def drop_exp_2(r_feat_val, r_feat_train, pred): # incep_score, mode_score, fid n_mode = len(Counter(pred)) scores = np.zeros((n_mode, 3)) t_feat = r_feat_train.clone() collapsed_order = torch.randperm(n_mode).long() index = torch.arange(0, r_feat_train.size(0)).long() collapsed = torch.zeros(r_feat_train.size(0)).byte() Mxx = distance(r_feat_val, r_feat_val, sqrt=True) for i in range(n_mode): # Compute Score Mxy = distance(r_feat_val, t_feat, sqrt=True) Myy = distance(t_feat, t_feat, sqrt=True) scores[i, 0] = inception_score(t_feat) scores[i, 1] = mode_score(t_feat, r_feat_val) scores[i, 2] = fid(t_feat, r_feat_val) # Do drop -- fill dropped slots with remaining samples c = collapsed_order[i] collapsed[pred.eq(c)] = 1 cidx = index[collapsed.eq(1)] ncidx = index[collapsed.ne(1)] if ncidx.dim() == 0 or cidx.dim() == 0 or ncidx.size(0) == 0: continue for j in cidx: copy_idx = np.random.randint(0, ncidx.size(0)) t_feat[j] = t_feat[ncidx[copy_idx]] return scores
def drop_exp_2(r_feat_val, r_feat_train, pred): # incep_score, mode_score, fid n_mode = len(Counter(pred)) scores = np.zeros((n_mode, 3)) t_feat = r_feat_train.clone() collapsed_order = torch.randperm(n_mode).long() index = torch.arange(0, r_feat_train.size(0)).long() collapsed = torch.zeros(r_feat_train.size(0)).byte() Mxx = distance(r_feat_val, r_feat_val, sqrt=True) for i in range(n_mode): # Compute Score Mxy = distance(r_feat_val, t_feat, sqrt=True) Myy = distance(t_feat, t_feat, sqrt=True) scores[i, 0] = inception_score(t_feat) scores[i, 1] = mode_score(t_feat, r_feat_val) scores[i, 2] = fid(t_feat, r_feat_val) # Do drop -- fill dropped slots with remaining samples c = collapsed_order[i] collapsed[pred.eq(c)] = 1 cidx = index[collapsed.eq(1)] ncidx = index[collapsed.ne(1)] if ncidx.dim() == 0 or cidx.dim() == 0 or ncidx.size(0) == 0: continue for j in cidx: copy_idx = np.random.randint(0, ncidx.size(0)) t_feat[j] = t_feat[ncidx[copy_idx]] return scores
def overfit_exp_2(r_feat_val, r_feat_train, step=200): # incep_score, mode_score, fid n_mode = r_feat_train.size(0) // step scores = np.zeros((n_mode + 1, 3)) t_feat = r_feat_train.clone() collapsed_order = torch.randperm(n_mode).long() index = torch.arange(0, r_feat_train.size(0)).long() collapsed = torch.zeros(r_feat_train.size(0)).byte() Mxx = distance(r_feat_val, r_feat_val, sqrt=True) for i in range(n_mode + 1): # Compute Score Mxy = distance(r_feat_val, t_feat, sqrt=True) Myy = distance(t_feat, t_feat, sqrt=True) scores[i, 0] = inception_score(t_feat) scores[i, 1] = mode_score(t_feat, r_feat_val) scores[i, 2] = fid(t_feat, r_feat_val) # Copy samples so as to overfit if i == n_mode: break t_feat[i * step:(i + 1) * step] = r_feat_val[i * step:(i + 1) * step] return scores
def overfit_exp_2(r_feat_val, r_feat_train, step=200): # incep_score, mode_score, fid n_mode = r_feat_train.size(0) // step scores = np.zeros((n_mode+1, 3)) t_feat = r_feat_train.clone() collapsed_order = torch.randperm(n_mode).long() index = torch.arange(0, r_feat_train.size(0)).long() collapsed = torch.zeros(r_feat_train.size(0)).byte() Mxx = distance(r_feat_val, r_feat_val, sqrt=True) for i in range(n_mode+1): # Compute Score Mxy = distance(r_feat_val, t_feat, sqrt=True) Myy = distance(t_feat, t_feat, sqrt=True) scores[i, 0] = inception_score(t_feat) scores[i, 1] = mode_score(t_feat, r_feat_val) scores[i, 2] = fid(t_feat, r_feat_val) # Copy samples so as to overfit if i == n_mode: break t_feat[i*step:(i+1)*step] = r_feat_val[i*step:(i+1)*step] return scores
def collapse_exp_2(r_feat_val, r_feat, c_feat, pred): # incep_score, mode_score, fid n_mode = c_feat.size(0) c_feat_repeat = c_feat[pred] scores = np.zeros((n_mode, 3)) t_feat = r_feat.clone() index = torch.arange(0, 2000).long() collapsed_order = torch.randperm(n_mode).long() Mxx = distance(r_feat_val, r_feat_val, sqrt=False) for i in range(n_mode): # Compute Score Mxy = distance(r_feat_val, t_feat, sqrt=False) Myy = distance(t_feat, t_feat, sqrt=False) scores[i, 0] = inception_score(t_feat) scores[i, 1] = mode_score(t_feat, r_feat_val) scores[i, 2] = fid(t_feat, r_feat_val) # Do collapse c = collapsed_order[i] cidx = index[pred.eq(c)] t_feat[cidx] = c_feat_repeat[cidx] return scores
def collapse_exp_2(r_feat_val, r_feat, c_feat, pred): # incep_score, mode_score, fid n_mode = c_feat.size(0) c_feat_repeat = c_feat[pred] scores = np.zeros((n_mode, 3)) t_feat = r_feat.clone() index = torch.arange(0, 2000).long() collapsed_order = torch.randperm(n_mode).long() Mxx = distance(r_feat_val, r_feat_val, sqrt=False) for i in range(n_mode): # Compute Score Mxy = distance(r_feat_val, t_feat, sqrt=False) Myy = distance(t_feat, t_feat, sqrt=False) scores[i, 0] = inception_score(t_feat) scores[i, 1] = mode_score(t_feat, r_feat_val) scores[i, 2] = fid(t_feat, r_feat_val) # Do collapse c = collapsed_order[i] cidx = index[pred.eq(c)] t_feat[cidx] = c_feat_repeat[cidx] return scores
def run_combine(t): print_prop(t) if t.mode == "Mix" or t.mode == "Entropy": mkdir(g.default_repo_dir + "samples/Mix") print("running mix mod..") mkdir(g.default_repo_dir + "rlt") for sp in range(0, 5 if t.mixOnly else 1): def getName(): ans = 'mix_' if t.mode == "Mix" else 'entropy_' ans = ans + t.data + '_' + t.featureType + '_' for entry in t.dataList: ans = ans + entry.data + '_' + entry.folder + '_' + \ ('%.2f' % entry.fraction) + '_' + \ str(entry.dup) + '_' + str(entry.imageMode) + '_' return ans + str(t.mixSize) + '_' + str(sp) saveFile = g.default_repo_dir + "rlt/" + getName() if os.path.exists(saveFile): print(saveFile + " already generated. Continue.") continue if t.mode == "Mix": mixDat = getDat(t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType, t.mixOnly) dataList = [Ent(1, t.data, 'true', None)] print('ftype', t.featureType) trueDat = getDat(t, dataList, g.default_repo_dir + "samples/Mix/M2" + hostname, t.featureType) score = compute_score(trueDat, mixDat, 1, 1, True) else: t.mixSize = t.mixSize * 10 mixDat = getDat(t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType, t.mixOnly) t.mixSize = t.mixSize / 10 dataList = [Ent(1, t.data, 'true_test', None)] trueDat = getDat(t, dataList, g.default_repo_dir + "samples/Mix/M2" + hostname, t.featureType) if t.featureType == 'pix': epsilons = [1000] elif t.featureType == 'smax': epsilons = [0.001] elif t.featureType == 'class': epsilons = [0.1] elif t.featureType == 'conv': epsilons = [1000] score = entropy_score(mixDat, trueDat, epsilons) f = open(saveFile, "wb") pickle.dump(score, f) f.close() elif t.mode == "Incep": mkdir(g.default_repo_dir + "samples/Mix") mkdir(g.default_repo_dir + "rlt") for sp in range(0, 5): def getIncepName(): ans = 'incep_' + t.data + '_' + t.featureType + '_' for entry in t.dataList: ans = ans + entry.data + '_' + entry.folder + '_' + \ ('%.2f' % entry.fraction) + '_' + \ str(entry.dup) + '_' + str(entry.imageMode) + '_' return ans + str(t.mixSize) + "_" + str(sp) incep_file_name = g.default_repo_dir + "rlt/" + getIncepName() if os.path.exists(incep_file_name): print(incep_file_name + " already generated. Continue.") return X = getDat(t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType) # get smax feature incep_score = inception_score(X) f = open(incep_file_name, "wb") pickle.dump(incep_score, f) f.close() elif t.mode == "ModeScore": mkdir(g.default_repo_dir + "samples/Mix") mkdir(g.default_repo_dir + "rlt") for sp in range(0, 5): def getMSName(): ans = 'mode_score_' + t.data + '_' + t.featureType + '_' for entry in t.dataList: ans = ans + entry.data + '_' + entry.folder + '_' + \ ('%.2f' % entry.fraction) + '_' + \ str(entry.dup) + '_' + str(entry.imageMode) + '_' return ans + str(t.mixSize) + "_" + str(sp) ms_file_name = g.default_repo_dir + "rlt/" + getMSName() if os.path.exists(ms_file_name): print(ms_file_name + " already generated. Continue.") return X = getDat(t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType) dataList = [Ent(1, t.data, 'true', None)] Y = getDat(t, dataList, g.default_repo_dir + "samples/Mix/M2" + hostname, t.featureType) modeScore = mode_score(X, Y) f = open(ms_file_name, "wb") pickle.dump(modeScore, f) f.close() elif t.mode == "FID": mkdir(g.default_repo_dir + "samples/Mix") mkdir(g.default_repo_dir + "rlt") for sp in range(0, 5): def getFidName(): ans = 'fid_' + t.data + '_' + t.featureType + '_' for entry in t.dataList: ans = ans + entry.data + '_' + entry.folder + '_' + \ ('%.2f' % entry.fraction) + '_' + \ str(entry.dup) + '_' + str(entry.imageMode) + '_' return ans + str(t.mixSize) + "_" + str(sp) fid_file_name = g.default_repo_dir + "rlt/" + getFidName() if os.path.exists(fid_file_name): print(fid_file_name + " already generated. Continue.") return X = getDat(t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType) dataList = [Ent(1, t.data, 'true', None)] Y = getDat(t, dataList, g.default_repo_dir + "samples/Mix/M2" + hostname, t.featureType) FID = fid(X, Y) f = open(fid_file_name, "wb") pickle.dump(FID, f) f.close() elif t.mode == 'collapse': run_collapse_exp(t.data) elif t.mode == 'drop': run_drop_exp(t.data) elif t.mode == 'overfit': run_overfit_exp(t.data) elif t.mode == 'Gen': # generate necessary data.. if t.data == 'noise': print("\nSampling noise...") noise_sampler(t) elif t.data == 'mnistsub': # subclass print("\nSampling subclass...") t.data = 'mnist' subclass_sampler(t) else: t.folderName = t.model + \ (str(t.epoch) if not t.model.startswith('true') else "") if t.model.startswith('true'): print("\nSampling " + t.folderName + " ...") folder_sampler(t) else: print("\nSampling " + t.folderName + " ...") generator_sampler(t) elif t.mode == "Gan": # generate some fake images.. if t.model == "DCGAN": DCGAN_main(t) elif t.model == "WGAN": WGAN_main(t) elif t.model == "NNGAN": NNGAN_main(t) elif t.model == "MGGAN": MGGAN_main(t) elif t.model == "DCGAN_cluster": DCGAN_cluster_main(t) else: print("error.. Unknown model.") else: print("not designed yet.")
def run_combine(t): print_prop(t) if t.mode == "Mix" or t.mode == "Entropy": mkdir(g.default_repo_dir + "samples/Mix") print("running mix mod..") mkdir(g.default_repo_dir + "rlt") for sp in range(0, 5 if t.mixOnly else 1): def getName(): ans = 'mix_' if t.mode == "Mix" else 'entropy_' ans = ans + t.data + '_' + t.featureType + '_' for entry in t.dataList: ans = ans + entry.data + '_' + entry.folder + '_' + \ ('%.2f' % entry.fraction) + '_' + \ str(entry.dup) + '_' + str(entry.imageMode) + '_' return ans + str(t.mixSize) + '_' + str(sp) saveFile = g.default_repo_dir + "rlt/" + getName() if os.path.exists(saveFile): print(saveFile + " already generated. Continue.") continue if t.mode == "Mix": mixDat = getDat( t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType, t.mixOnly) dataList = [Ent(1, t.data, 'true', None)] print('ftype', t.featureType) trueDat = getDat( t, dataList, g.default_repo_dir + "samples/Mix/M2" + hostname, t.featureType) score = compute_score(trueDat, mixDat, 1, 1, True) else: t.mixSize = t.mixSize * 10 mixDat = getDat( t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType, t.mixOnly) t.mixSize = t.mixSize / 10 dataList = [Ent(1, t.data, 'true_test', None)] trueDat = getDat( t, dataList, g.default_repo_dir + "samples/Mix/M2" + hostname, t.featureType) if t.featureType == 'pix': epsilons = [1000] elif t.featureType == 'smax': epsilons = [0.001] elif t.featureType == 'class': epsilons = [0.1] elif t.featureType == 'conv': epsilons = [1000] score = entropy_score(mixDat, trueDat, epsilons) f = open(saveFile, "wb") pickle.dump(score, f) f.close() elif t.mode == "Incep": mkdir(g.default_repo_dir + "samples/Mix") mkdir(g.default_repo_dir + "rlt") for sp in range(0, 5): def getIncepName(): ans = 'incep_' + t.data + '_' + t.featureType + '_' for entry in t.dataList: ans = ans + entry.data + '_' + entry.folder + '_' + \ ('%.2f' % entry.fraction) + '_' + \ str(entry.dup) + '_' + str(entry.imageMode) + '_' return ans + str(t.mixSize) + "_" + str(sp) incep_file_name = g.default_repo_dir + "rlt/" + getIncepName() if os.path.exists(incep_file_name): print(incep_file_name + " already generated. Continue.") return X = getDat(t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType) # get smax feature incep_score = inception_score(X) f = open(incep_file_name, "wb") pickle.dump(incep_score, f) f.close() elif t.mode == "ModeScore": mkdir(g.default_repo_dir + "samples/Mix") mkdir(g.default_repo_dir + "rlt") for sp in range(0, 5): def getMSName(): ans = 'mode_score_' + t.data + '_' + t.featureType + '_' for entry in t.dataList: ans = ans + entry.data + '_' + entry.folder + '_' + \ ('%.2f' % entry.fraction) + '_' + \ str(entry.dup) + '_' + str(entry.imageMode) + '_' return ans + str(t.mixSize) + "_" + str(sp) ms_file_name = g.default_repo_dir + "rlt/" + getMSName() if os.path.exists(ms_file_name): print(ms_file_name + " already generated. Continue.") return X = getDat(t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType) dataList = [Ent(1, t.data, 'true', None)] Y = getDat(t, dataList, g.default_repo_dir + "samples/Mix/M2" + hostname, t.featureType) modeScore = mode_score(X, Y) f = open(ms_file_name, "wb") pickle.dump(modeScore, f) f.close() elif t.mode == "FID": mkdir(g.default_repo_dir + "samples/Mix") mkdir(g.default_repo_dir + "rlt") for sp in range(0, 5): def getFidName(): ans = 'fid_' + t.data + '_' + t.featureType + '_' for entry in t.dataList: ans = ans + entry.data + '_' + entry.folder + '_' + \ ('%.2f' % entry.fraction) + '_' + \ str(entry.dup) + '_' + str(entry.imageMode) + '_' return ans + str(t.mixSize) + "_" + str(sp) fid_file_name = g.default_repo_dir + "rlt/" + getFidName() if os.path.exists(fid_file_name): print(fid_file_name + " already generated. Continue.") return X = getDat(t, t.dataList, g.default_repo_dir + "samples/Mix/M1" + hostname, t.featureType) dataList = [Ent(1, t.data, 'true', None)] Y = getDat(t, dataList, g.default_repo_dir + "samples/Mix/M2" + hostname, t.featureType) FID = fid(X, Y) f = open(fid_file_name, "wb") pickle.dump(FID, f) f.close() elif t.mode == 'collapse': run_collapse_exp(t.data) elif t.mode == 'drop': run_drop_exp(t.data) elif t.mode == 'overfit': run_overfit_exp(t.data) elif t.mode == 'Gen': # generate necessary data.. if t.data == 'noise': print("\nSampling noise...") noise_sampler(t) elif t.data == 'mnistsub': # subclass print("\nSampling subclass...") t.data = 'mnist' subclass_sampler(t) else: t.folderName = t.model + \ (str(t.epoch) if not t.model.startswith('true') else "") if t.model.startswith('true'): print("\nSampling " + t.folderName + " ...") folder_sampler(t) else: print("\nSampling " + t.folderName + " ...") generator_sampler(t) elif t.mode == "Gan": # generate some fake images.. if t.model == "DCGAN": DCGAN_main(t) elif t.model == "WGAN": WGAN_main(t) elif t.model == "NNGAN": NNGAN_main(t) elif t.model == "MGGAN": MGGAN_main(t) elif t.model == "DCGAN_cluster": DCGAN_cluster_main(t) else: print("error.. Unknown model.") else: print("not designed yet.")