init

train_old.py

@@ -0,0 +1,445 @@
+import os
+import time
+import argparse
+import wandb
+from cv2 import drawFrameAxes
+import numpy as np
+import tensorflow as tf
+
+from utils import str_to_bool
+from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
+from load_models import load_generator, load_discriminator
+from dataset_ffhq import get_ffhq_dataset
+from dataset import DatasetSimple
+from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='test_2')
+artifact = wandb.Artifact('code', type='code')
+artifact.add_file('train_old.py')
+run.log_artifact(artifact)
+
+def initiate_models(g_params, d_params, use_custom_cuda):
+    discriminator = load_discriminator(d_params, ckpt_dir=None, custom_cuda=use_custom_cuda)
+    generator = load_generator(g_params=g_params, is_g_clone=False, ckpt_dir=None, custom_cuda=use_custom_cuda)
+    g_clone = load_generator(g_params=g_params, is_g_clone=True, ckpt_dir=None, custom_cuda=use_custom_cuda)
+
+    # set initial g_clone weights same as generator
+    g_clone.set_weights(generator.get_weights())
+    return discriminator, generator, g_clone
+
+
+class Trainer(object):
+    def __init__(self, t_params, name):
+        self.cur_tf_ver = t_params['cur_tf_ver']
+        self.use_tf_function = t_params['use_tf_function']
+        self.use_custom_cuda = t_params['use_custom_cuda']
+        self.model_base_dir = t_params['model_base_dir']
+        self.global_batch_size = t_params['batch_size']
+        self.n_total_image = t_params['n_total_image']
+        self.max_steps = int(np.ceil(self.n_total_image / self.global_batch_size))
+        self.n_samples = min(t_params['batch_size'], t_params['n_samples'])
+        self.train_res = t_params['train_res']
+        self.print_step = 10
+        self.save_step = 1000
+        self.image_summary_step = 10000
+        self.reached_max_steps = False
+        self.log_template = '{:s}, {:s}, {:s}'.format(
+            'step {}: elapsed: {:.2f}s, d_loss: {:.3f}, g_loss: {:.3f}',
+            'd_gan_loss: {:.3f}, g_gan_loss: {:.3f}',
+            'r1_penalty: {:.3f}, pl_penalty: {:.3f}')
+
+        # copy network params
+        self.g_params = t_params['g_params']
+        self.d_params = t_params['d_params']
+
+        # set optimizer params
+        self.global_batch_scaler = 1.0 / float(self.global_batch_size)
+        self.r1_gamma = 10.0
+        self.g_opt = self.update_optimizer_params(t_params['g_opt'])
+        self.d_opt = self.update_optimizer_params(t_params['d_opt'])
+        self.pl_minibatch_shrink = 2
+        self.pl_weight = float(self.pl_minibatch_shrink)
+        self.pl_denorm = tf.math.rsqrt(float(self.train_res) * float(self.train_res))
+        self.pl_decay = 0.01
+        self.pl_mean = tf.Variable(initial_value=0.0, name='pl_mean', trainable=False,
+                                   synchronization=tf.VariableSynchronization.ON_READ,
+                                   aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)
+
+        # create model: model and optimizer must be created under `strategy.scope`
+        self.discriminator, self.generator, self.g_clone = initiate_models(self.g_params,
+                                                                           self.d_params,
+                                                                           self.use_custom_cuda)
+
+        # set optimizers
+        self.d_optimizer = tf.keras.optimizers.Adam(self.d_opt['learning_rate'],
+                                                    beta_1=self.d_opt['beta1'],
+                                                    beta_2=self.d_opt['beta2'],
+                                                    epsilon=self.d_opt['epsilon'])
+        self.g_optimizer = tf.keras.optimizers.Adam(self.g_opt['learning_rate'],
+                                                    beta_1=self.g_opt['beta1'],
+                                                    beta_2=self.g_opt['beta2'],
+                                                    epsilon=self.g_opt['epsilon'])
+
+        # setup saving locations (object based savings)
+        self.ckpt_dir = os.path.join(self.model_base_dir, name)
+        self.ckpt = tf.train.Checkpoint(d_optimizer=self.d_optimizer,
+                                        g_optimizer=self.g_optimizer,
+                                        discriminator=self.discriminator,
+                                        generator=self.generator,
+                                        g_clone=self.g_clone,
+                                        pl_mean=self.pl_mean)
+        self.manager = tf.train.CheckpointManager(self.ckpt, self.ckpt_dir, max_to_keep=10)
+
+        # try to restore
+        self.ckpt.restore(self.manager.latest_checkpoint)
+        if self.manager.latest_checkpoint:
+            print('Restored from {}'.format(self.manager.latest_checkpoint))
+
+            # check if already trained in this resolution
+            restored_step = self.g_optimizer.iterations.numpy()
+            if restored_step >= self.max_steps:
+                print('Already reached max steps {}/{}'.format(restored_step, self.max_steps))
+                self.reached_max_steps = True
+                return
+        else:
+            print('Not restoring from saved checkpoint')
+
+    @staticmethod
+    def update_optimizer_params(params):
+        params_copy = params.copy()
+        mb_ratio = params_copy['reg_interval'] / (params_copy['reg_interval'] + 1)
+        params_copy['learning_rate'] = params_copy['learning_rate'] * mb_ratio
+        params_copy['beta1'] = params_copy['beta1'] ** mb_ratio
+        params_copy['beta2'] = params_copy['beta2'] ** mb_ratio
+        return params_copy
+
+    def d_train_step(self, dist_inputs):
+        real_images = dist_inputs[0]
+
+        with tf.GradientTape() as d_tape:
+            # compute losses
+            d_loss = d_logistic(real_images, self.generator, self.discriminator, self.g_params['z_dim'])
+
+            # scale loss
+            d_loss = tf.reduce_sum(d_loss) * self.global_batch_scaler
+
+        d_gradients = d_tape.gradient(d_loss, self.discriminator.trainable_variables)
+        self.d_optimizer.apply_gradients(zip(d_gradients, self.discriminator.trainable_variables))
+        return d_loss
+
+    def d_train_step_reg(self, dist_inputs):
+        real_images = dist_inputs[0]
+
+        with tf.GradientTape() as d_tape:
+            # compute losses
+            d_gan_loss, r1_penalty = d_logistic_r1_reg(real_images, self.generator, self.discriminator,
+                                                       self.g_params['z_dim'])
+            r1_penalty = r1_penalty * (0.5 * self.r1_gamma) * self.d_opt['reg_interval']
+
+            # scale losses
+            r1_penalty = tf.reduce_sum(r1_penalty) * self.global_batch_scaler
+            d_gan_loss = tf.reduce_sum(d_gan_loss) * self.global_batch_scaler
+
+            # combine
+            d_loss = d_gan_loss + r1_penalty
+
+        d_gradients = d_tape.gradient(d_loss, self.discriminator.trainable_variables)
+        self.d_optimizer.apply_gradients(zip(d_gradients, self.discriminator.trainable_variables))
+        return d_loss, d_gan_loss, r1_penalty
+
+    def g_train_step(self, dist_inputs):
+        real_images = dist_inputs[0]
+
+        with tf.GradientTape() as g_tape:
+            # compute losses
+            g_loss = g_logistic_non_saturating(real_images, self.generator, self.discriminator, self.g_params['z_dim'])
+
+            # scale loss
+            g_loss = tf.reduce_sum(g_loss) * self.global_batch_scaler
+
+        g_gradients = g_tape.gradient(g_loss, self.generator.trainable_variables)
+        self.g_optimizer.apply_gradients(zip(g_gradients, self.generator.trainable_variables))
+        return g_loss
+
+    def g_train_step_reg(self, dist_inputs):
+        real_images = dist_inputs[0]
+
+        with tf.GradientTape() as g_tape:
+            # compute losses
+            g_gan_loss, pl_penalty = g_logistic_ns_pathreg(real_images, self.generator, self.discriminator,
+                                                           self.g_params['z_dim'], self.pl_mean,
+                                                           self.pl_minibatch_shrink, self.pl_denorm, self.pl_decay)
+            pl_penalty = pl_penalty * self.pl_weight * self.g_opt['reg_interval']
+
+            # scale loss
+            pl_penalty = tf.reduce_sum(pl_penalty) * self.global_batch_scaler
+            g_gan_loss = tf.reduce_sum(g_gan_loss) * self.global_batch_scaler
+
+            # combine
+            g_loss = g_gan_loss + pl_penalty
+
+        g_gradients = g_tape.gradient(g_loss, self.generator.trainable_variables)
+        self.g_optimizer.apply_gradients(zip(g_gradients, self.generator.trainable_variables))
+        return g_loss, g_gan_loss, pl_penalty
+
+#    @tf.function
+    def train(self, dist_datasets, strategy):
+        def dist_d_train_step(inputs):
+            if self.cur_tf_ver == '2.0.0':
+                per_replica_losses = strategy.experimental_run_v2(fn=self.d_train_step, args=(inputs,))
+            else:
+                per_replica_losses = strategy.run(fn=self.d_train_step, args=(inputs,))
+            mean_d_loss = strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)
+            return mean_d_loss
+
+        def dist_d_train_step_reg(inputs):
+            if self.cur_tf_ver == '2.0.0':
+                per_replica_losses = strategy.experimental_run_v2(fn=self.d_train_step_reg, args=(inputs,))
+            else:
+                per_replica_losses = strategy.run(fn=self.d_train_step_reg, args=(inputs,))
+            mean_d_loss = strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses[0], axis=None)
+            mean_d_gan_loss = strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses[1], axis=None)
+            mean_r1_penalty = strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses[2], axis=None)
+            return mean_d_loss, mean_d_gan_loss, mean_r1_penalty
+
+        def dist_g_train_step(inputs):
+            if self.cur_tf_ver == '2.0.0':
+                per_replica_losses = strategy.experimental_run_v2(fn=self.g_train_step, args=(inputs,))
+            else:
+                per_replica_losses = strategy.run(fn=self.g_train_step, args=(inputs,))
+            mean_g_loss = strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)
+            return mean_g_loss
+
+        def dist_g_train_step_reg(inputs):
+            if self.cur_tf_ver == '2.0.0':
+                per_replica_losses = strategy.experimental_run_v2(fn=self.g_train_step_reg, args=(inputs,))
+            else:
+                per_replica_losses = strategy.run(fn=self.g_train_step_reg, args=(inputs,))
+            mean_g_loss = strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses[0], axis=None)
+            mean_g_gan_loss = strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses[1], axis=None)
+            mean_pl_penalty = strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses[2], axis=None)
+            return mean_g_loss, mean_g_gan_loss, mean_pl_penalty
+
+        def dist_gen_samples(dist_inputs):
+            if self.cur_tf_ver == '2.0.0':
+                per_replica_samples = strategy.experimental_run_v2(self.gen_samples, args=(dist_inputs,))
+            else:
+                per_replica_samples = strategy.run(self.gen_samples, args=(dist_inputs,))
+            return per_replica_samples
+
+        # wrap with tf.function
+        if self.use_tf_function:
+            dist_d_train_step = tf.function(dist_d_train_step)
+            dist_g_train_step = tf.function(dist_g_train_step)
+            dist_d_train_step_reg = tf.function(dist_d_train_step_reg)
+            dist_g_train_step_reg = tf.function(dist_g_train_step_reg)
+            dist_gen_samples = tf.function(dist_gen_samples)
+
+        if self.reached_max_steps:
+            return
+
+        # start actual training
+        print('Start Training')
+
+        # setup tensorboards
+        train_summary_writer = tf.summary.create_file_writer(self.ckpt_dir)
+
+        # loss metrics
+        metric_d_loss = tf.keras.metrics.Mean('d_loss', dtype=tf.float32)
+        metric_g_loss = tf.keras.metrics.Mean('g_loss', dtype=tf.float32)
+        metric_d_gan_loss = tf.keras.metrics.Mean('d_gan_loss', dtype=tf.float32)
+        metric_g_gan_loss = tf.keras.metrics.Mean('g_gan_loss', dtype=tf.float32)
+        metric_r1_penalty = tf.keras.metrics.Mean('r1_penalty', dtype=tf.float32)
+        metric_pl_penalty = tf.keras.metrics.Mean('pl_penalty', dtype=tf.float32)
+
+        # start training
+        zero = tf.constant(0.0, dtype=tf.float32)
+        print('max_steps: {}'.format(self.max_steps))
+        t_start = time.time()
+        for real_images in dist_datasets:
+            step = self.g_optimizer.iterations.numpy()
+
+            # d train step
+            if (step + 1) % self.d_opt['reg_interval'] == 0:
+                d_loss, d_gan_loss, r1_penalty = dist_d_train_step_reg((real_images, ))
+            else:
+                d_loss = dist_d_train_step((real_images, ))
+                d_gan_loss = d_loss
+                r1_penalty = zero
+
+            # g train step
+            if (step + 1) % self.g_opt['reg_interval'] == 0:
+                g_loss, g_gan_loss, pl_penalty = dist_g_train_step_reg((real_images, ))
+            else:
+                g_loss = dist_g_train_step((real_images,))
+                g_gan_loss = g_loss
+                pl_penalty = zero
+
+            # update g_clone
+            self.g_clone.set_as_moving_average_of(self.generator)
+
+            # update metrics
+            metric_d_loss(d_loss)
+            metric_g_loss(g_loss)
+            metric_d_gan_loss(d_gan_loss)
+            metric_g_gan_loss(g_gan_loss)
+            metric_r1_penalty(r1_penalty)
+            metric_pl_penalty(pl_penalty)
+
+            # get current step
+            step = self.g_optimizer.iterations.numpy()
+
+            # save to tensorboard
+            with train_summary_writer.as_default():
+                tf.summary.scalar('d_loss', metric_d_loss.result(), step=step)
+                tf.summary.scalar('g_loss', metric_g_loss.result(), step=step)
+                tf.summary.scalar('d_gan_loss', metric_d_gan_loss.result(), step=step)
+                tf.summary.scalar('g_gan_loss', metric_g_gan_loss.result(), step=step)
+                tf.summary.scalar('r1_penalty', metric_r1_penalty.result(), step=step)
+                tf.summary.scalar('pl_penalty', metric_pl_penalty.result(), step=step)
+
+            # save every self.save_step
+            if step % self.save_step == 0:
+                self.manager.save(checkpoint_number=step)
+
+            # save every self.image_summary_step
+            if step % self.image_summary_step == 0:
+                # add summary image
+                test_z = tf.random.normal(shape=(self.n_samples, self.g_params['z_dim']), dtype=tf.dtypes.float32)
+                test_labels = tf.ones((self.n_samples, self.g_params['labels_dim']), dtype=tf.dtypes.float32)
+                summary_image = dist_gen_samples((test_z, test_labels))
+
+                # convert to tensor image
+                summary_image = self.convert_per_replica_image(summary_image, strategy)
+
+                with train_summary_writer.as_default():
+                    tf.summary.image('images', summary_image, step=step)
+
+            # print every self.print_steps
+            if step % self.print_step == 0:
+                elapsed = time.time() - t_start
+                print(self.log_template.format(step, elapsed, d_loss.numpy(), g_loss.numpy(),
+                                               d_gan_loss.numpy(), g_gan_loss.numpy(),
+                                               r1_penalty.numpy(), pl_penalty.numpy()))
+
+                # reset timer
+                t_start = time.time()
+
+            # check exit status
+            if step >= self.max_steps:
+                break
+
+        # save last checkpoint
+        step = self.g_optimizer.iterations.numpy()
+        self.manager.save(checkpoint_number=step)
+        return
+
+    def gen_samples(self, inputs):
+        test_z, test_labels = inputs
+
+        # run networks
+        fake_images_05 = self.g_clone([test_z, test_labels], truncation_psi=0.5, training=False)
+        fake_images_07 = self.g_clone([test_z, test_labels], truncation_psi=0.7, training=False)
+
+        # merge on batch dimension: [n_samples, 3, out_res, 2 * out_res]
+        final_image = tf.concat([fake_images_05, fake_images_07], axis=2)
+        return final_image
+
+    @staticmethod
+    def convert_per_replica_image(nchw_per_replica_images, strategy):
+        as_tensor = tf.concat(strategy.experimental_local_results(nchw_per_replica_images), axis=0)
+        as_tensor = tf.transpose(as_tensor, perm=[0, 2, 3, 1])
+        as_tensor = (tf.clip_by_value(as_tensor, -1.0, 1.0) + 1.0) * 127.5
+        as_tensor = tf.cast(as_tensor, tf.uint8)
+        return as_tensor
+
+
+def filter_resolutions_featuremaps(resolutions, featuremaps, res):
+    index = resolutions.index(res)
+    filtered_resolutions = resolutions[:index + 1]
+    filtered_featuremaps = featuremaps[:index + 1]
+    return filtered_resolutions, filtered_featuremaps
+
+
+def main():
+    # global program arguments parser
+    parser = argparse.ArgumentParser(description='')
+    parser.add_argument('--allow_memory_growth', type=str_to_bool, nargs='?', const=True, default=True)
+    parser.add_argument('--debug_split_gpu', type=str_to_bool, nargs='?', const=True, default=False)
+    parser.add_argument('--use_tf_function', type=str_to_bool, nargs='?', const=True, default=True)
+    parser.add_argument('--use_custom_cuda', type=str_to_bool, nargs='?', const=True, default=False)
+    parser.add_argument('--model_base_dir', default='./models', type=str)
+    parser.add_argument('--tfrecord_dir', default='./tfrecords', type=str)
+    parser.add_argument('--train_res', default=512, type=int)
+    parser.add_argument('--shuffle_buffer_size', default=1000, type=int)
+    parser.add_argument('--batch_size_per_replica', default=12, type=int)
+    args = vars(parser.parse_args())
+
+    # check tensorflow version
+    cur_tf_ver = check_tf_version()
+
+    # GPU environment settings
+    if args['allow_memory_growth']:
+        allow_memory_growth()
+    if args['debug_split_gpu']:
+        split_gpu_for_testing(mem_in_gb=4.5)
+
+    # network params
+    resolutions = [4, 8, 16, 32, 64, 128, 256, 512, 1024]
+    featuremaps = [512, 512, 512, 512, 512, 256, 128, 64, 32]
+    train_resolutions, train_featuremaps = filter_resolutions_featuremaps(resolutions, featuremaps, args['train_res'])
+    g_params = {
+        'z_dim': 512,
+        'w_dim': 512,
+        'labels_dim': 0,
+        'n_mapping': 8,
+        'resolutions': train_resolutions,
+        'featuremaps': train_featuremaps,
+    }
+    d_params = {
+        'labels_dim': 0,
+        'resolutions': train_resolutions,
+        'featuremaps': train_featuremaps,
+    }
+
+    # prepare distribute strategy
+    strategy = tf.distribute.MirroredStrategy()
+    global_batch_size = args['batch_size_per_replica'] * strategy.num_replicas_in_sync
+
+    # prepare dataset
+    # dataset = get_ffhq_dataset(args['tfrecord_dir'], args['train_res'], batch_size=global_batch_size, epochs=None)
+    dataset = DatasetSimple('/home/workspace/local-data/stylegan/npz').shuffle(buffer_size=1000).repeat(None).batch(global_batch_size).prefetch(buffer_size=tf.data.experimental.AUTOTUNE).cache()
+
+    with strategy.scope():
+        # distribute dataset
+        #dist_dataset = strategy.experimental_distribute_dataset(dataset)
+        dist_dataset = dataset
+        # training parameters
+        training_parameters = {
+            # global params
+            'cur_tf_ver': cur_tf_ver,
+            'use_tf_function': args['use_tf_function'],
+            'use_custom_cuda': args['use_custom_cuda'],
+            'model_base_dir': args['model_base_dir'],
+
+            # network params
+            'g_params': g_params,
+            'd_params': d_params,
+
+            # training params
+            'g_opt': {'learning_rate': 0.0002, 'beta1': 0.0, 'beta2': 0.99, 'epsilon': 1e-08, 'reg_interval': 8},
+            'd_opt': {'learning_rate': 0.0002, 'beta1': 0.0, 'beta2': 0.99, 'epsilon': 1e-08, 'reg_interval': 16},
+            'batch_size': global_batch_size,
+            'n_total_image': 25000000,
+            'n_samples': 12,
+            'train_res': args['train_res'],
+        }
+
+        trainer = Trainer(training_parameters, name=f'stylegan2-test-2-{args["train_res"]}x{args["train_res"]}')
+        trainer.train(dist_dataset, strategy)
+    return
+
+
+if __name__ == '__main__':
+    main()