# Copyright 2019 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from __future__ import absolute_import from __future__ import division from __future__ import print_function """Test person prediction model. See README for running instructions. """ import argparse import models import tensorflow as tf import utils parser = argparse.ArgumentParser() # inputs and outputs parser.add_argument("prepropath", type=str) parser.add_argument("outbasepath", type=str, help="full path will be outbasepath/modelname/runId") parser.add_argument("modelname", type=str) parser.add_argument("--runId", type=int, default=0, help="used for run the same model multiple times") # ---- gpu stuff. Now only one gpu is used parser.add_argument("--gpuid", default=0, type=int) parser.add_argument("--load", action="store_true", default=False, help="whether to load existing model") parser.add_argument("--load_best", action="store_true", default=False, help="whether to load the best model") # use for pre-trained model parser.add_argument("--load_from", type=str, default=None) # ------------- experiment settings parser.add_argument("--obs_len", type=int, default=8) parser.add_argument("--pred_len", type=int, default=12) parser.add_argument("--is_actev", action="store_true", help="is actev/virat dataset, has activity info") # ------------------- basic model parameters parser.add_argument("--emb_size", type=int, default=128) parser.add_argument("--enc_hidden_size", type=int, default=256, help="hidden size for rnn") parser.add_argument("--dec_hidden_size", type=int, default=256, help="hidden size for rnn") parser.add_argument("--activation_func", type=str, default="tanh", help="relu/lrelu/tanh") # ---- multi decoder parser.add_argument("--multi_decoder", action="store_true") # ----------- add person appearance features parser.add_argument("--person_feat_path", type=str, default=None) parser.add_argument("--person_feat_dim", type=int, default=256) parser.add_argument("--person_h", type=int, default=9, help="roi align resize to feature size") parser.add_argument("--person_w", type=int, default=5, help="roi align resize to feature size") # ---------------- other boxes parser.add_argument("--random_other", action="store_true", help="randomize top k other boxes") parser.add_argument("--max_other", type=int, default=15, help="maximum number of other box") parser.add_argument("--box_emb_size", type=int, default=64) # ---------- person pose features parser.add_argument("--add_kp", action="store_true") parser.add_argument("--kp_size", default=17, type=int) # --------- scene features parser.add_argument("--scene_conv_kernel", default=3, type=int) parser.add_argument("--scene_h", default=36, type=int) parser.add_argument("--scene_w", default=64, type=int) parser.add_argument("--scene_class", default=11, type=int) parser.add_argument("--scene_conv_dim", default=64, type=int) parser.add_argument("--pool_scale_idx", default=0, type=int) # --------- activity parser.add_argument("--add_activity", action="store_true") # --------- loss weight parser.add_argument("--act_loss_weight", default=1.0, type=float) parser.add_argument("--grid_loss_weight", default=0.1, type=float) parser.add_argument("--traj_class_loss_weight", default=1.0, type=float) # ---------------------------- training hparam parser.add_argument("--save_period", type=int, default=300, help="num steps to save model and eval") parser.add_argument("--batch_size", type=int, default=64) # num_step will be num_example/batch_size * epoch parser.add_argument("--num_epochs", type=int, default=100) # drop out rate parser.add_argument("--keep_prob", default=1.0, type=float, help="1.0 - drop out rate") # l2 weight decay rate parser.add_argument("--wd", default=0.0001, type=float, help="l2 weight decay loss") parser.add_argument("--clip_gradient_norm", default=10, type=float, help="gradient clipping") parser.add_argument("--optimizer", default="adadelta", help="momentum|adadelta|adam") parser.add_argument("--learning_rate_decay", default=0.95, type=float, help="learning rate decay") parser.add_argument("--num_epoch_per_decay", default=2.0, type=float, help="how epoch after which lr decay") parser.add_argument("--init_lr", default=0.5, type=float, help="Start learning rate") parser.add_argument("--emb_lr", type=float, default=1.0, help="learning scaling factor for 'emb' variables") def main(args): """Run testing.""" test_data = utils.read_data(args, "test") print("total test samples:%s" % test_data.num_examples) if args.random_other: print("warning, testing mode with 'random_other' will result in " "different results every run...") model = models.get_model(args, gpuid=args.gpuid) tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth = True tfconfig.gpu_options.visible_device_list = "%s" % ( ",".join(["%s" % i for i in [args.gpuid]])) with tf.Session(config=tfconfig) as sess: utils.initialize(load=True, load_best=args.load_best, args=args, sess=sess) # load the graph and variables tester = models.Tester(model, args, sess) perf = utils.evaluate(test_data, args, sess, tester) print("performance:") numbers = [] for k in sorted(perf.keys()): print("%s, %s" % (k, perf[k])) numbers.append("%s" % perf[k]) print(" ".join(sorted(perf.keys()))) print(" ".join(numbers)) if __name__ == "__main__": arguments = parser.parse_args() arguments.is_train = False arguments.is_test = True arguments = utils.process_args(arguments) main(arguments)