add auto encoder
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import os
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import time
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import argparse
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import wandb
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from cv2 import drawFrameAxes
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import numpy as np
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import tensorflow as tf
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from custom_utils.custom_utils import UV2Mesh, maskFacesBatch
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from tensorboardX import SummaryWriter
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from utils import str_to_bool
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from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
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from load_models import load_generator, load_discriminator
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from dataset import DatasetSimple
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from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg, edge_loss, uv_mask_loss
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# from losses import collision_penalty as collision_penalty
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from losses import collision_loss as collision_penalty
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from convnext.models.convnext_tf import ConvNeXt
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from stylegan2_ae.generator import Generator
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os.environ["CUDA_VISIBLE_DEVICES"] = "3"
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run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='stylegan', name='ae_1_2', notes='convnext + stylegan generator')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('auto_encoder.py')
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artifact.add_file('stylegan2_ae/layers/mapping_block.py')
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run.log_artifact(artifact)
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uv2mesh = UV2Mesh(res=256)
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def filter_resolutions_featuremaps(resolutions, featuremaps, res):
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index = resolutions.index(res)
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filtered_resolutions = resolutions[:index + 1]
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filtered_featuremaps = featuremaps[:index + 1]
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return filtered_resolutions, filtered_featuremaps
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class AutoEncoder(tf.keras.Model):
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def __init__(self):
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super(AutoEncoder, self).__init__()
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resolutions = [4, 8, 16, 32, 64, 128, 256, 512, 1024]
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g_featuremaps = [256, 256, 256, 256, 256, 128, 64, 32, 16]
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train_resolutions, train_g_featuremaps = filter_resolutions_featuremaps(resolutions, g_featuremaps, 512)
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g_params = {
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'z_dim': 256,
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'w_dim': 256,
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'labels_dim': -1,
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'n_mapping': 8,
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'resolutions': train_resolutions,
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'featuremaps': train_g_featuremaps,
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}
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# test_latent = tf.ones((1, g_params['z_dim']), dtype=tf.float32)
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self.decoder = Generator(g_params)
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# _ = generator(test_latent)
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self.encoder = ConvNeXt(depths=[3, 3, 27, 3], dims=[96, 192, 256, g_params['z_dim']], include_top=False)
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self.pooling = tf.keras.layers.GlobalAveragePooling2D()
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def call(self, inputs, training=None):
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x, uv_mask = inputs
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x = tf.transpose(x, [0,2,3,1])
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x = self.encoder(x, training=training)
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x = self.pooling(x)
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x = self.decoder(x, training=training)
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x = x * uv_mask[:,tf.newaxis,:,:]
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return x
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@tf.function
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def train_step(batch):
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posed_uv, conditions,_, mesh_body_posed, mesh_mask = batch
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uv_mask = conditions[3]
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with tf.GradientTape() as tape:
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pred = model([posed_uv, uv_mask])
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mesh_pred = uv2mesh(pred)
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mesh_orig = uv2mesh(posed_uv)
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e_loss = 0.0#edge_loss(mesh_pred, mesh_orig, mesh_mask)
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c_loss = 0.0#collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
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l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(posed_uv - pred)), axis=1) * uv_mask)
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loss = l1_loss + e_loss + c_loss
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metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mesh_mask[:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(mesh_mask)
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gradients = tape.gradient(loss, model.trainable_variables)
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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return { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric }
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@tf.function
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def valid_step(batch):
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posed_uv, conditions,_, mesh_body_posed, mesh_mask = batch
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uv_mask = conditions[3]
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pred = model([posed_uv, uv_mask])
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mesh_pred = uv2mesh(pred)
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mesh_orig = uv2mesh(posed_uv)
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e_loss = 0.0#edge_loss(mesh_pred, mesh_orig, mesh_mask)
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c_loss = 0.0#collision_penalty(mesh_pred, mesh_body_posed, mesh_mask)
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l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(posed_uv - pred)), axis=1) * uv_mask)
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loss = l1_loss + e_loss + c_loss
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metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(mesh_mask[:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(mesh_mask)
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return { 'loss': loss, 'edge_loss': e_loss, 'collision_loss': c_loss, 'l1_loss': l1_loss, 'euclid_error': metric }
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if __name__ == '__main__':
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allow_memory_growth()
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name = 'test-9'
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dataset_train = DatasetSimple('/home/workspace/data/dataset/stylegan_new/train').cache().batch(8).repeat()
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dataset_valid = DatasetSimple('/home/workspace/data/dataset/stylegan_new/valid').cache().batch(8).repeat()
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model = AutoEncoder()
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# optimizer = tf.optimizers.Adam(learning_rate=0.002, beta_1=0.0, beta_2=0.99, epsilon=1e-8)
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optimizer = tf.optimizers.Adam()
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folder = 'models/ae_logs/'
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train_summary_writer = tf.summary.create_file_writer(folder + name + '/train')
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valid_summary_writer = tf.summary.create_file_writer(folder + name + '/valid')
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mesh_writer = SummaryWriter(folder + name + '/mesh')
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step = 0
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for batch in dataset_train:
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train_loss = train_step(batch)
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for key in train_loss.keys():
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with train_summary_writer.as_default():
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tf.summary.scalar(key, train_loss[key], step=step)
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data_valid = iter(dataset_valid).get_next()
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valid_loss = valid_step(data_valid)
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for key in valid_loss.keys():
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with valid_summary_writer.as_default():
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tf.summary.scalar(key, valid_loss[key], step=step)
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if step % 250 == 0:
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pred = model([data_valid[0], data_valid[1][3]])
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mesh_pred = uv2mesh(pred)
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mesh_orig = uv2mesh(data_valid[0])
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orig_img = tf.transpose(data_valid[0], [0,2,3,1])
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pred_img = tf.transpose(pred, [0,2,3,1])
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img = tf.concat((orig_img, pred_img), axis=2)
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with train_summary_writer.as_default():
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tf.summary.image('images', img, step=step)
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F = np.load("custom_utils/smpl_faces.npy")
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garment, faces = maskFacesBatch(mesh_pred.numpy(), F, data_valid[4].numpy().astype(bool))
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gt, _ = maskFacesBatch(mesh_orig.numpy(), F, data_valid[4].numpy().astype(bool))
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for i in range(len(garment)):
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mesh_writer.add_mesh('garment_pred_' + str(i), vertices=garment[i][np.newaxis,:,:], faces=faces[i][np.newaxis,:,:], global_step=step)
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mesh_writer.add_mesh('garment_gt_' + str(i), vertices=gt[i][np.newaxis,:,:], faces=faces[i][np.newaxis,:,:], global_step=step)
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print(step)
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step += 1
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