def main(checkpoint, input_files): os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() model_config, _, track_config = load_cfgs(checkpoint) track_config['log_level'] = 1 track_config["is_video"] = False g = tf.Graph() with g.as_default(): model = inference_wrapper.InferenceWrapper() restore_fn = model.build_graph_from_config(model_config, track_config, checkpoint) g.finalize() if not osp.isdir(track_config['log_dir']): logging.info('Creating inference directory: %s', track_config['log_dir']) mkdir_p(track_config['log_dir']) video_dirs = [] for file_pattern in input_files.split(","): video_dirs.extend(glob(file_pattern)) logging.info("Running tracking on %d videos matching %s", len(video_dirs), input_files) gpu_options = tf.GPUOptions(allow_growth=True) sess_config = tf.ConfigProto(gpu_options=gpu_options) with tf.Session(graph=g, config=sess_config) as sess: restore_fn(sess) tracker = Tracker(model, model_config=model_config, track_config=track_config) for video_dir in video_dirs: if not osp.isdir(video_dir): logging.warning( '{} is not a directory, skipping...'.format(video_dir)) continue video_name = osp.basename(video_dir) video_log_dir = osp.join(track_config['log_dir'], video_name) mkdir_p(video_log_dir) filenames = sort_nicely(glob(video_dir + '/img/*.jpg')) first_line = open(video_dir + '/groundtruth_rect.txt').readline() bb = [int(v) for v in first_line.strip().split(',')] # Rectangle: [x,y,width,height] init_bb = Rectangle(bb[0] - 1, bb[1] - 1, bb[2], bb[3]) # 0-index in python trajectory = tracker.track(sess, init_bb, filenames, video_log_dir) with open(osp.join(video_log_dir, 'track_rect.txt'), 'w') as f: for region in trajectory: rect_str = '{},{},{},{}\n'.format(region.x + 1, region.y + 1, region.width, region.height) f.write(rect_str)
def run_SiamRPN_OPF(seq, rp, bSaveImage): os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() config_name = "SiamRPN_ftall" CHECKPOINT = '/home/lab-xiong.jiangfeng/Projects/SiameseRPN/Logs/%s/track_model_checkpoints/%s' % ( config_name, config_name) logging.info('Evaluating {}...'.format(CHECKPOINT)) # Read configurations from json model_config, _, track_config = load_cfgs(CHECKPOINT) track_config['log_level'] = 0 # Skip verbose logging for speed np.random.seed(1234) tf.set_random_seed(1234) g = tf.Graph() with g.as_default(): model = get_model(model_config['Model'])(model_config=model_config, mode='inference') model.build(reuse=tf.AUTO_REUSE) model.online_net = OnlineNet(online_config, is_training=True, reuse=False) model.online_valnet = OnlineNet(online_config, is_training=False, reuse=True) global_variables_init_op = tf.global_variables_initializer() gpu_options = tf.GPUOptions(allow_growth=True) sess_config = tf.ConfigProto(gpu_options=gpu_options) sess_config.gpu_options.per_process_gpu_memory_fraction = 0.2 with tf.Session(graph=g, config=sess_config) as sess: sess.run(global_variables_init_op) model.restore_weights_from_checkpoint(sess, 605000) tracker = OnlineTracker(sess, model, track_config, online_config, show_video=0) tic = time.clock() frames = seq.s_frames init_rect = seq.init_rect x, y, width, height = init_rect # OTB format init_bb = Rectangle(x - 1, y - 1, width, height) trajectory_py = tracker.track(init_bb, frames, bSaveImage, rp) #print(trajectory_py) trajectory = [ Rectangle(val.x + 1, val.y + 1, val.width, val.height) for val in trajectory_py ] # x, y add one to match OTB format duration = time.clock() - tic result = dict() result['res'] = trajectory result['type'] = 'rect' result['fps'] = round(seq.len / duration, 3) return result
def main(checkpoint, input_files): os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() model_config, _, track_config = load_cfgs(checkpoint) track_config['log_level'] = 1 g = tf.Graph() with g.as_default(): model = inference_wrapper.InferenceWrapper() restore_fn = model.build_graph_from_config(model_config, track_config, checkpoint) g.finalize() if not osp.isdir(track_config['log_dir']): logging.info('Creating inference directory: %s', track_config['log_dir']) mkdir_p(track_config['log_dir']) video_dirs = [] for file_pattern in input_files.split(","): video_dirs.extend(glob(file_pattern)) logging.info("Running tracking on %d videos matching %s", len(video_dirs), input_files) gpu_options = tf.GPUOptions(allow_growth=True) sess_config = tf.ConfigProto(gpu_options=gpu_options) with tf.Session(graph=g, config=sess_config) as sess: restore_fn(sess) tracker = Tracker(model, model_config=model_config, track_config=track_config) for video_dir in video_dirs: if not osp.isdir(video_dir): logging.warning('{} is not a directory, skipping...'.format(video_dir)) continue video_name = osp.basename(video_dir) video_log_dir = osp.join(track_config['log_dir'], video_name) mkdir_p(video_log_dir) filenames = sort_nicely(glob(video_dir + '/img/*.jpg')) first_line = open(video_dir + '/groundtruth_rect.txt').readline() bb = [int(v) for v in first_line.strip().split(',')] init_bb = Rectangle(bb[0] - 1, bb[1] - 1, bb[2], bb[3]) # 0-index in python trajectory = tracker.track(sess, init_bb, filenames, video_log_dir) with open(osp.join(video_log_dir, 'track_rect.txt'), 'w') as f: for region in trajectory: rect_str = '{},{},{},{}\n'.format(region.x + 1, region.y + 1, region.width, region.height) f.write(rect_str)
def __init__( self, debug=0, checkpoint='Logs/SiamFC/track_model_checkpoints/SiamFC-3s-color-pretrained' ): os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() # run only on cpu # os.environ['CUDA_VISIBLE_DEVICES'] = '-1' model_config, _, track_config = load_cfgs(checkpoint) track_config['log_level'] = debug g = tf.Graph() with g.as_default(): model = inference_wrapper.InferenceWrapper() restore_fn = model.build_graph_from_config(model_config, track_config, checkpoint) g.finalize() if not osp.isdir(track_config['log_dir']): logging.info('Creating inference directory: %s', track_config['log_dir']) mkdir_p(track_config['log_dir']) gpu_options = tf.GPUOptions(allow_growth=True) sess_config = tf.ConfigProto(gpu_options=gpu_options) sess = tf.Session(graph=g, config=sess_config) # sess.run(tf.global_variables_initializer()) restore_fn(sess) tracker = Tracker(model, model_config=model_config, track_config=track_config) video_name = "demo" video_log_dir = osp.join(track_config['log_dir'], video_name) rmdir(video_log_dir) mkdir_p(video_log_dir) self.tracker = tracker self.sess = sess self.video_log_dir = video_log_dir self.graph = g
import os import os.path as osp import sys import numpy as np import tensorflow as tf CURRENT_DIR = osp.dirname(__file__) sys.path.append(osp.join(CURRENT_DIR, '..')) import configuration import siamese_model from utils.misc_utils import auto_select_gpu, save_cfgs # Set GPU os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() tf.logging.set_verbosity(tf.logging.DEBUG) from sacred import Experiment ex = Experiment(configuration.RUN_NAME) @ex.config def configurations(): # Add configurations for current script, for more details please see the documentation of `sacred`. model_config = configuration.MODEL_CONFIG train_config = configuration.TRAIN_CONFIG track_config = configuration.TRACK_CONFIG
def main(model_config, train_config, track_config): os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() # Create training directory which will be used to save: configurations, model files, TensorBoard logs train_dir = train_config['train_dir'] if not osp.isdir(train_dir): logging.info('Creating training directory: %s', train_dir) mkdir_p(train_dir) g = tf.Graph() with g.as_default(): # Set fixed seed for reproducible experiments random.seed(train_config['seed']) np.random.seed(train_config['seed']) tf.set_random_seed(train_config['seed']) # Build the training and validation model model = siamese_model.SiameseModel(model_config, train_config, mode='train') model.build() model_va = siamese_model.SiameseModel(model_config, train_config, mode='validation') model_va.build(reuse=True) # Save configurations for future reference save_cfgs(train_dir, model_config, train_config, track_config) learning_rate = _configure_learning_rate(train_config, model.global_step) optimizer = _configure_optimizer(train_config, learning_rate) tf.summary.scalar('learning_rate', learning_rate) # Set up the training ops opt_op = tf.contrib.layers.optimize_loss( loss=model.total_loss, global_step=model.global_step, learning_rate=learning_rate, optimizer=optimizer, clip_gradients=train_config['clip_gradients'], learning_rate_decay_fn=None, summaries=['learning_rate']) with tf.control_dependencies([opt_op]): train_op = tf.no_op(name='train') saver = tf.train.Saver( tf.global_variables(), max_to_keep=train_config['max_checkpoints_to_keep']) summary_writer = tf.summary.FileWriter(train_dir, g) summary_op = tf.summary.merge_all() global_variables_init_op = tf.global_variables_initializer() local_variables_init_op = tf.local_variables_initializer() g.finalize() # Finalize graph to avoid adding ops by mistake # Dynamically allocate GPU memory gpu_options = tf.GPUOptions(allow_growth=True) sess_config = tf.ConfigProto(gpu_options=gpu_options) sess = tf.Session(config=sess_config) model_path = tf.train.latest_checkpoint(train_config['train_dir']) if not model_path: sess.run(global_variables_init_op) sess.run(local_variables_init_op) start_step = 0 if model_config['embed_config']['embedding_checkpoint_file']: model.init_fn(sess) else: logging.info('Restore from last checkpoint: {}'.format(model_path)) sess.run(local_variables_init_op) saver.restore(sess, model_path) start_step = tf.train.global_step(sess, model.global_step.name) + 1 # Training loop data_config = train_config['train_data_config'] total_steps = int(data_config['epoch'] * data_config['num_examples_per_epoch'] / data_config['batch_size']) logging.info('Train for {} steps'.format(total_steps)) for step in range(start_step, total_steps): start_time = time.time() _, loss, batch_loss = sess.run( [train_op, model.total_loss, model.batch_loss]) duration = time.time() - start_time if step % 10 == 0: examples_per_sec = data_config['batch_size'] / float(duration) time_remain = data_config['batch_size'] * ( total_steps - step) / examples_per_sec m, s = divmod(time_remain, 60) h, m = divmod(m, 60) format_str = ( '%s: step %d, total loss = %.2f, batch loss = %.2f (%.1f examples/sec; %.3f ' 'sec/batch; %dh:%02dm:%02ds remains)') logging.info(format_str % (datetime.now(), step, loss, batch_loss, examples_per_sec, duration, h, m, s)) if step % 100 == 0: summary_str = sess.run(summary_op) summary_writer.add_summary(summary_str, step) if step % train_config['save_model_every_n_step'] == 0 or ( step + 1) == total_steps: checkpoint_path = osp.join(train_config['train_dir'], 'model.ckpt') saver.save(sess, checkpoint_path, global_step=step)
def main(model_config, train_config, track_config): os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() # Create training directory which will be used to save: configurations, model files, TensorBoard logs train_dir = train_config['train_dir'] if not osp.isdir(train_dir): logging.info('Creating training directory: %s', train_dir) mkdir_p(train_dir) if have_cfgs(train_dir): model_config, train_config, track_config = load_cfgs(train_dir) print("=================== load cfg ") else: save_cfgs(train_dir, model_config, train_config, track_config) print("=================== save default cfg, please modify files in {}".format(train_dir)) return g = tf.Graph() with g.as_default(): # Set fixed seed for reproducible experiments random.seed(train_config['seed']) np.random.seed(train_config['seed']) tf.set_random_seed(train_config['seed']) # Build the training and validation model model = siamese_model.SiameseModel(model_config, train_config, track_config, mode='train') model.build() model_va = siamese_model.SiameseModel(model_config, train_config, track_config, mode='validation') model_va.build(reuse=True) learning_rate = _configure_learning_rate(train_config, model.global_step) optimizer = _configure_optimizer(train_config, learning_rate) tf.summary.scalar('learning_rate', learning_rate) # general way for run train: https://qiita.com/horiem/items/00ec6488b23895cc4fe2 # tensorflow 2.1: https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough # Set up the training ops opt_op = tensorflow.contrib.layers.optimize_loss( loss=model.total_loss, global_step=model.global_step, learning_rate=learning_rate, optimizer=optimizer, clip_gradients=train_config['clip_gradients'], learning_rate_decay_fn=None, summaries=['learning_rate']) with tf.control_dependencies([opt_op]): train_op = tf.no_op(name='train') saver = tf.train.Saver(tf.global_variables(), max_to_keep=train_config['max_checkpoints_to_keep']) summary_writer = tf.summary.FileWriter(train_dir, g) summary_op = tf.summary.merge_all() global_variables_init_op = tf.global_variables_initializer() local_variables_init_op = tf.local_variables_initializer() g.finalize() # Finalize graph to avoid adding ops by mistake # Dynamically allocate GPU memory gpu_options = tf.GPUOptions(allow_growth=True) sess_config = tf.ConfigProto(gpu_options=gpu_options) sess = tf.Session(config=sess_config) model_path = tf.train.latest_checkpoint(train_config['train_dir']) if not model_path: sess.run(global_variables_init_op) sess.run(local_variables_init_op) start_step = 0 if model_config['embed_config']['embedding_checkpoint_file']: model.init_fn(sess) else: logging.info('Restore from last checkpoint: {}'.format(model_path)) sess.run(local_variables_init_op) saver.restore(sess, model_path) start_step = tf.train.global_step(sess, model.global_step.name) + 1 # export if train_config["export"]: # still debugging ''' frozen_graph_def = tf.graph_util.convert_variables_to_constants(sess, tf.get_default_graph().as_graph_def(), ["train/detection/add"]) frozen_graph = tf.Graph() with frozen_graph.as_default(): tf.import_graph_def(frozen_graph_def) save_model_dir = osp.join(train_config['train_dir'], 'models') tf.train.write_graph(frozen_graph_def, save_model_dir, 'quantized_frozen_graph.pb', as_text=False) tf.train.write_graph(frozen_graph_def, save_model_dir, 'quantized_frozen_graph.pbtxt', as_text=True) output_op = sess.graph.get_tensor_by_name("validation/detection/add:0") input1_op = sess.graph.get_tensor_by_name("validation/template_image:0") input2_op = sess.graph.get_tensor_by_name("validation/input_image:0") converter = tf.lite.TFLiteConverter.from_session(sess, [input1_op, input2_op], [output_op]) converter.inference_type = tf.lite.constants.QUANTIZED_UINT8 input_arrays = converter.get_input_arrays() converter.quantized_input_stats = {input_arrays[0] : (0., 1.), input_arrays[1] : (0., 1.)} # mean, std_dev converter.default_ranges_stats = (0, 255) tflite_model = converter.convert() open(osp.join(save_model_dir, 'quantized_frozen_graph.tflite'), "wb").write(tflite_model) ''' return # Training loop data_config = train_config['train_data_config'] total_steps = int(data_config['epoch'] * data_config['num_examples_per_epoch'] / data_config['batch_size']) logging.info('Train for {} steps'.format(total_steps)) save_step = int(data_config['num_examples_per_epoch'] / data_config['batch_size']) print("=========== save_step: {}".format(save_step)) for step in range(start_step, total_steps): start_time = time.time() # no "feed_dict" # has "feed_dict" exmaple (mnist): https://qiita.com/SwitchBlade/items/6677c283b2402d060cd0 _, loss, batch_loss, instances, response = sess.run([train_op, model.total_loss, model.batch_loss, model.instances, model.response]) duration = time.time() - start_time if step % 10 == 0: examples_per_sec = data_config['batch_size'] / float(duration) time_remain = data_config['batch_size'] * (total_steps - step) / examples_per_sec m, s = divmod(time_remain, 60) h, m = divmod(m, 60) format_str = ('%s: step %d, total loss = %.2f, batch loss = %.2f (%.1f examples/sec; %.3f ' 'sec/batch; %dh:%02dm:%02ds remains)') logging.info(format_str % (datetime.now(), step, loss, batch_loss, examples_per_sec, duration, h, m, s)) if step % 100 == 0: summary_str = sess.run(summary_op) summary_writer.add_summary(summary_str, step) if step % save_step == 0 or (step + 1) == total_steps: checkpoint_path = osp.join(train_config['train_dir'], 'model.ckpt') saver.save(sess, checkpoint_path, global_step=step)
def main(model_config, train_config, track_config): os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() # Create training directory which will be used to save: configurations, model files, TensorBoard logs train_dir = train_config['train_dir'] if not osp.isdir(train_dir): logging.info('Creating training directory: %s', train_dir) mkdir_p(train_dir) g = tf.Graph() with g.as_default(): # Set fixed seed for reproducible experiments random.seed(train_config['seed']) np.random.seed(train_config['seed']) tf.set_random_seed(train_config['seed']) # Build the training and validation model model = siamese_model.SiameseModel(model_config, train_config, mode='train') model.build() model_va = siamese_model.SiameseModel(model_config, train_config, mode='validation') model_va.build(reuse=True) # Save configurations for future reference save_cfgs(train_dir, model_config, train_config, track_config) learning_rate = _configure_learning_rate(train_config, model.global_step) optimizer = _configure_optimizer(train_config, learning_rate) tf.summary.scalar('learning_rate', learning_rate) # Set up the training ops opt_op = tf.contrib.layers.optimize_loss( loss=model.total_loss, global_step=model.global_step, learning_rate=learning_rate, optimizer=optimizer, clip_gradients=train_config['clip_gradients'], learning_rate_decay_fn=None, summaries=['learning_rate']) with tf.control_dependencies([opt_op]): train_op = tf.no_op(name='train') saver = tf.train.Saver(tf.global_variables(), max_to_keep=train_config['max_checkpoints_to_keep']) summary_writer = tf.summary.FileWriter(train_dir, g) summary_op = tf.summary.merge_all() global_variables_init_op = tf.global_variables_initializer() local_variables_init_op = tf.local_variables_initializer() g.finalize() # Finalize graph to avoid adding ops by mistake # Dynamically allocate GPU memory gpu_options = tf.GPUOptions(allow_growth=True) sess_config = tf.ConfigProto(gpu_options=gpu_options) sess = tf.Session(config=sess_config) model_path = tf.train.latest_checkpoint(train_config['train_dir']) if not model_path: sess.run(global_variables_init_op) sess.run(local_variables_init_op) start_step = 0 if model_config['embed_config']['embedding_checkpoint_file']: model.init_fn(sess) else: logging.info('Restore from last checkpoint: {}'.format(model_path)) sess.run(local_variables_init_op) saver.restore(sess, model_path) start_step = tf.train.global_step(sess, model.global_step.name) + 1 # Training loop data_config = train_config['train_data_config'] total_steps = int(data_config['epoch'] * data_config['num_examples_per_epoch'] / data_config['batch_size']) logging.info('Train for {} steps'.format(total_steps)) for step in range(start_step, total_steps): start_time = time.time() _, loss, batch_loss = sess.run([train_op, model.total_loss, model.batch_loss]) duration = time.time() - start_time if step % 10 == 0: examples_per_sec = data_config['batch_size'] / float(duration) time_remain = data_config['batch_size'] * (total_steps - step) / examples_per_sec m, s = divmod(time_remain, 60) h, m = divmod(m, 60) format_str = ('%s: step %d, total loss = %.2f, batch loss = %.2f (%.1f examples/sec; %.3f ' 'sec/batch; %dh:%02dm:%02ds remains)') logging.info(format_str % (datetime.now(), step, loss, batch_loss, examples_per_sec, duration, h, m, s)) if step % 100 == 0: summary_str = sess.run(summary_op) summary_writer.add_summary(summary_str, step) if step % train_config['save_model_every_n_step'] == 0 or (step + 1) == total_steps: checkpoint_path = osp.join(train_config['train_dir'], 'model.ckpt') saver.save(sess, checkpoint_path, global_step=step)
def main(model_config, train_config, track_config): # GPU Config gpu_list = train_config['train_data_config'].get('gpu_ids', '0') num_gpus = len(gpu_list.split(',')) if num_gpus > 1: os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list else: os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() # Create training directory which will be used to save: configurations, model files, TensorBoard logs train_dir = train_config['train_dir'] if not osp.isdir(train_dir): logging.info('Creating training directory: %s', train_dir) mkdir_p(train_dir) g = tf.Graph() with g.as_default(): # Set fixed seed for reproducible experiments random.seed(train_config['seed']) np.random.seed(train_config['seed']) tf.set_random_seed(train_config['seed']) #Build global step with tf.name_scope('train/'): global_step = tf.Variable(initial_value=0, name='global_step', trainable=False, collections=[ tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES ]) Model = get_model(model_config['Model']) # build training dataloader and validation dataloader #---train train_dataloader = DataLoader(train_config['train_data_config'], is_training=True) train_dataloader.build() train_inputs = train_dataloader.get_one_batch() #---validation val_dataloader = DataLoader(train_config['validation_data_config'], is_training=False) val_dataloader.build() val_inputs = val_dataloader.get_one_batch() # Save configurations for future reference save_cfgs(train_dir, model_config, train_config, track_config) if train_config['lr_config'].get('lr_warmup', False): warmup_epoch_num = 10 init_lr_ratio = 0.8 warmup_steps = warmup_epoch_num * int( train_config['train_data_config']['num_examples_per_epoch'] ) // train_config['train_data_config']['batch_size'] inc_per_step = ( 1 - init_lr_ratio ) * train_config['lr_config']['initial_lr'] / warmup_steps warmup_lr = train_config['lr_config'][ 'initial_lr'] * init_lr_ratio + inc_per_step * tf.to_float( global_step) learning_rate = tf.cond( tf.less(global_step, warmup_steps), lambda: tf.identity(warmup_lr), lambda: _configure_learning_rate(train_config, global_step - warmup_steps)) else: learning_rate = _configure_learning_rate(train_config, global_step) optimizer = _configure_optimizer(train_config, learning_rate) tf.summary.scalar('learning_rate', learning_rate) # Set up the training ops examplars, instances, gt_examplar_boxes, gt_instance_boxes = tf.split(train_inputs[0],num_gpus), \ tf.split(train_inputs[1],num_gpus), \ tf.split(train_inputs[2],num_gpus), \ tf.split(train_inputs[3],num_gpus) if train_config['train_data_config'].get('time_decay', False): time_intervals = tf.split(train_inputs[4], num_gpus) tower_grads = [] with tf.variable_scope(tf.get_variable_scope()): for i in range(num_gpus): with tf.device('/gpu:%d' % i): if train_config['train_data_config'].get( 'time_decay', False): inputs = [ examplars[i], instances[i], gt_examplar_boxes[i], gt_instance_boxes[i], time_intervals[i] ] else: inputs = [ examplars[i], instances[i], gt_examplar_boxes[i], gt_instance_boxes[i] ] model = tower_model(Model, inputs, model_config, train_config, mode='train') # Reuse variables for the next tower. tf.get_variable_scope().reuse_variables() grads = optimizer.compute_gradients(model.total_loss) tower_grads.append(grads) grads = average_gradients(tower_grads) #Clip gradient gradients, tvars = zip(*grads) clip_gradients, _ = tf.clip_by_global_norm( gradients, train_config['clip_gradients']) train_op = optimizer.apply_gradients(zip(clip_gradients, tvars), global_step=global_step) #Build validation model with tf.device('/gpu:0'): model_va = Model(model_config, train_config, mode='validation', inputs=val_inputs) model_va.build(reuse=True) #Save Model setup saver = tf.train.Saver( tf.global_variables(), max_to_keep=train_config['max_checkpoints_to_keep']) summary_writer = tf.summary.FileWriter(train_dir, g) summary_op = tf.summary.merge_all() global_variables_init_op = tf.global_variables_initializer() local_variables_init_op = tf.local_variables_initializer() # Dynamically allocate GPU memory gpu_options = tf.GPUOptions(allow_growth=True) sess_config = tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True) #inter_op_parallelism_threads = 16, intra_op_parallelism_threads = 16, log_device_placement=True) ######Debug timeline if Debug: from tensorflow.python.client import timeline run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() ######Debug timeline sess = tf.Session(config=sess_config) model_path = tf.train.latest_checkpoint(train_config['train_dir']) if not model_path: sess.run(global_variables_init_op) sess.run(local_variables_init_op) start_step = 0 if model_config['embed_config']['embedding_checkpoint_file']: model.init_fn(sess) elif model_config['finetuned_checkpoint_file']: finetuned_checkpoint_file = tf.train.latest_checkpoint( model_config['finetuned_checkpoint_file']) logging.info('Restore from last checkpoint: {}'.format( finetuned_checkpoint_file)) sess.run(local_variables_init_op) sess.run(global_variables_init_op) restore_op = tf.contrib.slim.assign_from_checkpoint_fn( finetuned_checkpoint_file, tf.global_variables(), ignore_missing_vars=True) restore_op(sess) #reset global step saved in checkpoint global_step_reset_op = global_step.assign(0) sess.run(global_step_reset_op) else: logging.info('Restore from last checkpoint: {}'.format(model_path)) sess.run(local_variables_init_op) sess.run(global_variables_init_op) #saver.restore(sess, model_path) restore_op = tf.contrib.slim.assign_from_checkpoint_fn( model_path, tf.global_variables(), ignore_missing_vars=True) restore_op(sess) start_step = tf.train.global_step(sess, global_step.name) + 1 print_trainable(sess) #help function, can be disenable g.finalize() # Finalize graph to avoid adding ops by mistake # Training loop data_config = train_config['train_data_config'] total_steps = int(data_config['epoch'] * data_config['num_examples_per_epoch'] / data_config['batch_size']) logging.info('Train for {} steps'.format(total_steps)) for step in range(start_step, total_steps): try: start_time = time.time() if Debug: _, loss, batch_loss, current_lr = sess.run( [ train_op, model.total_loss, model.batch_loss, learning_rate ], run_metadata=run_metadata, options=run_options) t1 = timeline.Timeline(run_metadata.step_stats) ctf = t1.generate_chrome_trace_format() with open('timeline.json', 'w') as f: f.write(ctf) else: _, loss, batch_loss, current_lr = sess.run([ train_op, model.total_loss, model.batch_loss, learning_rate ]) duration = time.time() - start_time if step % 10 == 0: examples_per_sec = data_config['batch_size'] / float( duration) time_remain = data_config['batch_size'] * ( total_steps - step) / examples_per_sec current_epoch = ( step * data_config['batch_size'] ) // data_config['num_examples_per_epoch'] + 1 m, s = divmod(time_remain, 60) h, m = divmod(m, 60) format_str = ( '%s: epoch %d-step %d,lr = %f, total loss = %.3f, batch loss = %.3f (%.1f examples/sec; %.3f ' 'sec/batch; %dh:%02dm:%02ds remains)') logging.info( format_str % (datetime.now(), current_epoch, step, current_lr, loss, batch_loss, examples_per_sec, duration, h, m, s)) if step % 200 == 0: summary_str = sess.run(summary_op) summary_writer.add_summary(summary_str, step) if step % train_config['save_model_every_n_step'] == 0 or ( step + 1) == total_steps: checkpoint_path = osp.join(train_config['train_dir'], 'model.ckpt') saver.save(sess, checkpoint_path, global_step=step) except KeyboardInterrupt: checkpoint_path = osp.join(train_config['train_dir'], 'model.ckpt') saver.save(sess, checkpoint_path, global_step=step) print("save model.ckpt-%d" % (step)) break except: print(traceback.format_exc()) print("Error found in current step, continue")
def main(checkpoint, input_files): os.environ['CUDA_VISIBLE_DEVICES'] = auto_select_gpu() model_config, _, track_config = load_cfgs(checkpoint) track_config['log_level'] = 1 g = tf.Graph() with g.as_default(): model = inference_wrapper.InferenceWrapper() restore_fn = model.build_graph_from_config(model_config, track_config, checkpoint) g.finalize() if not osp.isdir(track_config['log_dir']): logging.info('Creating inference directory: %s', track_config['log_dir']) mkdir_p(track_config['log_dir']) video_dirs = [] for file_pattern in input_files.split(","): video_dirs.extend(glob(file_pattern)) logging.info("Running tracking on %d videos matching %s", len(video_dirs), input_files) gpu_options = tf.GPUOptions(allow_growth=True) sess_config = tf.ConfigProto(gpu_options=gpu_options) with tf.Session(graph=g, config=sess_config) as sess: restore_fn(sess) tracker = Tracker(model, model_config=model_config, track_config=track_config) for video_dir in video_dirs: if not osp.isdir(video_dir): logging.warning( '{} is not a directory, skipping...'.format(video_dir)) continue video_name = osp.basename(video_dir) video_log_dir = osp.join(track_config['log_dir'], video_name) mkdir_p(video_log_dir) filenames = sort_nicely( glob(video_dir + '/img/*.jpg') + glob(video_dir + '/img/*.png')) first_line = open(video_dir + '/groundtruth_rect.txt').readline() bb = [int(v) for v in first_line.strip().split(',')] init_bb = Rectangle(bb[0] - 1, bb[1] - 1, bb[2], bb[3]) # 0-index in python trajectory = tracker.track(sess, init_bb, filenames, video_log_dir) with open(osp.join(video_log_dir, 'track_rect.txt'), 'w') as f: for region in trajectory: rect_str = '{},{},{},{}\n'.format(region.x + 1, region.y + 1, region.width, region.height) f.write(rect_str) with open(osp.join(video_log_dir, 'bboxes.json'), 'r') as f: data = json.load(f) final_output = {} for i, fname in enumerate(data.keys()): img = np.array(Image.open(fname).convert('RGB')) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #print(img,img.shape) bboxes = data[fname] bboxes = list( map( lambda x: list( map(lambda y: float(y), x.strip().split(','))), bboxes)) arr = [] for x, y, w, h in bboxes: ymin, xmin, ymax, xmax = int(y), int(x), int(y + h), int(x + w) img = cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0, 255), 2) arr.append([ymin, xmin, ymax, xmax]) final_output[fname] = arr name = osp.basename(fname) name = osp.splitext(name)[0] W, H, _ = img.shape cv2.imshow("Pic", cv2.resize(img, (W // 2, H // 2))) cv2.waitKey(0) out_folder = osp.join(video_log_dir, "Outputs") mkdir_p(out_folder) cv2.imwrite(osp.join(out_folder, f"{name}_bbox.png"), img) with open(osp.join(out_folder, "output.json"), "w") as f: json.dump(final_output, f, indent=4) cv2.destroyAllWindows()