytr.append(y) if c in C_v and s in S_v: Xte.append(x) yte.append(y) return Xtr,Xte,ytr,yte import sys # insert at 1, 0 is the script path (or '' in REPL) sys.path.insert(1, '/media/erik/Mass/Dropbox/NIVA_GITLAB/pySMIfp') from smiles_fingerprints import smiles_fingerprint def load_smiles_fingerprints(): q = """ select ?chembl ?smiles where { ?c wdt:P233 ?smiles ; wdt:P592 ?chembl . } """ converter = {} sparql.setQuery(q) results = sparql.query().convert() for result in results["results"]["bindings"]: ch = result["chembl"]["value"] smi = result['smiles']['value'] smifp = smiles_fingerprint(smi) converter['http://rdf.ebi.ac.uk/resource/chembl/molecule/'+ch] = smifp return converter def save_smiles_fingerprints(fp,filename='data/smiles_fingerprints.csv'): a = {} for i in range(len(smiles_fingerprint('C'))): a['sig%s'%str(i)] = [array[i] for _,array in fp.items()] df = pd.DataFrame(data={'chemical':list(fp.keys()),**a}) df.to_csv(filename) def read_smiles_fingerprints(filename): df = pd.read_csv(filename) cols = [c for c in df.columns if 'sig' in c] chemicals = df['chemical'].values arrays = df[cols].values return dict(zip(chemicals,np.asarray(arrays))) def chemical_similarities(fingerprints): keys = fingerprints.keys() array = np.asarray([i for k,i in fingerprints.items()]) sim = [] for a in array: v = a @ array.T w = np.sum(a) + np.sum(array,axis=1) sim_score = 2*v/w sim.append(sim_score) return {k:s for k,s in zip(keys,sim)} def main(): """ organic = obo['CHEBI_50860'] inorganic = obo['CHEBI_24835'] """ model = 'ComplEx' g1_parts = [[0],[0,1],[0,1,2]] g2_parts = [[0],[0,1]] p = list(product(g1_parts,g2_parts)) p += [p[-1]] ul = (False,False) f1,f2=[],[] for g1p,g2p,in p: for lit,gp,fs,name in zip([*ul],[g1p,g2p],[f1,f2],['_chemical_','_taxonomy_']): fs.append(model+name+str(hash((lit,*gp)))) if (g1p,g2p) == p[-1]: ul = (True,True) organic_chemicals = set() inorganic_chemicals = set() salts = set() for i in range(1,10): df = pd.read_csv('./data/chemical_group_%s.csv' % str(i),index_col='parent') try: organic_chemicals |= set(df.loc['http://purl.obolibrary.org/obo/CHEBI_50860','children'].split(',')) except: pass try: inorganic_chemicals |= set(df.loc['http://purl.obolibrary.org/obo/CHEBI_24835','children'].split(',')) except: pass try: salts |= set(df.loc['http://purl.obolibrary.org/obo/CHEBI_24866','children'].split(',')) except: pass print('Num organic chemicals',len(organic_chemicals)) print('Num inorganic chemicals',len(inorganic_chemicals)) print('Num salts',len(salts)) C = organic_chemicals try: smiles_fingerprints = read_smiles_fingerprints('./data/smiles_fingerprints.csv') except FileNotFoundError: smiles_fingerprints = load_smiles_fingerprints() save_smiles_fingerprints(smiles_fingerprints,'./data/smiles_fingerprints.csv') mms = MinMaxScaler().fit_transform(np.asarray([smiles_fingerprints[k] for k in smiles_fingerprints])) smiles_fingerprints = dict(zip(smiles_fingerprints,mms)) X,Y,experimental_features = load_data('./data/experiments.csv',filter_chemicals=None, filter_species=None) pubchem_fingerprints = load_fingerprints('./data/chemicals_fingerprints.csv') Y = {k:y for k,y in zip(X,Y)} pubchem_fingerprints = chemical_similarities(pubchem_fingerprints) chemical_embedding = load_embeddings('./data/embeddings/%s_entity_embeddings.npy' % f1[0], './data/embeddings/%s_entity_ids.npy' % f1[0]) species_embedding = load_embeddings('./data/embeddings/%s_entity_embeddings.npy' % f2[0], './data/embeddings/%s_entity_ids.npy' % f2[0]) chemical_features = load_features('./data/chemicals_features.csv') chemical_features = dict(zip(chemical_features,MinMaxScaler().fit_transform(np.asarray([chemical_features[k] for k in chemical_features])))) for cf in [QuantileTransformer(n_quantiles=100,output_distribution='normal')]: chemical_embedding = dict(zip(chemical_embedding,cf.fit_transform(np.asarray([chemical_embedding[k] for k in chemical_embedding])))) for cf in [QuantileTransformer(n_quantiles=100,output_distribution='normal')]: species_embedding = dict(zip(species_embedding,cf.fit_transform(np.asarray([species_embedding[k] for k in species_embedding])))) species_divisions = defaultdict(set) for k in range(1,2): df = pd.read_csv('./data/species_groups_%s.csv' % str(k), index_col='parent') for s in df.index: species_divisions[s] |= set(df.loc[s,'children'].split(',')) species_divisions = dict(filter(lambda x:len(x[1])>5,species_divisions.items())) #for k in species_divisions: #print(get_species_name(k.split('/')[-1])) #species_divisions = defaultdict(set) #df = pd.read_csv('./data/species_divisions.csv', index_col='parent') #for s in df.index: #species_divisions[s] |= set(df.loc[s,'children'].split(',')) C = set.intersection(*map(lambda k:set(k.keys()),[smiles_fingerprints,pubchem_fingerprints,chemical_features,chemical_embedding])) for d in [smiles_fingerprints,pubchem_fingerprints,chemical_embedding,chemical_features]: for c in set(d.keys()): if not c in C: d.pop(c,None) n = 7 clusters, cluster_centers = define_chemical_clusters([smiles_fingerprints],k=max(-1,n),use_pca=False) print(*map(lambda x:len(x[1]),clusters.items())) data = {} all_runs = {} TOP_K = 10 while True: for C,S in tqdm(product(clusters,species_divisions),total=len(clusters)*len(species_divisions)): k = [C,S] C = list(clusters[C]) S = species_divisions[S] k[1] = get_species_name(k[1].split('/')[-1]) loo = LeaveOneOut() predictions = [] y_true = [] for train_index, test_index in loo.split(C): C_t = [C[i] for i in train_index] C_v = [C[i] for i in test_index] r1,r2,yte = run_model(C_t,C_v,S,S,Y, experimental_features, pubchem_fingerprints, chemical_embedding, species_embedding, chemical_features, merge_species=True) if r1 is None and r2 is None: continue r1 = np.mean(r1) r2 = np.mean(r2) y_true.append(np.mean(yte)) predictions.append((r1,r2)) y_true, predictions = map(np.asarray,[y_true,predictions]) if len(predictions) < 10: continue try: if len(predictions.shape) < 2: predictions = np.expand_dims(predictions,axis=1) rsq_1 = r2_score(y_true,predictions[:,0]) rsq_2 = r2_score(y_true,predictions[:,1]) all_runs[tuple(k)] = (rsq_1,rsq_2) except ValueError: pass all_runs = dict(sorted(all_runs.items(),key=lambda x: sum(x[1])/2,reverse=True)) print(all_runs) data[len(cluster_centers)] = all_runs if len(cluster_centers) > 0: clusters, cluster_centers = merge_closest(clusters,cluster_centers) for k in list(all_runs.keys())[:TOP_K]: _,s = k species_divisions.pop(k,None) else: break pd.to_pickle(data,'chemical_cluster_merging.pkl') exit() ks = set() for k in species_divisions: S = species_divisions[k] still_true = True for k_c in clusters: C = clusters[k_c] Xtr,Xte,ytr,yte = filter_data(X,Y,C,C,S,S) if count(Xtr,Xte) > 100: ks.add(k) for k in tqdm(ks): n=6 clusters, cluster_centers = define_chemical_clusters([smiles_fingerprints],k=max(-1,n)) S = species_divisions[k] sn = get_species_name(k.split('/')[-1]) results = defaultdict(list) i = 0 while True: k_c = sorted(clusters,key=lambda x:len(clusters[x]),reverse=True)[0] C_t = clusters[k_c] if len(C_t) < 1: continue C_t,C_v = train_test_split(list(C_t),test_size=0.25) S_t = S S_v = S Xtr,Xte,ytr,yte = filter_data(X,Y,C_t,C_v,S_t,S_v) try: assert count(Xtr,Xte) > 20 r1,r2 = run_model(Xtr, Xte, ytr, yte, experimental_features, pubchem_fingerprints, chemical_embedding, species_embedding, chemical_features, merge_species=True) except AssertionError: r1,r2 = float('nan'), float('nan') except np.AxisError: r1,r2 = float('nan'), float('nan') results[i].append((r1,r2)) clusters, cluster_centers = merge_closest(clusters,cluster_centers) if len(cluster_centers) < 1: break i += 1 v0 = [[v[0] for v in results[k]] for k in results] v1 = [[v[1] for v in results[k]] for k in results] fig, ax = plt.subplots() for x,color,ran in zip([v0,v1],['red','green'],[np.arange(0,len(v0)*2,2),np.arange(1,len(v1)*2,2)]): mins = [np.nanmin(a) for a in x] maxes = [np.nanmax(a) for a in x] means = [np.nanmean(a) for a in x] std = [np.nanstd(a) for a in x] mins,maxes,means,std = map(np.asarray,[mins,maxes,means,std]) ax.bar(ran,maxes,width=0.5,color=color) #plt.ylim(-1,1) ax.set_xticks(np.arange(0.5,len(v0)*2,2)) ax.set_xticklabels(('%s Clusters' % str(abs(i)) for i in range(-n,0))) plt.savefig('./plots/chemical_clusters_taxon_%s.png' % sn) exit() #def tqdm(x,**params): #return x for filter_chemicals,string,TOP_K in tqdm(zip([inorganic_chemicals | salts],['organic'],[4]),total=1,desc='Chemical Groups'): #if string=='organic': continue for division in tqdm(S_v,total=len(S_v),desc='Divisions'): if not len(S_v[division]) > 1: continue model_params={'encode':False,'train_ae_fingerprints':False,'train_ae_species':False} results = [[]]*TOP_K f = lambda _s: sum([1 for c,s in X if (s == _s and c in C-filter_chemicals)]) tmp_division = list(sorted(S_v[division],key=f,reverse=True))[:TOP_K] for i,s_v in tqdm(enumerate(tmp_division),desc='Species in division %s' % division,leave=False,total=len(tmp_division)): C_restriction = {'C_v':{'not content':filter_chemicals},'C_t':{'not content':filter_chemicals}} configs = [] #Method 1 configs.append((1, 1, {'S_v':{'content':set([s_v]),'max_len':1}})) configs.append((1, 2, {'S_v':{'content':set([s_v]),'max_len':1}})) #Method 2 configs.append((2, 1, {'S_v':{'content':set([s_v]),'max_len':1}})) #Method 3 configs.append((3, 1, {'S_v':{'content':set([s_v]),'max_len':1}})) configs.append((3, 2, {'S_v':{'content':set([s_v]),'max_len':1}})) #Method 4 configs.append((4, 1, {'S_v':{'content':set([s_v]),'max_len':1}})) configs.append((4, 2, {'S_v':{'content':set([s_v]),'max_len':1}})) tmp_res = np.zeros((len(configs),2)) for j,config in tqdm(enumerate(configs),total=len(configs),leave=False,desc='Configs'): m,v,res = config r1_tmp = [] r2_tmp = [] for _ in range(10): tf.keras.backend.clear_session() prop = 0.3 Xtr,Xte,ytr,yte = data_split(X,Y,restrictions={**res,**C_restriction},method=m,variant=v,prop=prop) try: r1,r2 = run_model(Xtr, Xte, ytr, yte, experimental_features, fingerprints, chemical_embedding, species_embedding, model_params=model_params) except: r1,r2=0,0 r1_tmp.append(r1) r2_tmp.append(r2) tmp_res[j,0] = np.mean(r1_tmp) tmp_res[j,1] = np.mean(r2_tmp) results[i] = tmp_res fig, axs = plt.subplots(1,len(results),figsize=(40, 10)) for i,ax in enumerate(axs): ms = results[i] baseline = ms[:,0] over = ms[:,1] baseline = np.nan_to_num(baseline, nan=0.0,posinf=0.0, neginf=0.0) over = np.nan_to_num(over, nan=0.0,posinf=0.0, neginf=0.0) width = 0.4 ax.bar(np.arange(0,len(baseline)*2,2),baseline,width,color='red') ax.bar(np.arange(1,len(baseline)*2,2),over,width,color='green') ax.set_title(get_species_name(tmp_division[i].split('/')[-1])) ax.set_xticks(np.arange(0.5,len(baseline)*2,2)) ax.set_xticklabels((str(i) for i in range(len(configs)))) ax.set_ylim(0,max(*over,*baseline)+0.1) plt.savefig('plots/division_%s_%s.png' % (division,string)) if __name__ == '__main__': main()

py

kge_ecotox_regression

kge_ecotox_regression-main/train_rdf2vec.py

from pyrdf2vec.graphs import KG from pyrdf2vec.samplers import UniformSampler from pyrdf2vec.walkers import RandomWalker from pyrdf2vec import RDF2VecTransformer import pandas as pd from rdflib import Graph, URIRef import numpy as np from main import load_data import rdflib d = './data/embeddings/' pdf = [pd.read_csv('./data/chemicals_%s.csv' % str(i)) for i in range(3)] kg1 = pd.concat(pdf) kg2 = pd.read_csv('./data/taxonomy.csv') X,_ = load_data('./data/experiments.csv') entities1 = list(set(map(rdflib.URIRef,list(zip(*X))[0]))) entities2 = list(set(map(rdflib.URIRef,list(zip(*X))[1]))) for kg,kg_name,entities in zip([kg1,kg2],['chemical','taxonomy'],[entities1,entities2]): g = Graph() for t in zip(kg['subject'],kg['predicate'],kg['object']): g.add(tuple(map(rdflib.URIRef,t))) g.serialize('tmp.ttl',format='ttl') kg = KG(location="tmp.ttl",file_type='ttl') walkers = [RandomWalker(4, 5, UniformSampler())] transformer = RDF2VecTransformer(walkers=walkers) embeddings = transformer.fit_transform(kg,entities) np.save(d + 'rdf2vec_%s_entity_embeddings.csv' % kg_name, embeddings) np.save(d + 'rdf2vec_%s_entity_ids.csv' % kg_name, np.asarray(list(enumerate(entities))))

py

kge_ecotox_regression

kge_ecotox_regression-main/embedding_model.py

from tensorflow.keras import Model, Sequential from tensorflow.keras.layers import Input, Embedding, Dense, Dropout, Conv2D, Flatten, Concatenate, Multiply import tensorflow as tf def min_distance_loss(w,epsilon=1.0): r = tf.reduce_sum(w*w, 1) r = tf.reshape(r, [-1, 1]) D = r - 2*tf.matmul(w, tf.transpose(w)) + tf.transpose(r) D = D + tf.linalg.diag(epsilon * tf.ones(D.shape[0])) return tf.reduce_sum(tf.where(D<epsilon,1.0,0.0))/tf.cast(w.shape[1],tf.float32) def TransE(entities,relations,dim=200,bias=1,lamb=1,norm_size=0.0,mdl=0.0): inp = Input((3,)) inp_label = Input(()) s,p,o = tf.unstack(inp,axis=-1) entity_embedding = Embedding(len(entities),dim,name='entity_embedding') relation_embedding = Embedding(len(relations),dim,name='relation_embedding') h,r,t = entity_embedding(s),relation_embedding(p),entity_embedding(o) score = bias - tf.norm(h+r-t, ord=2, axis=-1) loss = lamb - inp_label * score loss = tf.where(loss>0,loss,0) + \ norm_size * tf.norm(entity_embedding.weights[0],ord=2)**2 + \ min_distance_loss(entity_embedding.weights[0]) * mdl model = Model(inputs=[inp,inp_label],outputs=score) model.add_loss(loss) model.compile(optimizer='adam',loss=None) return model def DistMult(entities,relations,dim=200,norm_size=0.0,mdl=0.0): inp = Input((3,)) inp_label = Input(()) s,p,o = tf.unstack(inp,axis=-1) entity_embedding = Embedding(len(entities),dim,name='entity_embedding') relation_embedding = Embedding(len(relations),dim,name='relation_embedding') h,r,t = entity_embedding(s),relation_embedding(p),entity_embedding(o) score = tf.keras.layers.Activation('linear')(tf.reduce_sum(h*r*t,axis=-1)) model = Model(inputs=[inp,inp_label],outputs=score) loss = lambda true,pred: tf.reduce_sum(tf.math.log(1+tf.math.exp(-true*pred))) + \ norm_size * tf.norm(entity_embedding.weights[0],ord=2)**2 + \ min_distance_loss(entity_embedding.weights[0],mdl) * mdl model.compile(optimizer='adam',loss=loss) return model def ComplEx(entities,relations,dim=200,norm_size=0.0,mdl=0.0): inp = Input((3,)) inp_label = Input(()) s,p,o = tf.unstack(inp,axis=-1) entity_embedding = Embedding(len(entities),dim,name='entity_embedding') relation_embedding = Embedding(len(relations),dim,name='relation_embedding') h,r,t = entity_embedding(s),relation_embedding(p),entity_embedding(o) h_real,h_img = tf.split(h,2,axis=-1) r_real,r_img = tf.split(r,2,axis=-1) t_real,t_img = tf.split(t,2,axis=-1) score = tf.reduce_sum(r_real*h_real*t_real,axis=-1) + \ tf.reduce_sum(r_real*h_img*t_img,axis=-1) + \ tf.reduce_sum(r_img*h_real*t_img,axis=-1) - \ tf.reduce_sum(r_img*h_img*t_real,axis=-1) model = Model(inputs=[inp,inp_label],outputs=score) loss = lambda true,pred: tf.reduce_sum(tf.math.log(1+tf.math.exp(-true*pred))) + \ norm_size * tf.norm(entity_embedding.weights[0],ord=2)**2 + \ min_distance_loss(entity_embedding.weights[0]) * mdl model.compile(optimizer='adam',loss=loss) return model def ConvE(entities,relations): dim = 200 inp = Input((3,)) inp_label = Input(()) s,p,o = tf.unstack(inp,axis=-1) entity_embedding = Embedding(len(entities),dim,name='entity_embedding') relation_embedding = Embedding(len(relations),dim,name='relation_embedding') h,r,t = entity_embedding(s),relation_embedding(p),entity_embedding(o) h = tf.reshape(h,(-1,20,10,1)) r = tf.reshape(r,(-1,20,10,1)) x = Concatenate(axis=2)([h,r]) x = Conv2D(16,(5,5),activation='relu')(x) x = Dropout(0.2)(x) x = Conv2D(16,(3,3),activation='relu')(x) x = Dropout(0.2)(x) x = Flatten()(x) x = Dense(dim)(x) x = Multiply()([x,t]) x = Dense(1,activation='sigmoid')(x) model = Model(inputs=[inp,inp_label],outputs=x) model.compile(optimizer='adam',loss=tf.keras.losses.BinaryCrossentropy(label_smoothing=0.05)) return model

py

kge_ecotox_regression

kge_ecotox_regression-main/pretrained_embedding_models.py

import sys import os from itertools import product from KGEkeras import DistMult, HolE, TransE, HAKE, ConvE, ComplEx, ConvR, RotatE, pRotatE, ConvKB, CosinE from kerastuner import RandomSearch, HyperParameters, Objective, Hyperband, BayesianOptimization from random import choice from collections import defaultdict from tensorflow.keras.losses import binary_crossentropy,hinge,mean_squared_error from tensorflow.keras import Input from tensorflow.keras import Model import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split from tensorflow.keras.optimizers import Adam from tensorflow.keras.callbacks import EarlyStopping, Callback, TerminateOnNaN, ReduceLROnPlateau from sklearn.metrics.cluster import completeness_score from tensorflow.keras.optimizers import Adam import json import tensorflow as tf from tensorflow.keras.optimizers.schedules import ExponentialDecay from KGEkeras import loss_function_lookup from lib.utils import generate_negative, oversample_data, load_data from tqdm import tqdm import string import random from random import choices from lib.hptuner import HPTuner import pickle try: from tensorflow_addons.callbacks import TimeStopping except: pass from rdflib import Graph, URIRef, Literal, Namespace from KGEkeras import LiteralConverter from sklearn.decomposition import PCA SECONDS_PER_TRAIL = 600 SECONDS_TO_TERMINATE = 3600 SEARCH_MAX_EPOCHS = 10 MAX_EPOCHS = 200 MIN_EPOCHS = 50 MAX_TRIALS = 20 PATIENCE = 10 EPSILON = 10e-7 models = { #'DistMult':DistMult, #'TransE':TransE, #'HolE':HolE, 'ComplEx':ComplEx, #'HAKE':HAKE, #'pRotatE':pRotatE, #'RotatE':RotatE, #'ConvE':ConvE, #'ConvKB':ConvKB, } class DataGenerator(tf.keras.utils.Sequence): def __init__(self, kg, ns=10, batch_size=32, shuffle=True): self.batch_size = min(batch_size,len(kg)) self.kg = kg self.ns = ns self.num_e = len(set([s for s,_,_ in kg])|set([o for _,_,o in kg])) self.shuffle = shuffle self.indices = list(range(len(kg))) self.on_epoch_end() def __len__(self): return len(self.kg) // self.batch_size def __getitem__(self, index): index = self.index[index * self.batch_size:(index + 1) * self.batch_size] batch = [self.indices[k] for k in index] X, y = self.__get_data(batch) return X, y def on_epoch_end(self): self.index = np.arange(len(self.indices)) if self.shuffle == True: np.random.shuffle(self.index) def __get_data(self, batch): tmp_kg = np.asarray([self.kg[i] for i in batch]) negative_kg = generate_negative(tmp_kg,N=self.num_e,negative=self.ns) X = oversample_data(kgs=[tmp_kg,negative_kg]) return X, None def build_model(hp): params = hp.copy() params['e_dim'] = params['dim'] params['r_dim'] = params['dim'] params['name'] = 'embedding_model' embedding_model = models[params['embedding_model']] embedding_model = embedding_model(**params) triple = Input((3,)) ftriple = Input((3,)) inputs = [triple, ftriple] score = embedding_model(triple) fscore = embedding_model(ftriple) loss_function = loss_function_lookup(params['loss_function']) loss = loss_function(score,fscore,params['margin'] or 1, 1) model = Model(inputs=inputs, outputs=loss) model.add_loss(loss) model.compile(optimizer=Adam(learning_rate=ExponentialDecay(params['learning_rate'],decay_steps=100000,decay_rate=0.96)), loss=None) return model def optimize_model(model, kg, lit=False, name='name', hp=None): if lit: lc = LiteralConverter(kg) literals = lc.fit_transform() kg = lc.g literals = PCA(min(len(literals[0]),100)).fit_transform(literals) else: literals = None kg -= [(s,p,o) for s,p,o in kg if isinstance(o,Literal)] entities = set(kg.subjects()) | set(kg.objects()) relations = set(kg.predicates()) me = {k:i for i,k in enumerate(entities)} mr = {k:i for i,k in enumerate(relations)} kg = list(map(lambda x: (me[x[0]],mr[x[1]],me[x[2]]), kg)) bs = 512 kg = np.asarray(kg) model_name = model N = len(me) M = len(mr) hptuner = HPTuner(runs=MAX_TRIALS, objectiv_direction='min') hptuner.add_value_hp('gamma',0,21) hptuner.add_value_hp('dim',100,401,dtype=int) hptuner.add_value_hp('negative_samples',10,101,dtype=int) hptuner.add_value_hp('margin',1,11,dtype=int) hptuner.add_list_hp('loss_function',['pairwize_hinge','pairwize_logistic','pointwize_hinge','pointwize_logistic'],exhaustive=True) hptuner.add_fixed_hp('embedding_model',model) hptuner.add_fixed_hp('dp',0.2) hptuner.add_fixed_hp('hidden_dp',0.2) hptuner.add_fixed_hp('num_entities',N) hptuner.add_fixed_hp('num_relations',M) if hp: for k,i in hp.items(): hptuner.add_fixed_hp(k,i) hptuner.add_fixed_hp('num_entities',N) hptuner.add_fixed_hp('num_relations',M) hptuner.add_fixed_hp('learning_rate',0.001) hptuner.add_fixed_hp('regularization',0.001) if lit: hptuner.add_fixed_hp('literals',literals) hptuner.add_fixed_hp('literal_activation','tanh') if hp: hptuner.next_hp_config() hptuner.add_result(0.0) with tqdm(total=hptuner.runs, desc='Trials') as pbar: while hptuner.is_active and hp is None: hp = hptuner.next_hp_config() model = build_model(hp) tr_gen = DataGenerator(kg, batch_size=bs, shuffle=True, ns=hp['negative_samples']) hist = model.fit(tr_gen,epochs=SEARCH_MAX_EPOCHS,verbose=2, callbacks=[EarlyStopping('loss'),TerminateOnNaN()]) score = hist.history['loss'][-1]/hist.history['loss'][0] hptuner.add_result(score) tf.keras.backend.clear_session() pbar.update(1) hp = hptuner.best_config() #if hp is None: #with open('./pretrained_hp/%s%s_kg.json' % (model_name,name), 'w') as fp: #json.dump(hp, fp) model = build_model(hp) tr_gen = DataGenerator(kg, batch_size=bs, shuffle=True, ns=hp['negative_samples']) hist = model.fit(tr_gen,epochs=MAX_EPOCHS, verbose=2, callbacks=[EarlyStopping('loss',patience=PATIENCE), TerminateOnNaN()]) if np.isnan(hist.history['loss'][-1]): print(model_name,'nan loss.') return optimize_model(model_name,kg,lit,name,None) for l in model.layers: if isinstance(l,models[model_name]): m = l.name m, W1, W2 = model, model.get_layer(m).entity_embedding.get_weights()[0], model.get_layer(m).relational_embedding.get_weights()[0] m.save_weights('pretrained_models/model/'+name) np.save(name+'_entity_embeddings.npy', W1) np.save(name+'_entity_ids.npy',np.asarray(list(zip(entities,range(len(entities)))))) np.save(name+'_relational_embeddings.npy', W2) np.save(name+'_relation_ids.npy',np.asarray(list(zip(relations,range(len(relations)))))) def main(): d = './data/embeddings/' use_literals = product([False,True],[False,True]) g1_parts = [[0],[0,1],[0,1,2]] g2_parts = [[0],[0,1]] p = list(product(g1_parts,g2_parts)) p += [p[-1]] ul = (False,False) for g1p,g2p in tqdm(p): g1,g2 = Graph(),Graph() for i in g1p: g = Graph() g.load('./data/chemicals_%s.ttl' % str(i),format='ttl') g1 += g for i in g2p: g = Graph() g.load('./data/taxonomy_%s.ttl' % str(i),format='ttl') g2 += g for lit,gp,kg,name in zip([*ul],[g1p,g2p],[g1,g2],['_chemical_','_taxonomy_']): #hp_file = '../KGE-CEP/pretrained_hp/%s%s_kg.json' % (model,name) hp = {'e_dim':100, 'negative_samples':10, 'loss_function':'pairwize_logistic'} model = 'ComplEx' f = d+model+name+str(hash((lit,*gp))) optimize_model(model,kg,lit,name=f,hp=hp) tf.keras.backend.clear_session() if (g1p,g2p) == p[-1]: ul = (True,True) if __name__ == '__main__': main()

py

kge_ecotox_regression

kge_ecotox_regression-main/create_data.py

""" TODO: - Load LC50 data from ECOTOX. - Take median per chemical species pairs. - Defined chemical groups. - Export files per chemical groups and each species. - Forall chemicals and species export relevant KGs. """ from tera.DataAggregation import Taxonomy, Effects, Traits from tera.DataAccess import EffectsAPI from tera.DataIntegration import DownloadedWikidata, LogMapMapping from tera.utils import strip_namespace, unit_conversion from tqdm import tqdm from rdflib import Graph, URIRef, Literal, BNode, Namespace from rdflib.namespace import RDFS, RDF cco = Namespace('http://rdf.ebi.ac.uk/terms/chembl#') skos = Namespace('http://www.w3.org/2004/02/skos/core#') obo = Namespace('http://purl.obolibrary.org/obo/') import pandas as pd from collections import defaultdict import pubchempy as pcp import numpy as np def get_subgraph(to_visit, graph, backtracking=0): out = Graph() visited = set() while to_visit: curr = to_visit.pop() visited.add(curr) tmp = set(graph.triples((curr,None,None))) for t in tmp: out.add(t) to_visit |= set([o for _,_,o in tmp if not isinstance(o,Literal)]) to_visit -= visited if backtracking > 0: tmp = set() for s in set([s for s,_,_ in out]): tmp |= set(graph.subjects(object=s)) for t in out: graph.remove(t) return out + get_subgraph(tmp, graph, backtracking-1) return out def load_endpoint_data(): ed = Effects(directory='../ecotox_data/',verbose=False) species_mapping = LogMapMapping(filename='./data/final_mappings.txt') chemicals_mappings = DownloadedWikidata(filename='./data/cas_to_chembl.csv') species_mapping.load() chemicals_mappings.load() ncbi_namespace = Namespace('https://www.ncbi.nlm.nih.gov/taxonomy/') species_mapping = [(ed.namespace['taxon/'+k],ncbi_namespace['taxon/'+i.pop(0)]) for k,i in species_mapping.mappings.items()] ed.replace(species_mapping) chembl_namespace = Namespace('http://rdf.ebi.ac.uk/resource/chembl/molecule/') chemicals_mappings = [(ed.namespace['cas/'+k],chembl_namespace[i.pop(0)]) for k,i in chemicals_mappings.mappings.items()] ed.replace(chemicals_mappings) endpoints = EffectsAPI(dataobject=ed, verbose=True).get_endpoint(c=None, s=None) d = defaultdict(list) for c,s,cc,cu,ep,ef,sd,sdu in endpoints: try: sd = float(sd) except: continue if 'day' in str(sdu).lower(): sd *= 24 elif 'week' in str(sdu).lower(): sd *= (7*24) elif 'hour' in str(sdu).lower(): sd *= 1 else: continue if ('LC50' in str(ep) or 'LD50' in str(ep) or ('EC50' in str(ep) and 'MOR' in str(ef))) and ('ncbi' in str(s) and 'chembl' in str(c)): try: factor = unit_conversion(str(cu),'http://qudt.org/vocab/unit#MilligramPerLitre') except: factor = 0 if factor > 0: cc = float(cc) cc = cc*factor d['chemical'].append(str(c)) d['species'].append(str(s)) d['concentration'].append(cc) d['study_duration'].append(sd) df = pd.DataFrame(data=d) df.to_csv('./data/experiments.csv') def fingerprints(): df = pd.read_csv('./data/experiments.csv') mapping = DownloadedWikidata(filename='./data/chembl_to_cid.csv') to_look_for = mapping.convert(set(df['chemical']),reverse=False,strip=True) to_look_for = set([URIRef('http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID'+str(i)) for k,i in to_look_for.items() if i != 'no mapping']) out = [] fp = [] for c,c2 in tqdm(zip(to_look_for,set(df['chemical'])),total=len(to_look_for)): try: compound = pcp.Compound.from_cid(int(c.split('CID')[-1])) fp.append(bin(int(compound.fingerprint,16))[2:]) out.append(c2) except: pass df = pd.DataFrame(data={'chemical':out,'fingerprint':fp}) df.to_csv('./data/chemicals_fingerprints.csv') def chemical_features(): df = pd.read_csv('./data/experiments.csv') mapping = DownloadedWikidata(filename='./data/chembl_to_cid.csv') to_look_for = mapping.convert(set(df['chemical']),reverse=False,strip=True) to_look_for = set([URIRef('http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID'+str(i)) for k,i in to_look_for.items() if i != 'no mapping']) out = defaultdict(list) fp = [] fs = ['xlogp','exact_mass','tpsa','complexity','charge'] for c,c2 in tqdm(zip(to_look_for,set(df['chemical'])),total=len(to_look_for)): try: compound = pcp.Compound.from_cid(int(c.split('CID')[-1])) tmp = compound.to_dict(fs) for k in tmp: out[k].append(tmp[k]) out['chemical'].append(c2) except: pass df = pd.DataFrame(data=out) df.to_csv('./data/chemicals_features.csv') def load_chemical_groups(): """ Split (in)organic CHEBI:50860 (organic) CHEBI:24835 (inorganic) """ df = pd.read_csv('./data/experiments.csv') chemicals = df['chemical'] graph = Graph() graph.parse('../chembl/chembl_26.0_molecule_chebi_ls.ttl',format='ttl') mapping = defaultdict() for c in chemicals: c = URIRef(c) m = list(graph.objects(subject=c,predicate=skos['exactMatch'])) if m: mapping[c]=m.pop(0) chebi_graph = Graph() chebi_graph.parse('../chebi/chebi.ttl',format='ttl') chebi_graph = replace(chebi_graph,{i:k for k,i in mapping.items()}) for steps in range(1,20): out = defaultdict(set) desendents=set() for c in map(lambda x:URIRef(x), chemicals): p = ' / '.join('<'+r+'>' for r in [RDFS.subClassOf]*steps) qres = chebi_graph.query( """SELECT DISTINCT ?parent WHERE { <%s> %s ?parent }""" % (str(c),p)) for r in qres: desendents.add((c,r[-1])) for c,p in desendents: out[p].add(c) df = pd.DataFrame(data={'parent':list(out.keys()),'children':[','.join(out[k]) for k in out]}) df.to_csv('./data/chemical_group_%s.csv' % str(steps)) def load_species_groups(): df = pd.read_csv('./data/experiments.csv') t = Graph() t.load('./data/taxonomy_0.ttl',format='ttl') species = set(df['species']) for steps in range(0,20): out_taxon = defaultdict(set) out_division = defaultdict(set) desendents = set() for c in map(URIRef,species): p = ' / '.join('<'+r+'>' for r in [RDF.type,*[RDFS.subClassOf]*steps]) qres = t.query( """SELECT DISTINCT ?parent WHERE { <%s> %s ?parent }""" % (str(c),p)) for r in qres: desendents.add((c,r[-1])) for c,p in desendents: if 'division' in str(p): out_division[p].add(c) else: out_taxon[p].add(c) df = pd.DataFrame(data={'parent':list(out_division.keys()),'children':[','.join(out_division[k]) for k in out_division]}) df.to_csv('./data/species_divisions.csv') df = pd.DataFrame(data={'parent':list(out_taxon.keys()),'children':[','.join(out_taxon[k]) for k in out_taxon]}) df.to_csv('./data/species_groups_%s.csv' % str(steps)) def replace(graph,mapping): for s,p,o in graph: if s in mapping: graph.remove((s,p,o)) graph.add((mapping[s],p,o)) if o in mapping: graph.remove((s,p,o)) graph.add((s,p,mapping[o])) if p in mapping: graph.remove((s,p,o)) graph.add((s,mapping[p],o)) return graph def load_chemical_graph(): df = pd.read_csv('./data/experiments.csv') mapping = DownloadedWikidata(filename='./data/chembl_to_mesh.csv') mapping.load() mapping = {URIRef('http://id.nlm.nih.gov/mesh/'+i.pop(0)):URIRef('http://rdf.ebi.ac.uk/resource/chembl/molecule/'+k) for k,i in mapping.mappings.items()} mesh_graph = Graph() mesh_graph.parse('../mesh/mesh.nt',format='nt') mesh_graph = replace(mesh_graph,mapping) graph = Graph() graph.parse('../chembl/chembl_26.0_molecule_chebi_ls.ttl',format='ttl') mapping = defaultdict() for c in df['chemical']: c = URIRef(c) m = list(graph.objects(subject=c,predicate=skos['exactMatch'])) if m: mapping[c]=m.pop(0) chebi_graph = Graph() chebi_graph.parse('../chebi/chebi.ttl',format='ttl') chebi_graph = replace(chebi_graph,{i:k for k,i in mapping.items()}) chembl_graph = Graph() for f in [#'../chembl/chembl_26.0_molecule.ttl', '../chembl/chembl_26.0_molhierarchy.ttl', '../chembl/chembl_26.0_target.ttl', '../chembl/chembl_26.0_targetrel.ttl', '../chembl/chembl_26.0_moa.ttl']: chembl_graph.parse(f,format='ttl') for i,g in enumerate([mesh_graph,chebi_graph,chembl_graph]): graph = get_subgraph(set([URIRef(a) for a in set(df['chemical'])]), g, backtracking=0) graph.serialize('./data/chemicals_%s.ttl' % str(i),format='ttl') def load_taxonomy_graph(): df = pd.read_csv('./data/experiments.csv') t = Taxonomy(directory='../taxdump/', verbose=True, taxon_namespace='http://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info&id=') ne = DownloadedWikidata(filename='./data/ncbi_to_eol.csv', verbose=False) n = list(set(t.graph.subjects(predicate=t.namespace['rank'], object=t.namespace['rank/species']))) tr = Traits(directory='../eol/', verbose=True) conv = ne.convert(n, strip=True) converted = [(tr.namespace[i],k) for k,i in conv.items() if i != 'no mapping'] tr.replace(converted) for i,g in enumerate([t.graph,tr.graph]): tmp = set([URIRef(a) for a in set(df['species'])]) graph = get_subgraph(tmp, g, backtracking=0) graph.serialize('./data/taxonomy_%s.ttl' % str(i),format='ttl') if __name__ == '__main__': #load_endpoint_data() #fingerprints() #chemical_features() #load_species_groups() #load_chemical_groups() #load_chemical_graph() load_taxonomy_graph()

py

kge_ecotox_regression

kge_ecotox_regression-main/autoencoder.py

from tensorflow.keras.layers import Dense, GaussianNoise, Input, LayerNormalization from tensorflow.keras.models import Model from tensorflow import keras def create_auto_encoder(input_size, dense_layers = (10,), noise=0): autoencoder = keras.Sequential() if noise > 0: autoencoder.add(GaussianNoise(noise)) for l in dense_layers: autoencoder.add(Dense(l,activation='relu')) encoder = autoencoder for l in dense_layers[::-1]: autoencoder.add(Dense(l,activation='relu')) autoencoder.add(Dense(input_size,activation='sigmoid')) return encoder, autoencoder

py

lepard

lepard-main/main.py

import os, torch, json, argparse, shutil from easydict import EasyDict as edict import yaml from datasets.dataloader import get_dataloader, get_datasets from models.pipeline import Pipeline from lib.utils import setup_seed from lib.tester import get_trainer from models.loss import MatchMotionLoss from lib.tictok import Timers from configs.models import architectures from torch import optim setup_seed(0) def join(loader, node): seq = loader.construct_sequence(node) return '_'.join([str(i) for i in seq]) yaml.add_constructor('!join', join) if __name__ == '__main__': # load configs parser = argparse.ArgumentParser() parser.add_argument('config', type=str, help= 'Path to the config file.') args = parser.parse_args() with open(args.config,'r') as f: config = yaml.load(f, Loader=yaml.Loader) config['snapshot_dir'] = 'snapshot/%s/%s' % (config['dataset']+config['folder'], config['exp_dir']) config['tboard_dir'] = 'snapshot/%s/%s/tensorboard' % (config['dataset']+config['folder'], config['exp_dir']) config['save_dir'] = 'snapshot/%s/%s/checkpoints' % (config['dataset']+config['folder'], config['exp_dir']) config = edict(config) os.makedirs(config.snapshot_dir, exist_ok=True) os.makedirs(config.save_dir, exist_ok=True) os.makedirs(config.tboard_dir, exist_ok=True) if config.gpu_mode: config.device = torch.device("cuda:0") else: config.device = torch.device('cpu') # backup the if config.mode == 'train': os.system(f'cp -r models {config.snapshot_dir}') os.system(f'cp -r configs {config.snapshot_dir}') os.system(f'cp -r cpp_wrappers {config.snapshot_dir}') os.system(f'cp -r datasets {config.snapshot_dir}') os.system(f'cp -r kernels {config.snapshot_dir}') os.system(f'cp -r lib {config.snapshot_dir}') shutil.copy2('main.py',config.snapshot_dir) # model initialization config.kpfcn_config.architecture = architectures[config.dataset] config.model = Pipeline(config) # config.model = KPFCNN(config) # create optimizer if config.optimizer == 'SGD': config.optimizer = optim.SGD( config.model.parameters(), lr=config.lr, momentum=config.momentum, weight_decay=config.weight_decay, ) elif config.optimizer == 'ADAM': config.optimizer = optim.Adam( config.model.parameters(), lr=config.lr, betas=(0.9, 0.999), weight_decay=config.weight_decay, ) #create learning rate scheduler if 'overfit' in config.exp_dir : config.scheduler = optim.lr_scheduler.MultiStepLR( config.optimizer, milestones=[config.max_epoch-1], # fix lr during overfitting gamma=0.1, last_epoch=-1) else: config.scheduler = optim.lr_scheduler.ExponentialLR( config.optimizer, gamma=config.scheduler_gamma, ) config.timers = Timers() # create dataset and dataloader train_set, val_set, test_set = get_datasets(config) config.train_loader, neighborhood_limits = get_dataloader(train_set,config,shuffle=True) config.val_loader, _ = get_dataloader(val_set, config, shuffle=False, neighborhood_limits=neighborhood_limits) config.test_loader, _ = get_dataloader(test_set, config, shuffle=False, neighborhood_limits=neighborhood_limits) # config.desc_loss = MetricLoss(config) config.desc_loss = MatchMotionLoss (config['train_loss']) trainer = get_trainer(config) if(config.mode=='train'): trainer.train() else: trainer.test()

py

lepard

lepard-main/models/matching.py

import torch import torch.nn as nn import torch.nn.functional as F from models.position_encoding import VolumetricPositionEncoding as VolPE def log_optimal_transport(scores, alpha, iters, src_mask, tgt_mask ): b, m, n = scores.shape if src_mask is None: ms = m ns = n else : ms = src_mask.sum(dim=1, keepdim=True) ns = tgt_mask.sum(dim=1, keepdim=True) bins0 = alpha.expand(b, m, 1) bins1 = alpha.expand(b, 1, n) alpha = alpha.expand(b, 1, 1) Z = torch.cat([torch.cat([scores, bins0], -1), torch.cat([bins1, alpha], -1)], 1) norm = - (ms + ns).log() # [b, 1] log_mu = torch.cat([norm .repeat(1, m), ns.log() + norm], dim=1) log_nu = torch.cat([norm.repeat(1, n), ms.log() + norm], dim=1) u, v = torch.zeros_like(log_mu), torch.zeros_like(log_nu) for _ in range(iters): u = log_mu - torch.logsumexp( Z + v.unsqueeze(1), dim=2) v = log_nu - torch.logsumexp(Z + u.unsqueeze(2), dim=1) Z= Z + u.unsqueeze(2) + v.unsqueeze(1) Z = Z - norm.view(-1,1,1) return Z class Matching(nn.Module): def __init__(self, config): super().__init__() self.match_type = config['match_type'] self.confidence_threshold = config['confidence_threshold'] d_model = config['feature_dim'] self.src_proj = nn.Linear(d_model, d_model, bias=False) self.tgt_proj = nn.Linear(d_model, d_model, bias=False) self.entangled= config['entangled'] if self.match_type == "dual_softmax": self.temperature = config['dsmax_temperature'] elif self.match_type == 'sinkhorn': #sinkhorn algorithm self.skh_init_bin_score = config['skh_init_bin_score'] self.skh_iters = config['skh_iters'] self.skh_prefilter = config['skh_prefilter'] self.bin_score = nn.Parameter( torch.tensor( self.skh_init_bin_score, requires_grad=True)) else: raise NotImplementedError() @staticmethod @torch.no_grad() def get_match( conf_matrix, thr, mutual=True): mask = conf_matrix > thr #mutual nearest if mutual: mask = mask \ * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0]) \ * (conf_matrix == conf_matrix.max(dim=1, keepdim=True)[0]) #find all valid coarse matches index = (mask==True).nonzero() b_ind, src_ind, tgt_ind = index[:,0], index[:,1], index[:,2] mconf = conf_matrix[b_ind, src_ind, tgt_ind] return index, mconf, mask @staticmethod @torch.no_grad() def get_topk_match( conf_matrix, thr, mutual=True): mask = conf_matrix > thr #mutual nearest if mutual: mask = mask \ * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0]) \ * (conf_matrix == conf_matrix.max(dim=1, keepdim=True)[0]) #find all valid coarse matches index = (mask==True).nonzero() b_ind, src_ind, tgt_ind = index[:,0], index[:,1], index[:,2] mconf = conf_matrix[b_ind, src_ind, tgt_ind] return index, mconf, mask def forward(self, src_feats, tgt_feats, src_pe, tgt_pe, src_mask, tgt_mask, data, pe_type="rotary"): ''' @param src_feats: [B, S, C] @param tgt_feats: [B, T, C] @param src_mask: [B, S] @param tgt_mask: [B, T] @return: ''' src_feats = self.src_proj(src_feats) tgt_feats = self.src_proj(tgt_feats) data["src_feats_nopos"] = src_feats data["tgt_feats_nopos"] = tgt_feats if not self.entangled : src_feats = VolPE.embed_pos(pe_type, src_feats, src_pe) tgt_feats = VolPE.embed_pos(pe_type, tgt_feats, tgt_pe) data["src_feats"] = src_feats data["tgt_feats"] = tgt_feats src_feats, tgt_feats = map(lambda feat: feat / feat.shape[-1] ** .5, [src_feats, tgt_feats]) if self.match_type == "dual_softmax": # dual softmax matching sim_matrix_1 = torch.einsum("bsc,btc->bst", src_feats, tgt_feats) / self.temperature if src_mask is not None: sim_matrix_2 = sim_matrix_1.clone() sim_matrix_1.masked_fill_(~src_mask[:, :, None], float('-inf')) sim_matrix_2.masked_fill_(~tgt_mask[:, None, :], float('-inf')) conf_matrix = F.softmax(sim_matrix_1, 1) * F.softmax(sim_matrix_2, 2) else : conf_matrix = F.softmax(sim_matrix_1, 1) * F.softmax(sim_matrix_1, 2) elif self.match_type == "sinkhorn" : #optimal transport sinkhoron sim_matrix = torch.einsum("bsc,btc->bst", src_feats, tgt_feats) if src_mask is not None: sim_matrix.masked_fill_( ~(src_mask[..., None] * tgt_mask[:, None]).bool(), float('-inf')) log_assign_matrix = log_optimal_transport( sim_matrix, self.bin_score, self.skh_iters, src_mask, tgt_mask) assign_matrix = log_assign_matrix.exp() conf_matrix = assign_matrix[:, :-1, :-1].contiguous() coarse_match, _, _ = self.get_match(conf_matrix, self.confidence_threshold) return conf_matrix, coarse_match

py

lepard

lepard-main/models/loss.py

import torch import torch.nn as nn import numpy as np import open3d as o3d from lib.benchmark_utils import to_o3d_pcd from lib.visualization import * import nibabel.quaternions as nq from sklearn.metrics import precision_recall_fscore_support from datasets.utils import blend_scene_flow, multual_nn_correspondence, knn_point_np from models.matching import Matching as CM def ransac_pose_estimation(src_pcd, tgt_pcd, corrs, distance_threshold=0.05, ransac_n=3): src_pcd = to_o3d_pcd(src_pcd) tgt_pcd = to_o3d_pcd(tgt_pcd) corrs = o3d.utility.Vector2iVector(np.array(corrs).T) result_ransac = o3d.registration.registration_ransac_based_on_correspondence( source=src_pcd, target=tgt_pcd, corres=corrs, max_correspondence_distance=distance_threshold, estimation_method=o3d.registration.TransformationEstimationPointToPoint(False), ransac_n=ransac_n, criteria=o3d.registration.RANSACConvergenceCriteria(50000, 1000)) return result_ransac.transformation def computeTransformationErr(trans, info): """ Computer the transformation error as an approximation of the RMSE of corresponding points. More informaiton at http://redwood-data.org/indoor/registration.html Args: trans (numpy array): transformation matrices [n,4,4] info (numpy array): covariance matrices of the gt transformation paramaters [n,4,4] Returns: p (float): transformation error """ t = trans[:3, 3] r = trans[:3, :3] q = nq.mat2quat(r) er = np.concatenate([t, q[1:]], axis=0) p = er.reshape(1, 6) @ info @ er.reshape(6, 1) / info[0, 0] return p.item() class MatchMotionLoss(nn.Module): def __init__(self, config): super().__init__() self.focal_alpha = config['focal_alpha'] self.focal_gamma = config['focal_gamma'] self.pos_w = config['pos_weight'] self.neg_w = config['neg_weight'] self.mot_w = config['motion_weight'] self.mat_w = config['match_weight'] self.motion_loss_type = config['motion_loss_type'] self.match_type = config['match_type'] self.positioning_type = config['positioning_type'] self.registration_threshold = config['registration_threshold'] self.confidence_threshold_metric = config['confidence_threshold_metric'] self.inlier_thr = config['inlier_thr'] self.fmr_thr = config['fmr_thr'] self.mutual_nearest = config['mutual_nearest'] self.dataset = config['dataset'] def forward(self, data): loss_info = {} loss = self.ge_coarse_loss(data, loss_info) loss_info.update({ 'loss': loss }) return loss_info def ge_coarse_loss(self, data, loss_info, eval_metric=False): if self.dataset == "4dmatch": s2t_flow = torch.zeros_like(data['s_pcd']) for i, cflow in enumerate(data['coarse_flow']): s2t_flow[i][: len(cflow)] = cflow loss = 0. src_mask = data['src_mask'] tgt_mask = data['tgt_mask'] conf_matrix_pred = data['conf_matrix_pred'] match_gt = data['coarse_matches'] R_s2t_gt = data['batched_rot'] t_s2t_gt = data['batched_trn'] #get the overlap mask, for dense motion loss s_overlap_mask = torch.zeros_like(src_mask).bool() for bi, corr in enumerate (match_gt): s_overlap_mask[bi][ corr[0] ] = True # compute focal loss c_weight = (src_mask[:, :, None] * tgt_mask[:, None, :]).float() conf_matrix_gt = self.match_2_conf_matrix(match_gt, conf_matrix_pred) data['conf_matrix_gt'] = conf_matrix_gt focal_coarse = self.compute_correspondence_loss(conf_matrix_pred, conf_matrix_gt, weight=c_weight) recall, precision = self.compute_match_recall( conf_matrix_gt, data['coarse_match_pred']) loss_info.update( { "focal_coarse": focal_coarse, "recall_coarse": recall, "precision_coarse": precision } ) loss = loss + self.mat_w * focal_coarse if recall > 0.01 and self.mot_w > 0: R_s2t_pred = data["R_s2t_pred"] t_s2t_pred = data["t_s2t_pred"] #compute predicted flow. Note, if 4dmatch, the R_pred,t_pred try to find the best rigid fit of deformation src_pcd_wrapped_pred = (torch.matmul(R_s2t_pred, data['s_pcd'].transpose(1, 2)) + t_s2t_pred).transpose(1, 2) sflow_pred = src_pcd_wrapped_pred - data['s_pcd'] if self.dataset == '4dmatch': spcd_deformed = data['s_pcd'] + s2t_flow src_pcd_wrapped_gt = (torch.matmul(R_s2t_gt, spcd_deformed.transpose(1, 2)) + t_s2t_gt).transpose(1, 2) else : # 3dmatch src_pcd_wrapped_gt = (torch.matmul(R_s2t_gt, data['s_pcd'].transpose(1, 2)) + t_s2t_gt).transpose(1, 2) sflow_gt = src_pcd_wrapped_gt - data['s_pcd'] e1 = torch.sum(torch.abs(sflow_pred - sflow_gt), 2) e1 = e1[s_overlap_mask] # [data['src_mask']] l1_loss = torch.mean(e1) loss = loss + self.mot_w * l1_loss # # if eval_metric : # # match_pred, _, _ = CM.get_match(data['conf_matrix_pred'], thr=self.confidence_threshold_metric, mutual=self.mutual_nearest) # # '''Inlier Ratio (IR)''' # ir = self.compute_inlier_ratio(match_pred, data, self.inlier_thr, # s2t_flow=s2t_flow if self.dataset == "4dmatch" else None) # loss_info.update({"Inlier Ratio": ir.mean()}) # # if self.dataset == '3dmatch': # # '''Feature Matching Recall (FMR)''' # fmr = (ir > self.fmr_thr).float().sum() / len(ir) # loss_info.update({"Feature Matching Recall": fmr}) # # '''Registration Recall (RR)''' # rot_, trn_ = self.ransac_regist_coarse(data['s_pcd'], data['t_pcd'], src_mask, tgt_mask , match_pred) # rot, trn = rot_.to(data['s_pcd']) , trn_.to(data['s_pcd']) # rr = self.compute_registration_recall(rot, trn, data, self.registration_threshold) # loss_info.update({'Registration_Recall': rr}) if self.positioning_type == "procrustes": for layer_ind in data["position_layers"]: # compute focal loss rpe_conf_matrix = data["position_layers"][layer_ind]["conf_matrix"] focal_rpe = self.compute_correspondence_loss(rpe_conf_matrix, conf_matrix_gt, weight=c_weight) recall, precision = self.compute_match_recall(conf_matrix_gt, data["position_layers"][layer_ind]['match_pred']) # loss_info.update({'focal_layer_%d' % layer_ind: focal_rpe, 'recall_layer_%d' % layer_ind: recall, # 'precision_layer_%d' % layer_ind: precision}) loss = loss + self.mat_w * focal_rpe if recall >0.01 and self.mot_w > 0: R_s2t_pred = data["position_layers"][layer_ind]["R_s2t_pred"] t_s2t_pred = data["position_layers"][layer_ind]["t_s2t_pred"] src_pcd_wrapped_pred = (torch.matmul(R_s2t_pred, data['s_pcd'].transpose(1, 2)) + t_s2t_pred).transpose(1, 2) sflow_pred = src_pcd_wrapped_pred - data['s_pcd'] if self.dataset == '4dmatch': spcd_deformed = data['s_pcd'] + s2t_flow src_pcd_wrapped_gt = ( torch.matmul(R_s2t_gt, spcd_deformed.transpose(1, 2)) + t_s2t_gt).transpose(1, 2) else: # 3dmatch src_pcd_wrapped_gt = ( torch.matmul(R_s2t_gt, data['s_pcd'].transpose(1, 2)) + t_s2t_gt).transpose(1, 2) sflow_gt = src_pcd_wrapped_gt - data['s_pcd'] e1 = torch.sum(torch.abs(sflow_pred - sflow_gt), 2) #[data['src_mask']] e1 = e1[s_overlap_mask] # [data['src_mask']] l1_loss = torch.mean(e1) loss = loss + self.mot_w * l1_loss return loss @staticmethod def compute_nrfmr(match_pred, data, recall_thr=0.04): s_pcd, t_pcd = data['s_pcd'], data['t_pcd'] s_pcd_raw = data['src_pcd_list'] sflow_list = data['sflow_list'] metric_index_list = data['metric_index_list'] batched_rot = data['batched_rot'] # B,3,3 batched_trn = data['batched_trn'] nrfmr = 0. for i in range(len(s_pcd_raw)): # use the match prediction as the motion anchor match_pred_i = match_pred[match_pred[:, 0] == i] s_id, t_id = match_pred_i[:, 1], match_pred_i[:, 2] s_pcd_matched = s_pcd[i][s_id] t_pcd_matched = t_pcd[i][t_id] motion_pred = t_pcd_matched - s_pcd_matched if len(s_pcd_matched) >= 3 : # get the wrapped metric points metric_index = metric_index_list[i] sflow = sflow_list[i] s_pcd_raw_i = s_pcd_raw[i] metric_pcd = s_pcd_raw_i[metric_index] metric_sflow = sflow[metric_index] metric_pcd_deformed = metric_pcd + metric_sflow metric_pcd_wrapped_gt = (torch.matmul(batched_rot[i], metric_pcd_deformed.T) + batched_trn[i]).T # blend the motion for metric points try: metric_motion_pred, valid_mask = MatchMotionLoss.blend_anchor_motion( metric_pcd.cpu().numpy(), s_pcd_matched.cpu().numpy(), motion_pred.cpu().numpy(), knn=3, search_radius=0.1) metric_pcd_wrapped_pred = metric_pcd + torch.from_numpy(metric_motion_pred).to(metric_pcd) dist = torch.sqrt(torch.sum((metric_pcd_wrapped_pred - metric_pcd_wrapped_gt) ** 2, dim=1)) r = (dist < recall_thr).float().sum() / len(dist) except : r = 0 nrfmr = nrfmr + r debug = False if debug: import mayavi.mlab as mlab c_red = (224. / 255., 0 / 255., 125 / 255.) c_pink = (224. / 255., 75. / 255., 232. / 255.) c_blue = (0. / 255., 0. / 255., 255. / 255.) scale_factor = 0.013 metric_pcd_wrapped_gt = metric_pcd_wrapped_gt.cpu() metric_pcd_wrapped_pred = metric_pcd_wrapped_pred.cpu() err = metric_pcd_wrapped_pred - metric_pcd_wrapped_gt mlab.points3d(metric_pcd_wrapped_gt[:, 0], metric_pcd_wrapped_gt[:, 1], metric_pcd_wrapped_gt[:, 2], scale_factor=scale_factor, color=c_pink) mlab.points3d(metric_pcd_wrapped_pred[:, 0], metric_pcd_wrapped_pred[:, 1], metric_pcd_wrapped_pred[:, 2], scale_factor=scale_factor, color=c_blue) mlab.quiver3d(metric_pcd_wrapped_gt[:, 0], metric_pcd_wrapped_gt[:, 1], metric_pcd_wrapped_gt[:, 2], err[:, 0], err[:, 1], err[:, 2], scale_factor=1, mode='2ddash', line_width=1.) mlab.show() nrfmr = nrfmr / len(s_pcd_raw) return nrfmr @staticmethod def blend_anchor_motion(query_loc, reference_loc, reference_flow, knn=3, search_radius=0.1): '''approximate flow on query points this function assume query points are sub- or un-sampled from reference locations @param query_loc:[m,3] @param reference_loc:[n,3] @param reference_flow:[n,3] @param knn: @return: blended_flow:[m,3] ''' dists, idx = knn_point_np(knn, reference_loc, query_loc) dists[dists < 1e-10] = 1e-10 mask = dists > search_radius dists[mask] = 1e+10 weight = 1.0 / dists weight = weight / np.sum(weight, -1, keepdims=True) # [B,N,3] blended_flow = np.sum(reference_flow[idx] * weight.reshape([-1, knn, 1]), axis=1, keepdims=False) mask = mask.sum(axis=1) < 3 return blended_flow, mask def compute_correspondence_loss(self, conf, conf_gt, weight=None): ''' @param conf: [B, L, S] @param conf_gt: [B, L, S] @param weight: [B, L, S] @return: ''' pos_mask = conf_gt == 1 neg_mask = conf_gt == 0 pos_w, neg_w = self.pos_w, self.neg_w #corner case assign a wrong gt if not pos_mask.any(): pos_mask[0, 0, 0] = True if weight is not None: weight[0, 0, 0] = 0. pos_w = 0. if not neg_mask.any(): neg_mask[0, 0, 0] = True if weight is not None: weight[0, 0, 0] = 0. neg_w = 0. # focal loss conf = torch.clamp(conf, 1e-6, 1 - 1e-6) alpha = self.focal_alpha gamma = self.focal_gamma if self.match_type == "dual_softmax": pos_conf = conf[pos_mask] loss_pos = - alpha * torch.pow(1 - pos_conf, gamma) * pos_conf.log() if weight is not None: loss_pos = loss_pos * weight[pos_mask] loss = pos_w * loss_pos.mean() return loss elif self.match_type == "sinkhorn": # no supervision on dustbin row & column. loss_pos = - alpha * torch.pow(1 - conf[pos_mask], gamma) * (conf[pos_mask]).log() loss_neg = - alpha * torch.pow(conf[neg_mask], gamma) * (1 - conf[neg_mask]).log() loss = pos_w * loss_pos.mean() + neg_w * loss_neg.mean() return loss def match_2_conf_matrix(self, matches_gt, matrix_pred): matrix_gt = torch.zeros_like(matrix_pred) for b, match in enumerate (matches_gt) : matrix_gt [ b][ match[0], match[1] ] = 1 return matrix_gt @staticmethod def compute_match_recall(conf_matrix_gt, match_pred) : #, s_pcd, t_pcd, search_radius=0.3): ''' @param conf_matrix_gt: @param match_pred: @return: ''' pred_matrix = torch.zeros_like(conf_matrix_gt) b_ind, src_ind, tgt_ind = match_pred[:, 0], match_pred[:, 1], match_pred[:, 2] pred_matrix[b_ind, src_ind, tgt_ind] = 1. true_positive = (pred_matrix == conf_matrix_gt) * conf_matrix_gt recall = true_positive.sum() / conf_matrix_gt.sum() precision = true_positive.sum() / max(len(match_pred), 1) return recall, precision @staticmethod def ransac_regist_coarse(batched_src_pcd, batched_tgt_pcd, src_mask, tgt_mask, match_pred ): s_len = src_mask.sum(dim=1).int() t_len = tgt_mask.sum(dim=1).int() bsize = len(batched_src_pcd) batched_src_pcd = MatchMotionLoss.tensor2numpy( batched_src_pcd) batched_tgt_pcd = MatchMotionLoss.tensor2numpy( batched_tgt_pcd) match_pred = MatchMotionLoss.tensor2numpy(match_pred) rot = [] trn = [] for i in range(bsize): s_pcd = batched_src_pcd[i][:s_len[i]] t_pcd = batched_tgt_pcd[i][:t_len[i]] pair_i = match_pred[:, 0] == i n_pts = pair_i.sum() if n_pts < 3 : rot.append(torch.eye(3)) trn.append(torch.zeros((3,1))) continue ind = match_pred[pair_i] s_ind, t_ind = ind[:, 1], ind[:, 2] pose = ransac_pose_estimation(s_pcd, t_pcd, [s_ind, t_ind], distance_threshold=0.05) pose = pose.copy() rot.append(torch.from_numpy(pose[:3,:3])) trn.append(torch.from_numpy(pose[:3,3:])) return torch.stack(rot, dim=0 ), torch.stack(trn , dim=0)#ndarray @staticmethod def compute_inlier_ratio(match_pred, data, inlier_thr, s2t_flow=None): s_pcd, t_pcd = data['s_pcd'], data['t_pcd'] #B,N,3 batched_rot = data['batched_rot'] #B,3,3 batched_trn = data['batched_trn'] if s2t_flow is not None: # 4dmatch s_pcd_deformed = s_pcd + s2t_flow s_pcd_wrapped = (torch.matmul(batched_rot, s_pcd_deformed.transpose(1, 2)) + batched_trn).transpose(1,2) else: # 3dmatch s_pcd_wrapped = (torch.matmul(batched_rot, s_pcd.transpose(1, 2)) + batched_trn).transpose(1,2) s_pcd_matched = s_pcd_wrapped [match_pred[:,0], match_pred[:,1]] t_pcd_matched = t_pcd [match_pred[:,0], match_pred[:,2]] inlier = torch.sum( (s_pcd_matched - t_pcd_matched)**2 , dim= 1) < inlier_thr**2 bsize = len(s_pcd) IR=[] for i in range(bsize): pair_i = match_pred[:, 0] == i n_match = pair_i.sum() inlier_i = inlier[pair_i] n_inlier = inlier_i.sum().float() if n_match <3: IR.append( n_match.float()*0) else : IR.append(n_inlier/n_match) return torch.stack(IR, dim=0) @staticmethod def compute_registration_recall(R_est, t_est, data, thr=0.2): bs = len(R_est) success = 0. if data['gt_cov'] is not None: err2 = thr ** 2 gt = np.zeros( (bs, 4, 4)) gt[:, -1,-1] = 1 gt[:, :3, :3] = data['batched_rot'].cpu().numpy() gt[:, :3, 3:] = data['batched_trn'].cpu().numpy() pred = np.zeros((bs, 4, 4)) pred[:, -1, -1] = 1 pred[:, :3, :3] = R_est.detach().cpu().numpy() pred[:, :3, 3:] = t_est.detach().cpu().numpy() for i in range(bs): p = computeTransformationErr( np.linalg.inv(gt[i]) @ pred[i], data['gt_cov'][i]) if p <= err2: success += 1 rr = success / bs return rr else : return 0. @staticmethod def tensor2numpy(tensor): if tensor.requires_grad: tensor=tensor.detach() return tensor.cpu().numpy()

py

lepard

lepard-main/models/position_encoding.py

import math import torch from torch import nn class VolumetricPositionEncoding(nn.Module): def __init__(self, config): super().__init__() self.feature_dim = config.feature_dim self.vol_bnds = config.vol_bnds self.voxel_size = config.voxel_size self.vol_origin = self.vol_bnds[0] self.pe_type = config.pe_type def voxelize(self, xyz): ''' @param xyz: B,N,3 @return: B,N,3 ''' if type ( self.vol_origin ) == list : self.vol_origin = torch.FloatTensor(self.vol_origin ).view(1, 1, -1).to( xyz.device ) return (xyz - self.vol_origin) / self.voxel_size @staticmethod def embed_rotary(x, cos, sin): ''' @param x: [B,N,d] @param cos: [B,N,d] [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1] @param sin: [B,N,d] [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1] @return: ''' x2 = torch.stack([-x[..., 1::2], x[..., ::2]], dim=-1).reshape_as(x).contiguous() x = x * cos + x2 * sin return x @staticmethod def embed_pos(pe_type, x, pe): """ combine feature and position code """ if pe_type == 'rotary': return VolumetricPositionEncoding.embed_rotary(x, pe[..., 0], pe[..., 1]) elif pe_type == 'sinusoidal': return x + pe else: raise KeyError() def forward(self, XYZ): ''' @param XYZ: [B,N,3] @return: ''' bsize, npoint, _ = XYZ.shape vox = self.voxelize( XYZ) x_position, y_position, z_position = vox[..., 0:1], vox[...,1:2], vox[...,2:3] div_term = torch.exp( torch.arange(0, self.feature_dim // 3, 2, dtype=torch.float, device=XYZ.device) * (-math.log(10000.0) / (self.feature_dim // 3))) div_term = div_term.view( 1,1, -1) # [1, 1, d//6] sinx = torch.sin(x_position * div_term) # [B, N, d//6] cosx = torch.cos(x_position * div_term) siny = torch.sin(y_position * div_term) cosy = torch.cos(y_position * div_term) sinz = torch.sin(z_position * div_term) cosz = torch.cos(z_position * div_term) if self.pe_type == 'sinusoidal' : position_code = torch.cat( [ sinx, cosx, siny, cosy, sinz, cosz] , dim=-1 ) elif self.pe_type == "rotary" : # sin/cos [θ0,θ1,θ2......θd/6-1] -> sin/cos [θ0,θ0,θ1,θ1,θ2,θ2......θd/6-1,θd/6-1] sinx, cosx, siny, cosy, sinz, cosz = map( lambda feat:torch.stack([feat, feat], dim=-1).view(bsize, npoint, -1), [ sinx, cosx, siny, cosy, sinz, cosz] ) sin_pos = torch.cat([sinx,siny,sinz], dim=-1) cos_pos = torch.cat([cosx,cosy,cosz], dim=-1) position_code = torch.stack( [cos_pos, sin_pos] , dim=-1) else: raise KeyError() if position_code.requires_grad: position_code = position_code.detach() return position_code

py

lepard

lepard-main/models/backbone.py

from models.blocks import * import torch.nn.functional as F import numpy as np class KPFCN(nn.Module): def __init__(self, config): super(KPFCN, self).__init__() ############ # Parameters ############ layer = 0 r = config.first_subsampling_dl * config.conv_radius in_dim = config.in_feats_dim out_dim = config.first_feats_dim ##################### # List Encoder blocks ##################### self.encoder_blocks = nn.ModuleList() self.encoder_skip_dims = [] self.encoder_skips = [] # Loop over consecutive blocks for block_i, block in enumerate(config.architecture): # Check equivariance if ('equivariant' in block) and (not out_dim % 3 == 0): raise ValueError('Equivariant block but features dimension is not a factor of 3') # Detect change to next layer for skip connection if np.any([tmp in block for tmp in ['pool', 'strided', 'upsample', 'global']]): self.encoder_skips.append(block_i) self.encoder_skip_dims.append(in_dim) # Detect upsampling block to stop if 'upsample' in block: break # Apply the good block function defining tf ops self.encoder_blocks.append(block_decider(block, r, in_dim, out_dim, layer, config)) # Update dimension of input from output if 'simple' in block: in_dim = out_dim // 2 else: in_dim = out_dim # Detect change to a subsampled layer if 'pool' in block or 'strided' in block: # Update radius and feature dimension for next layer layer += 1 r *= 2 out_dim *= 2 ##################### # bottleneck output & input layer self.coarse_out = nn.Conv1d(in_dim//2, config.coarse_feature_dim, kernel_size=1, bias=True) coarse_in_dim = config.coarse_feature_dim self.coarse_in = nn.Conv1d(coarse_in_dim, in_dim//2, kernel_size=1, bias=True) ##################### # List Decoder blocks ##################### # Save all block operations in a list of modules self.decoder_blocks = nn.ModuleList() self.decoder_concats = [] # Find first upsampling block start_i = 0 for block_i, block in enumerate(config.architecture): if 'upsample' in block: start_i = block_i break # Loop over consecutive blocks for block_i, block in enumerate(config.architecture[start_i:]): # Add dimension of skip connection concat if block_i > 0 and 'upsample' in config.architecture[start_i + block_i - 1]: in_dim += self.encoder_skip_dims[layer] self.decoder_concats.append(block_i) # Apply the good block function defining tf ops self.decoder_blocks.append(block_decider(block, r, in_dim, out_dim, layer, config)) # Update dimension of input from output in_dim = out_dim # Detect change to a subsampled layer if 'upsample' in block: # Update radius and feature dimension for next layer layer -= 1 r *= 0.5 out_dim = out_dim // 2 ##################### # fine output layer ##################### fine_feature_dim = config.fine_feature_dim self.fine_out = nn.Conv1d(out_dim, fine_feature_dim, kernel_size=1, bias=True) def forward(self, batch, phase = 'encode'): # Get input features if phase == 'coarse' : x = batch['features'].clone().detach() # 1. joint encoder part self.skip_x = [] for block_i, block_op in enumerate(self.encoder_blocks): if block_i in self.encoder_skips: self.skip_x.append(x) x = block_op(x, batch) # [N,C] for block_i, block_op in enumerate(self.decoder_blocks): if block_i in self.decoder_concats: x = torch.cat([x, self.skip_x.pop()], dim=1) x = block_op(x, batch) if block_i == 1 : coarse_feats = x.transpose(0,1).unsqueeze(0) #[B, C, N] coarse_feats = self.coarse_out(coarse_feats) #[B, C, N] coarse_feats = coarse_feats.transpose(1,2).squeeze(0) return coarse_feats #[N,C2] # # elif phase == "fine": # # coarse_feats = batch['coarse_feats'] # coarse_feats = coarse_feats.transpose(0,1).unsqueeze(0) # coarse_feats = self.coarse_in(coarse_feats) # x = coarse_feats.transpose(1,2).squeeze(0) # # # for block_i, block_op in enumerate(self.decoder_blocks): # if block_i > 1 : # if block_i in self.decoder_concats: # x = torch.cat([x, self.skip_x.pop()], dim=1) # x = block_op(x, batch) # # fine_feats = x.transpose(0, 1).unsqueeze(0) # [1, C, N] # fine_feats = self.fine_out(fine_feats) # [1, C, N] # fine_feats = fine_feats.transpose(1, 2).squeeze(0) # # return fine_feats

py

lepard

lepard-main/models/transformer.py

import copy import math import torch from torch import nn from torch.nn import Module, Dropout from models.position_encoding import VolumetricPositionEncoding as VolPE from models.matching import Matching from models.procrustes import SoftProcrustesLayer import numpy as np import random from scipy.spatial.transform import Rotation class GeometryAttentionLayer(nn.Module): def __init__(self, config): super(GeometryAttentionLayer, self).__init__() d_model = config['feature_dim'] nhead = config['n_head'] self.dim = d_model // nhead self.nhead = nhead self.pe_type = config['pe_type'] # multi-head attention self.q_proj = nn.Linear(d_model, d_model, bias=False) self.k_proj = nn.Linear(d_model, d_model, bias=False) self.v_proj = nn.Linear(d_model, d_model, bias=False) # self.attention = Attention() #LinearAttention() if attention == 'linear' else FullAttention() self.merge = nn.Linear(d_model, d_model, bias=False) # feed-forward network self.mlp = nn.Sequential( nn.Linear(d_model*2, d_model*2, bias=False), nn.ReLU(True), nn.Linear(d_model*2, d_model, bias=False), ) # norm and dropout self.norm1 = nn.LayerNorm(d_model) self.norm2 = nn.LayerNorm(d_model) def forward(self, x, source, x_pe, source_pe, x_mask=None, source_mask=None): bs = x.size(0) q, k, v = x, source, source qp, kvp = x_pe, source_pe q_mask, kv_mask = x_mask, source_mask if self.pe_type == 'sinusoidal': #w(x+p), attention is all you need : https://arxiv.org/abs/1706.03762 if qp is not None: # disentangeld q = q + qp k = k + kvp qw = self.q_proj(q).view(bs, -1, self.nhead, self.dim) # [N, L, (H, D)] kw = self.k_proj(k).view(bs, -1, self.nhead, self.dim) # [N, S, (H, D)] vw = self.v_proj(v).view(bs, -1, self.nhead, self.dim) elif self.pe_type == 'rotary': #Rwx roformer : https://arxiv.org/abs/2104.09864 qw = self.q_proj(q) kw = self.k_proj(k) vw = self.v_proj(v) if qp is not None: # disentangeld q_cos, q_sin = qp[...,0] ,qp[...,1] k_cos, k_sin = kvp[...,0],kvp[...,1] qw = VolPE.embed_rotary(qw, q_cos, q_sin) kw = VolPE.embed_rotary(kw, k_cos, k_sin) qw = qw.view(bs, -1, self.nhead, self.dim) kw = kw.view(bs, -1, self.nhead, self.dim) vw = vw.view(bs, -1, self.nhead, self.dim) else: raise KeyError() # attention a = torch.einsum("nlhd,nshd->nlsh", qw, kw) if kv_mask is not None: a.masked_fill_( q_mask[:, :, None, None] * (~kv_mask[:, None, :, None]), float('-inf')) a = a / qw.size(3) **0.5 a = torch.softmax(a, dim=2) o = torch.einsum("nlsh,nshd->nlhd", a, vw).contiguous() # [N, L, (H, D)] message = self.merge(o.view(bs, -1, self.nhead*self.dim)) # [N, L, C] message = self.norm1(message) # feed-forward network message = self.mlp(torch.cat([x, message], dim=2)) message = self.norm2(message) e = x + message return e class RepositioningTransformer(nn.Module): def __init__(self, config): super(RepositioningTransformer, self).__init__() self.d_model = config['feature_dim'] self.nhead = config['n_head'] self.layer_types = config['layer_types'] self.positioning_type = config['positioning_type'] self.pe_type =config['pe_type'] self.entangled= config['entangled'] self.positional_encoding = VolPE(config) encoder_layer = GeometryAttentionLayer (config) self.layers = nn.ModuleList() for l_type in self.layer_types: if l_type in ['self','cross']: self.layers.append( copy.deepcopy(encoder_layer)) elif l_type == "positioning": if self.positioning_type == 'procrustes': positioning_layer = nn.ModuleList() positioning_layer.append( Matching(config['feature_matching'])) positioning_layer.append( SoftProcrustesLayer(config['procrustes']) ) self.layers.append(positioning_layer) elif self.positioning_type in ['oracle', 'randSO3']: self.layers.append( None) else : raise KeyError(self.positioning_type + " undefined positional encoding type") else: raise KeyError() self._reset_parameters() def forward(self, src_feat, tgt_feat, s_pcd, t_pcd, src_mask, tgt_mask, data, T = None, timers = None): self.timers = timers assert self.d_model == src_feat.size(2), "the feature number of src and transformer must be equal" if T is not None: R, t = T src_pcd_wrapped = (torch.matmul(R, s_pcd.transpose(1, 2)) + t).transpose(1, 2) tgt_pcd_wrapped = t_pcd else: src_pcd_wrapped = s_pcd tgt_pcd_wrapped = t_pcd src_pe = self.positional_encoding( src_pcd_wrapped) tgt_pe = self.positional_encoding( tgt_pcd_wrapped) if not self.entangled: position_layer = 0 data.update({"position_layers":{}}) for layer, name in zip(self.layers, self.layer_types) : if name == 'self': if self.timers: self.timers.tic('self atten') src_feat = layer(src_feat, src_feat, src_pe, src_pe, src_mask, src_mask,) tgt_feat = layer(tgt_feat, tgt_feat, tgt_pe, tgt_pe, tgt_mask, tgt_mask) if self.timers: self.timers.toc('self atten') elif name == 'cross': if self.timers: self.timers.tic('cross atten') src_feat = layer(src_feat, tgt_feat, src_pe, tgt_pe, src_mask, tgt_mask) tgt_feat = layer(tgt_feat, src_feat, tgt_pe, src_pe, tgt_mask, src_mask) if self.timers: self.timers.toc('cross atten') elif name =='positioning': if self.positioning_type == 'procrustes': conf_matrix, match_pred = layer[0](src_feat, tgt_feat, src_pe, tgt_pe, src_mask, tgt_mask, data, pe_type=self.pe_type) position_layer += 1 data["position_layers"][position_layer] = {"conf_matrix": conf_matrix, "match_pred": match_pred} if self.timers: self.timers.tic('procrustes_layer') R, t, R_forwd, t_forwd, condition, solution_mask = layer[1] (conf_matrix, s_pcd, t_pcd, src_mask, tgt_mask) if self.timers: self.timers.toc('procrustes_layer') data["position_layers"][position_layer].update({ "R_s2t_pred": R,"t_s2t_pred": t, "solution_mask": solution_mask, "condition": condition}) src_pcd_wrapped = (torch.matmul(R_forwd, s_pcd.transpose(1, 2)) + t_forwd).transpose(1, 2) tgt_pcd_wrapped = t_pcd src_pe = self.positional_encoding(src_pcd_wrapped) tgt_pe = self.positional_encoding(tgt_pcd_wrapped) elif self.positioning_type == 'randSO3': src_pcd_wrapped = self.rand_rot_pcd( s_pcd, src_mask) tgt_pcd_wrapped = t_pcd src_pe = self.positional_encoding(src_pcd_wrapped) tgt_pe = self.positional_encoding(tgt_pcd_wrapped) elif self.positioning_type == 'oracle': #Note R,t ground truth is only available for computing oracle position encoding rot_gt = data['batched_rot'] trn_gt = data['batched_trn'] src_pcd_wrapped = (torch.matmul(rot_gt, s_pcd.transpose(1, 2)) + trn_gt).transpose(1, 2) tgt_pcd_wrapped = t_pcd src_pe = self.positional_encoding(src_pcd_wrapped) tgt_pe = self.positional_encoding(tgt_pcd_wrapped) else: raise KeyError(self.positioning_type + " undefined positional encoding type") else : raise KeyError return src_feat, tgt_feat, src_pe, tgt_pe else : # pos. fea. entangeled position_layer = 0 data.update({"position_layers":{}}) src_feat = VolPE.embed_pos(self.pe_type, src_feat, src_pe) tgt_feat = VolPE.embed_pos(self.pe_type, tgt_feat, tgt_pe) for layer, name in zip(self.layers, self.layer_types): if name == 'self': if self.timers: self.timers.tic('self atten') src_feat = layer(src_feat, src_feat, None, None, src_mask, src_mask, ) tgt_feat = layer(tgt_feat, tgt_feat, None, None, tgt_mask, tgt_mask) if self.timers: self.timers.toc('self atten') elif name == 'cross': if self.timers: self.timers.tic('cross atten') src_feat = layer(src_feat, tgt_feat, None, None, src_mask, tgt_mask) tgt_feat = layer(tgt_feat, src_feat, None, None, tgt_mask, src_mask) if self.timers: self.timers.toc('cross atten') elif name == 'positioning': pass return src_feat, tgt_feat, src_pe, tgt_pe def rand_rot_pcd (self, pcd, mask): ''' @param pcd: B, N, 3 @param mask: B, N @return: ''' pcd[~mask]=0. N = mask.shape[1] n_points = mask.sum(dim=1, keepdim=True).view(-1,1,1) bs = pcd.shape[0] euler_ab = np.random.rand(bs, 3) * np.pi * 2 # anglez, angley, anglex rand_rot = torch.from_numpy( Rotation.from_euler('zyx', euler_ab).as_matrix() ).to(pcd) pcd_u = pcd.mean(dim=1, keepdim=True) * N / n_points pcd_centered = pcd - pcd_u pcd_rand_rot = torch.matmul( rand_rot, pcd_centered.transpose(1,2) ).transpose(1,2) + pcd_u return pcd_rand_rot def _reset_parameters(self): for p in self.parameters(): if p.dim() > 1: nn.init.xavier_uniform_(p)

py

lepard

lepard-main/models/__init__.py

py

lepard

lepard-main/models/procrustes.py

import torch import torch.nn as nn def topk(data, num_topk): sort, idx = data.sort(descending=True) return sort[:num_topk], idx[:num_topk] class SoftProcrustesLayer(nn.Module): def __init__(self, config): super(SoftProcrustesLayer, self).__init__() self.sample_rate = config.sample_rate self.max_condition_num= config.max_condition_num @staticmethod def batch_weighted_procrustes( X, Y, w, eps=0.0001): ''' @param X: source frame [B, N,3] @param Y: target frame [B, N,3] @param w: weights [B, N,1] @param eps: @return: ''' # https://ieeexplore.ieee.org/document/88573 bsize = X.shape[0] device = X.device W1 = torch.abs(w).sum(dim=1, keepdim=True) w_norm = w / (W1 + eps) mean_X = (w_norm * X).sum(dim=1, keepdim=True) mean_Y = (w_norm * Y).sum(dim=1, keepdim=True) Sxy = torch.matmul( (Y - mean_Y).transpose(1,2), w_norm * (X - mean_X) ) Sxy = Sxy.cpu().double() U, D, V = Sxy.svd() # small SVD runs faster on cpu condition = D.max(dim=1)[0] / D.min(dim=1)[0] S = torch.eye(3)[None].repeat(bsize,1,1).double() UV_det = U.det() * V.det() S[:, 2:3, 2:3] = UV_det.view(-1, 1,1) svT = torch.matmul( S, V.transpose(1,2) ) R = torch.matmul( U, svT).float().to(device) t = mean_Y.transpose(1,2) - torch.matmul( R, mean_X.transpose(1,2) ) return R, t, condition def forward(self, conf_matrix, src_pcd, tgt_pcd, src_mask, tgt_mask): ''' @param conf_matrix: @param src_pcd: @param tgt_pcd: @param src_mask: @param tgt_mask: @return: ''' bsize, N, M = conf_matrix.shape # subsample correspondence src_len = src_mask.sum(dim=1) tgt_len = tgt_mask.sum(dim=1) entry_max, _ = torch.stack([src_len,tgt_len], dim=0).max(dim=0) entry_max = (entry_max * self.sample_rate).int() sample_n_points = entry_max.float().mean().int() #entry_max.max() conf, idx = conf_matrix.view(bsize, -1).sort(descending=True,dim=1) w = conf [:, :sample_n_points] idx= idx[:, :sample_n_points] idx_src = idx//M #torch.div(idx, M, rounding_mode='trunc') idx_tgt = idx%M b_index = torch.arange(bsize).view(-1, 1).repeat((1, sample_n_points)).view(-1) src_pcd_sampled = src_pcd[b_index, idx_src.view(-1)].view(bsize, sample_n_points, -1) tgt_pcd_sampled = tgt_pcd[b_index, idx_tgt.view(-1)].view(bsize, sample_n_points, -1) w_mask = torch.arange(sample_n_points).view(1,-1).repeat(bsize,1).to(w) w_mask = w_mask < entry_max[:,None] w[~w_mask] = 0. # solve try : R, t, condition = self.batch_weighted_procrustes(src_pcd_sampled, tgt_pcd_sampled, w[...,None]) except: # fail to get valid solution, this usually happens at the early stage of training R = torch.eye(3)[None].repeat(bsize,1,1).type_as(conf_matrix) t = torch.zeros(3, 1)[None].repeat(bsize,1,1).type_as(conf_matrix) condition = torch.zeros(bsize).type_as(conf_matrix) #filter unreliable solution with condition nnumber solution_mask = condition < self.max_condition_num R_forwd = R.clone() t_forwd = t.clone() R_forwd[~solution_mask] = torch.eye(3).type_as(R) t_forwd[~solution_mask] = torch.zeros(3, 1).type_as(R) return R, t, R_forwd, t_forwd, condition, solution_mask

py

lepard

lepard-main/models/pipeline.py

from models.blocks import * from models.backbone import KPFCN from models.transformer import RepositioningTransformer from models.matching import Matching from models.procrustes import SoftProcrustesLayer class Pipeline(nn.Module): def __init__(self, config): super(Pipeline, self).__init__() self.config = config self.backbone = KPFCN(config['kpfcn_config']) self.pe_type = config['coarse_transformer']['pe_type'] self.positioning_type = config['coarse_transformer']['positioning_type'] self.coarse_transformer = RepositioningTransformer(config['coarse_transformer']) self.coarse_matching = Matching(config['coarse_matching']) self.soft_procrustes = SoftProcrustesLayer(config['coarse_transformer']['procrustes']) def forward(self, data, timers=None): self.timers = timers if self.timers: self.timers.tic('kpfcn backbone encode') coarse_feats = self.backbone(data, phase="coarse") if self.timers: self.timers.toc('kpfcn backbone encode') if self.timers: self.timers.tic('coarse_preprocess') src_feats, tgt_feats, s_pcd, t_pcd, src_mask, tgt_mask = self.split_feats (coarse_feats, data) data.update({ 's_pcd': s_pcd, 't_pcd': t_pcd }) if self.timers: self.timers.toc('coarse_preprocess') if self.timers: self.timers.tic('coarse feature transformer') src_feats, tgt_feats, src_pe, tgt_pe = self.coarse_transformer(src_feats, tgt_feats, s_pcd, t_pcd, src_mask, tgt_mask, data, timers=timers) if self.timers: self.timers.toc('coarse feature transformer') if self.timers: self.timers.tic('match feature coarse') conf_matrix_pred, coarse_match_pred = self.coarse_matching(src_feats, tgt_feats, src_pe, tgt_pe, src_mask, tgt_mask, data, pe_type = self.pe_type) data.update({'conf_matrix_pred': conf_matrix_pred, 'coarse_match_pred': coarse_match_pred }) if self.timers: self.timers.toc('match feature coarse') if self.timers: self.timers.tic('procrustes_layer') R, t, _, _, _, _ = self.soft_procrustes(conf_matrix_pred, s_pcd, t_pcd, src_mask, tgt_mask) data.update({"R_s2t_pred": R, "t_s2t_pred": t}) if self.timers: self.timers.toc('procrustes_layer') return data def split_feats(self, geo_feats, data): pcd = data['points'][self.config['kpfcn_config']['coarse_level']] src_mask = data['src_mask'] tgt_mask = data['tgt_mask'] src_ind_coarse_split = data[ 'src_ind_coarse_split'] tgt_ind_coarse_split = data['tgt_ind_coarse_split'] src_ind_coarse = data['src_ind_coarse'] tgt_ind_coarse = data['tgt_ind_coarse'] b_size, src_pts_max = src_mask.shape tgt_pts_max = tgt_mask.shape[1] src_feats = torch.zeros([b_size * src_pts_max, geo_feats.shape[-1]]).type_as(geo_feats) tgt_feats = torch.zeros([b_size * tgt_pts_max, geo_feats.shape[-1]]).type_as(geo_feats) src_pcd = torch.zeros([b_size * src_pts_max, 3]).type_as(pcd) tgt_pcd = torch.zeros([b_size * tgt_pts_max, 3]).type_as(pcd) src_feats[src_ind_coarse_split] = geo_feats[src_ind_coarse] tgt_feats[tgt_ind_coarse_split] = geo_feats[tgt_ind_coarse] src_pcd[src_ind_coarse_split] = pcd[src_ind_coarse] tgt_pcd[tgt_ind_coarse_split] = pcd[tgt_ind_coarse] return src_feats.view( b_size , src_pts_max , -1), \ tgt_feats.view( b_size , tgt_pts_max , -1), \ src_pcd.view( b_size , src_pts_max , -1), \ tgt_pcd.view( b_size , tgt_pts_max , -1), \ src_mask, \ tgt_mask

py

lepard

lepard-main/models/blocks.py

import time import math import torch import torch.nn as nn from torch.nn.parameter import Parameter from torch.nn.init import kaiming_uniform_ from kernels.kernel_points import load_kernels # from lib.ply import write_ply def gather(x, idx, method=2): """ implementation of a custom gather operation for faster backwards. :param x: input with shape [N, D_1, ... D_d] :param idx: indexing with shape [n_1, ..., n_m] :param method: Choice of the method :return: x[idx] with shape [n_1, ..., n_m, D_1, ... D_d] """ if method == 0: return x[idx] elif method == 1: x = x.unsqueeze(1) x = x.expand((-1, idx.shape[-1], -1)) idx = idx.unsqueeze(2) idx = idx.expand((-1, -1, x.shape[-1])) return x.gather(0, idx) elif method == 2: for i, ni in enumerate(idx.size()[1:]): x = x.unsqueeze(i+1) new_s = list(x.size()) new_s[i+1] = ni x = x.expand(new_s) n = len(idx.size()) for i, di in enumerate(x.size()[n:]): idx = idx.unsqueeze(i+n) new_s = list(idx.size()) new_s[i+n] = di idx = idx.expand(new_s) return x.gather(0, idx) else: raise ValueError('Unkown method') def radius_gaussian(sq_r, sig, eps=1e-9): """ Compute a radius gaussian (gaussian of distance) :param sq_r: input radiuses [dn, ..., d1, d0] :param sig: extents of gaussians [d1, d0] or [d0] or float :return: gaussian of sq_r [dn, ..., d1, d0] """ return torch.exp(-sq_r / (2 * sig**2 + eps)) def closest_pool(x, inds): """ Pools features from the closest neighbors. WARNING: this function assumes the neighbors are ordered. :param x: [n1, d] features matrix :param inds: [n2, max_num] Only the first column is used for pooling :return: [n2, d] pooled features matrix """ # Add a last row with minimum features for shadow pools x = torch.cat((x, torch.zeros_like(x[:1, :])), 0) # Get features for each pooling location [n2, d] return gather(x, inds[:, 0]) def max_pool(x, inds): """ Pools features with the maximum values. :param x: [n1, d] features matrix :param inds: [n2, max_num] pooling indices :return: [n2, d] pooled features matrix """ # Add a last row with minimum features for shadow pools x = torch.cat((x, torch.zeros_like(x[:1, :])), 0) # Get all features for each pooling location [n2, max_num, d] pool_features = gather(x, inds) # Pool the maximum [n2, d] max_features, _ = torch.max(pool_features, 1) return max_features def global_average(x, batch_lengths): """ Block performing a global average over batch pooling :param x: [N, D] input features :param batch_lengths: [B] list of batch lengths :return: [B, D] averaged features """ # Loop over the clouds of the batch averaged_features = [] i0 = 0 for b_i, length in enumerate(batch_lengths): # Average features for each batch cloud averaged_features.append(torch.mean(x[i0:i0 + length], dim=0)) # Increment for next cloud i0 += length # Average features in each batch return torch.stack(averaged_features) # ---------------------------------------------------------------------------------------------------------------------- # # KPConv class # \******************/ # class KPConv(nn.Module): def __init__(self, kernel_size, p_dim, in_channels, out_channels, KP_extent, radius, fixed_kernel_points='center', KP_influence='linear', aggregation_mode='sum', deformable=False, modulated=False): """ Initialize parameters for KPConvDeformable. :param kernel_size: Number of kernel points. :param p_dim: dimension of the point space. :param in_channels: dimension of input features. :param out_channels: dimension of output features. :param KP_extent: influence radius of each kernel point. :param radius: radius used for kernel point init. Even for deformable, use the config.conv_radius :param fixed_kernel_points: fix position of certain kernel points ('none', 'center' or 'verticals'). :param KP_influence: influence function of the kernel points ('constant', 'linear', 'gaussian'). :param aggregation_mode: choose to sum influences, or only keep the closest ('closest', 'sum'). :param deformable: choose deformable or not :param modulated: choose if kernel weights are modulated in addition to deformed """ super(KPConv, self).__init__() # Save parameters self.K = kernel_size self.p_dim = p_dim self.in_channels = in_channels self.out_channels = out_channels self.radius = radius self.KP_extent = KP_extent self.fixed_kernel_points = fixed_kernel_points self.KP_influence = KP_influence self.aggregation_mode = aggregation_mode self.deformable = deformable self.modulated = modulated # Running variable containing deformed KP distance to input points. (used in regularization loss) self.min_d2 = None self.deformed_KP = None self.offset_features = None # Initialize weights self.weights = Parameter(torch.zeros((self.K, in_channels, out_channels), dtype=torch.float32), requires_grad=True) # Initiate weights for offsets if deformable: if modulated: self.offset_dim = (self.p_dim + 1) * self.K else: self.offset_dim = self.p_dim * self.K self.offset_conv = KPConv(self.K, self.p_dim, self.in_channels, self.offset_dim, KP_extent, radius, fixed_kernel_points=fixed_kernel_points, KP_influence=KP_influence, aggregation_mode=aggregation_mode) self.offset_bias = Parameter(torch.zeros(self.offset_dim, dtype=torch.float32), requires_grad=True) else: self.offset_dim = None self.offset_conv = None self.offset_bias = None # Reset parameters self.reset_parameters() # Initialize kernel points self.kernel_points = self.init_KP() return def reset_parameters(self): kaiming_uniform_(self.weights, a=math.sqrt(5)) if self.deformable: nn.init.zeros_(self.offset_bias) return def init_KP(self): """ Initialize the kernel point positions in a sphere :return: the tensor of kernel points """ # Create one kernel disposition (as numpy array). Choose the KP distance to center thanks to the KP extent K_points_numpy = load_kernels(self.radius, self.K, dimension=self.p_dim, fixed=self.fixed_kernel_points) return Parameter(torch.tensor(K_points_numpy, dtype=torch.float32), requires_grad=False) def forward(self, q_pts, s_pts, neighb_inds, x): ################### # Offset generation ################### if self.deformable: # Get offsets with a KPConv that only takes part of the features self.offset_features = self.offset_conv(q_pts, s_pts, neighb_inds, x) + self.offset_bias if self.modulated: # Get offset (in normalized scale) from features unscaled_offsets = self.offset_features[:, :self.p_dim * self.K] unscaled_offsets = unscaled_offsets.view(-1, self.K, self.p_dim) # Get modulations modulations = 2 * torch.sigmoid(self.offset_features[:, self.p_dim * self.K:]) else: # Get offset (in normalized scale) from features unscaled_offsets = self.offset_features.view(-1, self.K, self.p_dim) # No modulations modulations = None # Rescale offset for this layer offsets = unscaled_offsets * self.KP_extent else: offsets = None modulations = None ###################### # Deformed convolution ###################### # Add a fake point in the last row for shadow neighbors s_pts = torch.cat((s_pts, torch.zeros_like(s_pts[:1, :]) + 1e6), 0) # Get neighbor points [n_points, n_neighbors, dim] neighbors = s_pts[neighb_inds, :] # Center every neighborhood neighbors = neighbors - q_pts.unsqueeze(1) # Apply offsets to kernel points [n_points, n_kpoints, dim] if self.deformable: self.deformed_KP = offsets + self.kernel_points deformed_K_points = self.deformed_KP.unsqueeze(1) else: deformed_K_points = self.kernel_points # Get all difference matrices [n_points, n_neighbors, n_kpoints, dim] neighbors.unsqueeze_(2) differences = neighbors - deformed_K_points # Get the square distances [n_points, n_neighbors, n_kpoints] sq_distances = torch.sum(differences ** 2, dim=3) # Optimization by ignoring points outside a deformed KP range if self.deformable: # Save distances for loss self.min_d2, _ = torch.min(sq_distances, dim=1) # Boolean of the neighbors in range of a kernel point [n_points, n_neighbors] in_range = torch.any(sq_distances < self.KP_extent ** 2, dim=2).type(torch.int32) # New value of max neighbors new_max_neighb = torch.max(torch.sum(in_range, dim=1)) # For each row of neighbors, indices of the ones that are in range [n_points, new_max_neighb] neighb_row_bool, neighb_row_inds = torch.topk(in_range, new_max_neighb.item(), dim=1) # Gather new neighbor indices [n_points, new_max_neighb] new_neighb_inds = neighb_inds.gather(1, neighb_row_inds, sparse_grad=False) # Gather new distances to KP [n_points, new_max_neighb, n_kpoints] neighb_row_inds.unsqueeze_(2) neighb_row_inds = neighb_row_inds.expand(-1, -1, self.K) sq_distances = sq_distances.gather(1, neighb_row_inds, sparse_grad=False) # New shadow neighbors have to point to the last shadow point new_neighb_inds *= neighb_row_bool new_neighb_inds -= (neighb_row_bool.type(torch.int64) - 1) * int(s_pts.shape[0] - 1) else: new_neighb_inds = neighb_inds # Get Kernel point influences [n_points, n_kpoints, n_neighbors] if self.KP_influence == 'constant': # Every point get an influence of 1. all_weights = torch.ones_like(sq_distances) all_weights = torch.transpose(all_weights, 1, 2) elif self.KP_influence == 'linear': # Influence decrease linearly with the distance, and get to zero when d = KP_extent. all_weights = torch.clamp(1 - torch.sqrt(sq_distances) / self.KP_extent, min=0.0) all_weights = torch.transpose(all_weights, 1, 2) elif self.KP_influence == 'gaussian': # Influence in gaussian of the distance. sigma = self.KP_extent * 0.3 all_weights = radius_gaussian(sq_distances, sigma) all_weights = torch.transpose(all_weights, 1, 2) else: raise ValueError('Unknown influence function type (config.KP_influence)') # In case of closest mode, only the closest KP can influence each point if self.aggregation_mode == 'closest': neighbors_1nn = torch.argmin(sq_distances, dim=2) all_weights *= torch.transpose(nn.functional.one_hot(neighbors_1nn, self.K), 1, 2) elif self.aggregation_mode != 'sum': raise ValueError("Unknown convolution mode. Should be 'closest' or 'sum'") # Add a zero feature for shadow neighbors x = torch.cat((x, torch.zeros_like(x[:1, :])), 0) # Get the features of each neighborhood [n_points, n_neighbors, in_fdim] neighb_x = gather(x, new_neighb_inds) # Apply distance weights [n_points, n_kpoints, in_fdim] weighted_features = torch.matmul(all_weights, neighb_x) # Apply modulations if self.deformable and self.modulated: weighted_features *= modulations.unsqueeze(2) # Apply network weights [n_kpoints, n_points, out_fdim] weighted_features = weighted_features.permute((1, 0, 2)) kernel_outputs = torch.matmul(weighted_features, self.weights) # Convolution sum [n_points, out_fdim] # return torch.sum(kernel_outputs, dim=0) output_features = torch.sum(kernel_outputs, dim=0, keepdim=False) # normalization term. neighbor_features_sum = torch.sum(neighb_x, dim=-1) neighbor_num = torch.sum(torch.gt(neighbor_features_sum, 0.0), dim=-1) neighbor_num = torch.max(neighbor_num, torch.ones_like(neighbor_num)) output_features = output_features / neighbor_num.unsqueeze(1) return output_features def __repr__(self): return 'KPConv(radius: {:.2f}, extent: {:.2f}, in_feat: {:d}, out_feat: {:d})'.format(self.radius, self.KP_extent, self.in_channels, self.out_channels) # ---------------------------------------------------------------------------------------------------------------------- # # Complex blocks # \********************/ # def block_decider(block_name, radius, in_dim, out_dim, layer_ind, config): if block_name == 'unary': return UnaryBlock(in_dim, out_dim, config.use_batch_norm, config.batch_norm_momentum) elif block_name in ['simple', 'simple_deformable', 'simple_invariant', 'simple_equivariant', 'simple_strided', 'simple_deformable_strided', 'simple_invariant_strided', 'simple_equivariant_strided']: return SimpleBlock(block_name, in_dim, out_dim, radius, layer_ind, config) elif block_name in ['resnetb', 'resnetb_invariant', 'resnetb_equivariant', 'resnetb_deformable', 'resnetb_strided', 'resnetb_deformable_strided', 'resnetb_equivariant_strided', 'resnetb_invariant_strided']: return ResnetBottleneckBlock(block_name, in_dim, out_dim, radius, layer_ind, config) elif block_name == 'max_pool' or block_name == 'max_pool_wide': return MaxPoolBlock(layer_ind) elif block_name == 'global_average': return GlobalAverageBlock() elif block_name == 'nearest_upsample': return NearestUpsampleBlock(layer_ind) else: raise ValueError('Unknown block name in the architecture definition : ' + block_name) class BatchNormBlock(nn.Module): def __init__(self, in_dim, use_bn, bn_momentum): """ Initialize a batch normalization block. If network does not use batch normalization, replace with biases. :param in_dim: dimension input features :param use_bn: boolean indicating if we use Batch Norm :param bn_momentum: Batch norm momentum """ super(BatchNormBlock, self).__init__() self.bn_momentum = bn_momentum self.use_bn = use_bn self.in_dim = in_dim if self.use_bn: #self.batch_norm = nn.BatchNorm1d(in_dim, momentum=bn_momentum) self.batch_norm = nn.InstanceNorm1d(in_dim, momentum=bn_momentum) else: self.bias = Parameter(torch.zeros(in_dim, dtype=torch.float32), requires_grad=True) return def reset_parameters(self): nn.init.zeros_(self.bias) def forward(self, x): if self.use_bn: x = x.unsqueeze(2) x = x.transpose(0, 2) x = self.batch_norm(x) x = x.transpose(0, 2) return x.squeeze() else: return x + self.bias def __repr__(self): return 'BatchNormBlock(in_feat: {:d}, momentum: {:.3f}, only_bias: {:s})'.format(self.in_dim, self.bn_momentum, str(not self.use_bn)) class UnaryBlock(nn.Module): def __init__(self, in_dim, out_dim, use_bn, bn_momentum, no_relu=False): """ Initialize a standard unary block with its ReLU and BatchNorm. :param in_dim: dimension input features :param out_dim: dimension input features :param use_bn: boolean indicating if we use Batch Norm :param bn_momentum: Batch norm momentum """ super(UnaryBlock, self).__init__() self.bn_momentum = bn_momentum self.use_bn = use_bn self.no_relu = no_relu self.in_dim = in_dim self.out_dim = out_dim self.mlp = nn.Linear(in_dim, out_dim, bias=False) self.batch_norm = BatchNormBlock(out_dim, self.use_bn, self.bn_momentum) if not no_relu: self.leaky_relu = nn.LeakyReLU(0.1) return def forward(self, x, batch=None): x = self.mlp(x) x = self.batch_norm(x) if not self.no_relu: x = self.leaky_relu(x) return x def __repr__(self): return 'UnaryBlock(in_feat: {:d}, out_feat: {:d}, BN: {:s}, ReLU: {:s})'.format(self.in_dim, self.out_dim, str(self.use_bn), str(not self.no_relu)) class LastUnaryBlock(nn.Module): def __init__(self, in_dim, out_dim, use_bn, bn_momentum, no_relu=False): """ Initialize a standard last_unary block without BN, ReLU. :param in_dim: dimension input features :param out_dim: dimension input features :param use_bn: boolean indicating if we use Batch Norm :param bn_momentum: Batch norm momentum """ super(LastUnaryBlock, self).__init__() self.in_dim = in_dim self.out_dim = out_dim self.mlp = nn.Linear(in_dim, out_dim, bias=False) return def forward(self, x, batch=None): x = self.mlp(x) return x def __repr__(self): return 'LastUnaryBlock(in_feat: {:d}, out_feat: {:d})'.format(self.in_dim, self.out_dim) class SimpleBlock(nn.Module): def __init__(self, block_name, in_dim, out_dim, radius, layer_ind, config): """ Initialize a simple convolution block with its ReLU and BatchNorm. :param in_dim: dimension input features :param out_dim: dimension input features :param radius: current radius of convolution :param config: parameters """ super(SimpleBlock, self).__init__() # get KP_extent from current radius current_extent = radius * config.KP_extent / config.conv_radius # Get other parameters self.bn_momentum = config.batch_norm_momentum self.use_bn = config.use_batch_norm self.layer_ind = layer_ind self.block_name = block_name self.in_dim = in_dim self.out_dim = out_dim # Define the KPConv class self.KPConv = KPConv(config.num_kernel_points, config.in_points_dim, in_dim, out_dim // 2, current_extent, radius, fixed_kernel_points=config.fixed_kernel_points, KP_influence=config.KP_influence, aggregation_mode=config.aggregation_mode, deformable='deform' in block_name, modulated=config.modulated) # Other opperations self.batch_norm = BatchNormBlock(out_dim // 2, self.use_bn, self.bn_momentum) self.leaky_relu = nn.LeakyReLU(0.1) return def forward(self, x, batch): if 'strided' in self.block_name: q_pts = batch['points'][self.layer_ind + 1] s_pts = batch['points'][self.layer_ind] neighb_inds = batch['pools'][self.layer_ind] else: q_pts = batch['points'][self.layer_ind] s_pts = batch['points'][self.layer_ind] neighb_inds = batch['neighbors'][self.layer_ind] x = self.KPConv(q_pts, s_pts, neighb_inds, x) return self.leaky_relu(self.batch_norm(x)) class ResnetBottleneckBlock(nn.Module): def __init__(self, block_name, in_dim, out_dim, radius, layer_ind, config): """ Initialize a resnet bottleneck block. :param in_dim: dimension input features :param out_dim: dimension input features :param radius: current radius of convolution :param config: parameters """ super(ResnetBottleneckBlock, self).__init__() # get KP_extent from current radius current_extent = radius * config.KP_extent / config.conv_radius # Get other parameters self.bn_momentum = config.batch_norm_momentum self.use_bn = config.use_batch_norm self.block_name = block_name self.layer_ind = layer_ind self.in_dim = in_dim self.out_dim = out_dim # First downscaling mlp if in_dim != out_dim // 4: self.unary1 = UnaryBlock(in_dim, out_dim // 4, self.use_bn, self.bn_momentum) else: self.unary1 = nn.Identity() # KPConv block self.KPConv = KPConv(config.num_kernel_points, config.in_points_dim, out_dim // 4, out_dim // 4, current_extent, radius, fixed_kernel_points=config.fixed_kernel_points, KP_influence=config.KP_influence, aggregation_mode=config.aggregation_mode, deformable='deform' in block_name, modulated=config.modulated) self.batch_norm_conv = BatchNormBlock(out_dim // 4, self.use_bn, self.bn_momentum) # Second upscaling mlp self.unary2 = UnaryBlock(out_dim // 4, out_dim, self.use_bn, self.bn_momentum, no_relu=True) # Shortcut optional mpl if in_dim != out_dim: self.unary_shortcut = UnaryBlock(in_dim, out_dim, self.use_bn, self.bn_momentum, no_relu=True) else: self.unary_shortcut = nn.Identity() # Other operations self.leaky_relu = nn.LeakyReLU(0.1) return def forward(self, features, batch): if 'strided' in self.block_name: q_pts = batch['points'][self.layer_ind + 1] s_pts = batch['points'][self.layer_ind] neighb_inds = batch['pools'][self.layer_ind] else: q_pts = batch['points'][self.layer_ind] s_pts = batch['points'][self.layer_ind] neighb_inds = batch['neighbors'][self.layer_ind] # First downscaling mlp x = self.unary1(features) # Convolution x = self.KPConv(q_pts, s_pts, neighb_inds, x) x = self.leaky_relu(self.batch_norm_conv(x)) # Second upscaling mlp x = self.unary2(x) # Shortcut if 'strided' in self.block_name: shortcut = max_pool(features, neighb_inds) else: shortcut = features shortcut = self.unary_shortcut(shortcut) return self.leaky_relu(x + shortcut) class GlobalAverageBlock(nn.Module): def __init__(self): """ Initialize a global average block with its ReLU and BatchNorm. """ super(GlobalAverageBlock, self).__init__() return def forward(self, x, batch): return global_average(x, batch['stack_lengths'][-1]) class NearestUpsampleBlock(nn.Module): def __init__(self, layer_ind): """ Initialize a nearest upsampling block with its ReLU and BatchNorm. """ super(NearestUpsampleBlock, self).__init__() self.layer_ind = layer_ind return def forward(self, x, batch): return closest_pool(x, batch['upsamples'][self.layer_ind - 1]) def __repr__(self): return 'NearestUpsampleBlock(layer: {:d} -> {:d})'.format(self.layer_ind, self.layer_ind - 1) class MaxPoolBlock(nn.Module): def __init__(self, layer_ind): """ Initialize a max pooling block with its ReLU and BatchNorm. """ super(MaxPoolBlock, self).__init__() self.layer_ind = layer_ind return def forward(self, x, batch): return max_pool(x, batch['pools'][self.layer_ind + 1])

py

lepard

lepard-main/cpp_wrappers/cpp_neighbors/setup.py

from distutils.core import setup, Extension import numpy.distutils.misc_util # Adding OpenCV to project # ************************ # Adding sources of the project # ***************************** SOURCES = ["../cpp_utils/cloud/cloud.cpp", "neighbors/neighbors.cpp", "wrapper.cpp"] module = Extension(name="radius_neighbors", sources=SOURCES, extra_compile_args=['-std=c++11', '-D_GLIBCXX_USE_CXX11_ABI=0']) setup(ext_modules=[module], include_dirs=numpy.distutils.misc_util.get_numpy_include_dirs())

py

lepard

lepard-main/cpp_wrappers/cpp_subsampling/setup.py

from distutils.core import setup, Extension import numpy.distutils.misc_util # Adding OpenCV to project # ************************ # Adding sources of the project # ***************************** SOURCES = ["../cpp_utils/cloud/cloud.cpp", "grid_subsampling/grid_subsampling.cpp", "wrapper.cpp"] module = Extension(name="grid_subsampling", sources=SOURCES, extra_compile_args=['-std=c++11', '-D_GLIBCXX_USE_CXX11_ABI=0']) setup(ext_modules=[module], include_dirs=numpy.distutils.misc_util.get_numpy_include_dirs())

py

lepard

lepard-main/datasets/_4dmatch.py

import os, sys, glob, torch # sys.path.append("../") [sys.path.append(i) for i in ['.', '..']] import numpy as np import torch import random from scipy.spatial.transform import Rotation from torch.utils.data import Dataset from lib.benchmark_utils import to_o3d_pcd, to_tsfm, KDTree_corr from lib.utils import load_obj HMN_intrin = np.array( [443, 256, 443, 250 ]) cam_intrin = np.array( [443, 256, 443, 250 ]) from lib.benchmark_utils import to_o3d_pcd, to_tsfm, get_correspondences class _4DMatch(Dataset): def __init__(self, config, split, data_augmentation=True): super(_4DMatch, self).__init__() assert split in ['train','val','test'] if 'overfit' in config.exp_dir: d_slice = config.batch_size else : d_slice = None self.entries = self.read_entries( config.split[split] , config.data_root, d_slice=d_slice ) self.base_dir = config.data_root self.data_augmentation = data_augmentation self.config = config self.rot_factor = 1. self.augment_noise = config.augment_noise self.max_points = 30000 self.overlap_radius = 0.0375 self.cache = {} self.cache_size = 30000 def read_entries (self, split, data_root, d_slice=None, shuffle= False): entries = glob.glob(os.path.join(data_root, split, "*/*.npz")) if shuffle: random.shuffle(entries) if d_slice: return entries[:d_slice] return entries def __len__(self): return len(self.entries ) def __getitem__(self, index, debug=False): if index in self.cache: entry = self.cache[index] else : entry = np.load(self.entries[index]) if len(self.cache) < self.cache_size: self.cache[index] = entry # get transformation rot = entry['rot'] trans = entry['trans'] s2t_flow = entry['s2t_flow'] src_pcd = entry['s_pc'] tgt_pcd = entry['t_pc'] correspondences = entry['correspondences'] # obtained with search radius 0.015 m src_pcd_deformed = src_pcd + s2t_flow if "metric_index" in entry: metric_index = entry['metric_index'].squeeze() else: metric_index = None # if we get too many points, we do some downsampling if (src_pcd.shape[0] > self.max_points): idx = np.random.permutation(src_pcd.shape[0])[:self.max_points] src_pcd = src_pcd[idx] if (tgt_pcd.shape[0] > self.max_points): idx = np.random.permutation(tgt_pcd.shape[0])[:self.max_points] tgt_pcd = tgt_pcd[idx] if debug: import mayavi.mlab as mlab c_red = (224. / 255., 0 / 255., 125 / 255.) c_pink = (224. / 255., 75. / 255., 232. / 255.) c_blue = (0. / 255., 0. / 255., 255. / 255.) scale_factor = 0.013 src_wrapped = (np.matmul( rot, src_pcd_deformed.T ) + trans ).T mlab.points3d(src_wrapped[:, 0], src_wrapped[:, 1], src_wrapped[:, 2], scale_factor=scale_factor, color=c_pink) mlab.points3d(src_pcd[ :, 0] , src_pcd[ :, 1], src_pcd[:, 2], scale_factor=scale_factor , color=c_red) mlab.points3d(tgt_pcd[ :, 0] , tgt_pcd[ :, 1], tgt_pcd[:, 2], scale_factor=scale_factor , color=c_blue) mlab.show() # add gaussian noise if self.data_augmentation: # rotate the point cloud euler_ab = np.random.rand(3) * np.pi * 2 / self.rot_factor # anglez, angley, anglex rot_ab = Rotation.from_euler('zyx', euler_ab).as_matrix() if (np.random.rand(1)[0] > 0.5): src_pcd = np.matmul(rot_ab, src_pcd.T).T src_pcd_deformed = np.matmul(rot_ab, src_pcd_deformed.T).T rot = np.matmul(rot, rot_ab.T) else: tgt_pcd = np.matmul(rot_ab, tgt_pcd.T).T rot = np.matmul(rot_ab, rot) trans = np.matmul(rot_ab, trans) src_pcd += (np.random.rand(src_pcd.shape[0], 3) - 0.5) * self.augment_noise tgt_pcd += (np.random.rand(tgt_pcd.shape[0], 3) - 0.5) * self.augment_noise s2t_flow = src_pcd_deformed - src_pcd if debug: # wrapp_src = (np.matmul(rot, src_pcd.T)+ trans).T src_wrapped = (np.matmul( rot, src_pcd_deformed.T ) + trans ).T mlab.points3d(src_wrapped[:, 0], src_wrapped[:, 1], src_wrapped[:, 2], scale_factor=scale_factor, color=c_red) mlab.points3d(tgt_pcd[:, 0], tgt_pcd[:, 1], tgt_pcd[:, 2], scale_factor=scale_factor, color=c_blue) mlab.show() if (trans.ndim == 1): trans = trans[:, None] src_feats = np.ones_like(src_pcd[:, :1]).astype(np.float32) tgt_feats = np.ones_like(tgt_pcd[:, :1]).astype(np.float32) rot = rot.astype(np.float32) trans = trans.astype(np.float32) #R * ( Ps + flow ) + t = Pt return src_pcd, tgt_pcd, src_feats, tgt_feats, correspondences, rot, trans, s2t_flow, metric_index if __name__ == '__main__': from lib.utils import load_config from easydict import EasyDict as edict from lib.tictok import Timers import yaml def join(loader, node): seq = loader.construct_sequence(node) return '_'.join([str(i) for i in seq]) yaml.add_constructor('!join', join) config = "/home/liyang/workspace/Regformer/configs/train/4dmatch.yaml" with open(config,'r') as f: config = yaml.load(f, Loader=yaml.Loader) config = edict(config) config.timers=Timers() D = _4DMatch(config, "test") for i in range (len(D)): try: if i%1000 == 0 : print (i,"/",len(D)) D.__getitem__(i, debug=True) except: # print(i, "/", len(D)) pass

py

lepard

lepard-main/datasets/dataloader.py

import numpy as np from functools import partial import torch import cpp_wrappers.cpp_subsampling.grid_subsampling as cpp_subsampling import cpp_wrappers.cpp_neighbors.radius_neighbors as cpp_neighbors from datasets._3dmatch import _3DMatch from datasets._4dmatch import _4DMatch from datasets.utils import blend_scene_flow, multual_nn_correspondence from lib.visualization import * from torch.utils.data import DataLoader def batch_grid_subsampling_kpconv(points, batches_len, features=None, labels=None, sampleDl=0.1, max_p=0, verbose=0, random_grid_orient=True): """ CPP wrapper for a grid subsampling (method = barycenter for points and features) """ if (features is None) and (labels is None): s_points, s_len = cpp_subsampling.subsample_batch(points, batches_len, sampleDl=sampleDl, max_p=max_p, verbose=verbose) return torch.from_numpy(s_points), torch.from_numpy(s_len) elif (labels is None): s_points, s_len, s_features = cpp_subsampling.subsample_batch(points, batches_len, features=features, sampleDl=sampleDl, max_p=max_p, verbose=verbose) return torch.from_numpy(s_points), torch.from_numpy(s_len), torch.from_numpy(s_features) elif (features is None): s_points, s_len, s_labels = cpp_subsampling.subsample_batch(points, batches_len, classes=labels, sampleDl=sampleDl, max_p=max_p, verbose=verbose) return torch.from_numpy(s_points), torch.from_numpy(s_len), torch.from_numpy(s_labels) else: s_points, s_len, s_features, s_labels = cpp_subsampling.subsample_batch(points, batches_len, features=features, classes=labels, sampleDl=sampleDl, max_p=max_p, verbose=verbose) return torch.from_numpy(s_points), torch.from_numpy(s_len), torch.from_numpy(s_features), torch.from_numpy(s_labels) def batch_neighbors_kpconv(queries, supports, q_batches, s_batches, radius, max_neighbors): """ Computes neighbors for a batch of queries and supports, apply radius search :param queries: (N1, 3) the query points :param supports: (N2, 3) the support points :param q_batches: (B) the list of lengths of batch elements in queries :param s_batches: (B)the list of lengths of batch elements in supports :param radius: float32 :return: neighbors indices """ neighbors = cpp_neighbors.batch_query(queries, supports, q_batches, s_batches, radius=radius) if max_neighbors > 0: return torch.from_numpy(neighbors[:, :max_neighbors]) else: return torch.from_numpy(neighbors) def collate_fn_3dmatch(list_data, config, neighborhood_limits ): batched_points_list = [] batched_features_list = [] batched_lengths_list = [] correspondences_list = [] src_pcd_list = [] tgt_pcd_list = [] batched_rot = [] batched_trn = [] gt_cov_list = [] for ind, ( src_pcd, tgt_pcd, src_feats, tgt_feats, correspondences, rot, trn, gt_cov) in enumerate(list_data): correspondences_list.append(correspondences ) src_pcd_list.append(torch.from_numpy(src_pcd) ) tgt_pcd_list.append(torch.from_numpy(tgt_pcd) ) batched_points_list.append(src_pcd) batched_points_list.append(tgt_pcd) batched_features_list.append(src_feats) batched_features_list.append(tgt_feats) batched_lengths_list.append(len(src_pcd)) batched_lengths_list.append(len(tgt_pcd)) batched_rot.append( torch.from_numpy(rot).float()) batched_trn.append( torch.from_numpy(trn).float()) gt_cov_list.append(gt_cov) gt_cov_list = None if gt_cov_list[0] is None \ else np.stack(gt_cov_list, axis=0) # if timers: cnter['collate_load_batch'] = time.time() - st batched_features = torch.from_numpy(np.concatenate(batched_features_list, axis=0)) batched_points = torch.from_numpy(np.concatenate(batched_points_list, axis=0)) batched_lengths = torch.from_numpy(np.array(batched_lengths_list)).int() batched_rot = torch.stack(batched_rot,dim=0) batched_trn = torch.stack(batched_trn,dim=0) # Starting radius of convolutions r_normal = config.first_subsampling_dl * config.conv_radius # Starting layer layer_blocks = [] layer = 0 # Lists of inputs input_points = [] input_neighbors = [] input_pools = [] input_upsamples = [] input_batches_len = [] # construt kpfcn inds for block_i, block in enumerate(config.architecture): # Stop when meeting a global pooling or upsampling if 'global' in block or 'upsample' in block: break # Get all blocks of the layer if not ('pool' in block or 'strided' in block): layer_blocks += [block] if block_i < len(config.architecture) - 1 and not ('upsample' in config.architecture[block_i + 1]): continue # Convolution neighbors indices # ***************************** if layer_blocks: # Convolutions are done in this layer, compute the neighbors with the good radius if np.any(['deformable' in blck for blck in layer_blocks[:-1]]): r = r_normal * config.deform_radius / config.conv_radius else: r = r_normal conv_i = batch_neighbors_kpconv(batched_points, batched_points, batched_lengths, batched_lengths, r, neighborhood_limits[layer]) else: # This layer only perform pooling, no neighbors required conv_i = torch.zeros((0, 1), dtype=torch.int64) # Pooling neighbors indices # ************************* # If end of layer is a pooling operation if 'pool' in block or 'strided' in block: # New subsampling length dl = 2 * r_normal / config.conv_radius # Subsampled points pool_p, pool_b = batch_grid_subsampling_kpconv(batched_points, batched_lengths, sampleDl=dl) # Radius of pooled neighbors if 'deformable' in block: r = r_normal * config.deform_radius / config.conv_radius else: r = r_normal # Subsample indices pool_i = batch_neighbors_kpconv(pool_p, batched_points, pool_b, batched_lengths, r, neighborhood_limits[layer]) # Upsample indices (with the radius of the next layer to keep wanted density) up_i = batch_neighbors_kpconv(batched_points, pool_p, batched_lengths, pool_b, 2 * r, neighborhood_limits[layer]) else: # No pooling in the end of this layer, no pooling indices required pool_i = torch.zeros((0, 1), dtype=torch.int64) pool_p = torch.zeros((0, 3), dtype=torch.float32) pool_b = torch.zeros((0,), dtype=torch.int64) up_i = torch.zeros((0, 1), dtype=torch.int64) # Updating input lists input_points += [batched_points.float()] input_neighbors += [conv_i.long()] input_pools += [pool_i.long()] input_upsamples += [up_i.long()] input_batches_len += [batched_lengths] # New points for next layer batched_points = pool_p batched_lengths = pool_b # Update radius and reset blocks r_normal *= 2 layer += 1 layer_blocks = [] # coarse infomation coarse_level = config.coarse_level pts_num_coarse = input_batches_len[coarse_level].view(-1, 2) b_size = pts_num_coarse.shape[0] src_pts_max, tgt_pts_max = pts_num_coarse.amax(dim=0) coarse_pcd = input_points[coarse_level] # .numpy() coarse_matches= [] src_ind_coarse_split= [] # src_feats shape :[b_size * src_pts_max] src_ind_coarse = [] tgt_ind_coarse_split= [] tgt_ind_coarse = [] accumu = 0 src_mask = torch.zeros([b_size, src_pts_max], dtype=torch.bool) tgt_mask = torch.zeros([b_size, tgt_pts_max], dtype=torch.bool) #grid subsample fine level points for differentiable matching fine_pts, fine_length = batch_grid_subsampling_kpconv(input_points[0], input_batches_len[0], sampleDl=dl*0.5*0.85) fine_ind = batch_neighbors_kpconv(fine_pts, input_points[0], fine_length, input_batches_len[0], dl*0.5*0.85, 1).squeeze().long() for entry_id, cnt in enumerate( pts_num_coarse ): #input_batches_len[-1].numpy().reshape(-1,2)) : n_s_pts, n_t_pts = cnt '''split mask for bottlenect feats''' src_mask[entry_id][:n_s_pts] = 1 tgt_mask[entry_id][:n_t_pts] = 1 '''split indices of bottleneck feats''' src_ind_coarse_split.append( torch.arange( n_s_pts ) + entry_id * src_pts_max ) tgt_ind_coarse_split.append( torch.arange( n_t_pts ) + entry_id * tgt_pts_max ) src_ind_coarse.append( torch.arange( n_s_pts ) + accumu ) tgt_ind_coarse.append( torch.arange( n_t_pts ) + accumu + n_s_pts ) '''get match at coarse level''' c_src_pcd = coarse_pcd[accumu : accumu + n_s_pts] c_tgt_pcd = coarse_pcd[accumu + n_s_pts: accumu + n_s_pts + n_t_pts] s_pc_wrapped = (torch.matmul( batched_rot[entry_id], c_src_pcd.T ) + batched_trn [entry_id]).T coarse_match_gt = torch.from_numpy( multual_nn_correspondence(s_pc_wrapped.numpy(), c_tgt_pcd.numpy(), search_radius=config['coarse_match_radius']) )# 0.1m scaled coarse_matches.append(coarse_match_gt) accumu = accumu + n_s_pts + n_t_pts vis=False # for debug if vis : viz_coarse_nn_correspondence_mayavi(c_src_pcd, c_tgt_pcd, coarse_match_gt, scale_factor=0.04) vis=False # for debug if vis : pass import mayavi.mlab as mlab # src_nei_valid = src_nei_mask[coarse_match_gt[0]].view(-1) # tgt_nei_valid = tgt_nei_mask[coarse_match_gt[1]].view(-1) # # f_src_pcd = src_m_nei_pts.view(-1, 3)[src_nei_valid] # f_tgt_pcd = tgt_m_nei_pts.view(-1,3)[tgt_nei_valid] # # mlab.points3d(f_src_pcd[:, 0], f_src_pcd[:, 1], f_src_pcd[:, 2], scale_factor=0.02,color=c_gray1) # mlab.points3d(f_tgt_pcd[:, 0], f_tgt_pcd[:, 1], f_tgt_pcd[:, 2], scale_factor=0.02,color=c_gray2) # # src_m_nn_pts =src_m_nn_pts.view(-1, 3) # src_m_nn_pts_wrapped = src_m_nn_pts_wrapped.view(-1,3) # tgt_m_nn_pts = tgt_m_nei_pts [ torch.arange(tgt_m_nei_pts.shape[0]), nni.view(-1), ... ] # mlab.points3d(src_m_nn_pts[:, 0], src_m_nn_pts[:, 1], src_m_nn_pts[:, 2], scale_factor=0.04,color=c_red) # mlab.points3d(src_m_nn_pts_wrapped[:, 0], src_m_nn_pts_wrapped[:, 1], src_m_nn_pts_wrapped[:, 2], scale_factor=0.04,color=c_red) # mlab.points3d(tgt_m_nn_pts[:, 0], tgt_m_nn_pts[:, 1], tgt_m_nn_pts[:, 2], scale_factor=0.04 ,color=c_blue) # mlab.show() # viz_coarse_nn_correspondence_mayavi(c_src_pcd, c_tgt_pcd, coarse_match_gt, # f_src_pcd=src_m_nei_pts.view(-1,3)[src_nei_valid], # f_tgt_pcd=tgt_m_nei_pts.view(-1,3)[tgt_nei_valid], scale_factor=0.08) src_ind_coarse_split = torch.cat(src_ind_coarse_split) tgt_ind_coarse_split = torch.cat(tgt_ind_coarse_split) src_ind_coarse = torch.cat(src_ind_coarse) tgt_ind_coarse = torch.cat(tgt_ind_coarse) dict_inputs = { 'src_pcd_list': src_pcd_list, 'tgt_pcd_list': tgt_pcd_list, 'points': input_points, 'neighbors': input_neighbors, 'pools': input_pools, 'upsamples': input_upsamples, 'features': batched_features.float(), 'stack_lengths': input_batches_len, 'coarse_matches': coarse_matches, 'src_mask': src_mask, 'tgt_mask': tgt_mask, 'src_ind_coarse_split': src_ind_coarse_split, 'tgt_ind_coarse_split': tgt_ind_coarse_split, 'src_ind_coarse': src_ind_coarse, 'tgt_ind_coarse': tgt_ind_coarse, 'batched_rot': batched_rot, 'batched_trn': batched_trn, 'gt_cov': gt_cov_list, #for refine 'correspondences_list': correspondences_list, 'fine_ind': fine_ind, 'fine_pts': fine_pts, 'fine_length': fine_length } return dict_inputs def collate_fn_4dmatch(list_data, config, neighborhood_limits ): batched_points_list = [] batched_features_list = [] batched_lengths_list = [] correspondences_list = [] src_pcd_list = [] tgt_pcd_list = [] batched_rot = [] batched_trn = [] sflow_list = [] metric_index_list = [] #for feature matching recall computation for ind, ( src_pcd, tgt_pcd, src_feats, tgt_feats, correspondences, rot, trn, s2t_flow, metric_index) in enumerate(list_data): correspondences_list.append(correspondences ) src_pcd_list.append(torch.from_numpy(src_pcd) ) tgt_pcd_list.append(torch.from_numpy(tgt_pcd) ) batched_points_list.append(src_pcd) batched_points_list.append(tgt_pcd) batched_features_list.append(src_feats) batched_features_list.append(tgt_feats) batched_lengths_list.append(len(src_pcd)) batched_lengths_list.append(len(tgt_pcd)) batched_rot.append( torch.from_numpy(rot).float()) batched_trn.append( torch.from_numpy(trn).float()) # gt_cov_list.append(gt_cov) sflow_list.append( torch.from_numpy(s2t_flow).float() ) if metric_index is None: metric_index_list = None else : metric_index_list.append ( torch.from_numpy(metric_index)) # if timers: cnter['collate_load_batch'] = time.time() - st batched_features = torch.from_numpy(np.concatenate(batched_features_list, axis=0)) batched_points = torch.from_numpy(np.concatenate(batched_points_list, axis=0)) batched_lengths = torch.from_numpy(np.array(batched_lengths_list)).int() batched_rot = torch.stack(batched_rot,dim=0) batched_trn = torch.stack(batched_trn,dim=0) # Starting radius of convolutions r_normal = config.first_subsampling_dl * config.conv_radius # Starting layer layer_blocks = [] layer = 0 # Lists of inputs input_points = [] input_neighbors = [] input_pools = [] input_upsamples = [] input_batches_len = [] # construt kpfcn inds for block_i, block in enumerate(config.architecture): # Stop when meeting a global pooling or upsampling if 'global' in block or 'upsample' in block: break # Get all blocks of the layer if not ('pool' in block or 'strided' in block): layer_blocks += [block] if block_i < len(config.architecture) - 1 and not ('upsample' in config.architecture[block_i + 1]): continue # Convolution neighbors indices # ***************************** if layer_blocks: # Convolutions are done in this layer, compute the neighbors with the good radius if np.any(['deformable' in blck for blck in layer_blocks[:-1]]): r = r_normal * config.deform_radius / config.conv_radius else: r = r_normal conv_i = batch_neighbors_kpconv(batched_points, batched_points, batched_lengths, batched_lengths, r, neighborhood_limits[layer]) else: # This layer only perform pooling, no neighbors required conv_i = torch.zeros((0, 1), dtype=torch.int64) # Pooling neighbors indices # ************************* # If end of layer is a pooling operation if 'pool' in block or 'strided' in block: # New subsampling length dl = 2 * r_normal / config.conv_radius # Subsampled points pool_p, pool_b = batch_grid_subsampling_kpconv(batched_points, batched_lengths, sampleDl=dl) # Radius of pooled neighbors if 'deformable' in block: r = r_normal * config.deform_radius / config.conv_radius else: r = r_normal # Subsample indices pool_i = batch_neighbors_kpconv(pool_p, batched_points, pool_b, batched_lengths, r, neighborhood_limits[layer]) # Upsample indices (with the radius of the next layer to keep wanted density) up_i = batch_neighbors_kpconv(batched_points, pool_p, batched_lengths, pool_b, 2 * r, neighborhood_limits[layer]) else: # No pooling in the end of this layer, no pooling indices required pool_i = torch.zeros((0, 1), dtype=torch.int64) pool_p = torch.zeros((0, 3), dtype=torch.float32) pool_b = torch.zeros((0,), dtype=torch.int64) up_i = torch.zeros((0, 1), dtype=torch.int64) # Updating input lists input_points += [batched_points.float()] input_neighbors += [conv_i.long()] input_pools += [pool_i.long()] input_upsamples += [up_i.long()] input_batches_len += [batched_lengths] # New points for next layer batched_points = pool_p batched_lengths = pool_b # Update radius and reset blocks r_normal *= 2 layer += 1 layer_blocks = [] # coarse infomation coarse_level = config.coarse_level pts_num_coarse = input_batches_len[coarse_level].view(-1, 2) b_size = pts_num_coarse.shape[0] src_pts_max, tgt_pts_max = pts_num_coarse.amax(dim=0) coarse_pcd = input_points[coarse_level] # .numpy() coarse_matches= [] coarse_flow = [] src_ind_coarse_split= [] # src_feats shape :[b_size * src_pts_max] src_ind_coarse = [] tgt_ind_coarse_split= [] tgt_ind_coarse = [] accumu = 0 src_mask = torch.zeros([b_size, src_pts_max], dtype=torch.bool) tgt_mask = torch.zeros([b_size, tgt_pts_max], dtype=torch.bool) for entry_id, cnt in enumerate( pts_num_coarse ): #input_batches_len[-1].numpy().reshape(-1,2)) : n_s_pts, n_t_pts = cnt '''split mask for bottlenect feats''' src_mask[entry_id][:n_s_pts] = 1 tgt_mask[entry_id][:n_t_pts] = 1 '''split indices of bottleneck feats''' src_ind_coarse_split.append( torch.arange( n_s_pts ) + entry_id * src_pts_max ) tgt_ind_coarse_split.append( torch.arange( n_t_pts ) + entry_id * tgt_pts_max ) src_ind_coarse.append( torch.arange( n_s_pts ) + accumu ) tgt_ind_coarse.append( torch.arange( n_t_pts ) + accumu + n_s_pts ) '''get match at coarse level''' c_src_pcd_np = coarse_pcd[accumu : accumu + n_s_pts].numpy() c_tgt_pcd_np = coarse_pcd[accumu + n_s_pts: accumu + n_s_pts + n_t_pts].numpy() #interpolate flow f_src_pcd = batched_points_list[entry_id * 2] c_flow = blend_scene_flow( c_src_pcd_np, f_src_pcd, sflow_list[entry_id].numpy(), knn=3) c_src_pcd_deformed = c_src_pcd_np + c_flow s_pc_wrapped = (np.matmul( batched_rot[entry_id].numpy(), c_src_pcd_deformed.T ) + batched_trn [entry_id].numpy()).T coarse_match_gt = torch.from_numpy( multual_nn_correspondence(s_pc_wrapped , c_tgt_pcd_np , search_radius=config['coarse_match_radius']) )# 0.1m scaled coarse_matches.append(coarse_match_gt) coarse_flow.append(torch.from_numpy(c_flow) ) accumu = accumu + n_s_pts + n_t_pts vis=False # for debug if vis : viz_coarse_nn_correspondence_mayavi(c_src_pcd_np, c_tgt_pcd_np, coarse_match_gt, scale_factor=0.02) src_ind_coarse_split = torch.cat(src_ind_coarse_split) tgt_ind_coarse_split = torch.cat(tgt_ind_coarse_split) src_ind_coarse = torch.cat(src_ind_coarse) tgt_ind_coarse = torch.cat(tgt_ind_coarse) dict_inputs = { 'src_pcd_list': src_pcd_list, 'tgt_pcd_list': tgt_pcd_list, 'points': input_points, 'neighbors': input_neighbors, 'pools': input_pools, 'upsamples': input_upsamples, 'features': batched_features.float(), 'stack_lengths': input_batches_len, 'coarse_matches': coarse_matches, 'coarse_flow' : coarse_flow, 'src_mask': src_mask, 'tgt_mask': tgt_mask, 'src_ind_coarse_split': src_ind_coarse_split, 'tgt_ind_coarse_split': tgt_ind_coarse_split, 'src_ind_coarse': src_ind_coarse, 'tgt_ind_coarse': tgt_ind_coarse, 'batched_rot': batched_rot, 'batched_trn': batched_trn, 'sflow_list': sflow_list, "metric_index_list": metric_index_list } return dict_inputs def calibrate_neighbors(dataset, config, collate_fn, keep_ratio=0.8, samples_threshold=2000): # From config parameter, compute higher bound of neighbors number in a neighborhood hist_n = int(np.ceil(4 / 3 * np.pi * (config.deform_radius + 1) ** 3)) neighb_hists = np.zeros((config.num_layers, hist_n), dtype=np.int32) # Get histogram of neighborhood sizes i in 1 epoch max. for i in range(len(dataset)): batched_input = collate_fn([dataset[i]], config, neighborhood_limits=[hist_n] * 5) # update histogram counts = [torch.sum(neighb_mat < neighb_mat.shape[0], dim=1).numpy() for neighb_mat in batched_input['neighbors']] hists = [np.bincount(c, minlength=hist_n)[:hist_n] for c in counts] neighb_hists += np.vstack(hists) # if timer.total_time - last_display > 0.1: # last_display = timer.total_time # print(f"Calib Neighbors {i:08d}: timings {timer.total_time:4.2f}s") if np.min(np.sum(neighb_hists, axis=1)) > samples_threshold: break cumsum = np.cumsum(neighb_hists.T, axis=0) percentiles = np.sum(cumsum < (keep_ratio * cumsum[hist_n - 1, :]), axis=0) neighborhood_limits = percentiles print('\n') return neighborhood_limits def get_datasets(config): if (config.dataset == '3dmatch'): train_set = _3DMatch(config, 'train', data_augmentation=True) val_set = _3DMatch(config, 'val', data_augmentation=False) test_set = _3DMatch(config, 'test', data_augmentation=False) elif(config.dataset == '4dmatch'): train_set = _4DMatch(config, 'train', data_augmentation=True) val_set = _4DMatch(config, 'val', data_augmentation=False) test_set = _4DMatch(config, 'test', data_augmentation=False) else: raise NotImplementedError return train_set, val_set, test_set def get_dataloader(dataset, config, shuffle=True, neighborhood_limits=None): if config.dataset=='4dmatch': collate_fn = collate_fn_4dmatch elif config.dataset == '3dmatch': collate_fn = collate_fn_3dmatch else: raise NotImplementedError() if neighborhood_limits is None: neighborhood_limits = calibrate_neighbors(dataset, config['kpfcn_config'], collate_fn=collate_fn) print("neighborhood:", neighborhood_limits) dataloader = torch.utils.data.DataLoader( dataset, batch_size=config['batch_size'], shuffle=shuffle, num_workers=config['num_workers'], collate_fn=partial(collate_fn, config=config['kpfcn_config'], neighborhood_limits=neighborhood_limits ), drop_last=False ) return dataloader, neighborhood_limits if __name__ == '__main__': pass

py

lepard

lepard-main/datasets/utils.py

import numpy as np # from lib.benchmark_utils import to_o3d_pcd, KDTree_corr def partition_arg_topK(matrix, K, axis=0): """ find index of K smallest entries along a axis perform topK based on np.argpartition :param matrix: to be sorted :param K: select and sort the top K items :param axis: 0 or 1. dimension to be sorted. :return: """ a_part = np.argpartition(matrix, K, axis=axis) if axis == 0: row_index = np.arange(matrix.shape[1 - axis]) a_sec_argsort_K = np.argsort(matrix[a_part[0:K, :], row_index], axis=axis) return a_part[0:K, :][a_sec_argsort_K, row_index] else: column_index = np.arange(matrix.shape[1 - axis])[:, None] a_sec_argsort_K = np.argsort(matrix[column_index, a_part[:, 0:K]], axis=axis) return a_part[:, 0:K][column_index, a_sec_argsort_K] def knn_point_np(k, reference_pts, query_pts): ''' :param k: number of k in k-nn search :param reference_pts: (N, 3) float32 array, input points :param query_pts: (M, 3) float32 array, query points :return: val: (batch_size, npoint, k) float32 array, L2 distances idx: (batch_size, npoint, k) int32 array, indices to input points ''' N, _ = reference_pts.shape M, _ = query_pts.shape reference_pts = reference_pts.reshape(1, N, -1).repeat(M, axis=0) query_pts = query_pts.reshape(M, 1, -1).repeat(N, axis=1) dist = np.sum((reference_pts - query_pts) ** 2, -1) idx = partition_arg_topK(dist, K=k, axis=1) val = np.take_along_axis ( dist , idx, axis=1) return np.sqrt(val), idx def blend_scene_flow (query_loc, reference_loc, reference_flow , knn=3) : '''approximate flow on query points this function assume query points are sub-/un-sampled from reference locations @param query_loc:[m,3] @param reference_loc:[n,3] @param reference_flow:[n,3] @param knn: @return: blended_flow:[m,3] ''' dists, idx = knn_point_np (knn, reference_loc, query_loc) dists[dists < 1e-10] = 1e-10 weight = 1.0 / dists weight = weight / np.sum(weight, -1, keepdims=True) # [B,N,3] blended_flow = np.sum (reference_flow [idx] * weight.reshape ([-1, knn, 1]), axis=1, keepdims=False) return blended_flow def multual_nn_correspondence(src_pcd_deformed, tgt_pcd, search_radius=0.3, knn=1): src_idx = np.arange(src_pcd_deformed.shape[0]) s2t_dists, ref_tgt_idx = knn_point_np (knn, tgt_pcd, src_pcd_deformed) s2t_dists, ref_tgt_idx = s2t_dists[:,0], ref_tgt_idx [:, 0] valid_distance = s2t_dists < search_radius _, ref_src_idx = knn_point_np (knn, src_pcd_deformed, tgt_pcd) _, ref_src_idx = _, ref_src_idx [:, 0] cycle_src_idx = ref_src_idx [ ref_tgt_idx ] is_mutual_nn = cycle_src_idx == src_idx mutual_nn = np.logical_and( is_mutual_nn, valid_distance) correspondences = np.stack([src_idx [ mutual_nn ], ref_tgt_idx[mutual_nn] ] , axis=0) return correspondences

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lepard

lepard-main/datasets/__init__.py

py

lepard

lepard-main/datasets/_3dmatch.py

import os, sys, glob, torch # sys.path.append("../") [sys.path.append(i) for i in ['.', '..']] import numpy as np import torch import random from scipy.spatial.transform import Rotation from torch.utils.data import Dataset from lib.benchmark_utils import to_o3d_pcd, to_tsfm, KDTree_corr from lib.utils import load_obj from lib.benchmark_utils import to_o3d_pcd, to_tsfm, get_correspondences class _3DMatch(Dataset): def __init__(self, config,split, data_augmentation=True): super(_3DMatch, self).__init__() assert split in ['train','val','test'] if 'overfit' in config.exp_dir: d_slice = config.batch_size else : d_slice = None self.infos = self.read_entries( config.split[split] , config.data_root, d_slice=d_slice ) self.base_dir = config.data_root self.data_augmentation = data_augmentation self.config = config self.rot_factor = 1. self.augment_noise = config.augment_noise self.max_points = 30000 self.overlap_radius = 0.0375 def read_entries (self, split, data_root, d_slice=None, shuffle= True): infos = load_obj(split) # we use the split prepared by Predator if d_slice: for k, v in infos.items(): infos[k] = v[:d_slice] return infos def __len__(self): return len(self.infos['rot']) def __getitem__(self, item, debug=False): # get transformation rot = self.infos['rot'][item] trans = self.infos['trans'][item] if 'gt_cov' in self.infos: gt_cov = self.infos['gt_cov'][item] else : gt_cov = None # get pointcloud src_path = os.path.join(self.base_dir, self.infos['src'][item]) tgt_path = os.path.join(self.base_dir, self.infos['tgt'][item]) src_pcd = torch.load(src_path) tgt_pcd = torch.load(tgt_path) # if we get too many points, we do some downsampling if (src_pcd.shape[0] > self.max_points): idx = np.random.permutation(src_pcd.shape[0])[:self.max_points] src_pcd = src_pcd[idx] if (tgt_pcd.shape[0] > self.max_points): idx = np.random.permutation(tgt_pcd.shape[0])[:self.max_points] tgt_pcd = tgt_pcd[idx] if debug: import mayavi.mlab as mlab c_red = (224. / 255., 0 / 255., 125 / 255.) c_pink = (224. / 255., 75. / 255., 232. / 255.) c_blue = (0. / 255., 0. / 255., 255. / 255.) scale_factor = 0.02 # mlab.points3d(s_pc[ :, 0] , s_pc[ :, 1], s_pc[:, 2], scale_factor=scale_factor , color=c_blue) mlab.points3d(src_pcd[ :, 0] , src_pcd[ :, 1], src_pcd[:, 2], scale_factor=scale_factor , color=c_red) mlab.points3d(tgt_pcd[ :, 0] , tgt_pcd[ :, 1], tgt_pcd[:, 2], scale_factor=scale_factor , color=c_blue) mlab.show() # add gaussian noise if self.data_augmentation: # rotate the point cloud euler_ab = np.random.rand(3) * np.pi * 2 / self.rot_factor # anglez, angley, anglex rot_ab = Rotation.from_euler('zyx', euler_ab).as_matrix() if (np.random.rand(1)[0] > 0.5): src_pcd = np.matmul(rot_ab, src_pcd.T).T rot = np.matmul(rot, rot_ab.T) else: tgt_pcd = np.matmul(rot_ab, tgt_pcd.T).T rot = np.matmul(rot_ab, rot) trans = np.matmul(rot_ab, trans) src_pcd += (np.random.rand(src_pcd.shape[0], 3) - 0.5) * self.augment_noise tgt_pcd += (np.random.rand(tgt_pcd.shape[0], 3) - 0.5) * self.augment_noise # get correspondence at fine level tsfm = to_tsfm(rot, trans) correspondences = get_correspondences(to_o3d_pcd(src_pcd), to_o3d_pcd(tgt_pcd), tsfm,self.overlap_radius) if debug: import mayavi.mlab as mlab c_red = (224. / 255., 0 / 255., 125 / 255.) c_pink = (224. / 255., 75. / 255., 232. / 255.) c_blue = (0. / 255., 0. / 255., 255. / 255.) scale_factor = 0.02 # mlab.points3d(s_pc[ :, 0] , s_pc[ :, 1], s_pc[:, 2], scale_factor=scale_factor , color=c_blue) mlab.points3d(src_pcd[ :, 0] , src_pcd[ :, 1], src_pcd[:, 2], scale_factor=scale_factor , color=c_red) mlab.points3d(tgt_pcd[ :, 0] , tgt_pcd[ :, 1], tgt_pcd[:, 2], scale_factor=scale_factor , color=c_blue) mlab.show() if (trans.ndim == 1): trans = trans[:, None] src_feats = np.ones_like(src_pcd[:, :1]).astype(np.float32) tgt_feats = np.ones_like(tgt_pcd[:, :1]).astype(np.float32) rot = rot.astype(np.float32) trans = trans.astype(np.float32) return src_pcd, tgt_pcd, src_feats, tgt_feats, correspondences, rot, trans, gt_cov if __name__ == '__main__': from lib.utils import load_config from easydict import EasyDict as edict from lib.tictok import Timers import yaml def join(loader, node): seq = loader.construct_sequence(node) return '_'.join([str(i) for i in seq]) yaml.add_constructor('!join', join) config = "/home/liyang/workspace/Regformer/configs/train/3dmatch.yaml" with open(config,'r') as f: config = yaml.load(f, Loader=yaml.Loader) config = edict(config) config.timers=Timers() D = _3DMatch(config, "test") for i in range (len(D)): try: if i%1000 == 0 : print (i,"/",len(D)) D.__getitem__(i, debug=True) except: pass # print ( D.data_entries[i] ) # print (os.remove(D.data_entries[i]) )

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lepard

lepard-main/configs/models.py

architectures = dict() kpfcn_backbone = [ 'simple', 'resnetb', 'resnetb_strided', 'resnetb', 'resnetb', 'resnetb_strided', 'resnetb', 'resnetb', 'resnetb_strided', 'resnetb', 'resnetb', 'nearest_upsample', 'unary', 'nearest_upsample', 'unary', 'nearest_upsample', 'unary' ] architectures['3dmatch'] = kpfcn_backbone architectures['4dmatch'] = kpfcn_backbone

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lepard

lepard-main/lib/tester.py

from lib.trainer import Trainer import torch from tqdm import tqdm from models.loss import MatchMotionLoss as MML import numpy as np from models.matching import Matching as CM import math class _3DMatchTester(Trainer): """ 3DMatch tester """ def __init__(self,args): Trainer.__init__(self, args) def test(self): n = 3 afmr = 0. arr = 0 air = 0 for i in range(n): # combat ransac nondeterministic thr =0.05 rr, ir, fmr = self.test_thr(thr) afmr+=fmr arr+=rr air+=ir print( "conf_threshold", thr, "registration recall:", rr, " Inlier rate:", ir, "FMR:", fmr) print("average registration recall:", arr / n, afmr/n, air/n) # print ("registration recall:", self.test_thr()) def test_thr(self, conf_threshold=None): # print('Start to evaluate on test datasets...') # os.makedirs(f'{self.snapshot_dir}/{self.config.dataset}',exist_ok=True) num_iter = math.ceil(len(self.loader['test'].dataset) // self.loader['test'].batch_size) c_loader_iter = self.loader['test'].__iter__() self.model.eval() success1 = 0. IR=0. FMR=0. with torch.no_grad(): for idx in tqdm(range(num_iter)): # loop through this epoch ################################## if self.timers: self.timers.tic('load batch') inputs = c_loader_iter.next() for k, v in inputs.items(): if type(v) == list: inputs[k] = [item.to(self.device) for item in v] elif type(v) in [dict, float, type(None), np.ndarray]: pass else: inputs[k] = v.to(self.device) if self.timers: self.timers.toc('load batch') ################################## if self.timers: self.timers.tic('forward pass') data = self.model(inputs, timers=self.timers) # [N1, C1], [N2, C2] if self.timers: self.timers.toc('forward pass') match_pred, _, _ = CM.get_match(data['conf_matrix_pred'], thr=conf_threshold, mutual=False) rot, trn = MML.ransac_regist_coarse(data['s_pcd'], data['t_pcd'], data['src_mask'], data['tgt_mask'], match_pred) ir = MML.compute_inlier_ratio(match_pred, data, inlier_thr=0.1).mean() rr1 = MML.compute_registration_recall(rot, trn, data, thr=0.2) # 0.2m vis = False if vis: pcd = data['points'][0].cpu().numpy() lenth = data['stack_lengths'][0][0] spcd, tpcd = pcd[:lenth] , pcd[lenth:] import mayavi.mlab as mlab c_red = (224. / 255., 0 / 255., 125 / 255.) c_pink = (224. / 255., 75. / 255., 232. / 255.) c_blue = (0. / 255., 0. / 255., 255. / 255.) scale_factor = 0.02 # mlab.points3d(s_pc[ :, 0] , s_pc[ :, 1], s_pc[:, 2], scale_factor=scale_factor , color=c_blue) mlab.points3d(spcd[:, 0], spcd[:, 1], spcd[:, 2], scale_factor=scale_factor, color=c_red) mlab.points3d(tpcd[:, 0], tpcd[:, 1], tpcd[:, 2], scale_factor=scale_factor, color=c_blue) mlab.show() spcd = ( np.matmul(rot, spcd.T) + trn ).T mlab.points3d(spcd[:, 0], spcd[:, 1], spcd[:, 2], scale_factor=scale_factor, color=c_red) mlab.points3d(tpcd[:, 0], tpcd[:, 1], tpcd[:, 2], scale_factor=scale_factor, color=c_blue) mlab.show() bs = len(rot) assert bs==1 success1 += bs * rr1 IR += bs*ir FMR += (ir>0.05).float() recall1 = success1/len(self.loader['test'].dataset) IRate = IR/len(self.loader['test'].dataset) FMR = FMR/len(self.loader['test'].dataset) return recall1, IRate, FMR def blend_anchor_motion (query_loc, reference_loc, reference_flow , knn=3, search_radius=0.1) : '''approximate flow on query points this function assume query points are sub- or un-sampled from reference locations @param query_loc:[m,3] @param reference_loc:[n,3] @param reference_flow:[n,3] @param knn: @return: blended_flow:[m,3] ''' from datasets.utils import knn_point_np dists, idx = knn_point_np (knn, reference_loc, query_loc) dists[dists < 1e-10] = 1e-10 mask = dists>search_radius dists[mask] = 1e+10 weight = 1.0 / dists weight = weight / np.sum(weight, -1, keepdims=True) # [B,N,3] blended_flow = np.sum (reference_flow [idx] * weight.reshape ([-1, knn, 1]), axis=1, keepdims=False) mask = mask.sum(axis=1)<3 return blended_flow, mask def compute_nrfmr( match_pred, data, recall_thr=0.04): s_pcd, t_pcd = data['s_pcd'], data['t_pcd'] s_pcd_raw = data ['src_pcd_list'] sflow_list = data['sflow_list'] metric_index_list = data['metric_index_list'] batched_rot = data['batched_rot'] # B,3,3 batched_trn = data['batched_trn'] nrfmr = 0. for i in range ( len(s_pcd_raw)): # get the metric points' transformed position metric_index = metric_index_list[i] sflow = sflow_list[i] s_pcd_raw_i = s_pcd_raw[i] metric_pcd = s_pcd_raw_i [ metric_index ] metric_sflow = sflow [ metric_index ] metric_pcd_deformed = metric_pcd + metric_sflow metric_pcd_wrapped_gt = ( torch.matmul( batched_rot[i], metric_pcd_deformed.T) + batched_trn[i] ).T # use the match prediction as the motion anchor match_pred_i = match_pred[ match_pred[:, 0] == i ] s_id , t_id = match_pred_i[:,1], match_pred_i[:,2] s_pcd_matched= s_pcd[i][s_id] t_pcd_matched= t_pcd[i][t_id] motion_pred = t_pcd_matched - s_pcd_matched metric_motion_pred, valid_mask = blend_anchor_motion( metric_pcd.cpu().numpy(), s_pcd_matched.cpu().numpy(), motion_pred.cpu().numpy(), knn=3, search_radius=0.1) metric_pcd_wrapped_pred = metric_pcd + torch.from_numpy(metric_motion_pred).to(metric_pcd) debug = False if debug: import mayavi.mlab as mlab c_red = (224. / 255., 0 / 255., 125 / 255.) c_pink = (224. / 255., 75. / 255., 232. / 255.) c_blue = (0. / 255., 0. / 255., 255. / 255.) scale_factor = 0.013 metric_pcd_wrapped_gt = metric_pcd_wrapped_gt.cpu() metric_pcd_wrapped_pred = metric_pcd_wrapped_pred.cpu() err = metric_pcd_wrapped_pred - metric_pcd_wrapped_gt mlab.points3d(metric_pcd_wrapped_gt[:, 0], metric_pcd_wrapped_gt[:, 1], metric_pcd_wrapped_gt[:, 2], scale_factor=scale_factor, color=c_pink) mlab.points3d(metric_pcd_wrapped_pred[ :, 0] , metric_pcd_wrapped_pred[ :, 1], metric_pcd_wrapped_pred[:, 2], scale_factor=scale_factor , color=c_blue) mlab.quiver3d(metric_pcd_wrapped_gt[:, 0], metric_pcd_wrapped_gt[:, 1], metric_pcd_wrapped_gt[:, 2], err[:, 0], err[:, 1], err[:, 2], scale_factor=1, mode='2ddash', line_width=1.) mlab.show() dist = torch.sqrt( torch.sum( (metric_pcd_wrapped_pred - metric_pcd_wrapped_gt)**2, dim=1 ) ) r = (dist < recall_thr).float().sum() / len(dist) nrfmr = nrfmr + r nrfmr = nrfmr /len(s_pcd_raw) return nrfmr class _4DMatchTester(Trainer): """ 3DMatch tester """ def __init__(self,args): Trainer.__init__(self, args) def test(self): for thr in [ 0.05, 0.1, 0.2]: # for thr in [ 0.1 ]: import time start = time.time() ir, fmr, nspl = self.test_thr(thr) print( "conf_threshold", thr, "NFMR:", fmr, " Inlier rate:", ir, "Number sample:", nspl) print( "time costs:", time.time() - start) def test_thr(self, conf_threshold=None): num_iter = math.ceil(len(self.loader['test'].dataset) // self.loader['test'].batch_size) c_loader_iter = self.loader['test'].__iter__() self.model.eval() assert self.loader['test'].batch_size == 1 IR=0. NR_FMR=0. inlier_thr = recall_thr = 0.04 n_sample = 0. with torch.no_grad(): for idx in tqdm(range(num_iter)): # loop through this epoch ################################## if self.timers: self.timers.tic('load batch') inputs = c_loader_iter.next() for k, v in inputs.items(): if type(v) == list: inputs[k] = [item.to(self.device) for item in v] elif type(v) in [ dict, float, type(None), np.ndarray]: pass else: inputs[k] = v.to(self.device) if self.timers: self.timers.toc('load batch') ################################## if self.timers: self.timers.tic('forward pass') data = self.model(inputs, timers=self.timers) # [N1, C1], [N2, C2] if self.timers: self.timers.toc('forward pass') match_pred, _, _ = CM.get_match(data['conf_matrix_pred'], thr=conf_threshold, mutual=True) ir = MML.compute_inlier_ratio(match_pred, data, inlier_thr=inlier_thr, s2t_flow=data['coarse_flow'][0][None] )[0] nrfmr = compute_nrfmr(match_pred, data, recall_thr=recall_thr) IR += ir NR_FMR += nrfmr n_sample += match_pred.shape[0] IRate = IR/len(self.loader['test'].dataset) NR_FMR = NR_FMR/len(self.loader['test'].dataset) n_sample = n_sample/len(self.loader['test'].dataset) if self.timers: self.timers.print() return IRate, NR_FMR, n_sample def get_trainer(config): if config.dataset == '3dmatch': return _3DMatchTester(config) elif config.dataset == '4dmatch': return _4DMatchTester(config) else: raise NotImplementedError

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lepard

lepard-main/lib/visualization.py

c_red = (224. / 255., 0 / 255., 125 / 255.) c_pink = (224. / 255., 75. / 255., 232. / 255.) c_blue = (0. / 255., 0. / 255., 255. / 255.) c_green = (0. / 255., 255. / 255., 0. / 255.) c_gray1 = (100. / 255., 100. / 255., 100. / 255.) c_gray2 = (175. / 255., 175. / 255., 175. / 255.) def viz_flow_mayavi( s_pc,flow = None, s_pc_deformed=None, t_pc=None, scale_factor = 0.02): import mayavi.mlab as mlab mlab.points3d(s_pc[:, 0], s_pc[:, 1], s_pc[:, 2], scale_factor=scale_factor, color=c_red) if flow is not None: mlab.quiver3d(s_pc[:, 0], s_pc[:, 1], s_pc[:, 2], flow[:, 0], flow[:, 1], flow[:, 2], scale_factor=1) if t_pc is not None: mlab.points3d(t_pc[:, 0], t_pc[:, 1], t_pc[:, 2], scale_factor=scale_factor, color=c_blue) if s_pc_deformed is not None: mlab.points3d(s_pc_deformed[:, 0], s_pc_deformed[:, 1], s_pc_deformed[:, 2], scale_factor=scale_factor, color=c_green) mlab.show() def viz_coarse_nn_correspondence_mayavi(s_pc, t_pc, correspondence, f_src_pcd=None, f_tgt_pcd=None, scale_factor = 0.02): ''' @param s_pc: [S,3] @param t_pc: [T,3] @param correspondence: [2,K] @param f_src_pcd: [S1,3] @param f_tgt_pcd: [T1,3] @param scale_factor: @return: ''' import mayavi import mayavi.mlab as mlab if f_src_pcd is not None: mlab.points3d(f_src_pcd[:, 0], f_src_pcd[:, 1], f_src_pcd[:, 2], scale_factor=scale_factor * 0.25, color=c_gray1) else: mlab.points3d(s_pc[:, 0], s_pc[:, 1], s_pc[:, 2], scale_factor=scale_factor*0.75, color=c_gray1) if f_tgt_pcd is not None: mlab.points3d(f_tgt_pcd[:, 0], f_tgt_pcd[:, 1], f_tgt_pcd[:, 2], scale_factor=scale_factor * 0.25, color=c_gray2) else : mlab.points3d(t_pc[:, 0], t_pc[:, 1], t_pc[:, 2], scale_factor=scale_factor*0.75, color=c_gray2) s_cpts = s_pc[correspondence[0]] t_cpts = t_pc[correspondence[1]] flow = t_cpts-s_cpts mlab.points3d(s_cpts[:, 0], s_cpts[:, 1], s_cpts[:, 2], scale_factor=scale_factor , color=c_red) mlab.points3d(t_cpts[:, 0], t_cpts[:, 1], t_cpts[:, 2], scale_factor=scale_factor , color=c_blue) mlab.quiver3d(s_cpts[:, 0], s_cpts[:, 1], s_cpts[:, 2], flow[:, 0], flow[:, 1], flow[:, 2], scale_factor=1, mode='2ddash', line_width=1.) mlab.show()

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lepard

lepard-main/lib/timer.py

import time class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0.0 self.sq_sum = 0.0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count self.sq_sum += val ** 2 * n self.var = self.sq_sum / self.count - self.avg ** 2 class Timer(object): """A simple timer.""" def __init__(self): self.total_time = 0. self.calls = 0 self.start_time = 0. self.diff = 0. self.avg = 0. def reset(self): self.total_time = 0 self.calls = 0 self.start_time = 0 self.diff = 0 self.avg = 0 def tic(self): # using time.time instead of time.clock because time time.clock # does not normalize for multithreading self.start_time = time.time() def toc(self, average=True): self.diff = time.time() - self.start_time self.total_time += self.diff self.calls += 1 self.avg = self.total_time / self.calls if average: return self.avg else: return self.diff

py

lepard

lepard-main/lib/benchmark_utils.py

import os,re,sys,json,yaml,random, glob, argparse, torch, pickle from tqdm import tqdm import numpy as np from scipy.spatial.transform import Rotation import open3d as o3d _EPS = 1e-7 # To prevent division by zero def viz_coarse_nn_correspondence_mayavi(s_pc, t_pc, good_c, bad_c, f_src_pcd=None, f_tgt_pcd=None, scale_factor=0.02): ''' @param s_pc: [S,3] @param t_pc: [T,3] @param correspondence: [2,K] @param f_src_pcd: [S1,3] @param f_tgt_pcd: [T1,3] @param scale_factor: @return: ''' import mayavi.mlab as mlab c_red = (224. / 255., 0 / 255., 0 / 255.) c_pink = (224. / 255., 75. / 255., 232. / 255.) c_blue = (0. / 255., 0. / 255., 255. / 255.) c_green = (0. / 255., 255. / 255., 0. / 255.) c_gray1 = (255 / 255., 255 / 255., 125 / 255.) c_gray2 = (125. / 255., 125. / 255., 255. / 255.) if f_src_pcd is not None: mlab.points3d(f_src_pcd[:, 0], f_src_pcd[:, 1], f_src_pcd[:, 2], scale_factor=scale_factor * 0.25, color=c_gray1) else: mlab.points3d(s_pc[:, 0], s_pc[:, 1], s_pc[:, 2], scale_factor=scale_factor * 0.75, color=c_gray1) if f_tgt_pcd is not None: mlab.points3d(f_tgt_pcd[:, 0], f_tgt_pcd[:, 1], f_tgt_pcd[:, 2], scale_factor=scale_factor * 0.25, color=c_gray2) else: mlab.points3d(t_pc[:, 0], t_pc[:, 1], t_pc[:, 2], scale_factor=scale_factor * 0.75, color=c_gray2) s_cpts_god = s_pc[good_c[0]] t_cpts_god = t_pc[good_c[1]] flow_good = t_cpts_god - s_cpts_god s_cpts_bd = s_pc[bad_c[0]] t_cpts_bd = t_pc[bad_c[1]] flow_bad = t_cpts_bd - s_cpts_bd def match_draw(s_cpts, t_cpts, flow, color): mlab.points3d(s_cpts[:, 0], s_cpts[:, 1], s_cpts[:, 2], scale_factor=scale_factor * 0.35, color=c_blue) mlab.points3d(t_cpts[:, 0], t_cpts[:, 1], t_cpts[:, 2], scale_factor=scale_factor * 0.35, color=c_pink) mlab.quiver3d(s_cpts[:, 0], s_cpts[:, 1], s_cpts[:, 2], flow[:, 0], flow[:, 1], flow[:, 2], scale_factor=1, mode='2ddash', line_width=1., color=color) match_draw(s_cpts_god, t_cpts_god, flow_good, c_green) match_draw(s_cpts_bd, t_cpts_bd, flow_bad, c_red) mlab.show() def correspondence_viz(src_raw, tgt_raw, src_pcd, tgt_pcd, corrs, inlier_mask, max=200): perm = np.random.permutation(corrs.shape[1]) ind = perm[:max] corrs = corrs[:, ind] inlier_mask = inlier_mask[ind] good_c = corrs[:, inlier_mask] bad_c = corrs[:, ~inlier_mask] offset = np.array([[1.45, 0, 0]]) # src_pcd = src_pcd + offset # src_raw = src_raw + offset tgt_pcd = tgt_pcd + offset tgt_raw = tgt_raw + offset viz_coarse_nn_correspondence_mayavi(src_pcd, tgt_pcd, good_c, bad_c, src_raw, tgt_raw, scale_factor=0.07) def fmr_wrt_distance(data,split,inlier_ratio_threshold=0.05): """ calculate feature match recall wrt distance threshold """ fmr_wrt_distance =[] for distance_threshold in range(1,21): inlier_ratios =[] distance_threshold /=100.0 for idx in range(data.shape[0]): inlier_ratio = (data[idx] < distance_threshold).mean() inlier_ratios.append(inlier_ratio) fmr = 0 for ele in split: fmr += (np.array(inlier_ratios[ele[0]:ele[1]]) > inlier_ratio_threshold).mean() fmr /= 8 fmr_wrt_distance.append(fmr*100) return fmr_wrt_distance def fmr_wrt_inlier_ratio(data, split, distance_threshold=0.1): """ calculate feature match recall wrt inlier ratio threshold """ fmr_wrt_inlier =[] for inlier_ratio_threshold in range(1,21): inlier_ratios =[] inlier_ratio_threshold /=100.0 for idx in range(data.shape[0]): inlier_ratio = (data[idx] < distance_threshold).mean() inlier_ratios.append(inlier_ratio) fmr = 0 for ele in split: fmr += (np.array(inlier_ratios[ele[0]:ele[1]]) > inlier_ratio_threshold).mean() fmr /= 8 fmr_wrt_inlier.append(fmr*100) return fmr_wrt_inlier def to_tensor(array): """ Convert array to tensor """ if(not isinstance(array,torch.Tensor)): return torch.from_numpy(array).float() else: return array def to_array(tensor): """ Conver tensor to array """ if(not isinstance(tensor,np.ndarray)): if(tensor.device == torch.device('cpu')): return tensor.numpy() else: return tensor.cpu().numpy() else: return tensor def to_tsfm(rot,trans): tsfm = np.eye(4) tsfm[:3,:3]=rot tsfm[:3,3]=trans.flatten() return tsfm def to_o3d_pcd(xyz): """ Convert tensor/array to open3d PointCloud xyz: [N, 3] """ pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(to_array(xyz)) return pcd def to_o3d_feats(embedding): """ Convert tensor/array to open3d features embedding: [N, 3] """ feats = o3d.registration.Feature() feats.data = to_array(embedding).T return feats def get_correspondences(src_pcd, tgt_pcd, trans, search_voxel_size, K=None): src_pcd.transform(trans) correspondences = KDTree_corr ( src_pcd, tgt_pcd, search_voxel_size, K=None) correspondences = torch.from_numpy(correspondences) return correspondences def KDTree_corr ( src_pcd_transformed, tgt_pcd, search_voxel_size, K=None): pcd_tree = o3d.geometry.KDTreeFlann(tgt_pcd) correspondences = [] for i, point in enumerate(src_pcd_transformed.points): [count, idx, _] = pcd_tree.search_radius_vector_3d(point, search_voxel_size) if K is not None: idx = idx[:K] for j in idx: correspondences.append([i, j]) correspondences = np.array(correspondences) return correspondences def get_blue(): """ Get color blue for rendering """ return [0, 0.651, 0.929] def get_yellow(): """ Get color yellow for rendering """ return [1, 0.706, 0] def random_sample(pcd, feats, N): """ Do random sampling to get exact N points and associated features pcd: [N,3] feats: [N,C] """ if(isinstance(pcd,torch.Tensor)): n1 = pcd.size(0) elif(isinstance(pcd, np.ndarray)): n1 = pcd.shape[0] if n1 == N: return pcd, feats if n1 > N: choice = np.random.permutation(n1)[:N] else: choice = np.random.choice(n1, N) return pcd[choice], feats[choice] def get_angle_deviation(R_pred,R_gt): """ Calculate the angle deviation between two rotaion matrice The rotation error is between [0,180] Input: R_pred: [B,3,3] R_gt : [B,3,3] Return: degs: [B] """ R=np.matmul(R_pred,R_gt.transpose(0,2,1)) tr=np.trace(R,0,1,2) rads=np.arccos(np.clip((tr-1)/2,-1,1)) # clip to valid range degs=rads/np.pi*180 return degs def ransac_pose_estimation(src_pcd, tgt_pcd, src_feat, tgt_feat, mutual = False, distance_threshold = 0.05, ransac_n = 3): """ RANSAC pose estimation with two checkers We follow D3Feat to set ransac_n = 3 for 3DMatch and ransac_n = 4 for KITTI. For 3DMatch dataset, we observe significant improvement after changing ransac_n from 4 to 3. """ if(mutual): if(torch.cuda.device_count()>=1): device = torch.device('cuda') else: device = torch.device('cpu') src_feat, tgt_feat = to_tensor(src_feat), to_tensor(tgt_feat) scores = torch.matmul(src_feat.to(device), tgt_feat.transpose(0,1).to(device)).cpu() selection = mutual_selection(scores[None,:,:])[0] row_sel, col_sel = np.where(selection) corrs = o3d.utility.Vector2iVector(np.array([row_sel,col_sel]).T) src_pcd = to_o3d_pcd(src_pcd) tgt_pcd = to_o3d_pcd(tgt_pcd) result_ransac = o3d.registration.registration_ransac_based_on_correspondence( source=src_pcd, target=tgt_pcd,corres=corrs, max_correspondence_distance=distance_threshold, estimation_method=o3d.registration.TransformationEstimationPointToPoint(False), ransac_n=4, criteria=o3d.registration.RANSACConvergenceCriteria(50000, 1000)) else: src_pcd = to_o3d_pcd(src_pcd) tgt_pcd = to_o3d_pcd(tgt_pcd) src_feats = to_o3d_feats(src_feat) tgt_feats = to_o3d_feats(tgt_feat) result_ransac = o3d.registration.registration_ransac_based_on_feature_matching( src_pcd, tgt_pcd, src_feats, tgt_feats,distance_threshold, o3d.registration.TransformationEstimationPointToPoint(False), ransac_n, [o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9), o3d.registration.CorrespondenceCheckerBasedOnDistance(distance_threshold)], o3d.registration.RANSACConvergenceCriteria(50000, 1000)) return result_ransac.transformation def get_inlier_ratio(src_pcd, tgt_pcd, src_feat, tgt_feat, rot, trans, inlier_distance_threshold = 0.1): """ Compute inlier ratios with and without mutual check, return both """ src_pcd = to_tensor(src_pcd) tgt_pcd = to_tensor(tgt_pcd) src_feat = to_tensor(src_feat) tgt_feat = to_tensor(tgt_feat) rot, trans = to_tensor(rot), to_tensor(trans) results =dict() results['w']=dict() results['wo']=dict() if(torch.cuda.device_count()>=1): device = torch.device('cuda') else: device = torch.device('cpu') src_pcd = (torch.matmul(rot, src_pcd.transpose(0,1)) + trans).transpose(0,1) scores = torch.matmul(src_feat.to(device), tgt_feat.transpose(0,1).to(device)).cpu() ######################################## # 1. calculate inlier ratios wo mutual check _, idx = scores.max(-1) dist = torch.norm(src_pcd- tgt_pcd[idx],dim=1) results['wo']['distance'] = dist.numpy() c_inlier_ratio = (dist < inlier_distance_threshold).float().mean() results['wo']['inlier_ratio'] = c_inlier_ratio ######################################## # 2. calculate inlier ratios w mutual check selection = mutual_selection(scores[None,:,:])[0] row_sel, col_sel = np.where(selection) dist = torch.norm(src_pcd[row_sel]- tgt_pcd[col_sel],dim=1) results['w']['distance'] = dist.numpy() c_inlier_ratio = (dist < inlier_distance_threshold).float().mean() results['w']['inlier_ratio'] = c_inlier_ratio return results def mutual_selection(score_mat): """ Return a {0,1} matrix, the element is 1 if and only if it's maximum along both row and column Args: np.array() score_mat: [B,N,N] Return: mutuals: [B,N,N] """ score_mat=to_array(score_mat) if(score_mat.ndim==2): score_mat=score_mat[None,:,:] mutuals=np.zeros_like(score_mat) for i in range(score_mat.shape[0]): # loop through the batch c_mat=score_mat[i] flag_row=np.zeros_like(c_mat) flag_column=np.zeros_like(c_mat) max_along_row=np.argmax(c_mat,1)[:,None] max_along_column=np.argmax(c_mat,0)[None,:] np.put_along_axis(flag_row,max_along_row,1,1) np.put_along_axis(flag_column,max_along_column,1,0) mutuals[i]=(flag_row.astype(np.bool)) & (flag_column.astype(np.bool)) return mutuals.astype(np.bool)

py

lepard

lepard-main/lib/utils.py

import os,re,sys,json,yaml,random, argparse, torch, pickle import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import numpy as np from scipy.spatial.transform import Rotation from sklearn.neighbors import NearestNeighbors from scipy.spatial.distance import minkowski _EPS = 1e-7 # To prevent division by zero class Logger: def __init__(self, path): self.path = path log_path = self.path + '/log' if os.path.exists(log_path): os.remove(log_path) self.fw = open(log_path,'a') def write(self, text): self.fw.write(text) self.fw.flush() def close(self): self.fw.close() def save_obj(obj, path ): """ save a dictionary to a pickle file """ with open(path, 'wb') as f: pickle.dump(obj, f) def load_obj(path): """ read a dictionary from a pickle file """ with open(path, 'rb') as f: return pickle.load(f) def load_config(path): """ Loads config file: Args: path (str): path to the config file Returns: config (dict): dictionary of the configuration parameters, merge sub_dicts """ with open(path,'r') as f: cfg = yaml.safe_load(f) config = dict() for key, value in cfg.items(): for k,v in value.items(): config[k] = v return config def setup_seed(seed): """ fix random seed for deterministic training """ torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True def square_distance(src, dst, normalised = False): """ Calculate Euclid distance between each two points. Args: src: source points, [B, N, C] dst: target points, [B, M, C] Returns: dist: per-point square distance, [B, N, M] """ B, N, _ = src.shape _, M, _ = dst.shape dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) if(normalised): dist += 2 else: dist += torch.sum(src ** 2, dim=-1)[:, :, None] dist += torch.sum(dst ** 2, dim=-1)[:, None, :] dist = torch.clamp(dist, min=1e-12, max=None) return dist def validate_gradient(model): """ Confirm all the gradients are non-nan and non-inf """ for name, param in model.named_parameters(): if param.grad is not None: if torch.any(torch.isnan(param.grad)): return False if torch.any(torch.isinf(param.grad)): return False return True def natural_key(string_): """ Sort strings by numbers in the name """ return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_)]

py

lepard

lepard-main/lib/ply.py

# # # 0===============================0 # | PLY files reader/writer | # 0===============================0 # # # ---------------------------------------------------------------------------------------------------------------------- # # function to read/write .ply files # # ---------------------------------------------------------------------------------------------------------------------- # # Hugues THOMAS - 10/02/2017 # # ---------------------------------------------------------------------------------------------------------------------- # # Imports and global variables # \**********************************/ # # Basic libs import numpy as np import sys # Define PLY types ply_dtypes = dict([ (b'int8', 'i1'), (b'char', 'i1'), (b'uint8', 'u1'), (b'uchar', 'u1'), (b'int16', 'i2'), (b'short', 'i2'), (b'uint16', 'u2'), (b'ushort', 'u2'), (b'int32', 'i4'), (b'int', 'i4'), (b'uint32', 'u4'), (b'uint', 'u4'), (b'float32', 'f4'), (b'float', 'f4'), (b'float64', 'f8'), (b'double', 'f8') ]) # Numpy reader format valid_formats = {'ascii': '', 'binary_big_endian': '>', 'binary_little_endian': '<'} # ---------------------------------------------------------------------------------------------------------------------- # # Functions # \***************/ # def parse_header(plyfile, ext): # Variables line = [] properties = [] num_points = None while b'end_header' not in line and line != b'': line = plyfile.readline() if b'element' in line: line = line.split() num_points = int(line[2]) elif b'property' in line: line = line.split() properties.append((line[2].decode(), ext + ply_dtypes[line[1]])) return num_points, properties def parse_mesh_header(plyfile, ext): # Variables line = [] vertex_properties = [] num_points = None num_faces = None current_element = None while b'end_header' not in line and line != b'': line = plyfile.readline() # Find point element if b'element vertex' in line: current_element = 'vertex' line = line.split() num_points = int(line[2]) elif b'element face' in line: current_element = 'face' line = line.split() num_faces = int(line[2]) elif b'property' in line: if current_element == 'vertex': line = line.split() vertex_properties.append((line[2].decode(), ext + ply_dtypes[line[1]])) elif current_element == 'vertex': if not line.startswith('property list uchar int'): raise ValueError('Unsupported faces property : ' + line) return num_points, num_faces, vertex_properties def read_ply(filename, triangular_mesh=False): """ Read ".ply" files Parameters ---------- filename : string the name of the file to read. Returns ------- result : array data stored in the file Examples -------- Store data in file >>> points = np.random.rand(5, 3) >>> values = np.random.randint(2, size=10) >>> write_ply('example.ply', [points, values], ['x', 'y', 'z', 'values']) Read the file >>> data = read_ply('example.ply') >>> values = data['values'] array([0, 0, 1, 1, 0]) >>> points = np.vstack((data['x'], data['y'], data['z'])).T array([[ 0.466 0.595 0.324] [ 0.538 0.407 0.654] [ 0.850 0.018 0.988] [ 0.395 0.394 0.363] [ 0.873 0.996 0.092]]) """ with open(filename, 'rb') as plyfile: # Check if the file start with ply if b'ply' not in plyfile.readline(): raise ValueError('The file does not start whith the word ply') # get binary_little/big or ascii fmt = plyfile.readline().split()[1].decode() if fmt == "ascii": raise ValueError('The file is not binary') # get extension for building the numpy dtypes ext = valid_formats[fmt] # PointCloud reader vs mesh reader if triangular_mesh: # Parse header num_points, num_faces, properties = parse_mesh_header(plyfile, ext) # Get point data vertex_data = np.fromfile(plyfile, dtype=properties, count=num_points) # Get face data face_properties = [('k', ext + 'u1'), ('v1', ext + 'i4'), ('v2', ext + 'i4'), ('v3', ext + 'i4')] faces_data = np.fromfile(plyfile, dtype=face_properties, count=num_faces) # Return vertex data and concatenated faces faces = np.vstack((faces_data['v1'], faces_data['v2'], faces_data['v3'])).T data = [vertex_data, faces] else: # Parse header num_points, properties = parse_header(plyfile, ext) # Get data data = np.fromfile(plyfile, dtype=properties, count=num_points) return data def header_properties(field_list, field_names): # List of lines to write lines = [] # First line describing element vertex lines.append('element vertex %d' % field_list[0].shape[0]) # Properties lines i = 0 for fields in field_list: for field in fields.T: lines.append('property %s %s' % (field.dtype.name, field_names[i])) i += 1 return lines def write_ply(filename, field_list, field_names, triangular_faces=None): """ Write ".ply" files Parameters ---------- filename : string the name of the file to which the data is saved. A '.ply' extension will be appended to the file name if it does no already have one. field_list : list, tuple, numpy array the fields to be saved in the ply file. Either a numpy array, a list of numpy arrays or a tuple of numpy arrays. Each 1D numpy array and each column of 2D numpy arrays are considered as one field. field_names : list the name of each fields as a list of strings. Has to be the same length as the number of fields. Examples -------- >>> points = np.random.rand(10, 3) >>> write_ply('example1.ply', points, ['x', 'y', 'z']) >>> values = np.random.randint(2, size=10) >>> write_ply('example2.ply', [points, values], ['x', 'y', 'z', 'values']) >>> colors = np.random.randint(255, size=(10,3), dtype=np.uint8) >>> field_names = ['x', 'y', 'z', 'red', 'green', 'blue', values'] >>> write_ply('example3.ply', [points, colors, values], field_names) """ # Format list input to the right form field_list = list(field_list) if (type(field_list) == list or type(field_list) == tuple) else list((field_list,)) for i, field in enumerate(field_list): if field.ndim < 2: field_list[i] = field.reshape(-1, 1) if field.ndim > 2: print('fields have more than 2 dimensions') return False # check all fields have the same number of data n_points = [field.shape[0] for field in field_list] if not np.all(np.equal(n_points, n_points[0])): print('wrong field dimensions') return False # Check if field_names and field_list have same nb of column n_fields = np.sum([field.shape[1] for field in field_list]) if (n_fields != len(field_names)): print('wrong number of field names') return False # Add extension if not there if not filename.endswith('.ply'): filename += '.ply' # open in text mode to write the header with open(filename, 'w') as plyfile: # First magical word header = ['ply'] # Encoding format header.append('format binary_' + sys.byteorder + '_endian 1.0') # Points properties description header.extend(header_properties(field_list, field_names)) # Add faces if needded if triangular_faces is not None: header.append('element face {:d}'.format(triangular_faces.shape[0])) header.append('property list uchar int vertex_indices') # End of header header.append('end_header') # Write all lines for line in header: plyfile.write("%s\n" % line) # open in binary/append to use tofile with open(filename, 'ab') as plyfile: # Create a structured array i = 0 type_list = [] for fields in field_list: for field in fields.T: type_list += [(field_names[i], field.dtype.str)] i += 1 data = np.empty(field_list[0].shape[0], dtype=type_list) i = 0 for fields in field_list: for field in fields.T: data[field_names[i]] = field i += 1 data.tofile(plyfile) if triangular_faces is not None: triangular_faces = triangular_faces.astype(np.int32) type_list = [('k', 'uint8')] + [(str(ind), 'int32') for ind in range(3)] data = np.empty(triangular_faces.shape[0], dtype=type_list) data['k'] = np.full((triangular_faces.shape[0],), 3, dtype=np.uint8) data['0'] = triangular_faces[:, 0] data['1'] = triangular_faces[:, 1] data['2'] = triangular_faces[:, 2] data.tofile(plyfile) return True def describe_element(name, df): """ Takes the columns of the dataframe and builds a ply-like description Parameters ---------- name: str df: pandas DataFrame Returns ------- element: list[str] """ property_formats = {'f': 'float', 'u': 'uchar', 'i': 'int'} element = ['element ' + name + ' ' + str(len(df))] if name == 'face': element.append("property list uchar int points_indices") else: for i in range(len(df.columns)): # get first letter of dtype to infer format f = property_formats[str(df.dtypes[i])[0]] element.append('property ' + f + ' ' + df.columns.values[i]) return element

py

lepard

lepard-main/lib/tictok.py

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import time import numpy as np from collections import defaultdict class Timer(object): def __init__(self): self.reset() def tic(self): self.start_time = time.time() def toc(self, average=True): self.diff = time.time() - self.start_time self.total_time += self.diff self.calls += 1 def tictoc(self, diff): self.diff = diff self.total_time += diff self.calls += 1 def total(self): """ return the total amount of time """ return self.total_time def avg(self): """ return the average amount of time """ return self.total_time / float(self.calls) def reset(self): self.total_time = 0. self.calls = 0 self.start_time = 0. self.diff = 0. class Timers(object): def __init__(self): self.timers = defaultdict(Timer) def tic(self, key): self.timers[key].tic() def toc(self, key): self.timers[key].toc() def tictoc(self, key, diff): self.timers[key].tictoc( diff) def print(self, key=None): if key is None: # print all time for k, v in self.timers.items(): print("{:}: \t average {:.4f}, total {:.4f} ,\t calls {:}".format(k.ljust(30), v.avg(), v.total_time, v.calls)) else: print("Average time for {:}: {:}".format(key, self.timers[key].avg())) def get_avg(self, key): return self.timers[key].avg()

py

lepard

lepard-main/lib/__init__.py

py

lepard

lepard-main/lib/trainer.py

import gc import os import torch import torch.nn as nn import numpy as np from tensorboardX import SummaryWriter from tqdm import tqdm from lib.timer import AverageMeter from lib.utils import Logger, validate_gradient from lib.tictok import Timers class Trainer(object): def __init__(self, args): self.config = args # parameters self.start_epoch = 1 self.max_epoch = args.max_epoch self.save_dir = args.save_dir self.device = args.device self.verbose = args.verbose self.model = args.model self.model = self.model.to(self.device) self.optimizer = args.optimizer self.scheduler = args.scheduler self.scheduler_freq = args.scheduler_freq self.snapshot_dir = args.snapshot_dir self.iter_size = args.iter_size self.verbose_freq = args.verbose_freq // args.batch_size + 1 if 'overfit' in self.config.exp_dir: self.verbose_freq = 1 self.loss = args.desc_loss self.best_loss = 1e5 self.best_recall = -1e5 self.summary_writer = SummaryWriter(log_dir=args.tboard_dir) self.logger = Logger(args.snapshot_dir) self.logger.write(f'#parameters {sum([x.nelement() for x in self.model.parameters()]) / 1000000.} M\n') if (args.pretrain != ''): self._load_pretrain(args.pretrain) self.loader = dict() self.loader['train'] = args.train_loader self.loader['val'] = args.val_loader self.loader['test'] = args.test_loader self.timers = args.timers with open(f'{args.snapshot_dir}/model', 'w') as f: f.write(str(self.model)) f.close() def _snapshot(self, epoch, name=None): state = { 'epoch': epoch, 'state_dict': self.model.state_dict(), 'optimizer': self.optimizer.state_dict(), 'scheduler': self.scheduler.state_dict(), 'best_loss': self.best_loss, 'best_recall': self.best_recall } if name is None: filename = os.path.join(self.save_dir, f'model_{epoch}.pth') else: filename = os.path.join(self.save_dir, f'model_{name}.pth') self.logger.write(f"Save model to {filename}\n") torch.save(state, filename, _use_new_zipfile_serialization=False) def _load_pretrain(self, resume): print ("loading pretrained", resume) if os.path.isfile(resume): state = torch.load(resume) self.model.load_state_dict(state['state_dict']) self.start_epoch = state['epoch'] self.scheduler.load_state_dict(state['scheduler']) self.optimizer.load_state_dict(state['optimizer']) self.best_loss = state['best_loss'] self.best_recall = state['best_recall'] self.logger.write(f'Successfully load pretrained model from {resume}!\n') self.logger.write(f'Current best loss {self.best_loss}\n') self.logger.write(f'Current best recall {self.best_recall}\n') else: raise ValueError(f"=> no checkpoint found at '{resume}'") def _get_lr(self, group=0): return self.optimizer.param_groups[group]['lr'] def inference_one_batch(self, inputs, phase): assert phase in ['train', 'val', 'test'] inputs ['phase'] = phase if (phase == 'train'): self.model.train() if self.timers: self.timers.tic('forward pass') data = self.model(inputs, timers=self.timers) # [N1, C1], [N2, C2] if self.timers: self.timers.toc('forward pass') if self.timers: self.timers.tic('compute loss') loss_info = self.loss( data) if self.timers: self.timers.toc('compute loss') if self.timers: self.timers.tic('backprop') loss_info['loss'].backward() if self.timers: self.timers.toc('backprop') else: self.model.eval() with torch.no_grad(): data = self.model(inputs, timers=self.timers) # [N1, C1], [N2, C2] loss_info = self.loss(data) return loss_info def inference_one_epoch(self, epoch, phase): gc.collect() assert phase in ['train', 'val', 'test'] # init stats meter stats_meter = None # self.stats_meter() num_iter = int(len(self.loader[phase].dataset) // self.loader[phase].batch_size) # drop last incomplete batch c_loader_iter = self.loader[phase].__iter__() self.optimizer.zero_grad() for c_iter in tqdm(range(num_iter)): # loop through this epoch if self.timers: self.timers.tic('one_iteration') ################################## if self.timers: self.timers.tic('load batch') inputs = c_loader_iter.next() # for gpu_div_i, _ in enumerate(inputs): for k, v in inputs.items(): if type(v) == list: inputs [k] = [item.to(self.device) for item in v] elif type(v) in [ dict, float, type(None), np.ndarray]: pass else: inputs [k] = v.to(self.device) if self.timers: self.timers.toc('load batch') ################################## if self.timers: self.timers.tic('inference_one_batch') loss_info = self.inference_one_batch(inputs, phase) if self.timers: self.timers.toc('inference_one_batch') ################################################### # run optimisation # if self.timers: self.timers.tic('run optimisation') if ((c_iter + 1) % self.iter_size == 0 and phase == 'train'): gradient_valid = validate_gradient(self.model) if (gradient_valid): self.optimizer.step() else: self.logger.write('gradient not valid\n') self.optimizer.zero_grad() # if self.timers: self.timers.toc('run optimisation') ################################ torch.cuda.empty_cache() if stats_meter is None: stats_meter = dict() for key, _ in loss_info.items(): stats_meter[key] = AverageMeter() for key, value in loss_info.items(): stats_meter[key].update(value) if phase == 'train' : if (c_iter + 1) % self.verbose_freq == 0 and self.verbose : curr_iter = num_iter * (epoch - 1) + c_iter for key, value in stats_meter.items(): self.summary_writer.add_scalar(f'{phase}/{key}', value.avg, curr_iter) dump_mess=True if dump_mess: message = f'{phase} Epoch: {epoch} [{c_iter + 1:4d}/{num_iter}]' for key, value in stats_meter.items(): message += f'{key}: {value.avg:.2f}\t' self.logger.write(message + '\n') if self.timers: self.timers.toc('one_iteration') # report evaluation score at end of each epoch if phase in ['val', 'test']: for key, value in stats_meter.items(): self.summary_writer.add_scalar(f'{phase}/{key}', value.avg, epoch) message = f'{phase} Epoch: {epoch}' for key, value in stats_meter.items(): message += f'{key}: {value.avg:.2f}\t' self.logger.write(message + '\n') return stats_meter def train(self): print('start training...') for epoch in range(self.start_epoch, self.max_epoch): with torch.autograd.set_detect_anomaly(True): if self.timers: self.timers.tic('run one epoch') stats_meter = self.inference_one_epoch(epoch, 'train') if self.timers: self.timers.toc('run one epoch') self.scheduler.step() if 'overfit' in self.config.exp_dir : if stats_meter['loss'].avg < self.best_loss: self.best_loss = stats_meter['loss'].avg self._snapshot(epoch, 'best_loss') if self.timers: self.timers.print() else : # no validation step for overfitting if self.config.do_valid: stats_meter = self.inference_one_epoch(epoch, 'val') if stats_meter['loss'].avg < self.best_loss: self.best_loss = stats_meter['loss'].avg self._snapshot(epoch, 'best_loss') if self.timers: self.timers.print() # finish all epoch print("Training finish!")

py

lepard

lepard-main/kernels/kernel_points.py

# # # 0=================================0 # | Kernel Point Convolutions | # 0=================================0 # # # ---------------------------------------------------------------------------------------------------------------------- # # Functions handling the disposition of kernel points. # # ---------------------------------------------------------------------------------------------------------------------- # # Hugues THOMAS - 11/06/2018 # import time import numpy as np from os import makedirs from os.path import join, exists from lib.ply import read_ply, write_ply # ------------------------------------------------------------------------------------------ # # Functions # \***************/ # # def create_3D_rotations(axis, angle): """ Create rotation matrices from a list of axes and angles. Code from wikipedia on quaternions :param axis: float32[N, 3] :param angle: float32[N,] :return: float32[N, 3, 3] """ t1 = np.cos(angle) t2 = 1 - t1 t3 = axis[:, 0] * axis[:, 0] t6 = t2 * axis[:, 0] t7 = t6 * axis[:, 1] t8 = np.sin(angle) t9 = t8 * axis[:, 2] t11 = t6 * axis[:, 2] t12 = t8 * axis[:, 1] t15 = axis[:, 1] * axis[:, 1] t19 = t2 * axis[:, 1] * axis[:, 2] t20 = t8 * axis[:, 0] t24 = axis[:, 2] * axis[:, 2] R = np.stack([t1 + t2 * t3, t7 - t9, t11 + t12, t7 + t9, t1 + t2 * t15, t19 - t20, t11 - t12, t19 + t20, t1 + t2 * t24], axis=1) return np.reshape(R, (-1, 3, 3)) def spherical_Lloyd(radius, num_cells, dimension=3, fixed='center', approximation='monte-carlo', approx_n=5000, max_iter=500, momentum=0.9, verbose=0): """ Creation of kernel point via Lloyd algorithm. We use an approximation of the algorithm, and compute the Voronoi cell centers with discretization of space. The exact formula is not trivial with part of the sphere as sides. :param radius: Radius of the kernels :param num_cells: Number of cell (kernel points) in the Voronoi diagram. :param dimension: dimension of the space :param fixed: fix position of certain kernel points ('none', 'center' or 'verticals') :param approximation: Approximation method for Lloyd's algorithm ('discretization', 'monte-carlo') :param approx_n: Number of point used for approximation. :param max_iter: Maximum nu;ber of iteration for the algorithm. :param momentum: Momentum of the low pass filter smoothing kernel point positions :param verbose: display option :return: points [num_kernels, num_points, dimension] """ ####################### # Parameters definition ####################### # Radius used for optimization (points are rescaled afterwards) radius0 = 1.0 ####################### # Kernel initialization ####################### # Random kernel points (Uniform distribution in a sphere) kernel_points = np.zeros((0, dimension)) while kernel_points.shape[0] < num_cells: new_points = np.random.rand(num_cells, dimension) * 2 * radius0 - radius0 kernel_points = np.vstack((kernel_points, new_points)) d2 = np.sum(np.power(kernel_points, 2), axis=1) kernel_points = kernel_points[np.logical_and(d2 < radius0 ** 2, (0.9 * radius0) ** 2 < d2), :] kernel_points = kernel_points[:num_cells, :].reshape((num_cells, -1)) # Optional fixing if fixed == 'center': kernel_points[0, :] *= 0 if fixed == 'verticals': kernel_points[:3, :] *= 0 kernel_points[1, -1] += 2 * radius0 / 3 kernel_points[2, -1] -= 2 * radius0 / 3 ############################## # Approximation initialization ############################## # Initialize figure if verbose > 1: fig = plt.figure() # Initialize discretization in this method is chosen if approximation == 'discretization': side_n = int(np.floor(approx_n ** (1. / dimension))) dl = 2 * radius0 / side_n coords = np.arange(-radius0 + dl/2, radius0, dl) if dimension == 2: x, y = np.meshgrid(coords, coords) X = np.vstack((np.ravel(x), np.ravel(y))).T elif dimension == 3: x, y, z = np.meshgrid(coords, coords, coords) X = np.vstack((np.ravel(x), np.ravel(y), np.ravel(z))).T elif dimension == 4: x, y, z, t = np.meshgrid(coords, coords, coords, coords) X = np.vstack((np.ravel(x), np.ravel(y), np.ravel(z), np.ravel(t))).T else: raise ValueError('Unsupported dimension (max is 4)') elif approximation == 'monte-carlo': X = np.zeros((0, dimension)) else: raise ValueError('Wrong approximation method chosen: "{:s}"'.format(approximation)) # Only points inside the sphere are used d2 = np.sum(np.power(X, 2), axis=1) X = X[d2 < radius0 * radius0, :] ##################### # Kernel optimization ##################### # Warning if at least one kernel point has no cell warning = False # moving vectors of kernel points saved to detect convergence max_moves = np.zeros((0,)) for iter in range(max_iter): # In the case of monte-carlo, renew the sampled points if approximation == 'monte-carlo': X = np.random.rand(approx_n, dimension) * 2 * radius0 - radius0 d2 = np.sum(np.power(X, 2), axis=1) X = X[d2 < radius0 * radius0, :] # Get the distances matrix [n_approx, K, dim] differences = np.expand_dims(X, 1) - kernel_points sq_distances = np.sum(np.square(differences), axis=2) # Compute cell centers cell_inds = np.argmin(sq_distances, axis=1) centers = [] for c in range(num_cells): bool_c = (cell_inds == c) num_c = np.sum(bool_c.astype(np.int32)) if num_c > 0: centers.append(np.sum(X[bool_c, :], axis=0) / num_c) else: warning = True centers.append(kernel_points[c]) # Update kernel points with low pass filter to smooth mote carlo centers = np.vstack(centers) moves = (1 - momentum) * (centers - kernel_points) kernel_points += moves # Check moves for convergence max_moves = np.append(max_moves, np.max(np.linalg.norm(moves, axis=1))) # Optional fixing if fixed == 'center': kernel_points[0, :] *= 0 if fixed == 'verticals': kernel_points[0, :] *= 0 kernel_points[:3, :-1] *= 0 if verbose: print('iter {:5d} / max move = {:f}'.format(iter, np.max(np.linalg.norm(moves, axis=1)))) if warning: print('{:}WARNING: at least one point has no cell{:}'.format(bcolors.WARNING, bcolors.ENDC)) if verbose > 1: plt.clf() plt.scatter(X[:, 0], X[:, 1], c=cell_inds, s=20.0, marker='.', cmap=plt.get_cmap('tab20')) #plt.scatter(kernel_points[:, 0], kernel_points[:, 1], c=np.arange(num_cells), s=100.0, # marker='+', cmap=plt.get_cmap('tab20')) plt.plot(kernel_points[:, 0], kernel_points[:, 1], 'k+') circle = plt.Circle((0, 0), radius0, color='r', fill=False) fig.axes[0].add_artist(circle) fig.axes[0].set_xlim((-radius0 * 1.1, radius0 * 1.1)) fig.axes[0].set_ylim((-radius0 * 1.1, radius0 * 1.1)) fig.axes[0].set_aspect('equal') plt.draw() plt.pause(0.001) plt.show(block=False) ################### # User verification ################### # Show the convergence to ask user if this kernel is correct if verbose: if dimension == 2: fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10.4, 4.8]) ax1.plot(max_moves) ax2.scatter(X[:, 0], X[:, 1], c=cell_inds, s=20.0, marker='.', cmap=plt.get_cmap('tab20')) # plt.scatter(kernel_points[:, 0], kernel_points[:, 1], c=np.arange(num_cells), s=100.0, # marker='+', cmap=plt.get_cmap('tab20')) ax2.plot(kernel_points[:, 0], kernel_points[:, 1], 'k+') circle = plt.Circle((0, 0), radius0, color='r', fill=False) ax2.add_artist(circle) ax2.set_xlim((-radius0 * 1.1, radius0 * 1.1)) ax2.set_ylim((-radius0 * 1.1, radius0 * 1.1)) ax2.set_aspect('equal') plt.title('Check if kernel is correct.') plt.draw() plt.show() if dimension > 2: plt.figure() plt.plot(max_moves) plt.title('Check if kernel is correct.') plt.show() # Rescale kernels with real radius return kernel_points * radius def kernel_point_optimization_debug(radius, num_points, num_kernels=1, dimension=3, fixed='center', ratio=0.66, verbose=0): """ Creation of kernel point via optimization of potentials. :param radius: Radius of the kernels :param num_points: points composing kernels :param num_kernels: number of wanted kernels :param dimension: dimension of the space :param fixed: fix position of certain kernel points ('none', 'center' or 'verticals') :param ratio: ratio of the radius where you want the kernels points to be placed :param verbose: display option :return: points [num_kernels, num_points, dimension] """ ####################### # Parameters definition ####################### # Radius used for optimization (points are rescaled afterwards) radius0 = 1 diameter0 = 2 # Factor multiplicating gradients for moving points (~learning rate) moving_factor = 1e-2 continuous_moving_decay = 0.9995 # Gradient threshold to stop optimization thresh = 1e-5 # Gradient clipping value clip = 0.05 * radius0 ####################### # Kernel initialization ####################### # Random kernel points kernel_points = np.random.rand(num_kernels * num_points - 1, dimension) * diameter0 - radius0 while (kernel_points.shape[0] < num_kernels * num_points): new_points = np.random.rand(num_kernels * num_points - 1, dimension) * diameter0 - radius0 kernel_points = np.vstack((kernel_points, new_points)) d2 = np.sum(np.power(kernel_points, 2), axis=1) kernel_points = kernel_points[d2 < 0.5 * radius0 * radius0, :] kernel_points = kernel_points[:num_kernels * num_points, :].reshape((num_kernels, num_points, -1)) # Optionnal fixing if fixed == 'center': kernel_points[:, 0, :] *= 0 if fixed == 'verticals': kernel_points[:, :3, :] *= 0 kernel_points[:, 1, -1] += 2 * radius0 / 3 kernel_points[:, 2, -1] -= 2 * radius0 / 3 ##################### # Kernel optimization ##################### # Initialize figure if verbose>1: fig = plt.figure() saved_gradient_norms = np.zeros((10000, num_kernels)) old_gradient_norms = np.zeros((num_kernels, num_points)) for iter in range(10000): # Compute gradients # ***************** # Derivative of the sum of potentials of all points A = np.expand_dims(kernel_points, axis=2) B = np.expand_dims(kernel_points, axis=1) interd2 = np.sum(np.power(A - B, 2), axis=-1) inter_grads = (A - B) / (np.power(np.expand_dims(interd2, -1), 3/2) + 1e-6) inter_grads = np.sum(inter_grads, axis=1) # Derivative of the radius potential circle_grads = 10*kernel_points # All gradients gradients = inter_grads + circle_grads if fixed == 'verticals': gradients[:, 1:3, :-1] = 0 # Stop condition # ************** # Compute norm of gradients gradients_norms = np.sqrt(np.sum(np.power(gradients, 2), axis=-1)) saved_gradient_norms[iter, :] = np.max(gradients_norms, axis=1) # Stop if all moving points are gradients fixed (low gradients diff) if fixed == 'center' and np.max(np.abs(old_gradient_norms[:, 1:] - gradients_norms[:, 1:])) < thresh: break elif fixed == 'verticals' and np.max(np.abs(old_gradient_norms[:, 3:] - gradients_norms[:, 3:])) < thresh: break elif np.max(np.abs(old_gradient_norms - gradients_norms)) < thresh: break old_gradient_norms = gradients_norms # Move points # *********** # Clip gradient to get moving dists moving_dists = np.minimum(moving_factor * gradients_norms, clip) # Fix central point if fixed == 'center': moving_dists[:, 0] = 0 if fixed == 'verticals': moving_dists[:, 0] = 0 # Move points kernel_points -= np.expand_dims(moving_dists, -1) * gradients / np.expand_dims(gradients_norms + 1e-6, -1) if verbose: print('iter {:5d} / max grad = {:f}'.format(iter, np.max(gradients_norms[:, 3:]))) if verbose > 1: plt.clf() plt.plot(kernel_points[0, :, 0], kernel_points[0, :, 1], '.') circle = plt.Circle((0, 0), radius, color='r', fill=False) fig.axes[0].add_artist(circle) fig.axes[0].set_xlim((-radius*1.1, radius*1.1)) fig.axes[0].set_ylim((-radius*1.1, radius*1.1)) fig.axes[0].set_aspect('equal') plt.draw() plt.pause(0.001) plt.show(block=False) print(moving_factor) # moving factor decay moving_factor *= continuous_moving_decay # Rescale radius to fit the wanted ratio of radius r = np.sqrt(np.sum(np.power(kernel_points, 2), axis=-1)) kernel_points *= ratio / np.mean(r[:, 1:]) # Rescale kernels with real radius return kernel_points * radius, saved_gradient_norms def load_kernels(radius, num_kpoints, dimension, fixed, lloyd=False): # Kernel directory kernel_dir = 'kernels/dispositions' if not exists(kernel_dir): makedirs(kernel_dir) # To many points switch to Lloyds if num_kpoints > 30: lloyd = True # Kernel_file kernel_file = join(kernel_dir, 'k_{:03d}_{:s}_{:d}D.ply'.format(num_kpoints, fixed, dimension)) # Check if already done if not exists(kernel_file): if lloyd: # Create kernels kernel_points = spherical_Lloyd(1.0, num_kpoints, dimension=dimension, fixed=fixed, verbose=0) else: # Create kernels kernel_points, grad_norms = kernel_point_optimization_debug(1.0, num_kpoints, num_kernels=100, dimension=dimension, fixed=fixed, verbose=0) # Find best candidate best_k = np.argmin(grad_norms[-1, :]) # Save points kernel_points = kernel_points[best_k, :, :] write_ply(kernel_file, kernel_points, ['x', 'y', 'z']) else: data = read_ply(kernel_file) kernel_points = np.vstack((data['x'], data['y'], data['z'])).T # Random roations for the kernel # N.B. 4D random rotations not supported yet R = np.eye(dimension) theta = np.random.rand() * 2 * np.pi if dimension == 2: if fixed != 'vertical': c, s = np.cos(theta), np.sin(theta) R = np.array([[c, -s], [s, c]], dtype=np.float32) elif dimension == 3: if fixed != 'vertical': c, s = np.cos(theta), np.sin(theta) R = np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]], dtype=np.float32) else: phi = (np.random.rand() - 0.5) * np.pi # Create the first vector in carthesian coordinates u = np.array([np.cos(theta) * np.cos(phi), np.sin(theta) * np.cos(phi), np.sin(phi)]) # Choose a random rotation angle alpha = np.random.rand() * 2 * np.pi # Create the rotation matrix with this vector and angle R = create_3D_rotations(np.reshape(u, (1, -1)), np.reshape(alpha, (1, -1)))[0] R = R.astype(np.float32) # Add a small noise kernel_points = kernel_points + np.random.normal(scale=0.01, size=kernel_points.shape) # Scale kernels kernel_points = radius * kernel_points # Rotate kernels kernel_points = np.matmul(kernel_points, R) return kernel_points.astype(np.float32)

py

sngan.pytorch

sngan.pytorch-master/test.py

# -*- coding: utf-8 -*- # @Date : 2019-07-25 # @Author : Xinyu Gong (xy_gong@tamu.edu) # @Link : None # @Version : 0.0 from __future__ import absolute_import from __future__ import division from __future__ import print_function import cfg import models from functions import validate from utils.utils import set_log_dir, create_logger from utils.inception_score import _init_inception from utils.fid_score import create_inception_graph, check_or_download_inception import torch import os import numpy as np from tensorboardX import SummaryWriter torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True def main(): args = cfg.parse_args() torch.cuda.manual_seed(args.random_seed) assert args.exp_name assert args.load_path.endswith('.pth') assert os.path.exists(args.load_path) args.path_helper = set_log_dir('logs_eval', args.exp_name) logger = create_logger(args.path_helper['log_path'], phase='test') # set tf env _init_inception() inception_path = check_or_download_inception(None) create_inception_graph(inception_path) # import network gen_net = eval('models.'+args.model+'.Generator')(args=args).cuda() # fid stat if args.dataset.lower() == 'cifar10': fid_stat = 'fid_stat/fid_stats_cifar10_train.npz' else: raise NotImplementedError(f'no fid stat for {args.dataset.lower()}') assert os.path.exists(fid_stat) # initial fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (25, args.latent_dim))) # set writer logger.info(f'=> resuming from {args.load_path}') checkpoint_file = args.load_path assert os.path.exists(checkpoint_file) checkpoint = torch.load(checkpoint_file) if 'avg_gen_state_dict' in checkpoint: gen_net.load_state_dict(checkpoint['avg_gen_state_dict']) epoch = checkpoint['epoch'] logger.info(f'=> loaded checkpoint {checkpoint_file} (epoch {epoch})') else: gen_net.load_state_dict(checkpoint) logger.info(f'=> loaded checkpoint {checkpoint_file}') logger.info(args) writer_dict = { 'writer': SummaryWriter(args.path_helper['log_path']), 'valid_global_steps': 0, } inception_score, fid_score = validate(args, fixed_z, fid_stat, gen_net, writer_dict) logger.info(f'Inception score: {inception_score}, FID score: {fid_score}.') if __name__ == '__main__': main()

py

sngan.pytorch

sngan.pytorch-master/functions.py

# -*- coding: utf-8 -*- # @Date : 2019-07-25 # @Author : Xinyu Gong (xy_gong@tamu.edu) # @Link : None # @Version : 0.0 import os import numpy as np import torch import torch.nn as nn from torchvision.utils import make_grid from imageio import imsave from tqdm import tqdm from copy import deepcopy import logging from utils.inception_score import get_inception_score from utils.fid_score import calculate_fid_given_paths logger = logging.getLogger(__name__) def train(args, gen_net: nn.Module, dis_net: nn.Module, gen_optimizer, dis_optimizer, gen_avg_param, train_loader, epoch, writer_dict, schedulers=None): writer = writer_dict['writer'] gen_step = 0 # train mode gen_net = gen_net.train() dis_net = dis_net.train() for iter_idx, (imgs, _) in enumerate(tqdm(train_loader)): global_steps = writer_dict['train_global_steps'] # Adversarial ground truths real_imgs = imgs.type(torch.cuda.FloatTensor) # Sample noise as generator input z = torch.cuda.FloatTensor(np.random.normal(0, 1, (imgs.shape[0], args.latent_dim))) # --------------------- # Train Discriminator # --------------------- dis_optimizer.zero_grad() real_validity = dis_net(real_imgs) fake_imgs = gen_net(z).detach() assert fake_imgs.size() == real_imgs.size() fake_validity = dis_net(fake_imgs) # cal loss d_loss = torch.mean(nn.ReLU(inplace=True)(1.0 - real_validity)) + \ torch.mean(nn.ReLU(inplace=True)(1 + fake_validity)) d_loss.backward() dis_optimizer.step() writer.add_scalar('d_loss', d_loss.item(), global_steps) # ----------------- # Train Generator # ----------------- if global_steps % args.n_critic == 0: gen_optimizer.zero_grad() gen_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (args.gen_batch_size, args.latent_dim))) gen_imgs = gen_net(gen_z) fake_validity = dis_net(gen_imgs) # cal loss g_loss = -torch.mean(fake_validity) g_loss.backward() gen_optimizer.step() # adjust learning rate if schedulers: gen_scheduler, dis_scheduler = schedulers g_lr = gen_scheduler.step(global_steps) d_lr = dis_scheduler.step(global_steps) writer.add_scalar('LR/g_lr', g_lr, global_steps) writer.add_scalar('LR/d_lr', d_lr, global_steps) # moving average weight for p, avg_p in zip(gen_net.parameters(), gen_avg_param): avg_p.mul_(0.999).add_(0.001, p.data) writer.add_scalar('g_loss', g_loss.item(), global_steps) gen_step += 1 # verbose if gen_step and iter_idx % args.print_freq == 0: tqdm.write( "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, args.max_epoch, iter_idx % len(train_loader), len(train_loader), d_loss.item(), g_loss.item())) writer_dict['train_global_steps'] = global_steps + 1 def validate(args, fixed_z, fid_stat, gen_net: nn.Module, writer_dict): writer = writer_dict['writer'] global_steps = writer_dict['valid_global_steps'] # eval mode gen_net = gen_net.eval() # generate images sample_imgs = gen_net(fixed_z) img_grid = make_grid(sample_imgs, nrow=5, normalize=True, scale_each=True) # get fid and inception score fid_buffer_dir = os.path.join(args.path_helper['sample_path'], 'fid_buffer') os.makedirs(fid_buffer_dir) eval_iter = args.num_eval_imgs // args.eval_batch_size img_list = list() for iter_idx in tqdm(range(eval_iter), desc='sample images'): z = torch.cuda.FloatTensor(np.random.normal(0, 1, (args.eval_batch_size, args.latent_dim))) # Generate a batch of images gen_imgs = gen_net(z).mul_(127.5).add_(127.5).clamp_(0.0, 255.0).permute(0, 2, 3, 1).to('cpu', torch.uint8).numpy() for img_idx, img in enumerate(gen_imgs): file_name = os.path.join(fid_buffer_dir, f'iter{iter_idx}_b{img_idx}.png') imsave(file_name, img) img_list.extend(list(gen_imgs)) # get inception score logger.info('=> calculate inception score') mean, std = get_inception_score(img_list) # get fid score logger.info('=> calculate fid score') fid_score = calculate_fid_given_paths([fid_buffer_dir, fid_stat], inception_path=None) os.system('rm -r {}'.format(fid_buffer_dir)) writer.add_image('sampled_images', img_grid, global_steps) writer.add_scalar('Inception_score/mean', mean, global_steps) writer.add_scalar('Inception_score/std', std, global_steps) writer.add_scalar('FID_score', fid_score, global_steps) writer_dict['valid_global_steps'] = global_steps + 1 return mean, fid_score class LinearLrDecay(object): def __init__(self, optimizer, start_lr, end_lr, decay_start_step, decay_end_step): assert start_lr > end_lr self.optimizer = optimizer self.delta = (start_lr - end_lr) / (decay_end_step - decay_start_step) self.decay_start_step = decay_start_step self.decay_end_step = decay_end_step self.start_lr = start_lr self.end_lr = end_lr def step(self, current_step): if current_step <= self.decay_start_step: lr = self.start_lr elif current_step >= self.decay_end_step: lr = self.end_lr else: lr = self.start_lr - self.delta * (current_step - self.decay_start_step) for param_group in self.optimizer.param_groups: param_group['lr'] = lr return lr def load_params(model, new_param): for p, new_p in zip(model.parameters(), new_param): p.data.copy_(new_p) def copy_params(model): flatten = deepcopy(list(p.data for p in model.parameters())) return flatten

py

sngan.pytorch

sngan.pytorch-master/cfg.py

# -*- coding: utf-8 -*- # @Date : 2019-07-25 # @Author : Xinyu Gong (xy_gong@tamu.edu) # @Link : None # @Version : 0.0 import argparse def str2bool(v): if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( '--max_epoch', type=int, default=200, help='number of epochs of training') parser.add_argument( '--max_iter', type=int, default=None, help='set the max iteration number') parser.add_argument( '-gen_bs', '--gen_batch_size', type=int, default=64, help='size of the batches') parser.add_argument( '-dis_bs', '--dis_batch_size', type=int, default=64, help='size of the batches') parser.add_argument( '--g_lr', type=float, default=0.0002, help='adam: gen learning rate') parser.add_argument( '--d_lr', type=float, default=0.0002, help='adam: disc learning rate') parser.add_argument( '--lr_decay', action='store_true', help='learning rate decay or not') parser.add_argument( '--beta1', type=float, default=0.0, help='adam: decay of first order momentum of gradient') parser.add_argument( '--beta2', type=float, default=0.9, help='adam: decay of first order momentum of gradient') parser.add_argument( '--num_workers', type=int, default=8, help='number of cpu threads to use during batch generation') parser.add_argument( '--latent_dim', type=int, default=128, help='dimensionality of the latent space') parser.add_argument( '--img_size', type=int, default=32, help='size of each image dimension') parser.add_argument( '--channels', type=int, default=3, help='number of image channels') parser.add_argument( '--n_critic', type=int, default=1, help='number of training steps for discriminator per iter') parser.add_argument( '--val_freq', type=int, default=20, help='interval between each validation') parser.add_argument( '--print_freq', type=int, default=50, help='interval between each verbose') parser.add_argument( '--load_path', type=str, help='The reload model path') parser.add_argument( '--exp_name', type=str, help='The name of exp') parser.add_argument( '--d_spectral_norm', type=str2bool, default=False, help='add spectral_norm on discriminator?') parser.add_argument( '--g_spectral_norm', type=str2bool, default=False, help='add spectral_norm on generator?') parser.add_argument( '--dataset', type=str, default='cifar10', help='dataset type') parser.add_argument( '--data_path', type=str, default='./data', help='The path of data set') parser.add_argument('--init_type', type=str, default='normal', choices=['normal', 'orth', 'xavier_uniform', 'false'], help='The init type') parser.add_argument('--gf_dim', type=int, default=64, help='The base channel num of gen') parser.add_argument('--df_dim', type=int, default=64, help='The base channel num of disc') parser.add_argument( '--model', type=str, default='sngan_cifar10', help='path of model') parser.add_argument('--eval_batch_size', type=int, default=100) parser.add_argument('--num_eval_imgs', type=int, default=50000) parser.add_argument( '--bottom_width', type=int, default=4, help="the base resolution of the GAN") parser.add_argument('--random_seed', type=int, default=12345) opt = parser.parse_args() return opt

py

sngan.pytorch

sngan.pytorch-master/datasets.py

import torch import torchvision.datasets as datasets import torchvision.transforms as transforms from torch.utils.data import Dataset class ImageDataset(object): def __init__(self, args): if args.dataset.lower() == 'cifar10': Dt = datasets.CIFAR10 transform = transforms.Compose([ transforms.Resize(args.img_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) args.n_classes = 10 elif args.dataset.lower() == 'stl10': Dt = datasets.STL10 transform = transforms.Compose([ transforms.Resize(args.img_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) else: raise NotImplementedError('Unknown dataset: {}'.format(args.dataset)) if args.dataset.lower() == 'stl10': self.train = torch.utils.data.DataLoader( Dt(root=args.data_path, split='train+unlabeled', transform=transform, download=True), batch_size=args.dis_batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) self.valid = torch.utils.data.DataLoader( Dt(root=args.data_path, split='test', transform=transform), batch_size=args.dis_batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True) self.test = self.valid else: self.train = torch.utils.data.DataLoader( Dt(root=args.data_path, train=True, transform=transform, download=True), batch_size=args.dis_batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True) self.valid = torch.utils.data.DataLoader( Dt(root=args.data_path, train=False, transform=transform), batch_size=args.dis_batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True) self.test = self.valid

py

sngan.pytorch

sngan.pytorch-master/train.py

# -*- coding: utf-8 -*- # @Date : 2019-07-25 # @Author : Xinyu Gong (xy_gong@tamu.edu) # @Link : None # @Version : 0.0 from __future__ import absolute_import from __future__ import division from __future__ import print_function import cfg import models import datasets from functions import train, validate, LinearLrDecay, load_params, copy_params from utils.utils import set_log_dir, save_checkpoint, create_logger from utils.inception_score import _init_inception from utils.fid_score import create_inception_graph, check_or_download_inception import torch import os import numpy as np import torch.nn as nn from tensorboardX import SummaryWriter from tqdm import tqdm from copy import deepcopy torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True def main(): args = cfg.parse_args() torch.cuda.manual_seed(args.random_seed) # set tf env _init_inception() inception_path = check_or_download_inception(None) create_inception_graph(inception_path) # import network gen_net = eval('models.'+args.model+'.Generator')(args=args).cuda() dis_net = eval('models.'+args.model+'.Discriminator')(args=args).cuda() # weight init def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv2d') != -1: if args.init_type == 'normal': nn.init.normal_(m.weight.data, 0.0, 0.02) elif args.init_type == 'orth': nn.init.orthogonal_(m.weight.data) elif args.init_type == 'xavier_uniform': nn.init.xavier_uniform(m.weight.data, 1.) else: raise NotImplementedError('{} unknown inital type'.format(args.init_type)) elif classname.find('BatchNorm2d') != -1: nn.init.normal_(m.weight.data, 1.0, 0.02) nn.init.constant_(m.bias.data, 0.0) gen_net.apply(weights_init) dis_net.apply(weights_init) # set optimizer gen_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, gen_net.parameters()), args.g_lr, (args.beta1, args.beta2)) dis_optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, dis_net.parameters()), args.d_lr, (args.beta1, args.beta2)) gen_scheduler = LinearLrDecay(gen_optimizer, args.g_lr, 0.0, 0, args.max_iter * args.n_critic) dis_scheduler = LinearLrDecay(dis_optimizer, args.d_lr, 0.0, 0, args.max_iter * args.n_critic) # set up data_loader dataset = datasets.ImageDataset(args) train_loader = dataset.train # fid stat if args.dataset.lower() == 'cifar10': fid_stat = 'fid_stat/fid_stats_cifar10_train.npz' elif args.dataset.lower() == 'stl10': fid_stat = 'fid_stat/stl10_train_unlabeled_fid_stats_48.npz' else: raise NotImplementedError(f'no fid stat for {args.dataset.lower()}') assert os.path.exists(fid_stat) # epoch number for dis_net args.max_epoch = args.max_epoch * args.n_critic if args.max_iter: args.max_epoch = np.ceil(args.max_iter * args.n_critic / len(train_loader)) # initial fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (25, args.latent_dim))) gen_avg_param = copy_params(gen_net) start_epoch = 0 best_fid = 1e4 # set writer if args.load_path: print(f'=> resuming from {args.load_path}') assert os.path.exists(args.load_path) checkpoint_file = os.path.join(args.load_path, 'Model', 'checkpoint.pth') assert os.path.exists(checkpoint_file) checkpoint = torch.load(checkpoint_file) start_epoch = checkpoint['epoch'] best_fid = checkpoint['best_fid'] gen_net.load_state_dict(checkpoint['gen_state_dict']) dis_net.load_state_dict(checkpoint['dis_state_dict']) gen_optimizer.load_state_dict(checkpoint['gen_optimizer']) dis_optimizer.load_state_dict(checkpoint['dis_optimizer']) avg_gen_net = deepcopy(gen_net) avg_gen_net.load_state_dict(checkpoint['avg_gen_state_dict']) gen_avg_param = copy_params(avg_gen_net) del avg_gen_net args.path_helper = checkpoint['path_helper'] logger = create_logger(args.path_helper['log_path']) logger.info(f'=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})') else: # create new log dir assert args.exp_name args.path_helper = set_log_dir('logs', args.exp_name) logger = create_logger(args.path_helper['log_path']) logger.info(args) writer_dict = { 'writer': SummaryWriter(args.path_helper['log_path']), 'train_global_steps': start_epoch * len(train_loader), 'valid_global_steps': start_epoch // args.val_freq, } # train loop lr_schedulers = (gen_scheduler, dis_scheduler) if args.lr_decay else None for epoch in tqdm(range(int(start_epoch), int(args.max_epoch)), desc='total progress'): train(args, gen_net, dis_net, gen_optimizer, dis_optimizer, gen_avg_param, train_loader, epoch, writer_dict, lr_schedulers) if epoch and epoch % args.val_freq == 0 or epoch == int(args.max_epoch)-1: backup_param = copy_params(gen_net) load_params(gen_net, gen_avg_param) inception_score, fid_score = validate(args, fixed_z, fid_stat, gen_net, writer_dict) logger.info(f'Inception score: {inception_score}, FID score: {fid_score} || @ epoch {epoch}.') load_params(gen_net, backup_param) if fid_score < best_fid: best_fid = fid_score is_best = True else: is_best = False else: is_best = False avg_gen_net = deepcopy(gen_net) load_params(avg_gen_net, gen_avg_param) save_checkpoint({ 'epoch': epoch + 1, 'model': args.model, 'gen_state_dict': gen_net.state_dict(), 'dis_state_dict': dis_net.state_dict(), 'avg_gen_state_dict': avg_gen_net.state_dict(), 'gen_optimizer': gen_optimizer.state_dict(), 'dis_optimizer': dis_optimizer.state_dict(), 'best_fid': best_fid, 'path_helper': args.path_helper }, is_best, args.path_helper['ckpt_path']) del avg_gen_net if __name__ == '__main__': main()

py

sngan.pytorch

sngan.pytorch-master/models/sngan_64.py

import torch.nn as nn class GenBlock(nn.Module): def __init__(self, in_channels, out_channels, hidden_channels=None, ksize=3, pad=1, activation=nn.ReLU(), upsample=False, n_classes=0): super(GenBlock, self).__init__() self.activation = activation self.upsample = upsample self.learnable_sc = in_channels != out_channels or upsample hidden_channels = out_channels if hidden_channels is None else hidden_channels self.n_classes = n_classes self.c1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=ksize, padding=pad) self.c2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=ksize, padding=pad) self.b1 = nn.BatchNorm2d(in_channels) self.b2 = nn.BatchNorm2d(hidden_channels) if self.learnable_sc: self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) def upsample_conv(self, x, conv): return conv(nn.UpsamplingNearest2d(scale_factor=2)(x)) def residual(self, x): h = x h = self.b1(h) h = self.activation(h) h = self.upsample_conv(h, self.c1) if self.upsample else self.c1(h) h = self.b2(h) h = self.activation(h) h = self.c2(h) return h def shortcut(self, x): if self.learnable_sc: x = self.upsample_conv(x, self.c_sc) if self.upsample else self.c_sc(x) return x else: return x def forward(self, x): return self.residual(x) + self.shortcut(x) class Generator(nn.Module): def __init__(self, args, activation=nn.ReLU(), n_classes=0): super(Generator, self).__init__() self.bottom_width = args.bottom_width self.activation = activation self.n_classes = n_classes self.ch = args.gf_dim self.l1 = nn.Linear(args.latent_dim, (self.bottom_width ** 2) * self.ch) self.block2 = GenBlock(self.ch, self.ch, activation=activation, upsample=True, n_classes=n_classes) self.block3 = GenBlock(self.ch, self.ch, activation=activation, upsample=True, n_classes=n_classes) self.block4 = GenBlock(self.ch, self.ch, activation=activation, upsample=True, n_classes=n_classes) self.b5 = nn.BatchNorm2d(self.ch) self.c5 = nn.Conv2d(self.ch, 3, kernel_size=3, stride=1, padding=1) def forward(self, z): h = z h = self.l1(h).view(-1, self.ch, self.bottom_width, self.bottom_width) h = self.block2(h) h = self.block3(h) h = self.block4(h) h = self.b5(h) h = self.activation(h) h = nn.Tanh()(self.c5(h)) return h """Discriminator""" def _downsample(x): # Downsample (Mean Avg Pooling with 2x2 kernel) return nn.AvgPool2d(kernel_size=2)(x) class OptimizedDisBlock(nn.Module): def __init__(self, args, in_channels, out_channels, ksize=3, pad=1, activation=nn.ReLU()): super(OptimizedDisBlock, self).__init__() self.activation = activation self.c1 = nn.Conv2d(in_channels, out_channels, kernel_size=ksize, padding=pad) self.c2 = nn.Conv2d(out_channels, out_channels, kernel_size=ksize, padding=pad) self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) if args.d_spectral_norm: self.c1 = nn.utils.spectral_norm(self.c1) self.c2 = nn.utils.spectral_norm(self.c2) self.c_sc = nn.utils.spectral_norm(self.c_sc) def residual(self, x): h = x h = self.c1(h) h = self.activation(h) h = self.c2(h) h = _downsample(h) return h def shortcut(self, x): return self.c_sc(_downsample(x)) def forward(self, x): return self.residual(x) + self.shortcut(x) class DisBlock(nn.Module): def __init__(self, args, in_channels, out_channels, hidden_channels=None, ksize=3, pad=1, activation=nn.ReLU(), downsample=False): super(DisBlock, self).__init__() self.activation = activation self.downsample = downsample self.learnable_sc = (in_channels != out_channels) or downsample hidden_channels = in_channels if hidden_channels is None else hidden_channels self.c1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=ksize, padding=pad) self.c2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=ksize, padding=pad) if args.d_spectral_norm: self.c1 = nn.utils.spectral_norm(self.c1) self.c2 = nn.utils.spectral_norm(self.c2) if self.learnable_sc: self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) if args.d_spectral_norm: self.c_sc = nn.utils.spectral_norm(self.c_sc) def residual(self, x): h = x h = self.activation(h) h = self.c1(h) h = self.activation(h) h = self.c2(h) if self.downsample: h = _downsample(h) return h def shortcut(self, x): if self.learnable_sc: x = self.c_sc(x) if self.downsample: return _downsample(x) else: return x else: return x def forward(self, x): return self.residual(x) + self.shortcut(x) class Discriminator(nn.Module): def __init__(self, args, activation=nn.ReLU()): super(Discriminator, self).__init__() self.ch = args.df_dim self.activation = activation self.block1 = OptimizedDisBlock(args, 3, self.ch) self.block2 = DisBlock(args, self.ch, self.ch, activation=activation, downsample=True) self.block3 = DisBlock(args, self.ch, self.ch, activation=activation, downsample=False) self.block4 = DisBlock(args, self.ch, self.ch, activation=activation, downsample=False) self.l5 = nn.Linear(self.ch, 1, bias=False) if args.d_spectral_norm: self.l5 = nn.utils.spectral_norm(self.l5) def forward(self, x): h = x h = self.block1(h) h = self.block2(h) h = self.block3(h) h = self.block4(h) h = self.activation(h) # Global average pooling h = h.sum(2).sum(2) output = self.l5(h) return output

py

sngan.pytorch

sngan.pytorch-master/models/sngan_stl10.py

import torch.nn as nn class GenBlock(nn.Module): def __init__(self, in_channels, out_channels, hidden_channels=None, ksize=3, pad=1, activation=nn.ReLU(), upsample=False, n_classes=0): super(GenBlock, self).__init__() self.activation = activation self.upsample = upsample self.learnable_sc = in_channels != out_channels or upsample hidden_channels = out_channels if hidden_channels is None else hidden_channels self.n_classes = n_classes self.c1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=ksize, padding=pad) self.c2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=ksize, padding=pad) self.b1 = nn.BatchNorm2d(in_channels) self.b2 = nn.BatchNorm2d(hidden_channels) if self.learnable_sc: self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) def upsample_conv(self, x, conv): return conv(nn.UpsamplingNearest2d(scale_factor=2)(x)) def residual(self, x): h = x h = self.b1(h) h = self.activation(h) h = self.upsample_conv(h, self.c1) if self.upsample else self.c1(h) h = self.b2(h) h = self.activation(h) h = self.c2(h) return h def shortcut(self, x): if self.learnable_sc: x = self.upsample_conv(x, self.c_sc) if self.upsample else self.c_sc(x) return x else: return x def forward(self, x): return self.residual(x) + self.shortcut(x) class Generator(nn.Module): def __init__(self, args, activation=nn.ReLU(), n_classes=0): super(Generator, self).__init__() self.bottom_width = args.bottom_width self.activation = activation self.n_classes = n_classes self.ch = 512 self.l1 = nn.Linear(args.latent_dim, (self.bottom_width ** 2) * self.ch) self.block2 = GenBlock(512, 256, activation=activation, upsample=True, n_classes=n_classes) self.block3 = GenBlock(256, 128, activation=activation, upsample=True, n_classes=n_classes) self.block4 = GenBlock(128, 64, activation=activation, upsample=True, n_classes=n_classes) self.b5 = nn.BatchNorm2d(64) self.c5 = nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1) def forward(self, z): h = z h = self.l1(h).view(-1, self.ch, self.bottom_width, self.bottom_width) h = self.block2(h) h = self.block3(h) h = self.block4(h) h = self.b5(h) h = self.activation(h) h = nn.Tanh()(self.c5(h)) return h """Discriminator""" def _downsample(x): # Downsample (Mean Avg Pooling with 2x2 kernel) return nn.AvgPool2d(kernel_size=2)(x) class OptimizedDisBlock(nn.Module): def __init__(self, args, in_channels, out_channels, ksize=3, pad=1, activation=nn.ReLU()): super(OptimizedDisBlock, self).__init__() self.activation = activation self.c1 = nn.Conv2d(in_channels, out_channels, kernel_size=ksize, padding=pad) self.c2 = nn.Conv2d(out_channels, out_channels, kernel_size=ksize, padding=pad) self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) if args.d_spectral_norm: self.c1 = nn.utils.spectral_norm(self.c1) self.c2 = nn.utils.spectral_norm(self.c2) self.c_sc = nn.utils.spectral_norm(self.c_sc) def residual(self, x): h = x h = self.c1(h) h = self.activation(h) h = self.c2(h) h = _downsample(h) return h def shortcut(self, x): return self.c_sc(_downsample(x)) def forward(self, x): return self.residual(x) + self.shortcut(x) class DisBlock(nn.Module): def __init__(self, args, in_channels, out_channels, hidden_channels=None, ksize=3, pad=1, activation=nn.ReLU(), downsample=False): super(DisBlock, self).__init__() self.activation = activation self.downsample = downsample self.learnable_sc = (in_channels != out_channels) or downsample hidden_channels = in_channels if hidden_channels is None else hidden_channels self.c1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=ksize, padding=pad) self.c2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=ksize, padding=pad) if args.d_spectral_norm: self.c1 = nn.utils.spectral_norm(self.c1) self.c2 = nn.utils.spectral_norm(self.c2) if self.learnable_sc: self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) if args.d_spectral_norm: self.c_sc = nn.utils.spectral_norm(self.c_sc) def residual(self, x): h = x h = self.activation(h) h = self.c1(h) h = self.activation(h) h = self.c2(h) if self.downsample: h = _downsample(h) return h def shortcut(self, x): if self.learnable_sc: x = self.c_sc(x) if self.downsample: return _downsample(x) else: return x else: return x def forward(self, x): return self.residual(x) + self.shortcut(x) class Discriminator(nn.Module): def __init__(self, args, activation=nn.ReLU()): super(Discriminator, self).__init__() self.activation = activation self.block1 = OptimizedDisBlock(args, 3, 64) self.block2 = DisBlock(args, 64, 128, activation=activation, downsample=True) self.block3 = DisBlock(args, 128, 256, activation=activation, downsample=True) self.block4 = DisBlock(args, 256, 512, activation=activation, downsample=True) self.block5 = DisBlock(args, 512, 1024, activation=activation, downsample=False) self.l6 = nn.Linear(1024, 1, bias=False) if args.d_spectral_norm: self.l6 = nn.utils.spectral_norm(self.l6) def forward(self, x): h = x h = self.block1(h) h = self.block2(h) h = self.block3(h) h = self.block4(h) h = self.block5(h) h = self.activation(h) # Global average pooling h = h.sum(2).sum(2) output = self.l6(h) return output

py

sngan.pytorch

sngan.pytorch-master/models/sngan_cifar10.py

import torch.nn as nn from .gen_resblock import GenBlock class Generator(nn.Module): def __init__(self, args, activation=nn.ReLU(), n_classes=0): super(Generator, self).__init__() self.bottom_width = args.bottom_width self.activation = activation self.n_classes = n_classes self.ch = args.gf_dim self.l1 = nn.Linear(args.latent_dim, (self.bottom_width ** 2) * self.ch) self.block2 = GenBlock(self.ch, self.ch, activation=activation, upsample=True, n_classes=n_classes) self.block3 = GenBlock(self.ch, self.ch, activation=activation, upsample=True, n_classes=n_classes) self.block4 = GenBlock(self.ch, self.ch, activation=activation, upsample=True, n_classes=n_classes) self.b5 = nn.BatchNorm2d(self.ch) self.c5 = nn.Conv2d(self.ch, 3, kernel_size=3, stride=1, padding=1) def forward(self, z): h = z h = self.l1(h).view(-1, self.ch, self.bottom_width, self.bottom_width) h = self.block2(h) h = self.block3(h) h = self.block4(h) h = self.b5(h) h = self.activation(h) h = nn.Tanh()(self.c5(h)) return h """Discriminator""" def _downsample(x): # Downsample (Mean Avg Pooling with 2x2 kernel) return nn.AvgPool2d(kernel_size=2)(x) class OptimizedDisBlock(nn.Module): def __init__(self, args, in_channels, out_channels, ksize=3, pad=1, activation=nn.ReLU()): super(OptimizedDisBlock, self).__init__() self.activation = activation self.c1 = nn.Conv2d(in_channels, out_channels, kernel_size=ksize, padding=pad) self.c2 = nn.Conv2d(out_channels, out_channels, kernel_size=ksize, padding=pad) self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) if args.d_spectral_norm: self.c1 = nn.utils.spectral_norm(self.c1) self.c2 = nn.utils.spectral_norm(self.c2) self.c_sc = nn.utils.spectral_norm(self.c_sc) def residual(self, x): h = x h = self.c1(h) h = self.activation(h) h = self.c2(h) h = _downsample(h) return h def shortcut(self, x): return self.c_sc(_downsample(x)) def forward(self, x): return self.residual(x) + self.shortcut(x) class DisBlock(nn.Module): def __init__(self, args, in_channels, out_channels, hidden_channels=None, ksize=3, pad=1, activation=nn.ReLU(), downsample=False): super(DisBlock, self).__init__() self.activation = activation self.downsample = downsample self.learnable_sc = (in_channels != out_channels) or downsample hidden_channels = in_channels if hidden_channels is None else hidden_channels self.c1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=ksize, padding=pad) self.c2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=ksize, padding=pad) if args.d_spectral_norm: self.c1 = nn.utils.spectral_norm(self.c1) self.c2 = nn.utils.spectral_norm(self.c2) if self.learnable_sc: self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) if args.d_spectral_norm: self.c_sc = nn.utils.spectral_norm(self.c_sc) def residual(self, x): h = x h = self.activation(h) h = self.c1(h) h = self.activation(h) h = self.c2(h) if self.downsample: h = _downsample(h) return h def shortcut(self, x): if self.learnable_sc: x = self.c_sc(x) if self.downsample: return _downsample(x) else: return x else: return x def forward(self, x): return self.residual(x) + self.shortcut(x) class Discriminator(nn.Module): def __init__(self, args, activation=nn.ReLU()): super(Discriminator, self).__init__() self.ch = args.df_dim self.activation = activation self.block1 = OptimizedDisBlock(args, 3, self.ch) self.block2 = DisBlock(args, self.ch, self.ch, activation=activation, downsample=True) self.block3 = DisBlock(args, self.ch, self.ch, activation=activation, downsample=False) self.block4 = DisBlock(args, self.ch, self.ch, activation=activation, downsample=False) self.l5 = nn.Linear(self.ch, 1, bias=False) if args.d_spectral_norm: self.l5 = nn.utils.spectral_norm(self.l5) def forward(self, x): h = x h = self.block1(h) h = self.block2(h) h = self.block3(h) h = self.block4(h) h = self.activation(h) # Global average pooling h = h.sum(2).sum(2) output = self.l5(h) return output

py

sngan.pytorch

sngan.pytorch-master/models/__init__.py

# -*- coding: utf-8 -*- # @Date : 2019-07-25 # @Author : Xinyu Gong (xy_gong@tamu.edu) # @Link : None # @Version : 0.0 from __future__ import absolute_import from __future__ import division from __future__ import print_function import models.sngan_cifar10 import models.sngan_stl10

py

sngan.pytorch

sngan.pytorch-master/models/gen_resblock.py

# -*- coding: utf-8 -*- # @Date : 3/26/20 # @Author : Xinyu Gong (xy_gong@tamu.edu) # @Link : None # @Version : 0.0 import torch.nn as nn class GenBlock(nn.Module): def __init__(self, in_channels, out_channels, hidden_channels=None, ksize=3, pad=1, activation=nn.ReLU(), upsample=False, n_classes=0): super(GenBlock, self).__init__() self.activation = activation self.upsample = upsample self.learnable_sc = in_channels != out_channels or upsample hidden_channels = out_channels if hidden_channels is None else hidden_channels self.n_classes = n_classes self.c1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=ksize, padding=pad) self.c2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=ksize, padding=pad) self.b1 = nn.BatchNorm2d(in_channels) self.b2 = nn.BatchNorm2d(hidden_channels) if self.learnable_sc: self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0) def upsample_conv(self, x, conv): return conv(nn.UpsamplingNearest2d(scale_factor=2)(x)) def residual(self, x): h = x h = self.b1(h) h = self.activation(h) h = self.upsample_conv(h, self.c1) if self.upsample else self.c1(h) h = self.b2(h) h = self.activation(h) h = self.c2(h) return h def shortcut(self, x): if self.learnable_sc: x = self.upsample_conv(x, self.c_sc) if self.upsample else self.c_sc(x) return x else: return x def forward(self, x): return self.residual(x) + self.shortcut(x)

py

sngan.pytorch

sngan.pytorch-master/utils/cal_fid_stat.py

# -*- coding: utf-8 -*- # @Date : 2019-07-26 # @Author : Xinyu Gong (xy_gong@tamu.edu) # @Link : None # @Version : 0.0 import os import glob import argparse import numpy as np from imageio import imread import tensorflow as tf import utils.fid_score as fid def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( '--data_path', type=str, required=True, help='set path to training set jpg images dir') parser.add_argument( '--output_file', type=str, default='fid_stat/fid_stats_cifar10_train.npz', help='path for where to store the statistics') opt = parser.parse_args() print(opt) return opt def main(): args = parse_args() ######## # PATHS ######## data_path = args.data_path output_path = args.output_file # if you have downloaded and extracted # http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz # set this path to the directory where the extracted files are, otherwise # just set it to None and the script will later download the files for you inception_path = None print("check for inception model..", end=" ", flush=True) inception_path = fid.check_or_download_inception(inception_path) # download inception if necessary print("ok") # loads all images into memory (this might require a lot of RAM!) print("load images..", end=" ", flush=True) image_list = glob.glob(os.path.join(data_path, '*.jpg')) images = np.array([imread(str(fn)).astype(np.float32) for fn in image_list]) print("%d images found and loaded" % len(images)) print("create inception graph..", end=" ", flush=True) fid.create_inception_graph(inception_path) # load the graph into the current TF graph print("ok") print("calculte FID stats..", end=" ", flush=True) config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: sess.run(tf.global_variables_initializer()) mu, sigma = fid.calculate_activation_statistics(images, sess, batch_size=100) np.savez_compressed(output_path, mu=mu, sigma=sigma) print("finished") if __name__ == '__main__': main()

py

sngan.pytorch

sngan.pytorch-master/utils/utils.py

# -*- coding: utf-8 -*- # @Date : 2019-07-25 # @Author : Xinyu Gong (xy_gong@tamu.edu) # @Link : None # @Version : 0.0 import os import torch import dateutil.tz from datetime import datetime import time import logging def create_logger(log_dir, phase='train'): time_str = time.strftime('%Y-%m-%d-%H-%M') log_file = '{}_{}.log'.format(time_str, phase) final_log_file = os.path.join(log_dir, log_file) head = '%(asctime)-15s %(message)s' logging.basicConfig(filename=str(final_log_file), format=head) logger = logging.getLogger() logger.setLevel(logging.INFO) console = logging.StreamHandler() logging.getLogger('').addHandler(console) return logger def set_log_dir(root_dir, exp_name): path_dict = {} os.makedirs(root_dir, exist_ok=True) # set log path exp_path = os.path.join(root_dir, exp_name) now = datetime.now(dateutil.tz.tzlocal()) timestamp = now.strftime('%Y_%m_%d_%H_%M_%S') prefix = exp_path + '_' + timestamp os.makedirs(prefix) path_dict['prefix'] = prefix # set checkpoint path ckpt_path = os.path.join(prefix, 'Model') os.makedirs(ckpt_path) path_dict['ckpt_path'] = ckpt_path log_path = os.path.join(prefix, 'Log') os.makedirs(log_path) path_dict['log_path'] = log_path # set sample image path for fid calculation sample_path = os.path.join(prefix, 'Samples') os.makedirs(sample_path) path_dict['sample_path'] = sample_path return path_dict def save_checkpoint(states, is_best, output_dir, filename='checkpoint.pth'): torch.save(states, os.path.join(output_dir, filename)) if is_best: torch.save(states, os.path.join(output_dir, 'checkpoint_best.pth'))

py

sngan.pytorch

sngan.pytorch-master/utils/inception_score.py

# Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py from __future__ import absolute_import from __future__ import division from __future__ import print_function from tqdm import tqdm import os.path import tarfile import numpy as np from six.moves import urllib import tensorflow as tf import math import sys MODEL_DIR = '/tmp/imagenet' DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz' softmax = None config = tf.ConfigProto() config.gpu_options.allow_growth = True # Call this function with list of images. Each of elements should be a # numpy array with values ranging from 0 to 255. def get_inception_score(images, splits=10): assert (type(images) == list) assert (type(images[0]) == np.ndarray) assert (len(images[0].shape) == 3) assert (np.max(images[0]) > 10) assert (np.min(images[0]) >= 0.0) inps = [] for img in images: img = img.astype(np.float32) inps.append(np.expand_dims(img, 0)) bs = 100 with tf.Session(config=config) as sess: preds = [] n_batches = int(math.ceil(float(len(inps)) / float(bs))) for i in tqdm(range(n_batches), desc="Calculate inception score"): sys.stdout.flush() inp = inps[(i * bs):min((i + 1) * bs, len(inps))] inp = np.concatenate(inp, 0) pred = sess.run(softmax, {'ExpandDims:0': inp}) preds.append(pred) preds = np.concatenate(preds, 0) scores = [] for i in range(splits): part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :] kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0))) kl = np.mean(np.sum(kl, 1)) scores.append(np.exp(kl)) sess.close() return np.mean(scores), np.std(scores) # This function is called automatically. def _init_inception(): global softmax if not os.path.exists(MODEL_DIR): os.makedirs(MODEL_DIR) filename = DATA_URL.split('/')[-1] filepath = os.path.join(MODEL_DIR, filename) if not os.path.exists(filepath): def _progress(count, block_size, total_size): sys.stdout.write('\r>> Downloading %s %.1f%%' % ( filename, float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress) print() statinfo = os.stat(filepath) print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR) with tf.gfile.FastGFile(os.path.join( MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(graph_def, name='') # Works with an arbitrary minibatch size. with tf.Session(config=config) as sess: pool3 = sess.graph.get_tensor_by_name('pool_3:0') ops = pool3.graph.get_operations() for op_idx, op in enumerate(ops): for o in op.outputs: shape = o.get_shape() if shape._dims != []: shape = [s.value for s in shape] new_shape = [] for j, s in enumerate(shape): if s == 1 and j == 0: new_shape.append(None) else: new_shape.append(s) o.__dict__['_shape_val'] = tf.TensorShape(new_shape) w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1] logits = tf.matmul(tf.squeeze(pool3, [1, 2]), w) softmax = tf.nn.softmax(logits) sess.close()

py

sngan.pytorch

sngan.pytorch-master/utils/__init__.py

# -*- coding: utf-8 -*- # @Date : 2019-07-25 # @Author : Xinyu Gong (xy_gong@tamu.edu) # @Link : None # @Version : 0.0 from __future__ import absolute_import from __future__ import division from __future__ import print_function from utils import utils

py

sngan.pytorch

sngan.pytorch-master/utils/fid_score.py

#!/usr/bin/env python3 """ Calculates the Frechet Inception Distance (FID) to evaluate GANs. The FID metric calculates the distance between two distributions of images. Typically, we have summary statistics (mean & covariance matrix) of one of these distributions, while the 2nd distribution is given by a GAN. When run as a stand-alone program, it compares the distribution of images that are stored as PNG/JPEG at a specified location with a distribution given by summary statistics (in pickle format). The FID is calculated by assuming that X_1 and X_2 are the activations of the pool_3 layer of the inception net for generated samples and real world samples respectively. See --help to see further details. """ from __future__ import absolute_import, division, print_function import numpy as np import os import tensorflow as tf from imageio import imread from scipy import linalg import pathlib import warnings class InvalidFIDException(Exception): pass def create_inception_graph(pth): """Creates a graph from saved GraphDef file.""" # Creates graph from saved graph_def.pb. with tf.gfile.FastGFile(pth, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(graph_def, name='FID_Inception_Net') # ------------------------------------------------------------------------------- # code for handling inception net derived from # https://github.com/openai/improved-gan/blob/master/inception_score/model.py def _get_inception_layer(sess): """Prepares inception net for batched usage and returns pool_3 layer. """ layername = 'FID_Inception_Net/pool_3:0' pool3 = sess.graph.get_tensor_by_name(layername) ops = pool3.graph.get_operations() for op_idx, op in enumerate(ops): for o in op.outputs: shape = o.get_shape() if shape._dims != []: shape = [s.value for s in shape] new_shape = [] for j, s in enumerate(shape): if s == 1 and j == 0: new_shape.append(None) else: new_shape.append(s) o.__dict__['_shape_val'] = tf.TensorShape(new_shape) return pool3 # ------------------------------------------------------------------------------- def get_activations(images, sess, batch_size=50, verbose=False): """Calculates the activations of the pool_3 layer for all images. Params: -- images : Numpy array of dimension (n_images, hi, wi, 3). The values must lie between 0 and 256. -- sess : current session -- batch_size : the images numpy array is split into batches with batch size batch_size. A reasonable batch size depends on the disposable hardware. -- verbose : If set to True and parameter out_step is given, the number of calculated batches is reported. Returns: -- A numpy array of dimension (num images, 2048) that contains the activations of the given tensor when feeding inception with the query tensor. """ inception_layer = _get_inception_layer(sess) d0 = images.shape[0] if batch_size > d0: print("warning: batch size is bigger than the data size. setting batch size to data size") batch_size = d0 n_batches = d0 // batch_size n_used_imgs = n_batches * batch_size pred_arr = np.empty((n_used_imgs, 2048)) for i in range(n_batches): if verbose: print("\rPropagating batch %d/%d" % (i + 1, n_batches), end="", flush=True) start = i * batch_size end = start + batch_size batch = images[start:end] pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch}) pred_arr[start:end] = pred.reshape(batch_size, -1) if verbose: print(" done") return pred_arr # ------------------------------------------------------------------------------- def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Params: -- mu1 : Numpy array containing the activations of the pool_3 layer of the inception net ( like returned by the function 'get_predictions') for generated samples. -- mu2 : The sample mean over activations of the pool_3 layer, precalcualted on an representive data set. -- sigma1: The covariance matrix over activations of the pool_3 layer for generated samples. -- sigma2: The covariance matrix over activations of the pool_3 layer, precalcualted on an representive data set. Returns: -- : The Frechet Distance. """ mu1 = np.atleast_1d(mu1) mu2 = np.atleast_1d(mu2) sigma1 = np.atleast_2d(sigma1) sigma2 = np.atleast_2d(sigma2) assert mu1.shape == mu2.shape, "Training and test mean vectors have different lengths" assert sigma1.shape == sigma2.shape, "Training and test covariances have different dimensions" diff = mu1 - mu2 # product might be almost singular covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) if not np.isfinite(covmean).all(): msg = "fid calculation produces singular product; adding %s to diagonal of cov estimates" % eps warnings.warn(msg) offset = np.eye(sigma1.shape[0]) * eps covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) # numerical error might give slight imaginary component if np.iscomplexobj(covmean): if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): m = np.max(np.abs(covmean.imag)) raise ValueError("Imaginary component {}".format(m)) covmean = covmean.real tr_covmean = np.trace(covmean) return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean # ------------------------------------------------------------------------------- def calculate_activation_statistics(images, sess, batch_size=50, verbose=False): """Calculation of the statistics used by the FID. Params: -- images : Numpy array of dimension (n_images, hi, wi, 3). The values must lie between 0 and 255. -- sess : current session -- batch_size : the images numpy array is split into batches with batch size batch_size. A reasonable batch size depends on the available hardware. -- verbose : If set to True and parameter out_step is given, the number of calculated batches is reported. Returns: -- mu : The mean over samples of the activations of the pool_3 layer of the incption model. -- sigma : The covariance matrix of the activations of the pool_3 layer of the incption model. """ act = get_activations(images, sess, batch_size, verbose) mu = np.mean(act, axis=0) sigma = np.cov(act, rowvar=False) return mu, sigma # ------------------ # The following methods are implemented to obtain a batched version of the activations. # This has the advantage to reduce memory requirements, at the cost of slightly reduced efficiency. # - Pyrestone # ------------------ def load_image_batch(files): """Convenience method for batch-loading images Params: -- files : list of paths to image files. Images need to have same dimensions for all files. Returns: -- A numpy array of dimensions (num_images,hi, wi, 3) representing the image pixel values. """ return np.array([imread(str(fn)).astype(np.float32) for fn in files]) def get_activations_from_files(files, sess, batch_size=50, verbose=False): """Calculates the activations of the pool_3 layer for all images. Params: -- files : list of paths to image files. Images need to have same dimensions for all files. -- sess : current session -- batch_size : the images numpy array is split into batches with batch size batch_size. A reasonable batch size depends on the disposable hardware. -- verbose : If set to True and parameter out_step is given, the number of calculated batches is reported. Returns: -- A numpy array of dimension (num images, 2048) that contains the activations of the given tensor when feeding inception with the query tensor. """ inception_layer = _get_inception_layer(sess) d0 = len(files) if batch_size > d0: print("warning: batch size is bigger than the data size. setting batch size to data size") batch_size = d0 n_batches = d0 // batch_size n_used_imgs = n_batches * batch_size pred_arr = np.empty((n_used_imgs, 2048)) for i in range(n_batches): if verbose: print("\rPropagating batch %d/%d" % (i + 1, n_batches), end="", flush=True) start = i * batch_size end = start + batch_size batch = load_image_batch(files[start:end]) pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch}) pred_arr[start:end] = pred.reshape(batch_size, -1) del batch # clean up memory if verbose: print(" done") return pred_arr def calculate_activation_statistics_from_files(files, sess, batch_size=50, verbose=False): """Calculation of the statistics used by the FID. Params: -- files : list of paths to image files. Images need to have same dimensions for all files. -- sess : current session -- batch_size : the images numpy array is split into batches with batch size batch_size. A reasonable batch size depends on the available hardware. -- verbose : If set to True and parameter out_step is given, the number of calculated batches is reported. Returns: -- mu : The mean over samples of the activations of the pool_3 layer of the incption model. -- sigma : The covariance matrix of the activations of the pool_3 layer of the incption model. """ act = get_activations_from_files(files, sess, batch_size, verbose) mu = np.mean(act, axis=0) sigma = np.cov(act, rowvar=False) return mu, sigma # ------------------------------------------------------------------------------- # ------------------------------------------------------------------------------- # The following functions aren't needed for calculating the FID # they're just here to make this module work as a stand-alone script # for calculating FID scores # ------------------------------------------------------------------------------- def check_or_download_inception(inception_path): """ Checks if the path to the inception file is valid, or downloads the file if it is not present. """ INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz' if inception_path is None: inception_path = '/tmp' inception_path = pathlib.Path(inception_path) model_file = inception_path / 'classify_image_graph_def.pb' if not model_file.exists(): print("Downloading Inception model") from urllib import request import tarfile fn, _ = request.urlretrieve(INCEPTION_URL) with tarfile.open(fn, mode='r') as f: f.extract('classify_image_graph_def.pb', str(model_file.parent)) return str(model_file) def _handle_path(path, sess, low_profile=False): if path.endswith('.npz'): f = np.load(path) m, s = f['mu'][:], f['sigma'][:] f.close() else: path = pathlib.Path(path) files = list(path.glob('*.jpg')) + list(path.glob('*.png')) if low_profile: m, s = calculate_activation_statistics_from_files(files, sess) else: x = np.array([imread(str(fn)).astype(np.float32) for fn in files]) m, s = calculate_activation_statistics(x, sess) del x # clean up memory return m, s def calculate_fid_given_paths(paths, inception_path, low_profile=False): """ Calculates the FID of two paths. """ # inception_path = check_or_download_inception(inception_path) for p in paths: if not os.path.exists(p): raise RuntimeError("Invalid path: %s" % p) # from utils import memory # memory() config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: sess.run(tf.global_variables_initializer()) m1, s1 = _handle_path(paths[0], sess, low_profile=low_profile) m2, s2 = _handle_path(paths[1], sess, low_profile=low_profile) fid_value = calculate_frechet_distance(m1, s1, m2, s2) sess.close() del m1, s1, m2, s2 return fid_value

py

neu-nbv

neu-nbv-main/scripts/planning/simulator_planning.py

import rospy import os import sys root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) sys.path.insert(0, root_dir) import yaml import argparse from planner import get_planner from datetime import datetime def main(): # planning experiment in simulator using a specific planner args = parse_args() rospy.init_node(args.planner_type) # find planner configuration file experiment_path = os.path.join( root_dir, "experiments", "simulator", datetime.now().strftime("%d-%m-%Y-%H-%M"), ) planner_cfg_path = os.path.join( "planning/config", f"{args.planner_type}_planner.yaml" ) assert os.path.exists(planner_cfg_path) with open(planner_cfg_path, "r") as config_file: planner_cfg = yaml.safe_load(config_file) planner_cfg.update(args.__dict__) planner_cfg["planner_type"] = args.planner_type planner_cfg["experiment_path"] = experiment_path planner_cfg["experiment_id"] = "record" nbv_planner = get_planner(planner_cfg) nbv_planner.start() def parse_args(): parser = argparse.ArgumentParser() # mandatory arguments parser.add_argument( "--planner_type", "-P", type=str, required=True, help="planner_type" ) # arguments with default values parser.add_argument( "--planning_budget", "-BG", type=int, default=20, help="maximal measurments for the mission", ) parser.add_argument( "--device", type=str, default="cuda", help="config file path", ) parser.add_argument( "--gpu_id", type=str, default="0", help="gpu to use, space delimited", ) parser.add_argument( "--initial_view", type=list, default=[0, 0], help="prefixed initial camera view angle", ) parser.add_argument( "--random_initial", action="store_true", help="use random inital camera pose", ) args = parser.parse_args() return args if __name__ == "__main__": main()

py

neu-nbv

neu-nbv-main/scripts/planning/dtu_experiment.py

import sys import os root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) sys.path.insert(0, root_dir) from neural_rendering.evaluation.pretrained_model import PretrainedModel from neural_rendering.data import get_data from neural_rendering.utils import parser, util import yaml from dotmap import DotMap import torch import warnings import numpy as np import pandas import seaborn as sb import copy from scipy.spatial import distance from datetime import datetime import random import pickle from dotmap import DotMap warnings.filterwarnings("ignore") # follow pixelnerf setup candidate_index_list = [ 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 21, 22, 23, 24, 25, 31, 32, 33, 34, 35, 41, 42, 43, 44, 45, ] def setup_random_seed(seed): torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True def get_nbv_ref_index( model, images, poses, focal, c, z_near, z_far, candidate_list, budget, ref_index ): _, _, H, W = images.shape for i in range(budget): remain_candidate_list = list(set(candidate_list) - set(ref_index)) reward_list = [] model.network.encode( images[ref_index].unsqueeze(0), poses[ref_index].unsqueeze(0), focal.unsqueeze(0), c.unsqueeze(0), ) for target_view in remain_candidate_list: novel_pose = poses[target_view] target_rays = util.gen_rays( novel_pose.unsqueeze(0), W, H, focal, z_near, z_far, c ) target_rays = target_rays.reshape(1, H * W, -1) predict = DotMap(model.renderer_par(target_rays)) uncertainty = predict["uncertainty"][0] reward = torch.sum(uncertainty**2).cpu().numpy() reward_list.append(reward) nbv_index = np.argmax(reward_list) new_ref_index = remain_candidate_list[nbv_index] ref_index.append(new_ref_index) return ref_index def get_camera_view_direction(poses): poses = poses.cpu().numpy() view_direction = -poses[..., :3, 2] view_direction = view_direction / np.linalg.norm(view_direction) return view_direction def get_max_dist_ref_index(poses, ref_index, candidate_list, budget): view_direction = get_camera_view_direction(poses) for i in range(budget): remain_candidate_list = list(set(candidate_list) - set(ref_index)) cos_distance_list = [] for idx in remain_candidate_list: cos_dist = 0.0 for image_idx in ref_index: cos_dist += distance.cosine( view_direction[idx], view_direction[image_idx] ) cos_distance_list.append(cos_dist) new_ref_index = remain_candidate_list[np.argmax(cos_distance_list)] ref_index.append(new_ref_index) return ref_index def main(): # planning experiment on DTU using baseline planners and our planner setup_random_seed(10) args = parser.parse_args(planning_args) dtu_nbv_planner = DTUNBVPlanning(args) experiment_path = args.experiment_path if args.evaluation_only: with open(f"{experiment_path}/saved_index_dict.pkl", "rb") as f: index_record = pickle.load(f) else: experiment_path = os.path.join( root_dir, "experiments", "dtu", datetime.now().strftime("%d-%m-%Y-%H-%M"), ) os.makedirs(experiment_path) index_record = dtu_nbv_planner.planning() with open(f"{experiment_path}/saved_index_dict.pkl", "wb") as f: pickle.dump(index_record, f) total_df = dtu_nbv_planner.evaluation(index_record) total_df.to_csv(f"{experiment_path}/dataframe.csv") class DTUNBVPlanning: """ planning on DTU using different view selection methods: max_view_distance, random, and our uncertainty guided """ def __init__(self, args): log_path = os.path.join(root_dir, "neural_rendering", "logs", args.model_name) assert os.path.exists(log_path), "experiment does not exist" with open(f"{log_path}/training_setup.yaml", "r") as config_file: cfg = yaml.safe_load(config_file) checkpoint_path = os.path.join(log_path, "checkpoints", "best.ckpt") assert os.path.exists(checkpoint_path), "checkpoint does not exist" ckpt_file = torch.load(checkpoint_path) gpu_id = list(map(int, args.gpu_id.split())) self.device = util.get_cuda(gpu_id[0]) self.repeat = args.repeat self.model = PretrainedModel(cfg["model"], ckpt_file, self.device, gpu_id) cfg["data"]["dataset"]["data_rootdir"] = os.path.join( root_dir, "neural_rendering/data/dataset/dtu_dataset/rs_dtu_4/DTU" ) datamodule = get_data(cfg["data"]) self.dataset = datamodule.load_dataset("val") self.z_near = self.dataset.z_near self.z_far = self.dataset.z_far def planning(self): print(f"---------- planning ---------- \n") ON = len(self.dataset) selection_type = ["Max. View Distance", "Random", "Ours"] nview_list = [2, 3, 4, 5, 6, 7, 8, 9] # maximal budget = 9 scene_index = range(ON) ref_index_record = {} with torch.no_grad(): for nviews in nview_list: ref_index_record[nviews] = {} print(f"---------- {nviews} views experiment---------- \n") for i in scene_index: data_instance = self.dataset.__getitem__(i) scene_title = data_instance["scan_name"] ref_index_record[nviews][i] = {} print(f"test on {scene_title}") images = data_instance["images"].to(self.device) focal = data_instance["focal"].to(self.device) c = data_instance["c"].to(self.device) poses = data_instance["poses"].to(self.device) # random initialize first 2 ref images for all methods for r in range(self.repeat): ref_index_record[nviews][i][r] = {} initial_ref_index = list( np.random.choice(candidate_index_list, 2, replace=False) ) candidate_list = list( set(candidate_index_list) - set(initial_ref_index) ) budget = nviews - 2 for stype in selection_type: print(f"---------- repeat: {r}, {stype} ---------- \n") if stype == "Max. View Distance": ref_index = get_max_dist_ref_index( poses, copy.deepcopy(initial_ref_index), candidate_list, budget, ) print(ref_index) elif stype == "Random": random_ref_index = list( np.random.choice( candidate_index_list, budget, replace=True ) ) ref_index = initial_ref_index + random_ref_index print(ref_index) ref_index = np.unique(ref_index) elif stype == "Ours": ref_index = get_nbv_ref_index( self.model, images, poses, focal, c, self.z_near, self.z_far, candidate_list, budget, copy.deepcopy(initial_ref_index), ) print(ref_index) ref_index_record[nviews][i][r][stype] = ref_index return ref_index_record def evaluation(self, index_record): print(f"---------- evaluation ---------- \n") total_df = pandas.DataFrame( { "Planning Type": [], "Reference Image Number": [], "PSNR": [], "SSIM": [], "Scene": [], } ) with torch.no_grad(): for nviews, nviews_dict in index_record.items(): print(f"---------- {nviews} views experiment---------- \n") for scene_id, scene_dict in nviews_dict.items(): data_instance = self.dataset.__getitem__(scene_id) scene_title = data_instance["scan_name"] print(f"test on {scene_title}") images = data_instance["images"].to(self.device) images_0to1 = images * 0.5 + 0.5 _, _, H, W = images.shape focal = data_instance["focal"].to(self.device) c = data_instance["c"].to(self.device) poses = data_instance["poses"].to(self.device) psnr_per_scene = [] ssim_per_scene = [] # random initialize first 2 ref images for all methods for repeat, repeat_dict in scene_dict.items(): for stype, ref_index in repeat_dict.items(): print(f"---------- repeat: {repeat}, {stype} ---------- \n") print(ref_index) self.model.network.encode( images[ref_index].unsqueeze(0), poses[ref_index].unsqueeze(0), focal.unsqueeze(0), c.unsqueeze(0), ) test_index = list( set(candidate_index_list) - set(ref_index) ) psnr_per_test = [] ssim_per_test = [] for target_view in test_index: gt = ( images_0to1[target_view] .permute(1, 2, 0) .cpu() .numpy() ) novel_pose = poses[target_view] target_rays = util.gen_rays( novel_pose.unsqueeze(0), W, H, focal, self.z_near, self.z_far, c, ) target_rays = target_rays.reshape(1, H * W, -1) predict = DotMap(self.model.renderer_par(target_rays)) metrics_dict = util.calc_metrics( predict, torch.tensor(gt) ) psnr_per_test.append(metrics_dict["psnr"]) ssim_per_test.append(metrics_dict["ssim"]) psnr_per_scene = np.mean(psnr_per_test) ssim_per_scene = np.mean(ssim_per_test) print(psnr_per_scene, ssim_per_scene) dataframe = pandas.DataFrame( { "Planning Type": stype, "Reference Image Number": nviews, "PSNR": psnr_per_scene, "SSIM": ssim_per_scene, "Scene": scene_id, }, index=[repeat], ) total_df = total_df.append(dataframe) return total_df def planning_args(parser): """ Parse arguments for evaluation setup. """ parser.add_argument( "--model_name", "-M", type=str, required=True, help="model name of pretrained model", ) parser.add_argument( "--repeat", "-R", type=int, default=5, help="repeat times for planning experiment", ) # arguments with default values parser.add_argument( "--evaluation_only", action="store_true", help="evaluation mode" ) parser.add_argument( "--experiment_path", type=str, default="not defined", help="must be defined in evaluation mode", ) parser.add_argument( "--gpu_id", type=str, default="0", help="GPU(s) to use, space delimited" ) return parser if __name__ == "__main__": main()

py

neu-nbv

neu-nbv-main/scripts/planning/simulator_experiment.py

import rospy import os import sys root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) sys.path.insert(0, root_dir) import yaml import argparse from planner import get_planner from planner.utils import uniform_sampling import numpy as np import scipy.spatial as spatial from datetime import datetime import imageio import glob from dotmap import DotMap import torch from neural_rendering.utils import util from neural_rendering.evaluation.pretrained_model import PretrainedModel import pandas import torch.nn.functional as F planner_title = { "max_distance": "Max. View Distance", "random": "Random", "neural_nbv": "Ours", } def setup_random_seed(seed): np.random.seed(seed) def main(): # planning experiment in simulator using baseline planners and our planner setup_random_seed(10) rospy.init_node("simulator_experiment") args = parse_args() planner_type_list = ["max_distance", "random", "neural_nbv"] repeat = args.repeat experiment_path = args.experiment_path if not args.evaluation_only: experiment_path = os.path.join( root_dir, "experiments", "simulator", datetime.now().strftime("%d-%m-%Y-%H-%M"), ) os.makedirs(experiment_path, exist_ok=True) print("---------- planning ----------") for i in range(repeat): # initialize planning with 2 same views random_initial_view = [] for _ in range(2): random_initial_view.append( uniform_sampling(radius=2, phi_min=0.15) ) # hard-coded, should be the same for config file for planner_type in planner_type_list: # find planner configuration file print( f"---------- {planner_type} planner, experiment ID {i} ----------\n" ) planner_cfg_path = os.path.join( "planning/config", f"{planner_type}_planner.yaml" ) assert os.path.exists(planner_cfg_path) with open(planner_cfg_path, "r") as config_file: planner_cfg = yaml.safe_load(config_file) planner_cfg.update(args.__dict__) planner_cfg["planner_type"] = planner_type planner_cfg["experiment_path"] = experiment_path planner_cfg["experiment_id"] = i nbv_planner = get_planner(planner_cfg) nbv_planner.start(initial_view=random_initial_view) print("---------- evaluation ----------") gpu_id = list(map(int, args.gpu_id.split())) device = util.get_cuda(gpu_id[0]) log_path = os.path.join(root_dir, "neural_rendering", "logs", args.model_name) assert os.path.exists(log_path), "experiment does not exist" with open(f"{log_path}/training_setup.yaml", "r") as config_file: cfg = yaml.safe_load(config_file) checkpoint_path = os.path.join(log_path, "checkpoints", "best.ckpt") assert os.path.exists(checkpoint_path), "checkpoint does not exist" ckpt_file = torch.load(checkpoint_path) model = PretrainedModel(cfg["model"], ckpt_file, device, gpu_id) # load test view data as ground truth test_rgbs, test_poses, focal, c = get_image_data( args.test_data_path, "normal", device ) # configure rendering information nview = int(args.nviews) _, _, H, W = test_rgbs.shape z_near = cfg["data"]["dataset"]["z_near"] z_far = cfg["data"]["dataset"]["z_far"] step_list = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] total_df = pandas.DataFrame( { "Planning Type": [], "Reference Image Num.": [], "PSNR": [], "SSIM": [], } ) for r in range(repeat): for planner_type in planner_type_list: ref_data_path = os.path.join(experiment_path, planner_type, str(r)) ref_rgbs, ref_poses, _, _ = get_image_data(ref_data_path, "normal", device) for step in step_list: print( f"---------- planner:{planner_type}, repeat {r}, step {step} ----------\n" ) ref_kd_tree = spatial.KDTree(ref_poses[:step, :3, 3].cpu().numpy()) psnr_list = [] ssim_list = [] with torch.no_grad(): for i, rgb in enumerate(test_rgbs): pose = test_poses[i] gt = rgb * 0.5 + 0.5 gt = gt.permute(1, 2, 0).cpu().numpy() _, ref_index = ref_kd_tree.query( pose[:3, 3].cpu().numpy(), np.minimum(nview, step) ) model.network.encode( ref_rgbs[ref_index].unsqueeze(0), ref_poses[ref_index].unsqueeze(0), focal.unsqueeze(0), c.unsqueeze(0), ) target_rays = util.gen_rays( pose.unsqueeze(0), W, H, focal, z_near, z_far, c ) target_rays = target_rays.reshape(1, H * W, -1) predict = DotMap(model.renderer_par(target_rays)) metrics_dict = util.calc_metrics(predict, torch.tensor(gt)) psnr_list.append(metrics_dict["psnr"]) ssim_list.append(metrics_dict["ssim"]) psnr_mean = np.mean(psnr_list) ssim_mean = np.mean(ssim_list) print("psnr:", psnr_mean, "ssim:", ssim_mean) dataframe = pandas.DataFrame( { "Planning Type": planner_title[planner_type], "Reference Image Num.": step, "PSNR": psnr_mean, "SSIM": ssim_mean, }, index=[r], ) total_df = total_df.append(dataframe) total_df.to_csv(f"{experiment_path}/dataframe.csv") image_to_tensor = util.get_image_to_tensor_balanced() def get_image_data(data_path, coordinate_format, device, rescale=0.5): assert os.path.exists(data_path) rgb_paths = [ x for x in glob.glob(f"{data_path}/images/*") if (x.endswith(".jpg") or x.endswith(".png")) ] rgb_paths = sorted(rgb_paths) images = [] poses = [] for image_path in rgb_paths: image = imageio.imread(image_path)[..., :3] image = image_to_tensor(image) images.append(image) pose_list = np.load(f"{data_path}/trajectory.npy") for pose in pose_list: pose = util.coordinate_transformation(pose, format=coordinate_format) poses.append(pose) with open(f"{data_path}/camera_info.yaml") as file: intrinsic = yaml.safe_load(file) images = torch.stack(images).to(device) poses = torch.stack(poses).to(device) if rescale != 1: _, _, H, W = images.shape H = int(rescale * H) W = int(rescale * W) images = F.interpolate(images, size=[W, H], mode="area") focal = rescale * torch.tensor(intrinsic["focal"], dtype=torch.float32).to(device) c = rescale * torch.tensor(intrinsic["c"], dtype=torch.float32).to(device) assert len(images) == len(poses) return images, poses, focal, c def test_visualize(results_dict): import matplotlib.pyplot as plt H = 400 W = 400 rgb = results_dict.rgb[0].cpu().numpy().reshape(H, W, 3) depth = results_dict.depth[0].cpu().numpy().reshape(H, W) uncertainty = results_dict.uncertainty[0].cpu().numpy().reshape(H, W) fig, axs = plt.subplots(1, 3) axs[0].imshow(rgb) axs[1].imshow(uncertainty) axs[2].imshow(depth) plt.show() def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "--model_name", "-M", type=str, required=True, help="model name of pretrained model", ) parser.add_argument( "--test_data_path", "-TD", type=str, required=True, help="data path", ) # mandatory arguments parser.add_argument( "--repeat", "-rp", type=int, default=10, help="repeat experiment", ) # arguments with default values parser.add_argument( "--nviews", "-nv", type=int, default=5, help="number of reference views" ) parser.add_argument( "--planning_budget", "-BG", type=int, default=20, help="maximal measurments for the mission", ) parser.add_argument( "--device", type=str, default="cuda", help="config file path", ) parser.add_argument( "--gpu_id", type=str, default="0", help="gpu to use, space delimited", ) parser.add_argument( "--evaluation_only", action="store_true", help="evaluation mode" ) parser.add_argument( "--experiment_path", type=str, default="not defined", help="must be defined in evaluation mode", ) args = parser.parse_args() return args if __name__ == "__main__": main()

py

neu-nbv

neu-nbv-main/scripts/planning/planner/simulator_bridge.py

import rospy from gazebo_msgs.msg import ModelState from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge from . import utils import numpy as np class SimulatorBridge: def __init__(self, cfg): self.cv_bridge = CvBridge() self.current_rgb = None self.current_depth = None self.camera_type = cfg["camera_type"] self.sensor_noise = cfg["sensor_noise"] self.get_simulator_camera_info() self.pose_pub = rospy.Publisher( "/gazebo/set_model_state", ModelState, queue_size=1, latch=True ) if self.camera_type == "rgb_camera": self.rgb_sub = rospy.Subscriber( "/rgb_camera/rgb_image_raw", Image, self.update_rgb ) elif self.camera_type == "rgbd_camera": self.rgb_sub = rospy.Subscriber( "/rgbd_camera/rgb_image_raw", Image, self.update_rgb ) self.depth_sub = rospy.Subscriber( "/rgbd_camera/depth_image_raw", Image, self.update_depth ) def get_simulator_camera_info(self): camera_info_raw = rospy.wait_for_message( f"/{self.camera_type}/camera_info", CameraInfo ) K = camera_info_raw.K # intrinsic matrix H = int(camera_info_raw.height) # image height W = int(camera_info_raw.width) # image width self.camera_info = { "image_resolution": [H, W], "c": [K[2], K[5]], "focal": [K[0], K[4]], } def move_camera(self, pose): quaternion = utils.rotation_2_quaternion(pose[:3, :3]) translation = pose[:3, -1] camera_pose_msg = ModelState() camera_pose_msg.model_name = self.camera_type camera_pose_msg.pose.position.x = translation[0] camera_pose_msg.pose.position.y = translation[1] camera_pose_msg.pose.position.z = translation[2] camera_pose_msg.pose.orientation.x = quaternion[0] camera_pose_msg.pose.orientation.y = quaternion[1] camera_pose_msg.pose.orientation.z = quaternion[2] camera_pose_msg.pose.orientation.w = quaternion[3] self.pose_pub.publish(camera_pose_msg) def update_rgb(self, data): self.current_rgb = data def update_depth(self, data): self.current_depth = data def get_image(self): rgb = self.cv_bridge.imgmsg_to_cv2(self.current_rgb, "rgb8") rgb = np.array(rgb, dtype=float) if self.sensor_noise != 0: noise = np.random.normal(0.0, self.sensor_noise, rgb.shape) rgb += noise if self.camera_type == "rgb_camera": depth = None elif self.camera_type == "rgbd_camera": depth = self.cv_bridge.imgmsg_to_cv2(self.current_depth, "32FC1") return np.asarray(rgb), np.asarray(depth)

py

neu-nbv

neu-nbv-main/scripts/planning/planner/utils.py

from scipy.spatial.transform import Rotation as R import cv2 import numpy as np import json # from rembg import remove import os import imageio def get_roi_mask(rgb): """binary mask for ROIs using color thresholding""" hsv = cv2.cvtColor(np.array(rgb, dtype=np.uint8), cv2.COLOR_RGB2HSV) lower_red = np.array([0, 50, 50]) upper_red = np.array([20, 255, 255]) mask0 = cv2.inRange(hsv, lower_red, upper_red) lower_red = np.array([160, 50, 50]) upper_red = np.array([180, 255, 255]) mask1 = cv2.inRange(hsv, lower_red, upper_red) mask = mask0 + mask1 mask = mask + 1e-5 return mask def get_black_mask(rgb): """binary mask for ROIs using color thresholding""" lower_black = np.array([250, 250, 250]) upper_black = np.array([255, 255, 255]) mask = cv2.inRange(rgb, lower_black, upper_black) return mask def visualize_uncertainty(uncertainty): variance = np.exp(uncertainty) def rotation_2_quaternion(rotation_matrix): r = R.from_matrix(rotation_matrix) return r.as_quat() def xyz_to_view(xyz, radius): phi = np.arcsin(xyz[2] / radius) # phi from 0 to 0.5*pi theta = np.arctan2(xyz[1], xyz[0]) % (2 * np.pi) # theta from 0 to 2*pi return [phi, theta] def view_to_pose(view, radius): phi, theta = view # phi should be within [min_phi, 0.5*np.pi) if phi >= 0.5 * np.pi: phi = np.pi - phi pose = np.eye(4) x = radius * np.cos(theta) * np.cos(phi) y = radius * np.sin(theta) * np.cos(phi) z = radius * np.sin(phi) translation = np.array([x, y, z]) rotation = R.from_euler("ZYZ", [theta, -phi, np.pi]).as_matrix() pose[:3, -1] = translation pose[:3, :3] = rotation return pose def view_to_pose_batch(views, radius): num = len(views) phi = views[:, 0] theta = views[:, 1] # phi should be within [min_phi, 0.5*np.pi) index = phi >= 0.5 * np.pi phi[index] = np.pi - phi[index] poses = np.broadcast_to(np.identity(4), (num, 4, 4)).copy() x = radius * np.cos(theta) * np.cos(phi) y = radius * np.sin(theta) * np.cos(phi) z = radius * np.sin(phi) translations = np.stack((x, y, z), axis=-1) angles = np.stack((theta, -phi, np.pi * np.ones(num)), axis=-1) rotations = R.from_euler("ZYZ", angles).as_matrix() poses[:, :3, -1] = translations poses[:, :3, :3] = rotations return poses def random_view(current_xyz, radius, phi_min, min_view_change, max_view_change): """ random scatter view direction changes by given current position and view change range. """ u = current_xyz / np.linalg.norm(current_xyz) # pick a random vector: r = np.random.multivariate_normal(np.zeros_like(u), np.eye(len(u))) # form a vector perpendicular to u: uperp = r - r.dot(u) * u uperp = uperp / np.linalg.norm(uperp) # random view angle change in radian random_view_change = np.random.uniform(low=min_view_change, high=max_view_change) cosine = np.cos(random_view_change) w = cosine * u + np.sqrt(1 - cosine**2 + 1e-8) * uperp w = radius * w / np.linalg.norm(w) view = xyz_to_view(w, radius) if view[0] < phi_min: view[0] = phi_min return view def uniform_sampling(radius, phi_min): """ uniformly generate unit vector on hemisphere. then calculate corresponding view direction targeting coordinate origin. """ xyz = np.array([0.0, 0.0, 0.0]) # avoid numerical error while np.linalg.norm(xyz) < 0.001: xyz[0] = np.random.uniform(low=-1.0, high=1.0) xyz[1] = np.random.uniform(low=-1.0, high=1.0) xyz[2] = np.random.uniform(low=0.0, high=1.0) xyz = radius * xyz / np.linalg.norm(xyz) view = xyz_to_view(xyz, radius) if view[0] < phi_min: view[0] = phi_min return view def focal_len_to_fov(focal, resolution): """ calculate FoV based on given focal length adn image resolution Args: focal: [fx, fy] resolution: [W, H] Returns: FoV: [HFoV, VFoV] """ focal = np.asarray(focal) resolution = np.asarray(resolution) return 2 * np.arctan(0.5 * resolution / focal) def mask_out_background(image_path): """remove background""" rgb = imageio.imread(image_path) masked_rgb = remove(rgb) # H, W, _ = rgb.shape # masked_rgb = np.ones((H, W, 4)) * 255 # masked_rgb[..., :3] = rgb # mask_white = rgb >= np.array([254, 254, 254]) # mask_white = np.all(mask_white, axis=-1) # mask_black = rgb <= np.array([1, 1, 1]) # mask_black = np.all(mask_black, axis=-1) # masked_rgb[mask_white] = [0, 0, 0, 0] # masked_rgb[mask_black] = [0, 0, 0, 0] return masked_rgb def record_render_data(path, camera_info, trajectory, use_masked_image=False): transformation = np.array( [[0, 0, -1, 0], [-1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]] ) # transform gazebo coordinate to opengl format opencv_trajectory = np.empty(trajectory.shape) for i, pose in enumerate(trajectory): opencv_trajectory[i] = pose @ transformation resolution = camera_info["image_resolution"] c = camera_info["c"] focal = camera_info["focal"] fov = focal_len_to_fov(focal, resolution) record_dict = {} record_dict["camera_angle_x"] = fov[0] record_dict["camera_angle_y"] = fov[1] record_dict["fl_x"] = focal[0] record_dict["fl_y"] = focal[1] record_dict["k1"] = 0.000001 record_dict["k2"] = 0.000001 record_dict["p1"] = 0.000001 record_dict["p2"] = 0.000001 record_dict["cx"] = c[0] record_dict["cy"] = c[1] record_dict["w"] = resolution[0] record_dict["h"] = resolution[1] record_dict["frames"] = [] record_dict["scale"] = 1.0 record_dict["aabb_scale"] = 2.0 for i, pose in enumerate(opencv_trajectory): image_file = f"images/{i+1:04d}.png" image_path = os.path.join(path, image_file) if use_masked_image: masked_image = mask_out_background(image_path) image_file = f"images/masked_{i+1:04d}.png" image_path = os.path.join(path, image_file) imageio.imwrite(image_path, masked_image) data_frame = { "file_path": image_file, # "sharpness": 30.0, "transform_matrix": pose.tolist(), } record_dict["frames"].append(data_frame) with open(f"{path}/transforms.json", "w") as f: json.dump(record_dict, f, indent=4) # for test only # for i, pose in enumerate(opencv_trajectory[50:]): # data_frame = { # "file_path": f"images/{i+51:04d}.jpg", # "sharpness": 30.0, # "transform_matrix": pose.tolist(), # } # record_dict["frames"].append(data_frame) # with open(f"{path}/test_transforms.json", "w") as f: # json.dump(record_dict, f, indent=4) def test(): view = [] # for i in range(5): # new_view = uniform_sampling(2, 0.15) # view.append(new_view) # print("view:", new_view) current_xyz = [0, 0, 2] for i in range(500): local = random_view(current_xyz, 2, 0.15, 0.2, 1.05) view.append(local) xyz_list = view_to_pose_batch(np.array(view), 2)[..., :3, 3] print(xyz_list) for xyz in xyz_list: view = xyz_to_view(xyz, 2) print(view) fig = plt.figure() ax = fig.add_subplot(projection="3d") ax.scatter(xyz_list[..., 0], xyz_list[..., 1], xyz_list[..., 2]) ax.set_xlabel("X Label") ax.set_ylabel("Y Label") ax.set_zlabel("Z Label") ax.set_zlim(0, 2.5) ax.set_xlim(-2.5, 2.5) ax.set_ylim(-2.5, 2.5) plt.show() if __name__ == "__main__": import matplotlib.pyplot as plt test()

py

neu-nbv

neu-nbv-main/scripts/planning/planner/__init__.py

from .neural_nbv.neural_nbv_planner import NeuralNBVPlanner from .baselines.random_planner import RandomPlanner from .baselines.max_distance_planner import MaxDistancePlanner def get_planner(cfg): planner_type = cfg["planner_type"] if planner_type == "neural_nbv": return NeuralNBVPlanner(cfg) elif planner_type == "random": return RandomPlanner(cfg) elif planner_type == "max_distance": return MaxDistancePlanner(cfg)

py

neu-nbv

neu-nbv-main/scripts/planning/planner/planner.py

from .simulator_bridge import SimulatorBridge from . import utils import time import os from datetime import datetime import numpy as np import imageio.v2 as imageio import yaml class Planner: def __init__(self, cfg): self.simulator_bridge = SimulatorBridge(cfg["simulation_bridge"]) self.camera_info = self.simulator_bridge.camera_info self.record_path = os.path.join( cfg["experiment_path"], cfg["planner_type"], str(cfg["experiment_id"]) ) self.planning_budget = cfg["planning_budget"] self.initial_type = cfg["initial_type"] self.H, self.W = self.camera_info[ "image_resolution" ] # original image resolution from simulator self.trajectory = np.empty((self.planning_budget, 4, 4)) self.view_trajectory = np.empty((self.planning_budget, 2)) # [phi, theta] self.rgb_measurements = np.empty((self.planning_budget, self.H, self.W, 3)) self.depth_measurements = np.empty((self.planning_budget, self.H, self.W)) self.step = 0 self.config_actionspace(cfg["action_space"]) def config_actionspace(self, cfg): """set hemisphere actionspace parameters""" self.min_height = cfg["min_height"] self.radius = cfg["radius"] self.phi_min = np.arcsin(self.min_height / self.radius) self.phi_max = 0.5 * np.pi self.theta_min = 0 self.theta_max = 2 * np.pi def init_camera_pose(self, initial_view): print("------ start mission ------ \n") print("------ initialize camera pose ------ \n") if initial_view is None: if self.initial_type == "random": initial_view = utils.uniform_sampling(self.radius, self.phi_min) elif self.initial_type == "pre_calculated": self.get_view_list() initial_view = next(self.view_list) self.move_sensor(initial_view) else: for view in initial_view: self.move_sensor(view) def start(self, initial_view=None): self.init_camera_pose(initial_view) while self.step < self.planning_budget: next_view = self.plan_next_view() self.move_sensor(next_view) self.record_experiment() print("------ complete mission ------\n") # rospy.signal_shutdown("shut down ros node") def move_sensor(self, view): pose = utils.view_to_pose(view, self.radius) self.simulator_bridge.move_camera(pose) self.current_view = view self.current_pose = pose print(pose) print( f"------ reach given pose and take measurement No.{self.step + 1} ------\n" ) time.sleep(1) # lazy solution to make sure we receive correct images rgb, depth = self.simulator_bridge.get_image() self.record_step(view, pose, rgb, depth) self.step += 1 def plan_next_view(self): raise NotImplementedError("plan_next_view method is not implemented") def record_experiment(self): print("------ record experiment data ------\n") os.makedirs(self.record_path, exist_ok=True) images_path = os.path.join(self.record_path, "images") os.mkdir(images_path) depths_path = os.path.join(self.record_path, "depths") os.mkdir(depths_path) for i, rgb in enumerate(self.rgb_measurements): imageio.imwrite( f"{images_path}/{i+1:04d}.png", (rgb * 255).astype(np.uint8) ) if len(self.depth_measurements) > 0: for i, depth in enumerate(self.depth_measurements): with open(f"{depths_path}/depth_{i+1:04d}.npy", "wb") as f: depth_array = np.array(depth, dtype=np.float32) np.save(f, depth_array) with open(f"{self.record_path}/trajectory.npy", "wb") as f: np.save(f, self.trajectory) with open(f"{self.record_path}/camera_info.yaml", "w") as f: yaml.safe_dump(self.camera_info, f) # record json data required for instant-ngp training utils.record_render_data(self.record_path, self.camera_info, self.trajectory) def record_step(self, view, pose, rgb, depth): self.record_trajectory(view, pose) self.record_rgb_measurement(rgb) if depth is not None: self.record_depth_measurement(depth) def record_rgb_measurement(self, rgb): rgb = np.clip(rgb, a_min=0, a_max=255) rgb = rgb / 255 self.rgb_measurements[self.step] = rgb def record_depth_measurement(self, depth): self.depth_measurements[self.step] = depth def record_trajectory(self, view, pose): self.view_trajectory[self.step] = view self.trajectory[self.step] = pose

py

neu-nbv

neu-nbv-main/scripts/planning/planner/neural_nbv/neural_nbv_planner.py

import numpy as np from scipy.spatial.transform import Rotation as R from planner.planner import Planner from planner.utils import view_to_pose_batch, random_view, uniform_sampling from neural_rendering.evaluation.pretrained_model import PretrainedModel import torch from dotmap import DotMap from neural_rendering.utils import util import torch.nn.functional as F import scipy.spatial as spatial import matplotlib.pyplot as plt import time import yaml import os class NeuralNBVPlanner(Planner): def __init__(self, cfg): super().__init__(cfg) self.device = cfg["device"] self.gpu_id = list(map(int, cfg["gpu_id"].split())) self.init_sensor_model(cfg) self.image_to_tensor = util.get_image_to_tensor_balanced() self.num_candidates = cfg["num_candidates"] self.sample_type = cfg["sample_type"] self.view_change = cfg["view_change"] self.local_view_change = cfg["local_view_change"] self.selection_range = cfg["selection_range"] self.hierachical_sampling = cfg["use_hierachical_sampling"] self.sample_ratio = cfg["sample_ratio"] self.K = cfg["top_k"] self.max_ref_num = cfg["maximal_ref"] self.reward_type = cfg["reward_type"] self.render_batch_size = cfg["render_batch_size"] self.uncertainty_th = cfg["uncertainty_threshold"] self.candidate_views = None self.candidate_poses = None self.render_pairs = None self.trajectory_kdtree = None # self.depth_for_renderer = torch.empty( # (self.planning_budget, self.H, self.W) # ).to(self.device) def init_sensor_model(self, cfg): assert os.path.exists(cfg["config_path"]) assert os.path.exists(cfg["checkpoint_path"]) with open(cfg["config_path"], "r") as config_file: model_cfg = yaml.safe_load(config_file)["model"] ckpt_file = torch.load(cfg["checkpoint_path"]) self.model = PretrainedModel(model_cfg, ckpt_file, self.device, self.gpu_id) # original image format H, W = self.camera_info["image_resolution"] # (H, W) focal = self.camera_info["focal"] # (f_x, f_y) c = self.camera_info["c"] # (c_x, c_y) # desired image format for redendering input render_info = cfg["render_info"] H_ref, W_ref = render_info["ref_image_resolution"] ref_focal = [0, 0] ref_c = [0, 0] if np.any([H, W] != [H_ref, W_ref]): scale_h = H_ref / H scale_w = W_ref / W ref_focal[0] = scale_w * focal[0] ref_focal[1] = scale_h * focal[1] ref_c[0] = scale_w * c[0] ref_c[1] = scale_h * c[1] self.ref_focal = torch.tensor(ref_focal, dtype=torch.float32).to(self.device) self.ref_c = torch.tensor(ref_c, dtype=torch.float32).to(self.device) self.ref_image_resolution = (H_ref, W_ref) self.trajectory_for_renderer = torch.empty((self.planning_budget, 4, 4)).to( self.device ) self.rgb_for_renderer = torch.empty((self.planning_budget, 3, H_ref, W_ref)).to( self.device ) # desired image format for redendering output render_scale = render_info["render_scale"] self.H_render = int(render_scale * H_ref) self.W_render = int(render_scale * W_ref) render_scale = torch.tensor( [ self.W_render / W_ref, self.H_render / H_ref, ] ).to(self.device) self.render_focal = render_scale * self.ref_focal self.render_c = render_scale * self.ref_c self.z_near, self.z_far = render_info["scene_range"] def render_novel_views(self, candidate_poses): candidate_num = len(candidate_poses) reward_list = np.zeros(candidate_num) distance_all, ref_index_all = self.trajectory_kdtree.query( candidate_poses[:, :3, 3], np.minimum(self.max_ref_num, self.step) ) # distance_all = torch.tensor(distance_all) # ref_index_all = torch.tensor(ref_index_all) bool_mask = ~np.isinf(distance_all) novel_poses = util.coordinate_transformation( candidate_poses, format="normal" ).to(self.device) # render novel view in batch split_novel_view = torch.split( torch.arange(candidate_num), self.render_batch_size, dim=0 ) for i in split_novel_view: ref_index = torch.tensor(ref_index_all[i] * bool_mask[i]) ref_images = self.rgb_for_renderer[ref_index] ref_poses = self.trajectory_for_renderer[ref_index] render_results = self.rendering(ref_images, ref_poses, novel_poses[i]) reward_list[i] = self.cal_reward(render_results) return reward_list def rendering(self, ref_images, ref_poses, novel_poses): NP = len(novel_poses) with torch.no_grad(): self.model.network.encode( ref_images, ref_poses, self.ref_focal.unsqueeze(0), self.ref_c.unsqueeze(0), ) target_rays = util.gen_rays( novel_poses, self.W_render, self.H_render, self.render_focal, self.z_near, self.z_far, self.render_c, ) # (IN, H, W, 8) target_rays = target_rays.reshape(NP, self.H_render * self.W_render, -1) predict = DotMap(self.model.renderer_par(target_rays)) return predict def cal_reward(self, render_results): uncertainty = render_results["uncertainty"] reward = torch.mean(uncertainty**2, dim=-1).cpu().numpy() reward = np.log10(reward) return reward # one stage planning def start_planning(self): candidate_views, candidate_poses = self.local_sampling( self.num_candidates, self.current_pose[:3, 3], view_change=self.view_change ) reward_list = self.render_novel_views(candidate_poses) nbv_index = np.argmax(reward_list) return candidate_views[nbv_index] def global_sampling(self, num): view_list = np.empty((num, 2)) for i in range(num): view_list[i] = uniform_sampling(self.radius, self.phi_min) pose_list = view_to_pose_batch(view_list, self.radius) return view_list, pose_list def local_sampling(self, num, xyz, view_change, min_view_change=0.2): view_list = np.empty((num, 2)) for i in range(num): view_list[i] = random_view( xyz, self.radius, self.phi_min, min_view_change, view_change ) pose_list = view_to_pose_batch(view_list, self.radius) return view_list, pose_list def plan_next_view(self): import time if self.step > 1: t1 = time.time() nbv = self.start_planning() t2 = time.time() print((t2 - t1)) return nbv # need at least two views to start the planning else: random_next_view = random_view( self.current_pose[:3, 3], self.radius, self.phi_min, self.view_change - 0.1, self.view_change, ) return random_next_view def record_trajectory(self, view, pose): self.view_trajectory[self.step] = view self.trajectory[self.step] = pose # maintain current measurment positions in kd tree self.trajectory_kdtree = spatial.KDTree(self.trajectory[: self.step + 1, :3, 3]) self.trajectory_for_renderer[self.step] = util.coordinate_transformation( pose, format="normal" ).to(self.device) def record_rgb_measurement(self, rgb): rgb = np.clip(rgb, a_min=0, a_max=255) rgb = rgb / 255 self.rgb_measurements[self.step] = rgb ref_image = self.image_to_tensor(rgb).to(self.device) ref_image = F.interpolate( ref_image.unsqueeze(0), size=self.ref_image_resolution, mode="area" ).squeeze(0) self.rgb_for_renderer[self.step] = ref_image def test_visualize(self, ref_images, results_dict): import matplotlib.pyplot as plt H = 60 W = 60 for i in range(self.render_batch_size): rgb = results_dict.rgb[i].cpu().numpy().reshape(H, W, 3) depth = results_dict.depth[i].cpu().numpy().reshape(H, W) uncertainty = results_dict.uncertainty[i].cpu().numpy().reshape(H, W) fig, axs = plt.subplots(1, 3) axs[0].imshow(rgb) axs[1].imshow(uncertainty) axs[2].imshow(depth) plt.show()

py

neu-nbv

neu-nbv-main/scripts/planning/planner/baselines/random_planner.py

from planner.planner import Planner from planner.utils import random_view, uniform_sampling import numpy as np class RandomPlanner(Planner): def __init__(self, cfg): super().__init__(cfg) print("initial ") self.num_candidates = cfg["num_candidates"] self.view_change = cfg["view_change"] self.planning_type = cfg["planning_type"] def plan_next_view(self): view_list = np.empty((self.num_candidates, 2)) if self.planning_type == "local": for i in range(self.num_candidates): view_list[i] = random_view( self.current_pose[:3, 3], self.radius, self.phi_min, min_view_change=0.2, max_view_change=self.view_change, ) elif self.planning_type == "global": for i in range(self.num_candidates): view_list[i] = uniform_sampling(self.radius, self.phi_min) nbv_index = np.random.choice(len(view_list)) nbv = view_list[nbv_index] return nbv

py

neu-nbv

neu-nbv-main/scripts/planning/planner/baselines/max_distance_planner.py

from planner.planner import Planner from planner.utils import view_to_pose_batch, random_view, uniform_sampling import numpy as np from scipy.spatial import distance class MaxDistancePlanner(Planner): def __init__(self, cfg): super().__init__(cfg) self.num_candidates = cfg["num_candidates"] self.view_change = cfg["view_change"] self.planning_type = cfg["planning_type"] def get_camera_view_direction(self, poses): view_direction = poses[..., :3, 0] view_direction = view_direction / np.linalg.norm(view_direction) return view_direction def plan_next_view(self): view_list = np.empty((self.num_candidates, 2)) if self.planning_type == "local": for i in range(self.num_candidates): view_list[i] = random_view( self.current_pose[:3, 3], self.radius, self.phi_min, min_view_change=0.2, max_view_change=self.view_change, ) elif self.planning_type == "global": for i in range(self.num_candidates): view_list[i] = uniform_sampling(self.radius, self.phi_min) pose_list = view_to_pose_batch(view_list, self.radius) new_view_list = self.get_camera_view_direction(pose_list) reference_pose_list = self.trajectory[: self.step] reference_view_list = self.get_camera_view_direction(reference_pose_list) dist_list = [] for view in new_view_list: dist = 0 count = 0 for ref_view in reference_view_list: # print(view, ref_view) cos_dist = distance.cosine(view, ref_view) if cos_dist < 0.6: dist += cos_dist count += 1 # print(dist) dist_list.append(dist / count) nbv = view_list[np.argmax(dist_list)] return nbv

py

neu-nbv

neu-nbv-main/scripts/utils/plot.py

import seaborn as sb import matplotlib.pyplot as plt import pandas import os import argparse from matplotlib.ticker import FormatStrFormatter from matplotlib.ticker import MaxNLocator from matplotlib import rc PLOT_FONT_SIZE = 22 PLOT_LEGEND_SIZE = 18 PLOT_TICKS_SIZE = 18 PLOT_LINE_WIDTH = 4 plt.rcParams["figure.figsize"] = [12, 8] plt.rcParams["figure.autolayout"] = True sb.set_palette("bright") def main(): args = plot_args() dataframe_path = args.dataframe_path os.path.exists(dataframe_path), "dataframe does not exist!" plot(dataframe_path) def plot(dataframe_path): # DTU experiment plot save_path = os.path.dirname(dataframe_path) total_df = pandas.read_csv(dataframe_path) fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True) sb.lineplot( total_df, x="Reference Image Number", y="PSNR", hue="Planning Type", linewidth=PLOT_LINE_WIDTH, ax=ax1, errorbar=("sd", 1), palette=["C2", "C0", "C3"], ) ax1.set_ylabel("PSNR", fontsize=PLOT_FONT_SIZE) ax1.set_xlabel("Number of collected images", fontsize=PLOT_FONT_SIZE) ax1.yaxis.set_major_formatter(FormatStrFormatter("%.1f")) ax1.yaxis.set_major_locator(MaxNLocator(nbins=4)) ax1.tick_params(axis="both", labelsize=PLOT_TICKS_SIZE) handles, labels = ax1.get_legend_handles_labels() order = [2, 0, 1] ax1.legend( [handles[idx] for idx in order], [labels[idx] for idx in order], loc="lower right", fontsize=PLOT_LEGEND_SIZE, frameon=False, ) sb.lineplot( total_df, x="Reference Image Number", y="SSIM", hue="Planning Type", linewidth=PLOT_LINE_WIDTH, ax=ax2, errorbar=("sd", 1), palette=["C2", "C0", "C3"], ) ax2.set_xlabel("Number of collected images", fontsize=PLOT_FONT_SIZE) ax2.set_ylabel("SSIM", fontsize=PLOT_FONT_SIZE) ax2.yaxis.set_major_formatter(FormatStrFormatter("%.2f")) ax2.yaxis.set_major_locator(MaxNLocator(nbins=4)) ax2.get_legend().remove() ax2.tick_params(axis="both", labelsize=PLOT_TICKS_SIZE) plt.xticks([2, 3, 4, 5, 6, 7, 8, 9]) plt.savefig(f"{save_path}/plot_results.svg", bbox_inches="tight") plt.clf() def plot_args(): # mandatory arguments args = None parser = argparse.ArgumentParser() parser.add_argument( "--dataframe_path", type=str, required=True, help="path to dataframe", ) args = parser.parse_args() return args if __name__ == "__main__": main() # # gazebo car experiment plot # total_df = pandas.read_csv("dataframe/experiment_gazebo_car_0.csv") # total_df = total_df.append(pandas.read_csv("dataframe/experiment_gazebo_car_1.csv")) # fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True) # sb.lineplot( # total_df, # x="Reference Image Num.", # y="PSNR", # hue="Planning Type", # linewidth=PLOT_LINE_WIDTH, # ax=ax1, # errorbar=("sd", 1), # palette=["C2", "C0", "C3"], # ) # ax1.set_xlabel("Number of collected images", fontsize=PLOT_FONT_SIZE) # ax1.set_ylabel("PSNR", fontsize=PLOT_FONT_SIZE) # ax1.yaxis.set_major_formatter(FormatStrFormatter("%.1f")) # ax1.yaxis.set_major_locator(MaxNLocator(nbins=4)) # # ax1.tick_params(labelsize=PLOT_TICKS_SIZE) # ax1.legend(loc="lower right", fontsize=PLOT_FONT_SIZE) # ax1.tick_params(axis="both", labelsize=PLOT_TICKS_SIZE) # handles, labels = ax1.get_legend_handles_labels() # order = [2, 0, 1] # ax1.legend( # [handles[idx] for idx in order], # [labels[idx] for idx in order], # loc="lower right", # fontsize=PLOT_LEGEND_SIZE, # frameon=False, # ) # sb.lineplot( # total_df, # x="Reference Image Num.", # y="SSIM", # hue="Planning Type", # linewidth=PLOT_LINE_WIDTH, # ax=ax2, # errorbar=("sd", 1), # palette=["C2", "C0", "C3"], # ) # ax2.set_xlabel("Number of collected images", fontsize=PLOT_FONT_SIZE) # plt.xticks([2, 4, 6, 8, 10, 12, 14, 16, 18, 20]) # ax2.set_ylabel("SSIM", fontsize=PLOT_FONT_SIZE) # ax2.yaxis.set_major_formatter(FormatStrFormatter("%.2f")) # ax2.yaxis.set_major_locator(MaxNLocator(nbins=4)) # ax2.get_legend().remove() # ax2.tick_params(axis="both", labelsize=PLOT_TICKS_SIZE) # # ax2.tick_params(labelsize=PLOT_TICKS_SIZE) # # plt.show() # plt.xticks(fontsize=PLOT_TICKS_SIZE) # plt.yticks(fontsize=PLOT_TICKS_SIZE) # plt.savefig("dataframe/gazebo_car.svg", bbox_inches="tight") # plt.clf() # # # gazebo indoor experiment plot # total_df = pandas.read_csv("dataframe/experiment_gazebo_indoor_0.csv") # total_df = total_df.append(pandas.read_csv("dataframe/experiment_gazebo_indoor_1.csv")) # fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True) # sb.lineplot( # total_df, # x="Reference Image Num.", # y="PSNR", # hue="Planning Type", # linewidth=PLOT_LINE_WIDTH, # ax=ax1, # errorbar=("sd", 1), # palette=["C2", "C0", "C3"], # ) # ax1.set_ylabel("PSNR", fontsize=PLOT_FONT_SIZE) # ax1.set_xlabel("Number of collected images", fontsize=PLOT_FONT_SIZE) # ax1.yaxis.set_major_formatter(FormatStrFormatter("%.1f")) # ax1.set_yticks([13.4, 14.6, 15.8, 17.0]) # # ax1.yaxis.set_major_locator(MaxNLocator(nbins=5)) # ax1.tick_params(axis="both", labelsize=PLOT_TICKS_SIZE) # # ax1.tick_params(labelsize=PLOT_TICKS_SIZE) # ax1.legend(loc="lower right", fontsize=PLOT_FONT_SIZE) # handles, labels = ax1.get_legend_handles_labels() # order = [2, 0, 1] # ax1.legend( # [handles[idx] for idx in order], # [labels[idx] for idx in order], # loc="lower right", # fontsize=PLOT_LEGEND_SIZE, # frameon=False, # ) # sb.lineplot( # total_df, # x="Reference Image Num.", # y="SSIM", # hue="Planning Type", # linewidth=PLOT_LINE_WIDTH, # ax=ax2, # errorbar=("sd", 1), # palette=["C2", "C0", "C3"], # ) # ax2.set_xlabel("Number of collected images", fontsize=PLOT_FONT_SIZE) # ax2.set_ylabel("SSIM", fontsize=PLOT_FONT_SIZE) # ax2.yaxis.set_major_formatter(FormatStrFormatter("%.2f")) # ax2.yaxis.set_major_locator(MaxNLocator(nbins=4)) # ax2.get_legend().remove() # ax2.tick_params(axis="both", labelsize=PLOT_TICKS_SIZE) # # ax2.tick_params(labelsize=PLOT_TICKS_SIZE) # # plt.show() # plt.xticks([2, 4, 6, 8, 10, 12, 14, 16, 18, 20]) # plt.xticks(fontsize=PLOT_TICKS_SIZE) # plt.yticks(fontsize=PLOT_TICKS_SIZE) # plt.savefig("dataframe/gazebo_indoor.svg", bbox_inches="tight") # plt.clf()

py

PolSF

PolSF-master/label2dto3d.py

import numpy as np from skimage import io from scipy import misc ### The original PolSAR data is downloaded from www.ietr.fr/polsarpro-bio/sanfrancisco. ### Courtesy of CNSA, CSA, ESA, IECAS, ISRO, JAXA, MDA, NASA-JPL, NSOAS. ### The color code and the class are shown as follows: # SF-ALOS2 # https://www.ietr.fr/polsarpro-bio/san-francisco/dataset/SAN_FRANCISCO_ALOS2.zip # color = [[0,0,0],[132,112,255],[0,0,255],[0,255,0],[192,0,0],[0,255,255],[255,255,0]] ## 0, unlabel, 1,Montain,2,Water,3,Vegetation,4,High-Density Urban,5,Low-Density Urban,6,Developd # SF-GF3 # https://www.ietr.fr/polsarpro-bio/san-francisco/dataset/SAN_FRANCISCO_GF3.zip # color = [[0,0,0],[132,112,255],[0,0,255],[0,255,0],[192,0,0],[0,255,255],[255,255,0]] ## 0, unlabel, 1,Montain,2,Water,3,Vegetation,4,High-Density Urban,5,Low-Density Urban,6,Developd # SF-RISAT # https://www.ietr.fr/polsarpro-bio/san-francisco/dataset/SAN_FRANCISCO_RISAT.zip # color = [[0,0,0],[132,112,255],[0,0,255],[0,255,0],[192,0,0],[0,255,255],[255,255,0]] ## 0, unlabel, 1,Montain,2,Water,3,Vegetation,4,High-Density Urban,5,Low-Density Urban,6,Developd # SF-RS2 # https://www.ietr.fr/polsarpro-bio/san-francisco/dataset/SAN_FRANCISCO_RS2.zip # color = [[0,0,0], [0,0,255],[0,255,0],[255,0,0],[255,255,0],[255,0,255]] ## 0, unlabel, 1,Water,2,Vegetation,3,High-Density Urban,4,Low-Density Urban,5,Developd # SF-AIRSAR # https://www.ietr.fr/polsarpro-bio/san-francisco/dataset/SAN_FRANCISCO_AIRSAR.zip color = [[0,0,0], [0,255,255],[255,255,0],[0,0,255],[255,0,0],[0,255,0]] ## 0, unlabel, 1,Montain,2,Water,3,Urban,4,Vegetation ,5, Bare soil # label_files = '/home/bidlc/labelposar/PolSF/SF-AIRSAR/SF-AIRSAR-label2d.png' datanameall = ['SF-AIRSAR','SF-AIRSAR','SF-AIRSAR','SF-AIRSAR','SF-AIRSAR'] dataname = datanameall[4] label_files = './'+dataname+'/'+dataname+'-label2d.png' label = io.imread(label_files) mm = label.shape[0] nn = label.shape[1] label3d = np.zeros([mm, nn, 3]) for j in range(mm): for k in range(nn): print(j,k) label3d[j][k]=color[int(label[j][k])] misc.imsave('/home/bidlc/labelposar/PolSF/'+dataname+'/'+dataname+'-label3d-test.png', label3d)

py

MP-FedXGB

MP-FedXGB-main/VerticalXGBoost.py

import numpy as np import pandas as pd from mpi4py import MPI from datetime import * from SSCalculation import * from Tree import * import math import time np.random.seed(10) clientNum = 4 class LeastSquareLoss: def gradient(self, actual, predicted): return -(actual - predicted) def hess(self, actual, predicted): return np.ones_like(actual) class LogLoss(): def gradient(self, actual, predicted): prob = 1.0 / (1.0 + np.exp(-predicted)) return prob - actual def hess(self, actual, predicted): prob = 1.0 / (1.0 + np.exp(-predicted)) return prob * (1.0 - prob) # Mind the dimension class VerticalXGBoostClassifier: def __init__(self, rank, lossfunc, splitclass, _lambda=1, _gamma=0.5, _epsilon=0.1, n_estimators=3, max_depth=3): if lossfunc == 'LogLoss': self.loss = LogLoss() else: self.loss = LeastSquareLoss() self._lambda = _lambda self._gamma = _gamma self._epsilon = _epsilon self.n_estimators = n_estimators # Number of trees self.max_depth = max_depth # Maximum depth for tree self.rank = rank self.trees = [] self.splitclass = splitclass for _ in range(n_estimators): tree = VerticalXGBoostTree(rank=self.rank, lossfunc=self.loss, splitclass=self.splitclass, _lambda=self._lambda, _gamma=self._gamma, _epsilon=self._epsilon, _maxdepth=self.max_depth, clientNum=clientNum) self.trees.append(tree) def getQuantile(self, colidx): split_list = [] if self.rank != 0: # For client nodes data = self.data.copy() idx = np.argsort(data[:, colidx], axis=0) data = data[idx] value_list = sorted(list(set(list(data[:, colidx])))) # Record all the different value hess = np.ones_like(data[:, colidx]) data = np.concatenate((data, hess.reshape(-1, 1)), axis=1) sum_hess = np.sum(hess) last = value_list[0] i = 1 if len(value_list) == 1: # For those who has only one value, do such process. last_cursor = last else: last_cursor = value_list[1] split_list.append((-np.inf, value_list[0])) # if len(value_list) == 15000: # print(self.rank, colidx) # print(value_list) while i < len(value_list): cursor = value_list[i] small_hess = np.sum(data[:, -1][data[:, colidx] <= last]) / sum_hess big_hess = np.sum(data[:, -1][data[:, colidx] <= cursor]) / sum_hess # print(colidx, self.rank, np.abs(big_hess - small_hess), last, cursor) if np.abs(big_hess - small_hess) < self._epsilon: last_cursor = cursor else: judge = value_list.index(cursor) - value_list.index(last) if judge == 1: # Although it didn't satisfy the criterion, it has no more split, so we must add it. split_list.append((last, cursor)) last = cursor else: # Move forward and record the last. split_list.append((last, last_cursor)) last = last_cursor last_cursor = cursor i += 1 if split_list[-1][1] != value_list[-1]: split_list.append((split_list[-1][1], value_list[-1])) # Add the top value into split_list. split_list = np.array(split_list) return split_list def getAllQuantile(self): # Global quantile, must be calculated before tree building, avoiding recursion. self_maxlen = 0 if self.rank != 0: dict = {i:self.getQuantile(i) for i in range(self.data.shape[1])} # record all the split self_maxlen = max([len(dict[i]) for i in dict.keys()]) else: dict = {} recv_maxlen = comm.gather(self_maxlen, root=1) maxlen = None if self.rank == 1: maxlen = max(recv_maxlen) self.maxSplitNum = comm.bcast(maxlen, root=1) # print('MaxSplitNum: ', self.maxSplitNum) self.quantile = dict def fit(self, X, y): data_num = X.shape[0] y = np.reshape(y, (data_num, 1)) y_pred = np.zeros(np.shape(y)) self.data = X.copy() self.getAllQuantile() for i in range(self.n_estimators): # print('In classifier fit, rank: ', self.rank) tree = self.trees[i] tree.data, tree.maxSplitNum, tree.quantile = self.data, self.maxSplitNum, self.quantile y_and_pred = np.concatenate((y, y_pred), axis=1) tree.fit(y_and_pred, i) if i == self.n_estimators - 1: # The last tree, no need for prediction update. continue else: update_pred = tree.predict(X) if self.rank == 1: # print('test') update_pred = np.reshape(update_pred, (data_num, 1)) y_pred += update_pred def predict(self, X): y_pred = None data_num = X.shape[0] # Make predictions for tree in self.trees: # Estimate gradient and update prediction update_pred = tree.predict(X) if y_pred is None: y_pred = np.zeros_like(update_pred).reshape(data_num, -1) if self.rank == 1: update_pred = np.reshape(update_pred, (data_num, 1)) y_pred += update_pred return y_pred comm = MPI.COMM_WORLD rank = comm.Get_rank() def main1(): data = pd.read_csv('./iris.csv').values zero_index = data[:, -1] == 0 one_index = data[:, -1] == 1 zero_data = data[zero_index] one_data = data[one_index] train_size_zero = int(zero_data.shape[0] * 0.8) train_size_one = int(one_data.shape[0] * 0.8) X_train, X_test = np.concatenate((zero_data[:train_size_zero, :-1], one_data[:train_size_one, :-1]), 0), \ np.concatenate((zero_data[train_size_zero:, :-1], one_data[train_size_one:, :-1]), 0) y_train, y_test = np.concatenate((zero_data[:train_size_zero, -1].reshape(-1,1), one_data[:train_size_one, -1].reshape(-1, 1)), 0), \ np.concatenate((zero_data[train_size_zero:, -1].reshape(-1, 1), one_data[train_size_one:, -1].reshape(-1, 1)), 0) X_train_A = X_train[:, 0].reshape(-1, 1) X_train_B = X_train[:, 2].reshape(-1, 1) X_train_C = X_train[:, 1].reshape(-1, 1) X_train_D = X_train[:, 3].reshape(-1, 1) X_test_A = X_test[:, 0].reshape(-1, 1) X_test_B = X_test[:, 2].reshape(-1, 1) X_test_C = X_test[:, 1].reshape(-1, 1) X_test_D = X_test[:, 3].reshape(-1, 1) splitclass = SSCalculate() model = VerticalXGBoostClassifier(rank=rank, lossfunc='LogLoss', splitclass=splitclass) if rank == 1: model.fit(X_train_A, y_train) print('end 1') elif rank == 2: model.fit(X_train_B, np.zeros_like(y_train)) print('end 2') elif rank == 3: model.fit(X_train_C, np.zeros_like(y_train)) print('end 3') elif rank == 4: model.fit(X_train_D, np.zeros_like(y_train)) print('end 4') else: model.fit(np.zeros_like(X_train_B), np.zeros_like(y_train)) print('end 0') if rank == 1: y_pred = model.predict(X_test_A) elif rank == 2: y_pred = model.predict(X_test_B) elif rank == 3: y_pred = model.predict(X_test_C) elif rank == 4: y_pred = model.predict(X_test_D) else: model.predict(np.zeros_like(X_test_A)) if rank == 1: y_ori = y_pred.copy() y_pred = 1.0 / (1.0 + np.exp(-y_pred)) y_pred[y_pred > 0.5] = 1 y_pred[y_pred <= 0.5] = 0 result = y_pred - y_test print(np.sum(result == 0) / y_pred.shape[0]) # for i in range(y_test.shape[0]): # print(y_test[i], y_pred[i], y_ori[i]) def main2(): data = pd.read_csv('./GiveMeSomeCredit/cs-training.csv') data.dropna(inplace=True) data = data[['SeriousDlqin2yrs', 'RevolvingUtilizationOfUnsecuredLines', 'age', 'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate', 'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse', 'NumberOfDependents']].values ori_data = data.copy() # Add features # for i in range(1): # data = np.concatenate((data, ori_data[:, 1:]), axis=1) data = data / data.max(axis=0) ratio = 10000 / data.shape[0] zero_index = data[:, 0] == 0 one_index = data[:, 0] == 1 zero_data = data[zero_index] one_data = data[one_index] zero_ratio = len(zero_data) / data.shape[0] one_ratio = len(one_data) / data.shape[0] num = 7500 train_size_zero = int(zero_data.shape[0] * ratio) + 1 train_size_one = int(one_data.shape[0] * ratio) X_train, X_test = np.concatenate((zero_data[:train_size_zero, 1:], one_data[:train_size_one, 1:]), 0), \ np.concatenate((zero_data[train_size_zero:train_size_zero+int(num * zero_ratio)+1, 1:], one_data[train_size_one:train_size_one+int(num * one_ratio), 1:]), 0) y_train, y_test = np.concatenate( (zero_data[:train_size_zero, 0].reshape(-1, 1), one_data[:train_size_one, 0].reshape(-1, 1)), 0), \ np.concatenate((zero_data[train_size_zero:train_size_zero+int(num * zero_ratio)+1, 0].reshape(-1, 1), one_data[train_size_one:train_size_one+int(num * one_ratio), 0].reshape(-1, 1)), 0) X_train_A = X_train[:, :2] X_train_B = X_train[:, 2:4] X_train_C = X_train[:, 4:7] X_train_D = X_train[:, 7:] X_test_A = X_test[:, :2] X_test_B = X_test[:, 2:4] X_test_C = X_test[:, 4:7] X_test_D = X_test[:, 7:] splitclass = SSCalculate() model = VerticalXGBoostClassifier(rank=rank, lossfunc='LogLoss', splitclass=splitclass, max_depth=3, n_estimators=3, _epsilon=0.1) start = datetime.now() if rank == 1: model.fit(X_train_A, y_train) end = datetime.now() print('In fitting 1: ', end - start) time = end - start for i in range(clientNum + 1): if i == 1: pass else: time += comm.recv(source=i) print(time / (clientNum + 1)) final_time = time / (clientNum + 1) print('end 1') print(final_time) elif rank == 2: model.fit(X_train_B, np.zeros_like(y_train)) end = datetime.now() comm.send(end - start, dest=1) print('In fitting 2: ', end - start) print('end 2') elif rank == 3: model.fit(X_train_C, np.zeros_like(y_train)) end = datetime.now() print('In fitting 3: ', end - start) comm.send(end - start, dest=1) print('end 3') elif rank == 4: model.fit(X_train_D, np.zeros_like(y_train)) end = datetime.now() print('In fitting 4: ', end - start) comm.send(end - start, dest=1) print('end 4') else: model.fit(np.zeros_like(X_train_B), np.zeros_like(y_train)) end = datetime.now() print('In fitting 0: ', end - start) comm.send(end - start, dest=1) print('end 0') if rank == 1: y_pred = model.predict(X_test_A) elif rank == 2: y_pred = model.predict(X_test_B) elif rank == 3: y_pred = model.predict(X_test_C) elif rank == 4: y_pred = model.predict(X_test_D) else: model.predict(np.zeros_like(X_test_A)) if rank == 1: y_pred = 1.0 / (1.0 + np.exp(-y_pred)) y_pred2 = y_pred.copy() y_pred2[y_pred2 > 0.5] = 1 y_pred2[y_pred2 <= 0.5] = 0 y_pred2 = y_pred2.reshape(-1,1) y_test = y_test.reshape(-1,1) result = y_pred2 - y_test print(np.sum(result == 0) / y_pred.shape[0]) # for i in range(y_test.shape[0]): # print(y_test[i], y_pred[i]) def main3(): data = np.load('./adult.npy') data = data / data.max(axis=0) ratio = 0.8 zero_index = data[:, 0] == 0 one_index = data[:, 0] == 1 zero_data = data[zero_index] one_data = data[one_index] train_size_zero = int(zero_data.shape[0] * ratio) + 1 train_size_one = int(one_data.shape[0] * ratio) X_train, X_test = np.concatenate((zero_data[:train_size_zero, 1:], one_data[:train_size_one, 1:]), 0), \ np.concatenate((zero_data[train_size_zero:, 1:], one_data[train_size_one:, 1:]), 0) y_train, y_test = np.concatenate( (zero_data[:train_size_zero, 0].reshape(-1, 1), one_data[:train_size_one, 0].reshape(-1, 1)), 0), \ np.concatenate((zero_data[train_size_zero:, 0].reshape(-1, 1), one_data[train_size_one:, 0].reshape(-1, 1)), 0) segment_A = int(0.2 * (data.shape[1] - 1)) segment_B = segment_A + int(0.2 * (data.shape[1] - 1)) segment_C = segment_B + int(0.3 * (data.shape[1] - 1)) X_train_A = X_train[:, 0:segment_A] X_train_B = X_train[:, segment_A:segment_B] X_train_C = X_train[:, segment_B:segment_C] X_train_D = X_train[:, segment_C:] X_test_A = X_test[:, :segment_A] X_test_B = X_test[:, segment_A:segment_B] X_test_C = X_test[:, segment_B:segment_C] X_test_D = X_test[:, segment_C:] splitclass = SSCalculate() model = VerticalXGBoostClassifier(rank=rank, lossfunc='LogLoss', splitclass=splitclass, max_depth=3, n_estimators=3, _epsilon=0.1) if rank == 1: start = datetime.now() model.fit(X_train_A, y_train) end = datetime.now() print('In fitting: ', end - start) print('end 1') elif rank == 2: model.fit(X_train_B, np.zeros_like(y_train)) print('end 2') elif rank == 3: model.fit(X_train_C, np.zeros_like(y_train)) print('end 3') elif rank == 4: model.fit(X_train_D, np.zeros_like(y_train)) else: model.fit(np.zeros_like(X_train_B), np.zeros_like(y_train)) print('end 0') if rank == 1: y_pred = model.predict(X_test_A) elif rank == 2: y_pred = model.predict(X_test_B) elif rank == 3: y_pred = model.predict(X_test_C) elif rank == 4: y_pred = model.predict(X_test_D) else: model.predict(np.zeros_like(X_test_A)) if rank == 1: y_ori = y_pred.copy() y_pred = 1.0 / (1.0 + np.exp(-y_pred)) y_pred2 = y_pred.copy() y_pred2[y_pred2 > 0.5] = 1 y_pred2[y_pred2 <= 0.5] = 0 y_pred2 = y_pred2.reshape(-1,1) y_test = y_test.reshape(-1,1) result = y_pred2 - y_test print(np.sum(result == 0) / y_pred.shape[0]) # for i in range(y_test.shape[0]): # print(y_test[i], y_pred[i]) if __name__ == '__main__': main2()

py

MP-FedXGB

MP-FedXGB-main/XGBoost.py

import numpy as np import progressbar import pandas as pd from datetime import * np.random.seed(10) class LeastSquareLoss(): def gradient(self, actual, predicted): return -(actual - predicted) def hess(self, actual, predicted): return np.ones_like(actual) class LogLoss(): def gradient(self, actual, predicted): prob = 1.0 / (1.0 + np.exp(-predicted)) return prob - actual def hess(self, actual, predicted): prob = 1.0 / (1.0 + np.exp(-predicted)) return prob * (1.0 - prob) # Mind the dimension class Tree: def __init__(self, value=None, leftBranch=None, rightBranch=None, col=-1, result=None): self.value = value self.leftBranch = leftBranch self.rightBranch = rightBranch self.col = col self.result = result class XGBoostTree: def __init__(self, lossfunc, _lambda, _gamma, _max_depth, _epsilon=0.05): self.loss = lossfunc self._lambda = _lambda self._gamma = _gamma self._epsilon = _epsilon self._max_depth = _max_depth def _split(self, y): y, y_pred = y[:, 0].reshape(-1, 1), y[:, 1].reshape(-1, 1) return y, y_pred def _leaf_gain(self, g, h): nominator = np.power(g, 2) denominator = h + self._lambda return nominator / denominator def _split_criterion(self, left_g, left_h, right_g, right_h): gain = (self._leaf_gain(left_g, left_h) + self._leaf_gain(right_g, right_h) - self._leaf_gain(left_g + right_g, left_h + right_h)) * 0.5 - self._gamma # print('In split criterion') # print(self._leaf_gain(left_g, left_h)) # print(self._leaf_gain(right_g, right_h)) # print(self._leaf_gain(left_g + right_g, left_h + right_h)) # print('Out split criterion') return gain def getQuantile(self, colidx, X, y, y_pred): split_list = [] data = np.concatenate((X, y, y_pred), axis=1) data = data.copy() idx = np.argsort(data[:, colidx], axis=0) data = data[idx] value_list = sorted(list(set(list(data[:, colidx])))) # Record all the different value hess = np.ones_like(data[:, colidx]) data = np.concatenate((data, hess.reshape(-1, 1)), axis=1) sum_hess = np.sum(hess) last = value_list[0] i = 1 if len(value_list) == 1: # For those who has only one value, do such process. last_cursor = last else: last_cursor = value_list[1] split_list.append((-np.inf, value_list[0])) while i < len(value_list): cursor = value_list[i] small_hess = np.sum(data[:, -1][data[:, colidx] <= last]) / sum_hess big_hess = np.sum(data[:, -1][data[:, colidx] <= cursor]) / sum_hess # print(colidx, self.rank, np.abs(big_hess - small_hess), last, cursor) if np.abs(big_hess - small_hess) < self._epsilon: last_cursor = cursor else: judge = value_list.index(cursor) - value_list.index(last) if judge == 1: # Although it didn't satisfy the criterion, it has no more split, so we must add it. split_list.append((last, cursor)) last = cursor else: # Move forward and record the last. split_list.append((last, last_cursor)) last = last_cursor last_cursor = cursor i += 1 if split_list[-1][1] != value_list[-1]: split_list.append((split_list[-1][1], value_list[-1])) # Add the top value into split_list. split_list = np.array(split_list) return split_list def getAllQuantile(self, X, y): # Global quantile, must be calculated before tree building, avoiding recursion. y, y_pred = self._split(y) column_length = X.shape[1] dict = {i: self.getQuantile(i, X, y, y_pred) for i in range(column_length)} # record all the split self.quantile = dict def buildTree(self, X, y, depth=1): data = np.concatenate((X, y), axis=1) y, y_pred = self._split(y) column_length = X.shape[1] gradient = self.loss.gradient(y, y_pred) # Calculate the loss at begining, avoiding later calculation. hessian = self.loss.hess(y, y_pred) # print('*' * 10, 'Gradient') # print(np.concatenate([gradient, hessian], axis=1)[:20]) G = np.sum(gradient) H = np.sum(hessian) if depth > self._max_depth: return Tree(result=- G / (H + self._lambda)) bestGain = 0 bestSplit = 0 bestSet = () for col in range(column_length): splitList = self.quantile[col] GL = 0 HL = 0 for k in range(splitList.shape[0]): left = splitList[k][0] right = splitList[k][1] idx = ((data[:, col] <= right) & (data[:, col] > left)) GL += np.sum(gradient[idx]) HL += np.sum(hessian[idx]) GR = G - GL HR = H - HL gain = self._split_criterion(GL, HL, GR, HR) if gain > bestGain: bestGain = gain bestSplit = (col, right) bestSet = (data[data[:, col] <= right], data[data[:, col] > right]) if bestGain > 0: # print('Split value: ', bestSplit[1]) # print('Into left') leftBranch = self.buildTree(bestSet[0][:, :-2], bestSet[0][:, -2:], depth + 1) # print('Out left') # print('Into right') rightBranch = self.buildTree(bestSet[1][:, :-2], bestSet[1][:, -2:], depth + 1) # print('Out right') return Tree(value=bestSplit[1], leftBranch=leftBranch, rightBranch=rightBranch, col=bestSplit[0]) else: # print(-G/(H + self._lambda)) return Tree(result=- G / (H + self._lambda)) def fit(self, X, y): self.getAllQuantile(X, y) self.Tree = self.buildTree(X, y) def classify(self, tree, data): if tree.result != None: return tree.result else: branch = None v = data[tree.col] if isinstance(v, int) or isinstance(v, float): if v > tree.value: branch = tree.rightBranch else: branch = tree.leftBranch return self.classify(branch, data) def predict(self, data): data_num = data.shape[0] result = [] for i in range(data_num): result.append(self.classify(self.Tree, data[i])) result = np.array(result).reshape((-1, 1)) return result class XGBoostClassifier: def __init__(self, lossfunc, _lambda=1, _gamma=0.5, _epsilon=0.1, n_estimators=3, learning_rate=1, min_samples_split=2, max_depth=3): if lossfunc == 'LogLoss': self.loss = LogLoss() else: self.loss = LeastSquareLoss() self._lambda = _lambda self._gamma = _gamma self._epsilon = _epsilon self.n_estimators = n_estimators # Number of trees self.learning_rate = learning_rate # Step size for weight update self.min_samples_split = min_samples_split # The minimum n of sampels to justify split self.max_depth = max_depth # Maximum depth for tree self.bar = progressbar.ProgressBar() self.trees = [] for _ in range(n_estimators): tree = XGBoostTree( lossfunc=self.loss, _lambda=self._lambda, _gamma=self._gamma, _max_depth=self.max_depth, _epsilon=self._epsilon,) self.trees.append(tree) def fit(self, X, y): data_num = X.shape[0] y = np.reshape(y, (data_num, 1)) y_pred = np.zeros(np.shape(y)) # y_pred = np.random.rand(y.shape[0]).reshape(-1, 1) for i in range(self.n_estimators): tree = self.trees[i] y_and_pred = np.concatenate((y, y_pred), axis=1) # print(y_and_pred) tree.fit(X, y_and_pred) print('-' * 100) update_pred = tree.predict(X) update_pred = np.reshape(update_pred, (data_num, 1)) y_pred += update_pred def predict(self, X): y_pred = None data_num = X.shape[0] # Make predictions for tree in self.trees: # Estimate gradient and update prediction update_pred = tree.predict(X) update_pred = np.reshape(update_pred, (data_num, 1)) if y_pred is None: y_pred = np.zeros_like(update_pred).reshape(data_num, -1) y_pred += update_pred return y_pred def main(): data = pd.read_csv('./filtered_data_median.csv').values # train_size = int(data.shape[0] * 0.9) # X_train, X_test = data[:train_size, :-2], data[train_size:, :-2] # y_train, y_test = data[:train_size, -2].reshape(-1, 1), data[train_size:, -2].reshape(-1, 1) zero_index = data[:, -2] == 0 one_index = data[:, -2] == 1 zero_data = data[zero_index] one_data = data[one_index] train_size_zero = int(zero_data.shape[0] * 0.8) train_size_one = int(one_data.shape[0] * 0.8) X_train, X_test = np.concatenate((zero_data[:train_size_zero, :-2], one_data[:train_size_one, :-2]), 0), \ np.concatenate((zero_data[train_size_zero:, :-2], one_data[train_size_one:, :-2]), 0) y_train, y_test = np.concatenate((zero_data[:train_size_zero, -2].reshape(-1,1), one_data[:train_size_one, -2].reshape(-1, 1)), 0), \ np.concatenate((zero_data[train_size_zero:, -2].reshape(-1, 1), one_data[train_size_one:, -2].reshape(-1, 1)), 0) model = XGBoostClassifier(lossfunc='LogLoss') model.fit(X_train, y_train) y_pred = model.predict(X_test) y_ori = y_pred.copy() y_pred = 1.0 / (1.0 + np.exp(-y_pred)) y_pred[y_pred > 0.5] = 1 y_pred[y_pred <= 0.5] = 0 result = y_pred - y_test print(np.sum(result == 0) / y_pred.shape[0]) # for i in range(y_test.shape[0]): # print(y_test[i], y_pred[i], y_ori[i]) def main2(): data = pd.read_csv('./iris.csv').values zero_index = data[:, -1] == 0 one_index = data[:, -1] == 1 zero_data = data[zero_index] one_data = data[one_index] train_size_zero = int(zero_data.shape[0] * 0.8) train_size_one = int(one_data.shape[0] * 0.8) X_train, X_test = np.concatenate((zero_data[:train_size_zero, :-1], one_data[:train_size_one, :-1]), 0), \ np.concatenate((zero_data[train_size_zero:, :-1], one_data[train_size_one:, :-1]), 0) y_train, y_test = np.concatenate((zero_data[:train_size_zero, -1].reshape(-1,1), one_data[:train_size_one, -1].reshape(-1, 1)), 0), \ np.concatenate((zero_data[train_size_zero:, -1].reshape(-1, 1), one_data[train_size_one:, -1].reshape(-1, 1)), 0) model = XGBoostClassifier(lossfunc='LogLoss', n_estimators=3) model.fit(X_train, y_train) y_pred = model.predict(X_test) y_ori = y_pred.copy() y_pred = 1.0 / (1.0 + np.exp(-y_pred)) y_pred[y_pred > 0.5] = 1 y_pred[y_pred <= 0.5] = 0 result = y_pred - y_test print(np.sum(result == 0) / y_pred.shape[0]) for i in range(y_test.shape[0]): print(y_test[i], y_pred[i], y_ori[i]) def main3(): from sklearn.ensemble import GradientBoostingClassifier data = pd.read_csv('./iris.csv').values zero_index = data[:, -1] == 0 one_index = data[:, -1] == 1 zero_data = data[zero_index] one_data = data[one_index] train_size_zero = int(zero_data.shape[0] * 0.8) train_size_one = int(one_data.shape[0] * 0.8) X_train, X_test = np.concatenate((zero_data[:train_size_zero, :-1], one_data[:train_size_one, :-1]), 0), \ np.concatenate((zero_data[train_size_zero:, :-1], one_data[train_size_one:, :-1]), 0) y_train, y_test = np.concatenate((zero_data[:train_size_zero, -1].reshape(-1,1), one_data[:train_size_one, -1].reshape(-1, 1)), 0), \ np.concatenate((zero_data[train_size_zero:, -1].reshape(-1, 1), one_data[train_size_one:, -1].reshape(-1, 1)), 0) model = model = XGBoostClassifier(lossfunc='LogLoss') model.fit(X_train, y_train) y_pred = model.predict(X_test) print(y_pred) y_pred[y_pred > 0.5] = 1 y_pred[y_pred <= 0.5] = 0 result = y_pred - y_test print(np.sum(result == 0) / y_pred.shape[0]) for i in range(y_test.shape[0]): print(y_test[i], y_pred[i]) def main6(): data_train = pd.read_csv('./GiveMeSomeCredit/cs-training.csv') data_train = data_train[['SeriousDlqin2yrs', 'RevolvingUtilizationOfUnsecuredLines', 'age', 'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate', 'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse', 'NumberOfDependents']].values data_train.dtype = 'float16' data_test = pd.read_csv('./GiveMeSomeCredit/cs-training.csv') data_test = data_test[['SeriousDlqin2yrs', 'RevolvingUtilizationOfUnsecuredLines', 'age', 'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate', 'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse', 'NumberOfDependents']].values data_test.dtype = 'float16' y_train = data_train[:, 0] X_train = data_train[:, 1:] X_test = data_test[:, 1:] model = XGBoostClassifier(lossfunc='LogLoss') model.fit(X_train, y_train) y_pred = model.predict(X_test) y_ori = y_pred.copy() y_pred = 1.0 / (1.0 + np.exp(-y_pred)) y_pred2 = y_pred.copy() y_pred2[y_pred2 > 0.5] = 1 y_pred2[y_pred2 <= 0.5] = 0 print(y_pred) # result = y_pred2 - y_test # print(np.sum(result == 0) / y_pred.shape[0]) if __name__ == '__main__': start = datetime.now() main3() end = datetime.now() print(end - start)

py

MP-FedXGB

MP-FedXGB-main/SSCalculation.py

import numpy as np import pandas as pd from mpi4py import MPI from datetime import * import math import time from VerticalXGBoost import * from Tree import * np.random.seed(10) clientNum = 4 comm = MPI.COMM_WORLD class SSCalculate: def SSSplit(self, data, clientNum): r = np.array([np.random.uniform(0, 4, (data.shape[0], data.shape[1])) for i in range(clientNum - 1)]) data = data.astype('float64') data -= np.sum(r, axis=0).astype('float64') data = np.expand_dims(data, axis=0) dataList = np.concatenate([r, data], axis=0) return dataList def SMUL(self, data_A, data_B, rank): if len(data_A.shape) <= 1: data_A = data_A.reshape(-1, 1) data_B = data_B.reshape(-1, 1) if rank == 0: # Send shared data a = np.random.rand(data_A.shape[0], data_A.shape[1]) b = np.random.rand(data_A.shape[0], data_A.shape[1]) c = a * b dataList_a = self.SSSplit(a, clientNum) dataList_b = self.SSSplit(b, clientNum) dataList_c = self.SSSplit(c, clientNum) for i in range(1, clientNum + 1): comm.send([dataList_a[i - 1], dataList_b[i - 1], dataList_c[i - 1]], dest=i) return a elif rank == 1: ra, rb, rc = comm.recv(source=0) ei = data_A - ra fi = data_B - rb eList = [] fList = [] for i in range(2, clientNum + 1): temp_e, temp_f = comm.recv(source=i) eList.append(temp_e) fList.append(temp_f) e = np.sum(np.array(eList), axis=0) + ei f = np.sum(np.array(fList), axis=0) + fi for i in range(2, clientNum + 1): comm.send((e, f), dest=i) zi = e * f + f * ra + e * rb + rc return zi else: ra, rb, rc = comm.recv(source=0) ei = data_A - ra fi = data_B - rb comm.send((ei, fi), dest=1) e, f = comm.recv(source=1) zi = f * ra + e * rb + rc return zi def SDIV(self, data_A, data_B, rank): # iter = 8 # factor = 1.9 if len(data_A.shape) <= 1: data_A = data_A.reshape(-1, 1) data_B = data_B.reshape(-1, 1) iter = 20 if rank == 0: # Send shared data divisor_list = [] for i in range(1, clientNum + 1): divisor_list.append(comm.recv(source=i)) divisor_list = np.array(divisor_list) divisor = np.min(divisor_list, axis=0) / 10 divisor /= clientNum # Equally share the divisor to parties. # divisor = divisor / np.ceil(clientNum / 2) for i in range(1, clientNum + 1): comm.send(divisor, dest=i) for i in range(iter): self.SMUL(data_B, divisor, rank) self.SMUL(divisor, divisor, rank) self.SMUL(data_A, data_B, rank) return divisor else: divisor = np.zeros_like(data_B) divisor.dtype = np.float64 for i in range(data_B.shape[0]): for j in range(data_B.shape[1]): k = 0 data = abs(data_B[i, j]) if data > 1: while data >= 1: data /= 10 k += 1 divisor[i, j] = 1 / pow(10, k) else: while data <= 1: data *= 10 k += 1 k -= 1 divisor[i, j] = 1 * pow(10, k) comm.send(divisor, dest=0) divisor = comm.recv(source=0) for i in range(iter): t = 2 / clientNum - self.SMUL(data_B, divisor, rank) divisor_next = self.SMUL(divisor, t, rank) divisor = divisor_next result = self.SMUL(data_A, divisor, rank) return result # Implement ARGMAX by calculating SS division in build_tree. def SARGMAX(self, data, rank): new_col_index_list = None new_row_index_list = None row_idx_dict = {} for k in range(data.shape[0]): ori_value_list = data[k, :] value_list = ori_value_list.copy() col_index_list = [i for i in range(0, len(ori_value_list))] while ori_value_list.shape[0] > 1: if rank != 0: if len(ori_value_list) % 2 == 0: # Even value_list = [ori_value_list[i] - ori_value_list[i + 1] for i in range(0, len(ori_value_list), 2)] else: value_list = [ori_value_list[i] - ori_value_list[i + 1] for i in range(0, len(ori_value_list) - 1, 2)] value_list.append(value_list[-1]) value_list = np.array(value_list) total_value_list = comm.gather(value_list, root=0) if rank == 0: total_value_list = total_value_list[1:] # Rip out the nonsense list from rank 0. shared_value_sum = np.sum(np.array(total_value_list), axis=0) sign_list = np.array(shared_value_sum >= 0) # Record the judgement. new_col_index_list = [] iter_size = len(sign_list) if len(ori_value_list) % 2 != 0: iter_size -= 1 for j in range(iter_size): if sign_list[j]: # True, or the former value is bigger than the latter. new_col_index_list.append(col_index_list[j * 2]) else: new_col_index_list.append(col_index_list[j * 2 + 1]) if len(ori_value_list) % 2 != 0: # Odd new_col_index_list.append(col_index_list[-1]) new_col_index_list = comm.bcast(new_col_index_list, root=0) ori_value_list = np.array([data[k, i] for i in new_col_index_list]) col_index_list = new_col_index_list col_idx = col_index_list[0] # Retrieve out the only col index. row_idx_dict[k] = col_idx ori_value_list = np.array([data[i, row_idx_dict[i]] for i in row_idx_dict.keys()]) value_list = ori_value_list.copy() row_index_list = [i for i in range(0, len(ori_value_list))] while ori_value_list.shape[0] > 1: if rank != 0: if len(ori_value_list) % 2 == 0: # Even value_list = [ori_value_list[i] - ori_value_list[i + 1] for i in range(0, len(ori_value_list), 2)] else: value_list = [ori_value_list[i] - ori_value_list[i + 1] for i in range(0, len(ori_value_list) - 1, 2)] value_list.append(ori_value_list[-1]) value_list = np.array(value_list) total_value_list = comm.gather(value_list, root=0) if rank == 0: total_value_list = total_value_list[1:] # Rip out the nonsense list from rank 0. shared_value_sum = np.sum(np.array(total_value_list), axis=0) sign_list = np.array(shared_value_sum >= 0) # Record the judgement. new_row_index_list = [] iter_size = len(sign_list) if len(ori_value_list) % 2 != 0: iter_size -= 1 for j in range(iter_size): if sign_list[j]: # True, or the former value is bigger than the latter. new_row_index_list.append(row_index_list[j * 2]) else: new_row_index_list.append(row_index_list[j * 2 + 1]) if len(ori_value_list) % 2 != 0: # Odd new_row_index_list.append(row_index_list[-1]) new_row_index_list = comm.bcast(new_row_index_list, root=0) ori_value_list = np.array([data[i, row_idx_dict[i]] for i in new_row_index_list]) row_index_list = new_row_index_list return row_index_list[0], row_idx_dict[row_index_list[0]] # Return feature and split position # Implement ARGMAX and rip out SS division. def SARGMAX_ver2(self, gain_left_up, gain_left_down, gain_right_up, gain_right_down, rank): new_col_index_list = None new_row_index_list = None row_idx_dict = {} row_num = gain_left_up.shape[0] nominator_sign_list = denominator_sign_list = None for k in range(row_num): col_index_list = [i for i in range(0, len(gain_right_down[0, :]))] while len(col_index_list) > 1: iter_size = len(col_index_list) if iter_size % 2 != 0: # Odd iter_size -= 1 list1 = [gain_left_up[k, col_index_list[i]] for i in range(0, iter_size, 2)] list2 = [gain_right_down[k, col_index_list[i]] for i in range(0, iter_size, 2)] list3 = [gain_right_up[k, col_index_list[i]] for i in range(0, iter_size, 2)] list4 = [gain_left_down[k, col_index_list[i]] for i in range(0, iter_size, 2)] list5 = [gain_left_up[k, col_index_list[i + 1]] for i in range(0, iter_size, 2)] list6 = [gain_right_down[k, col_index_list[i + 1]] for i in range(0, iter_size, 2)] list7 = [gain_right_up[k, col_index_list[i + 1]] for i in range(0, iter_size, 2)] list8 = [gain_left_down[k, col_index_list[i + 1]] for i in range(0, iter_size, 2)] nominator1 = self.SMUL(np.array(list1), np.array(list8), rank) - self.SMUL(np.array(list5), np.array(list4), rank) nominator2 = self.SMUL(np.array(list3), np.array(list6), rank) - self.SMUL(np.array(list7), np.array(list2), rank) denominator1 = self.SMUL(np.array(list4), np.array(list8), rank) denominator2 = self.SMUL(np.array(list2), np.array(list6), rank) total_nominator = self.SMUL(nominator1, denominator2, rank) + self.SMUL(nominator2, denominator1, rank) total_denominator = self.SMUL(denominator1, denominator2, rank) total_nominator_list = comm.gather(total_nominator, root=2) total_deominator_list = comm.gather(total_denominator, root=1) if rank == 2: total_nominator_list = total_nominator_list[1:] nominator_sign_list = np.sum(np.array(total_nominator_list), axis=0) nominator_sign_list[nominator_sign_list >= 0] = 1 nominator_sign_list[nominator_sign_list < 0] = -1 comm.send(nominator_sign_list, dest=1) elif rank == 1: total_denominator_list = total_deominator_list[1:] denominator_sign_list = np.sum(np.array(total_denominator_list), axis=0) denominator_sign_list[denominator_sign_list >= 0] = 1 denominator_sign_list[denominator_sign_list < 0] = -1 nominator_sign_list = comm.recv(source=2) sign_list = denominator_sign_list * nominator_sign_list # Record the judgement. sign_list = sign_list >= 0 + 0 new_col_index_list = [] iter_size = len(sign_list) for j in range(iter_size): if sign_list[j]: # True, or the former value is bigger than the latter. new_col_index_list.append(col_index_list[j * 2]) else: new_col_index_list.append(col_index_list[j * 2 + 1]) if len(col_index_list) % 2 != 0: # Odd new_col_index_list.append(col_index_list[-1]) new_col_index_list = comm.bcast(new_col_index_list, root=1) col_index_list = new_col_index_list col_idx = col_index_list[0] # Retrieve out the only col index. row_idx_dict[k] = col_idx row_index_list = [i for i in row_idx_dict.keys()] nominator_sign_list = denominator_sign_list = None while len(row_index_list) > 1: iter_size = len(row_index_list) if len(row_index_list) % 2 != 0: # Odd iter_size -= 1 list1 = [gain_left_up[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list2 = [gain_right_down[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list3 = [gain_right_up[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list4 = [gain_left_down[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list5 = [gain_left_up[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] list6 = [gain_right_down[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] list7 = [gain_right_up[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] list8 = [gain_left_down[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] nominator1 = self.SMUL(np.array(list1), np.array(list8), rank) - self.SMUL(np.array(list5), np.array(list4), rank) nominator2 = self.SMUL(np.array(list3), np.array(list6), rank) - self.SMUL(np.array(list7), np.array(list2), rank) denominator1 = self.SMUL(np.array(list4), np.array(list8), rank) denominator2 = self.SMUL(np.array(list2), np.array(list6), rank) total_nominator = self.SMUL(nominator1, denominator2, rank) + self.SMUL(nominator2, denominator1, rank) total_denominator = self.SMUL(denominator1, denominator2, rank) total_nominator_list = comm.gather(total_nominator, root=2) total_deominator_list = comm.gather(total_denominator, root=1) if rank == 2: total_nominator_list = total_nominator_list[1:] nominator_sign_list = np.sum(np.array(total_nominator_list), axis=0) nominator_sign_list[nominator_sign_list >= 0] = 1 nominator_sign_list[nominator_sign_list < 0] = -1 comm.send(nominator_sign_list, dest=1) elif rank == 1: total_denominator_list = total_deominator_list[1:] denominator_sign_list = np.sum(np.array(total_denominator_list), axis=0) denominator_sign_list[denominator_sign_list >= 0] = 1 denominator_sign_list[denominator_sign_list < 0] = -1 nominator_sign_list = comm.recv(source=2) sign_list = denominator_sign_list * nominator_sign_list # Record the judgement. sign_list = sign_list >= 0 + 0 new_row_index_list = [] iter_size = len(sign_list) for j in range(iter_size): if sign_list[j]: # True, or the former value is bigger than the latter. new_row_index_list.append(row_index_list[j * 2]) else: new_row_index_list.append(row_index_list[j * 2 + 1]) if len(row_index_list) % 2 != 0: # Odd new_row_index_list.append(row_index_list[-1]) new_row_index_list = comm.bcast(new_row_index_list, root=1) row_index_list = new_row_index_list # if rank == 0: # print(row_index_list[0], row_idx_dict[row_index_list[0]]) return row_index_list[0], row_idx_dict[row_index_list[0]] # Return feature and split position # Implement the Fisrt-tree trick from Kewei Cheng's paper. def SARGMAX_ver3(self, gain_left_up, gain_left_down, gain_right_up, gain_right_down, rank, tree_num, legal_featureList): new_col_index_list = None new_row_index_list = None row_idx_dict = {} row_num = gain_left_up.shape[0] permission = True for k in range(row_num): if tree_num == 0: # The first tree. if rank == 1: # The first party who holds labels. if k not in legal_featureList: permission = False else: permission = True comm.send(permission, dest=0) for i in range(2, clientNum + 1): comm.send(permission, dest=i) else: permission = comm.recv(source=1) if not permission: continue # Jump to the next feature gain_left_up_ori = gain_left_up[k, :] gain_left_down_ori = gain_left_down[k, :] gain_right_up_ori = gain_right_up[k, :] gain_right_down_ori = gain_right_down[k, :] value_list = np.zeros_like(gain_left_up_ori) col_index_list = [i for i in range(0, len(value_list))] while len(col_index_list) > 1: iter_size = len(col_index_list) if len(col_index_list) % 2 != 0: # Odd iter_size -= 1 list1 = [gain_left_up_ori[col_index_list[i]] for i in range(0, iter_size, 2)] list2 = [gain_right_down_ori[col_index_list[i]] for i in range(0, iter_size, 2)] list3 = [gain_right_up_ori[col_index_list[i]] for i in range(0, iter_size, 2)] list4 = [gain_left_down_ori[col_index_list[i]] for i in range(0, iter_size, 2)] list5 = [gain_left_up_ori[col_index_list[i + 1]] for i in range(0, iter_size, 2)] list6 = [gain_right_down_ori[col_index_list[i + 1]] for i in range(0, iter_size, 2)] list7 = [gain_right_up_ori[col_index_list[i + 1]] for i in range(0, iter_size, 2)] list8 = [gain_left_down_ori[col_index_list[i + 1]] for i in range(0, iter_size, 2)] nominator1 = self.SMUL(np.array(list1), np.array(list8), rank) - self.SMUL(np.array(list5), np.array(list4), rank) nominator2 = self.SMUL(np.array(list3), np.array(list6), rank) - self.SMUL(np.array(list7), np.array(list2), rank) denominator1 = self.SMUL(np.array(list4), np.array(list8), rank) denominator2 = self.SMUL(np.array(list2), np.array(list6), rank) total_nominator1_list = comm.gather(nominator1, root=2) total_nominator2_list = comm.gather(nominator2, root=2) total_denominator1_list = comm.gather(denominator1, root=2) total_denominator2_list = comm.gather(denominator2, root=2) if rank == 2: total_nominator1_list = total_nominator1_list[1:] # Rip out the nonsense list from rank 0. total_nominator2_list = total_nominator2_list[1:] total_denominator1_list = total_denominator1_list[1:] total_denominator2_list = total_denominator2_list[1:] shared_nominator1_sum = np.sum(np.array(total_nominator1_list), axis=0) shared_nominator2_sum = np.sum(np.array(total_nominator2_list), axis=0) shared_denominator1_sum = np.sum(np.array(total_denominator1_list), axis=0) shared_denominator2_sum = np.sum(np.array(total_denominator2_list), axis=0) shared_value_final = shared_nominator1_sum / shared_denominator1_sum + shared_nominator2_sum / shared_denominator2_sum sign_list = np.array(shared_value_final >= 0) # Record the judgement. new_col_index_list = [] iter_size = len(sign_list) for j in range(iter_size): if sign_list[j]: # True, or the former value is bigger than the latter. new_col_index_list.append(col_index_list[j * 2]) else: new_col_index_list.append(col_index_list[j * 2 + 1]) if len(col_index_list) % 2 != 0: # Odd new_col_index_list.append(col_index_list[-1]) new_col_index_list = comm.bcast(new_col_index_list, root=2) col_index_list = new_col_index_list col_idx = col_index_list[0] # Retrieve out the only col index. row_idx_dict[k] = col_idx row_index_list = [i for i in row_idx_dict.keys()] while len(row_index_list) > 1: iter_size = len(row_index_list) if len(row_index_list) % 2 != 0: # Odd iter_size -= 1 list1 = [gain_left_up[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list2 = [gain_right_down[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list3 = [gain_right_up[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list4 = [gain_left_down[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list5 = [gain_left_up[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] list6 = [gain_right_down[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] list7 = [gain_right_up[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] list8 = [gain_left_down[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] nominator1 = self.SMUL(np.array(list1), np.array(list8), rank) - self.SMUL(np.array(list5), np.array(list4), rank) nominator2 = self.SMUL(np.array(list3), np.array(list6), rank) - self.SMUL(np.array(list7), np.array(list2), rank) denominator1 = self.SMUL(np.array(list4), np.array(list8), rank) denominator2 = self.SMUL(np.array(list2), np.array(list6), rank) total_nominator1_list = comm.gather(nominator1, root=2) total_nominator2_list = comm.gather(nominator2, root=2) total_denominator1_list = comm.gather(denominator1, root=2) total_denominator2_list = comm.gather(denominator2, root=2) if rank == 2: total_nominator1_list = total_nominator1_list[1:] # Rip out the nonsense list from rank 0. total_nominator2_list = total_nominator2_list[1:] total_denominator1_list = total_denominator1_list[1:] total_denominator2_list = total_denominator2_list[1:] shared_nominator1_sum = np.sum(np.array(total_nominator1_list), axis=0) shared_nominator2_sum = np.sum(np.array(total_nominator2_list), axis=0) shared_denominator1_sum = np.sum(np.array(total_denominator1_list), axis=0) shared_denominator2_sum = np.sum(np.array(total_denominator2_list), axis=0) shared_value_final = shared_nominator1_sum / shared_denominator1_sum + shared_nominator2_sum / shared_denominator2_sum sign_list = np.array(shared_value_final >= 0) # Record the judgement. new_row_index_list = [] iter_size = len(sign_list) for j in range(iter_size): if sign_list[j]: # True, or the former value is bigger than the latter. new_row_index_list.append(row_index_list[j * 2]) else: new_row_index_list.append(row_index_list[j * 2 + 1]) if len(row_index_list) % 2 != 0: # Odd new_row_index_list.append(row_index_list[-1]) new_row_index_list = comm.bcast(new_row_index_list, root=2) row_index_list = new_row_index_list return row_index_list[0], row_idx_dict[row_index_list[0]] # Return feature and split position # Implement the First-layer mask and optimize the judgment. def SARGMAX_ver4(self, gain_left_up, gain_left_down, gain_right_up, gain_right_down, rank, depth, legal_featureList): new_col_index_list = None new_row_index_list = None row_idx_dict = {} row_num = gain_left_up.shape[0] nominator_sign_list = denominator_sign_list = None for k in range(row_num): if depth == 1: # The first layer. if rank == 1: # The first party who holds labels. if k not in legal_featureList: permission = False else: permission = True comm.send(permission, dest=0) for i in range(2, clientNum + 1): comm.send(permission, dest=i) else: permission = comm.recv(source=1) if not permission: continue # Jump to the next feature col_index_list = [i for i in range(0, len(gain_right_down[0, :]))] while len(col_index_list) > 1: iter_size = len(col_index_list) if iter_size % 2 != 0: # Odd iter_size -= 1 list1 = [gain_left_up[k, col_index_list[i]] for i in range(0, iter_size, 2)] list2 = [gain_right_down[k, col_index_list[i]] for i in range(0, iter_size, 2)] list3 = [gain_right_up[k, col_index_list[i]] for i in range(0, iter_size, 2)] list4 = [gain_left_down[k, col_index_list[i]] for i in range(0, iter_size, 2)] list5 = [gain_left_up[k, col_index_list[i + 1]] for i in range(0, iter_size, 2)] list6 = [gain_right_down[k, col_index_list[i + 1]] for i in range(0, iter_size, 2)] list7 = [gain_right_up[k, col_index_list[i + 1]] for i in range(0, iter_size, 2)] list8 = [gain_left_down[k, col_index_list[i + 1]] for i in range(0, iter_size, 2)] nominator1 = self.SMUL(np.array(list1), np.array(list8), rank) - self.SMUL(np.array(list5), np.array(list4), rank) nominator2 = self.SMUL(np.array(list3), np.array(list6), rank) - self.SMUL(np.array(list7), np.array(list2), rank) denominator1 = self.SMUL(np.array(list4), np.array(list8), rank) denominator2 = self.SMUL(np.array(list2), np.array(list6), rank) total_nominator = self.SMUL(nominator1, denominator2, rank) + self.SMUL(nominator2, denominator1, rank) total_denominator = self.SMUL(denominator1, denominator2, rank) total_nominator_list = comm.gather(total_nominator, root=2) total_deominator_list = comm.gather(total_denominator, root=1) if rank == 2: total_nominator_list = total_nominator_list[1:] nominator_sign_list = np.sum(np.array(total_nominator_list), axis=0) nominator_sign_list[nominator_sign_list >= 0] = 1 nominator_sign_list[nominator_sign_list < 0] = -1 comm.send(nominator_sign_list, dest=1) elif rank == 1: total_denominator_list = total_deominator_list[1:] denominator_sign_list = np.sum(np.array(total_denominator_list), axis=0) denominator_sign_list[denominator_sign_list >= 0] = 1 denominator_sign_list[denominator_sign_list < 0] = -1 nominator_sign_list = comm.recv(source=2) sign_list = denominator_sign_list * nominator_sign_list # Record the judgement. sign_list = sign_list >= 0 + 0 new_col_index_list = [] iter_size = len(sign_list) for j in range(iter_size): if sign_list[j]: # True, or the former value is bigger than the latter. new_col_index_list.append(col_index_list[j * 2]) else: new_col_index_list.append(col_index_list[j * 2 + 1]) if len(col_index_list) % 2 != 0: # Odd new_col_index_list.append(col_index_list[-1]) new_col_index_list = comm.bcast(new_col_index_list, root=1) col_index_list = new_col_index_list col_idx = col_index_list[0] # Retrieve out the only col index. row_idx_dict[k] = col_idx row_index_list = [i for i in row_idx_dict.keys()] nominator_sign_list = denominator_sign_list = None while len(row_index_list) > 1: iter_size = len(row_index_list) if len(row_index_list) % 2 != 0: # Odd iter_size -= 1 list1 = [gain_left_up[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list2 = [gain_right_down[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list3 = [gain_right_up[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list4 = [gain_left_down[row_index_list[i], row_idx_dict[row_index_list[i]]] for i in range(0, iter_size, 2)] list5 = [gain_left_up[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] list6 = [gain_right_down[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] list7 = [gain_right_up[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] list8 = [gain_left_down[row_index_list[i + 1], row_idx_dict[row_index_list[i + 1]]] for i in range(0, iter_size, 2)] nominator1 = self.SMUL(np.array(list1), np.array(list8), rank) - self.SMUL(np.array(list5), np.array(list4), rank) nominator2 = self.SMUL(np.array(list3), np.array(list6), rank) - self.SMUL(np.array(list7), np.array(list2), rank) denominator1 = self.SMUL(np.array(list4), np.array(list8), rank) denominator2 = self.SMUL(np.array(list2), np.array(list6), rank) total_nominator = self.SMUL(nominator1, denominator2, rank) + self.SMUL(nominator2, denominator1, rank) total_denominator = self.SMUL(denominator1, denominator2, rank) total_nominator_list = comm.gather(total_nominator, root=2) total_deominator_list = comm.gather(total_denominator, root=1) if rank == 2: total_nominator_list = total_nominator_list[1:] nominator_sign_list = np.sum(np.array(total_nominator_list), axis=0) nominator_sign_list[nominator_sign_list >= 0] = 1 nominator_sign_list[nominator_sign_list < 0] = -1 comm.send(nominator_sign_list, dest=1) elif rank == 1: total_denominator_list = total_deominator_list[1:] denominator_sign_list = np.sum(np.array(total_denominator_list), axis=0) denominator_sign_list[denominator_sign_list >= 0] = 1 denominator_sign_list[denominator_sign_list < 0] = -1 nominator_sign_list = comm.recv(source=2) sign_list = denominator_sign_list * nominator_sign_list # Record the judgement. sign_list = sign_list >= 0 + 0 new_row_index_list = [] iter_size = len(sign_list) for j in range(iter_size): if sign_list[j]: # True, or the former value is bigger than the latter. new_row_index_list.append(row_index_list[j * 2]) else: new_row_index_list.append(row_index_list[j * 2 + 1]) if len(row_index_list) % 2 != 0: # Odd new_row_index_list.append(row_index_list[-1]) new_row_index_list = comm.bcast(new_row_index_list, root=1) row_index_list = new_row_index_list # if rank == 0: # print(row_index_list[0], row_idx_dict[row_index_list[0]]) return row_index_list[0], row_idx_dict[row_index_list[0]] # Return feature and split position # The initial version of judging the best loss reduction's sign, but will recover the value with random factor. def SSIGN(self, data, rank): random_num = 0 result_list = None sign = None if rank == 1: nowTime = datetime.now().strftime("%Y%m%d%H%M%S") # Generate one time data to be the random factor. uniqueFactor = int(str(nowTime)[-3:]) random_num = np.random.rand(1) * uniqueFactor random_num = comm.bcast(random_num, root=1) result_list = comm.gather(random_num * data, root=0) if rank == 0: result_sum = np.sum(np.array(result_list[1:])) if result_sum > 0: sign = '+' elif result_sum == 0: sign = '=' else: sign = '-' sign = comm.bcast(sign, root=0) return sign def SSIGN_ver2(self, gain_left_up, gain_left_down, gain_right_up, gain_right_down, cgain_up, cgain_down, gamma, rank): sign = None nominator = self.SMUL(self.SMUL(gain_left_up, gain_right_down, rank) + self.SMUL(gain_left_down, gain_right_up, rank) - self.SMUL(gain_left_down, gain_right_down, rank) * gamma * 2, cgain_down, rank)\ - self.SMUL(self.SMUL(gain_left_down, gain_right_down, rank), cgain_up, rank) denominator = self.SMUL(self.SMUL(gain_left_down, gain_right_down, rank), cgain_down, rank) * 2 if rank * rank > 1: # Select rank exclude 0 and 1 comm.send(nominator, dest=1) elif rank == 1: nominator_list = [] nominator_list.append(nominator) for i in range(2, clientNum + 1): nominator_list.append(comm.recv(source=i)) # nominator += comm.recv(source=i) nominator = np.sum(nominator_list) if nominator > 0: sign = 1 elif nominator == 0: sign = 0 else: sign = -1 if rank != 0 and rank != 2: comm.send(denominator, dest=2) elif rank == 2: denominator_list = [] denominator_list.append(denominator) for i in range(1, clientNum + 1): if i == 2: pass else: denominator_list.append(comm.recv(source=i)) # denominator += comm.recv(source=i) denominator = np.sum(denominator_list) if denominator > 0: sign = 1 elif denominator == 0: sign = 0 else: sign = -1 if rank == 2: comm.send(sign, dest=1) elif rank == 1: sign *= comm.recv(source=2) # Judge the final sign. if sign == 1: sign = '+' sign = comm.bcast(sign, root=1) return sign def S_GD(self, a, b, rank, lamb): temp_a = 0 shared_step = 0 coef = 2 m = coef * lamb iter = 0 if rank != 0: temp_a = a.copy() + np.random.uniform(0.1*m, m) * 0.5 if rank != 1: comm.send(temp_a, dest=1) # if rank == 1: # for i in range(2, clientNum + 1): # temp_a += comm.recv(source=i) # shared_step = np.array(1 / (2 * temp_a)).reshape(-1, 1) # if temp_a >= 2 * clientNum * m: # max_step = math.log(1e-14, math.e) # worst_case = math.log(0.5, math.e) # iter = max_step / worst_case # z = temp_a / (clientNum * m) # iter *= worst_case / math.log(1 / z, math.e) # iter = int(np.ceil(iter)) # else: # max_step = math.log(1e-14, math.e) # z = temp_a / (clientNum * m) # worst_case = math.log(1 - (z * coef - 1) / (z * coef)) # worst_case_iter = int(np.ceil(max_step / worst_case)) # if z <= 1: # iter = worst_case_iter # else: # step1 = worst_case_iter # step2 = int(np.ceil(max_step / math.log(1 / z, math.e))) # iter = min(step1, step2) if rank == 1: temp_a_list = [] temp_a_list.append(temp_a) for i in range(2, clientNum + 1): temp_a_list.append(comm.recv(source=i)) temp_a = np.sum(temp_a_list) temp_a *= 2 # The a is transmitted as a/2 from each client, we must restore it first. shared_step = np.array(1 / temp_a).reshape(-1, 1) if temp_a <= clientNum * m: max_step = math.log(1e-14, math.e) z = temp_a / (clientNum * m) iter = max_step / math.log((z * coef - 1) / (z * coef), math.e) iter = int(np.ceil(iter)) else: max_step = math.log(1e-14, math.e) z = temp_a / (clientNum * m) iter1 = max_step / math.log((z * coef - 1) / (z * coef), math.e) iter2 = max_step / math.log(1 / z, math.e) iter1 = int(np.ceil(iter1)) iter2 = int(np.ceil(iter2)) iter = min(iter1, iter2) # print('*' * 20, iter) eta = comm.bcast(shared_step, root=1) iter = comm.bcast(iter, root=1) w = np.array([[0]]) for j in range(iter): wi = w - eta * (2 * self.SMUL(a, w, rank) + b) w = wi return w

py

MP-FedXGB

MP-FedXGB-main/data_adult_process.py

import pandas as pd import numpy as np training_data = './adult.data' columns = ['Age','Workclass','fnlwgt','Education','EdNum','MaritalStatus', 'Occupation','Relationship','Race','Sex','CapitalGain', 'CapitalLoss','HoursPerWeek','Country','Income'] income = pd.read_csv(training_data, names=columns) income.dropna(inplace=True) income['Income'].replace(' <=50K', 0,inplace=True) income['Income'].replace(' >50K', 1,inplace=True) y = income['Income'] temp = income.iloc[:, :-1] # 将文本转换为数值型用于拟合模型 income_=pd.get_dummies(temp,columns=['Relationship','Sex','MaritalStatus','Workclass', 'Education','Country','Occupation','Race']) income = np.concatenate([y.values.reshape(-1, 1), income_.values,], axis=1) print(income.shape) np.save('adult.npy', income)

py

MP-FedXGB

MP-FedXGB-main/Tree.py

import numpy as np import pandas as pd from mpi4py import MPI from datetime import * import math import time from SSCalculation import * from VerticalXGBoost import * np.random.seed(10) clientNum = 4 comm = MPI.COMM_WORLD class Tree: def __init__(self, value=None, leftBranch=None, rightBranch=None, col=-1, result=None, isDummy=False): self.value = value self.leftBranch = leftBranch self.rightBranch = rightBranch self.col = col self.result = result self.isDummy = isDummy class VerticalXGBoostTree: def __init__(self, rank, lossfunc, splitclass, _lambda, _gamma, _epsilon, _maxdepth, clientNum): self.featureList = [] self.featureIdxMapping = {} self._maxdepth = _maxdepth self.rank = rank self.loss = lossfunc self.split = splitclass self._lambda = _lambda / clientNum self._gamma = _gamma self._epsilon = _epsilon def setMapping(self): rand = np.random.permutation(self.data.shape[1]) # Get column number if self.rank == 0: return if len(self.featureList) == 0: self.featureIdxMapping = {self.featureList[0]:rand[0]} else: self.featureIdxMapping = {self.featureList[i]:rand[i] for i in range(len(self.featureList))} def _split(self, y_and_pred): y, y_pred = y_and_pred[:, 0].reshape(-1, 1), y_and_pred[:, 1].reshape(-1, 1) return y, y_pred def AggBucket(self, shared_G, shared_H): bg_Matrix = np.zeros((self.featureNum, self.maxSplitNum)) bh_Matrix = np.zeros((self.featureNum, self.maxSplitNum)) for j in range(self.featureNum): indexMatrix = np.zeros((self.maxSplitNum, self.data.shape[0])) indexMatrixArray = None currentRank = None if self.rank != 0: if j in self.featureList: # print('Rank ' + str(self.rank) + ' I have: ', j) mapped_idx = self.featureIdxMapping[j] splitNum = len(self.quantile[mapped_idx]) splitList = self.quantile[mapped_idx] for k in range(splitNum): left = splitList[k][0] right = splitList[k][1] indexMatrix[k, :] = ((self.data[:, self.featureIdxMapping[j]] <= right) & ( self.data[:, self.featureIdxMapping[j]] > left)) + 0 # Type conversion indexMatrixArray = self.split.SSSplit(indexMatrix, clientNum) temp = np.zeros_like(indexMatrixArray[0]) temp = np.expand_dims(temp, axis=0) indexMatrixArray = np.concatenate([temp, indexMatrixArray], axis=0) comm.send(self.rank, dest=1) if self.rank == 1: currentRank = comm.recv() currentRank = comm.bcast(currentRank, root=1) indexMatrix = comm.scatter(indexMatrixArray, root=currentRank) bg_Matrix[j, :] = np.sum(self.split.SMUL(indexMatrix, np.tile(shared_G.copy(), (1, self.maxSplitNum)).T, self.rank), axis=1).T bh_Matrix[j, :] = np.sum(self.split.SMUL(indexMatrix, np.tile(shared_H.copy(), (1, self.maxSplitNum)).T, self.rank), axis=1).T return bg_Matrix, bh_Matrix # Implemented SARGMAX, but didn't rip out division. def buildTree(self, shared_G, shared_H, shared_S, depth=1): shared_gsum = np.sum(shared_G).reshape(-1, 1) shared_hsum = np.sum(shared_H).reshape(-1, 1) if depth > self._maxdepth: a = -shared_gsum a = a.reshape(-1, 1) b = shared_hsum b = b.reshape(-1, 1) + self._lambda value = self.split.SDIV(a, b, self.rank) return Tree(result=value) currentRank = None cgain = self.split.SDIV(self.split.SMUL(shared_gsum, shared_gsum, self.rank), shared_hsum + self._lambda, self.rank) BG, BH = self.AggBucket(shared_G, shared_H) shared_gain = np.zeros((self.featureNum, self.maxSplitNum)) shared_sl = np.ones((self.data.shape[0], 1)) shared_sr = np.ones((self.data.shape[0], 1)) shared_gsum_L = np.array([0.0]).reshape(-1, 1) shared_hsum_L = np.array([0.0]).reshape(-1, 1) start = None if self.rank == 1: start = datetime.now() for j in range(self.featureNum): if self.rank != 0: if j in self.featureList: gsum_L, hsum_L = 0, 0 gsum_L_array = self.split.SSSplit(np.array([gsum_L]).reshape(-1, 1), clientNum) hsum_L_array = self.split.SSSplit(np.array([hsum_L]).reshape(-1, 1), clientNum) temp = np.zeros_like(gsum_L_array[0]) temp = np.expand_dims(temp, axis=0) shared_gsum_L = np.concatenate([temp.copy(), gsum_L_array], axis=0) # Add zero matrix to rank 0. shared_hsum_L = np.concatenate([temp.copy(), hsum_L_array], axis=0) # Add zero matrix to rank 0. comm.send(self.rank, dest=1) if self.rank == 1: currentRank = comm.recv() currentRank = comm.bcast(currentRank, root=1) shared_gsum_L = comm.scatter(shared_gsum_L, root=currentRank) shared_hsum_L = comm.scatter(shared_hsum_L, root=currentRank) for k in range(self.maxSplitNum): shared_gsum_L += BG[j, k] shared_hsum_L += BH[j, k] shared_gsum_R = shared_gsum - shared_gsum_L shared_hsum_R = shared_hsum - shared_hsum_L gain_left = self.split.SDIV(self.split.SMUL(shared_gsum_L, shared_gsum_L, self.rank), shared_hsum_L + self._lambda, self.rank) gain_right = self.split.SDIV(self.split.SMUL(shared_gsum_R, shared_gsum_R, self.rank), shared_hsum_R + self._lambda, self.rank) shared_gain[j, k] = gain_left + gain_right - cgain # Combine all the gain from clients, and find the max gain at client 1 (the party who holds label). shared_gain /= 2 shared_gain -= self._gamma / clientNum j_best, k_best = self.split.SARGMAX(shared_gain, self.rank) if self.rank == 1: print(datetime.now() - start) gain_sign = self.split.SSIGN(shared_gain[j_best, k_best], self.rank) if gain_sign == '+': if self.rank != 0: # Avoid entering calculator node. if j_best in self.featureList: sl = np.ones((self.data.shape[0], 1)) idx = self.data[:, self.featureIdxMapping[j_best]] > self.quantile[self.featureIdxMapping[j_best]][k_best][1] sl[idx] = 0 sr = 1 - sl sl_array = self.split.SSSplit(sl, clientNum) sr_array = self.split.SSSplit(sr, clientNum) temp = np.zeros_like(sl_array[0]) temp = np.expand_dims(temp, axis=0) shared_sl = np.concatenate([temp, sl_array], axis=0) # Add zero matrix to rank 0. shared_sr = np.concatenate([temp, sr_array], axis=0) # Add zero matrix to rank 0. comm.send(self.rank, dest=1) if self.rank == 1: currentRank = comm.recv() currentRank = comm.bcast(currentRank, root=1) shared_sl = comm.scatter(shared_sl, root=currentRank) shared_sr = comm.scatter(shared_sr, root=currentRank) shared_sl = self.split.SMUL(shared_S, shared_sl, self.rank) shared_sr = self.split.SMUL(shared_S, shared_sr, self.rank) shared_gl = self.split.SMUL(shared_sl, shared_G, self.rank) shared_gr = self.split.SMUL(shared_sr, shared_G, self.rank) shared_hl = self.split.SMUL(shared_sl, shared_H, self.rank) shared_hr = self.split.SMUL(shared_sr, shared_H, self.rank) # print('In build tree, into left', self.rank) leftBranch = self.buildTree(shared_gl, shared_hl, shared_sl, depth + 1) # print('In build tree, out of left', self.rank) rightBranch = self.buildTree(shared_gr, shared_hr, shared_sr, depth + 1) # print('In build tree, out of right', self.rank) if self.rank != 0: if j_best in self.featureList: return Tree(value=self.quantile[self.featureIdxMapping[j_best]][k_best][1], leftBranch=leftBranch, rightBranch=rightBranch, col=j_best, isDummy=False) else: return Tree(leftBranch=leftBranch, rightBranch=rightBranch, isDummy=True) # Return a dummy node else: return else: a = -shared_gsum a = a.reshape(-1, 1) b = shared_hsum b = b.reshape(-1, 1) + self._lambda value = self.split.SDIV(a, b, self.rank) return Tree(result=value) # Implemented both SARGMAX and rip out division in LSplit, but calculates leaf weight with SS division. def buildTree_ver2(self, shared_G, shared_H, shared_S, depth=1): shared_gsum = np.sum(shared_G).reshape(-1, 1) shared_hsum = np.sum(shared_H).reshape(-1, 1) if depth > self._maxdepth: a = -shared_gsum a = a.reshape(-1, 1) b = shared_hsum b = b.reshape(-1, 1) + self._lambda value = self.split.SDIV(a, b, self.rank) return Tree(result=value) currentRank = None cgain_up = self.split.SMUL(shared_gsum, shared_gsum, self.rank) cgain_down = shared_hsum + self._lambda gain_left_up, gain_left_down, gain_right_up, gain_right_down = np.zeros((self.featureNum, self.maxSplitNum)), np.zeros((self.featureNum, self.maxSplitNum)), np.zeros((self.featureNum, self.maxSplitNum)), np.zeros((self.featureNum, self.maxSplitNum)) BG, BH = self.AggBucket(shared_G, shared_H) shared_sl = np.ones((self.data.shape[0], 1)) shared_sr = np.ones((self.data.shape[0], 1)) for j in range(self.featureNum): shared_gsum_L = np.array([0.0]).reshape(-1, 1) shared_hsum_L = np.array([0.0]).reshape(-1, 1) for k in range(self.maxSplitNum): shared_gsum_L += BG[j, k] shared_hsum_L += BH[j, k] shared_gsum_R = shared_gsum - shared_gsum_L shared_hsum_R = shared_hsum - shared_hsum_L gain_left_up[j, k] = self.split.SMUL(shared_gsum_L, shared_gsum_L, self.rank) gain_left_down[j, k] = shared_hsum_L + self._lambda gain_right_up[j, k] = self.split.SMUL(shared_gsum_R, shared_gsum_R, self.rank) gain_right_down[j, k] = shared_hsum_R + self._lambda # Combine all the gain from clients, and find the max gain at client 1 (the party who holds label). j_best, k_best = self.split.SARGMAX_ver2(gain_left_up, gain_left_down, gain_right_up, gain_right_down, self.rank) gain_sign = self.split.SSIGN_ver2(gain_left_up[j_best, k_best], gain_left_down[j_best, k_best], gain_right_up[j_best, k_best], gain_right_down[j_best, k_best], cgain_up, cgain_down, self._gamma, self.rank) if gain_sign == '+': if self.rank != 0: # Avoid entering calculator node. if j_best in self.featureList: sl = np.ones((self.data.shape[0], 1)) idx = self.data[:, self.featureIdxMapping[j_best]] > \ self.quantile[self.featureIdxMapping[j_best]][k_best][1] sl[idx] = 0 sr = 1 - sl sl_array = self.split.SSSplit(sl, clientNum) sr_array = self.split.SSSplit(sr, clientNum) temp = np.zeros_like(sl_array[0]) temp = np.expand_dims(temp, axis=0) shared_sl = np.concatenate([temp, sl_array], axis=0) # Add zero matrix to rank 0. shared_sr = np.concatenate([temp, sr_array], axis=0) # Add zero matrix to rank 0. comm.send(self.rank, dest=1) if self.rank == 1: currentRank = comm.recv() currentRank = comm.bcast(currentRank, root=1) shared_sl = comm.scatter(shared_sl, root=currentRank) shared_sr = comm.scatter(shared_sr, root=currentRank) shared_sl = self.split.SMUL(shared_S, shared_sl, self.rank) shared_sr = self.split.SMUL(shared_S, shared_sr, self.rank) shared_gl = self.split.SMUL(shared_sl, shared_G, self.rank) shared_gr = self.split.SMUL(shared_sr, shared_G, self.rank) shared_hl = self.split.SMUL(shared_sl, shared_H, self.rank) shared_hr = self.split.SMUL(shared_sr, shared_H, self.rank) leftBranch = self.buildTree_ver2(shared_gl, shared_hl, shared_sl, depth + 1) rightBranch = self.buildTree_ver2(shared_gr, shared_hr, shared_sr, depth + 1) if self.rank != 0: if j_best in self.featureList: # print(depth, self.rank) return Tree(value=self.quantile[self.featureIdxMapping[j_best]][k_best][1], leftBranch=leftBranch, rightBranch=rightBranch, col=j_best, isDummy=False) else: # print(depth, 'None', self.rank) return Tree(leftBranch=leftBranch, rightBranch=rightBranch, isDummy=True) # Return a dummy node else: return else: a = -shared_gsum a = a.reshape(-1, 1) b = shared_hsum b = b.reshape(-1, 1) + self._lambda value = self.split.SDIV(a, b, self.rank) return Tree(result=value) # Implement the Fisrt-tree trick from Kewei Cheng's paper, but calculates leaf weight with SS division. def buildTree_ver3(self, shared_G, shared_H, shared_S, depth=1, tree_num=0): shared_gsum = np.sum(shared_G).reshape(-1, 1) shared_hsum = np.sum(shared_H).reshape(-1, 1) if depth > self._maxdepth: a = -shared_gsum a = a.reshape(-1, 1) b = shared_hsum b = b.reshape(-1, 1) + self._lambda value = self.split.SDIV(a, b, self.rank) return Tree(result=value) currentRank = None cgain_up = self.split.SMUL(shared_gsum, shared_gsum, self.rank) cgain_down = shared_hsum + self._lambda gain_left_up, gain_left_down, gain_right_up, gain_right_down = np.zeros( (self.featureNum, self.maxSplitNum)), np.zeros((self.featureNum, self.maxSplitNum)), np.zeros( (self.featureNum, self.maxSplitNum)), np.zeros((self.featureNum, self.maxSplitNum)) BG, BH = self.AggBucket(shared_G, shared_H) shared_sl = np.ones((self.data.shape[0], 1)) shared_sr = np.ones((self.data.shape[0], 1)) for j in range(self.featureNum): if tree_num == 0: # The first tree. if self.rank == 1: # The first party who holds labels. if j not in self.featureList: permission = False else: permission = True comm.send(permission, dest=0) for i in range(2, clientNum + 1): comm.send(permission, dest=i) else: permission = comm.recv(source=1) if not permission: continue # Jump to the next feature shared_gsum_L = np.array([0.0]).reshape(-1, 1) shared_hsum_L = np.array([0.0]).reshape(-1, 1) for k in range(self.maxSplitNum): shared_gsum_L += BG[j, k] shared_hsum_L += BH[j, k] shared_gsum_R = shared_gsum - shared_gsum_L shared_hsum_R = shared_hsum - shared_hsum_L gain_left_up[j, k] = self.split.SMUL(shared_gsum_L, shared_gsum_L, self.rank) gain_left_down[j, k] = shared_hsum_L + self._lambda gain_right_up[j, k] = self.split.SMUL(shared_gsum_R, shared_gsum_R, self.rank) gain_right_down[j, k] = shared_hsum_R + self._lambda # Combine all the gain from clients, and find the max gain at client 1 (the party who holds label). j_best, k_best = self.split.SARGMAX_ver3(gain_left_up, gain_left_down, gain_right_up, gain_right_down, self.rank, tree_num, self.featureList) gain_sign = self.split.SSIGN_ver2(gain_left_up[j_best, k_best], gain_left_down[j_best, k_best], gain_right_up[j_best, k_best], gain_right_down[j_best, k_best], cgain_up, cgain_down, self._gamma, self.rank) if gain_sign == '+': if self.rank != 0: # Avoid entering calculator node. if j_best in self.featureList: sl = np.ones((self.data.shape[0], 1)) idx = self.data[:, self.featureIdxMapping[j_best]] > \ self.quantile[self.featureIdxMapping[j_best]][k_best][1] sl[idx] = 0 sr = 1 - sl sl_array = self.split.SSSplit(sl, clientNum) sr_array = self.split.SSSplit(sr, clientNum) temp = np.zeros_like(sl_array[0]) temp = np.expand_dims(temp, axis=0) shared_sl = np.concatenate([temp, sl_array], axis=0) # Add zero matrix to rank 0. shared_sr = np.concatenate([temp, sr_array], axis=0) # Add zero matrix to rank 0. comm.send(self.rank, dest=0) if self.rank == 0: currentRank = comm.recv() currentRank = comm.bcast(currentRank, root=0) shared_sl = comm.scatter(shared_sl, root=currentRank) shared_sr = comm.scatter(shared_sr, root=currentRank) shared_sl = self.split.SMUL(shared_S, shared_sl, self.rank) shared_sr = self.split.SMUL(shared_S, shared_sr, self.rank) shared_gl = self.split.SMUL(shared_sl, shared_G, self.rank) shared_gr = self.split.SMUL(shared_sr, shared_G, self.rank) shared_hl = self.split.SMUL(shared_sl, shared_H, self.rank) shared_hr = self.split.SMUL(shared_sr, shared_H, self.rank) leftBranch = self.buildTree_ver3(shared_gl, shared_hl, shared_sl, depth + 1, tree_num) rightBranch = self.buildTree_ver3(shared_gr, shared_hr, shared_sr, depth + 1, tree_num) if self.rank != 0: if j_best in self.featureList: return Tree(value=self.quantile[self.featureIdxMapping[j_best]][k_best][1], leftBranch=leftBranch, rightBranch=rightBranch, col=j_best, isDummy=False) else: return Tree(leftBranch=leftBranch, rightBranch=rightBranch, isDummy=True) # Return a dummy node else: return else: a = -shared_gsum a = a.reshape(-1, 1) b = shared_hsum b = b.reshape(-1, 1) + self._lambda value = self.split.SDIV(a, b, self.rank) return Tree(result=value) # Implement the first-layer mask and gradient descent. def buildTree_ver4(self, shared_G, shared_H, shared_S, depth=1): shared_gsum = np.sum(shared_G).reshape(-1, 1) shared_hsum = np.sum(shared_H).reshape(-1, 1) iter = 10 if depth > self._maxdepth: a = shared_hsum a = a.reshape(-1, 1) + self._lambda a *= 0.5 b = shared_gsum b = b.reshape(-1, 1) value = self.split.S_GD(a, b, self.rank, lamb=self._lambda) return Tree(result=value) currentRank = None cgain_up = self.split.SMUL(shared_gsum, shared_gsum, self.rank) cgain_down = shared_hsum + self._lambda gain_left_up, gain_left_down, gain_right_up, gain_right_down = np.zeros( (self.featureNum, self.maxSplitNum)), np.zeros((self.featureNum, self.maxSplitNum)), np.zeros( (self.featureNum, self.maxSplitNum)), np.zeros((self.featureNum, self.maxSplitNum)) BG, BH = self.AggBucket(shared_G, shared_H) shared_sl = np.ones((self.data.shape[0], 1)) shared_sr = np.ones((self.data.shape[0], 1)) start = None if self.rank == 1: start = datetime.now() for j in range(self.featureNum): shared_gsum_L = np.array([np.sum(BG[j, :k+1]) for k in range(self.maxSplitNum)]) shared_hsum_L = np.array([np.sum(BH[j, :k+1]) for k in range(self.maxSplitNum)]) shared_gsum_R = (shared_gsum - shared_gsum_L).reshape(-1,) shared_hsum_R = (shared_hsum - shared_hsum_L).reshape(-1,) gain_left_up[j, :] = self.split.SMUL(shared_gsum_L, shared_gsum_L, self.rank).T gain_left_down[j, :] = shared_hsum_L + self._lambda gain_right_up[j, :] = self.split.SMUL(shared_gsum_R, shared_gsum_R, self.rank).T gain_right_down[j, :] = shared_hsum_R + self._lambda # Original j_best, k_best = self.split.SARGMAX_ver2(gain_left_up, gain_left_down, gain_right_up, gain_right_down, self.rank) if self.rank == 1: print(datetime.now() - start) gain_sign = self.split.SSIGN_ver2(gain_left_up[j_best, k_best], gain_left_down[j_best, k_best], gain_right_up[j_best, k_best], gain_right_down[j_best, k_best], cgain_up, cgain_down, self._gamma, self.rank) if gain_sign == '+' or depth == 1: # For layer 1, splitte by the first party, we should pass it. if self.rank != 0: # Avoid entering calculator node. if j_best in self.featureList: sl = np.ones((self.data.shape[0], 1)) idx = self.data[:, self.featureIdxMapping[j_best]] > \ self.quantile[self.featureIdxMapping[j_best]][k_best][1] sl[idx] = 0 sr = 1 - sl sl_array = self.split.SSSplit(sl, clientNum) sr_array = self.split.SSSplit(sr, clientNum) temp = np.zeros_like(sl_array[0]) temp = np.expand_dims(temp, axis=0) shared_sl = np.concatenate([temp, sl_array], axis=0) # Add zero matrix to rank 0. shared_sr = np.concatenate([temp, sr_array], axis=0) # Add zero matrix to rank 0. comm.send(self.rank, dest=1) if self.rank == 1: currentRank = comm.recv() currentRank = comm.bcast(currentRank, root=1) shared_sl = comm.scatter(shared_sl, root=currentRank) shared_sr = comm.scatter(shared_sr, root=currentRank) shared_sl = self.split.SMUL(shared_S, shared_sl, self.rank) shared_sr = self.split.SMUL(shared_S, shared_sr, self.rank) shared_gl = self.split.SMUL(shared_sl, shared_G, self.rank) shared_gr = self.split.SMUL(shared_sr, shared_G, self.rank) shared_hl = self.split.SMUL(shared_sl, shared_H, self.rank) shared_hr = self.split.SMUL(shared_sr, shared_H, self.rank) leftBranch = self.buildTree_ver4(shared_gl, shared_hl, shared_sl, depth + 1) rightBranch = self.buildTree_ver4(shared_gr, shared_hr, shared_sr, depth + 1) if self.rank != 0: if j_best in self.featureList: return Tree(value=self.quantile[self.featureIdxMapping[j_best]][k_best][1], leftBranch=leftBranch, rightBranch=rightBranch, col=j_best, isDummy=False) else: return Tree(leftBranch=leftBranch, rightBranch=rightBranch, isDummy=True) # Return a dummy node else: return else: a = shared_hsum a = a.reshape(-1, 1) + self._lambda a *= 0.5 b = shared_gsum b = b.reshape(-1, 1) value = self.split.S_GD(a, b, self.rank, lamb=self._lambda) return Tree(result=value) # This function contains no communication operation. def getInfo(self, tree, data, belongs=1): if self.rank == 0: return if tree.result != None: return np.array([belongs]).reshape(-1, 1), np.array([tree.result]).reshape(-1, 1) else: left_belongs = 0 right_belongs = 0 if tree.isDummy: if belongs == 1: left_belongs = 1 right_belongs = 1 left_idx, left_result = self.getInfo(tree.leftBranch, data, left_belongs) right_idx, right_result = self.getInfo(tree.rightBranch, data, right_belongs) idx = np.concatenate((left_idx, right_idx), axis=0) result = np.concatenate((left_result, right_result), axis=0) return idx, result v = data[0, self.featureIdxMapping[tree.col]] if belongs == 1: # In selected branch if v > tree.value: right_belongs = 1 else: left_belongs = 1 left_idx, left_result = self.getInfo(tree.leftBranch, data, left_belongs) right_idx, right_result = self.getInfo(tree.rightBranch, data, right_belongs) idx = np.concatenate((left_idx, right_idx), axis=0) result = np.concatenate((left_result, right_result), axis=0) return idx, result def fit(self, y_and_pred, tree_num): size = None size_list = comm.gather(self.data.shape[1], root=2) # Gather all the feature size. if self.rank == 2: size = sum(size_list[1:]) self.featureNum = comm.bcast(size, root=2) # Broadcast how many feature there are in total. if self.rank == 2: random_list = np.random.permutation(self.featureNum) start = 0 for i in range(1, clientNum + 1): rand = random_list[start:start + size_list[i]] if i == 2: self.featureList = rand else: comm.send(rand, dest=i) # Send random_list to all the client, mask their feature index. start += size_list[i] elif self.rank != 0: self.featureList = comm.recv(source=2) self.setMapping() shared_G, shared_H, shared_S = None, None, None if self.rank == 1: # Calculate gradients on the node who have labels. y, y_pred = self._split(y_and_pred) G = self.loss.gradient(y, y_pred) H = self.loss.hess(y, y_pred) S = np.ones_like(y) shared_G = self.split.SSSplit(G, clientNum) # Split G/H/indicator. shared_H = self.split.SSSplit(H, clientNum) shared_S = self.split.SSSplit(S, clientNum) temp = np.zeros_like(shared_G[0]) temp = np.expand_dims(temp, axis=0) shared_G = np.concatenate([temp.copy(), shared_G], axis=0) shared_H = np.concatenate([temp.copy(), shared_H], axis=0) shared_S = np.concatenate([temp.copy(), shared_S], axis=0) shared_G = comm.scatter(shared_G, root=1) shared_H = comm.scatter(shared_H, root=1) shared_S = comm.scatter(shared_S, root=1) self.Tree = self.buildTree_ver4(shared_G, shared_H, shared_S) # self.Tree = self.buildTree_ver3(shared_G, shared_H, shared_S, depth=1, tree_num=tree_num) # self.Tree = self.buildTree_ver2(shared_G, shared_H, shared_S) # self.Tree = self.buildTree(shared_G, shared_H, shared_S) def classify(self, tree, data): idx_list = [] shared_idx = None final_result = 0 if self.rank != 0: idx, result = self.getInfo(tree, data) for i in range(1, clientNum + 1): if self.rank == i: shared_idx = self.split.SSSplit(idx, clientNum) temp = np.zeros_like(shared_idx[0]) temp = np.expand_dims(temp, axis=0) shared_idx = np.concatenate([temp, shared_idx], axis=0) shared_idx = comm.scatter(shared_idx, root=i) idx_list.append(shared_idx) final_idx = idx_list[0] for i in range(1, clientNum): final_idx = self.split.SMUL(final_idx, idx_list[i], self.rank) if self.rank == 0: result = np.zeros_like(final_idx) temp_result = np.sum(self.split.SMUL(final_idx, result, self.rank)) temp_result = comm.gather(temp_result, root=1) if self.rank == 1: final_result = np.sum(temp_result[1:]) return final_result def predict(self, data): # Encapsulated for many data data_num = data.shape[0] result = [] for i in range(data_num): temp_result = self.classify(self.Tree, data[i].reshape(1, -1)) if self.rank == 1: result.append(temp_result) else: pass result = np.array(result).reshape((-1, 1)) return result

py

fitclip

fitclip-main/gunicorn.conf.py

bind = "0.0.0.0:5000" reload = True timeout = 3600 wsgi_app = "demo.app"

py

fitclip

fitclip-main/util/structured_group_utils.py

"""Useful utils when using `DataModuleStructuredGroup`.""" from typing import Any, Mapping, Sequence, Tuple import torch from aligner.video_text_module import TYPE_INPUT from util.tensor_utils import pad TYPE_MULTI_INPUT = Mapping[str, TYPE_INPUT] # It's like `default_collate` but instead of a sequence we have a mapping, and we do `cat` instead of `stack`. # It makes sense to be similar because we're merging multiple batches together. # Note that using collate from the dataloader side. It's simpler, and more GPU-memory efficient. def _cat_collate(batch: Sequence[Any]) -> Any: elem = batch[0] elem_type = type(batch) if isinstance(elem, torch.Tensor): return torch.cat(batch) # noqa elif isinstance(elem, Mapping): return {k: _cat_collate([d[k] for d in batch]) for k in elem} elif isinstance(elem, (float, int, bytes, str)): return batch elif isinstance(elem, tuple) and hasattr(elem, '_fields'): # namedtuple return elem_type(*(_cat_collate(samples) for samples in zip(*batch))) # noqa elif isinstance(elem, Sequence): return [x for d in batch for x in d] else: raise TypeError(f"Not sure how to collate type {elem_type}") def _merge_datasets_batch(batches_by_dataset: TYPE_MULTI_INPUT) -> Tuple[TYPE_INPUT, Sequence[int]]: lengths = [len(batch["video"]) for batch in batches_by_dataset.values()] max_text_len = max(batch["text"]["input_ids"].shape[-1] for batch in batches_by_dataset.values()) for batch in batches_by_dataset.values(): batch["text"] = {k: pad(v, min_size=max_text_len, dim=-1) for k, v in batch["text"].items()} batch = _cat_collate(list(batches_by_dataset.values())) return batch, lengths

py

fitclip

fitclip-main/util/viz_utils.py

import numpy as np import torch import torchvision from matplotlib import pyplot as plt from matplotlib.pyplot import subplots_adjust from torchvision.transforms.functional import to_pil_image from aligner.encoder.video_text_encoder import VideoTextEncoder def visualize_images_tensor(images: torch.Tensor) -> plt.Axes: """`images` has shape (N, C, H, W).""" grid = torchvision.utils.make_grid(images) fig, ax = plt.subplots() fig.tight_layout() subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) ax.autoscale_view("tight") ax.imshow(np.asarray(to_pil_image(grid))) ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[]) return ax def debug_batch(video: torch.Tensor, text: torch.Tensor, encoder: VideoTextEncoder) -> None: video, text = video.detach().cpu(), text.detach().cpu() video = encoder.to_bchw(video) denormalized_images = encoder.denormalize_video_tensor(video).reshape(-1, *video.shape[2:]) visualize_images_tensor(denormalized_images) plt.show() for decoded in encoder.decode_text(text): print(decoded)

py

fitclip

fitclip-main/util/tensor_utils.py

from typing import Any, Mapping, Optional, Sequence, TypeVar, Union import pytorch_lightning as pl import torch import torch.nn.functional as F from pytorch_lightning.utilities.apply_func import apply_to_collection T = TypeVar("T") def pad(t: torch.Tensor, min_size: int, dim: int = 1, value: Any = 0) -> torch.Tensor: """Pads the dim `dim` in `t` with the value `value` so the size is at least `min_size`.""" if dim < 0: dim += len(t.shape) if (count := t.shape[dim]) < min_size: # `pad` keyword arg goes from the last dim to the first one in pairs, where the first value of the pair is # for left padding and the other one for right padding. return F.pad(t, pad=(0, 0) * (len(t.shape) - 1 - dim) + (0, min_size - count), value=value) else: return t def split_in_collection(data: T, split_size_or_sections: Union[int, Sequence[int]]) -> Sequence[T]: """Applies `split` to the inside tensors of the collections and also generates one collection for each of the returned elements from `split`.""" type_ = type(data) if isinstance(data, torch.Tensor): return data.split(split_size_or_sections) elif isinstance(data, Mapping): zipped = zip(*(split_in_collection(v, split_size_or_sections) for v in data.values())) return [type_((k, v) for k, v in zip(data.keys(), z)) for z in zipped] elif isinstance(data, Sequence): return [type_(z) for z in zip(*(split_in_collection(e, split_size_or_sections) for e in data))] else: raise ValueError(f"Unsupported type for split: {type_}") def _first_tensor_in_collection(data: Any) -> torch.Tensor: if isinstance(data, torch.Tensor): return data elif isinstance(data, Mapping): return _first_tensor_in_collection(data.values()) else: return _first_tensor_in_collection(next(iter(data))) def all_gather(lightning_module: pl.LightningModule, data: Any, group: Optional[Any] = None, sync_grads: bool = False, return_world_size_dim: bool = False) -> Any: """Gathers a tensor, or multiple tensors inside a collection, so that the output number of dimensions is the same regardless of the accelerator. Note this is different from `pl.LightningModule.all_gather`, that for a single GPU it doesn't return a new dimension but for the parallel settings it does. """ first_tensor_old_shape = _first_tensor_in_collection(data).shape output = lightning_module.all_gather(data, group=group, sync_grads=sync_grads) if len(first_tensor_new_shape := _first_tensor_in_collection(output).shape) == len(first_tensor_old_shape) + 1: return output if return_world_size_dim else apply_to_collection(output, torch.Tensor, lambda t: t.view(-1, *t.shape[2:])) elif len(first_tensor_new_shape) == len(first_tensor_old_shape): return apply_to_collection(output, torch.Tensor, torch.Tensor.unsqueeze, 0) if return_world_size_dim else output else: raise ValueError(f"Unexpected new shape for the first tensor in the collection: {first_tensor_new_shape} (old " f"was {first_tensor_old_shape}). " f"The new shape was expected to have the same number of dimensions or one more.")

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fitclip-main/util/typing_utils.py

from os import PathLike from typing import Union # See https://stackoverflow.com/a/53418245/1165181 TYPE_PATH = Union[PathLike, str, bytes]

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fitclip-main/util/checkpoint_utils.py

from typing import MutableMapping import torch from cached_path import cached_path from util.typing_utils import TYPE_PATH def state_dict_from_checkpoint_path(checkpoint_path: TYPE_PATH, prefix: str = "") -> MutableMapping[str, torch.Tensor]: prefix += ("" if prefix.endswith(".") or not prefix else ".") checkpoint = torch.load(cached_path(checkpoint_path)) return {k[len(prefix):]: v for k, v in checkpoint["state_dict"].items() if k.startswith(prefix)}

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fitclip-main/util/video_utils.py

import os from typing import Any, Callable, Iterable, Iterator, Optional, Sequence from torchvision.datasets.video_utils import VideoClips from util.typing_utils import TYPE_PATH # From https://en.wikipedia.org/wiki/Video_file_format VIDEO_FILE_EXTENSIONS = (".3g2", ".3gp", ".amv", ".asf", ".avi", ".drc", ".f4a", ".f4b", ".f4p", ".f4v", ".flv", ".gif", ".gifv", ".m2ts", ".m2v", ".m4p", ".m4v", ".mkv", ".mng", ".mov", ".mp2", ".mp4", ".mpe", ".mpeg", ".mpg", ".mpv", ".mts", ".mxf", ".nsv", ".ogg", ".ogv", ".qt", ".rm", ".rmvb", ".roq", ".svi", ".ts", ".viv", ".vob", ".webm", ".wmv", ".yuv") def get_videos_in_folder(path: TYPE_PATH, extensions: Optional[Iterable[str]] = VIDEO_FILE_EXTENSIONS) -> Iterator[str]: extensions = None if extensions is None else tuple(extensions) for folder, _, filenames in os.walk(path, followlinks=True): for filename in filenames: if os.path.isfile(full_path := os.path.join(folder, filename)) \ and (not extensions or filename.lower().endswith(extensions)): yield full_path def get_sorted_videos_in_folder(path: TYPE_PATH, extensions: Optional[Iterable[str]] = VIDEO_FILE_EXTENSIONS, key: Optional[Callable[[str], Any]] = None, reverse: bool = False) -> Iterator[str]: """Returns a sorted version of `get_videos_in_folder`. Even though this can be simply applied by the caller, the fact that the main use case of `get_videos_in_folder` is from a video dataset and that its order should be deterministic (but that `get_videos_in_folder` doesn't guarantee it) makes this function handy and a wake-up call for this issue. The videos in a PyTorch `Dataset` need to be deterministic e.g. for a distributed setting, when e.g. using `DistributedSampler` for it to guarantee each data sample is used once and only once between all processes. """ return sorted(get_videos_in_folder(path, extensions), key=key, reverse=reverse) def resample(num_frames: int, original_fps: float, new_fps: float) -> Sequence[int]: """Returns essentially the same as `VideoClips._resample_video_idx`. Unlike it, it always checks for the max frames (the mentioned function doesn't do it when it returns a `slice`).""" indices = VideoClips._resample_video_idx(num_frames, original_fps, new_fps) if isinstance(indices, slice) and indices.stop is None: indices = range(*indices.indices((indices.start or 0) + num_frames * indices.step)) return indices

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fitclip-main/util/__init__.py

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fitclip-main/util/argparse_with_defaults.py

import argparse from typing import Any # Copied from https://github.com/allenai/allennlp/blob/3aafb92/allennlp/commands/__init__.py class ArgumentParserWithDefaults(argparse.ArgumentParser): """Custom argument parser that will display the default value for an argument in the help message. """ _action_defaults_to_ignore = {"help", "store_true", "store_false", "store_const"} @staticmethod def _is_empty_default(default: Any) -> bool: return default is None or (isinstance(default, (str, list, tuple, set)) and not default) def add_argument(self, *args, **kwargs) -> argparse.Action: # Add default value to the help message when the default is meaningful. default = kwargs.get("default") if kwargs.get("action") not in self._action_defaults_to_ignore and not self._is_empty_default(default): kwargs["help"] = f"{kwargs.get('help', '')} (default = {default})" return super().add_argument(*args, **kwargs)

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fitclip-main/util/iter_utils.py

import collections import itertools from typing import Any, Iterable, Iterator, Literal, Optional, Sequence, Tuple, TypeVar T = TypeVar("T") # See https://stackoverflow.com/a/50938015/1165181 def consume(it: Iterator[Any]) -> None: collections.deque(it, maxlen=0) # See https://docs.python.org/3/library/itertools.html#itertools-recipes def pairwise(it: Iterable[T]) -> Iterable[Tuple[T, T]]: a, b = itertools.tee(it) next(b, None) return zip(a, b) # See https://stackoverflow.com/a/9001089/1165181 def can_be_iterated_more_than_once(it: Iterable[Any]) -> bool: try: object.__getattribute__(it, "__iter__") except AttributeError: return False try: object.__getattribute__(it, "__next__") except AttributeError: return True return False # See `grouper` in https://docs.python.org/3/library/itertools.html#itertools-recipes. def batch(iterable: Iterable[T], n: int, *, incomplete: Literal["fill", "ignore"] = "ignore", fill_value: Optional[Any] = None) -> Iterator[Iterable[T]]: """Batches the data into non-overlapping fixed-length batches. Examples: grouper("ABCDEFGH", 3) --> ABC DEF grouper("ABCDEFGH", 3, incomplete="fill", fill_value="x") --> ABC DEF GHx """ args = [iter(iterable)] * n if incomplete == "fill": return itertools.zip_longest(*args, fillvalue=fill_value) elif incomplete == "ignore": return zip(*args) else: raise ValueError(f"Expected 'fill' or 'ignore'; got '{incomplete}'") # See https://stackoverflow.com/a/312464/1165181 def batch_sequence(seq: Sequence[T], n: int) -> Iterator[Sequence[T]]: """Yield successive n-sized chunks from `seq`.""" for i in range(0, len(seq), n): yield seq[i:i + n]

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fitclip-main/scripts/apply_wise_ft.py

#!/usr/bin/env python import argparse import torch from aligner.encoder.clip_video_text_encoder import load_clip_model from aligner.wise import wise_state_dict from util.argparse_with_defaults import ArgumentParserWithDefaults def parse_args() -> argparse.Namespace: parser = ArgumentParserWithDefaults("Applies weight-space ensembles for fine-tuning (WiSE-FT) on 2 CLIP " "checkpoints.", description="See https://arxiv.org/abs/2109.01903 for more info.") parser.add_argument("input_path_or_name1", metavar="INPUT_FILE_OR_NAME_1") parser.add_argument("input_path_or_name2", metavar="INPUT_FILE_OR_NAME_2") parser.add_argument("output_path", metavar="OUTPUT_FILE") parser.add_argument("--weight-for-2", type=float, default=0.5) return parser.parse_args() def main() -> None: args = parse_args() model1 = load_clip_model(args.input_path_or_name1) model2 = load_clip_model(args.input_path_or_name2) # We don't use the logic scale from CLIP but ours, so we had deleted it. Here we need to re-create the variable, # so it doesn't fail when using the checkpoints. model1.logit_scale = getattr(model1, "logit_scale", torch.tensor(float("nan"))) model2.logit_scale = getattr(model2, "logit_scale", torch.tensor(float("nan"))) state_dict = wise_state_dict(model1, model2, weight_for_2=args.weight_for_2) torch.save(state_dict, args.output_path) if __name__ == "__main__": main()

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fitclip-main/scripts/csv_diff.py

#!/usr/bin/env python import argparse import pandas as pd from util.argparse_with_defaults import ArgumentParserWithDefaults def parse_args() -> argparse.Namespace: parser = ArgumentParserWithDefaults() parser.add_argument("path1", metavar="FILE1") parser.add_argument("path2", metavar="FILE2") return parser.parse_args() def main() -> None: args = parse_args() df1 = pd.read_csv(args.path1) df2 = pd.read_csv(args.path2) # From https://stackoverflow.com/a/48647840/1165181 print(pd.concat([df1, df2]).drop_duplicates(keep=False).to_csv(index=False)) if __name__ == "__main__": main()

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fitclip-main/scripts/subcorr.py

#!/usr/bin/env python import argparse import sys from typing import Any, Callable, Iterable, MutableMapping, Optional, Sequence, Union import PIL.Image import clip import decord import numpy as np import seaborn as sns import torch from clip.model import CLIP from matplotlib import pyplot as plt from matplotlib.offsetbox import AnnotationBbox, OffsetImage from spacy.tokens import Doc, Span def get_video_info(path: str) -> MutableMapping[str, Any]: video_reader = decord.VideoReader(path) frame_indices = list(range(0, len(video_reader), 10)) frames = [PIL.Image.fromarray(f) for f in video_reader.get_batch(frame_indices).asnumpy()] thumbnails_frame_indices = video_reader.get_key_indices() thumbnails = [PIL.Image.fromarray(f) for f in video_reader.get_batch(thumbnails_frame_indices).asnumpy()] thumbnails = [f.copy() for f in thumbnails] for thumbnail in thumbnails: thumbnail.thumbnail((64, 64)) return { "frames": frames, "frame_times": video_reader.get_frame_timestamp(frame_indices).mean(axis=-1), # noqa "thumbnails": thumbnails, "thumbnail_times": video_reader.get_frame_timestamp(thumbnails_frame_indices).mean(axis=-1), # noqa } def encode_visual(images: Iterable[PIL.Image.Image], clip_model: CLIP, image_preprocessor: Callable[[PIL.Image.Image], torch.Tensor], device: Optional[Any] = None) -> torch.Tensor: images = torch.stack([image_preprocessor(image) for image in images]) if device is not None: images = images.to(device) with torch.inference_mode(): encoded_images = clip_model.encode_image(images) return encoded_images / encoded_images.norm(dim=-1, keepdim=True) def encode_text(text: str, clip_model: CLIP, device: Optional[Any] = None) -> torch.Tensor: tokenized_texts = clip.tokenize([text]) if device is not None: tokenized_texts = tokenized_texts.to(device) with torch.inference_mode(): encoded_texts = clip_model.encode_text(tokenized_texts) return encoded_texts / encoded_texts.norm(dim=-1, keepdim=True) def text_probs(encoded_images: torch.Tensor, encoded_texts: torch.Tensor) -> np.ndarray: with torch.inference_mode(): # clip_model.logit_scale.exp() == 100 return (100 * encoded_images @ encoded_texts.T).softmax(dim=0).squeeze(-1).cpu().numpy() # noqa def create_figure(times: Sequence[float], probs: Sequence[float], thumbnail_times: Sequence[float], thumbnails: Iterable[PIL.Image.Image], title: Union[Doc, Span, str]) -> plt.Axes: # noinspection SpellCheckingInspection sns.set(rc={"figure.figsize": (1.0 * len(thumbnail_times), 1.5)}) ax = sns.lineplot(x=times, y=probs) plt.xticks(thumbnail_times) ax.set_title(title.text if isinstance(title, (Doc, Span)) else title, fontsize=35, y=0.6) ax.set(xlabel="time", ylabel="probability") plt.fill_between(times, probs) if isinstance(title, (Doc, Span)): start_time = title[0]._.start_time end_time = title[-1]._.end_time plt.axvspan(start_time, end_time, alpha=0.5, color="red") for i, (time, thumbnail) in enumerate(zip(thumbnail_times, thumbnails)): im = OffsetImage(thumbnail, axes=ax) ab = AnnotationBbox(im, (time, 0), xybox=(0, -60), frameon=False, boxcoords="offset points", pad=0) ax.add_artist(ab) plt.margins(x=0, tight=True) plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) return ax def create_figure_for_text(encoded_frames: torch.Tensor, text: Union[Doc, Span, str], clip_model: CLIP, times: Sequence[float], thumbnail_times: Sequence[float], thumbnails: Iterable[PIL.Image.Image]) -> plt.Axes: encoded_texts = encode_text(text.text if isinstance(text, (Doc, Span)) else text, clip_model, device=encoded_frames.device) probs = text_probs(encoded_frames, encoded_texts) return create_figure(times, probs, thumbnail_times, thumbnails, text) def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("path", metavar="PATH") return parser.parse_args() def main() -> None: sns.set_theme() args = parse_args() device = "cuda" if torch.cuda.is_available() else "cpu" clip_model, image_preprocessor = clip.load("ViT-B/16", device=device) # noinspection SpellCheckingInspection video_info = get_video_info(args.path) encoded_frames = encode_visual(video_info["frames"], clip_model, image_preprocessor, device=device) for text in sys.stdin: if text := text.strip(): create_figure_for_text(encoded_frames, text, clip_model, video_info["frame_times"], video_info["thumbnail_times"], video_info["thumbnails"]) plt.show() if __name__ == "__main__": main()

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fitclip-main/scripts/sample_csv.py

#!/usr/bin/env python import argparse import sys import pandas as pd from util.argparse_with_defaults import ArgumentParserWithDefaults def parse_args() -> argparse.Namespace: parser = ArgumentParserWithDefaults() parser.add_argument("path", metavar="FILE", nargs="?", default="-") parser.add_argument("-s", "--size", type=int, default=10) args = parser.parse_args() args.path = sys.stdin if args.path == "-" else args.path return args def main() -> None: args = parse_args() print(pd.read_csv(args.path).sample(args.size).to_csv(index=False)) if __name__ == "__main__": main()

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fitclip-main/scripts/prepare_trained_clip_checkpoint_for_evaluation.py

#!/usr/bin/env python import argparse import torch from util.checkpoint_utils import state_dict_from_checkpoint_path def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("input_path", metavar="INPUT_FILE") parser.add_argument("output_path", metavar="OUTPUT_FILE") parser.add_argument("--prefix", default="encoder.model.") return parser.parse_args() def main() -> None: args = parse_args() state_dict = state_dict_from_checkpoint_path(args.input_path, prefix=args.prefix) # We don't use the logic scale from CLIP but ours, so we had deleted it. Here we need to re-create the variable, # so it doesn't fail when loading this `state_dict`. state_dict["logit_scale"] = torch.tensor(float("nan")) torch.save(state_dict, args.output_path) if __name__ == "__main__": main()

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fitclip-main/scripts/checkpoint_to_state_dict.py

#!/usr/bin/env python import argparse import sys import torch from util.checkpoint_utils import state_dict_from_checkpoint_path def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("input_path", metavar="INPUT_FILE") parser.add_argument("--prefix", default="encoder.model.") return parser.parse_args() def main() -> None: args = parse_args() state_dict = state_dict_from_checkpoint_path(args.input_path, prefix=args.prefix) torch.save(state_dict, sys.stdout.buffer) if __name__ == "__main__": main()

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fitclip-main/scripts/list_possible_pos.py

#!/usr/bin/env python import fileinput from nltk.corpus import wordnet as wn from nltk.corpus.reader import POS_LIST if __name__ == "__main__": for line in fileinput.input(): if line := line.strip(): print("".join(pos for pos in POS_LIST if wn.synsets(line, pos=pos)))

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fitclip-main/scripts/prepare_trained_checkpoint_for_evaluation.py

#!/usr/bin/env python import argparse import torch from cached_path import cached_path def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("input_path", metavar="INPUT_FILE", type=cached_path) parser.add_argument("output_path", metavar="OUTPUT_FILE") parser.add_argument("--prefix", default="encoder.model.") return parser.parse_args() def main() -> None: args = parse_args() checkpoint = torch.load(args.input_path) prefix = args.prefix + ("" if args.prefix.endswith(".") else ".") checkpoint["state_dict"] = {k[len(prefix):]: v for k, v in checkpoint["state_dict"].items() if k.startswith(prefix)} torch.save(checkpoint, args.output_path) if __name__ == "__main__": main()

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fitclip-main/scripts/speech_to_text.py

#!/usr/bin/env python import sys from google.cloud.speech_v1p1beta1 import RecognitionAudio, RecognitionConfig, RecognitionMetadata, \ SpeakerDiarizationConfig, SpeechClient # We use a Python script as the `gcloud` equivalent command doesn't support the enhanced models # (`gcloud ml speech recognize-long-running`, not even the alpha and beta ones). # See https://cloud.google.com/sdk/gcloud/reference/alpha/ml/speech/recognize-long-running for more info. # noinspection PyTypeChecker def main() -> None: assert len(sys.argv) == 2, f"Valid syntax: {sys.argv[0]} GS_PATH" path = sys.argv[1] # -Cv9h3ic2JI.opus, czQwCto9O80.opus, mono: --BLA_8Qixs if path.startswith("gs://"): audio = RecognitionAudio(uri=path) else: with open(path, "rb") as file: audio = RecognitionAudio(content=file.read()) kwargs = { "audio_channel_count": 2, # It fails otherwise for many audios. FIXME: fails with mono } if path.endswith(".opus"): kwargs["encoding"] = RecognitionConfig.AudioEncoding.OGG_OPUS # All our Opus videos are in an Ogg container. # When using Ogg-Opus, the endpoint needs the following fields. # See https://cloud.google.com/speech-to-text/docs/encoding kwargs["sample_rate"] = 48000 # All our Opus audios I've seen use this rate (at least 100). else: kwargs["encoding"] = RecognitionConfig.AudioEncoding.ENCODING_UNSPECIFIED metadata = RecognitionMetadata(original_media_type=RecognitionMetadata.OriginalMediaType.VIDEO) config = RecognitionConfig(language_code="en-US", enable_word_time_offsets=True, enable_word_confidence=True, # Option not supported in the enhanced video model: # alternative_language_codes=["en-GB", "en-IN", "en-AU"], enable_automatic_punctuation=True, use_enhanced=True, model="video", metadata=metadata, diarization_config=SpeakerDiarizationConfig(enable_speaker_diarization=True, min_speaker_count=1, max_speaker_count=10), **kwargs) response = SpeechClient().long_running_recognize(config=config, audio=audio) result = response.result(timeout=10000) print(type(result).to_json(result)) if __name__ == "__main__": main()

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fitclip-main/scripts/open_clip_checkpoint_to_model.py

#!/usr/bin/env python import argparse import torch from cached_path import cached_path def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("input_path", metavar="INPUT_FILE", type=cached_path) parser.add_argument("output_path", metavar="OUTPUT_FILE") return parser.parse_args() def main() -> None: args = parse_args() checkpoint = torch.load(args.input_path) state_dict = checkpoint["state_dict"] first_key = next(iter(state_dict)) prefix = next(prefix for prefix in ["model", "module"] if first_key.startswith(prefix + ".")) torch.save({k[len(prefix + "."):]: v for k, v in state_dict.items()}, args.output_path) if __name__ == "__main__": main()

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fitclip-main/demo/app.py

import json import random from typing import Tuple from flask import Flask, Response, jsonify, request from flask_cors import CORS, cross_origin from demo.search import search_in_subtitles app = application = Flask(__name__, static_url_path="/") # `application` is gunicorn's default, `app` is flask's. cors = CORS(app) app.config["CORS_HEADERS"] = "Content-Type" @app.route("/search") @cross_origin() def search() -> Tuple[Response, int]: try: query = request.args.get("q", "[]") pattern = json.loads(query) # [{"LOWER": "this", "DEP": {"IN": ["nsubj", "dobj", "iobj"]}}] top_k = int(request.args.get("top_k", "10")) results = list(search_in_subtitles(pattern)) return jsonify([ { "video_id": span.doc._.video_id, "start_time": span[0]._.start_time, "end_time": span[-1]._.end_time, "text": span.text, } for span in random.sample(results, min(top_k, len(results))) ]), 200 except Exception as e: # noqa return jsonify(status=500, message=repr(e)), 500 @app.route("/") def root() -> Response: return app.send_static_file("index.html")

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fitclip-main/demo/__init__.py

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fitclip

fitclip-main/demo/search.py

import json import os import re from typing import Any, Iterable, Iterator, Mapping, Optional, Sequence import spacy import spacy_alignments from cached_path import cached_path from spacy.matcher import Matcher from spacy.tokens import Doc, DocBin, Span, Token from tqdm.auto import tqdm RE_MULTIPLE_SPACES = re.compile(r" {2,}") CAPTIONS_DIR = os.path.join(os.environ["SCRATCH_DIR"], "captions") spacy.prefer_gpu() NLP = spacy.load("en_core_web_trf") Doc.set_extension("video_id", default=None) Token.set_extension("start_time", default=None) Token.set_extension("end_time", default=None) def _list_caption_paths(dir_path: str) -> Iterator[str]: with os.scandir(dir_path) as it: for entry in it: if entry.is_file() and entry.name.endswith(".json"): # noqa yield entry.path # noqa def _captions_to_text(caption_full_dict: Mapping[str, Any]) -> str: return RE_MULTIPLE_SPACES.sub(" ", " ".join(d["alternatives"][0]["transcript"].strip() for d in caption_full_dict["results"][:-1])).strip() def _parse_caption_time(s: str) -> float: return float(s[:-1]) def _load_caption(path: str) -> Optional[Mapping[str, Any]]: with open(path) as file: caption_full_dict = json.load(file) if results := caption_full_dict["results"]: tokens_info = results[-1]["alternatives"][0]["words"] else: tokens_info = None if tokens_info: return { # Save some memory by just keeping what we actually use. "text": _captions_to_text(caption_full_dict), "video_id": os.path.basename(path).rsplit(".", maxsplit=1)[0], "tokens_info": [{ "word": wi["word"], "start_time": _parse_caption_time(wi["startTime"]), "end_time": _parse_caption_time(wi["endTime"]), } for wi in tokens_info], } else: return None # There are around 750/150k that fall here for different reasons. def _add_caption_info_to_doc(doc: Doc, tokens_info: Sequence[Mapping[str, Any]]) -> Doc: spacy2caption = spacy_alignments.get_alignments([t.text for t in doc], [w["word"] for w in tokens_info])[0] for token, caption_token_indices in zip(doc, spacy2caption): token._.start_time = tokens_info[caption_token_indices[0]]["start_time"] token._.end_time = tokens_info[caption_token_indices[-1]]["end_time"] return doc def _create_docs() -> Iterator[Doc]: caption_paths = list(_list_caption_paths(CAPTIONS_DIR)) # caption_paths = random.sample(caption_paths, min(len(caption_paths), 100)) # We don't keep the captions in memory as it can be a lot. caption_it = (caption for path in caption_paths if (caption := _load_caption(path))) doc_and_context_it = NLP.pipe(((c["text"], (c["video_id"], c["tokens_info"])) # noqa for c in caption_it), as_tuples=True) for doc, (video_id, tokens_info) in tqdm(doc_and_context_it, total=len(caption_paths), desc="Parsing"): doc._.trf_data = None # It takes up a lot of memory. doc._.video_id = video_id yield _add_caption_info_to_doc(doc, tokens_info) def _load_cached_docs() -> Iterator[Doc]: print("Loading cached docs…") with open(cached_path("parsed_docs"), "rb") as file: return DocBin().from_bytes(file.read()).get_docs(NLP.vocab) def _save_docs(docs: Iterable[Doc]) -> None: print("Saving cached docs…") with open("/tmp/.docs", "wb") as file: file.write(DocBin(store_user_data=True, docs=docs).to_bytes()) if os.environ.get("LOAD_CACHED_DOCS", "0").lower() in {"1", "true", "y"}: DOCS = list(_load_cached_docs()) else: DOCS = list(_create_docs()) if os.environ.get("SAVE_CACHED_DOCS", "0").lower() in {"1", "true", "y"}: _save_docs(DOCS) print("Docs ready") def search_in_subtitles(pattern: Iterable[Mapping[str, Any]]) -> Iterator[Span]: matcher = Matcher(NLP.vocab) matcher.add("search", [pattern]) for doc in DOCS: for m in matcher(doc): yield doc[m[1]:m[2]]

py

fitclip

fitclip-main/aligner/video_text_module.py

from typing import Any, Literal, Mapping, MutableMapping, Optional, Sequence, Tuple, Union import math import pytorch_lightning as pl import torch.distributed.nn from overrides import overrides from torch import nn from torch.nn.modules.loss import _Loss from aligner.encoder.video_text_encoder import TYPE_OUTPUT, VideoTextEncoder from aligner.loss import NCELoss from util.tensor_utils import all_gather TYPE_INPUT = MutableMapping[str, Any] TYPE_SPLIT = Literal["train", "val"] def log_lr(pl_module: pl.LightningModule, **kwargs) -> None: for i, optimizer in enumerate(pl_module.trainer.optimizers): for j, param_group in enumerate(optimizer.param_groups): if (lr := param_group.get("lr")) is not None: # noqa pl_module.log(f"lr_{i}_group_{j}", lr, **kwargs) class VideoTextLightningModule(pl.LightningModule): # noqa def __init__(self, encoder: VideoTextEncoder, init_temperature: float = 0.05, min_temperature: float = 0.001, fit_temperature: bool = True, loss: Optional[_Loss] = None) -> None: super().__init__() self.encoder = encoder # Use the temperature as in CLIP: save it in log-space and fit it along with the model. self.logit_scale = nn.Parameter(torch.tensor([- math.log(init_temperature)]), requires_grad=fit_temperature) # The following constant is set also as a parameter, so it's moved to the correct device automatically. self.max_logit_scale = nn.Parameter(torch.tensor([- math.log(min_temperature)]), requires_grad=False) self.loss = loss or NCELoss() @overrides(check_signature=False) def forward(self, batch: TYPE_INPUT, _batch_idx: int = 0) -> Union[TYPE_OUTPUT, Tuple[torch.Tensor, torch.Tensor, Sequence[str]]]: batch.pop("video_id", None) return self.encoder(**batch) def _step(self, batch: TYPE_INPUT, batch_idx: int = 0) -> TYPE_OUTPUT: return self(batch, batch_idx) @overrides(check_signature=False) def training_step(self, batch: TYPE_INPUT, _batch_idx: int = 0) -> TYPE_OUTPUT: output = self._step(batch, _batch_idx) # Need to log the step because PL doesn't log it in Neptune. # See https://github.com/PyTorchLightning/pytorch-lightning/pull/5510 first_video_value = next(v for k, v in batch.items() if k.startswith("video")) self.log("step", float(self.global_step), batch_size=len(first_video_value)) return output def _step_end(self, output: TYPE_OUTPUT, split: TYPE_SPLIT, log_kwargs: Optional[Mapping[str, Any]] = None) -> Union[torch.Tensor, TYPE_OUTPUT]: log_kwargs = log_kwargs or {} encoded_video, encoded_text = all_gather(self, output, sync_grads=split == "train") batch_size = len(encoded_video) logit_scale = self.logit_scale.exp() scores = logit_scale * encoded_video @ encoded_text.T loss = self.loss(scores) # Note train loss it's already shown in the progress bar by PL by default. # # Note that we need to pass the batch size in the first step log # as it can't be easily inferred by PL in our case. self.log(f"loss/{split}", loss, prog_bar=split != "train", batch_size=batch_size, **log_kwargs) if split == "train": self.log("batch_size", float(batch_size), batch_size=batch_size) self.log("temperature", 1 / logit_scale, batch_size=batch_size) return loss if split == "train" else (encoded_video, encoded_text) @overrides(check_signature=False) def training_step_end(self, output: TYPE_OUTPUT) -> torch.Tensor: loss = self._step_end(output, split="train") log_lr(self) return loss @overrides(check_signature=False) def predict_step(self, batch: TYPE_INPUT, batch_idx: int = 0) -> Mapping[str, torch.Tensor]: encoded_video, encoded_text = self._step(batch, batch_idx) return { "encoded_videos": encoded_video, "encoded_texts": encoded_text, "video_ids": batch["video_id"] } @overrides(check_signature=False) def optimizer_step(self, *args, **kwargs) -> None: super().optimizer_step(*args, **kwargs) if self.logit_scale >= self.max_logit_scale: self.logit_scale.copy_(self.max_logit_scale)

py

fitclip

fitclip-main/aligner/__main__.py

#!/usr/bin/env python import logging import os from time import strftime from typing import Mapping, Optional import hydra import torch from omegaconf import DictConfig from pytorch_lightning.loggers import NeptuneLogger, TensorBoardLogger from aligner.cli import create_model_data_module_trainer_and_ckpt_path, init_cli from aligner.logger_utils import get_logger_by_type # Note it's better to have this as a module, so it's importable and DDP works fine in debug mode. # Maybe this issue is caused by Hydra moving the CWD to somewhere else. LOGGER = logging.getLogger(__name__) # Set an env var, if empty, to the desired working directory in sweep mode. Then we read it from the config. # This way we make sure all processes use the same folder. # See https://github.com/PyTorchLightning/pytorch-lightning/issues/2727 os.environ.setdefault("SWEEP_DIR", f"multirun/{strftime('%Y-%m-%d')}/{strftime('%H-%M-%S')}") @hydra.main(config_path="../config", config_name="trainer") def main(cfg: DictConfig) -> Optional[float]: init_cli(cfg) if cfg.get("trainer", {}).get("strategy") == "dp": LOGGER.warning("DP strategy not supported by the current metric logging scheme." " See https://torchmetrics.readthedocs.io/en/stable/pages/lightning.html#logging-torchmetrics") model, data_module, trainer, ckpt_path = create_model_data_module_trainer_and_ckpt_path(cfg) output = None if cfg.command == "train": if cfg.get("validate_before_training"): LOGGER.info("Validation before training started.") with torch.inference_mode(): metrics_list = trainer.validate(model, datamodule=data_module, ckpt_path=ckpt_path) LOGGER.info("Validation before training finished.") if (tb_logger := get_logger_by_type(trainer, TensorBoardLogger)) and not tb_logger._default_hp_metric: tb_logger.log_hyperparams(model.hparams_initial, metrics={k: v for metrics in metrics_list for k, v in metrics.items()}) LOGGER.info("Training started.") trainer.fit(model, datamodule=data_module, ckpt_path=ckpt_path) if optimized_metric_name := cfg.get("optimized_metric_name"): output = trainer.callback_metrics.get(optimized_metric_name) elif cfg.command == "tune": assert ckpt_path is None, "Checkpoint path not supported when tuning." if trainer._accelerator_connector.is_distributed: LOGGER.warning("Tuning with the PL Trainer is known to have some issues in distributed settings." " See e.g. https://github.com/PyTorchLightning/pytorch-lightning/issues/4280") LOGGER.info("Tuning started.") trainer.tune(model, datamodule=data_module) elif cfg.command in {"evaluate", "validate"}: with torch.inference_mode(): trainer.validate(model, datamodule=data_module, ckpt_path=ckpt_path) elif cfg.command == "test": with torch.inference_mode(): trainer.test(model, datamodule=data_module, ckpt_path=ckpt_path) elif cfg.command == "predict": if trainer._accelerator_connector.is_distributed: LOGGER.warning("Predicting with the PL Trainer is known to have some issues in distributed settings." " See e.g. https://github.com/PyTorchLightning/pytorch-lightning/issues/10618") output_path = cfg.get("output_path", "predictions.pt") with torch.inference_mode(): predictions = trainer.predict(model, datamodule=data_module, ckpt_path=ckpt_path) assert predictions first_prediction = predictions[0] assert isinstance(first_prediction, Mapping) keys = first_prediction predictions_map = {k: torch.cat([prediction[k] for prediction in predictions]) if isinstance(first_prediction[k], torch.Tensor) else [p for prediction in predictions for p in prediction[k]] for k in keys} torch.save(predictions_map, output_path) else: raise ValueError(f"Unrecognized command: {cfg.command}") if (neptune_logger := get_logger_by_type(trainer, NeptuneLogger)) and trainer.is_global_zero: # In a Hydra multirun (sweep) scenario, Neptune experiments from finished runs are marked as still running # unless we stop them manually. See https://github.com/PyTorchLightning/pytorch-lightning/issues/11368 neptune_logger.run.stop() # Return the optimized metric value for hparam search. return output if __name__ == "__main__": main()

py

fitclip

fitclip-main/aligner/logger_utils.py

from typing import Optional, Type, TypeVar import pytorch_lightning as pl from pytorch_lightning.loggers import LightningLoggerBase, LoggerCollection T = TypeVar("T", bound=LightningLoggerBase) def get_logger_by_type(trainer: pl.Trainer, logger_class: Type[T]) -> Optional[T]: if isinstance(trainer.logger, LoggerCollection): return next((logger for logger in trainer.logger._logger_iterable if isinstance(logger, logger_class)), None) elif isinstance(trainer.logger, logger_class): return trainer.logger else: return None

py

fitclip

fitclip-main/aligner/teacher_student.py

import itertools from typing import Iterable, Mapping, MutableMapping, Optional, Tuple, Union import torch.distributed.nn from overrides import overrides from torch import nn from aligner.encoder import video_text_encoder from aligner.encoder.video_text_encoder import TYPE_TOKENIZER, VideoTextEncoder from aligner.loss import TeacherStudentNCELoss from aligner.text_video_retrieval import TextVideoRetrievalLightningModule from aligner.video_text_module import TYPE_INPUT, TYPE_SPLIT, log_lr from util.tensor_utils import all_gather, pad, split_in_collection TYPE_OUTPUT = Tuple[video_text_encoder.TYPE_OUTPUT, video_text_encoder.TYPE_OUTPUT] TYPE_MULTI_OUTPUT = Mapping[str, TYPE_OUTPUT] def _replace_in_tokenized_text(tokenized_text: MutableMapping[str, torch.Tensor], new_tokenized_text: Mapping[str, torch.Tensor], start_idx: int, end_idx: int, tokenizer: TYPE_TOKENIZER) -> None: """Replaces the content in the tensor `tokenized_text` from the index `start_idx` to `end_idx` (exclusive) for `new_tokenized_text`. When it needs to know details about the tokenization, it uses `tokenizer`. """ for k in tokenized_text: padding_value = 0 if "mask" in k else getattr(tokenizer, "pad_token_id", 0) # We suppose right padding. if tokenized_text[k].shape[1] > new_tokenized_text[k].shape[1]: padded = pad(new_tokenized_text[k], min_size=tokenized_text[k].shape[1], value=padding_value) tokenized_text[k] = torch.cat((tokenized_text[k][:start_idx], padded, tokenized_text[k][end_idx:])) elif tokenized_text[k].shape[1] < new_tokenized_text[k].shape[1]: padded = pad(tokenized_text[k], min_size=new_tokenized_text[k].shape[1], value=padding_value) tokenized_text[k] = torch.cat((padded[:start_idx], new_tokenized_text[k], padded[end_idx:])) else: tokenized_text[k] = torch.cat((tokenized_text[k][:start_idx], new_tokenized_text[k], tokenized_text[k][end_idx:])) class TeacherStudentLightningModule(TextVideoRetrievalLightningModule): # noqa """ Distillation training module. If specified, `prompts` is used with the unlabeled dataset videos instead of the labels it provides (if any). """ def __init__(self, encoder: VideoTextEncoder, teacher: VideoTextEncoder, labeled_dataset_name: str = "labeled", labeled_dataset_loss_share: Optional[float] = None, dataset_names: Iterable[str] = ("labeled", "unlabeled"), prompts: Optional[Iterable[str]] = None, **kwargs) -> None: super().__init__(encoder=encoder, dataset_names=dataset_names, **kwargs) self.teacher = teacher assert self.dataset_names, "This module uses dataset names." assert len(self.dataset_names) == 2, "The current implementation needs exactly 2 datasets." # FIXME: it doesn't work with different datasets for training and evaluation, because it needs certain names # for training; and this logic assumes the same dataset names for both. if labeled_dataset_loss_share is None: self.dataset_loss_share = {name: 1 / len(self.dataset_names) for name in self.dataset_names} else: self.dataset_loss_share = {labeled_dataset_name: labeled_dataset_loss_share} self.dataset_loss_share.update((name, (1 - labeled_dataset_loss_share) / (len(self.dataset_names) - 1)) for name in self.dataset_names if name != labeled_dataset_name) self.teacher_student_logit_scale = nn.Parameter(self.logit_scale.clone(), requires_grad=self.logit_scale.requires_grad) # noinspection SpellCheckingInspection self.teacher_student_loss = TeacherStudentNCELoss(reduction="batchmean") for p in self.teacher.parameters(): p.requires_grad = False self.labeled_dataset_name = labeled_dataset_name self.unlabeled_dataset_name = next(k for k in self.dataset_names if k != labeled_dataset_name) if prompts is None: self.tokenized_prompts = None self.teacher_tokenized_prompts = None else: prompts = list(prompts) # We use parameters so the device and dtype are moved correctly along with this module. self.tokenized_prompts = nn.ParameterDict((k, nn.Parameter(v, requires_grad=False)) # noqa for k, v in encoder.get_tokenizer()(prompts).items()) self.teacher_tokenized_prompts = nn.ParameterDict((k, nn.Parameter(v, requires_grad=False)) # noqa for k, v in teacher.get_tokenizer()(prompts).items()) @overrides(check_signature=False) def _step(self, batch: TYPE_INPUT, _batch_idx: int = 0) -> TYPE_OUTPUT: # Note we pass the labeled dataset portion to the teacher, but then we don't use it. return self({"video": batch["video_student"], "text": batch["text_student"]}), \ self.teacher(video=batch["video_teacher"], text=batch["text_teacher"]) @overrides(check_signature=False) def training_step(self, batch: TYPE_INPUT, _batch_idx: int = 0) -> TYPE_MULTI_OUTPUT: keys, lengths = zip(*((key, sum(1 for _ in group)) for key, group in itertools.groupby(dataset for dataset in batch.pop("dataset")))) assert len(keys) == len(self.dataset_names), "All datasets should be present in each batch." if self.tokenized_prompts is None: unlabeled_dataset_idx = None else: unlabeled_dataset_idx = keys.index(self.unlabeled_dataset_name) start_idx_in_batch = sum(lengths[i] for i in range(unlabeled_dataset_idx)) end_idx_in_batch = start_idx_in_batch + lengths[unlabeled_dataset_idx] _replace_in_tokenized_text(tokenized_text=batch["text_student"], new_tokenized_text=self.tokenized_prompts, start_idx=start_idx_in_batch, end_idx=end_idx_in_batch, tokenizer=self.encoder.get_tokenizer()) _replace_in_tokenized_text(tokenized_text=batch["text_teacher"], new_tokenized_text=self.teacher_tokenized_prompts, start_idx=start_idx_in_batch, end_idx=end_idx_in_batch, tokenizer=self.teacher.get_tokenizer()) output = self._step(batch, _batch_idx) # Need to log the step because PL doesn't log it in Neptune. # See https://github.com/PyTorchLightning/pytorch-lightning/pull/5510 first_video_value = next(v for k, v in batch.items() if k.startswith("video")) self.log(f"step", self.global_step, batch_size=len(first_video_value)) if self.tokenized_prompts is None: split_output = split_in_collection(output, lengths) else: text_split_sections = list(lengths) text_split_sections[unlabeled_dataset_idx] = len(next(iter(self.tokenized_prompts.values()))) student_video_sections = split_in_collection(output[0][0], lengths) student_text_sections = split_in_collection(output[0][1], text_split_sections) teacher_video_sections = split_in_collection(output[1][0], lengths) teacher_text_sections = split_in_collection(output[1][1], text_split_sections) split_output = (((student_video_sections[i], student_text_sections[i]), (teacher_video_sections[i], teacher_text_sections[i])) for i in range(len(student_video_sections))) return dict(zip(keys, split_output)) def _dataset_step_end(self, output: TYPE_OUTPUT, split: TYPE_SPLIT, dataset_name: Optional[str] = None) -> Union[torch.Tensor, video_text_encoder.TYPE_OUTPUT]: gathered_output = all_gather(self, output, sync_grads=split == "train") (encoded_video, encoded_text), (teacher_encoded_video, teacher_encoded_text) = gathered_output batch_size = len(encoded_video) logit_scale = self.logit_scale.exp() scores = logit_scale * encoded_video @ encoded_text.T if dataset_name == self.labeled_dataset_name: loss = self.loss(scores) else: teacher_student_logit_scale = self.teacher_student_logit_scale.exp() teacher_scores = teacher_student_logit_scale * teacher_encoded_video @ teacher_encoded_text.T loss = self.teacher_student_loss(scores, teacher_scores) * teacher_student_logit_scale ** 2 if split == "train": # Note that we need to pass the batch size in the first step log # as it can't be easily inferred by PL in our case. self.log("batch_size", float(batch_size), batch_size=batch_size) self.log("temperature/labeled", 1 / logit_scale) self.log("temperature/unlabeled", 1 / teacher_student_logit_scale) prefix = f"loss/{split}_{dataset_name}" if dataset_name else f"loss/{split}" # Note that we need to pass the batch size in the first step log # as it can't be easily inferred by PL in our case. self.log(prefix, loss, prog_bar=split != "train", batch_size=batch_size, add_dataloader_idx=False) return loss if split == "train" else (encoded_video, encoded_text) @overrides(check_signature=False) def training_step_end(self, output: TYPE_MULTI_OUTPUT) -> torch.Tensor: loss = sum(self._dataset_step_end(batch, split="train", dataset_name=name) * self.dataset_loss_share[name] for name, batch in output.items()) self.log("loss/train", loss) # Note train loss it's already shown in the progress bar by PL by default. log_lr(self) return loss @overrides(check_signature=False) def _validation_dataset_step_end(self, output: TYPE_OUTPUT, dataset_name: Optional[str] = None) -> video_text_encoder.TYPE_OUTPUT: return self._dataset_step_end(output, split="val", dataset_name=dataset_name) @overrides(check_signature=False) def optimizer_step(self, *args, **kwargs) -> None: super().optimizer_step(*args, **kwargs) if self.teacher_student_logit_scale >= self.max_logit_scale: self.teacher_student_logit_scale.copy_(self.max_logit_scale)

py

fitclip

fitclip-main/aligner/loss.py

from typing import Literal import torch from overrides import overrides from torch.nn import functional as F from torch.nn.modules.loss import _Loss TYPE_REDUCTION = Literal["none", "mean", "sum"] # noinspection SpellCheckingInspection TYPE_REDUCTION_KL_DIV = Literal["none", "batchmean", "mean", "sum"] def _rows_to_columns_nce_loss(scores: torch.Tensor, reduction: TYPE_REDUCTION = "mean") -> torch.Tensor: loss = - F.log_softmax(scores, dim=-1).diag() if reduction == "mean": return loss.mean() elif reduction == "sum": return loss.sum() else: return loss def nce_loss(scores: torch.Tensor, reduction: TYPE_REDUCTION = "mean") -> torch.Tensor: return (_rows_to_columns_nce_loss(scores, reduction=reduction) + _rows_to_columns_nce_loss(scores.T, reduction=reduction)) def _rows_to_columns_teacher_student_nce_loss(scores: torch.Tensor, teacher_scores: torch.Tensor, reduction: TYPE_REDUCTION_KL_DIV = "mean") -> torch.Tensor: logits = F.log_softmax(scores, dim=-1) teacher_probs = F.softmax(teacher_scores, dim=-1) return F.kl_div(logits, teacher_probs, reduction=reduction) def teacher_student_nce_loss(scores: torch.Tensor, teacher_scores: torch.Tensor, reduction: TYPE_REDUCTION_KL_DIV = "mean") -> torch.Tensor: return (_rows_to_columns_teacher_student_nce_loss(scores, teacher_scores, reduction=reduction) + _rows_to_columns_teacher_student_nce_loss(scores.T, teacher_scores.T, reduction=reduction)) class NCELoss(_Loss): @overrides(check_signature=False) def forward(self, scores: torch.Tensor) -> torch.Tensor: return nce_loss(scores, reduction=self.reduction) # noqa class TeacherStudentNCELoss(_Loss): @overrides(check_signature=False) def forward(self, scores: torch.Tensor, teacher_scores: torch.Tensor) -> torch.Tensor: return teacher_student_nce_loss(scores, teacher_scores, reduction=self.reduction) # noqa class SimilarityLoss(_Loss): @overrides(check_signature=False) def forward(self, scores: torch.Tensor) -> torch.Tensor: # Note we actually don't need all the scores. loss = - torch.log(torch.sigmoid(scores.diag())) if self.reduction == "mean": return loss.mean() elif self.reduction == "sum": return loss.sum() else: return loss

py

fitclip

fitclip-main/aligner/text_video_retrieval.py

from collections import OrderedDict from typing import Iterable, Mapping, Optional, Sequence, Tuple, Union import torch import torch.distributed.nn from overrides import overrides from torch import nn from torchmetrics import Metric, Recall from aligner.encoder.video_text_encoder import TYPE_OUTPUT from aligner.metrics import MedianRank, Rank from aligner.video_text_module import TYPE_INPUT, VideoTextLightningModule from util.tensor_utils import all_gather class TextVideoRetrievalLightningModule(VideoTextLightningModule): # noqa def __init__(self, *args, dataset_names: Optional[Iterable[str]] = None, compute_rank: bool = False, **kwargs) -> None: super().__init__(*args, **kwargs) metrics_dict = {"r1": Recall(), "r5": Recall(top_k=5), "r10": Recall(top_k=10), "mr": MedianRank()} if compute_rank: metrics_dict["rank"] = Rank() self.dataset_names = list(dataset_names) if dataset_names else None self.multiple_datasets = self.dataset_names is not None and len(self.dataset_names) > 1 if self.multiple_datasets: assert all("_" not in name for name in self.dataset_names), \ "Underscores in dataset names are problematic because of how we get their corresponding metrics." self.metrics: Mapping[str, Metric] = nn.ModuleDict((f"{name}_{dataset_name}", metric.clone()) # noqa for dataset_name in self.dataset_names for name, metric in metrics_dict.items()) else: self.metrics: Mapping[str, Metric] = nn.ModuleDict(metrics_dict) @overrides(check_signature=False) def validation_step(self, batch: TYPE_INPUT, batch_idx: int = 0, dataloader_idx: Optional[int] = None) -> Tuple[TYPE_OUTPUT, Optional[int]]: return self._step(batch, batch_idx), dataloader_idx def _validation_dataset_step_end(self, output: TYPE_OUTPUT, dataset_name: Optional[str] = None) -> TYPE_OUTPUT: encoded_video, encoded_text = all_gather(self, output) batch_size = len(encoded_video) logit_scale = self.logit_scale.exp() scores = logit_scale * encoded_video @ encoded_text.T loss = self.loss(scores) # Note that we need to pass the batch size in the first step log # as it can't be easily inferred by PL in our case. key = "loss/val" + ("" if dataset_name is None else f"_{dataset_name}") self.log(key, loss, prog_bar=True, batch_size=batch_size, add_dataloader_idx=False) return encoded_video, encoded_text @overrides(check_signature=False) def validation_step_end(self, output: Tuple[TYPE_OUTPUT, int]) -> TYPE_OUTPUT: step_output, data_loader_idx = output assert self.multiple_datasets == (data_loader_idx is not None) dataset_name = self.dataset_names[data_loader_idx] if self.multiple_datasets else None return self._validation_dataset_step_end(step_output, dataset_name=dataset_name) def _validate_dataset(self, outputs: Sequence[TYPE_OUTPUT], dataset_name: Optional[str] = None) -> None: assert self.multiple_datasets == (dataset_name is not None) encoded_videos, encoded_texts = (torch.cat(x) for x in zip(*outputs)) batch_size = len(encoded_videos) scores = encoded_texts @ encoded_videos.T target = torch.arange(scores.shape[-1], device=scores.device) for name, metric in self.metrics.items(): if not dataset_name or name.endswith(f"_{dataset_name}"): metric(scores, target) # Note that we need to pass the batch size in the first step log # as it can't be easily inferred by PL in our case. self.log(name, metric, prog_bar=True, batch_size=batch_size, add_dataloader_idx=False) @overrides(check_signature=False) def validation_epoch_end(self, outputs: Union[Sequence[TYPE_OUTPUT], Sequence[Sequence[TYPE_OUTPUT]]]) -> None: if self.multiple_datasets: for i, (name, dataset_output) in enumerate(zip(self.dataset_names, outputs)): # Necessary to set the current data loader ID so PL knows to which one the logged metrics belong # (because it returns the metrics by data loader). self._current_dataloader_idx = i self._validate_dataset(dataset_output, dataset_name=name) # noqa self._current_dataloader_idx = None else: self._validate_dataset(outputs) if "rank" in self.metrics: self.print(self.metrics["rank"].compute().tolist()) @overrides def load_state_dict(self, state_dict: "OrderedDict[str, torch.Tensor]", strict: bool = True): # If it's exactly this class, then ignore any teacher-related thing. # We do it here, so we can control it more, and avoid bugs with a general solution. if type(self) is TextVideoRetrievalLightningModule: incompatible_keys = super().load_state_dict(state_dict, strict=False) unexpected_keys = set(incompatible_keys.unexpected_keys) for key in incompatible_keys.unexpected_keys: if key.startswith("teacher"): unexpected_keys.remove(key) # We then do as in super: if strict: error_msgs = [] if unexpected_keys: unexpected_key_str = ", ".join(f'"{k}"' for k in unexpected_keys) error_msgs.append(f"Unexpected key(s) in state_dict: {unexpected_key_str}. ") if incompatible_keys.missing_keys: missing_keys_str = ', '.join(f'"{k}"' for k in incompatible_keys.missing_keys) error_msgs.append(f"Missing key(s) in state_dict: {missing_keys_str}. ") if error_msgs: error_msgs_str = "\n\t".join(error_msgs) raise RuntimeError(f"Error(s) in loading state_dict for {self.__class__.__name__}:\n\t" f"{error_msgs_str}") return incompatible_keys else: return super().load_state_dict(state_dict, strict)

py

fitclip

fitclip-main/aligner/video_text_classification.py

import logging import math from typing import Any, Iterable, Mapping, Optional, Sequence, TypeVar import torch from overrides import overrides from pytorch_lightning.callbacks import RichProgressBar from pytorch_lightning.utilities.apply_func import apply_to_collection from torch import nn from torchmetrics import Accuracy, Metric from aligner.encoder.video_text_encoder import VideoTextEncoder from aligner.metrics import MedianRank from aligner.video_text_module import VideoTextLightningModule from util import iter_utils LOGGER = logging.getLogger(__name__) T = TypeVar("T") def batch_tokenized_text(tokenized: Mapping[str, Sequence[T]], n: int) -> Iterable[Mapping[str, T]]: tokenized_dicts = {k: iter(iter_utils.batch_sequence(v, n)) for k, v in tokenized.items()} length = math.ceil(len(next(iter(tokenized.values()))) / n) for _ in range(length): yield {k: next(tokenized_dicts[k]) for k in tokenized} class VideoTextClassificationLightningModule(VideoTextLightningModule): # noqa def __init__(self, encoder: VideoTextEncoder, labels: Iterable[str], templates: Optional[Iterable[str]], return_metrics_by_class: bool = False, **kwargs) -> None: super().__init__(encoder, **kwargs) labels = list(labels) label_count = len(labels) # If different templates are provided, we used them for each label # and reset the labels to be {labels} x {templates}. if templates: templates = list(templates) self.template_count = len(templates) labels = [template.format(label) for label in labels for template in templates] else: self.template_count = 1 # We tokenize all the labels but defer the encoding until the model is in the device. tokenized_labels = encoder.get_tokenizer()(labels) device = next(encoder.parameters()).device tokenized_labels = apply_to_collection(tokenized_labels, torch.Tensor, torch.Tensor.to, device) self.tokenized_labels = nn.ParameterDict(apply_to_collection(tokenized_labels, torch.Tensor, nn.Parameter, requires_grad=False)) # We encode just one label to allocate the size correctly. encoded_text = self.encoder.encode_text({k: v[:1] for k, v in tokenized_labels.items()}) self.encoded_labels = nn.Parameter(torch.empty(label_count, encoded_text.shape[-1]), requires_grad=False) self.metrics: Mapping[str, Metric] = nn.ModuleDict({"a1": Accuracy(), "a5": Accuracy(top_k=5), "mr": MedianRank()}) if return_metrics_by_class: self.metrics_by_class = nn.ModuleDict({f"a1_{k}": Accuracy() for k in range(label_count)}) else: self.metrics_by_class = None def _on_start(self) -> None: # Note that for training they should be encoded at running time, not here. # But we aren't training any text classification model but evaluating them. # # We compute them here and not during init because here the model is already in the device. # This is especially much faster than in CPU (init) when using templates. batch_size = 32 callback = next(callback for callback in self.trainer.callbacks if isinstance(callback, RichProgressBar)) progress = callback.progress if self.trainer.is_global_zero: progress_task = progress.add_task( description="Encoding the labels", total=math.ceil(len(next(iter(self.tokenized_labels.values()))) / batch_size)) else: progress_task = None encoded_label_list = [] for tokenized_labels_batch in batch_tokenized_text(self.tokenized_labels, batch_size): encoded_label_list.append(self.encoder.encode_text(tokenized_labels_batch)) if progress_task is not None: progress.update(progress_task, advance=1) encoded_labels = torch.cat(encoded_label_list) encoded_labels = encoded_labels.reshape(-1, self.template_count, encoded_labels.shape[1]).mean(dim=1) self.encoded_labels.copy_(encoded_labels) if progress_task is not None: # If we remove it, it later fails, not sure why. So we just hide it. progress.update(progress_task, visible=False) @overrides def on_validation_start(self) -> None: self._on_start() @overrides def on_test_start(self) -> None: self._on_start() @overrides def on_predict_start(self) -> None: self._on_start() @overrides(check_signature=False) def forward(self, video: torch.Tensor) -> torch.Tensor: return self.encoder.encode_video(video) @ self.encoded_labels.T @overrides(check_signature=False) def validation_step(self, batch: Mapping[str, Any], _batch_idx: int = 0) -> None: scores = self(batch["video"]) label_id = batch["target"][1] for name, metric in self.metrics.items(): metric(scores, label_id) # Note that we need to pass the batch size in the first step log # as it can't be easily inferred by PL in our case. self.log(name, metric, prog_bar=True, batch_size=len(batch["video"])) if self.metrics_by_class is not None: for scores_instance, label_id_instance in zip(scores, label_id): metric = self.metrics_by_class[f"a1_{label_id_instance}"] metric(scores_instance.unsqueeze(0), label_id_instance.unsqueeze(0)) self.log(f"a1_{label_id_instance}", metric, batch_size=1) @overrides(check_signature=False) def predict_step(self, batch: Mapping[str, Any], _batch_idx: int = 0) -> Mapping[str, torch.Tensor]: return { "predictions": self(batch["video"]).argmax(dim=-1), "labels": batch["target"][1], "video_ids": batch["video_id"], }

py

fitclip

fitclip-main/aligner/cli.py

#!/usr/bin/env python import copy import logging import warnings from types import MethodType from typing import Any, Mapping, Optional, Tuple, Type import hydra import pytorch_lightning as pl from cached_path import cached_path from omegaconf import DictConfig from pytorch_lightning import seed_everything from torch.optim import Optimizer from aligner.data.data_module_group import DataModuleStructuredGroup, EvalDataModuleGroup, MixedBatchDataModule, \ TrainAndEvalDataModules from aligner.data.video_data_module import ENCODER_OR_ENCODER_MAP, VideoClassificationDataModule from aligner.encoder.video_text_encoder import VideoTextEncoder from aligner.video_text_classification import VideoTextClassificationLightningModule from aligner.video_text_module import VideoTextLightningModule LOGGER = logging.getLogger(__name__) # This is because PL can't automatically infer the batch size, that's needed for logging. But we set it manually # within the modules. warnings.filterwarnings("ignore", message=r"^Trying to infer the `batch_size` from an ambiguous collection\. .+") # From https://stackoverflow.com/a/2020083/1165181 def fullname(klass: Type[Any]) -> str: return f"{klass.__module__}.{klass.__qualname__}" def set_logging_level(level: int) -> None: logging.basicConfig(level=level) # `basicConfig` will only work for new loggers, so we also need to set up the existing ones: for logger in logging.root.manager.loggerDict.values(): if isinstance(logger, logging.Logger): # Otherwise, it could be a `logging.PlaceHolder`. logger.setLevel(level) logging.getLogger().setLevel(level) # The root logger is not present in the previous iterable. def init_cli(cfg: DictConfig) -> None: if cfg.get("silent"): set_logging_level(logging.WARNING) else: set_logging_level(logging.INFO) if "seed" in cfg: seed_everything(cfg.seed, workers=True) def instantiate_data_module(cfg: DictConfig, encoder: ENCODER_OR_ENCODER_MAP) -> pl.LightningDataModule: kwargs = {} if cfg._target_ in {fullname(klass) for klass in [DataModuleStructuredGroup, EvalDataModuleGroup, MixedBatchDataModule]}: if isinstance(cfg.data_modules, Mapping): kwargs["data_modules"] = {k: instantiate_data_module(v, encoder=encoder) # noqa for k, v in cfg.data_modules.items()} else: kwargs["data_modules"] = {instantiate_data_module(cfg_dm, encoder=encoder) for cfg_dm in cfg.data_modules} # Convert because otherwise the passed `data_modules` is a `DictConfig` instead of a `dict` and # `train_dataloader` can't respect the same collection type as `DictConfig` can't have normal classes. kwargs["_convert_"] = "all" elif cfg._target_ == fullname(TrainAndEvalDataModules): kwargs["train_data_module"] = instantiate_data_module(cfg.train_data_module, encoder=encoder) kwargs["eval_data_module"] = instantiate_data_module(cfg.eval_data_module, encoder=encoder) else: kwargs["encoder"] = encoder # Necessary as well when the encoder is a dict. kwargs["_convert_"] = "all" return hydra.utils.instantiate(cfg, **kwargs) def create_model_data_module_trainer_and_ckpt_path( cfg: DictConfig, model_kwargs: Optional[Mapping[str, Any]] = None) -> Tuple[VideoTextLightningModule, pl.LightningDataModule, pl.Trainer, str]: model_kwargs = model_kwargs or {} LOGGER.info(f"Instantiating encoder <{getattr(cfg.encoder, '_target_', type(cfg.encoder).__name__)}>…") encoder: ENCODER_OR_ENCODER_MAP = hydra.utils.instantiate(cfg.encoder) if isinstance(encoder, Mapping) and cfg.get("use_student_encoder_for_data_preprocessing"): encoder_for_data_preprocessing = encoder["student"] else: encoder_for_data_preprocessing = encoder LOGGER.info("Encoder instantiated.") LOGGER.info(f"Instantiating data module <{cfg.data._target_}>…") data_module = instantiate_data_module(cfg.data, encoder=encoder_for_data_preprocessing) LOGGER.info("Data module instantiated.") LOGGER.info(f"Instantiating model <{cfg.model._target_}>…") if isinstance(encoder, Mapping): model_kwargs.setdefault("encoder", encoder["student"]) model_kwargs.setdefault("teacher", encoder["teacher"]) else: model_kwargs.setdefault("encoder", encoder) if isinstance(data_module, VideoClassificationDataModule): assert isinstance(encoder_for_data_preprocessing, VideoTextEncoder), \ "Encoder can't be a mapping and has to support text when doing classification." cfg.model._target_ = fullname(VideoTextClassificationLightningModule) model_kwargs.setdefault("labels", data_module.categories) model_kwargs.setdefault("templates", data_module.templates) if prompts_path := cfg.get("prompts"): # noqa with open(cached_path(prompts_path)) as file: model_kwargs.setdefault("prompts", [stripped_line for line in file if (stripped_line := line.strip())]) # noqa model: VideoTextLightningModule = hydra.utils.instantiate(cfg.model, **model_kwargs) LOGGER.info("Model instantiated.") if "optimizer" in cfg: LOGGER.info(f"Instantiating Optimizer <{cfg.optimizer._target_}>…") def configure_optimizers(self: pl.LightningModule) -> Optimizer: if (lr_ := self.hparams.get("lr")) is not None: # To be used by auto LR find. cfg.optimizer["lr"] = lr_ return hydra.utils.instantiate(cfg.optimizer, self.parameters()) model.configure_optimizers = MethodType(configure_optimizers, model) LOGGER.info("Optimizer instantiated.") LOGGER.info(f"Instantiating trainer <{cfg.trainer._target_}>…") trainer: pl.Trainer = hydra.utils.instantiate(cfg.trainer) LOGGER.info("Trainer instantiated.") # We do what `model.save_hyperparameters(cfg)` would do but without needing a current frame to get the args from. # It turns out that, even if you provide args, it still checks the current frame for args, and set those # conditioned by the provided args. model._log_hyperparams = trainer.logger model._set_hparams(cfg) # noqa model._hparams_initial = copy.deepcopy(model._hparams) ckpt_path = cached_path(cfg.checkpoint_path) if cfg.get("path") else None return model, data_module, trainer, ckpt_path

py

fitclip

fitclip-main/aligner/wise.py

import copy from typing import Mapping, TypeVar import torch from torch import nn T = TypeVar("T", bound=nn.Module) def wise_state_dict(model1: T, model2: T, weight_for_2: float = 0.5) -> Mapping[str, torch.Tensor]: state_dict1 = dict(model1.named_parameters()) state_dict2 = dict(model2.named_parameters()) assert set(state_dict1) == set(state_dict2) return {k: (1 - weight_for_2) * state_dict1[k] + weight_for_2 * state_dict2[k] for k in state_dict1} def wise(model1: T, model2: T, weight_for_2: float = 0.5, copy_model1: bool = True) -> T: assert type(model1) is type(model2) model = copy.deepcopy(model1 if copy_model1 else model2) model.load_state_dict(wise_state_dict(model1, model2, weight_for_2=weight_for_2)) # noqa return model

py

fitclip

fitclip-main/aligner/param_freezer.py

# Inspired from https://github.com/allenai/allennlp/blob/0d8c0fc/allennlp/training/optimizers.py import logging import re from typing import Iterable, Optional, Union import pytorch_lightning as pl from overrides import overrides LOGGER = logging.getLogger(__name__) class ParamFreezer(pl.Callback): def __init__(self, regexes: Iterable[Union[str, re.Pattern]]) -> None: super().__init__() self.regexes = [re.compile(regex) for regex in regexes] @overrides def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None: unused_regexes = {p.pattern for p in self.regexes} params_to_tune = [] frozen_params = [] for name, param in pl_module.named_parameters(): for regex in self.regexes: if regex.search(name): param.requires_grad = False if regex.pattern in unused_regexes: unused_regexes.remove(regex.pattern) frozen_params.append(name) break else: params_to_tune.append(name) LOGGER.debug(f"Params to tune: {params_to_tune}") LOGGER.debug(f"Frozen params: {frozen_params}") if unused_regexes: LOGGER.warning(f"The following param regexes used for freezing didn't match any param name: " f"{unused_regexes}")

py

fitclip

fitclip-main/aligner/metrics.py

import torch from overrides import overrides from torchmetrics import Metric class Rank(Metric): is_differentiable: bool = False higher_is_better: bool = False full_state_update: bool = False def __init__(self, **kwargs) -> None: super().__init__(**kwargs) self.add_state("ranks", default=[], dist_reduce_fx="cat") @overrides(check_signature=False) def update(self, predictions: torch.Tensor, target: torch.Tensor) -> None: sorted_predicted_positions = predictions.argsort(dim=1, descending=True) ranks = torch.where(sorted_predicted_positions == target.unsqueeze(-1))[1] # noqa self.ranks.append(ranks) @overrides def compute(self) -> torch.Tensor: # It could be already reduced depending on when we call it (e.g., at the epoch end). return self.ranks if isinstance(self.ranks, torch.Tensor) else torch.cat(self.ranks) class MeanRank(Rank): @overrides def compute(self) -> torch.Tensor: return super().compute().mean() + 1 class MedianRank(Rank): @overrides def compute(self) -> torch.Tensor: return super().compute().median() + 1

py