def train_setup(self): tf.set_random_seed(self.conf.random_seed) # Create queue coordinator. self.coord = tf.train.Coordinator() # Input size h, w = (self.conf.input_height, self.conf.input_width) input_size = (h, w) # Devices gpu_list = get_available_gpus() zip_encoder, zip_decoder_b, zip_decoder_w = [], [], [] restore_vars = [] self.loaders = [] self.im_list = [] for i in range(len(gpu_list)): with tf.device(gpu_list[i]): # Load reader with tf.name_scope("create_inputs"): reader = ImageReader(self.conf.data_dir, self.conf.data_list, input_size, self.conf.random_scale, self.conf.random_mirror, self.conf.ignore_label, IMG_MEAN, self.coord) self.image_batch, self.label_batch, names = reader.dequeue( self.conf.batch_size) self.im_list.append(self.image_batch) image_batch_075 = tf.image.resize_images( self.image_batch, [int(h * 0.75), int(w * 0.75)]) image_batch_05 = tf.image.resize_images( self.image_batch, [int(h * 0.5), int(w * 0.5)]) # Create network with tf.variable_scope('', reuse=False): net = Deeplab_v2(self.image_batch, self.conf.num_classes, True) with tf.variable_scope('', reuse=True): net075 = Deeplab_v2(image_batch_075, self.conf.num_classes, True) with tf.variable_scope('', reuse=True): net05 = Deeplab_v2(image_batch_05, self.conf.num_classes, True) # Variables that load from pre-trained model. restore_var = [ v for v in tf.global_variables() if 'fc' not in v.name ] restore_vars.append(restore_var) # Trainable Variables all_trainable = tf.trainable_variables() # Fine-tune part encoder_trainable = [ v for v in all_trainable if 'fc' not in v.name ] # lr * 1.0 # Decoder part decoder_trainable = [ v for v in all_trainable if 'fc' in v.name ] decoder_w_trainable = [ v for v in decoder_trainable if 'weights' in v.name or 'gamma' in v.name ] # lr * 10.0 decoder_b_trainable = [ v for v in decoder_trainable if 'biases' in v.name or 'beta' in v.name ] # lr * 20.0 # Check assert (len(all_trainable) == len(decoder_trainable) + len(encoder_trainable)) assert (len(decoder_trainable) == len(decoder_w_trainable) + len(decoder_b_trainable)) # Network raw output raw_output100 = net.outputs raw_output075 = net075.outputs raw_output05 = net05.outputs raw_output = tf.reduce_max(tf.stack([ raw_output100, tf.image.resize_images(raw_output075, tf.shape(raw_output100)[1:3, ]), tf.image.resize_images(raw_output05, tf.shape(raw_output100)[1:3, ]) ]), axis=0) # Groud Truth: ignoring all labels greater or equal than n_classes label_proc = prepare_label(self.label_batch, tf.stack( raw_output.get_shape()[1:3]), num_classes=self.conf.num_classes, one_hot=False) # [batch_size, h, w] label_proc075 = prepare_label( self.label_batch, tf.stack(raw_output075.get_shape()[1:3]), num_classes=self.conf.num_classes, one_hot=False) label_proc05 = prepare_label( self.label_batch, tf.stack(raw_output05.get_shape()[1:3]), num_classes=self.conf.num_classes, one_hot=False) raw_gt = tf.reshape(label_proc, [ -1, ]) raw_gt075 = tf.reshape(label_proc075, [ -1, ]) raw_gt05 = tf.reshape(label_proc05, [ -1, ]) indices = tf.squeeze( tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)), 1) indices075 = tf.squeeze( tf.where( tf.less_equal(raw_gt075, self.conf.num_classes - 1)), 1) indices05 = tf.squeeze( tf.where(tf.less_equal(raw_gt05, self.conf.num_classes - 1)), 1) gt = tf.cast(tf.gather(raw_gt, indices), tf.int32) gt075 = tf.cast(tf.gather(raw_gt075, indices075), tf.int32) gt05 = tf.cast(tf.gather(raw_gt05, indices05), tf.int32) raw_prediction = tf.reshape(raw_output, [-1, self.conf.num_classes]) raw_prediction100 = tf.reshape(raw_output100, [-1, self.conf.num_classes]) raw_prediction075 = tf.reshape(raw_output075, [-1, self.conf.num_classes]) raw_prediction05 = tf.reshape(raw_output05, [-1, self.conf.num_classes]) prediction = tf.gather(raw_prediction, indices) prediction100 = tf.gather(raw_prediction100, indices) prediction075 = tf.gather(raw_prediction075, indices075) prediction05 = tf.gather(raw_prediction05, indices05) # Pixel-wise softmax_cross_entropy loss loss = tf.nn.sparse_softmax_cross_entropy_with_logits( logits=prediction, labels=gt) loss100 = tf.nn.sparse_softmax_cross_entropy_with_logits( logits=prediction100, labels=gt) loss075 = tf.nn.sparse_softmax_cross_entropy_with_logits( logits=prediction075, labels=gt075) loss05 = tf.nn.sparse_softmax_cross_entropy_with_logits( logits=prediction05, labels=gt05) # L2 regularization l2_losses = [ self.conf.weight_decay * tf.nn.l2_loss(v) for v in all_trainable if 'weights' in v.name ] # Loss function self.reduced_loss = tf.reduce_mean(loss) + tf.reduce_mean( loss100) + tf.reduce_mean(loss075) + tf.reduce_mean( loss05) + tf.add_n(l2_losses) # Define optimizers # 'poly' learning rate base_lr = tf.constant(self.conf.learning_rate) self.curr_step = tf.placeholder(dtype=tf.float32, shape=()) learning_rate = tf.scalar_mul( base_lr, tf.pow((1 - self.curr_step / self.conf.num_steps), self.conf.power)) # We have several optimizers here in order to handle the different lr_mult # which is a kind of parameters in Caffe. This controls the actual lr for each # layer. opt_encoder = tf.train.MomentumOptimizer( learning_rate, self.conf.momentum) opt_decoder_w = tf.train.MomentumOptimizer( learning_rate * 10.0, self.conf.momentum) opt_decoder_b = tf.train.MomentumOptimizer( learning_rate * 20.0, self.conf.momentum) # Gradient accumulation # Define a variable to accumulate gradients. accum_grads = [ tf.Variable(tf.zeros_like(v.initialized_value()), trainable=False) for v in encoder_trainable + decoder_w_trainable + decoder_b_trainable ] # Define an operation to clear the accumulated gradients for next batch. self.zero_op = [ v.assign(tf.zeros_like(v)) for v in accum_grads ] # To make sure each layer gets updated by different lr's, we do not use 'minimize' here. # Instead, we separate the steps compute_grads+update_params. # Compute grads grads = tf.gradients( self.reduced_loss, encoder_trainable + decoder_w_trainable + decoder_b_trainable) # Accumulate and normalise the gradients. self.accum_grads_op = [ accum_grads[i].assign_add(grad / self.conf.grad_update_every) for i, grad in enumerate(grads) ] grads = tf.gradients( self.reduced_loss, encoder_trainable + decoder_w_trainable + decoder_b_trainable) grads_encoder = accum_grads[:len(encoder_trainable)] grads_decoder_w = accum_grads[len(encoder_trainable):( len(encoder_trainable) + len(decoder_w_trainable))] grads_decoder_b = accum_grads[(len(encoder_trainable) + len(decoder_w_trainable)):] zip_encoder.append(list(zip(grads_encoder, encoder_trainable))) zip_decoder_b.append( list(zip(grads_decoder_w, decoder_w_trainable))) zip_decoder_w.append( list(zip(grads_decoder_b, decoder_b_trainable))) avg_grads_encoder = average_gradients(zip_encoder) avg_grads_decoder_w = average_gradients(zip_decoder_w) avg_grads_decoder_b = average_gradients(zip_decoder_b) for i in range(len(gpu_list)): with tf.device(gpu_list[i]): # Update params train_op_conv = opt_encoder.apply_gradients(avg_grads_encoder) train_op_fc_w = opt_decoder_w.apply_gradients( avg_grads_decoder_w) train_op_fc_b = opt_decoder_b.apply_gradients( avg_grads_decoder_b) # Finally, get the train_op! update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS ) # for collecting moving_mean and moving_variance with tf.control_dependencies(update_ops): self.train_op = tf.group(train_op_conv, train_op_fc_w, train_op_fc_b) # Saver for storing checkpoints of the model self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=0) # Loader for loading the pre-trained model for i in range(len(gpu_list)): with tf.device(gpu_list[i]): #print(restore_var) #print("restoring gpu ", i) self.loaders.append(tf.train.Saver(var_list=restore_vars[i])) #print("restored gpu ", i) # Training summary # Processed predictions: for visualisation. raw_output_up = tf.image.resize_bilinear(raw_output, input_size) raw_output_up = tf.argmax(raw_output_up, axis=3) self.pred = tf.expand_dims(raw_output_up, axis=3) # Image summary. images_summary = tf.py_func(inv_preprocess, [self.image_batch, 1, IMG_MEAN], tf.uint8) labels_summary = tf.py_func( decode_labels, [self.label_batch, 1, self.conf.num_classes], tf.uint8) preds_summary = tf.py_func(decode_labels, [self.pred, 1, self.conf.num_classes], tf.uint8) self.total_summary = tf.summary.image( 'images', tf.concat(axis=2, values=[images_summary, labels_summary, preds_summary]), max_outputs=1) # Concatenate row-wise. if not os.path.exists(self.conf.logdir): os.makedirs(self.conf.logdir) self.summary_writer = tf.summary.FileWriter( self.conf.logdir, graph=tf.get_default_graph())
def train(args): ## set hyparameter img_mean = np.array((104.00698793, 116.66876762, 122.67891434), dtype=np.float32) tf.set_random_seed(args.random_seed) coord = tf.train.Coordinator() LAMBDA = 10 print("d_model_name:", args.d_name) print("lambda:", args.lamb) print("learning_rate:", args.learning_rate) print("is_val:", args.is_val) print("---------------------------------") ## load data with tf.name_scope("create_inputs"): reader = ImageReader(args.data_dir, args.img_size, args.random_scale, args.random_mirror, args.random_crop, args.ignore_label, args.is_val, img_mean, coord) image_batch, label_batch = reader.dequeue(args.batch_size) print("Data is ready!") ## load model g_net = choose_generator(args.g_name, image_batch) score_map = g_net.get_output() fk_batch = tf.nn.softmax(score_map, dim=-1) gt_batch = tf.one_hot(label_batch, args.num_classes, dtype=tf.float32) x_batch = tf.train.batch([ (reader.image + img_mean) / 255., ], args.batch_size, dynamic_pad=True) # normalization d_fk_net, d_gt_net = choose_discriminator(args.d_name, fk_batch, gt_batch, x_batch) d_fk_pred = d_fk_net.get_output() # fake segmentation result in d d_gt_pred = d_gt_net.get_output() # ground-truth result in d label, logits = convert_to_calculateloss(score_map, args.num_classes, label_batch) predict_label = tf.argmax(logits, axis=1) predict_batch = g_net.topredict(score_map, tf.shape(image_batch)[1:3]) print("The model has been created!") ## get all kinds of variables list g_restore_var = [ v for v in tf.global_variables() if 'discriminator' not in v.name ] vgg_restore_var = [ v for v in tf.global_variables() if 'discriminator' in v.name and 'image' in v.name ] g_var = [ v for v in tf.trainable_variables() if 'discriminator' not in v.name ] d_var = [ v for v in tf.trainable_variables() if 'discriminator' in v.name and 'image' not in v.name ] # g_trainable_var = [v for v in g_var if 'beta' not in v.name or 'gamma' not in v.name] #batch_norm training open g_trainable_var = g_var d_trainable_var = d_var ## set loss mce_loss = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label, logits=logits)) g_bce_loss = -tf.reduce_mean(d_fk_pred) g_loss = mce_loss + args.lamb * g_bce_loss fk_score_var = tf.reduce_mean(d_fk_pred) gt_score_var = tf.reduce_mean(d_gt_pred) d_loss = fk_score_var - gt_score_var alpha = tf.random_uniform(shape=tf.shape(gt_batch), minval=0., maxval=1.) differences = fk_batch - gt_batch interpolates = gt_batch + (alpha * differences) gradients = tf.gradients( Discriminator_add_vgg({ 'seg': interpolates, 'data': x_batch }, reuse=True).get_output(), [interpolates])[0] slopes = tf.sqrt( tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2, 3])) gradient_penalty = tf.reduce_mean((slopes - 1.)**2) d_loss += gradient_penalty mce_loss_var, mce_loss_op = tf.metrics.mean(mce_loss) g_bce_loss_var, g_bce_loss_op = tf.metrics.mean(g_bce_loss) g_loss_var, g_loss_op = tf.metrics.mean(g_loss) d_loss_var, d_loss_op = tf.metrics.mean(d_loss) iou_var, iou_op = tf.metrics.mean_iou(label, predict_label, args.num_classes) accuracy_var, acc_op = tf.metrics.accuracy(label, predict_label) metrics_op = tf.group(mce_loss_op, g_bce_loss_op, g_loss_op, d_loss_op, iou_op, acc_op) ## set optimizer iterstep = tf.placeholder(dtype=tf.float32, shape=[], name='iteration_step') base_lr = tf.constant(args.learning_rate, dtype=tf.float32, shape=[]) lr = tf.scalar_mul(base_lr, tf.pow( (1 - iterstep / args.num_steps), args.power)) # learning rate reduce with the time g_gradients = tf.train.AdamOptimizer(learning_rate=lr).compute_gradients( g_loss, g_trainable_var) d_gradients = tf.train.AdamOptimizer( learning_rate=lr * 10).compute_gradients(d_loss, d_trainable_var) grad_fk_oi = tf.gradients(d_fk_pred, fk_batch, name='grad_fk_oi')[0] grad_gt_oi = tf.gradients(d_gt_pred, gt_batch, name='grad_gt_oi')[0] grad_fk_img_oi = tf.gradients(d_fk_pred, image_batch, name='grad_fk_img_oi')[0] grad_gt_img_oi = tf.gradients(d_gt_pred, image_batch, name='grad_gt_img_oi')[0] train_g_op = tf.train.AdamOptimizer(learning_rate=lr).minimize( g_loss, var_list=g_trainable_var) train_d_op = tf.train.AdamOptimizer(learning_rate=lr * 10).minimize( d_loss, var_list=d_trainable_var) ## set summary vs_image = tf.py_func(inv_preprocess, [image_batch, args.save_num_images, img_mean], tf.uint8) vs_label = tf.py_func( decode_labels, [label_batch, args.save_num_images, args.num_classes], tf.uint8) vs_predict = tf.py_func( decode_labels, [predict_batch, args.save_num_images, args.num_classes], tf.uint8) tf.summary.image(name='image collection_train', tensor=tf.concat(axis=2, values=[vs_image, vs_label, vs_predict]), max_outputs=args.save_num_images) tf.summary.scalar('fk_score', tf.reduce_mean(d_fk_pred)) tf.summary.scalar('gt_score', tf.reduce_mean(d_gt_pred)) tf.summary.scalar('g_loss_train', g_loss_var) tf.summary.scalar('d_loss_train', d_loss_var) tf.summary.scalar('mce_loss_train', mce_loss_var) tf.summary.scalar('g_bce_loss_train', -1. * g_bce_loss_var) tf.summary.scalar('iou_train', iou_var) tf.summary.scalar('accuracy_train', accuracy_var) tf.summary.scalar('grad_fk_oi', tf.reduce_mean(tf.abs(grad_fk_oi))) tf.summary.scalar('grad_gt_oi', tf.reduce_mean(tf.abs(grad_gt_oi))) tf.summary.scalar('grad_fk_img_oi', tf.reduce_mean(tf.abs(grad_fk_img_oi))) tf.summary.scalar('grad_gt_img_oi', tf.reduce_mean(tf.abs(grad_gt_img_oi))) for grad, var in g_gradients + d_gradients: tf.summary.histogram(var.op.name + "/gradients", grad) for var in tf.trainable_variables(): tf.summary.histogram(var.op.name + "/values", var) summary_op = tf.summary.merge_all() summary_writer = tf.summary.FileWriter(args.log_dir, graph=tf.get_default_graph(), max_queue=3) ## set session print("GPU index:" + str(os.environ['CUDA_VISIBLE_DEVICES'])) config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) global_init = tf.global_variables_initializer() local_init = tf.local_variables_initializer() sess.run(global_init) sess.run(local_init) ## set saver saver_all = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=2) trained_step = 0 if os.path.exists(args.restore_from + 'checkpoint'): trained_step = load_weight(args.restore_from, saver_all, sess) else: load_weight(args.baseweight_from['d_vgg'], vgg_restore_var, sess) saver_g = tf.train.Saver(var_list=g_restore_var, max_to_keep=2) load_weight(args.baseweight_from['g'], saver_g, sess) threads = tf.train.start_queue_runners(sess, coord) print("all setting has been done,training start!") ## start training def auto_setting_train_steps(mode): if mode == 0: return 5, 1 elif mode == 1: return 1, 5 else: return 1, 1 d_train_steps = 5 g_train_steps = 1 flags = [0 for i in range(3)] for step in range(args.num_steps): now_step = int( trained_step) + step if trained_step is not None else step feed_dict = {iterstep: now_step} for i in range(d_train_steps): _, _ = sess.run([train_d_op, metrics_op], feed_dict) for i in range(g_train_steps): g_loss_, mce_loss_, g_bce_loss_, d_loss_, _, _ = sess.run([ g_loss_var, mce_loss_var, g_bce_loss_var, d_loss_var, train_g_op, metrics_op ], feed_dict) ######################## fk_score_, gt_score_ = sess.run([fk_score_var, gt_score_var], feed_dict) if fk_score_ > 0.48 and fk_score_ < 0.52: flags[0] += 1 flags[1] = flags[2] = 0 elif gt_score_ - fk_score_ > 0.3: flags[1] += 1 flags[0] = flags[2] = 0 else: flags[2] += 1 flags[0] = flags[1] = 0 if max(flags) > 100: d_train_steps, g_train_steps = auto_setting_train_steps( flags.index(max(flags))) ######################## if step > 0 and step % args.save_pred_every == 0: save_weight(args.restore_from, saver_all, sess, now_step) if step % 50 == 0 or step == args.num_steps - 1: print('step={} d_loss={} g_loss={} mce_loss={} g_bce_loss_={}'. format(now_step, d_loss_, g_loss_, mce_loss_, g_bce_loss_)) summary_str = sess.run(summary_op, feed_dict) summary_writer.add_summary(summary_str, now_step) sess.run(local_init) ## end training coord.request_stop() coord.join(threads) print('end....')
def train_setup(self): tf.set_random_seed(self.conf.random_seed) # Create queue coordinator. self.coord = tf.train.Coordinator() # Input size input_size = (self.conf.input_height, self.conf.input_width) # Load reader with tf.name_scope("create_inputs"): reader = ImageReader( self.conf.data_dir, self.conf.data_list, input_size, self.conf.random_scale, self.conf.random_mirror, self.conf.ignore_label, IMG_MEAN, self.coord) self.image_batch, self.label_batch = reader.dequeue(self.conf.batch_size) # Create network if self.conf.encoder_name not in ['res101', 'res50', 'deeplab']: print('encoder_name ERROR!') print("Please input: res101, res50, or deeplab") sys.exit(-1) elif self.conf.encoder_name == 'deeplab': net = Deeplab_v2(self.image_batch, self.conf.num_classes, True) # Variables that load from pre-trained model. restore_var = [v for v in tf.global_variables() if 'fc' not in v.name] # Trainable Variables all_trainable = tf.trainable_variables() # Fine-tune part encoder_trainable = [v for v in all_trainable if 'fc' not in v.name] # lr * 1.0 # Decoder part decoder_trainable = [v for v in all_trainable if 'fc' in v.name] else: net = ResNet_segmentation(self.image_batch, self.conf.num_classes, True, self.conf.encoder_name) # Variables that load from pre-trained model. restore_var = [v for v in tf.global_variables() if 'resnet_v1' in v.name] # Trainable Variables all_trainable = tf.trainable_variables() # Fine-tune part encoder_trainable = [v for v in all_trainable if 'resnet_v1' in v.name] # lr * 1.0 # Decoder part decoder_trainable = [v for v in all_trainable if 'decoder' in v.name] decoder_w_trainable = [v for v in decoder_trainable if 'weights' in v.name or 'gamma' in v.name] # lr * 10.0 decoder_b_trainable = [v for v in decoder_trainable if 'biases' in v.name or 'beta' in v.name] # lr * 20.0 # Check assert(len(all_trainable) == len(decoder_trainable) + len(encoder_trainable)) assert(len(decoder_trainable) == len(decoder_w_trainable) + len(decoder_b_trainable)) # Network raw output raw_output = net.outputs # [batch_size, h, w, 21] # Output size output_shape = tf.shape(raw_output) output_size = (output_shape[1], output_shape[2]) # Groud Truth: ignoring all labels greater or equal than n_classes label_proc = prepare_label(self.label_batch, output_size, num_classes=self.conf.num_classes, one_hot=False) raw_gt = tf.reshape(label_proc, [-1,]) indices = tf.squeeze(tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)), 1) gt = tf.cast(tf.gather(raw_gt, indices), tf.int32) raw_prediction = tf.reshape(raw_output, [-1, self.conf.num_classes]) prediction = tf.gather(raw_prediction, indices) # Pixel-wise softmax_cross_entropy loss loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=gt) # L2 regularization l2_losses = [self.conf.weight_decay * tf.nn.l2_loss(v) for v in all_trainable if 'weights' in v.name] # Loss function self.reduced_loss = tf.reduce_mean(loss) + tf.add_n(l2_losses) # Define optimizers # 'poly' learning rate base_lr = tf.constant(self.conf.learning_rate) self.curr_step = tf.placeholder(dtype=tf.float32, shape=()) learning_rate = tf.scalar_mul(base_lr, tf.pow((1 - self.curr_step / self.conf.num_steps), self.conf.power)) # We have several optimizers here in order to handle the different lr_mult # which is a kind of parameters in Caffe. This controls the actual lr for each # layer. opt_encoder = tf.train.MomentumOptimizer(learning_rate, self.conf.momentum) opt_decoder_w = tf.train.MomentumOptimizer(learning_rate * 10.0, self.conf.momentum) opt_decoder_b = tf.train.MomentumOptimizer(learning_rate * 20.0, self.conf.momentum) # To make sure each layer gets updated by different lr's, we do not use 'minimize' here. # Instead, we separate the steps compute_grads+update_params. # Compute grads grads = tf.gradients(self.reduced_loss, encoder_trainable + decoder_w_trainable + decoder_b_trainable) grads_encoder = grads[:len(encoder_trainable)] grads_decoder_w = grads[len(encoder_trainable) : (len(encoder_trainable) + len(decoder_w_trainable))] grads_decoder_b = grads[(len(encoder_trainable) + len(decoder_w_trainable)):] # Update params train_op_conv = opt_encoder.apply_gradients(zip(grads_encoder, encoder_trainable)) train_op_fc_w = opt_decoder_w.apply_gradients(zip(grads_decoder_w, decoder_w_trainable)) train_op_fc_b = opt_decoder_b.apply_gradients(zip(grads_decoder_b, decoder_b_trainable)) # Finally, get the train_op! update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) # for collecting moving_mean and moving_variance with tf.control_dependencies(update_ops): self.train_op = tf.group(train_op_conv, train_op_fc_w, train_op_fc_b) # Saver for storing checkpoints of the model self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=0) # Loader for loading the pre-trained model self.loader = tf.train.Saver(var_list=restore_var) # Training summary # Processed predictions: for visualisation. raw_output_up = tf.image.resize_bilinear(raw_output, input_size) raw_output_up = tf.argmax(raw_output_up, axis=3) self.pred = tf.expand_dims(raw_output_up, dim=3) # Image summary. images_summary = tf.py_func(inv_preprocess, [self.image_batch, 2, IMG_MEAN], tf.uint8) labels_summary = tf.py_func(decode_labels, [self.label_batch, 2, self.conf.num_classes], tf.uint8) preds_summary = tf.py_func(decode_labels, [self.pred, 2, self.conf.num_classes], tf.uint8) self.total_summary = tf.summary.image('images', tf.concat(axis=2, values=[images_summary, labels_summary, preds_summary]), max_outputs=2) # Concatenate row-wise. if not os.path.exists(self.conf.logdir): os.makedirs(self.conf.logdir) self.summary_writer = tf.summary.FileWriter(self.conf.logdir, graph=tf.get_default_graph())
def train_setup(self): tf.set_random_seed(self.conf.random_seed) # Create queue coordinator. self.coord = tf.train.Coordinator() # Input size h, w = (self.conf.input_height, self.conf.input_width) input_size = (h, w) # Devices gpu_list = get_available_gpus() zip_encoder, zip_decoder_b, zip_decoder_w, zip_crf = [], [], [], [] previous_crf_names = [] restore_vars = [] self.loaders = [] self.im_list = [] for i in range(len(gpu_list)): with tf.device(gpu_list[i]): # Load reader with tf.name_scope("create_inputs"): reader = ImageReader(self.conf.data_dir, self.conf.data_list, input_size, self.conf.random_scale, self.conf.random_mirror, self.conf.ignore_label, IMG_MEAN, self.coord) self.image_batch, self.label_batch, self.sp_batch = reader.dequeue( self.conf.batch_size) self.im_list.append(self.image_batch) image_batch_075 = tf.image.resize_images( self.image_batch, [int(h * 0.75), int(w * 0.75)]) image_batch_05 = tf.image.resize_images( self.image_batch, [int(h * 0.5), int(w * 0.5)]) sp_batch_075 = tf.image.resize_images( self.sp_batch, [int(h * 0.75), int(w * 0.75)]) sp_batch_05 = tf.image.resize_images( self.sp_batch, [int(h * 0.5), int(w * 0.5)]) #for i in range(1): # self.image_batch = tf.Print(self.image_batch, [self.image_batch[i]], message = 'image batch ', summarize=5) #for i in range(1): # self.label_batch = tf.Print(self.label_batch, [self.label_batch[i]], message = 'label batch ', summarize=5) #for i in range(1): # self.sp_batch = tf.Print(self.sp_batch, [self.sp_batch[i]], message = 'sp batch ', summarize=5) # Create network with tf.variable_scope('', reuse=False): if self.conf.crf_type == 'crf': net = Deeplab_v2(self.image_batch, self.conf.num_classes, True, rescale075=False, rescale05=False, crf_type=self.conf.crf_type) else: net = Deeplab_v2(self.image_batch, self.conf.num_classes, True, rescale075=False, rescale05=False, crf_type=self.conf.crf_type, superpixels=self.sp_batch) ''' with tf.variable_scope('', reuse=True): if self.conf.crf_type == 'crfSP': net075 = Deeplab_v2(image_batch_075, self.conf.num_classes, True, rescale075=True, rescale05=False, crf_type = self.conf.crf_type, superpixels=sp_batch_075) else: net075 = Deeplab_v2(image_batch_075, self.conf.num_classes, True, rescale075=True, rescale05=False, crf_type = self.conf.crf_type) with tf.variable_scope('', reuse=True): if self.conf.crf_type == 'crfSP': net05 = Deeplab_v2(image_batch_05, self.conf.num_classes, True, rescale075=False, rescale05=True, crf_type = self.conf.crf_type, superpixels=sp_batch_05) else: net05 = Deeplab_v2(image_batch_05, self.conf.num_classes, True, rescale075=False, rescale05=True, crf_type = self.conf.crf_type) ''' # Variables that load from pre-trained model. restore_var = [ v for v in tf.global_variables() if ('fc' not in v.name and 'crfrnn' not in v.name) ] # when don't want to train using previous crf weights #restore_var = [v for v in tf.global_variables() if ('fc' not in v.name and 'superpixel' not in v.name)] restore_vars.append(restore_var) # Trainable Variables all_trainable = tf.trainable_variables() # Fine-tune part for name in previous_crf_names: for v in all_trainable: if v.name == name: all_trainable.remove(v) crf_trainable = [ v for v in all_trainable if ('crfrnn' in v.name and v.name not in previous_crf_names ) ] previous_crf_names.extend(v.name for v in crf_trainable) encoder_trainable = [ v for v in all_trainable if 'fc' not in v.name and 'crfrnn' not in v.name ] # lr * 1.0 # Remove encoder_trainable from all_trainable #all_trainable = [v for v in all_trainable if v not in encoder_trainable] # Decoder part decoder_trainable = [ v for v in all_trainable if 'fc' in v.name and 'crfrnn' not in v.name ] decoder_w_trainable = [ v for v in decoder_trainable if ('weights' in v.name or 'gamma' in v.name) and 'crfrnn' not in v.name ] # lr * 10.0 decoder_b_trainable = [ v for v in decoder_trainable if ('biases' in v.name or 'beta' in v.name) and 'crfrnn' not in v.name ] # lr * 20.0 # Check assert (len(all_trainable) == len(encoder_trainable) + len(decoder_trainable) + len(crf_trainable) ) #+ len(encoder_trainable) assert (len(decoder_trainable) == len(decoder_w_trainable) + len(decoder_b_trainable)) # Network raw output raw_output100 = net.outputs raw_output = raw_output100 ''' raw_output075 = net075.outputs raw_output05 = net05.outputs raw_output = tf.reduce_max(tf.stack([raw_output100, tf.image.resize_images(raw_output075, tf.shape(raw_output100)[1:3,]), tf.image.resize_images(raw_output05, tf.shape(raw_output100)[1:3,])]), axis=0) ''' # Ground Truth: ignoring all labels greater or equal than n_classes label_proc = prepare_label(self.label_batch, tf.stack( raw_output.get_shape()[1:3]), num_classes=self.conf.num_classes, one_hot=True) # [batch_size, h, w] ''' label_proc075 = prepare_label(self.label_batch, tf.stack(raw_output075.get_shape()[1:3]), num_classes=self.conf.num_classes, one_hot=True) label_proc05 = prepare_label(self.label_batch, tf.stack(raw_output05.get_shape()[1:3]), num_classes=self.conf.num_classes, one_hot=True) ''' raw_gt = tf.reshape(label_proc, [ -1, ]) ''' raw_gt075 = tf.reshape(label_proc075, [-1,]) raw_gt05 = tf.reshape(label_proc05, [-1,]) ''' indices = tf.squeeze( tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)), 1) ''' indices075 = tf.squeeze(tf.where(tf.less_equal(raw_gt075, self.conf.num_classes - 1)), 1) indices05 = tf.squeeze(tf.where(tf.less_equal(raw_gt05, self.conf.num_classes - 1)), 1) ''' gt = tf.cast(tf.gather(raw_gt, indices), tf.int32) ''' gt075 = tf.cast(tf.gather(raw_gt075, indices075), tf.int32) gt05 = tf.cast(tf.gather(raw_gt05, indices05), tf.int32) ''' raw_prediction = tf.reshape(raw_output, [-1, self.conf.num_classes]) raw_prediction100 = tf.reshape(raw_output100, [-1, self.conf.num_classes]) ''' raw_prediction075 = tf.reshape(raw_output075, [-1, self.conf.num_classes]) raw_prediction05 = tf.reshape(raw_output05, [-1, self.conf.num_classes]) ''' prediction = tf.gather(raw_prediction, indices) prediction100 = tf.gather(raw_prediction100, indices) ''' prediction075 = tf.gather(raw_prediction075, indices075) prediction05 = tf.gather(raw_prediction05, indices05) ''' # Pixel-wise softmax_cross_entropy loss #loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=gt) loss = tf.nn.softmax_cross_entropy_with_logits( logits=raw_prediction, labels=tf.reshape(label_proc[0], (h * w, self.conf.num_classes))) # NOTE used to be loss=tf.nn.softmax_cross_entropy_with_logits_v2 ''' coefficients = [0.01460247, 1.25147725, 2.88479363, 1.20348121, 1.65261654, 1.67514772, 0.62338799, 0.7729363, 0.42038501, 0.98557268, 1.31867536, 0.85313332, 0.67227604, 1.21317965, 1. , 0.24263748, 1.80877607, 1.3082213, 0.79664027, 0.72543945, 1.27823374] ''' #loss = weighted_loss(self.conf.num_classes, coefficients, labels=tf.reshape(label_proc[0], (h*w, self.conf.num_classes)), logits=raw_prediction) #loss100 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction100, labels=gt) loss100 = tf.nn.softmax_cross_entropy_with_logits( logits=raw_prediction100, labels=tf.reshape(label_proc[0], (h * w, self.conf.num_classes))) # NOTE used to be loss=tf.nn.softmax_cross_entropy_with_logits_v2 #loss100 = weighted_loss(self.conf.num_classes, coefficients, labels=tf.reshape(label_proc[0], (h*w, self.conf.num_classes)), logits=raw_prediction100) #loss075 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction075, labels=gt075) #loss075 = tf.nn.softmax_cross_entropy_with_logits_v2(logits=raw_prediction075, labels=tf.reshape(label_proc075[0], (int(h * 0.75) * int(w * 0.75), self.conf.num_classes))) #loss075 = weighted_loss(self.conf.num_classes, coefficients, labels=tf.reshape(label_proc075[0], (int(h * 0.75) * int(w * 0.75), self.conf.num_classes)), logits=raw_prediction075) #loss05 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction05, labels=gt05) #loss05 = tf.nn.softmax_cross_entropy_with_logits_v2(logits=raw_prediction05, labels=tf.reshape(label_proc05[0], (int(h * 0.5) * int(w * 0.5), self.conf.num_classes))) #loss05 = weighted_loss(self.conf.num_classes, coefficients, labels=tf.reshape(label_proc05[0], (int(h * 0.5) * int(w * 0.5), self.conf.num_classes)), logits=raw_prediction05) # L2 regularization l2_losses = [ self.conf.weight_decay * tf.nn.l2_loss(v) for v in all_trainable if 'weights' in v.name ] # Loss function self.reduced_loss = tf.reduce_mean(loss) + tf.reduce_mean( loss100 ) #+ tf.reduce_mean(loss075) + tf.reduce_mean(loss05) + tf.add_n(l2_losses) # Define optimizers # 'poly' learning rate base_lr = tf.constant(self.conf.learning_rate) self.curr_step = tf.placeholder(dtype=tf.float32, shape=()) learning_rate = tf.scalar_mul( base_lr, tf.pow((1 - self.curr_step / self.conf.num_steps), self.conf.power)) # We have several optimizers here in order to handle the different lr_mult # which is a kind of parameters in Caffe. This controls the actual lr for each # layer. opt_encoder = tf.train.MomentumOptimizer( learning_rate, self.conf.momentum) opt_decoder_w = tf.train.MomentumOptimizer( learning_rate * 10.0, self.conf.momentum) opt_decoder_b = tf.train.MomentumOptimizer( learning_rate * 20.0, self.conf.momentum) opt_crf = tf.train.MomentumOptimizer(learning_rate, self.conf.momentum) # Gradient accumulation # Define a variable to accumulate gradients. accum_grads = [ tf.Variable(tf.zeros_like(v.initialized_value()), trainable=False) for v in encoder_trainable + decoder_w_trainable + decoder_b_trainable + crf_trainable ] #encoder_trainable + # Define an operation to clear the accumulated gradients for next batch. self.zero_op = [ v.assign(tf.zeros_like(v)) for v in accum_grads ] # To make sure each layer gets updated by different lr's, we do not use 'minimize' here. # Instead, we separate the steps compute_grads+update_params. # Compute grads grads = tf.gradients(self.reduced_loss, encoder_trainable + decoder_w_trainable + decoder_b_trainable + crf_trainable) #encoder_trainable + # Accumulate and normalise the gradients. self.accum_grads_op = [ accum_grads[i].assign_add(grad / self.conf.grad_update_every) for i, grad in enumerate(grads) ] #''' grads_encoder = accum_grads[:len(encoder_trainable)] grads_decoder_w = accum_grads[len(encoder_trainable ):len(encoder_trainable) + len(decoder_w_trainable)] grads_decoder_b = accum_grads[( len(encoder_trainable) + len(decoder_w_trainable)):(len(encoder_trainable) + len(decoder_w_trainable) + len(decoder_b_trainable))] grads_crf = accum_grads[ len(encoder_trainable) + len(decoder_w_trainable) + len(decoder_b_trainable ):] # assuming crf gradients are appended to the end #''' ''' grads_decoder_w = accum_grads[: len(decoder_w_trainable)] grads_decoder_b = accum_grads[(len(decoder_w_trainable)):(len(decoder_w_trainable)+len(decoder_b_trainable))] grads_crf = accum_grads[len(decoder_w_trainable)+len(decoder_b_trainable):] # assuming crf gradients are appended to the end ''' zip_encoder.append(list(zip(grads_encoder, encoder_trainable))) zip_decoder_b.append( list(zip(grads_decoder_b, decoder_b_trainable))) zip_decoder_w.append( list(zip(grads_decoder_w, decoder_w_trainable))) zip_crf.append(list(zip(grads_crf, crf_trainable))) avg_grads_encoder = average_gradients(zip_encoder) avg_grads_decoder_w = average_gradients(zip_decoder_w) avg_grads_decoder_b = average_gradients(zip_decoder_b) avg_grads_crf = average_gradients(zip_crf) for i in range(len(gpu_list)): with tf.device(gpu_list[i]): # Update params train_op_conv = opt_encoder.apply_gradients(avg_grads_encoder) train_op_fc_w = opt_decoder_w.apply_gradients( avg_grads_decoder_w) train_op_fc_b = opt_decoder_b.apply_gradients( avg_grads_decoder_b) train_op_crf = opt_crf.apply_gradients(avg_grads_crf) # Finally, get the train_op! update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS ) # for collecting moving_mean and moving_variance with tf.control_dependencies(update_ops): self.train_op = tf.group(train_op_conv, train_op_fc_w, train_op_fc_b, train_op_crf) # train_op_conv # Saver for storing checkpoints of the model self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=0) # Loader for loading the pre-trained model for i in range(len(gpu_list)): with tf.device(gpu_list[i]): self.loaders.append(tf.train.Saver(var_list=restore_vars[i])) #self.loaders.append(tf.train.Saver(var_list=tf.global_variables())) # Training summary # Processed predictions: for visualisation. raw_output_up = tf.image.resize_bilinear(raw_output, input_size) raw_output_up = tf.argmax(raw_output_up, axis=3) self.pred = tf.expand_dims(raw_output_up, axis=3) # Image summary. images_summary = tf.py_func(inv_preprocess, [self.image_batch, 1, IMG_MEAN], tf.uint8) labels_summary = tf.py_func( decode_labels, [self.label_batch, 1, self.conf.num_classes], tf.uint8) preds_summary = tf.py_func(decode_labels, [self.pred, 1, self.conf.num_classes], tf.uint8) self.total_summary = tf.summary.image( 'images', tf.concat(axis=2, values=[images_summary, labels_summary, preds_summary]), max_outputs=1) # Concatenate row-wise. if not os.path.exists(self.conf.logdir): os.makedirs(self.conf.logdir) self.summary_writer = tf.summary.FileWriter( self.conf.logdir, graph=tf.get_default_graph())
random_scale = False random_mirror = True random_crop = True batch_size = 8 learning_rate = 0.00001 power = 0.9 num_steps = 300000 restore_from = './weights/dvn/20171119/' g_weight_from = '' d_weight_from = '' data_dir = '/data/rui.wu/irfan/gan_seg/dvn/data/' is_train = True with tf.name_scope("create_inputs"): reader = ImageReader(data_dir, img_size, crop_size, random_scale, random_mirror, random_crop, is_train, coord) image_batch, label_batch = reader.dequeue(batch_size) print("Data is ready!") ## load model label_batch = tf.cast(label_batch, tf.uint8) image_batch = tf.cast(image_batch, tf.float32) # b = tf.zeros(label_batch.get_shape()) # a = tf.ones(label_batch.get_shape()) # label_batch_b = tf.where(tf.greater(label_batch, 0.5), a, b) real_iou = tf.placeholder(tf.float32, [batch_size, 1]) train_seg = tf.placeholder(tf.float32, [batch_size, 128, 128, 1]) train_image = tf.placeholder(tf.float32, [batch_size, 128, 128, 3]) train_seg_new = tf.cast(train_seg, tf.uint8) train_seg_new = tf.squeeze(train_seg_new, squeeze_dims=[3]) train_seg_new = tf.one_hot(train_seg_new, 2)
def build(self): config = self.__dict__.copy() num_labels = self.num_labels #for segmentation (pixel labels) ignore_label = 255 #for segmentation (pixel labels) random_seed = 1234 generator = self.resnetG discriminator = self.resnetD GEN_A2B_NAME = 'GEN_A2B' GEN_B2A_NAME = 'GEN_B2A' DIS_A_NAME = 'DIS_A' DIS_B_NAME = 'DIS_B' global_step = tf.train.get_or_create_global_step() slim.add_model_variable(global_step) global_step_update = tf.assign_add(global_step, 1, name='global_step_update') def resize_and_onehot(tensor, shape, depth): with tf.device('/device:CPU:0'): onehot_tensor = tf.one_hot(tf.squeeze( tf.image.resize_nearest_neighbor( tf.cast(tensor, tf.int32), shape), -1), depth=depth) return onehot_tensor def convert_to_labels(onehot_seg, crop_size=None): fake_segments_output = onehot_seg print ('%s | ' % fake_segments_output.device, fake_segments_output) if crop_size: fake_segments_output = tf.image.resize_bilinear(fake_segments_output, crop_size) #tf.shape(source_segments_batch)[1:3]) fake_segments_output = tf.argmax(fake_segments_output, axis=-1) # generate segment indices matrix fake_segments_output = tf.expand_dims(fake_segments_output, dim=-1) # Create 4-d tensor. return fake_segments_output target_data_queue = [] tf.set_random_seed(random_seed) coord = tf.train.Coordinator() with tf.name_scope("create_inputs"): for i, data in enumerate([config['source_data']] + config['target_data']): reader = ImageReader( data['data_dir'], data['data_list'], config['crop_size'], # Original size: [1024, 2048] random_scale=config['random_scale'], random_mirror=True, ignore_label=ignore_label, img_mean=0, # set IMG_MEAN to centralize image pixels (set NONE for automatic choosing) img_channel_format='RGB', # Default: BGR in deeplab_v2. See here: https://github.com/zhengyang-wang/Deeplab-v2--ResNet-101--Tensorflow/issues/30 coord=coord, rgb_label=False) data_queue = reader.dequeue(config['batch_size']) if i == 0: # ---[ source: training data source_images_batch = data_queue[0] #A: 3 chaanels source_segments_batch = data_queue[1] #B: 1-label channels source_images_batch = tf.cast(source_images_batch, tf.float32) / 127.5 - 1. source_images_batch = tf.image.resize_bilinear(source_images_batch, config['resize']) #A: 3 chaanels source_segments_batch = tf.image.resize_nearest_neighbor(source_segments_batch, config['resize']) #B: 1-label channels source_segments_batch = tf.cast(tf.one_hot(tf.squeeze(source_segments_batch, -1), depth=num_labels), tf.float32) - 0.5 #B: 19 channels else: # ---[ target: validation data / testing data target_images_batch = data_queue[0] #A: 3 chaanels target_segments_batch = data_queue[1] #B: 1-label channels target_images_batch = tf.cast(target_images_batch, tf.float32) / 127.5 - 1. target_images_batch = tf.image.resize_bilinear(target_images_batch, config['resize']) #A: 3 chaanels target_segments_batch = tf.image.resize_nearest_neighbor(target_segments_batch, config['resize']) #B: 1-label channels target_segments_batch = tf.cast(tf.one_hot(tf.squeeze(target_segments_batch, -1), depth=num_labels), tf.float32) - 0.5 #B: 19 channels target_data_queue.append([target_images_batch, target_segments_batch]) size_list = cuttool(config['batch_size'], config['gpus']) source_images_batches = tf.split(source_images_batch, size_list) source_segments_batches = tf.split(source_segments_batch, size_list) fake_1_segments_output = [None] * len(size_list) fake_2_segments_output = [None] * len(size_list) fake_1_images_output = [None] * len(size_list) fake_2_images_output = [None] * len(size_list) d_real_img_output = [None] * len(size_list) d_fake_img_output = [None] * len(size_list) d_real_seg_output = [None] * len(size_list) d_fake_seg_output = [None] * len(size_list) for gid, (source_images_batch, source_segments_batch) in \ enumerate(zip(source_images_batches, source_segments_batches)): # ---[ Generator A2B & B2A with tf.device('/device:GPU:{}'.format((gid-1) % config['gpus'])): fake_seg = generator(source_images_batch, output_channel=num_labels, reuse=tf.AUTO_REUSE, phase_train=True, scope=GEN_A2B_NAME) fake_seg = tf.nn.softmax(fake_seg) - 0.5 fake_img_ = generator(fake_seg, output_channel=3, reuse=tf.AUTO_REUSE, phase_train=True, scope=GEN_B2A_NAME) fake_img_ = tf.nn.tanh(fake_img_) fake_img = generator(source_segments_batch, output_channel=3, reuse=tf.AUTO_REUSE, phase_train=True, scope=GEN_B2A_NAME) fake_img = tf.nn.tanh(fake_img) fake_seg_ = generator(fake_img, output_channel=num_labels, reuse=tf.AUTO_REUSE, phase_train=True, scope=GEN_A2B_NAME) fake_seg_ = tf.nn.softmax(fake_seg_) - 0.5 # ---[ Discriminator A & B with tf.device('/device:GPU:{}'.format((gid-1) % config['gpus'])): d_real_img = discriminator(source_images_batch, reuse=tf.AUTO_REUSE, phase_train=True, scope=DIS_A_NAME) d_fake_img = discriminator(fake_img, reuse=tf.AUTO_REUSE, phase_train=True, scope=DIS_A_NAME) d_real_seg = discriminator(source_segments_batch, reuse=tf.AUTO_REUSE, phase_train=True, scope=DIS_B_NAME) d_fake_seg = discriminator(fake_seg, reuse=tf.AUTO_REUSE, phase_train=True, scope=DIS_B_NAME) #d_fake_img_val = discriminator(fake_img_val, reuse=tf.AUTO_REUSE, phase_train=False, scope=DIS_A_NAME) #d_fake_seg_val = discriminator(fake_seg_val, reuse=tf.AUTO_REUSE, phase_train=False, scope=DIS_B_NAME) fake_1_segments_output [gid] = fake_seg fake_2_segments_output [gid] = fake_seg_ fake_1_images_output [gid] = fake_img fake_2_images_output [gid] = fake_img_ d_real_img_output [gid] = d_real_img d_fake_img_output [gid] = d_fake_img d_real_seg_output [gid] = d_real_seg d_fake_seg_output [gid] = d_fake_seg source_images_batch = tf.concat(source_images_batches, axis=0) #-1~1 source_segments_batch = tf.concat(source_segments_batches, axis=0) #onehot: -0.5~+0.5 fake_1_segments_output = tf.concat(fake_1_segments_output, axis=0) ; print('fake_1_segments_output', fake_1_segments_output) fake_2_segments_output = tf.concat(fake_2_segments_output, axis=0) ; print('fake_2_segments_output', fake_2_segments_output) fake_1_images_output = tf.concat(fake_1_images_output , axis=0) ; print('fake_1_images_output ', fake_1_images_output ) fake_2_images_output = tf.concat(fake_2_images_output , axis=0) ; print('fake_2_images_output ', fake_2_images_output ) d_real_img_output = tf.concat(d_real_img_output , axis=0) d_fake_img_output = tf.concat(d_fake_img_output , axis=0) d_real_seg_output = tf.concat(d_real_seg_output , axis=0) d_fake_seg_output = tf.concat(d_fake_seg_output , axis=0) source_data_color = [ (1.+source_images_batch ) / 2. , # source_images_batch_color sgtools.decode_labels(tf.cast(convert_to_labels(source_segments_batch + 0.5), tf.int32), num_labels), # source_segments_batch_colo sgtools.decode_labels(tf.cast(convert_to_labels(fake_1_segments_output + 0.5), tf.int32), num_labels), # fake_1_segments_output_col sgtools.decode_labels(tf.cast(convert_to_labels(fake_2_segments_output + 0.5), tf.int32), num_labels), # fake_2_segments_output_col (1.+fake_1_images_output ) / 2. , # fake_1_images_output_color (1.+fake_2_images_output ) / 2. , # fake_2_images_output_color ] # ---[ Validation Model target_data_color_queue = [] for target_data in target_data_queue: with tf.device('/device:GPU:{}'.format((2) % config['gpus'])): fake_seg = generator(val_images_holder, output_channel=num_labels, reuse=tf.AUTO_REUSE, phase_train=False, scope=GEN_A2B_NAME) fake_seg = tf.nn.softmax(fake_seg) - 0.5 fake_img_ = generator(fake_seg, output_channel=3, reuse=tf.AUTO_REUSE, phase_train=False, scope=GEN_B2A_NAME) fake_img_ = tf.nn.tanh(fake_img_) fake_img = generator(val_segments_holder, output_channel=3, reuse=tf.AUTO_REUSE, phase_train=False, scope=GEN_B2A_NAME) fake_img = tf.nn.tanh(fake_img) fake_seg_ = generator(fake_img, output_channel=num_labels, reuse=tf.AUTO_REUSE, phase_train=False, scope=GEN_A2B_NAME) fake_seg_ = tf.nn.softmax(fake_seg) - 0.5 target_data_color_queue.append([ (1.+target_images_batch ) / 2. , # target_images_batch_color sgtools.decode_labels(tf.cast(convert_to_labels(target_segments_batch + 0.5), tf.int32), num_labels) , # target_segments_batch_color sgtools.decode_labels(tf.cast(convert_to_labels(fake_seg + 0.5), tf.int32), num_labels) , # val_fake_1_segments_output_color sgtools.decode_labels(tf.cast(convert_to_labels(fake_seg_ + 0.5), tf.int32), num_labels) , # val_fake_2_segments_output_color (1.+val_fake_1_images_output ) / 2. , # val_fake_1_images_output_color (1.+val_fake_2_images_output ) / 2. , # val_fake_2_images_output_color ]) # ---[ Segment-level loss: pixelwise loss # d_seg_batch = tf.image.resize_nearest_neighbor(seg_gt, tf.shape(_d_real['segment'])[1:3]) # d_seg_batch = tf.squeeze(d_seg_batch, -1) # d_seg_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=d_seg_batch, logits=_d_real['segment'], name='segment_pixelwise_loss') # pixel-wise loss # d_seg_loss = tf.reduce_mean(d_seg_loss) # d_seg_loss = tf.identity(d_seg_loss, name='d_seg_loss') # ---[ GAN Loss: crite loss #d_loss_old = - (tf.reduce_mean(d_source_output['critic']) - tf.reduce_mean(d_target_output['critic'])) #g_loss = - (tf.reduce_mean(d_target_output['critic'])) ## gradient penalty #LAMBDA = 10 ##alpha = tf.placeholder(tf.float32, shape=[None], name='alpha') #alpha = tf.random_uniform([config['batch_size']], 0.0, 1.0, dtype=tf.float32) #for _ in source_segments_batch.shape[1:]: #alpha = tf.expand_dims(alpha, axis=1) #shape=[None,1,1,1] #interpolates = alpha * source_segments_batch + (1.-alpha) * target_segments_output #print ('source_segments_batch:', source_segments_batch) #print ('target_segments_output:',target_segments_output) #print ('interpolates:', interpolates) #interpolates = resize_and_onehot(interpolates, target_raw_segments_output.shape.as_list()[1:3], num_labels) #print ('interpolates:', interpolates) #_d_intp = discriminator(interpolates, reuse=True, phase_train=True, scope=DIS_NAME) #intp_grads = tf.gradients(_d_intp['critic'], [interpolates])[0] #slopes = tf.sqrt(tf.reduce_sum(tf.square(intp_grads), reduction_indices=[1])) #L2-distance #grads_penalty = tf.reduce_mean(tf.square(slopes-1), name='grads_penalty') #d_loss = d_loss_old + LAMBDA * grads_penalty def sigmoid_cross_entropy(labels, logits): return tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits) ) def least_square(labels, logits): return tf.reduce_mean( (labels - logits) ** 2 ) if config['loss_mode'] == 'lsgan': # ---[ GAN loss: LSGAN loss (chi-square, or called least-square) loss_func = least_square else: # ---[ GAN loss: sigmoid BCE loss loss_func = sigmoid_cross_entropy # ---[ LOSS _img_recovery = config['L1_lambda'] * tf.reduce_mean( tf.abs(source_images_batch - fake_2_images_output)) #_seg_recovery = config['L1_lambda'] * tf.reduce_mean( tf.abs(source_segments_batch - fake_1_segments_output)) #r1.0: error #_seg_recovery = config['L1_lambda'] * tf.reduce_mean( tf.abs(source_segments_batch - fake_2_segments_output)) #r2.0 _seg_recovery = config['L1_lambda'] * tf.reduce_mean( tf.abs(source_segments_batch_color - fake_2_segments_output_color)) #r2.0.5: not sure because, in theory, no gradient if using decode_labels() g_loss_a2b = \ loss_func( labels=tf.ones_like(d_fake_seg_output), logits=d_fake_seg_output ) + \ _img_recovery + _seg_recovery g_loss_b2a = \ loss_func( labels=tf.ones_like(d_fake_img_output), logits=d_fake_img_output ) + \ _img_recovery + _seg_recovery g_loss = \ loss_func( labels=tf.ones_like(d_fake_seg_output), logits=d_fake_seg_output ) + \ loss_func( labels=tf.ones_like(d_fake_img_output), logits=d_fake_img_output ) + \ _img_recovery + _seg_recovery da_loss = \ loss_func( labels=tf.ones_like(d_real_img_output), logits=d_real_img_output ) + \ loss_func( labels=tf.zeros_like(d_fake_img_output), logits=d_fake_img_output ) db_loss = \ loss_func( labels=tf.ones_like(d_real_seg_output), logits=d_real_seg_output ) + \ loss_func( labels=tf.zeros_like(d_fake_seg_output), logits=d_fake_seg_output ) d_loss = \ (da_loss + db_loss) / 2. # D will output [BATCH_SIZE, 32, 32, 1] num_da_real_img_acc = tf.size( tf.where(tf.reduce_mean(tf.nn.sigmoid(d_real_img_output), axis=[1,2,3]) > 0.5)[:,0], name='num_da_real_img_acc' ) num_da_fake_img_acc = tf.size( tf.where(tf.reduce_mean(tf.nn.sigmoid(d_fake_img_output), axis=[1,2,3]) < 0.5)[:,0], name='num_da_fake_img_acc' ) num_db_real_seg_acc = tf.size( tf.where(tf.reduce_mean(tf.nn.sigmoid(d_real_seg_output), axis=[1,2,3]) > 0.5)[:,0], name='num_db_real_seg_acc' ) num_db_fake_seg_acc = tf.size( tf.where(tf.reduce_mean(tf.nn.sigmoid(d_fake_seg_output), axis=[1,2,3]) < 0.5)[:,0], name='num_db_fake_seg_acc' ) ## limit weights to 0 #g_weight_regularizer = [0.0001 * tf.nn.l2_loss(v) for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, GEN_NAME) if 'weight' in v.name] #g_weight_regularizer = tf.add_n(g_weight_regularizer, name='g_weight_regularizer_loss') #g_loss += g_weight_regularizer #d_weight_regularizer = [0.0001 * tf.nn.l2_loss(v) for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, DIS_NAME) if 'weight' in v.name] #d_weight_regularizer = tf.add_n(d_weight_regularizer, name='d_weight_regularizer_loss') #d_loss += d_weight_regularizer d_loss = tf.identity(d_loss, name='d_loss') g_loss = tf.identity(g_loss, name='g_loss') ## --- Training Set Validation --- # Predictions. #pred_gt = tf.reshape(target_segments_batch, [-1,]) #pred = tf.reshape(target_segments_output, [-1,]) #indices = tf.squeeze(tf.where(tf.not_equal(pred_gt, ignore_label)), 1) #pred_gt = tf.cast(tf.gather(pred_gt, indices), tf.int32) #pred = tf.cast(tf.gather(pred, indices), tf.int32) ## mIoU ### Allowing to use indices matrices in mean_iou() with `num_classes=indices.max()` #weights = tf.cast(tf.less_equal(pred_gt, num_labels), tf.int32) # Ignoring all labels greater than or equal to n_classes. #mIoU, mIoU_update_op = tf.metrics.mean_iou(pred, pred_gt, num_classes=num_labels, weights=weights) # ---[ Variables g_a2b_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, GEN_A2B_NAME) g_b2a_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, GEN_B2A_NAME) d_a_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, DIS_A_NAME) d_b_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, DIS_B_NAME) g_vars = g_a2b_vars + g_b2a_vars d_vars = d_a_vars + d_b_vars print_list(g_a2b_vars, GEN_A2B_NAME) print_list(g_b2a_vars, GEN_B2A_NAME) print_list(d_a_vars, DIS_A_NAME) print_list(d_b_vars, DIS_B_NAME) # ---[ Optimizer ## `colocate_gradients_with_ops = True` to reduce GPU MEM utils, and fasten training speed OPT_NAME = 'Optimizer' g_opts = []; d_opts = [] update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): with tf.variable_scope(OPT_NAME): #with tf.device('/device:GPU:{}'.format(config['gpus']-1)): if True: if len(g_vars) > 0: g_opt = tf.train.AdamOptimizer(learning_rate=config['g_lr'], beta1=0.5, beta2=0.9).minimize(g_loss, var_list=g_vars, colocate_gradients_with_ops=True) g_opts.append(g_opt) if len(d_vars) > 0: d_opt = tf.train.AdamOptimizer(learning_rate=config['d_lr'], beta1=0.5, beta2=0.9).minimize(d_loss, var_list=d_vars, colocate_gradients_with_ops=True) d_opts.append(d_opt) g_opt = tf.group(*g_opts) d_opt = tf.group(*d_opts) opt_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, OPT_NAME) print_list(opt_vars, OPT_NAME) # --- [ Summary scalars = [d_loss, g_loss] #scalars += [mIoU] scalars += [num_da_real_img_acc, num_da_fake_img_acc, num_db_real_seg_acc, num_db_fake_seg_acc] scalars += [g_loss_a2b, g_loss_b2a, da_loss, db_loss] writer, summarys = create_summary(summary_dir=config['summary_dir'], name=config['suffix'], scalar = scalars, ) ''' Training ''' with tf.Session(config=GpuConfig) as sess: sess.run(tf.global_variables_initializer()) #DONOT put it after ``saver.restore`` sess.run(tf.local_variables_initializer()) #DONOT put it after ``saver.restore`` saver = tf.train.Saver(g_vars + d_vars, max_to_keep=1) #g_saver = tf.train.Saver(g_vars, max_to_keep=1) #d_saver = tf.train.Saver(d_vars, max_to_keep=1) #if self.ckpt: #saver.restore(sess, self.ckpt) #print ("Training starts at %d iteration..." % sess.run(global_step)) feeds = {} # Start queue threads. threads = tf.train.start_queue_runners(coord=coord, sess=sess) inside_epoch = int(config['print_epoch']) if config['print_epoch'] < config['max_epoch'] else int(config['max_epoch'] / 1) outside_epoch = int(config['max_epoch'] / inside_epoch) start = int(sess.run(global_step) / inside_epoch) if start >= outside_epoch: raise ValueError("initial iteration:%d >= max iteration:%d. please reset '--max_epoch' value." % (sess.run(global_step), config['max_epoch'])) start_time = time.time() for epo in range(start, outside_epoch): bar = IncrementalBar('[epoch {:<4d}/{:<4d}]'.format(epo, outside_epoch), max=inside_epoch) for epi in range(inside_epoch): iters = sess.run(global_step) # save summary if epo == 0: save_summarys = sess.run(summarys, feed_dict=feeds) writer.add_summary(save_summarys, iters) for _ in range(config['d_epoch']): sess.run(d_opt, feed_dict=feeds) if iters > self.pretrain_D_epoch: for _ in range(config['g_epoch']): sess.run(g_opt, feed_dict=feeds) sess.run(global_step_update) bar.next() duration = time.time() - start_time disc_loss, gen_loss = \ sess.run([d_loss, g_loss], feed_dict=feeds) na_real, na_fake, nb_real, nb_fake = \ sess.run([num_da_real_img_acc, num_da_fake_img_acc, num_db_real_seg_acc, num_db_fake_seg_acc], feed_dict=feeds) #sess.run(mIoU_update_op, feed_dict=feeds) #miou = sess.run(mIoU, feed_dict=feeds) print (' -', 'DLoss: %-8.2e' % disc_loss, #'(W: %-8.2e)' % disc_wloss, 'GLoss: %-8.2e' % gen_loss, #'(W: %-8.2e)' % gen_wloss, '|', '[Da_img] #real: %d, #fake: %d' % (na_real, na_fake), '[Db_seg] #real: %d, #fake: %d' % (nb_real, nb_fake), '|', #'[train_mIoU] %.2f' % miou, '[ETA] %s' % format_time(duration) ) bar.finish() iters = sess.run(global_step) # save checkpoint if epo % 2 == 0: saver_path = os.path.join(config['ckpt_dir'], '{}.ckpt'.format(config['name'])) saver.save(sess, save_path=saver_path, global_step=global_step) # save summary if epo % 1 == 0: save_summarys = sess.run(summarys, feed_dict=feeds) writer.add_summary(save_summarys, iters) # output samples if epo % 5 == 0: img_gt, seg_gt, seg_1, seg_2, img_1, img_2 = sess.run(source_data_color) print ("Range %10s:" % "seg_gt", seg_gt.min(), seg_gt.max()) print ("Range %10s:" % "seg_1", seg_1.min(), seg_1.max()) print ("Range %10s:" % "seg_2", seg_2.min(), seg_2.max()) print ("Range %10s:" % "img_gt", img_gt.min(), img_gt.max()) print ("Range %10s:" % "img_1", img_1.min(), img_1.max()) print ("Range %10s:" % "img_2", img_2.min(), img_2.max()) _output = np.concatenate([img_gt, seg_gt, seg_1, img_1, img_2, seg_2], axis=0) save_visualization(_output, save_path=os.path.join(config['result_dir'], 'tr-{}.jpg'.format(iters)), size=[3, 2*config['batch_size']]) #seg_output = np.concatenate([seg_gt, seg_2, seg_1], axis=0) #img_output = np.concatenate([img_gt, img_2, img_1], axis=0) #save_visualization(seg_output, save_path=os.path.join(config['result_dir'], 'tr-seg-1gt_2mapback_3map-{}.jpg'.format(iters)), size=[3, config['batch_size']]) #save_visualization(img_output, save_path=os.path.join(config['result_dir'], 'tr-img-1gt_2mapback_3map-{}.jpg'.format(iters)), size=[3, config['batch_size']]) for i,target_data_color in enumerate(target_data_color_queue): val_img_gt, val_seg_gt, val_seg_1, val_seg_2, val_img_1, val_img_2 = sess.run(target_data_color) print ("Val Range %10s:" % "seg_gt", val_seg_gt.min(), val_seg_gt.max()) print ("Val Range %10s:" % "seg_1", val_seg_1.min(), val_seg_1.max()) print ("Val Range %10s:" % "seg_2", val_seg_2.min(), val_seg_2.max()) print ("Val Range %10s:" % "img_gt", val_img_gt.min(), val_img_gt.max()) print ("Val Range %10s:" % "img_1", val_img_1.min(), val_img_1.max()) print ("Val Range %10s:" % "img_2", val_img_2.min(), val_img_2.max()) _output = np.concatenate([val_img_gt, val_seg_gt, val_seg_1, val_img_1, val_img_2, val_seg_2], axis=0) save_visualization(_output, save_path=os.path.join(config['result_dir'], 'val{}-{}.jpg'.format(i,iters)), size=[3, 2*config['batch_size']]) #val_seg_output = np.concatenate([val_seg_gt, val_seg_2, val_seg_1], axis=0) #val_img_output = np.concatenate([val_img_gt, val_img_2, val_img_1], axis=0) #save_visualization(seg_output, save_path=os.path.join(config['result_dir'], 'val{}-seg-1gt_2mapback_3map-{}.jpg'.format(i,iters)), size=[3, config['batch_size']]) #save_visualization(img_output, save_path=os.path.join(config['result_dir'], 'val{}-img-1gt_2mapback_3map-{}.jpg'.format(i,iters)), size=[3, config['batch_size']]) writer.flush() writer.close()
def train(args): ## set hyparameter img_mean = np.array((104.00698793, 116.66876762, 122.67891434), dtype=np.float32) tf.set_random_seed(args.random_seed) coord = tf.train.Coordinator() print("g_model_name:", args.g_name) print("lambda:", args.lambd) print("learning_rate:", args.learning_rate) print("is_val:", args.is_val) print("---------------------------------") ## load data with tf.name_scope("create_inputs"): reader = ImageReader(args.data_dir, args.img_size, args.random_scale, args.random_mirror, args.random_crop, args.ignore_label, args.is_val, img_mean, coord) image_batch, label_batch = reader.dequeue(args.batch_size) print("Data is ready!") ## load model g_net = choose_generator(args.g_name, image_batch) score_map = g_net.get_output() # [batch_size, h, w, num_classes] label, logits = convert_to_calculateloss(score_map, args.num_classes, label_batch) predict_label = tf.argmax(logits, axis=1) predict_batch = g_net.topredict(score_map, tf.shape(image_batch)[1:3]) print("The model has been created!") ## get all kinds of variables list if '50' not in args.g_name: # aim at vgg16 g_restore_var = [ v for v in tf.global_variables() if 'generator' in v.name and 'image' in v.name ] g_trainable_var = [ v for v in tf.trainable_variables() if 'generator' in v.name and 'upscore' not in v.name ] else: # aim at resnet50 g_restore_var = [ v for v in tf.global_variables() if 'fc' not in v.name ] g_trainable_var = [ v for v in tf.trainable_variables() if 'beta' not in v.name or 'gamma' not in v.name ] ## set loss mce_loss = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label, logits=logits)) # l2_losses = [args.weight_decay * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name] # g_loss = tf.reduce_mean(mce_loss) + tf.add_n(l2_losses) g_loss = mce_loss # don't add the penalization g_loss_var, g_loss_op = tf.metrics.mean(g_loss) iou_var, iou_op = tf.metrics.mean_iou(label, predict_label, args.num_classes) accuracy_var, acc_op = tf.metrics.accuracy(label, predict_label) metrics_op = tf.group(g_loss_op, iou_op, acc_op) ## set optimizer iterstep = tf.placeholder(dtype=tf.float32, shape=[], name='iteration_step') base_lr = tf.constant(args.learning_rate, dtype=tf.float32, shape=[]) lr = tf.scalar_mul(base_lr, tf.pow( (1 - iterstep / args.num_steps), args.power)) # learning rate reduce with the time # g_gradients = tf.train.MomentumOptimizer(learning_rate=lr, momentum=args.momentum).compute_gradients(g_loss, # g_trainable_var) train_g_op = tf.train.MomentumOptimizer(learning_rate=lr, momentum=args.momentum).minimize( g_loss, var_list=g_trainable_var) train_all_op = train_g_op ## set summary vs_image = tf.py_func(inv_preprocess, [image_batch, args.save_num_images, img_mean], tf.uint8) vs_label = tf.py_func( decode_labels, [label_batch, args.save_num_images, args.num_classes], tf.uint8) vs_predict = tf.py_func( decode_labels, [predict_batch, args.save_num_images, args.num_classes], tf.uint8) tf.summary.image(name='image collection_train', tensor=tf.concat(axis=2, values=[vs_image, vs_label, vs_predict]), max_outputs=args.save_num_images) tf.summary.scalar('g_loss_train', g_loss_var) tf.summary.scalar('iou_train', iou_var) tf.summary.scalar('accuracy_train', accuracy_var) # for grad, var in g_gradients: # tf.summary.histogram(var.op.name + "/gradients", grad) # # for var in tf.trainable_variables(): # tf.summary.histogram(var.op.name + "/values", var) summary_op = tf.summary.merge_all() summary_writer = tf.summary.FileWriter(args.log_dir, graph=tf.get_default_graph(), max_queue=10) ## set session print("GPU index:" + str(os.environ['CUDA_VISIBLE_DEVICES'])) config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) global_init = tf.global_variables_initializer() local_init = tf.local_variables_initializer() sess.run(global_init) sess.run(local_init) ## set saver saver_all = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=5) trained_step = 0 if os.path.exists(args.restore_from + 'checkpoint'): trained_step = load_weight(args.restore_from, saver_all, sess) else: if '50' in args.g_name: saver_g = tf.train.Saver(var_list=g_restore_var) load_weight(args.baseweight_from['res50'], saver_g, sess) elif 'vgg' in args.g_name: load_weight(args.baseweight_from['vgg16'], g_restore_var, sess) threads = tf.train.start_queue_runners(sess, coord) print("all setting has been done,training start!") ## start training for step in range(args.num_steps): now_step = int( trained_step) + step if trained_step is not None else step feed_dict = {iterstep: now_step} _, _, g_loss_ = sess.run([train_all_op, metrics_op, g_loss], feed_dict) if step > 0 and step % args.save_pred_every == 0: save_weight(args.restore_from, saver_all, sess, now_step) if step % 50 == 0 or step == args.num_steps - 1: print('step={} g_loss={}'.format(now_step, g_loss_)) summary_str = sess.run(summary_op, feed_dict) summary_writer.add_summary(summary_str, now_step) sess.run(local_init) ## end training coord.request_stop() coord.join(threads) print('end....')
def train(args): ## set hyparameter img_mean = np.array((104.00698793, 116.66876762, 122.67891434), dtype=np.float32) tf.set_random_seed(args.random_seed) coord = tf.train.Coordinator() print("g_name:", args.g_name) print("d_name:", args.d_name) print("lambda:", args.lambd) print("learning_rate:", args.learning_rate) print("is_val:", args.is_val) print("---------------------------------") ## load data with tf.name_scope("create_inputs"): reader = ImageReader(args.data_dir, args.img_size, args.random_scale, args.random_mirror, args.random_crop, args.ignore_label, args.is_val, img_mean, coord) image_batch, label_batch = reader.dequeue(args.batch_size) print("Data is ready!") ## load model image_normal_batch = tf.train.batch([ (reader.image + img_mean) / 255., ], args.batch_size, dynamic_pad=True) g_net, g_net_x = choose_generator(args.g_name, image_batch, image_normal_batch) score_map = g_net.get_output() fk_batch = tf.nn.softmax(score_map, dim=-1) pre_batch = tf.expand_dims(tf.cast(tf.argmax(fk_batch, axis=-1), tf.uint8), axis=-1) gt_batch = tf.image.resize_nearest_neighbor(label_batch, tf.shape(score_map)[1:3]) gt_batch = tf.where(tf.equal(gt_batch, args.ignore_label), pre_batch, gt_batch) gt_batch = convert_to_scaling(fk_batch, args.num_classes, gt_batch) x_batch = g_net_x.get_appointed_layer('generator/image_conv5_3') d_fk_net, d_gt_net = choose_discriminator(args.d_name, fk_batch, gt_batch, x_batch) d_fk_pred = d_fk_net.get_output() # fake segmentation result in d d_gt_pred = d_gt_net.get_output() # ground-truth result in d label, logits = convert_to_calculateloss(score_map, args.num_classes, label_batch) predict_label = tf.argmax(logits, axis=1) predict_batch = g_net.topredict(score_map, tf.shape(image_batch)[1:3]) print("The model has been created!") ## get all kinds of variables list g_restore_var = [ v for v in tf.global_variables() if 'discriminator' not in v.name ] g_var = [ v for v in tf.trainable_variables() if 'generator' in v.name and 'deconv' not in v.name ] d_var = [v for v in tf.trainable_variables() if 'discriminator' in v.name] # g_trainable_var = [v for v in g_var if 'beta' not in v.name or 'gamma' not in v.name] # batch_norm training open g_trainable_var = g_var d_trainable_var = d_var ## set loss mce_loss = tf.reduce_mean( tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label, logits=logits)) # l2_losses = [args.weight_decay * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name] # mce_loss = tf.reduce_mean(mce_loss) + tf.add_n(l2_losses) # g_bce_loss = tf.reduce_mean(tf.log(d_fk_pred + eps)) g_bce_loss = args.lambd * tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_fk_pred), logits=d_fk_pred)) g_loss = mce_loss + g_bce_loss # d_loss = tf.reduce_mean(tf.constant(-1.0) * [tf.log(d_gt_pred + eps) + tf.log(1. - d_fk_pred + eps)]) d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_gt_pred), logits=d_gt_pred) \ + tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_fk_pred), logits=d_fk_pred)) fk_score_var = tf.reduce_mean(tf.sigmoid(d_fk_pred)) gt_score_var = tf.reduce_mean(tf.sigmoid(d_gt_pred)) mce_loss_var, mce_loss_op = tf.metrics.mean(mce_loss) g_bce_loss_var, g_bce_loss_op = tf.metrics.mean(g_bce_loss) g_loss_var, g_loss_op = tf.metrics.mean(g_loss) d_loss_var, d_loss_op = tf.metrics.mean(d_loss) iou_var, iou_op = tf.metrics.mean_iou(label, predict_label, args.num_classes) accuracy_var, acc_op = tf.metrics.accuracy(label, predict_label) metrics_op = tf.group(mce_loss_op, g_bce_loss_op, g_loss_op, d_loss_op, iou_op, acc_op) ## set optimizer iterstep = tf.placeholder(dtype=tf.float32, shape=[], name='iteration_step') base_lr = tf.constant(args.learning_rate, dtype=tf.float32, shape=[]) lr = tf.scalar_mul(base_lr, tf.pow( (1 - iterstep / args.num_steps), args.power)) # learning rate reduce with the time # g_gradients = tf.train.MomentumOptimizer(learning_rate=lr, # momentum=args.momentum).compute_gradients(g_loss, # var_list=g_trainable_var) g_gradients = tf.train.AdamOptimizer(learning_rate=lr).compute_gradients( g_loss, var_list=g_trainable_var) d_gradients = tf.train.MomentumOptimizer( learning_rate=lr * 10, momentum=args.momentum).compute_gradients(d_loss, var_list=d_trainable_var) grad_fk_oi = tf.gradients(d_fk_pred, fk_batch, name='grad_fk_oi')[0] grad_gt_oi = tf.gradients(d_gt_pred, gt_batch, name='grad_gt_oi')[0] grad_fk_img_oi = tf.gradients(d_fk_pred, image_batch, name='grad_fk_img_oi')[0] grad_gt_img_oi = tf.gradients(d_gt_pred, image_batch, name='grad_gt_img_oi')[0] train_g_op = tf.train.AdamOptimizer(learning_rate=lr).minimize( g_loss, var_list=g_trainable_var) train_d_op = tf.train.MomentumOptimizer(learning_rate=lr * 10, momentum=args.momentum).minimize( d_loss, var_list=d_trainable_var) ## set summary vs_image = tf.py_func(inv_preprocess, [image_batch, args.save_num_images, img_mean], tf.uint8) vs_label = tf.py_func( decode_labels, [label_batch, args.save_num_images, args.num_classes], tf.uint8) vs_predict = tf.py_func( decode_labels, [predict_batch, args.save_num_images, args.num_classes], tf.uint8) tf.summary.image(name='image collection_train', tensor=tf.concat(axis=2, values=[vs_image, vs_label, vs_predict]), max_outputs=args.save_num_images) tf.summary.scalar('fk_score', fk_score_var) tf.summary.scalar('gt_score', gt_score_var) tf.summary.scalar('g_loss_train', g_loss_var) tf.summary.scalar('d_loss_train', d_loss_var) tf.summary.scalar('mce_loss_train', mce_loss_var) tf.summary.scalar('g_bce_loss_train', g_bce_loss_var) tf.summary.scalar('iou_train', iou_var) tf.summary.scalar('accuracy_train', accuracy_var) tf.summary.scalar('grad_fk_oi', tf.reduce_mean(tf.abs(grad_fk_oi))) tf.summary.scalar('grad_gt_oi', tf.reduce_mean(tf.abs(grad_gt_oi))) tf.summary.scalar('grad_fk_img_oi', tf.reduce_mean(tf.abs(grad_fk_img_oi))) tf.summary.scalar('grad_gt_img_oi', tf.reduce_mean(tf.abs(grad_gt_img_oi))) for grad, var in g_gradients + d_gradients: tf.summary.histogram(var.op.name + "/gradients", grad) for var in tf.trainable_variables(): tf.summary.histogram(var.op.name + "/values", var) summary_op = tf.summary.merge_all() summary_writer = tf.summary.FileWriter(args.log_dir, graph=tf.get_default_graph(), max_queue=3) ## set session print("GPU index:" + str(os.environ['CUDA_VISIBLE_DEVICES'])) config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) global_init = tf.global_variables_initializer() local_init = tf.local_variables_initializer() sess.run(global_init) sess.run(local_init) ## set saver saver_all = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=2) trained_step = 0 if os.path.exists(args.restore_from + 'checkpoint'): trained_step = load_weight(args.restore_from, saver_all, sess) else: saver_g = tf.train.Saver(var_list=g_restore_var, max_to_keep=2) load_weight(args.baseweight_from['g'], saver_g, sess) # the weight is the completely g model threads = tf.train.start_queue_runners(sess, coord) print("all setting has been done,training start!") ## start training # def auto_setting_train_steps(mode): # if mode == 0: # return 5, 1 # elif mode == 1: # return 1, 5 # else: # return 1, 1 d_train_steps = 10 g_train_steps = 1 # flags = [0 for i in range(3)] for step in range(args.num_steps): now_step = int( trained_step) + step if trained_step is not None else step feed_dict = {iterstep: step} for i in range(d_train_steps): _, _ = sess.run([train_d_op, metrics_op], feed_dict) for i in range(g_train_steps): g_loss_, mce_loss_, g_bce_loss_, d_loss_, _, _ = sess.run([ g_loss_var, mce_loss_var, g_bce_loss_var, d_loss_var, train_g_op, metrics_op ], feed_dict) ######################## # fk_score_, gt_score_ = sess.run([fk_score_var, gt_score_var], feed_dict) # if fk_score_ > 0.48 and fk_score_ < 0.52: # flags[0] += 1 # flags[1] = flags[2] = 0 # elif gt_score_ - fk_score_ > 0.3: # flags[1] += 1 # flags[0] = flags[2] = 0 # else: # flags[2] += 1 # flags[0] = flags[1] = 0 # if max(flags) > 100: # d_train_steps, g_train_steps = auto_setting_train_steps(flags.index(max(flags))) ######################## if step > 0 and step % args.save_pred_every == 0: save_weight(args.restore_from, saver_all, sess, now_step) if step % 50 == 0 or step == args.num_steps - 1: print('step={} d_loss={} g_loss={} mce_loss={} g_bce_loss_={}'. format(now_step, d_loss_, g_loss_, mce_loss_, g_bce_loss_)) summary_str = sess.run(summary_op, feed_dict) summary_writer.add_summary(summary_str, now_step) sess.run(local_init) ## end training coord.request_stop() coord.join(threads) print('end....')