Datasets:
jblitzar
/
github-python-corpus

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wrap_class, wrap_params = parse_wrapper_class(wrapper_class_str)
env = wrap_class(env, *wrap_params)
# Load the model based on the algorithm type
if CONFIG.algorithm == "CLIP-SAC":
model = CLIPRewardedSAC.load(model_ckpt, env=env, config=CONFIG, device=args["device"], load_clip=False)
model.inference_only = True
elif CONFIG.algorithm == "CLIP-PPO":
model = CLIPRewardedPPO.load(model_ckpt, env=env, config=CONFIG, device=args["device"], load_clip=False)
model.inference_only = True
else:
model = AlgorithmRL.load(model_ckpt, env=env, device=args["device"])
print("Model loaded successfully...")
run_eval(env, model, model_ckpt, record_video=args["no_record_video"], eval_suffix=eval_suffix)
env.close()
# <FILESEP>
import numpy as np
import tensorflow as tf
import os, sys
from utils import process
from utils.faiss_rerank import compute_jaccard_distance
from tensorflow.python.layers.core import Dense
from sklearn.preprocessing import label_binarize
from sklearn.cluster import DBSCAN
import torch
import torch.nn.functional as F
import collections
from sklearn.metrics import average_precision_score
from sklearn import metrics as mr
from sklearn.metrics.cluster import adjusted_mutual_info_score as AMI_score
import gc
from functools import partial
from collections import Counter
dataset = ''
probe = ''
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
nb_nodes = 20
ft_size = 3 # originial node feature dimension (D)
time_step = 6 # sequence length (F)
# training params
batch_size = 256
nb_epochs = 100000
patience = 250 # patience for early stopping
k1, k2 = 20, 6 # parameters to compute feature distance matrix
tf.app.flags.DEFINE_string('save_model', '1', "") # save best model
tf.app.flags.DEFINE_string('batch_size', '256', "")
tf.app.flags.DEFINE_string('model_size', '0', "") # output model size and computational complexity
tf.app.flags.DEFINE_string('dataset', 'KS20', "Dataset: IAS, KS20, BIWI, CASIA-B or KGBD")
tf.app.flags.DEFINE_string('probe', 'probe', "for testing probe")
tf.app.flags.DEFINE_string('length', '6', "4, 6, 8 or 10")
tf.app.flags.DEFINE_string('H', '256', "") # embedding size (h) for skeleton representations
tf.app.flags.DEFINE_string('M', '8', "") # number (M) of meta-transformation heads
tf.app.flags.DEFINE_string('eps', '', "distance parameter in DBSCAN")
tf.app.flags.DEFINE_string('min_samples', '', "minimum sample number in DBSCAN")
tf.app.flags.DEFINE_string('gpu', '0', "GPU number")
tf.app.flags.DEFINE_string('probe_type', '', "probe.gallery") # probe and gallery setting for CASIA-B
tf.app.flags.DEFINE_string('patience', '50', "epochs for early stopping")
tf.app.flags.DEFINE_string('mode', 'Train', "Training (Train) or Evaluation (Eval)")
tf.app.flags.DEFINE_string('lr', '0.00035', "learning rate")
tf.app.flags.DEFINE_string('k1', '20', "")
tf.app.flags.DEFINE_string('k2', '6', "")
tf.app.flags.DEFINE_string('focus', '1', "")
FLAGS = tf.app.flags.FLAGS
k1, k2 = int(FLAGS.k1), int(FLAGS.k2)
# check parameters
if FLAGS.dataset not in ['IAS', 'KGBD', 'KS20', 'BIWI', 'CASIA_B']:
raise Exception('Dataset must be IAS, KGBD, KS20, BIWI or CASIA B.')
if FLAGS.dataset == 'CASIA_B':
FLAGS.length = '40'
if FLAGS.length not in ['40', '50', '60']:
raise Exception('Length number must be 40, 50 or 60')
else:
if FLAGS.length not in ['4', '6', '8', '10']:
raise Exception('Length number must be 4, 6, 8 or 10')
if FLAGS.probe not in ['probe', 'Walking', 'Still', 'A', 'B']:
raise Exception('Dataset probe must be "A" (for IAS-A), "B" (for IAS-B), "probe" (for KS20, KGBD).')
if FLAGS.mode not in ['Train', 'Eval']:
raise Exception('Mode must be Train or Eval.')