diff --git a/blueoil/cmd/convert_weight_from_darknet.py b/blueoil/cmd/convert_weight_from_darknet.py index 70fd995b0..da3c6fb07 100644 --- a/blueoil/cmd/convert_weight_from_darknet.py +++ b/blueoil/cmd/convert_weight_from_darknet.py @@ -53,14 +53,14 @@ def convert(config, weight_file): model.inference(images_placeholder, is_training) - init_op = tf.global_variables_initializer() + init_op = tf.compat.v1.global_variables_initializer() saver = tf.compat.v1.train.Saver(max_to_keep=None) - variables = tf.global_variables() + variables = tf.compat.v1.global_variables() session_config = None - sess = tf.Session(graph=graph, config=session_config) + sess = tf.compat.v1.Session(graph=graph, config=session_config) sess.run([init_op, ]) suffixes = ['bias', 'beta', 'gamma', 'moving_mean', 'moving_variance', 'kernel'] convert_variables = [] diff --git a/blueoil/cmd/evaluate.py b/blueoil/cmd/evaluate.py index a390974b3..cb25ed10e 100644 --- a/blueoil/cmd/evaluate.py +++ b/blueoil/cmd/evaluate.py @@ -106,15 +106,15 @@ def evaluate(config, restore_path, output_dir): metrics_summary_op, metrics_placeholders = executor.prepare_metrics(metrics_ops_dict) - init_op = tf.global_variables_initializer() - reset_metrics_op = tf.local_variables_initializer() + init_op = tf.compat.v1.global_variables_initializer() + reset_metrics_op = tf.compat.v1.local_variables_initializer() saver = tf.compat.v1.train.Saver(max_to_keep=None) session_config = None # tf.ConfigProto(log_device_placement=True) - sess = tf.Session(graph=graph, config=session_config) + sess = tf.compat.v1.Session(graph=graph, config=session_config) sess.run([init_op, reset_metrics_op]) - validation_writer = tf.summary.FileWriter(environment.TENSORBOARD_DIR + "/evaluate") + validation_writer = tf.compat.v1.summary.FileWriter(environment.TENSORBOARD_DIR + "/evaluate") saver.restore(sess, restore_path) diff --git a/blueoil/cmd/export.py b/blueoil/cmd/export.py index 0db75c5c2..81825e1d0 100644 --- a/blueoil/cmd/export.py +++ b/blueoil/cmd/export.py @@ -95,12 +95,12 @@ def _export(config, restore_path, image_path): images_placeholder, _ = model.placeholders() model.inference(images_placeholder, is_training) - init_op = tf.global_variables_initializer() + init_op = tf.compat.v1.global_variables_initializer() saver = tf.compat.v1.train.Saver(max_to_keep=50) - session_config = tf.ConfigProto() - sess = tf.Session(graph=graph, config=session_config) + session_config = tf.compat.v1.ConfigProto() + sess = tf.compat.v1.Session(graph=graph, config=session_config) sess.run(init_op) saver.restore(sess, restore_path) diff --git a/blueoil/cmd/measure_latency.py b/blueoil/cmd/measure_latency.py index f8f8e9390..c2e4f1ad5 100644 --- a/blueoil/cmd/measure_latency.py +++ b/blueoil/cmd/measure_latency.py @@ -55,13 +55,13 @@ def _measure_time(config, restore_path, step_size): images_placeholder, labels_placeholder = model.placeholders() output = model.inference(images_placeholder, is_training) - init_op = tf.global_variables_initializer() + init_op = tf.compat.v1.global_variables_initializer() saver = tf.compat.v1.train.Saver() session_config = None # tf.ConfigProto(log_device_placement=True) # session_config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 - sess = tf.Session(graph=graph, config=session_config) + sess = tf.compat.v1.Session(graph=graph, config=session_config) sess.run(init_op) if restore_path: diff --git a/blueoil/cmd/predict.py b/blueoil/cmd/predict.py index 5f9599902..3db97e939 100644 --- a/blueoil/cmd/predict.py +++ b/blueoil/cmd/predict.py @@ -77,12 +77,12 @@ def _run(input_dir, output_dir, config, restore_path, save_images): images_placeholder, _ = model.placeholders() output_op = model.inference(images_placeholder, is_training) - init_op = tf.global_variables_initializer() + init_op = tf.compat.v1.global_variables_initializer() saver = tf.compat.v1.train.Saver(max_to_keep=None) - session_config = tf.ConfigProto() - sess = tf.Session(graph=graph, config=session_config) + session_config = tf.compat.v1.ConfigProto() + sess = tf.compat.v1.Session(graph=graph, config=session_config) sess.run(init_op) saver.restore(sess, restore_path) diff --git a/blueoil/cmd/profile_model.py b/blueoil/cmd/profile_model.py index f4c161620..b06ec0a69 100644 --- a/blueoil/cmd/profile_model.py +++ b/blueoil/cmd/profile_model.py @@ -54,11 +54,11 @@ def _profile(config, restore_path, bit, unquant_layers): images_placeholder, _ = model.placeholders() model.inference(images_placeholder, is_training) - init_op = tf.global_variables_initializer() + init_op = tf.compat.v1.global_variables_initializer() saver = tf.compat.v1.train.Saver(max_to_keep=50) - session_config = tf.ConfigProto() - sess = tf.Session(graph=graph, config=session_config) + session_config = tf.compat.v1.ConfigProto() + sess = tf.compat.v1.Session(graph=graph, config=session_config) sess.run(init_op) if restore_path: @@ -145,7 +145,8 @@ def _save_json(name, image_size, num_classes, node_param_dict, node_flops_dict): def _profile_flops(graph, res, scopes): - float_prof = tf.profiler.profile(graph, options=tf.profiler.ProfileOptionBuilder.float_operation()) + float_prof = tf.compat.v1.profiler.profile( + graph, options=tf.compat.v1.profiler.ProfileOptionBuilder.float_operation()) float_res_dict = collections.defaultdict(int) float_res_dict["total"] = float_prof.total_float_ops for node in float_prof.children: @@ -172,7 +173,8 @@ def _profile_flops(graph, res, scopes): def _profile_params(graph, res, bit, unquant_layers): - prof = tf.profiler.profile(graph, options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter()) + prof = tf.compat.v1.profiler.profile( + graph, options=tf.compat.v1.profiler.ProfileOptionBuilder.trainable_variables_parameter()) # helper func to make profile res def helper(node, level): diff --git a/blueoil/cmd/train.py b/blueoil/cmd/train.py index 378f9b293..8ec7abe96 100644 --- a/blueoil/cmd/train.py +++ b/blueoil/cmd/train.py @@ -129,8 +129,8 @@ def start_training(config): metrics_summary_op, metrics_placeholders = executor.prepare_metrics(metrics_ops_dict) - init_op = tf.global_variables_initializer() - reset_metrics_op = tf.local_variables_initializer() + init_op = tf.compat.v1.global_variables_initializer() + reset_metrics_op = tf.compat.v1.local_variables_initializer() if use_horovod: # add Horovod broadcasting variables from rank 0 to all bcast_global_variables_op = hvd.broadcast_global_variables(0) @@ -141,7 +141,7 @@ def start_training(config): saver = tf.compat.v1.train.Saver(max_to_keep=config.KEEP_CHECKPOINT_MAX) if config.IS_PRETRAIN: - all_vars = tf.global_variables() + all_vars = tf.compat.v1.global_variables() pretrain_var_list = [ var for var in all_vars if var.name.startswith(tuple(config.PRETRAIN_VARS)) ] @@ -152,8 +152,8 @@ def start_training(config): if use_horovod: # For distributed training - session_config = tf.ConfigProto( - gpu_options=tf.GPUOptions( + session_config = tf.compat.v1.ConfigProto( + gpu_options=tf.compat.v1.GPUOptions( allow_growth=True, visible_device_list=str(hvd.local_rank()) ) @@ -166,23 +166,24 @@ def start_training(config): # per_process_gpu_memory_fraction=0.1 # ) # ) - session_config = tf.ConfigProto() # tf.ConfigProto(log_device_placement=True) + session_config = tf.compat.v1.ConfigProto() # tf.ConfigProto(log_device_placement=True) # TODO(wakisaka): XLA JIT # session_config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 - sess = tf.Session(graph=graph, config=session_config) + sess = tf.compat.v1.Session(graph=graph, config=session_config) sess.run([init_op, reset_metrics_op]) if rank == 0: - train_writer = tf.summary.FileWriter(environment.TENSORBOARD_DIR + "/train", sess.graph) + train_writer = tf.compat.v1.summary.FileWriter(environment.TENSORBOARD_DIR + "/train", sess.graph) if use_train_validation_saving: - train_val_saving_writer = tf.summary.FileWriter(environment.TENSORBOARD_DIR + "/train_validation_saving") - val_writer = tf.summary.FileWriter(environment.TENSORBOARD_DIR + "/validation") + train_val_saving_writer = tf.compat.v1.summary.FileWriter( + environment.TENSORBOARD_DIR + "/train_validation_saving") + val_writer = tf.compat.v1.summary.FileWriter(environment.TENSORBOARD_DIR + "/validation") if config.IS_PRETRAIN: print("------- Load pretrain data ----------") pretrain_saver.restore(sess, os.path.join(config.PRETRAIN_DIR, config.PRETRAIN_FILE)) - sess.run(tf.assign(global_step, 0)) + sess.run(tf.compat.v1.assign(global_step, 0)) last_step = 0 @@ -301,7 +302,7 @@ def start_training(config): if step == 0: # check create pb on only first step. - minimal_graph = tf.graph_util.convert_variables_to_constants( + minimal_graph = tf.compat.v1.graph_util.convert_variables_to_constants( sess, sess.graph.as_graph_def(add_shapes=True), ["output"], diff --git a/blueoil/cmd/tune_ray.py b/blueoil/cmd/tune_ray.py index c7260aa5b..961b9875f 100644 --- a/blueoil/cmd/tune_ray.py +++ b/blueoil/cmd/tune_ray.py @@ -116,14 +116,14 @@ def update_parameters_for_each_trial(network_kwargs, chosen_kwargs): network_kwargs['optimizer_kwargs'][key] = chosen_kwargs['optimizer_class'][key] network_kwargs['learning_rate_func'] = chosen_kwargs['learning_rate_func']['scheduler'] base_lr = chosen_kwargs['learning_rate'] - if network_kwargs['learning_rate_func'] is tf.train.piecewise_constant: + if network_kwargs['learning_rate_func'] is tf.compat.v1.train.piecewise_constant: lr_factor = chosen_kwargs['learning_rate_func']['scheduler_factor'] network_kwargs['learning_rate_kwargs']['values'] = [base_lr, base_lr * lr_factor, base_lr * lr_factor * lr_factor, base_lr * lr_factor * lr_factor * lr_factor] network_kwargs['learning_rate_kwargs']['boundaries'] = chosen_kwargs['learning_rate_func']['scheduler_steps'] - elif network_kwargs['learning_rate_func'] is tf.train.polynomial_decay: + elif network_kwargs['learning_rate_func'] is tf.compat.v1.train.polynomial_decay: network_kwargs['learning_rate_kwargs']['learning_rate'] = base_lr network_kwargs['learning_rate_kwargs']['power'] = chosen_kwargs['learning_rate_func']['scheduler_power'] network_kwargs['learning_rate_kwargs']['decay_steps'] = chosen_kwargs['learning_rate_func']['scheduler_decay'] @@ -210,12 +210,12 @@ def _setup(self, config): self.metrics_ops_dict = metrics_ops_dict self.metrics_update_op = metrics_update_op - init_op = tf.global_variables_initializer() - self.reset_metrics_op = tf.local_variables_initializer() + init_op = tf.compat.v1.global_variables_initializer() + self.reset_metrics_op = tf.compat.v1.local_variables_initializer() - session_config = tf.ConfigProto( - gpu_options=tf.GPUOptions(allow_growth=True)) - self.sess = tf.Session(config=session_config) + session_config = tf.compat.v1.ConfigProto( + gpu_options=tf.compat.v1.GPUOptions(allow_growth=True)) + self.sess = tf.compat.v1.Session(config=session_config) self.sess.run([init_op, self.reset_metrics_op]) self.iterations = 0 self.saver = tf.compat.v1.train.Saver()